From f8383b25110077709afe935513b543e054a5d833 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Mon, 6 Feb 2017 16:14:33 -0800
Subject: [PATCH 01/31] Adding small data caching for test automation

---
 .../Timeseries/DataSets/Stock/stock_SPY.pkl    | Bin 0 -> 240560 bytes
 .../CNTK_103B_MNIST_FeedForwardNetwork.ipynb   |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)
 create mode 100644 Examples/Timeseries/DataSets/Stock/stock_SPY.pkl

diff --git a/Examples/Timeseries/DataSets/Stock/stock_SPY.pkl b/Examples/Timeseries/DataSets/Stock/stock_SPY.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..1e10dcd6dfd98607ceb5e5b4a2091d43f8bebb46
GIT binary patch
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diff --git a/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb b/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
index 339a80f67..a33d07cd2 100644
--- a/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+++ b/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
@@ -841,7 +841,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -855,7 +855,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

From 11e4e4a6ee3ec3062af0735365c63237d38491c8 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Mon, 6 Feb 2017 16:17:19 -0800
Subject: [PATCH 02/31] Revert "Adding small data caching for test automation"

This reverts commit f8383b25110077709afe935513b543e054a5d833.
---
 .../Timeseries/DataSets/Stock/stock_SPY.pkl    | Bin 240560 -> 0 bytes
 .../CNTK_103B_MNIST_FeedForwardNetwork.ipynb   |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 Examples/Timeseries/DataSets/Stock/stock_SPY.pkl

diff --git a/Examples/Timeseries/DataSets/Stock/stock_SPY.pkl b/Examples/Timeseries/DataSets/Stock/stock_SPY.pkl
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb b/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
index a33d07cd2..339a80f67 100644
--- a/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+++ b/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
@@ -841,7 +841,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -855,7 +855,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

From 777c9cca075bfb4dfbc0dfa64f76a9557e8bc980 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Mon, 6 Feb 2017 16:21:14 -0800
Subject: [PATCH 03/31] Adding small data caching for test automation

---
 Examples/Timeseries/Stock/stock_SPY.pkl       | Bin 0 -> 240560 bytes
 ...e_Timeseries_Basic_with_Pandas_Numpy.ipynb | 130 +++++++++++-------
 2 files changed, 81 insertions(+), 49 deletions(-)
 create mode 100644 Examples/Timeseries/Stock/stock_SPY.pkl

diff --git a/Examples/Timeseries/Stock/stock_SPY.pkl b/Examples/Timeseries/Stock/stock_SPY.pkl
new file mode 100644
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diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index 0dd7ef76a..2cebea20d 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -102,10 +102,42 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File already exists ..\\Examples\\TimeSeries\\DataSets\\Stock\\stock_SPY.pkl\n"
+     ]
+    }
+   ],
    "source": [
     "# Please check if there is trade data for the chosen stock symbol during this period\n",
-    "data = get_stock_data(\"SPY\", 2000, 1,2,2017,1,1)"
+    "# Ensure the data is generated and available for this tutorial.\n",
+    "# We search in two locations in the toolkit for cached stock data set with symbol SPY.\n",
+    "data_found = False\n",
+    "\n",
+    "for data_dir in [os.path.join(\"..\", \"Examples\", \"TimeSeries\", \"DataSets\", \"Stock\"),\n",
+    "                 os.path.join(\"data\", \"Stock\")]:\n",
+    "    train_file = os.path.join(data_dir, \"stock_SPY.pkl\")\n",
+    "    if os.path.isfile(train_file):\n",
+    "        data_found = True\n",
+    "        print(\"File already exists\", train_file)\n",
+    "        data = pd.read_pickle(train_file)\n",
+    "        break\n",
+    "\n",
+    "if not data_found:        \n",
+    "    # Download the data and save the file to a local directory\n",
+    "    data = get_stock_data(\"SPY\", 2000, 1,2,2017,1,1)\n",
+    "    train_file = os.path.join(\"data\" , \"Stock\", \"stock_SPY.pkl\")\n",
+    "    dir = os.path.dirname(train_file)\n",
+    "\n",
+    "    if not os.path.exists(dir):\n",
+    "        os.makedirs(dir)\n",
+    "        \n",
+    "    if not os.path.isfile(train_file):\n",
+    "        print(\"Saving\", train_file )\n",
+    "        data.to_pickle(train_file)"
    ]
   },
   {
@@ -632,26 +664,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Minibatch: 0, Loss: 0.6971, Error: 49.00%\n",
-      "Minibatch: 1, Loss: 0.6826, Error: 50.00%\n",
-      "Minibatch: 2, Loss: 0.7160, Error: 54.00%\n",
-      "Minibatch: 3, Loss: 0.7018, Error: 49.00%\n",
-      "Minibatch: 4, Loss: 0.7088, Error: 52.00%\n",
-      "Minibatch: 5, Loss: 0.6905, Error: 42.00%\n",
-      "Minibatch: 6, Loss: 0.7083, Error: 54.00%\n",
-      "Minibatch: 7, Loss: 0.7015, Error: 51.00%\n",
-      "Minibatch: 8, Loss: 0.7068, Error: 57.00%\n",
-      "Minibatch: 9, Loss: 0.7128, Error: 60.00%\n",
-      "Minibatch: 10, Loss: 0.6881, Error: 46.00%\n",
-      "Minibatch: 11, Loss: 0.6859, Error: 40.00%\n",
-      "Minibatch: 12, Loss: 0.7035, Error: 52.00%\n",
-      "Minibatch: 13, Loss: 0.6803, Error: 40.00%\n",
-      "Minibatch: 14, Loss: 0.6927, Error: 51.00%\n",
-      "Minibatch: 15, Loss: 0.7098, Error: 55.00%\n",
-      "Minibatch: 16, Loss: 0.6880, Error: 48.00%\n",
-      "Minibatch: 17, Loss: 0.6885, Error: 45.00%\n",
-      "Minibatch: 18, Loss: 0.6895, Error: 45.00%\n",
-      "Minibatch: 19, Loss: 0.7105, Error: 51.00%\n"
+      "Minibatch: 0, Loss: 0.8458, Error: 52.00%\n",
+      "Minibatch: 1, Loss: 0.7292, Error: 53.00%\n",
+      "Minibatch: 2, Loss: 0.7177, Error: 52.00%\n",
+      "Minibatch: 3, Loss: 0.7022, Error: 52.00%\n",
+      "Minibatch: 4, Loss: 0.7118, Error: 53.00%\n",
+      "Minibatch: 5, Loss: 0.7118, Error: 53.00%\n",
+      "Minibatch: 6, Loss: 0.6900, Error: 44.00%\n",
+      "Minibatch: 7, Loss: 0.7228, Error: 61.00%\n",
+      "Minibatch: 8, Loss: 0.7242, Error: 61.00%\n",
+      "Minibatch: 9, Loss: 0.7087, Error: 48.00%\n",
+      "Minibatch: 10, Loss: 0.7006, Error: 54.00%\n",
+      "Minibatch: 11, Loss: 0.7013, Error: 54.00%\n",
+      "Minibatch: 12, Loss: 0.6948, Error: 53.00%\n",
+      "Minibatch: 13, Loss: 0.7122, Error: 56.00%\n",
+      "Minibatch: 14, Loss: 0.6950, Error: 49.00%\n",
+      "Minibatch: 15, Loss: 0.7011, Error: 55.00%\n",
+      "Minibatch: 16, Loss: 0.7015, Error: 48.00%\n",
+      "Minibatch: 17, Loss: 0.6794, Error: 46.00%\n",
+      "Minibatch: 18, Loss: 0.6874, Error: 49.00%\n",
+      "Minibatch: 19, Loss: 0.7077, Error: 49.00%\n"
      ]
     }
    ],
@@ -682,9 +714,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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fs1czbiyMHg39+ydtRX7E/V8ZlzZXKzNbAGBm84HtcjHqlFPCkE2uTJrUON/M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\nBfP/AYjIoLThXwd4AAAAAElFTkSuQmCC\n",
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XAUOAHYFJwBhJa9SyyZlAD2Cd7OfXgI+Bu/L2uQtwO3A9sANwD3C3pK2a6Wm0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8ea668>"
+       "<matplotlib.figure.Figure at 0x945f9e8>"
       ]
      },
      "metadata": {},
@@ -692,9 +724,9 @@
     },
     {
      "data": {
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53xujLCCkMp09OyzQd+5cWNkqkUMOCYmwjj46vPX/6lfF7e/BB8Nvl2Ce7nyo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\n0AAAAABJRU5ErkJggg==\n"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nWt54w2uFDjsMrr668DH77AMvvuhJSseOzRtfqIhK1pxkSU5GAPeZ2bC87ccC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\nIYRQU/4fIXIuPgDsW8EAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0cc198>"
+       "<matplotlib.figure.Figure at 0x19f95390>"
       ]
      },
      "metadata": {},
@@ -740,7 +772,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average error: 47.48%\n"
+      "Average error: 49.00%\n"
      ]
     }
    ],
@@ -847,9 +879,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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0+O/evHkzl1xyCRdeeCEvvvgiDz/8MOPHj+eLX/xiiz5/55138q1vfYuvfe1r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\nYzzJkhRjjDHGeJIlKcYYY4zxpP8H4qHiDWIZvp8AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xf1e9b70>"
+       "<matplotlib.figure.Figure at 0x1a1a9dd8>"
       ]
      },
      "metadata": {},
@@ -879,25 +911,25 @@
      "output_type": "stream",
      "text": [
       "TRADING STATS\n",
-      "AVG Monthly Return :: 0.40%\n",
-      "STD Monthly        :: 3.09%\n",
-      "SHARPE             :: 0.45\n",
-      "MAX DRAWDOWN       :: 10.45%, 30.0 months\n",
-      "Correlation to SPY :: 0.01\n",
-      "NUMBER OF TRADES   :: 563\n",
-      "TOTAL TRADING DAYS :: 4268\n",
+      "AVG Monthly Return :: 0.03%\n",
+      "STD Monthly        :: 2.94%\n",
+      "SHARPE             :: 0.03\n",
+      "MAX DRAWDOWN       :: 35.16%, 75.0 months\n",
+      "Correlation to SPY :: 0.00\n",
+      "NUMBER OF TRADES   :: 769\n",
+      "TOTAL TRADING DAYS :: 4277\n",
       "SPY MONTHLY RETURN :: 0.76%\n",
       "SPY STD RETURN     :: 4.44%\n",
       "SPY SHARPE         :: 0.59\n",
       "SPY DRAWDOWN       :: 55.37%, 8.0 months\n",
-      "0.104531465492\n"
+      "0.351648144393\n"
      ]
     },
     {
      "data": {
-      "image/png": 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eiy80FDJkiFurCA2FVatsgvj9d9s08t13UKuWXf7ttzBkCKxZA4UKeS6+pOrY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\nMl+UISg0yJMhetypq6do8E0DBvw+IM6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WH17MtTvXeKP6GwD4FPFhT+89fFD/A6oXqc6mrpsI7B3Iq0++SqYMmeKsJ2vG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\nlgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11e97940>"
+       "<matplotlib.figure.Figure at 0x1a1e84a8>"
       ]
      },
      "metadata": {},
@@ -967,18 +999,18 @@
      "output_type": "stream",
      "text": [
       "TRADING STATS\n",
-      "AVG Monthly Return :: 1.10%\n",
-      "STD Monthly        :: 7.11%\n",
-      "SHARPE             :: 0.54\n",
-      "MAX DRAWDOWN       :: 30.30%, 33.0 months\n",
-      "Correlation to SPY :: 0.04\n"
+      "AVG Monthly Return :: -1.01%\n",
+      "STD Monthly        :: 7.03%\n",
+      "SHARPE             :: -0.50\n",
+      "MAX DRAWDOWN       :: 170.53%, 105.0 months\n",
+      "Correlation to SPY :: -0.05\n"
      ]
     },
     {
      "data": {
-      "image/png": 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2fT/GW4L5saqXjd273bhD4H7h164NCxdenH/27KXL//WX66+f0JVA//7w8cdu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\nLCZ5DTyoqrut7eZcu+32A0N7XWnsvMAtSepyGUq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ppQKUUn8rpQraK97EIG/evPj6+nLq1KlY1w0KCuLGjRtkzZr1zb5KlSrxySef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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161ff8d0>"
+       "<matplotlib.figure.Figure at 0x19fb2748>"
       ]
      },
      "metadata": {},
@@ -1038,7 +1070,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.51\n"
+      "0.49\n"
      ]
     }
    ],
@@ -1064,7 +1096,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -1078,7 +1110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

From d27353e2471e374c71d730c4fd48bbb1a86d09a5 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 15:02:59 -0800
Subject: [PATCH 04/31] Incorporated CR feedback with retry logic

---
 ...e_Timeseries_Basic_with_Pandas_Numpy.ipynb | 87 ++++++++++++-------
 1 file changed, 58 insertions(+), 29 deletions(-)

diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index 2cebea20d..9d7932181 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -71,6 +71,7 @@
    "source": [
     "# A method which obtains stock data from Yahoo finance\n",
     "# Requires that you have an internet connection to retreive stock data from Yahoo finance\n",
+    "import time\n",
     "try:\n",
     "    from pandas_datareader import data\n",
     "except ImportError:\n",
@@ -92,8 +93,20 @@
     "    \"\"\"\n",
     "    start = datetime.datetime(s_year, s_month, s_day)\n",
     "    end = datetime.datetime(e_year, e_month, e_day)\n",
-    "    bars = data.get_data_yahoo(contract, start, end)\n",
-    "    return bars"
+    "    \n",
+    "    retry = True\n",
+    "    retry_cnt, max_num_retry = 0, 3\n",
+    "    \n",
+    "    while(retry_cnt < max_num_retry):\n",
+    "        try:\n",
+    "            bars = data.get_data_yahoo(contract, start, end)\n",
+    "            return bars\n",
+    "        except:\n",
+    "            retry_cnt += 1\n",
+    "            time.sleep(np.random.randint(1,10)) \n",
+    "            \n",
+    "    print(\"Yahoo Finance is not reachable\")\n",
+    "    return None"
    ]
   },
   {
@@ -107,37 +120,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "File already exists ..\\Examples\\TimeSeries\\DataSets\\Stock\\stock_SPY.pkl\n"
+      "File already exists data\\Stock\\stock_SPY.pkl\n"
      ]
     }
    ],
    "source": [
-    "# Please check if there is trade data for the chosen stock symbol during this period\n",
-    "# Ensure the data is generated and available for this tutorial.\n",
-    "# We search in two locations in the toolkit for cached stock data set with symbol SPY.\n",
-    "data_found = False\n",
+    "# We search in cached stock data set with symbol SPY.               \n",
+    "# Check for an environment variable defined in CNTK's test infrastructure\n",
+    "envvar = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'\n",
+    "def is_test(): return envvar in os.environ\n",
     "\n",
-    "for data_dir in [os.path.join(\"..\", \"Examples\", \"TimeSeries\", \"DataSets\", \"Stock\"),\n",
-    "                 os.path.join(\"data\", \"Stock\")]:\n",
-    "    train_file = os.path.join(data_dir, \"stock_SPY.pkl\")\n",
-    "    if os.path.isfile(train_file):\n",
-    "        data_found = True\n",
-    "        print(\"File already exists\", train_file)\n",
-    "        data = pd.read_pickle(train_file)\n",
-    "        break\n",
-    "\n",
-    "if not data_found:        \n",
-    "    # Download the data and save the file to a local directory\n",
+    "def download(data_file):\n",
     "    data = get_stock_data(\"SPY\", 2000, 1,2,2017,1,1)\n",
-    "    train_file = os.path.join(\"data\" , \"Stock\", \"stock_SPY.pkl\")\n",
-    "    dir = os.path.dirname(train_file)\n",
-    "\n",
+    "    dir = os.path.dirname(data_file)\n",
+    "        \n",
     "    if not os.path.exists(dir):\n",
     "        os.makedirs(dir)\n",
     "        \n",
-    "    if not os.path.isfile(train_file):\n",
-    "        print(\"Saving\", train_file )\n",
-    "        data.to_pickle(train_file)"
+    "    if not os.path.isfile(data_file):\n",
+    "        print(\"Saving\", data_file )\n",
+    "        data.to_pickle(data_file)\n",
+    "    return data\n",
+    "\n",
+    "data_found = False\n",
+    "data_file = os.path.join(\"data\", \"Stock\", \"stock_SPY.pkl\")\n",
+    "\n",
+    "# Check for data in local cache\n",
+    "if os.path.exists(data_file):\n",
+    "        data_found = True\n",
+    "        print(\"File already exists\", data_file)\n",
+    "        data = pd.read_pickle(data_file) \n",
+    "else: \n",
+    "    # If not there we might be running in CNTK's test infrastructure\n",
+    "    if is_test():\n",
+    "        test_file = os.path.join(os.environ[envvar], 'Tutorials','data','stock','stock_SPY.pkl')\n",
+    "        if os.path.isfile(test_file):\n",
+    "            print(\"Reading data from test data directory\")\n",
+    "            data = pd.read_pickle(test_file)\n",
+    "        else:\n",
+    "            print(\"Test data directory missing file\", test_file)\n",
+    "            print(\"Downloading data from Yahoo Finance\")\n",
+    "            data = download(data_file)\n",
+    "            \n",
+    "    else:\n",
+    "        # Local cache is not present and not test env\n",
+    "        # download the data from Yahoo finance and cache it in a local directory\n",
+    "        # Please check if there is trade data for the chosen stock symbol during this period\n",
+    "        data = data_file"
    ]
   },
   {
@@ -716,7 +745,7 @@
      "data": {
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XAUOAHYFJwBhJa9SyyZlAD2Cd7OfXgI+Bu/L2uQtwO3A9sANwD3C3pK2a6Wm0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x945f9e8>"
+       "<matplotlib.figure.Figure at 0x5486be0>"
       ]
      },
      "metadata": {},
@@ -726,7 +755,7 @@
      "data": {
       "image/png": 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nWt54w2uFDjsMrr668DH77AMvvuhJSseOzRtfqIhK1pxkSU5GAPeZ2bC87ccC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\nApOA24FhwB+BHvjjcmXeOesA+ye3uyt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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19f95390>"
+       "<matplotlib.figure.Figure at 0x9146048>"
       ]
      },
      "metadata": {},
@@ -881,7 +910,7 @@
      "data": {
       "image/png": 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0+O/evHkzl1xyCRdeeCEvvvgiDz/8MOPHj+eLX/xiiz5/55138q1vfYuvfe1r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\n0rbFddlYX4xEb4Ldr+yms839B0rXwS4i3RFaN7ey85WdbH1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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1a9dd8>"
+       "<matplotlib.figure.Figure at 0x9765c18>"
       ]
      },
      "metadata": {},
@@ -929,7 +958,7 @@
      "data": {
       "image/png": 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WH17MtTvXeKP6GwD4FPFhT+89fFD/A6oXqc6mrpsI7B3Iq0++SqYMmeKsJ2vG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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1e84a8>"
+       "<matplotlib.figure.Figure at 0x99f3e80>"
       ]
      },
      "metadata": {},
@@ -1010,7 +1039,7 @@
      "data": {
       "image/png": 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ppQKUUn8rpQraK97EIG/evPj6+nLq1KlY1w0KCuLGjRtkzZr1zb5KlSrxySef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\nN28Ot90WtTedO0dTzaJFse/cc2HaNHj4YWjZMq/ZFJEMUVB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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x19fb2748>"
+       "<matplotlib.figure.Figure at 0x99edeb8>"
       ]
      },
      "metadata": {},

From 775d85a4061d0149e206542c27991f5d7f1418c5 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 16:17:22 -0800
Subject: [PATCH 05/31] Remove the cached pkl file

---
 Examples/Timeseries/Stock/stock_SPY.pkl | Bin 240560 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 Examples/Timeseries/Stock/stock_SPY.pkl

diff --git a/Examples/Timeseries/Stock/stock_SPY.pkl b/Examples/Timeseries/Stock/stock_SPY.pkl
deleted file mode 100644
index 5808595bd1cc5f6b48f956ec65932d3764688236..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 43ca6174251ef673f457da0f9b5a7f53328411a2 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 16:26:20 -0800
Subject: [PATCH 06/31] Fixed a data download condition

---
 ...TK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index 9d7932181..bf171f8d4 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -142,12 +142,10 @@
     "        data.to_pickle(data_file)\n",
     "    return data\n",
     "\n",
-    "data_found = False\n",
     "data_file = os.path.join(\"data\", \"Stock\", \"stock_SPY.pkl\")\n",
     "\n",
     "# Check for data in local cache\n",
     "if os.path.exists(data_file):\n",
-    "        data_found = True\n",
     "        print(\"File already exists\", data_file)\n",
     "        data = pd.read_pickle(data_file) \n",
     "else: \n",
@@ -160,13 +158,12 @@
     "        else:\n",
     "            print(\"Test data directory missing file\", test_file)\n",
     "            print(\"Downloading data from Yahoo Finance\")\n",
-    "            data = download(data_file)\n",
-    "            \n",
+    "            data = download(data_file)         \n",
     "    else:\n",
     "        # Local cache is not present and not test env\n",
     "        # download the data from Yahoo finance and cache it in a local directory\n",
     "        # Please check if there is trade data for the chosen stock symbol during this period\n",
-    "        data = data_file"
+    "        data = download(data_file)"
    ]
   },
   {

From 3f68277bdd22e1eb5c2f09b194c224aea6e840aa Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 16:34:06 -0800
Subject: [PATCH 07/31] Updated caching of test data and retry logic

---
 ...e_Timeseries_Basic_with_Pandas_Numpy.ipynb | 123 +++++++++---------
 1 file changed, 61 insertions(+), 62 deletions(-)

diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index bf171f8d4..13ff3c42b 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -94,7 +94,6 @@
     "    start = datetime.datetime(s_year, s_month, s_day)\n",
     "    end = datetime.datetime(e_year, e_month, e_day)\n",
     "    \n",
-    "    retry = True\n",
     "    retry_cnt, max_num_retry = 0, 3\n",
     "    \n",
     "    while(retry_cnt < max_num_retry):\n",
@@ -111,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -189,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -475,7 +474,7 @@
        "2000-01-14    1  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -552,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -578,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
@@ -610,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
@@ -637,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -681,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -690,26 +689,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Minibatch: 0, Loss: 0.8458, Error: 52.00%\n",
-      "Minibatch: 1, Loss: 0.7292, Error: 53.00%\n",
-      "Minibatch: 2, Loss: 0.7177, Error: 52.00%\n",
-      "Minibatch: 3, Loss: 0.7022, Error: 52.00%\n",
-      "Minibatch: 4, Loss: 0.7118, Error: 53.00%\n",
-      "Minibatch: 5, Loss: 0.7118, Error: 53.00%\n",
-      "Minibatch: 6, Loss: 0.6900, Error: 44.00%\n",
-      "Minibatch: 7, Loss: 0.7228, Error: 61.00%\n",
-      "Minibatch: 8, Loss: 0.7242, Error: 61.00%\n",
-      "Minibatch: 9, Loss: 0.7087, Error: 48.00%\n",
-      "Minibatch: 10, Loss: 0.7006, Error: 54.00%\n",
-      "Minibatch: 11, Loss: 0.7013, Error: 54.00%\n",
-      "Minibatch: 12, Loss: 0.6948, Error: 53.00%\n",
-      "Minibatch: 13, Loss: 0.7122, Error: 56.00%\n",
-      "Minibatch: 14, Loss: 0.6950, Error: 49.00%\n",
-      "Minibatch: 15, Loss: 0.7011, Error: 55.00%\n",
-      "Minibatch: 16, Loss: 0.7015, Error: 48.00%\n",
-      "Minibatch: 17, Loss: 0.6794, Error: 46.00%\n",
-      "Minibatch: 18, Loss: 0.6874, Error: 49.00%\n",
-      "Minibatch: 19, Loss: 0.7077, Error: 49.00%\n"
+      "Minibatch: 0, Loss: 0.7004, Error: 53.00%\n",
+      "Minibatch: 1, Loss: 0.6930, Error: 47.00%\n",
+      "Minibatch: 2, Loss: 0.7122, Error: 56.00%\n",
+      "Minibatch: 3, Loss: 0.6941, Error: 44.00%\n",
+      "Minibatch: 4, Loss: 0.6901, Error: 38.00%\n",
+      "Minibatch: 5, Loss: 0.6797, Error: 49.00%\n",
+      "Minibatch: 6, Loss: 0.6960, Error: 50.00%\n",
+      "Minibatch: 7, Loss: 0.6986, Error: 52.00%\n",
+      "Minibatch: 8, Loss: 0.7059, Error: 49.00%\n",
+      "Minibatch: 9, Loss: 0.6972, Error: 52.00%\n",
+      "Minibatch: 10, Loss: 0.6962, Error: 45.00%\n",
+      "Minibatch: 11, Loss: 0.7007, Error: 44.00%\n",
+      "Minibatch: 12, Loss: 0.6800, Error: 46.00%\n",
+      "Minibatch: 13, Loss: 0.6853, Error: 42.00%\n",
+      "Minibatch: 14, Loss: 0.6927, Error: 47.00%\n",
+      "Minibatch: 15, Loss: 0.6794, Error: 47.00%\n",
+      "Minibatch: 16, Loss: 0.6913, Error: 47.00%\n",
+      "Minibatch: 17, Loss: 0.7049, Error: 48.00%\n",
+      "Minibatch: 18, Loss: 0.6713, Error: 41.00%\n",
+      "Minibatch: 19, Loss: 0.7048, Error: 54.00%\n"
      ]
     }
    ],
@@ -733,16 +732,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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XAUOAHYFJwBhJa9SyyZlAD2Cd7OfXgI+Bu/L2uQtwO3A9sANwD3C3pK2a6Wm0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\ns6ri5MTMzMyqipMTMzMzqyp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2SjuSUIw//cnXjymX/kKh9Mxi1uRyNHeuD+/dddeWmxxtxgyYPLllzpVryBBv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\nhJApkZyEEEIIIVMiOQkhhBBCpvw/qr1QKBFhkjAAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5486be0>"
+       "<matplotlib.figure.Figure at 0x1b890ac8>"
       ]
      },
      "metadata": {},
@@ -750,9 +749,9 @@
     },
     {
      "data": {
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nWt54w2uFDjsMrr668DH77AMvvuhJSseOzRtfqIhK1pxkSU5GAPeZ2bC87ccC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\nApOA24FhwB+BHvjjcmXeOesA+ye3uyt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Srx988klMLz5rls/QevbZPkNrly4+v8yZZ8L332d/TavRuHELz4lSU1O5ydxC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\nQgghhFBV/h+ak11DUWCsCQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9146048>"
+       "<matplotlib.figure.Figure at 0x1ace5b70>"
       ]
      },
      "metadata": {},
@@ -789,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -798,7 +797,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average error: 49.00%\n"
+      "Average error: 46.50%\n"
      ]
     }
    ],
@@ -822,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
@@ -860,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 50,
    "metadata": {
     "collapsed": true
    },
@@ -898,16 +897,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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0+O/evHkzl1xyCRdeeCEvvvgiDz/8MOPHj+eLX/xiiz5/55138q1vfYuvfe1r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\n0rbFddlYX4xEb4Ldr+yms839B0rXwS4i3RFaN7ey85WdbH1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X06dPn0N9u3btSlFREevXrycSiRxqv+iii3j//feJxWLNFiktndLpKKd6wIoU\nJyxevDjsEALhSp7gTq7333+/E9Pid/Tx7NKlCwX5BdS9WhfqrK8F+QV06dIl7fVFhMWLF3PHHXdw\n22230blzZ77//e8zY8aMVm9jyJAhLF26lNtvv50f//jHnHHGGSxYsICKiopDd+g07uuxxx7jpz/9\nKYsXL2bBggWcfvrp3HPPPRQXFzfZ7sCBA9mwYUNCMdKrVy/69u3L66+/3myR0tL1NNlwnU1rWZFi\njDGmTfLz8xk3elxOPAW5Z8+eLFmypE3buOqqq5ocGbvyyiub9MvLy+Oee+7hnnvuOeI2p0+fzvTp\n05u0v/rqq832v+6667juuuuatF988cUcOHDgiPvLFlakGGOMabP8/Hx7sJ9pd1akGGOMMUewZ88e\n9u07/KmsXr16BRSNO+zuHgeMGTMm7BAC4Uqe4E6uybNx5ipXxrMjmzRpEieddFKLr5NPPjnsEHOS\nHUlxgCuzWbqSJ7iT66BBg+ggs3e3iSvjmc2mTp3K1KlTW1w+ZcoUvv3tbwcYkQErUpwwcuTIsEMI\nhCt5gju5Dhs2zIm7e1wZz46sX79+9OvXL+wwnGOne4wxxhiTlaxIMcYYY0xWsiLFAZWVlWGHEAhX\n8gR3cl2/fn3YIQTClfE0JlV2TYoDZsyYwYUXXhh2GBnnSp7gTq5z5syhqGhY2GFkXEcaz8YH4Jnc\nli3jbEWKAxYtWhR2CIFwJU9wJ9d58+Yxd+7KsMPIuI4wnj169CAvL49Ro0aFHYoJSF5eHj169Ag1\nBitSHJCXlxd2CIFwJU9wJ1fLM3v07t2bLVu2UFtbG3YoJiA9evSgd+/eocZgRYoxxphW6d27d+g/\nWsYtduGsMcYYY7KSFSkOmDx5ctghBMKVPMGdXEtKSsIOIRCujKflaVKVFUWKiJwsIg+KSK2IxETk\nRRHpn9TnZyKy01/+pIj0TVp+jIiU+dvYKyLLROTEYDPJTq4cnnUlT3An11NOOSXsEALhynhaniZV\noRcpItIdeA7YDwwBzgZuAd6L6zMFmACMA84D3gdWicjRcZuaCVwOXANcBJwM/CGAFLKeKw9pcyVP\ncCfXG2+8MewQAuHKeFqeJlXZcOHsj4BqVf1OXFvyI8UmAXep6uMAIjIaqAGuApaIyHHAWOBaVX3G\n7zMG2CIi56nqxkwnYYwxxpj2FfqRFOAK4K8iskREakSkSkQOFSwi0gcoBJ5qbFPVPcAGYIDf9EW8\ngiu+zyuIO60tAAAgAElEQVRAdVwfY4wxxnQg2VCkfBK4CXgFGAz8GpglIo3PxC4EFO/ISbwafxlA\nL6DBL15a6uOsrVu3hh1CIFzJE9zJddu2bWGHEAhXxtPyNKnKhiKlE7BJVe9Q1RdV9X+A/wH+M+S4\ncsatt94adgiBcCVPcCfXO++8M+wQAuHKeFqeJlXZUKT8E0h+SMAWoPHy6F2A4B0tidfLX9bY52j/\n2pSW+jRr6NChRCKRhNeAAQOoqKhI6Ld69WoikUiT9cePH8/8+fMT2qqqqohEIk1mZpw6dSrTp09P\naKuuriYSiTSpvGfPnt3kNrZYLEYkEmnyMLLy8nLGjBnTJLYRI0ZQUVHBnDlzciKPeM3lMWfOnJzI\nA448HvFjms15rKxYyYYnNiTGtrWasuIyorujCe2Pzn2UlQsSp8CfNGkSS5eWUVOTeERlzZrZLFuW\nmEdDQ4yysgjbtwc/HvHSGY/48Qzzc9XWPOI1l8ecOXNyIg84/HhcccUVOZFH43iUl5cf+m0sLCwk\nEolQXFzcZJ1MEFUNZEctBiDyEHCqql4c11YKfElVL/Tf7wTuVtVS//1xeKdyRqvqUv/9u3gXzv7R\n73MWXrHz5eYunPVvcd60adMm+vfvn7zYGNNGtbW1lP62lIKiAvK757d6vejuKHWb6ige6/0lWFq6\nnIKCYeTnt/4ZItFoLXV1yykuHhb6s0eMyUVVVVUUFRUBFKlqVab2kw1395QCz4nIbcAS4HzgO0D8\nvYczgdtFZDvwBnAX8BbwCHgX0orIfOBeEXkP2AvMAp6zO3uMMcaYjin0IkVV/yoiVwPTgDuAHcAk\nVV0U12eGiOQBc4HuwFrgMlVtiNtUMXAAWAYcA6wExgeThTHGGGPaWzZck4KqrlDVz6lqnqp+RlV/\n20yfElU92e8zRFW3Jy3fr6oTVbWHqnZT1W+q6jvBZZG9ks9j5ipX8gR3cp01a1bYIQTClfG0PE2q\nsqJIMZkVi8XCDiEQruQJ7uS6b9++sEMIhCvjaXmaVFmR4gBXbuN0JU9wJ9cpU6aEHUIgXBlPy9Ok\nyooUY4wxxmQlK1KMMcYYk5WsSHFA8qRAucqVPMGdXOvq6sIOIRCujKflaVJlRYoDxo4dG3YIgXAl\nT3An10mTJoUdQiBcGU/L06TKihQHlJSUhB1CIFzJE9zJNXlq71zlynhaniZVVqQ4wJVp/13JE9zJ\n9dxzzw07hEC4Mp6Wp0mVFSnGGGOMyUpWpBhjjDEmK1mR4oDkR4HnKlfyBHdyXbhwYdghBMKV8bQ8\nTaqsSHFAVVXGnqKdVVzJE9zJdfPmzWGHEAhXxtPyNKmyIsUBZWVlYYcQCFfyBHdynTFjRtghBMKV\n8bQ8TaqsSDHGGGNMVrIixRhjjDFZyYoUY4wxxmQlK1IcEIlEwg4hEK7kCe7kOmrUqLBDCIQr42l5\nmlRZkeKACRMmhB1CIFzJE9zJ9YYbbgg7hEC4Mp6Wp0mVFSkOGDx4cNghBMKVPMGdXAcNGhR2CIFw\nZTwtT5MqK1KMMcYYk5WsSDHGGGNMVrIixQEVFRVhhxAIV/IEd3JdsWJF2CEEwpXxtDxNqqxIcUB5\neXnYIQTClTzBnVyXL18edgiBcGU8LU+TKitSHLB48eKwQwiEK3mCO7nef//9YYcQCFfG0/I0qbIi\nxRhjjDFZyYoUY4wxxmQlK1KMMcYYk5WsSHHAmDFjwg4hEK7kCe7kOnHixLBDCIQr42l5mlSFXqSI\nyFQROZj0ejmpz89EZKeIxETkSRHpm7T8GBEpE5FaEdkrIstE5MRgM8lersx+6Eqe4E6uNuNsbrE8\nTapCL1J8fwN6AYX+68LGBSIyBZgAjAPOA94HVonI0XHrzwQuB64BLgJOBv4QSOQdwMiRI8MOIRCu\n5Anu5Dps2LCwQwiEK+NpeZpUdQ47AN+HqvpuC8smAXep6uMAIjIaqAGuApaIyHHAWOBaVX3G7zMG\n2CIi56nqxsyHb4wxxpj2li1HUj4lIm+LyGsislBEPgEgIn3wjqw81dhRVfcAG4ABftMX8Yqt+D6v\nANVxfYwxxhjTwWRDkbIeuB4YAvwn0Ad4VkSOxStQFO/ISbwafxl4p4ka/OKlpT5Oq6ysDDuEQLiS\nJ7iT6/r168MOIRCujKflaVIVepGiqqtU9Q+q+jdVfRIYCpwADA85tJwxY8aMsEMIhCt5gju5zpkz\nJ+wQAuHKeFqeJlWhFynJVPXfwKtAX2AXIHhHS+L18pfh//do/9qUlvq0aOjQoUQikYTXgAEDmjwg\navXq1UQikSbrjx8/nvnz5ye0VVVVEYlEqK2tTWifOnUq06dPT2irrq4mEomwdevWhPbZs2czefLk\nhLZYLEYkEmlSpZeXlzd7y9uIESOoqKhg0aJFOZFHvObyWLRoUU7kAUcej/gxzeY8VlasZMMTGxJj\n21pNWXEZ0d3RhPZH5z7KygUrE9qmTp3K0qVl1NRsS2hfs2Y2y5Yl5tHQEKOsLML27cGPR3LMqY5H\n/HiG+blqax7xmstj0aJFOZEHHH48vvWtb+VEHo3jUV5efui3sbCwkEgkQnFxcZN1MkFUNZAdtZaI\n5ONdT3KHqpaJyE7gblUt9Zcfh3cqZ7SqLvXfv4t34ewf/T5nAVuAL7d04ayI9Ac2bdq0if79+2c+\nMWMcU1tbS+lvSykoKiC/e36r14vujlK3qY7isd5fgqWlyykoGEZ+fo/WbyNaS13dcoqLh9GjR+vX\nM8a0TlVVFUVFRQBFqlqVqf2EfnePiNwNPAa8CZwC3Al8ADT+02ImcLuIbAfeAO4C3gIeAe9CWhGZ\nD9wrIu8Be4FZwHN2Z48xxhjTcYVepACnAg8DBXhHRCrxjoDUAajqDBHJA+YC3YG1wGWq2hC3jWLg\nALAMOAZYCYwPLANjOoBoNEp9fX1a63bp0oX8/NYfDeno0v2zcu3PyZhMC71IUdUjznqjqiVAyWGW\n7wcm+i+TZPLkydx9991hh5FxruQJqecajUaZ9/t51EXr0tpfQX4B40aPC/wHuKSkhOOP/1yg+4xG\no8ybt4S6ug9TXregoDPjxg1P+c/Jlc+u5WlSlVaRIiLfBpaqanr/LDOB6t27d9ghBMKVPCH1XOvr\n66mL1tH1zK7kdctLad3Y3hh1r9ZRX18feJFyyimnEI0euV97qq+vp67uQ7p2/Qp5ed1bvV4stpu6\nujVp/Tm58tm1PE2q0j2SUgrMFpHFwHy79iO7ufKQNlfyhPRzzeuWl9JFrI32sS+t/bXVjTfeSGnp\n8lD2nZfXPaWLdQH2pfnH5Mpn1/I0qUr3FuSTgRvxrid5TkT+JiK3iEjP9gvNGGOMMS5Lq0hR1QZV\nXaqqlwO9gQeBG4C3RGS5iFwuItKegRpjjDHGLW2ezE1V/wn8L/A03hT2XwTKgW0iMrCt2zdtlzzR\nT65yJU9wJ9dt27YduVMOcGU8LU+TqrSLFBHpISI3i8iLwHPAiXhPJj4Nb76TCuD37RKlaZNbb701\n7BAC4Uqe4E6ud955Z9ghBMKV8bQ8TarSKlJE5I/A23gPBHwQ+ISqflNVV6pnLzADr2AxIXPl+Seu\n5Anu5Dpt2rSwQwiEK+NpeZpUpXt3zx7gq6q69jB93gU+leb2TTty5XY4V/IEd3I99dRTgdy/edCV\n8bQ8TarSKlJU9bpW9FHgtXS2b4wxxhiT7umeUhFpMu28iIwXkV+1PSxjjDHGuC7dC2e/Caxrpn09\nMCL9cEwmJD/eO1e5kie4k+usWbPCDiEQroyn5WlSlW6R0gPvupRk//aXmSwSi8XCDiEQruQJ7uS6\nL90pXDsYV8bT8jSpSrdIeQ0Y0kz7EGBH+uGYTHDlNk5X8gR3cp0yZUrYIQTClfG0PE2q0r27ZyYw\nU0QKgDV+26XArcAP2yMwY4wxxrgt3bt7/kdEugA/BhpLxreA76vqb9srOGOMMca4K+0ZZ1V1tqqe\nhDe77MdVtbcVKNmptrY27BAC4Uqe4E6udXV1YYcQCFfG0/I0qWqXZ/eo6u72CMZkxtixY8MOIRCu\n5Anu5Dpp0qSwQwiEK+NpeZpUpTtPSk8R+Z2IVItIvYg0xL/aO0jTNiUlJWGHEAhX8gR3cp08eXLY\nIQTClfG0PE2q0r1wdgFwBnA38E+8px+bLNW/f/+wQwiEK3mCO7mee+65rFmT+xNXuzKelqdJVbpF\nykXARar6fHsGY4wxxhjTKN1rUt7Cjp4YY4wxJoPSLVKKgV+KyKntGYzJjPnz54cdQiBcyRPcyXXh\nwoVhhxAIV8bT8jSpSrdIeRAYBLwpIu+JyDvxr3aMz7SDqqqqsEMIhCt5gju5bt68OewQAuHKeFqe\nJlXpXpPyo3aNwmRUWVlZ2CEEwpU8wZ1cZ8yYQWnp8rDDyDhXxtPyNKlKd8ZZO5ZljDHGmIxKezI3\nETldREpE5EEROdFvGywiZ7dfeMYYY4xxVbqTuQ0E/g5cDAwH8v1FRcDP2ic0Y4wxxrgs3SMp04ES\nVR0ExM8w+xTw5TZHZdpVJBIJO4RAuJInuJPrqFGjwg4hEK6Mp+VpUpVukfI5YFkz7e8APdMPB0Tk\nRyJyUETuTWr/mYjsFJGYiDwpIn2Tlh8jImUiUisie0VkWeNpKNdNmDAh7BAC4Uqe4E6uN9xwQ9gh\nBMKV8bQ8TarSLVL+DRQ2034u8Ha6wYjIl4BxwItJ7VOACf6y84D3gVUicnRct5nA5cA1eDPingz8\nId1YcsngwYPDDiEQruQJ7uQ6aNCgsEMIhCvjaXmaVKVbpCwGpolIT/yZZ0XkfOBXQFqzL4lIvr/u\nd4DkpypPAu5S1cdV9W/AaLwi5Cp/3eOAsUCxqj7jT9c/BrhARM5LJx5jjDHGhCvdIuU24HVgJ95F\nsy8D64C/AHeluc0y4DFVXRPfKCJ98I7aPNXYpqp7gA3AAL/pi3i3U8f3eQWojutjjDHGmA4krSJF\nVfer6hjgTLyjGWOBz6jqSFX9MNXtici1wOfxip9khXhHa2qS2mv46JRTL6DBL15a6uOsioqKsEMI\nhCt5gju5rlixIuwQAuHKeFqeJlVpz5MCoKo7VPVRVX1YVbemsw3/+T8zgW+p6gdticc0r7y8POwQ\nAuFKnuBOrsuX5/5ss+DOeFqeJlXpzpMy73CvFDdXhHdHUJWIfCAiH+DNvzJJRBrwjoYI3tGSeL2A\nXf7/7wKO9q9NaalPs4YOHUokEkl4DRgwoEklvHr16mZvKxs/fnyTh0lVVVURiUSora1NaJ86dSrT\np09PaKuuriYSibB1a2KNN3v2bCZPnpzQFovFiEQiVFZWJrSXl5czZsyYJrGNGDGCiooKFi9enBN5\nxGsuj8WLF+dEHnDk8Ygf01TyWPvHtSy7L/HGvIb6BsqKy9j+wvaE9o0rN7LgzgVtymNlxUo2PLEh\nMbat1ZQVlxHdHU1of3Tuo6xcsDKhraSkhKVLy6ip2ZbQvmbNbJYtSxyPhoYYZWURtm9v23g0d9vz\nww+Pp7IycTyqq6soK4sQjSZ+rqZPn57y5yp+PMP8XMXLxPdj8eLFOZEHHH48Ro4cmRN5NI5HeXn5\nod/GwsJCIpEIxcXFTdbJBFHV1FcSeSyp6WPAZ4BuwLOq2uqbxEXkWOC0pOYFwBZgmqpuEZGdwN2q\nWuqvcxxe8TJaVZf6798FrlXVP/p9zvK38WVV3djMfvsDmzZt2kT//v1bG64xHVJtbS2lvy2loKiA\n/O75R14hTnR3lLpNdRSPLaZHjx4Z32f8/gBKS5dTUDCM/PzW7zsaraWubjnFxcNSivlQ3AHv05iO\npqqqiqKiIoAiVc3YExXTfXbPFcltItIZ+A3eRbSpbOv95HVE5H2gTlW3+E0zgdtFZDvwBt7FuW8B\nj/jb2CMi84F7ReQ9YC8wC3iuuQLFGGOMMdkv3acgN6GqH4rI3cCfgXuP0P2Im0va9gwRyQPmAt2B\ntcBlqho/220xcABvkrljgJXA+DbGYYwxxpiQtOnC2Wb0wTv10yaq+hVV/UFSW4mqnqyqeao6RFW3\nJy3fr6oTVbWHqnZT1W+q6jttjSUXNHe+MRe5kie4k+vEiRPDDiEQroyn5WlSldaRFBGZkdwEnARE\nSHMyN5M5rsx+6Eqe4E6ugwYN4s03w44i81wZT8vTpCrdIykDkl7nAV2AH+HNDmuySPKV5rnKlTzB\nnVyHDRsWdgiBcGU8LU+TqnQvnB3Y3oEYY4wxxsRr72tSjDHGGGPaRbqTuf1FRDa25tXeAZvUJU/e\nk6tcyRPcyXX9+vVhhxAIV8bT8jSpSvdIytPAWXgXzK73X/htfwZWxb1MyGbMSL7OOTe5kie4k+uc\nOXPCDiEQroyn5WlSle48Kd2BMlX9cXyjiPwc6KWq32lzZKbdLFq0KOwQAuFKnuBOrvPmzWPu3JVH\n7tjBuTKelqdJVbpHUoYDv2umfQHwzbSjMRmRl5cXdgiBcCVPcCdXyzO3WJ4mVekWKfuBLzfT/mV/\nmTHGGGNMm6R7umcWMFdEvgA0Xhx7PnAj8Mv2CMwYY4wxbkvrSIqq/hz4DnABMM9//X/AOH+ZySLJ\nj+zOVa7kCe7kWlJSEnYIgXBlPC1Pk6q0HzCoqg8DD7djLCZDevfuHXYIgXAlT3An11NOOYVoNOwo\nMs+V8bQ8TarSnsxNRI4TketF5GcicoLfdq6InNR+4Zn24MpD2lzJE9zJ9cYbbww7hEC4Mp6Wp0lV\nug8YPAf4XyAGfALvrp73gBHAKcB17RSfMcYYYxyV7pGUUrxTPWcA9XHtfwIuamtQxhhjjDHpFilf\nAv5bVTWp/W3ATvdkma1bt4YdQiBcyRPcyXXbtm1hhxAIV8bT8jSpSrdI+QDIb6a9L1CbfjgmE269\n9dawQwiEK3mCO7neeeedYYcQCFfG0/I0qUq3SHkMuENEGq9pURE5BZgGLG+XyEy7ceX5J67kCe7k\nOm3atLBDCIQr42l5mlSlW6TcAnwc2AV0BdYAr+Ndn/Ljw6xnQuDK7XCu5Anu5HrqqaeGHUIgXBlP\ny9OkKq27e1T1PWCQiFwMnIt36qcKWNXMdSrGGGOMMSlLuUgRkY8BjwMTVPUZ4Jl2j8oYY4wxzkv5\ndI+qfgAUAXbEpIOYPn162CEEwpU8wZ1cZ82aFXYIgXBlPC1Pk6p0r0l5CBjTnoGYzInFYmGHEAhX\n8gR3ct23b1/YIQTClfG0PE2q0n12jwITROSrwF+B9xMWqtr9V1nElds4XckT3Ml1ypQplJbm/g2D\nroyn5WlSlW6RUgRs9v//c0nL7DSQMcYYY9ospSJFRD4J7FDVgRmKxxhjjDEGSP2alG1Az8Y3IrJY\nRHq1b0imvdXWujEJsCt5gju51tXVhR1CIFwZT8vTpCrVIkWS3g8Fjm2nWEyGjB07NuwQAuFKnuBO\nrpMmTQo7hEC4Mp6Wp0lVunf3tBsR+U8ReVFE/u2/1onI15P6/ExEdopITESeFJG+ScuPEZEyEakV\nkb0iskxETgw2k+xVUlISdgiBcCVPcCfXyZMnhx1CIFwZT8vTpCrVIkVpemFsWy+U/QcwBeiPd0Hu\nGuARETkbQESmABOAccB5eHcSrRKRo+O2MRO4HLgGuAg4GfhDG+PKGf379w87hEC4kie4k+u5554b\ndgiBcGU8LU+TqlTv7hFggYjs9993AX4jIsm3IA9r7QZV9U9JTbeLyE3Al4EtwCTgLlV9HEBERgM1\nwFXAEhE5DhgLXOvPgIuIjAG2iMh5qroxxRyNMcYYkwVSPZLyAPAO8G//tRDYGfe+8ZUWEekkItcC\necA6EekDFAJPNfZR1T3ABmCA3/RFvGIrvs8rQHVcH2OMMcZ0MCkVKao6pjWvVIMQkXNEZC+wH/hv\n4Gq/0CjEO51Uk7RKjb8MoBfQ4BcvLfVx2vz588MOIRCu5Anu5Lpw4cKwQwiEK+NpeZpUhX7hrG8r\n3tOUzwN+DfxeRPoFseOhQ4cSiUQSXgMGDKCioiKh3+rVq4lEIk3WHz9+fJMPZFVVFZFIpMltaFOn\nTm3yTIfq6moikQhbt25NaJ89e3aTiwZjsRiRSITKysqE9vLycsaMaVobjhgxgoqKCqqqqnIij3jN\n5VFVVZUTecCRxyN+TFPJY+0f17LsvmUJbQ31DZQVl7H9he0J7RtXbmTBnQvalMfKipVseGJDYmxb\nqykrLiO6O5rQ/ujcR1m5YGVC27p161i6tIyamm0J7WvWzGbZssTxaGiIUVYWYfv2to3HqFGjmvR9\n+OHxVFYmjkd1dRVlZRGi0cTP1fTp01P+XMWPZ5ifq3iZ+H5UVVXlRB5w+PFYtizxO9ZR82gcj/Ly\n8kO/jYWFhUQiEYqLi5uskwmimn0TxIrIk8B2YAbwGvB5Vd0ct/zPwPOqWiwig4D/BU6IP5oiIm8A\npap6Xwv76A9s2rRpk13kZHJebW0tpb8tpaCogPzu+SmtG90dpW5THcVji+nRo0fG9xm/P4DS0uUU\nFAwjP7/1+45Ga6mrW05x8bCUYj4Ud8D7NKajqaqqoqioCKBIVauO1D9d2XIkJVkn4BhV3QHsAi5t\nXOBfKHs+sM5v2gR8mNTnLKA38H9BBWyMMcaY9pXus3vajYj8AngC70LXbsC3gIuBwX6XmXh3/GwH\n3gDuAt4CHgHvQloRmQ/cKyLvAXuBWcBzdmePMcYY03GFXqQAJ+LdNXQS3p1Bm4HBqroGQFVniEge\nMBfoDqwFLlPVhrhtFAMHgGXAMcBKYHxgGRhjjDGm3YV+ukdVv6Oqn1TVrqpaqKqHCpS4PiWqerKq\n5qnqEFXdnrR8v6pOVNUeqtpNVb+pqu8Em0n2au6CrVzkSp7gTq7NXcSai1wZT8vTpCr0IsVk3oQJ\nE8IOIRCu5Anu5HrDDTeEHUIgXBlPy9OkyooUBwwePPjInXKAK3mCO7kOGjQo7BAC4cp4Wp4mVVak\nGGOMMSYrWZFijDHGmKxkRYoDkmdxzFWu5Anu5LpixYqwQwiEK+NpeZpUWZHigPLy8rBDCIQreYI7\nuS5fvjzsEALhynhaniZVVqQ4YPHixWGHEAhX8gR3cr3//vvDDiEQroyn5WlSZUWKMcYYY7KSFSnG\nGGOMyUpWpBhjjDEmK1mR4oAxY8aEHUIgXMkT3Ml14sSJYYcQCFfG0/I0qbIixQGuzH7oSp7gTq42\n42xusTxNqqxIccDIkSPDDiEQruQJ7uQ6bNiwsEMIhCvjaXmaVFmRYowxxpisZEWKMcYYY7KSFSkO\nqKysDDuEQLiSJ7iT6/r168MOIRCujKflaVJlRYoDZsyYEXYIgXAlT3An1zlz5oQdQiBcGU/L06Sq\nc9gBmMxbtGhR2CEEwpU8o9Eoc+bMoba2ttXr1NXV0fBBQwajyox58+Yxd+7KsMNotYaGeurq6lJe\nz5Xp/135jrqSZxCsSHFAXl5e2CEEwoU8o9Eo834/j7poaj+EsfdjvLT1JU74wgnkk5+h6NpfRxrT\n/fujPP/8S/zmNwfIyzs2pXULCjozbtxw8vM7ztikoyONZ1u4kmcQrEgxpgOpr6+nLlpH1zO7ktet\n9X8RHnz7IPte2seHH3yYwejc9sEH+9m37yi6dh1EQcGprV4vFttNXd0a6uvrc75IMSZVVqQY0wHl\ndcsjv3vrf9Ci/45mMBoTr0uX7uTn90hpnX37MhSMMR2cXTjrgMmTJ4cdQiBcyRPgsbmPhR1CIEpK\nSsIOIRBPPbUs7BAC4cp31JU8g2BFigN69+4ddgiBcCVPgO4ndg87hECccsopYYcQiOOO+3jYIQTC\nle+oK3kGwYoUB7jykDZX8gQYePXAsEMIxI033hh2CIH40pe+EnYIgXDlO+pKnkGwIsUYY4wxWcmK\nFGOMMcZkJStSHLB169awQwiEK3kC1FTXhB1CILZt2xZ2CIGord0VdgiBcOU76kqeQQi9SBGR20Rk\no4jsEZEaEfmjiJzZTL+fichOEYmJyJMi0jdp+TEiUiYitSKyV0SWiciJwWWSvW699dawQwiEK3kC\nPP4/j4cdQiDuvPPOsEMIxNNP/yHsEALhynfUlTyDkA3zpAwEZgN/xYvnl8BqETlbVfcBiMgUYAIw\nGngD+C9gld+nca7vmcBlwDXAHqAM+IO/fae58vwTV/IEGDZhWKD7a9jfkPJ07+0xFf+0adNYunRj\nWuumO0V9XV0dDQ3BPkJg8OCRge4vLK58R13JMwihFymqOjT+vYhcD7wDFAGNj5KcBNylqo/7fUYD\nNcBVwBIROQ4YC1yrqs/4fcYAW0TkPFVN72+5HOHK7XCu5AlwQq8TAtvX/n37ef7F5/nNgd+kNN13\ne0zFf+qppwKpf33bMkV9LBblpZe2c8IJ9QQ1Aezxx9styLnElTyDEHqR0ozugAL/AhCRPkAh8FRj\nB1XdIyIbgAHAEuCLeLnE93lFRKr9Pk4XKca0xQcNH7Dv4D66fqorBYUFrV4vzKn4052iHuDgwdfZ\nt+9VPvzQHiFgTNiyqkgREcE7bVOpqi/7zYV4RUvylYI1/jKAXkCDqu45TB9jTBt0ye/S4abiT2eK\n+miKD280xmRO6BfOJvlv4NPAtWEHkkumT58edgiBcCVPgDWL1oQdQiBmzZoVdgiB+L//Wxl2CIFw\n5TvqSp5ByJoiRUTmAEOBS1T1n3GLdgGCd7QkXi9/WWOfo/1rU1rq06yhQ4cSiUQSXgMGDKCioiKh\n3+rVq4lEIk3WHz9+PPPnz09oq6qqIhKJUFtbm9A+derUJh/e6upqIpFIk1vWZs+e3eT5D7FYjEgk\nQmVlZUJ7eXk5Y8aMaRLbiBEjqKioIBaL5UQe8ZrLIxaL5UQe0PJ4jBo1itj7MRr2f3Rh56NzH2Xl\ngsQfuX/t+hdlxWXseiPx47/+8fUsuy/xOTEN9Q2UFZex/YXtCe0bV25kwZ0LmsQ277Z5vPDnFxLa\nXl7/MmXFZU36vv7C62x6clNCW/XWasqKy4juTjzS0lweNTU1LF1aRk1N4q3Ia9bMZtmyxPFoaIhR\nVgTQrZ4AACAASURBVBZh+/bE8di4sZwFC5qOx7x5I3jhhcTxePnl1Tz88E1N+j788HgqKxPHo7q6\nirKyCNFo4udqzZpSVq5M/Fz961/VlJVF2LVra1Lf2Tz2WAkfxF1gHMbnKqjvRywWy4k84PDjUVVV\nlRN5NI5HeXn5od/GwsJCIpEIxcXFTdbJBFHVQHZ02CC8AuVK4GJVfb2Z5TuBu1W11H9/HN6pnNGq\nutR//y7ehbN/9PucBWwBvtzchbMi0h/YtGnTJvr375+p1IxpV7W1tZT+tpSCooKUTr3senMXqxau\nYsh1Qyg8NbUzoOmum+560d1R6jbVUTzW+0uwtHQ5BQXDUjpts2vXK6xaVcqQIT+isPD0Vq/XlnXT\nXS8araWubjnFxcPo0SO1U1PGhKWqqoqioiKAIlWtOlL/dIV+TYqI/DcwEogA74tI4xGTf6tqvf//\nM4HbRWQ73i3IdwFvAY/AoQtp5wP3ish7wF5gFvCc63f2GGOMMR1V6EUK8J94F8b+Oal9DPB7AFWd\nISJ5wFy8u3/WApfFzZECUAwcAJYBxwArgfEZjdwYY4wxGRP6NSmq2klVj2rm9fukfiWqerKq5qnq\nEFXdnrR8v6pOVNUeqtpNVb+pqu8Em012Sj63matcyROy486ZIKQzGVtHFIu5MZ6ufEddyTMIoRcp\nJvPGjh0bdgiBcCVPgMX3LA47hEBMmjQp7BAC8ac/PRB2CIFw5TvqSp5BsCLFASUlJWGHEAhX8gQY\nMnpI2CEEIvkOhVw1cOAVYYcQCFe+o67kGQQrUhzgyt1LruQJcOqnUptFtaM699xzww4hEIWFbkyj\n7sp31JU8g2BFijHGGGOykhUpxhhjjMlKVqQ4IHlGw1zlSp4AG57YEHYIgVi4cGHYIQTihRcqj9wp\nB7jyHXUlzyBYkeKA5Cmac5UreQK8te2tsEMIxObNm8MOIRC7dlWHHUIgXPmOupJnEKxIcUBZWdNn\nquQiV/IEuOb714QdQiBmzJgRdgiB+PrX/1/YIQTCle+oK3kGwYoUY4wxxmQlK1KMMcYYk5WsSDHG\nGGNMVrIixQGRSCTsEALhSp4A8+9w4+6BUaNGhR1CIJYudeMaBle+o67kGQQrUhwwYcKEsEMIhCt5\nAlx45YVhhxCIG264IewQAlFUNCjsEALhynfUlTyDYEWKAwYPHhx2CIFwJU+As754VtghBGLQIDd+\nvD/5yU+HHUIgXPmOupJnEKxIMcYYY0xWsiLFGGOMMVnJihQHVFRUhB1CIFzJE+Cl514KO4RArFix\nIuwQAvHKKy+EHUIgXPmOupJnEKxIcUB5eXnYIQTClTwBnl/zfNghBGL58uVhhxCIl1/eGHYIgXDl\nO+pKnkGwIsUBixcvDjuEQLiSJ8DoO0aHHUIg7r///rBDCMTVV48LO4RAuPIddSXPIFiRYowxxpis\nZEWKMcYYY7KSFSnGGGOMyUpWpDhgzJgxYYcQCFfyBFh096KwQwjExIkTww4hEI8/viDsEALhynfU\nlTyDYEWKA1yZ/dCVPAHOLDoz7BAC4cqMs3362IyzucSVPINgRYoDRo4cGXYIgXAlT4D+X+kfdgiB\nGDZsWNghBOIznzkv7BAC4cp31JU8g2BFijHGGGOykhUpxhhjjMlKVqQ4oLKyMuwQAuFKngCvv/R6\n2CEEYv369WGHEIh//GN72CEEwpXvqCt5BiErihQRGSgij4rI2yJyUEQizfT5mYjsFJGYiDwpIn2T\nlh8jImUiUisie0VkmYicGFwW2WvGjBlhhxAIV/IEeHrJ02GHEIg5c+aEHUIg1q9fFXYIgXDlO+pK\nnkHIiiIFOBZ4AfgeoMkLRWQKMAEYB5wHvA+sEpGj47rNBC4HrgEuAk4G/pDZsDuGRYvcuF3VlTwB\nvv2Tb4cdQiDmzZsXdgiBuOqqG8MOIRCufEddyTMIncMOAEBVVwIrAUREmukyCbhLVR/3+4wGaoCr\ngCUichwwFrhWVZ/x+4wBtojIearqxtO7WpCXlxd2CIHIy8sjGo1SX1+f1vpdunQhPz+/naPKjKO7\nHH3kTjnAlc/uxz7WccYz3e9YR/p+tZUrn9sgZEWRcjgi0gcoBJ5qbNP/v717D5OyuhM8/v31/Qrd\naYLtNV6I4BgDAXVDHI06M2F1Yq9jshqTPF7IDBODs4bMoJl5JiO6T3bFbMIGpydIhshks5BVkyC6\nK5qArqDghYuidAMtIk1D34ruhqque5/5463W6qIv9VZ1vW9dfp/nqQfqrXPe95w+VW/96rznvMeY\nkyLyOjAfeBK4HKsu8Wn2i8iRWJqCDlIKhdfrZfUvV+PxelLK31DTwKI7FhXMiVQpu7xeL6tXP4nH\nE7Gdt6GhhEWLbtXPl7Il64MUrADFYPWcxOuKvQZ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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9765c18>"
+       "<matplotlib.figure.Figure at 0x9b220b8>"
       ]
      },
      "metadata": {},
@@ -927,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
@@ -938,24 +937,24 @@
      "text": [
       "TRADING STATS\n",
       "AVG Monthly Return :: 0.03%\n",
-      "STD Monthly        :: 2.94%\n",
+      "STD Monthly        :: 3.03%\n",
       "SHARPE             :: 0.03\n",
-      "MAX DRAWDOWN       :: 35.16%, 75.0 months\n",
-      "Correlation to SPY :: 0.00\n",
-      "NUMBER OF TRADES   :: 769\n",
+      "MAX DRAWDOWN       :: 26.51%, 55.0 months\n",
+      "Correlation to SPY :: -0.04\n",
+      "NUMBER OF TRADES   :: 439\n",
       "TOTAL TRADING DAYS :: 4277\n",
       "SPY MONTHLY RETURN :: 0.76%\n",
       "SPY STD RETURN     :: 4.44%\n",
       "SPY SHARPE         :: 0.59\n",
       "SPY DRAWDOWN       :: 55.37%, 8.0 months\n",
-      "0.351648144393\n"
+      "0.265131925445\n"
      ]
     },
     {
      "data": {
-      "image/png": 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WH17MtTvXeKP6GwD4FPFhT+89fFD/A6oXqc6mrpsI7B3Iq0++SqYMmeKsJ2vG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jrwDgd48fO7rt4P3H3+eRex7hp04/EdgtkHYV25E5Q+Z4+8mWKRvz28znXN9z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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99f3e80>"
+       "<matplotlib.figure.Figure at 0xb228be0>"
       ]
      },
      "metadata": {},
@@ -1015,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false
    },
@@ -1025,18 +1024,18 @@
      "output_type": "stream",
      "text": [
       "TRADING STATS\n",
-      "AVG Monthly Return :: -1.01%\n",
-      "STD Monthly        :: 7.03%\n",
-      "SHARPE             :: -0.50\n",
-      "MAX DRAWDOWN       :: 170.53%, 105.0 months\n",
-      "Correlation to SPY :: -0.05\n"
+      "AVG Monthly Return :: 0.68%\n",
+      "STD Monthly        :: 4.98%\n",
+      "SHARPE             :: 0.47\n",
+      "MAX DRAWDOWN       :: 49.38%, 11.0 months\n",
+      "Correlation to SPY :: 0.03\n"
      ]
     },
     {
      "data": {
-      "image/png": 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ppQKUUn8rpQraK97EIG/evPj6+nLq1KlY1w0KCuLGjRtkzZr1zb5KlSrxySef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\n",
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VKDq5KPUX1OfG/RsseHcB53qe46MyH+Gion4MZk2blc2em2n8amMOXjvIGo81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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99edeb8>"
+       "<matplotlib.figure.Figure at 0x1ac48438>"
       ]
      },
      "metadata": {},
@@ -1087,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
@@ -1096,7 +1095,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.49\n"
+      "0.54\n"
      ]
     }
    ],

From 937dab567581ed10c4bcb5d66bfbb517b06e344c Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 16:39:17 -0800
Subject: [PATCH 08/31] Incorporated CR feedback with retry logic

---
 ...e_Timeseries_Basic_with_Pandas_Numpy.ipynb | 122 +++++++++---------
 1 file changed, 61 insertions(+), 61 deletions(-)

diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index 13ff3c42b..e7ee9dfae 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -474,7 +474,7 @@
        "2000-01-14    1  "
       ]
      },
-     "execution_count": 40,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -609,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -680,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -689,26 +689,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Minibatch: 0, Loss: 0.7004, Error: 53.00%\n",
-      "Minibatch: 1, Loss: 0.6930, Error: 47.00%\n",
-      "Minibatch: 2, Loss: 0.7122, Error: 56.00%\n",
-      "Minibatch: 3, Loss: 0.6941, Error: 44.00%\n",
-      "Minibatch: 4, Loss: 0.6901, Error: 38.00%\n",
-      "Minibatch: 5, Loss: 0.6797, Error: 49.00%\n",
-      "Minibatch: 6, Loss: 0.6960, Error: 50.00%\n",
-      "Minibatch: 7, Loss: 0.6986, Error: 52.00%\n",
-      "Minibatch: 8, Loss: 0.7059, Error: 49.00%\n",
-      "Minibatch: 9, Loss: 0.6972, Error: 52.00%\n",
-      "Minibatch: 10, Loss: 0.6962, Error: 45.00%\n",
-      "Minibatch: 11, Loss: 0.7007, Error: 44.00%\n",
-      "Minibatch: 12, Loss: 0.6800, Error: 46.00%\n",
-      "Minibatch: 13, Loss: 0.6853, Error: 42.00%\n",
-      "Minibatch: 14, Loss: 0.6927, Error: 47.00%\n",
-      "Minibatch: 15, Loss: 0.6794, Error: 47.00%\n",
-      "Minibatch: 16, Loss: 0.6913, Error: 47.00%\n",
-      "Minibatch: 17, Loss: 0.7049, Error: 48.00%\n",
-      "Minibatch: 18, Loss: 0.6713, Error: 41.00%\n",
-      "Minibatch: 19, Loss: 0.7048, Error: 54.00%\n"
+      "Minibatch: 0, Loss: 0.8458, Error: 52.00%\n",
+      "Minibatch: 1, Loss: 0.7292, Error: 53.00%\n",
+      "Minibatch: 2, Loss: 0.7177, Error: 52.00%\n",
+      "Minibatch: 3, Loss: 0.7022, Error: 52.00%\n",
+      "Minibatch: 4, Loss: 0.7118, Error: 53.00%\n",
+      "Minibatch: 5, Loss: 0.7118, Error: 53.00%\n",
+      "Minibatch: 6, Loss: 0.6900, Error: 44.00%\n",
+      "Minibatch: 7, Loss: 0.7228, Error: 61.00%\n",
+      "Minibatch: 8, Loss: 0.7242, Error: 61.00%\n",
+      "Minibatch: 9, Loss: 0.7087, Error: 48.00%\n",
+      "Minibatch: 10, Loss: 0.7006, Error: 54.00%\n",
+      "Minibatch: 11, Loss: 0.7013, Error: 54.00%\n",
+      "Minibatch: 12, Loss: 0.6948, Error: 53.00%\n",
+      "Minibatch: 13, Loss: 0.7122, Error: 56.00%\n",
+      "Minibatch: 14, Loss: 0.6950, Error: 49.00%\n",
+      "Minibatch: 15, Loss: 0.7011, Error: 55.00%\n",
+      "Minibatch: 16, Loss: 0.7015, Error: 48.00%\n",
+      "Minibatch: 17, Loss: 0.6794, Error: 46.00%\n",
+      "Minibatch: 18, Loss: 0.6874, Error: 49.00%\n",
+      "Minibatch: 19, Loss: 0.7077, Error: 49.00%\n"
      ]
     }
    ],
@@ -732,16 +732,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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2SjuSUIw//cnXjymX/kKh9Mxi1uRyNHeuD+/dddeWmxxtxgyYPLllzpVryBBv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\nAN1qmockrQ3sCTzaDJcRMqB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XAUOAHYFJwBhJa9SyyZlAD2Cd7OfXgI+Bu/L2uQtwO3A9sANwD3C3pK2a6Wm0\nC0uWwNNPp66h+uy1V3Wtm/Hd78KLL6YZJ7//ff1jXqy8Pv0U3nknDXTedts0Q+nQQ+EPf0jjVZYu\nrXSE1U1Ks+9K8cc/wkYbpRaXefPKG5dVrw8/TBf43GuvtGTB7be3om7tiKj4DRgHDM27L+Bd4NwG\nbn8IsARYP6/sDuDegnrPAFfXsZ+eQIwfPz7au5qaiKOOirjhhoj334+47baIAQMiVl01AiLWWCNi\nzpxKR1m6loh94sSI/faL+Oij5j9WazJvXsQjj0Scf37E7rtHdO6c/qa22abSkbVdH3wQ8ZOfRKy4\nYsSaa0ZccUXEwoWVjsqay9KlEddcE7HKKhGrrRZx442prLmNHz8+gAB6RhPzgoqvECupE9AL+GJo\nZkSEpEeA3g3czYnAIxHxTl5Zb1JrTL4xwMFNCLfdmDcvTVU8+eQvy3r2hNNPT2NHdt65dQ/AW3nl\n5t3/pElpfMuGGzbvcVqjrl3Ta7PPPun+okWpu6Kps02WLk3fCjvU0R78wQcwdWrqf1+8eNmfXbrA\nDjukafct7b330uDW3r3huOPKv/8ePdK043POgYsuSj8vuywtM3D88WnV6ZY2fXo6F0uWwC671P3/\nZNw4eP31dI6L3TbYIM3Ms9R9etpp6TU78cS0ztUatfVBVLGKJyfAGkBHYEZB+Qxg8/o2lrQO0A84\nquChHrXss0dpYbYvX/0q/P3vaRXT559PK5muu26lo2odJk5MH7wbbQQPP5yumWO169Ilzdyqz9y5\nKUH+7LPlE4vFi9MYlmeeSVNra3P77emDuTZf+1rqemops2bB//1fGofTtSt861vNe7wNNkgDws89\nFy68MM1e+93v0t/pRhs1zzGXLk0z9yZNSu+N3G1G3n/nOXPq/sJw/fVw003LlnXs+OXtu991cpLz\n8MPpvfLkk7DrrpWOpnQVn62TJRfvAb0j4tm88t8Bu0dEna0nks4DBgHrRsSSvPLPgGMj4s68stOA\nCyKi6NC93Gyd3Xffne4Fi2gMGDCAAQMGNPr5WesTkdbxKGVGz/PPpxkVG28MDz3kxKSc3nkHhg1L\nM8A6dUoXvcz9zP1+wAF1r7Y7a1bqh8/ftlOndJs7F95/H775zbrjGDUKNt0UNtmk7laausydC5df\nnlovAH7607RmSXO36BV64YW0FtLQoc3XEjp+fLpIKaTkaIcd0uDMHXZIr2OnTulnXcf/7LOUfK6w\nQqrX2NdzJJXNAAAP5ElEQVT9nXegW7f28X5cvDj9bO7WsJEjRzJy5MhlyubMmcO//vUvKMNsnWoY\nb9IJWAwcVFA+AvhHA7Z/Dfh9kfK3gTMLyi4Enq9jXx5zYnHrrWkcxCWXRCxZ0vDtJkxIY3J22ini\nk0+aLz6rnIULIzp2TGNkunWL2G23iLPPjrjlloiXX67/72XRoojLLktjtjp3jhg8OGLmzJaJvZxq\naiLefTdi1KiIf/6z7rqLFkU89lhlx14dcEDEuutG3H9/5WJoD8o55qTis3UiYjEwHtgnVyZJ2f2x\ndW0raU/gG8CNRR5+Jn+fmf2ycrNaHXZYGlvz85/Dd76TlmyvT26MySabpGbVVVZp/jit5XXpkroj\nHn4Yfv3rNH36/vvTjIitt04tH2Pr+K9VU5PGfhx6aBpDcdll1T8eYPHiNPX7ttvgZz+D/fZLrVNf\n+1pqqRo2rO7tc2sdrbZay8RbzLXXptaaAw9M4zBmz65cLNZATc1uynED+gMLgGOBLYBrgY+ANbPH\nLwZuLrLdrcDYWvbZG/gMGEwau3IhsAjYqo443HJiXxg7NmLzzdMMh4svjli8uPa6M2ZEHHusW0za\nq9mzIx5/PLWKzJpVd90FC1okpLI57bTUUgQRX/96xCGHRFx4YcTdd0e89VZqRWkNamrSrJWVV45Y\nb72I0aMrHVFpHn004rnnKh1FceVsOal4YvJFIDAQeAtYSGrd2CnvseHAYwX1VwbmASfWsc/vA69m\n+3wB6FNPDE5ObBkLFkSce25Ehw6pu+bFFysdkVnLeuGF1HXTVhLvadMivvvd9Ol30kkpsWwNpk+P\nOOaYFPfJJ1c6muLKmZxUfEBsNWkvy9db4z37bGoOXrQIpkxp/msCmVnziUizlgYPTqtXN+c1xppq\n6dI0aPm889L/nUsvTdPNSx2M3ZzKuXy9/8WaNcC3vpVWMX3jDScmZq2dBD/6UZqCvPrqlY6mds8/\nD6eemtYBOvnkNO28muMtpyrMvcyqU+fOsJUvfmDWZmy4YZpiXI1+8Ys0BXvhwnS17+uvbz+JCTg5\nMTMzqzprrAGXXJLWifnOdyodTctzA7WZmVkRixdXZml/qHsl4/bALSdmZmYFampg//3TgNn585u+\nv4UL4Ykn0rWN9tsvrb1itXNyYmZmVsT3vgc33gjbbQdpVfaGmzMHRo9Os2y+8x3o3j0tRnf55Wkx\nvx6+yludnJyYmZkV6NABzjwzrf68zjrp4qdnnw0LFjRs+x//OLW8DB8O662XkpKJE+Gjj+C+++Dg\ng5s1/FbPY07MzMxqsemm8M9/pssO/PKX6cKPI0bALrukKcm1GTIEfvvbdEmLuupZcW45MTMzq0PH\njumq0RMnplk0u+4Kjz1W9zZbbpkSGycmpXHLiZmZWQNsvnlac+SOO1JXjzUfJydmZmYN1LEjHHNM\npaNo+9ytY2ZmZlXFyYmZmZlVFScnZmZmVlWcnJiZmVlVcXJiZmZmVcXJiZmZmVUVJydmZmZWVZyc\nmJmZWVVxcmJmZmZVxcmJmZmZVRUnJ2ZmZlZVnJyYmZlZVXFyYmZmZlWlapITSadLmippoaRxknau\np/6Kkv5H0luSFkl6U9LxeY8fJ6lG0tLsZ42kBc3+RKyqjBw5stIhWBn5fLYtPp9Wm6pITiQdCVwG\nDAF2BCYBYyStUcdmfwH2Ak4ANgMGAFMK6swBeuTdNixv5Fbt/M+vbfH5bFt8Pq02K1Q6gMwg4NqI\nuAVA0qnAAcCJwCWFlSX1BXYDNo6I2VnxtCL7jYiY2Twhm5mZWXOoeMuJpE5AL+DRXFlEBPAI0LuW\nzb4H/Af4uaR3JU2RdKmkLgX1umXdPtMk3S1pq+Z4DmZmZlY+1dBysgbQEZhRUD4D2LyWbTYmtZws\nAg7J9vEnYDXgpKzOFFLLywtAd+BnwFhJW0XE++V8AmZmZlY+1ZCclKIDUAMcHRHzACQNBv4iaWBE\nfBYR44BxuQ0kPQNMBn5MGttSTBeAk08+ma9+9avLPNCnTx/69u1b9idizWvOnDlMmDCh0mFYmfh8\nti0+n63Xgw8+yJgxY5Ypmzt3bu7Xwl6MRlPqQamcrFtnAfD9iLg3r3wE0D0iDi2yzQhgl4jYLK9s\nC+BlYLOIeKOWY90FLI6IY2p5/Gjgz6U/GzMzs3bvmIi4vSk7qHjLSUQsljQe2Ae4F0CSsvtX1rLZ\n08DhklaKiNz04M1JrSnvFttAUgdgW2BUHeGMAY4B3iJ1GZmZmVnDdAG+TvosbZKKt5wASOoPjABO\nBZ4jzd45HNgiImZKuhhYNyKOy+p3BV4hddtcCKwJXA88HhGnZnXOzx5/HVgFOBc4COgVEa+22JMz\nMzOzRql4ywlARNyVrWlyEbA2MBHokzcNuAewfl79+ZL2A/4I/Bv4CLgTOD9vt6sC12XbfgKMB3o7\nMTEzM6tuVdFyYmZmZpZT8XVOzMzMzPI5Ock09to+Vp0kDcm7llLu9kql47KGk7SbpHslvZedv4OK\n1LlI0vuSFkh6WNImlYjV6lff+ZQ0vMh79oFKxWt1k3SepOckfSpphqR/SNqsSL0mvUednFDytX2s\ner1EGruUu6bSrpUNxxqpK2nc2UBguX5nST8HzgBOAb4JzCe9X1dsySCtweo8n5nRLPueHdAyoVkJ\ndiON9/wWsC/QCXhI0ldyFcrxHvWYE0DSOODZiDgruy/gHeDKiFju2j5WvSQNAQ6OiJ6VjsWaTlIN\ncEjBGkjvA5dGxBXZ/ZVJK0ofFxF3VSZSa4hazudw0ppWh1UuMitV9iX+Q2D3iHgqK2vye7Tdt5yU\neG0fq26bZk3Ib0i6TdL69W9irYGkjUjfrPPfr58Cz+L3a2u2Z9ZF8KqkqyWtVumArMFWIbWIfQzl\ne4+2++SEuq/t06Plw7EmGgccD/QhrZuzEfCvbG0ca/16kP4R+v3adowGjgX2Jq1HtQfwQNaCbVUs\nO0d/AJ6KiNzYvrK8R6tinROzcomI/JUJX5L0HPA20B8YXpmozKw2Bc38L0t6EXgD2BN4vCJBWUNd\nDWwFfKfcO3bLCcwClpIGY+VbG5je8uFYOUXEHOA1wLM52obpgPD7tc2KiKmk/8t+z1YxScOA/YE9\nI+KDvIfK8h5t98lJRCwmrR67T64s79o+YysVl5WHpG6kf3If1FfXql/2wTWdZd+vK5NmDvj92gZI\n+hqwOn7PVq0sMTkY2CsipuU/Vq73qLt1ksuBEdkFCHPX9lmJdL0fa0UkXQrcR+rKWQ/4DbAYGFnJ\nuKzhsvFBm5C+fQFsLGl74OOIeIfUx/1rSa+TLtL5W9IFP++pQLhWj7rOZ3YbAvyN9IG2CfA7Umtn\nky8eZ+Un6WrSVO+DgPmSci0kcyIid8HcJr9HPZU4I2kgaTBW7to+P4mI/1Q2KmssSSNJ8/BXB2YC\nTwG/yrJ5awUk7UEaa1D4z+nmiDgxq3MhaQ2FVYAngdMj4vWWjNMapq7zSVr75G5gB9K5fJ+UlFyQ\nd201qyLZdPBiicMJEXFLXr0LacJ71MmJmZmZVZV2P+bEzMzMqouTEzMzM6sqTk7MzMysqjg5MTMz\ns6ri5MTMzMyqipMTMzMzqypOTszMzKyqODkxMzOzquLkxMzMzKqKkxOzdkDS45Iub0T9DSXVSNou\nu79Hdn/l5ouy1liGS/p7Sx+3VJKGSHq+0nGYtWZOTsxaIUkjsmTh6iKPXZU9dlNe8aHA+Y04xDSg\nB/BSXlmTr3XR2CSpFfN1QcyawMmJWesUpATiKEmdc4XZ7wNIV2X+snLE7IiY3+CdJx9GRE25Aram\nkeSryFu74eTErPV6HngHOCyv7DBSYrJMt0Jhi4WkqZLOk3SjpE8lvS3pR3mPL9Otk2dXSZMkLZT0\njKSt87ZZTdLtkt6VNF/SC5KOynt8OLAHcFa276WSNsge21rSfZLmZPH8U9JGBc/hp5LelzRL0jBJ\nHWt7YXJdK5J+kD3X2ZJGSupa8BqcWbDd85IuyLtfI+mULLb5kl6R9G1J38he03mSni6MNdv2FEnT\nsu3ulPTVgsdPzva3MPt5WpHXv7+kJyQtAI6u7fmatTVOTsxarwBuAk7MKzsRGA6oAdsPBv5Nulz9\n1cCfJG1asP98Ai4BBgE7ATOBe/OShC7Af4B+wNbAtcAtknbKHj8LeAa4HlgbWAd4R9K6wD+BhcCe\nwI5ZnfyWgr2BjbPHjwWOz251+QZwMLA/cAApMfpFPdsU82tgBLA9MBm4HbgG+B+gF+l1GVawzabA\nEdlx+5Ce0xddcJKOAS4EzgO2AH4JXCTphwX7uRi4AtgSGFNC7GatkpsJzVq3PwP/J2l90peNXYAj\ngb0asO2oiLgm+/13kgZl2/03KyuW4FwYEY8BSDoOeJc0nuWvEfE+kD+e5CpJfYH+wH8i4lNJnwML\nImJmrpKkM4DZwICIWJoVv1Fw3I+BMyIigNckjQL2AW6s4/kJOC4iFmTHuTXbpjFjbwBuioi/Zfu4\nhJRg/SYiHsnKhpKSxHydgR9GxPSszk+AUZJ+GhEfkhKTn0bEPVn9t7NWqFOBW/P2c0VeHbN2w8mJ\nWSsWEbMk3Q+cQPowHhURH0sNaTjhxYL704G16jocMC7v2J9ImkL6Vo+kDsCvSC0G6wErZrf6xrps\nDzyZl5gU83KWmOR8AGxTz37fyiUmedvU9fxqk/86zch+vlRQ1kVSt4iYl5VNyyUmmWdIyePmkuaR\nWnVulHRDXp2OpCQt3/gS4jVr9ZycmLV+w0ndCgEMbMR2iwvuB03r6j0X+Amp++YlUlIylJSg1GVh\nA/ZdSqz1bVPD8q1DnerZT9RR1tDXrlv282TguYLHChO0Bg9iNmtLPObErPV7kJQArAA81IzHEfDt\nL+5IqwKbAa9kRbsA90TEyIh4EZiaPZ7vc1ILQb4XgN3qGuDaTGaSxr0AkK3hstzA1iIaMk14A0k9\n8u73JiUer2bdOu8D34iINwtu+bOsPB3Z2i0nJ2atXDbddwtg64Kuj+ZwgaS9JW1DGiQ6E8iNifgv\nsJ+k3pK2JA2IXbtg+7eAb2WzUVbPyoYBKwN3SuolaZNsls2mNK/HgB9K2lXSttnzWdKA7Yr1mRWW\nfQbcLGk7SbuRWpDuzBtrMwQ4T9JPJG0qaRtJx0s6u57jmLULTk7M2oCImJc33qFolXruN6ROkGa7\nDCXN8lkT+F5E5D7Q/x8wgdSS8xhpjMc/Cvbxe1ILwivAh5I2iIiPSbNxugJPkGb8nMzy3TLldjFp\nltB92e0fLD8QtyGvU7Gy/wJ/Bx4gvR4TgdO/qBxxI+k5nkBqOXoCOI7U2lTXcczaBTX/Fy0zMzOz\nhnPLiZmZmVUVJydmZmZWVZycmJmZWVVxcmJmZmZVxcmJmZmZVRUnJ2ZmZlZVnJyYmZlZVXFyYmZm\nZlXFyYmZmZlVFScnZmZmVlWcnJiZmVlVcXJiZmZmVeX/A/kWwZ4Yxlp7AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1b890ac8>"
+       "<matplotlib.figure.Figure at 0x996d978>"
       ]
      },
      "metadata": {},
@@ -749,9 +749,9 @@
     },
     {
      "data": {
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Srx988klMLz5rls/QevbZPkNrly4+v8yZZ8L332d/TavRuHELz4lSU1O5ydxC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\nFFZfve7zBw3yxeFGjfJrQ/U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nWt54w2uFDjsMrr668DH77AMvvuhJSseOzRtfqIhK1pxkSU5GAPeZ2bC87ccC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\nIYRQU/4fIXIuPgDsW8EAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ace5b70>"
+       "<matplotlib.figure.Figure at 0x9a23160>"
       ]
      },
      "metadata": {},
@@ -788,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -797,7 +797,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average error: 46.50%\n"
+      "Average error: 49.00%\n"
      ]
     }
    ],
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -859,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -897,16 +897,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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X06dPn0N9u3btSlFREevXrycSiRxqv+iii3j//feJxWLNFiktndLpKKd6wIoU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\nTY0vlUtMkzHgVo3P7mUibwq3dkhXur0+8+cvpr7+jKTz9fV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0+O/evHkzl1xyCRdeeCEvvvgiDz/8MOPHj+eLX/xiiz5/55138q1vfYuvfe1r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\n0rbFddlYX4xEb4Ldr+yms839B0rXwS4i3RFaN7ey85WdbH1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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b220b8>"
+       "<matplotlib.figure.Figure at 0x185c6cf8>"
       ]
      },
      "metadata": {},
@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -937,24 +937,24 @@
      "text": [
       "TRADING STATS\n",
       "AVG Monthly Return :: 0.03%\n",
-      "STD Monthly        :: 3.03%\n",
+      "STD Monthly        :: 2.94%\n",
       "SHARPE             :: 0.03\n",
-      "MAX DRAWDOWN       :: 26.51%, 55.0 months\n",
-      "Correlation to SPY :: -0.04\n",
-      "NUMBER OF TRADES   :: 439\n",
+      "MAX DRAWDOWN       :: 35.16%, 75.0 months\n",
+      "Correlation to SPY :: 0.00\n",
+      "NUMBER OF TRADES   :: 769\n",
       "TOTAL TRADING DAYS :: 4277\n",
       "SPY MONTHLY RETURN :: 0.76%\n",
       "SPY STD RETURN     :: 4.44%\n",
       "SPY SHARPE         :: 0.59\n",
       "SPY DRAWDOWN       :: 55.37%, 8.0 months\n",
-      "0.265131925445\n"
+      "0.351648144393\n"
      ]
     },
     {
      "data": {
-      "image/png": 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jrwDgd48fO7rt4P3H3+eRex7hp04/EdgtkHYV25E5Q+Z4+8mWKRvz28znXN9z\nPFUq5m0ZWpdvTY7MOZi5Z2aCsfx0/CciTAQNSzdMNO66JepS9Z6qjNg8IsF2xy4dY+uprTz/wPOJ\n9pkWadGoUkqpNOlC0AVe++E1boTdIF+2fOTNmpd82fLxdNmnqVS4Upz2E3ZM4KlST3H/XfffXJY5\nQ2beq/NekvctIuTKEve2DdkzZ6dthbbM3DOT9+u8H2eEJMqqI6sok68MJfOWdGlfb9d6m2cXPMuv\nf/9K1SJVnbabv38+2TJmo3nZ5k7Xp3U6wqGUUirNCY0IpfX81qw7tg6D4c9//2TlkZV8uvVT6s6s\ny9FLMa+a2312N7+c+oVXH3nV7bF1ebgLx/87zvrj6+Nts+rIKhqUSvx0SpRnyj1DmXxlGLllZLxt\n5u2fR/OyzcmROUeS4k0rNOFQSimVphhj6LG8B1tPbeX7579nqf9SNr+4mQOvHeBYr2Pky5aP1vNb\ncyPsxs1tJvw6gXty3sPTZZ92e3y1itbivnz3MX33dKfrj/x7hCOXjtCwTOKnU6Jk8MlA35p9+fbA\ntxz+93Cc9YcuHGL32d1eezoFNOFQSimVxozdOpapu6YyuflkHi36aIx1ebLmYVHbRRy6cIgeP/TA\nGMOVkCvM+W0O3ap0S9bMnUklInSu3JmFBxZyJeRKnPWrjqwio09G6paom6R+O1bqSIHsBRi9ZXSc\ndfP2zSNXllw0vq9xcsP2OK3hSIKDBw96OoR0T19jpe5sP/z5A31X96VfrX50rNTRaZtKhSsxsdlE\nOn3XiZr31iQsIozg8GBervJyqsXZoWIHBq4byIL9C3ipyksx1q06uoqa99Z0WgOSkGyZsvFGtTcY\nsmEIg+oOolCOQoAd8Zm3bx7PlHsmTU9dnhhNOFyQP39+fH19ad++vadDuSP4+vqSP39+T4ehlEpl\n+8/v5/mFz9Ps/mZ8XO/jBNt2rNSRbae20fPHnhTMXpAW5VpQJFeRVIoUiuYuSoPSDRixeQQtyrUg\nv6/9mxUWEcbao2vpV6tfsvrtUbUHwzYNo3lAc4rkKkJoRCg3wm7w+8XfGdtobEoeQqrThMMFxYoV\n4+DBg1y4cMHTodwR8ufPT7FixTwdhlIqFZ28fJJGcxpRMm9JZj8zO96rP6Ib02gMO8/uZOuprUxv\n4byewp3GNR5H7em1aTi7Ies6riN31txs+3sbV0OvujT/hjN5s+VlVINRLDywkNCIUDL5ZCK7b3Z6\nVutJvZIBzgnoAAAgAElEQVT1UvgIUpcmHC4qVqyYfggqpVQyRZpItp7aSvUi1ePckfWf6//QYFYD\nMvlkYkW7FeTMktOlPjNnyMzi5xaz9PeYk3Ollvvvup/VHVZTd0Zdms5tysr2K1l1ZBX5suWjyt1V\nkt3vK4+8cnMukfREi0aVUkq53ditY6k1rRY1ptbg179/vbn8ashVmsxtwqXgS6zqsIq7c96dpH4L\n5yhMV7+uLo2IuEPFQhVZ0X4Fe87toeU3LVn+53Lql6of723u72SacCillHKrc9fOMXj9YFqUbUFY\nRBjVp1Sn29JunL56mlbzW/H7hd9Z0e7WHVq9TbUi1Vjmv4xNJzex88zOZJ9OSe804VBKKeVWA9YN\nIINkYOrTU9nRbQfjGo9j/v75FBtTjI0nNrLEfwkP3/2wp8O8LXVK1GHxc4t55J5HaHpfU0+HkyZ5\nZcIhIveIyCwRuSAiQSKyR0SqxGrzoYicdqxfLSLemTorpZQXCzwdyLRd0xjyxBDu8r2LjD4Zeb3a\n6/zR8w96VuvJ4ucWJ3m+irSqUZlG/Nr115uXs6qYvC7hEJE8wGYgBGgIlAfeAi5Fa9MPeB3oBlQD\nrgMrRST+u/UopZRKtsDTgQSFBcVYZoyh14pePFDwAbo/0j3GuoLZCzKm0RivnshKJY3XJRxAf+Ck\nMeZlY0ygMeaEMWaNMSb6vYJ7AUOMMcuMMfuAjsA9QEtPBKyUUumVMYZBPw/ikcmPUG58Oebtm4cx\nBoCAfQFs/msznzX6LFVmAFVpmzcmHM2BHSIyX0TOichOEbk5vZyIlAQKA2ujlhljrgDbgJqpHq1S\nSqVTEZERvPbDawxeP5h3H3uXqkWq4v+tP7Wn12bDiQ28vfptWpVv5ZFLVlXa440pZyngVWA0MBR7\nymSciIQYY2Zhkw0DnIu13TnHOqWUUrcpODyY9ovas/jQYqY0n3Jzeu91x9bRa0Uv6syoQ5YMWRhV\nf5SHI1VphTcmHD7AdmPMe47ne0TkQeAVYJbnwlJKqTvDlZArtJjXgq2ntrL4ucUx7tD6ZMkn2dV9\nF9N3TSdXllyUzFvSg5GqtMQbE44zQOw7fB0EWjn+fRYQoBAxRzkKAbsS6rh3797kzp07xjJ/f3/8\n/f1vJ16llEo3roZcpdHsRhz45wCr2q+idvHacdpk9MlIV7+uHohOuVtAQAABAQExll2+fNmlbSWq\nuMdbiMgc4F5jTJ1oy8YAVY0xjzmenwY+McaMcTzPhU0+OhpjFjjpswoQGBgYSJUqyZ+OViml0rPr\noddpPKcxe87tYU2HNVQtUtXTIak0YOfOnfj5+QH4GWN2xtfOG0c4xgCbReQdYD5QHXgZiJ5OjwUG\nishh4DgwBDgFfJ+6oSqlVPpwI+wGT897ml1nd7Gy/UpNNlSSeV3CYYzZISLPAMOB94BjQC9jzLxo\nbUaKiC8wEcgDbAQaG2NCPRGzUkp5s+DwYFp+05Ktp7byY7sfebToo54OSXkhr0s4AIwxPwA/JNJm\nEDAoNeJRSqn06ELQBRYfXMzknZP57fxvLH9hOY8Xf9zTYSkv5ZUJh1JKKfe4HHyZhQcWMv/AfNYe\nXYvBULdEXVa0W0GdEnUS70CpeGjCoZRSd7hIE8mGExuYtmsaCw8sJCQihDrF6zC+yXhalW9FwewF\nPR2iSgc04VBKqTvYisMreO2H1zh66Shl8pXhvcffo2OljhTJVcTToal0RhMOpZS6Q4WEh9B1aVeK\n5y7OjBYzeKzYY4iIp8NS6ZQ33ktFKaWUC7ae2krXJV0JDg92un7armn8feVvJjefTO3itTXZUG6l\nIxxKKZUO/X7hd5rObcq/N/6lYPaCDK03NMb6kPAQPt70Mf4P+VO+QHkPRanuJDrCoZRS6cw/1/+h\nydwmFMpeiLdqvsXILSPZc3ZPjDZTd03l9NXTvPf4e/H0olTK0oRDKaXSkagZQa+HXueHdj/wcb2P\nKZe/HC8vfZnwyHDAMbqx8WP8H/SnXP5yHo5Y3Sk04VBKqXQiIjKC9ovbs/fcXpa9sIwSeUqQOUNm\npjSfQuDpQMZtGwfY0Y0z187o6IZKVZpwKKVUOtF/TX++O/Qd81rP45F7Hrm5vPq91Xmj+hsMXDeQ\ng/8c5OONH/PCQy9QNn9ZD0ar7jSacCilVDqw8MBCRv0yitENRtO8bPM46z968iMKZi9I7em1OXPt\nDANrD/RAlOpOpgmHUkp5uT8u/sGL379I2wfa0qt6L6dtcmTOwcRmE7l446KObiiP0MtilVLKiwWF\nBdF6fmvuyXkPU5pPSXAujYZlGrL8heXUuLdGKkaolKUJh1JKeSljDK8uf5Wjl46y/eXt5MySM9Ft\nmtzXJBUiUyouTTiUUspLTdk5ha/3fM3XLb/mgYIPeDocpRKkNRxKKeWFjl46Ss8fe9LdrzsdKnXw\ndDhKJUoTDqWU8kLDNg4jT9Y8fNrwU0+HopRLNOFQSikvc+K/E8zYM4P/Pfo/fDP5ejocpVyiCYdS\nSnmZ4ZuGkydrHl555BVPh6KUyzThUEopL/LX5b+YumsqfWv2JXvm7J4ORymXacKhlFJeZMTmEeTM\nkpMeVXt4OhSlkkQTDqWU8hJ/X/mbyTsn06dGH5fm3FAqLdGEQymlvMQnWz7BN5Mvr1d73dOhKJVk\nmnAopZQXOHvtLBMDJ/Jm9TfJnTW3p8NRKsk04VBKKQ/78+KfPLfwOc5fPx9vmwFrB5A5Q2beqP5G\nKkamVMrRqc2VUsrDhmwYwvz98/kv+D9+bPcjPhLzu+DCAwuZtnsak5pNIm+2vB6KUqnboyMcSinl\nQX9f+ZuAfQG0Kt+K1UdW8/HGj2Os/+vyX3Rd2pXW5VvzcpWXPRSlUrdPRziUUsqDxm8fT7aM2Zj2\n9DQeKvgQH/z8AbWK1uKJkk8QERlB+8XtyZE5B5OaT0rw1vNKpXVePcIhIv1FJFJEPo21/EMROS0i\nQSKyWkTKeCpGpZSKz7XQa3wV+BVdq3Qld9bcvPf4ezxR4gn8v/Xn7LWzDNs0jI0nNjL7mdnky5bP\n0+EqdVu8NuEQkapAN2BPrOX9gNcd66oB14GVIpI51YNUSqkEzNg9gyshV24WgmbwycCcVnMQERrN\nbsSgnwcxoPYA6pSo4+FIlbp9XplwiEgOYDbwMvBfrNW9gCHGmGXGmH1AR+AeoGXqRqmUUvGLiIxg\n7NaxtKnQhuJ5it9cXihHIQJaB/Db+d+oWqQq79d534NRKpVyvDLhAL4Alhpj1kVfKCIlgcLA2qhl\nxpgrwDagZqpGqJRSgDGGNUfXcOnGpRjLl/y+hCOXjvBWzbfibFO3RF02ddnEUv+lZMqQKbVCVcqt\nvC7hEJHngcrAO05WFwYMcC7W8nOOdUoplaoGrx9M/Vn1KT2uNKO2jCI4PBiAT7d+Sq2itahWpJrT\n7WoWrUl+3/ypGapSbuVVV6mIyL3AWOApY0yYp+NRSqmEjN4ymsHrBzOg9gAuBl2k/5r+fL79czpX\n6symk5tY1HaRp0NUKtV4VcIB+AEFgJ1y6/qwDMDjIvI6UA4QoBAxRzkKAbsS67x3797kzh1zymB/\nf3/8/f1TIHSl1J3kqx1f0Xd1X9597F0+evIjAN6s8SbvrnuXDzd8SKm8pXi67NMejlKppAkICCAg\nICDGssuXL7u0rRhj3BGTW4hIdqB4rMUzgIPAcGPMQRE5DXxijBnj2CYXNvnoaIxZEE+/VYDAwMBA\nqlSp4rb4lVJ3htl7Z9NxcUd6VuvJ2EZj48yfEXg6EN9MvpQvUN5DESqVcnbu3Imfnx+AnzFmZ3zt\nvGqEwxhzHTgQfZmIXAcuGmMOOhaNBQaKyGHgODAEOAV8n4qhKqXuUAG/BdD5u850qdyFMY3GOJ2s\ny+8ePw9EppRneVXCEY8YQzTGmJEi4gtMBPIAG4HGxphQTwSnlLozGGMYtWUUb695m46VOjKp+aQ4\n90RR6k7m9QmHMeZJJ8sGAYNSPRil1B0pIjKCN358gy93fMnA2gP58IkPdRpypWLx+oRDKaU8KSgs\nCP9v/Vn+x3ImNZtEV7+ung5JqTRJEw6llEqmSBNJs7nN2P73dpb4L6HJfU08HZJSaZYmHEoplUzT\nd03np+M/sbbjWp4sGefsrlIqmmQlHCLiA5QBChJrtlJjzIYUiEsppdK0f2/8S781/ehQsYMmG0q5\nIMkJh4jUAOZi58OIXRVlsBNxKaVUuvbu2ncJiwxjZP2Rng5FKa+QnBGOr4AdQFPgDLEuS1VKqfTu\n179/ZVLgJD5r9BmFc+htmpRyRXISjvuANsaYwykdjFJKpXURkRH0+KEHlQpX4tWqr3o6HKW8RnIS\njm3Y+g1NOJRSd5wpO6ew4/QONr+4mYw+WnevlKuS87/lc2C0iBQGfgNi3LXVGLM3JQJTSqm0ZteZ\nXbyz9h26VO7Co0Uf9XQ4SnmV5CQc3zp+Tou2zGALSLVoVCmV7lwMusjAdQOZGDiRCgUqMOKpEZ4O\nSSmvk5yEo2SKR6GUUmlQeGQ4kwInMXDdQCJMBJ82/JTXqr5GpgyZPB2aUl4nSQmHiGQCPgCGGGOO\nuSckpZRKG3r+0JOvAr/ixcovMuypYRTMXtDTISnltZJ0K0NjTBjQ2k2xKKVUmnHm6hmm7prKsHrD\nmNpiqiYbSt2m5Nw7+TugZUoHopRSacn47ePJkjELrzzyiqdDUSpdSE4Nx5/A+yJSCwgErkdfaYwZ\nlxKBKaWUp1wPvc6EHRN4+eGXyZM1j6fDUSpdSE7C8RLwH+DneERnAE04lFJebcbuGVwOuUyvGr08\nHYpS6UaSEw5jjF6lopRKtyIiIxizdQxtKrShRJ4Sng5HqXRDp8lTSqlolvy+hCOXjjC39VxPh6JU\nupKcu8VOS2i9MebF5IejlFKeNfqX0TxW7DGqFanm6VCUSleSM8KRN9bzTMCDQB5g3W1HpJRSHrLt\n1DY2/7WZxc8t9nQoSqU7yanheCb2MhHxASYAR1IiKKWUuh0Xgi4wZP0QelTtQdn8ZV3aJtJEMnLL\nSMrkK0Pz+5u7OUKl7jzJmYcjDmNMJPAp0Dsl+lNKqeQKCgui2dxmjNs+jmpTqvH9oe8TbH/pxiXG\n/DKGcuPLsejgIvrX6k8GH70llFIpLUUSDofSaBGqUsqDwiPD8f/Wn9/O/8a6jut4qtRTtPymJe+t\ne4+IyIib7ULCQ1h9ZDUvff8SRT4tQr81/XjknkfY2GUjLz6sZWhKuUNyikY/jb0IuBtoCsxMiaCU\nUiqpjDH0/KEny/9YzhL/JTxR8gnqlqjLiM0jGLBuADvO7KBl2Zb8ePhH1hxdw/Ww6xTPXZyBjw/k\npYdfolCOQp4+BKXSteSMSDwc63kk8A/wFjFvWa+UUqlm+KbhfBX4FVOaT6HJfU0AEBH6P9afKndX\nwf9bf1YdWcWjRR9l4OMDaXJfEx4q+BAi4uHIlbozJKdo9Al3BKKUUsn19Z6veXfdu3xQ5wNeqvJS\nnPUNSjfgeK/jhEeGkzdb7AvtlFKpIck1HCKyTkTi3FxARHKJiF4Wq5RKVSsOr+ClJS/x0sMv8UGd\nD+JtlzNLTk02lPKg5BSN1gUyO1meFah9W9EopdK1f67/w5Lfl3Ax6GKK9Lf97+20nt+axmUa81Wz\nr/T0iFJpmMunVESkYrSnFUSkcLTnGYBGwN8pFVgCcbwDPAOUA24AW4B+xpg/YrX7EHgZOyHZZuBV\nY8xhd8enlHIuIjKC1vNbs/HkRgShcuHK1CtZj4ZlGlKvZL0kJwt/XPyDpnObUqlQJea1mUdGH71I\nTqm0LCn/Q3dj7wZrcD6j6A2gZ0oElYjawOfADmz8w4BVIlLeGHMDQET6Aa8DHYHjwEfASkeb0FSI\nUSkVy6gto9h0chMLnl3AtdBrrD22ljm/zWHUL6OY13oezz34nMt9nbl6hoazG1LAtwDLXliGbyZf\nN0aulEoJSUk4SmIvgT0KVMNemRIlFDhvjIlwtmFKMsY0if5cRDoD5wE/YJNjcS9giDFmmaNNR+Ac\n0BKY7+4YlVIx7Tyzk/d+eo9+tfrRpkIbADpX7owxhsdnPM7UXVNdTjjWHVtHj+U9CIsIY0PnDeTL\nls+doSulUojLNRzGmBPGmOPGGB9jzA7H86jHmdRINuKRBzvq8i+AiJQECgNroxoYY64A24CanghQ\nqTtZUFgQ7Ra148GCDzL4icEx1okInSp1Ys3RNZy6cirBfg78c4Bmc5tR7+t65MuWjzUd11A0d1F3\nhq6USkHJmmlURDqIyGYROS0ixR3LeotIi5QNL9E4BBgLbDLGHHAsLoxNQM7Fan7OsU4plYr6re7H\n8f+OM7vVbDJniFtv/myFZ8mSMQtz9s5xuv210Gt0X9qdhyY8xMELB1nw7AI2v7iZcvnLuTt0pVQK\nSs5lsa9i75vyA3Z0IeqmA5eAN1MuNJd8CVQAnk/l/SqlXPDjnz8y/tfxfFL/EyoUqOC0Te6suWlZ\nriUz98zEGBNn/eCfBzNr7yxGNxjNwdcO0qZCG70aRSkvlJyy7p5AV2PMdyLSP9ryHcColAkrcSIy\nHmgC1DbGnIm26iy21qQQMUc5CgG7Euqzd+/e5M6dO8Yyf39//P39UyRmpe4kN8Ju0HVpVxqWbshr\nVV9LsG2nSp1ovK8xO07voGqRqjeXn/jvBOO2j2NA7QG8WSO1v88opWILCAggICAgxrLLly+7tG1y\nEo6SOP/gDgGyJ6O/JHMkGy2AOsaYk9HXGWOOichZoB6w19E+F1Ad+CKhfseMGUOVKlXcE7RSd5jx\n28dz7vo51jdZn+iIRP1S9bk7x93M3DMzRsLx3k/vkS9bPvrU7OPucJVSLnD2JXznzp34+fklum1y\najiOAZWdLG8EHExGf0kiIl8C7YAXgOsiUsjxyBqt2VhgoIg0F5GHgK+BU0DC96lWSqWIy8GXGb55\nOC8//DKl85VOtH0Gnwy0r9iegH0BhEbYK9d3ndnF7L2zGVRnEDky53B3yEopN0vOCMenwBeOD3gB\nqomIP/AOdqItd3sFWxT6c6zlXbCJBcaYkSLiC0zE1plsBBrrHBxKpY5RW0ZxI+wG79V5L866y5fh\n5En7OHECrl6Frl2hY6WOfLLlE5b/sZxnyj9DvzX9uP+u+53eGyUtCwmxx1iwoKcjUSptSc7N26aI\nyA3sZFq+wFzgNNDLGDMvheNztn+XRmWMMYOAQW4NRikVx7lr5xizdQyvPfIGE0fdw/bt8M8/tx43\nbtxqmzEjiMDWrbBo0YNUubsKM/fMJHvm7Kw+uprFzy1GTEb++APuv99zx5SYoCD48Uf49ltYtswm\nUQ0bwiuvQLNm9jiVutMl67+BMWYOMMcxipDDGHM+ZcNSSnmroRuHktEnE5tG9CNwEzRpAlWqQIEC\n9lt/oUJQrBgULw6FC8OSJdCqFcycaYtH31r1Fn9c/INaRWvx9P0teOUVmDwZ/vc/GDYMMmRIPAZ3\nOX8e5syBc+fg0iX7uHgRfvnFJlIVK0LfvnD33TBlCjzzDBQpYkdw3n4bsmXzXOxKedpt5d3GmCAg\nCMBxiuV1Y0yqXamilEpbjl06xoRfvyLHr4M5uj8vP/0EtWolvM0zz0CnTvDGG/DTdn/gLQ5eOMiW\nF7fwySfC5Mng7w+jR8O+fTB3LuSJc79q97p6FT79FEaNgvBwm1DkzWsf+fLBBx/YpOm++25t07Ur\n7NwJEyfaROniRRg3LnXjViotEWfXvcfbWKQA9mqPUGCtMSZCRDIBPbA1HBmNMfndEqkbiUgVIDAw\nMFCvUlHKReHhtg4jY8Zbj2fndmLD36uotP4wSxZmp1gx1/q6fNmODpQoAaV6v0gkETQOnom/P7z/\nPgweDCtXwvPP2xGSJUtS5xRLUBBMnw4ffgj//Qc9e8I778BddyWtnzFj4K237EhI9eruiVUpT4l2\nlYqfMWZnfO2ScrfYx4BlQC5s0eYOEekCfAeEY+slZt5GzEqpRBy7dIxIE+nSlR/uFBICTz4JW7ZE\nW1jgAPSYhd+18Wz4OTu+SbifWu7c9pTKk09C8+bTqFED6tWDDh1g0CDbpmFD2LYNnn4aqlWDfv2g\nRQsoX97WgdyuyEgYOhS2b4e//rKPf/+1fXfqZJMeVxOo2Hr2tKdiunaFwEDIlOn241XK27g8wiEi\nP2OLQ4dirwjpA/wJDDDGLHRXgKlBRziUN/jr8l/4TfLjQtAFnn3gWd597F0qFa6U6nEYAy++CAEB\nMGOG/bYfFgbD/nyeP29s5UTfP8iSMe4U5q7o2xc+/xyyZ4dKleyoRuZYXV2+DL16wcKFcP06lClj\nEw9/f3BhKoB4jR5t99+0qU0sihaFe++1yU3ZssnvN8quXVC1KgwZYkdJlEovXB3hwBjj0gO4CFRw\n/DsbEAG0cHX7tPwAqgAmMDDQKJUWBYcFm2qTq5minxY147aOMyXHljQMwjSd09RsObklVWMZO9YY\nMObrr28t239+v5FBYibumHhbfd+4YcxDDxlTvrwx//6beNtly4zp2tWYQoVsTM88Y8zBg87bh4QY\nExnpfN3OncZkymRM3763FX6i/vc/Y7JkMeaPP9y7H6VSU2BgoMGe+ahiEvqsTWilifmhHAkUjPb8\nKlDa1e3T8kMTDpXWdV/a3WQektlsP7XdGGNMWESYmbVnlqnwRQXDIEzPH3qaoNAgt8exerUxGTIY\n89ZbMZf7L/Q3RT8takLCQ257H9euGROUxEMJDzdm1ixjihc3xsfHmJdfNubYMWM2bTLmww+NqVPH\nmMyZjale3ZjTp2Nue/26MeXKGVO5sjHBwbcdfoKuXzemZEljnnwy/uRHKW/jroSjLlDR8biGvZdJ\nxegPV/tLSw9NOFRaNm3nNMMgzOTAyXHWRURGmHFbx5msH2U15ceXN4Gn3fce/vNPY/LmNaZhQ/sB\nH+XgPweNDBLz5fYv3bZvVwUH2xGYu+6yf93AmNy5jWnRwpgRI4wpUsQ+ov9Xf/VVY7JmNebAgdSJ\nceVKG1fnzsb06GFM27Y2AalTx5jp0+1IjFLexNWEIyk1HJGODp2VZ0UtN8YYD14lnzxaw6HSqsDT\ngdSaVov2Fdsz5ekp8bY78M8B2i1qx/7z+/nwiQ/536P/I4NPyv1XvHbNXl0RFmYLN/PmvbWu/aL2\nrD+xnsM9D5MlY5YU2+ftuHIFvv/eFpQ+/PCtuTtOn4aWLe3ltbNm2fqQp5+GL7+EV19Nvfj+9z9b\nA5M/v30UKGDn9Fi50s7b8eab0K2brWU5dMgWsm7fbq8MeuWV26tVUSqluVrDkZSEo7gr7YwxJ1zq\nMA3RhEOlBSf+O8HMPTP5L/g/Lgdf5kroFTad3MS9ue5lY5eNZM2YNcHtQyNC+eCnDxixeQSN72vM\nvNbzyJklZ4rE1qmTnUXz11/th3iUPy7+QfkvyjOu0Theq5bwHWHTihs3oEsX+OYbyJED6ta1l9mm\nhTveHzhgi1dnzYKsjl/31as2tvLlbZHsiRNQuzb07m2TJR8fOxHZ77/bR6VKeumtSl0pnnCkZ5pw\nKE8Liwij6uSqHL10lHtz3UuuLLnInTU3hbIXYuiTQymau6jLfa08vJK2C9tSIk8JlvkvS9K2zsyY\nYT+gZ82C9u1jruu4uCNrj63lyBtHEk2I0hJj4KOPYNEiO6qQ1u57cvq0nak0SxZ7lYyfH+TKZUc4\nvv/ezuuxebOdqfXGDXvlThQRO+fHRx/Z7dOKyEg7ipPUOUxSQ0iInSPl7FmbwEU9Spa0I2QqYSl+\nlUp6fqA1HMrDhqwfYjIMzmB2nt6ZIv3tO7fPFB9T3BQeVdj8+vevye9nnzHZshnz4otx1/1x4Q/j\nM9jHjNs67jYiVcm1fbsxffoY8/HHxixaZGtQbtwwZuRIWyD70EPG7N7t2RivXDHm22+N6dLFmIIF\nbe1K797uK84NCTHms8+MKVzYmEqVbMGws9qcsDBjDh0yZvx4Y5o1MyZ79ls1P9EfIsb89JN7Yk1P\nUryGIz3TEQ7lSQf+OcDDEx/mrZpv8XG9j1Os33PXztHym5bsObuHgNYBtCjXIknbX79uv12L2PqB\nqIm8jDGsPrqavqv6ciHoAkd7HfWq0Y07wd69dtK0gwdh+HDo08f9+wwJgf377Xwju3fbn7/+CqGh\nUKGCvYldjhx25KVCBVvDUq7cre23bbNzsGzaZD/uoxhjR3bCwm79LFHC1uK0bHmrnuW77+xkcIcP\nQ8eOdr9Ll9r6o/LloXJlO5nbiRPw9992xCVTJjv1fqNGdmK50qXt/iIjISIC2ra1NTR79thaG+Wc\nnlJJAk04lKdEREZQa1otLodcZlf3XSn+wX0j7Ab+3/rz8/GfOfHmCXJnze3yti++aOscduy4Vbex\n6eQmBqwbwIYTG6h5b00+a/QZVYtUTdGYVcoICbE3jBs3Dn77DR580D37iYiACRPg3Xdv1ZuULWs/\n4GvWtBOplY42Me7u3XaSthMn7Kmh7NltorF9O5QqBW3axD0VlDGjTQ6iptDfvdsmE5cu2cnZChe2\n79MGDeCTT+w0+QDBwbB6ta0/Onr01k0DixWzp0tq1oScCZQ5nT5t+6pVyyY0aaHOJy3SUyp6SkV5\ngdFbRhsZJGbzyc1u28fpK6dNliFZzNANQ40xxpw8aczDDxuzcWP82wz4cpuhS21z/7Da5okZT5j6\nX9c3NabUMAzCVJpQySz7fZmJ1Ikk0ryQEGPy57enXtxh7147twkY0727Mb/8YudRScy1a8Z063br\n1EX9+sYsXRrzcuvEhIYas3atMT172ku1V6xI/nEkZOlSG+Pnn7un//QgVU6piEh+7M3cMgC/GmPO\nJLszD9IRDuUJh/89TMUJFelapSufNf7MrfvqsbwHCw4s4Hiv40yflJ2ePe0Q8fbt9ptedD//DE+O\n6EPmajNoW7kZ4ZHhhEeGYzA8W+FZ2lRog4/4uDVelXLefNOevjh1KuXu4RIcbKdoHznS3iF30iR4\n7Irg8tMAACAASURBVLGk97Ntmy2GjX7lU1rUq5e96++2bfYqIBWT20+piEhrYCrwB5AJKAu8ZoyZ\nnqwOPUgTDpXagsODqT+rPqeunOK3V38jR+Ycbt3f8f+OU2ZcGUY1GMVPw97k1Cl7ZUPWrPYGbLly\n2XZ//gk1akBEp8doUONe5red59a4lPvt2WNPb3z/vb2M9nYFB9t71/z8sz2N0r9/2roaxh2Cg+3/\ni5AQe+ome3ZPR5S2uJpwuPw1RURi/0X8AKhmjKlmjHkYeBZ7YzelVAJCI0Jpu6AtO07vYNYzs9ye\nbACUyFOC9hXbM2rLKNZtCKF1a1i2zH7rff55W4z377/2fHuBQmGE3rWTGkWruT0u5X6VKtlLO6en\nwFfBkBBo3Ro2bIAff4QPPkj/yQbYxHzePFsHMnWqp6PxXkkZFw0Ukehl7uFA9KvXCwGhKRKVUulU\neGQ47Ra1Y+WRlSx+bjGPFUvGOHQy9X+sP6evnuZaqa+pX99eITB/PqxaZSeRatPGJh2fzNzPjfAb\nVCuiCUd60aWLTTDPn09+H2Fh8NxzsHatHS158smUi88blCtnJ4lbtszTkXivpCQcDYFuIrJYRO4B\negHfiMhZEbkADAd6uCNIpdKDiMgIOn/Xme8OfceCZxfQqEyjVN1/ufzlKGda4/P4cCpWDgdsVf9n\nn8H48fZyxMWL4YzPdjJIBh4urDMepRcvvGAnspozJ3nbh4fbK0t+/NFOltagQcrG5y2aNoX16+2l\ntirpXE44jDHHjTFNgfnAeqAyUAaoDzwFFDPG/OCWKJXycpEmku7LuhOwL4C5rebydNkUOJmeDJl+\neZfIPEdZePCbm8teew0+/RQWLrRTZm//ezsPFnyQ7Jn1RHV6cdddtn5j+vSYc1y4Ijzczunx/few\nYAE0aeKeGL1B06Z2fo81azwdiXdKcqm5MSYAqApUAn6G/7d33+FVFdvDx79DQgi9hBpaKJIIAlKC\nCAhEehOuioKFYr8oIna8FizXn9dyQS9gRUBKRF5FlCIqAgqI9EgX6b0TSihJznr/mBM4Cek5NVmf\n5zmPZu/Zs2c4bZ2pFBKRdSJywc1lUyrfmLl5JuPXjmdi74n0bdDXJ2U4dQo2/NyE64p0580lb+IQ\nx+VzKftygA04tDsl/xk82K7HsSbjVRKukpxs12OZMcOOYXDHoNNAVqeOXWNkzhxflyQw5SjgMMZ0\nN8Y8BTQXkQeAZ4Gpxph3jDFFPVJCpfKBKeun0Dy8Ofc2vtdnZVi40K6g+HLMCDYd3cQvO3+5Ks3Z\nS2fZeHSjBhz5UOfOUKVK9gePOhzw4IO2G2bqVDtYVNlWjjlzct5SpHI2S+U9YAK2deNjY8xLIrIY\nu2jWBWCtMaabZ4rp/xwO2wf+8cd2GeEePezc8ldf1RdmQXfqwinmbptL/+v6+7QcP/1k10y4vUVr\napWpxYyNM65Ks+bgGhzi0IAjHwoOtkt+T5tmp3lmRgT++U+7cd+kSXawqLJ69ICDB+3S7SpngnOQ\ndhDQWURWG2PKAcuB10XkEvCSMSYW+BiY5/5i+lZ8vP110KePXau/UJow7fRpu333t99CUJBdnjcy\nEpo3h5EjYft2u/NjSIhPiq98bObmmSQmJ3Jng9Sf2mfO2P1JgoLSv87hsDNIihWzr6nw8Ktfeznx\n4492vwhjDLfXv50J6yYwtsdYggtd+RhYsX8FxQsXp0GFBrm/kfJbgwfDf/5jP59cp7OWKGEXgEt5\nrF1rp39OmHD1DsEFXZs2djn0OXNAl23KmZwEHOeAWsBqoDq2VeMyEdkE3OS+ovmPceNsv+fKlXZK\n2OTJtmkSYOtWG4gcOGDX6+/ZM3Vg0aOHDUZSzpfO/lYWKp+I3RBL25ptqVqq6uVjR47YvS1q1bKr\nQNaunfqakyftQD3XvuIiRWz6wYPtHhk5sWOHDXw7dbJ/963fl3eWvcOvu3/l5lpX5jf+sf8PmoU3\nI6hQBlGQCmiRkXbfkx07Uh+Pj4edO+2Ppt27bbD7yScwaJBPiunXQkJs4D5nDrz0kq9LE1hyEnCM\nAL4wxnwAFAMGeqZI/iUhwW4w9MADdp2Ce+6xC+l88YUdvX333faX54oV9s2cVr9+Njjp0wfatrVB\nR2iofYOfPm3zaNUq41+5KrAdPnuYBTsXMK77uFTHhw2zH+rHjtlFmT791O5MCXZjqttus0HHd99B\n3br2C2LHDli92rayJSfDiBHZL8dPP9nXWEyM/bt5eHNqlq7JjI0zUgUcK/av4I76d+S12sqPPfJI\n5ueTkuD8+cw3NSvoevSwg2mPHoUKFXxdGs9KTIQNG+z3VtGi9lGsmG0Vy/FmdplttJL2AYRhx3CU\nycl1/v4gk83bRo8WCQoS2bHD/n34sEi3bnYzH2NEbrlFJD4+gx1tXGzcKFKjxpXNilwfPXqInDmT\ndR4q8Pzvj/9J8GvBcuzcscvHvvvOPu9TpoicOiVy553274ceEhk/XiQ01G6ulvKaS2vkSJt+9Ojs\nl+O220RatUp97Kn5T0nFdypKUrLdMevgmYPCSOSrDV/ltJpKFSiHDtn34KRJvi6JZzkcdmO99L63\ngoJEwsJE6tYVqV8/e5u35aSFAxE5DhzPYUzjE8aYR4GngcpAHDBURFbmJI+LF+1Wx3fffWWDq4oV\n7UpzH35oB14NH569fvX69e0a/IsW2ciwdGn72LbNDuRq29bmGx6ew4oqn0tMtJtY9e599RbgsRti\n6VynM2HFwgDbsvXPf0K3bnYxJmNsl0rHjvD441easceNs78k0vPyy3bhoSeesHs6PPBA5uVLTrZd\ngcOGpT7et35f3vv9PX7b8xvtI9qzcr99e+iAUaUyV6kSREfbbpUBA3xdGs+ZONG2jk6YYAecnz9v\nH+fO2Wn2J0/ax7ZtsGlTNjLMLBoJ1AdwJ3aMyQAgCjuY9QRQPoP06bZwfPqpbcXYtCk3sWH2xcWJ\nVKtmH3Fxnr2Xcr9nnrERf2ioyMcf218FIiK7Tu4SRiKT4yZfTvvwwyIlSojs3n11Pps3i8yceeX6\nzDgcIkOG2Nfn1KmZp12+3JZv6dK0eTik+n+ry5DZQ0RE5MUFL0rFdyrqtvNKZcPIkSKlSolcupR5\nuvffF7nrLpF77xUZPFjkgQds66S/v80OHhQpU8aWOyvZ3Z7e58GBJx7YGTTvu/xtgH3Asxmkvyrg\nSEwUqVPHNkV7w/79thm9ZEmRhQu9c8+CICHBs/nPmmXfRf/+tw0mQKRvX5GTJ0Xe+u0tCX0jVE5f\nOC0iIosW2fNjxrjn3snJIoMG2aDjscdETp++Os2pUyL9+9sPxsTEq88P/2G4VH63siQlJ0nnyZ2l\n57Se7imcUvncypX2/ZzZ5/WMGTZNy5YiN91kuzWbN7fHxo1zb3kOHhT58kvbVTtxou2e/eKL3HfX\n9+0rUr68yNGjWactsAEHUBhIBG5Jc3wiMDODa64KOKZOtf86a9Zk/Y/tLmfO2Bdm2r52lTujR9sI\n/cIFz+S/c6fNv3fvK79WvvrKfrlHRIhUe6OxRL/dVyZOtB8A11wj0rq1DRTcJSnJ1rNYMdtCNnu2\nPX7hgj0eFiZStGjGQc6yPcuEkciinYukzFtl5LVFr7mvcErlY8nJIpUqiTz9dPrn9+0TKVdO5Pbb\nr27NGDpUJCREZMWKvJfD4RCZNk2kbFlJd6xFjRoi33+fszy//dZeO21a9tIX5ICjCuAAbkhz/D/A\n7xlckyrgSE4WadDADg71tnHjRIKD0/+1qrJv926R4sU9FzReuCASHS1Sq5bIiROpz+3YIXJ9p03C\nSISoby6/8cuXt90mnrBzp0iXLvY+vXvbchUqZJtv9+3L+LpkR7JUfa+qdJncRRiJ/LDtB88UUKl8\n6NFHbUA/a1bq48nJdrBllSoix45dfd3FiyI33CBSs6bI8eO5v/+RIzagAZF+/WxLeUKCzT8pSWT7\n9iufC7ffbs+nXPfll/bzoUMHkf/8R+Tvv+25U6dEwsNFunfPfrdPdgOOHA0aze8ee2w4ly6V5tAh\n2L8fypSB2Nj+9O/vvRUiO3a009J+/dVOvVK5M2yYndaXkGCnmTZx88anzzwDcXGwdCmUKSN8vekb\ntp3YRkJiAgmJCZS+axWlDpVi35puhDrfZYUKeW76c0SE3clz2jR48UVo2NAOQq5fP/PrCplC3F7/\ndt7/430AoqtGe6aASuVD775rVx39xz/go4/sUvBgd1/+6SeYP99unJdWSAh89ZX9XBowwE5/z8mi\nfkeP2ryfesoOCp8+/cq0ele1a9vPhenT7WfitdfaCRBxcfZ8/fpQsya88oqdbt+4sf3eO33aToxI\nb9prbGwssbGxqY7Fx8dnr+CZRSOB+CAPXSqwWkAkKkrkjTeyF9m5m8MhUr26yBNP+Ob++UHKuIrp\n0203xrBh7s1/5kxJNRbjm03fCCORcv8pJ9X/W10i/xcpTT5qIm8vedu9N/aQJbuXCCORaz64xtdF\nUSrgJCXZlg4QeeUVkfXrRYoUyd7nzrx5dgzWm29mnu7iRTuJYcAAOw01pdW0Vy87diM7Tpyw3ysD\nBtixHSmtHSK2O3/GDNtKUqaMHfyeEwW2S0Ukw0Gje4FnMkjfFJDXXludafOztwweLNKwoa9LEZjO\nnrV9ll262OCtb1+R9u3dl//Jk7aZtFcvm/+5S+ek5qia0m1Kt4Cd3ZHSrXLPN/f4uihKBSSHwwYN\nYAf+16+f/QHrL71kuz8zG3w6bJhN06yZHSA+bZrtRvUXBT3guANIIPW02ONAhQzSZ7jwV3bN2DhD\nesf2ljMX876C15Qp9pk5dCjPWRU4zz5rf12k9Ee+8YaN2N0VCzzyiJ3WumeP/fulX16SkNdDZNvx\nbe65gY9sOLxB9p/en3VCpVSGJk604zLWrs3+NUlJIjffbH/IHDly9flffrHfB6NGua2YbpfdgCMP\nW0H5LxH5Crvo12vAWqAR0EVEjrr7XqcvnmbgtwPpO6Mvs7bOSnfL75y62bnS9C95z6pA2bAB/vtf\nO4ahdm3hi7gv+KX0QE7FJ7N3b97zX7bM9tO++SZUrw7bT2zn7aVv80yrZ6hbrm7eb+BDDSo2ILyk\nrjqnVF4MHAi7dsH112f/mqAgmDLFjt0bONBueZDi9Gm7d1K7dnZhwECXLwMOABEZJyIRIlJURG4U\nkVXuvseSPUto/FFjZm6eyaQ+k4goE8HPO37Oc75VqkCDBvBz3rPK9w4ftm/WAQPsPiF16sCDQ09x\n1zd3MfDbgfxy/AsIX826ddnLLynJbs536lTq45cuwUMPQYsWMGSIPTbsh2FUKlGJF256wb2VUkoV\nKFWq2P255s2zP5pSPPkkHD9uV/rMy07R/iIfVME3Plz5Ie0mtqNqyarEPRLHgMYD6FirIwt2LnBL\n/h062IDD9viotE6cgNatoXJlu6vq+vVw//3w2sQl3DDheuZtm8fUW6dSJrQMoY3mXh6VnZVvvrHB\nS7169k2e8mvjnXdgyxa79HhQEMz+azZzts1hVJdRFCtczHMVVUoVCF272l2gR4yA5cvtsunjx9sA\nJGVrjUCnAUcuxF+I5/kFzzOg8QAWD1pMrbL21dChdgc2Hd3EgTMH8nyPjh1hzx67pbi62tixsHat\n/VVw8KD9/2q3jqH/j+2oVqoacY/EcVfDu+hcpzMh187LdsAxc6adOtapk90NsnVru8Pv66/bKWiN\nG8OFpAsM+2EYnet05h9R//BsRZVSBcYbb0Dz5tC/v90jqVu3rPdKCiQacOTCh6s+5ELSBf59878J\nKnRlYYWUbb4X7Mh7K0e7dvaX9AL3NJjkKxcu2HnuAwfa1o3KleHcpXOMWDCCQY0HsWjQImqWqQlA\n97rdOV1yJau2HMky34sX7a+Kfv1g6lS70d7Zs3D77XZTvVdesenGrxnPnvg9fND1A0yO92dWSqn0\nFS5sN3M8dcp+Hn32WS62gPdjGnDk0PnE84xaPoqBjQdeNciuYvGKNK7U2C3dKqVK2fECGnBcbepU\nu/DN8OFXjn29+WvOXjrLi21fJLjQlfXsutbtCkbYHTSfM2cyz3fRIjhzBvr0sX+3awdr1thmzW++\ngWLOnpMftv9A25ptiSwf6d6KKaUKvIgIO2Hg55/z3+7hGnC42H1qd5ZpJqybwLGEYzzb+tl0z3eo\n1YGfd/ycMt02Tzp0sC8811HLBZ2I7dPs1cuOs0jx+drPiYmIudy9laJSiUrUL9MMrpnH+vWZ5z1z\npl2Zr2HDK8cKF7ZdKymjzpMcSSzetZgOtTq4qUZKKZVakybQtKmvS+F+GnC4GDJnSKbjL5IcSbyz\n7B361u+b4TTIjrU7sv/MfrYe35rn8nTsaEcoZ3f8QUHwww+waZMdT5Fi+4ntLN69mPua3JfuNX0a\ndIc681mzNjnDfB0OmDXLtm5k1oS56sAqzlw6owGHUkrlkAYcLhw46DqlK6cunEr3/PQN09l1ahfP\nt3k+wzxuqnkThQsVdss4jpYtoWhRnR7r6r337KCqm266cmziuomUKlKKW6+9Nd1rekZ2g2In+Hnz\nigzzXbECDh260p2SkQU7FlCqSCmahTfLTfGVUqrA0oDDxZhuY9h3eh+3xN7C+cTzqc45xMFbS9+i\nW91uXF8541VdSoSUoGW1lvy8M+9RQpEi0LatjuNIsW6d/bd46qkrrRDJjmQmxk2kX4N+GU5PbVG1\nBSHJ5VgZPzfDvGfOhAoVoFWrzMuwYOcC2tVsl2qciFJKqaxpwOGiTrk6zL5rNqsOrKLHtB58tfEr\njiccB2DOX3PYcGQDI9qMyDKfjrU7snDnQpIcSXkuU4cOdufYS5fynFXA++9/oUYNO2skxYKdC9h3\neh+DmwzO8LqgQkE0KNKVgyXmkpxOr4qIDThuuSXz3VzPJ55n2d5l2p2ilFK5oAFHGq2qt2LmnTM5\nePYgd/6/O6nwTgWaf9Kc4fOH07p6a26qeVOWeXSs3ZH4i/GsObgmz+Vp1w7On7ezJQqy/fvtdLFh\nwyDYpXHh87Wfc235a7mh6g2ZXt+lTjek8hp+X3/oqnNbtsC2bVl3pyzbu4yLyRcvT39WSimVfRpw\npKNL3S5sfnQze4fv5fPenxNZPhJBeD3m9WxdHx0eTYmQEm5Z5vz66yE01O7jUVAtXw633mqnpbou\ngnPi/Am+3fIt9zW5L8v1MAa16QJimPrHD1edmzkTihe3g3R3ntxJ9KfR7Dq166p0C3YuoEKxClxX\n8bq8VkkppQocDTgyUa1UNQZdP4ipt05l++PbiakVk63rCgcVpn1E+2wFHBeSLmR6PiTErsdREAOO\n7dvhjjvgxhvtIjizZ9v1SVLEro8lyZHEPY3uyTKvyGoVKHw0mkX7rx7H8e23dkW/0FCY/OdkVh1Y\nxeuLrw4uf9n5CzfXulkX+1JKqVzQgMNDOtbqyNK9S0lITEj3fLIjmVcXvUqJN0vw2+7fMs2rVStY\nurTg7Kuy7fg2qo68gcg7J7BsGUycCKtXp56ZAnZNlB71elC5ROVs5RuR2J2/5cdUY2v27YOVK690\np3y18SvKhpZlUtwk/j7x9+V08RfiWXlgpY7fUEqpXNKAw0M61O7ApeRL/Lr716vOHTxzkE6TO/Ha\nr68REhTC7L9mZ5pXq1Z2yuburNclC3i/7v6V6I9bciA5jkI9H+eXVXsZOPDqwZzz/57P6oOrue/6\n9NfeSE/rit1JCo5n2V7bXCRiN2gLDoYePWDT0U1sPLqRD3t8SMXiFXnj1zcuX7t492Ic4qBDbQ04\nlFIqNzTg8JAGFRpQu2xtesX2otPkToxZMYa98Xv5cfuPNP6oMVuObWHBgAX0ierDwl0LM83rxhvt\nf5cu9ULBfWjKn1Po+EVHipxqTLVvt1C+ZEmeXTT0qnRHzx1l0KxBdK7TmV6RvbKdf5eGzSC+GuN+\nn8CMGdCsGbz8st2PpUwZmLFxBqWKlKJ3VG9GtBnB5D8n89fxvwDbnVKzdE1qlckn2zYqpZSXacDh\nIcYYlt23jPe7vk8hU4jh84dTY3QNukzpQpMqTVj3yDraR7QnJiKG1QdXc/ri6QzzKl8eIiPz7zgO\nEWHkopHcO/Neete6h2Ojf+D5RyL4oNsHzNo6i5mbZ6ZK++D3D5KYnMjE3hMpZLL/Em7apBCsGMr0\nTVO5476DhIXZdT3Gj7fnZ2yawS2RtxAaHMqDzR6kSokqvLb4NcAOGO1Qq4OO31BKqVzSgMODKpWo\nxJDoIcy/Zz7HnjlG7G2xTOoziXl3z6Ni8YoAxNSKwSGObI3jyK8Bx1cbv+LVxa/y5s1vUnnFeMqW\nCmHwYLjt2tvoWa8nQ+cNvRyQfbrmU2ZtncX4W8ZTpWSVHN2nTh247tJDBFOEQR+O4aef4Oab7SJi\nG49sZOPRjdxR/w4AQoND+ddN/yJ2QyyLdy1mw5EN2p2ilFJ5oAGHl5QOLU2/6/oxoPGAVL/K65St\nQ7VS1bLsVmnVCv78k3R3PD140A589LTkZNuts20bJOV9TbPLxq8dz001buLBa0cw/jPD0KF2Cqwx\nhrHdx3Lqwin+teBfbDm2hSd+eIKHmz1M76jeOb5PUBCsX1GGx1o9wKz9H3Lu0rnL52Zsst0pnet0\nvnzsvib3UbVkVe78f3cCEBORvVlKSimlrqYBh48ZY4iJiMlWwOFw2D0/0rr3XrsE+rZtHiokdsBq\n+/bQpo3dpbVoUYiKsqtzjh8PiYm5y3ff6X38vONnBjYeyNix9tijj145X6N0DV6PeZ2xK8fSK7YX\nNUrX4L3O7+WpLsNaDiP+YjwT1028fGzGphn0juxNkeAil48VCS7Ci21f5PC5w9SvUD/HLSpKKaWu\n0IDDD8RExLD24FpOnj+ZYZqoKDuwMe3A0bg4Ow7BGHjoIc9MnZ0+HRo3hj17YO5cu5ncBx9A165w\n7pxdjKtevdwFHpPjJhMaHEqPWn353//g/vvtmBVXQ28YSpMqTdh9ajfTbptG8ZDieapPRJkIbq9/\nO6OWjyLZkczGIxvZdHQTdzS446q0g64fxDXlrqHnNT3zdE+llCroNODwA+0j2iNIulNoUxQqlP44\njlGjoHp1+OYbWLQIPv/cfeU6cwYGD4Z+/WxwERdnF8jq0AH++U8YPdoGO3/+aXdwTQk8pkzJXv4i\nwqS4Sdx67a18Pa0UJ0/Ck09enS64UDDf9/+exYMW07RKU7fU7akbn2L7ye18t/U7vtr4FaWKlKJT\n7U5XpQsJCmHtw2v5d4d/u+W+SilVUGnA4Qdqla1FzdI1s9Wt8vvvtmsF7NiNadPg8cdtQDBokN1J\n9eDBvJXn0iUYN84GDzNm2IW3YmNtC0t6Gja06f78E5o2tV08Tz55pZwZ+WP/H2w9vpV7Gw7ivffs\nqqK1Mph1Gl4ynBur35inerlqUbUFbWq04d3f32XGphn0ieqTqjvFVfGQ4ro7rFJK5ZEGHH4iplYM\ni3YtyjRNq1Zw+jRs2mT/HjvWbmGfsr/Iu+/av4devXRFtiQnw+TJtvvmscegc2fYsAEGDryyHXxm\nGjaEr7+23S2jR9sA4vz59NPu3QsvTJ9EscRqDGofw86d8OyzuSt3bj1141Ms27uMzcc2X56dopRS\nyjM04PATMRExxB2O43jC8QzTtGhhZ1osWwYJCfDRR3bMQ0rLQ1iY/bL/+mu7IVlOnDsHN9wAAwbY\n8Rrr18OkSRARkfO6DB1q7z93rt0Q7dgx29rxxx/wwgvQoAHUqH2BhUe/pOTOAdx7dxBLlkCTJjm/\nV170qteLa8pdQ+kipelU5+ruFKWUUu6j7cR+ImXK5eLdi7n12lvTTVO8uN09dtkyOzj05EnbneLq\njjtg6lQ706NDh9SbnWXmzTdta8aSJdC6dV5qYvXuDQsXQo/bT1Lj2fsI3TyYk8tvISwMevWCLk9+\nx6h9p1j8/gAiy2ednycEFQri016fcujsIUKCQnxTCKWUKiC0hcNPVC9dnTpl67BwZ9bjOJYssYNF\n+/SB2rVTnzcGxoyBo0dt90h2/PUXvPMOPP+8e4KNFDfcAJ3eep7zNWZxsmtv7p4wgv0Hk5gwAbYW\nnUTLai2JLB/pvhvmQruIdtx53Z0+LYNSShUEGnD4keyux7F9O2zdmv6MDoAaNWwrwiefZD1NVsR2\ngVSrBs89l8uCZ+C33b/x5d+fMLbHGN7u+Daxe96me2wX4g7F8cPfPzCo8SD33lAppZTfCpiAwxhT\n0xjzmTFmhzEmwRizzRgz0hhTOE266saYOcaYc8aYQ8aYt43JwYYbPhRTK4aNRzdy5NyRDNO0amX/\n26LFlf9Pz0MP2VkjWa1A+s038OOPduxH0aK5KHQGLiZd5KHZD3FjtRt5pPkjPNP6GRYMWMDGIxtp\n/mlzChcqrC0LSilVgATEF7FTFGCAB4H6wHDgEeDyAgnOwGIudmxKS2AgMAh4zctlzZX2Ee0BMp2t\nUqMG3H23HXOR2cyRTp1s2k8+yTjNuXPwxBO2NaSnm9e1emvJW/x94m8+6fXJ5aXc20e0Z83Da4iJ\niOGhZg9RJjSDebZKKaXynYAJOERkvojcLyILRGSXiMwG3gVcR1h2wQYmd4vIehGZD7wEPGqM8fsB\nsuElw6kXVi/L6bFTptgBoZkJCrIzWL780k6lTc8bb9ixHqNH5668Gdl8dDNvLnmT51o/x3UVr0t1\nLrxkOD/e+yMfdPvAvTdVSinl1wIm4MhAGeCEy98tgfUicszl2HygNNDAmwXLrRZVWxB3OM4ted13\nn10HIzb26nObNsF778GIEVcPPM0Lhzh4ePbD1Cxdkxfbvui+jJVSSgW0gA04jDF1gceAj1wOVwYO\np0l62OWc34sMi2Trsa1uyataNejeHT79NPXxo0ftpmt16rh/sa3xa8bz257f+Ljnx4QGh7o3c6WU\nUgHL5wGHMeb/jDGOTB7Jxph6aa6pCswDpouIG3cP8b2o8lEcP3+cYwnHsk6cDQ89BKtXw5o1Ri87\nnwAAGCtJREFU9u+EBDtm48wZuzCXOweKHks4xvMLnmdA4wHE1NKt3JVSSl3hD+Ma3gUmZJFmR8r/\nGGPCgV+AJSLycJp0h4DoNMcquZzL1PDhwyldunSqY/3796d///5ZXeo2UeWjANhybAttarTJc37d\nukF4uG3lGDMG7rnHriK6eHHG+5bk1vM/P49DHLzT6R33ZqyUUsovxMbGEpumnz4+Pj5b1/o84BCR\n40DG63m7cLZs/AKsBO5LJ8nvwAvGmPIu4zg6A/HApqzyHzVqFE2bumc30tyqW64uhUwhtwUcwcF2\n8Ojo0XbNjVmz4Ntv7e6u7vT73t8Zv3Y8Y7uPpWLxiu7NXCmllF9I70f4mjVraNasWZbX+rxLJbuc\nLRuLgN3As0BFY0wlY0wll2Q/YgOLycaYRsaYLsDrwBgRSfR2mXMjNDiUiDIRbhvHATbgOHsWPv4Y\n/vc/26XiTkmOJIbMHUKzKs14uFnaRiellFLKD1o4cqATUNv52Os8ZgABggBExGGM6Ql8CCwDzgET\ngVe8Xdi8iCofxZbjW9yWX82aMHw4lC8PQ4a4LdvLPlz5IXGH4lj+wHKCCgW5/wZKKaUCXsAEHCIy\nCZiUjXR7ATcvY+VdUWFRfPfXd27N87333JrdZYfOHuLFhS/yYNMHaVG1hWduopRSKuAFTJdKQRJV\nPoodJ3dwMemir4uSped+fo7ChQrzZoc3fV0UpZRSfkwDDj8UVT4Khzj4+8Tfvi5Kpk5fPE3s+lhG\ntBlBWLEwXxdHKaWUH9OAww+lbNm+9bj7Bo56wg9//0CiI5Hb6t/m66IopZTycxpw+KEKxSpQNrQs\nW465b+CoJ3y39TsaVWpERJkIXxdFKaWUn9OAww8ZY+xMFT8OOBKTE5mzbQ69I3v7uihKKaUCgAYc\nfsrfA47f9vzGqQunNOBQSimVLRpw+KmUgENEfF2UdH239TuqlqxK0yq+XZlVKaVUYNCAw09FhkVy\n5tIZDp3NcgsYrxMRZm2dxS2Rt2CM8XVxlFJKBQANOPyU6yZu/mbDkQ3sOrVLu1OUUkplmwYcfqp2\n2doEFwr2y4Bj1tZZlAwpSfuI9r4uilJKqQChAYefKhxUmLrl6vptwNG1bleKBBfxdVGUUkoFCA04\n/Ji7N3Fzh/2n97PqwCrtTlFKKZUjGnD4sciwSLduU+8O3//1PUEmiG7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WH17MtTvXeKP6GwD4FPFhT+89fFD/A6oXqc6mrpsI7B3Iq0++SqYMmeKsJ2vG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\nlgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb228be0>"
+       "<matplotlib.figure.Figure at 0x189e96a0>"
       ]
      },
      "metadata": {},
@@ -1014,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -1024,18 +1024,18 @@
      "output_type": "stream",
      "text": [
       "TRADING STATS\n",
-      "AVG Monthly Return :: 0.68%\n",
-      "STD Monthly        :: 4.98%\n",
-      "SHARPE             :: 0.47\n",
-      "MAX DRAWDOWN       :: 49.38%, 11.0 months\n",
-      "Correlation to SPY :: 0.03\n"
+      "AVG Monthly Return :: -1.01%\n",
+      "STD Monthly        :: 7.03%\n",
+      "SHARPE             :: -0.50\n",
+      "MAX DRAWDOWN       :: 170.53%, 105.0 months\n",
+      "Correlation to SPY :: -0.05\n"
      ]
     },
     {
      "data": {
-      "image/png": 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VKDq5KPUX1OfG/RsseHcB53qe46MyH+Gion4MZk2blc2em2n8amMOXjvIGo81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\nGmMeS1XuONA61bFAp3MZGjFiBAEBASmODR48mMGDB/P3v2d2tc290r4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WrSlXqFyIvcDm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ppQKUUn8rpQraK97EIG/evPj6+nLq1KlY1w0KCuLGjRtkzZr1zb5KlSrxySef\n8MMPP3Dy5Ek++ugj8ufPz4gRI2wZthAiCvee38P7T29qzq1Jn3V9aPl7S/xf+Ycrs+b8Grqs7EKX\nMl3Y0W0Ho+qMokXRFuTKkEsSEhEnkmRSYnIScAXcTFu10ANKqcFAX6AXUBF4DmxUSqW0Q5yJwqBB\ngwgICMDT05OqVavyxRdf8PfffxMUFBSpbGBgIA8ePODBgwccP36cLl26cO/ePdq2bRuu3LfffouL\niwu1atXiyJEj/PLLL29u5Qgh4kZwSDDTDk2jyNQirDm/ht+a/cbqDqvZdmUbVWdX5erjqwBsu7KN\nNkvb0KxwM2Y1nyW3ZUS8SMq3b4K01vejONYfGKW1XgOglOoK3AXeA5bGR3ABgQGc9Tsbp+co6lIU\nZydnm7RVt25d9u3bx3fffcfGjRvZv38/33//PdmyZeO3336jWbNmb8pu3LiRbNmyvfnZ0dGRrl27\nMnbs2HBtpk+fnkmTJtG2bVs6dOgQbtCrEML2zj84j/ef3uz/dz8feH7AuHrjcHE2br/u+3AfzZc0\np8LMCnxd62sGbx5Mjbw18Gntg6NDUv6qEAlJUn6nFVJK3QReAvuAIVrrG0qpfBg9J1tCC2qtnyql\nDgBViKek5KzfWbxmeMXpOXx7+VIuRzmbtefl5cUff/xBUFAQx44dY+XKlUycOJH333+fo0ePUrRo\nUQAqV67MmDFjAHB2dqZYsWJkyJDBbJsVKlR407YQIm5orZnuO53PNn1GzvQ52dV9F9Xcq4UrUyJ7\nCQ70OECbpW3os64P7+R5h5XtVsqkZiJeJdWkZD/QDTgH5AC+AnYqpUpiJCQao2ckrLumY/GiqEtR\nfHv5xvk54oKjoyNeXl54eXlRqFAhunfvzrJly/jyyy8BY+Br7dq14+TcQojYue1/mw//+pD1F9fz\nsdfHjK8/nrQp05ot6+LswqYum1hycgnNizSPspwQcSVJJiVa641hfjyplDoIXAPaAm91z2TAgAFk\nzBh+YagOHTpQpEiRWLXj7ORs014MeylfvjwAt2/ftnMkQoiIDt48SONFjXFK4cTajmtpXKhxjHVS\npkhJ1zJd4yE6kVT5+Pjg4+MTbt+TJ08sqpskk5KItNZPlFLngYLAdkBhDIIN21viChyJqa2JEydS\nrlzkZOLw4cM2iTWh2r59O7Vq1Yq0f+3atQBvbt0IIRKG0/dP02hRI4q6FGVV+1Vvxo4IEdc6dOgQ\nbq4qML5Hp/onAAAgAElEQVQjLblNnyySEqVUOoyEZJ7W+opS6g7wLnDcdDwDUAn42X5RJmz9+vUj\nICCAli1bUrRoUV6/fs2ePXtYunQp+fPnp1u3bvYOUQhhcu3xNeovqE/uDLlZ23EtmVJnsndIQlgk\nSSYlSqkfgNUYt2xyAV8DgcASU5FJwHCl1EXgKjAK+BdYFe/BJhITJkxg2bJlrF+/npkzZ/L69Wvc\n3d3p27cvw4YNezOQVSkV6/kLrKkjhDDv3vN71FtQj1SOqdjQaYMkJCJRSZJJCZAbWAxkBe4Du4HK\nWusHAFrr75VSzsB0IBOwC2iktX5tp3gTvPr161O/fszrWVy+fDlW7ebNm5fg4GBrwxJChPHk5RMa\nLmyI/2t/9nywhxzpc9g7JCFiJUnOhqO17qC1zq21TqO1dtdad9RaX4lQ5iutdU6ttbPWuoHW+qK9\n4hVCiJjMOTKH8jPKs/fGXrPHLzy4QK15tbjy+AobO28kf+b88RyhEG8vSSYlQgiRlGy9spVea3px\n4+kNqs+pzohtIwgMDnxzfPGJxZSbUY6AwAC2e2+ntGtpO0YrhPUkKRFCiATs/IPztFnahjr56nDt\n02uMrDmSb3d9S/U51Tl+9zg9/upBpxWdaFGkBYd6HqKMWxl7hyyE1ZLqmBIhhEj0Hr14RDOfZrim\nc+X3Nr+T2jE1I2qOoH6B+nRe0Zky08rg7OTM7Oaz6ebZTQaMi0RPkhIhhLCj56+fM37veGYfnU0Z\n1zI0LtSYxoUakyNdDtosa4NfgB8HehwI9xRN5dyVOfrxUaYenErzIs0pnq24Ha9ACNuRpEQIIewg\nRIcw/9h8hm0dhl+AH11Kd+Hiw4v0XdeXYB2MWzo3/AL82NxlMwWzFIxUP13KdHxR7Qs7RC5E3JGk\nRAgh4pHWmo2XNjJ0y1CO3DlC2xJtGfvuWPJlzgfA45eP2Xx5MxsvbqRu/rrU9Khp54iFiD+SlAgh\nRDwICgnij9N/MHb3WI7dPUaV3FXY88Ee3snzTrhymVJnok3xNrQp3sZOkQphP5KU2NiZM2fsHUKS\nJ6+xSEy01sw5Oocxu8Zw+dFl6heoz9YGW6nlUUsGpgoRgSQlNuLi4oKzszOdO3e2dyjJgrOzMy4u\nssCYSNiCQoLot64f03yn8X7x91n2/rIksTq4EHFFkhIbcXd358yZM/j5+dk7lGTBxcUFd3d3e4ch\nRJQCAgNo/0d71l1Yx2/NfuPDch/aOyQhEjxJSmzI3d1dviiFENx/fp9mPs04ee8kqzusplGhRvYO\nSYhEQZISIYSwEf9X/my4uIGhW4fi/8qfHd124JXTy95hCZFoSFIihBBv4dGLRyw/s5w/z/7J5sub\neRX8ikq5KrGp86Y3j/kKISwjSYkQQljp/vP7VPqtEteeXKO6e3XG1h3Le0XfwyOTh71DEyJRkqRE\nCCGs8CroFS1/b8nzwOec73ueAlkK2DskIRI9SUqEECKWtNb0XN2TQ7cOsb3bdklIhLARSUqEECKW\nxu4ey4LjC1jcajGVc1e2dzhCJBkO9g5ACCHsQWvN3Wd3Y11v+enlDN06lJE1R9KhVIc4iEyI5Et6\nSoQQyc7Jeyfps64PO6/tpFKuSvSt2Jf3i79PKsdUkcre9r/Nvn/3se/GPvb9u48DNw/QvmR7RtYc\naYfIhUjaJCkRQiQb/q/8+XrH10zaP4kCWQowtdFUVp1bRZeVXRi4cSA9y/UkS5osnPU7y7kH5zjr\nd5b7AfcByJMhD1XyVOHHEj/S06unrFsjRByQpEQIkeRprVlycgmD/h7EoxePGFV7FAOrDCSVYyr6\nVOzDWb+z/PLPL0w5OIVgHUxRl6IUyVqEuvnrUiJbCSrnrkyuDLnsfRlCJHmSlAghEpT7z+9z1u8s\nBbIUIEe6HG/dI3H87nH6re/Hzms7aVWsFRMbTMQ9Y/jlIIq6FGVyo8n82OBHHJQDDkqG2wlhD5KU\nCCESjE2XNtFpRSf8AoyFLdM4pqFAlgKUcS3D5EaTyZImi8VtPXrxiBHbRvDLoV8onLUwmzpvol6B\netHWcXSQj0Qh7En+DxRC2F1wSDBf7/ia0TtHU79AfcbUGcMt/1tcfHiRS48usejEIgJDAlnSekm0\nPScPXzxk48WNrLmwhrXn1xKiQ/i+7vf0q9SPlClSxuMVCSGsIUmJEMKu7j67S8cVHdl+dTujao9i\nSPUhOCgHvPhvIbvq7tVpv7w9LYq0oGOpjpHa2HN9D0O3DmXP9T0E62DKupWlX8V+9K7Qmxzpc8Tn\n5Qgh3oIkJUIIuznnd4468+sQHBLM5i6bqZ2vttly7Uq2Y9W5VfRe25vq7tXJkzHPm2Nbr2ylmU8z\nSmQrwS9NfqFxocbkzpA7vi5BCGFDMppLCGEX5x+cp/a82mRKnYnDHx2OMiEJ9XPjn0mXMh3dVnUj\nRIcAxhiUJoubUN29Oju67aCXVy9JSIRIxKSnRAgR7y4+vPgmIdnadSuu6VxjrJM5TWbmvjeXegvq\nMeXAFApmKUirpa2oX6A+y95fRmrH1PEQuRAiLklSIoSIV5ceXqL2vNpkSJWBrd6WJSSh6uavS/9K\n/Rm8eTAhOoSmhZuypM0SGcQqRBIhSYkQScy1x9dwUA7hxl0kFFceXaH2vNo4OzmztetW3NK5xbqN\n7979jt3Xd1PUpShzWszBKYVTHEQqhLAHSUqESCICgwMZs2sMo3eOJlgHkz9zfup41KF2vtq8m+/d\nWPVIxIXb/repu6AuqRxTsc17m9VPxaRxSsM/Pf+Rad6FSIIkKREiCTh57yRdV3bl+N3jDKs+jDJu\nZdh2ZRvbrm7jtyO/4aAcqF+gPt3KdKNF0RbxPv7i0YtHNFjYgFdBr9j9wW5yps/5Vu1JQiJE0iRJ\niRCJyOvg11x6eImXQS/fbHtv7OWbnd9QMEtBDvQ4gFdOY36PVsVaAcY8IKvOrWLesXm0X96ejKky\n0r5ke/pU6EMp11JxHvPz189psrgJt/xvsbP7TjwyecT5OYUQiZMkJUIkEhceXKD5kuac9Tsbbr9C\n8VmVzxhVZ5TZHhDXdK708upFL69enH9wnvnH5jP36Fym+06nYcGGDKoyiDr56sRJ78OroFe0WtqK\nE/dOsLXrVopnK27zcwghkg5JSoRIBDZd2kS7P9rhmtaVTZ03kSVNFlI7pia1Y2oypc5EVuesFrVT\nOGthRtcZzciaI1l6aik/7P2BugvqUtatLN/X+566+eu+daxaa07eO8nGSxtZdnoZx+4cY32n9VTI\nVeGt2xZCJG2SlAiRgGmtmbR/EoP+HkTDgg1Z3GoxGVNnfOt2nVI40al0JzqW6siWK1v4Zsc3NFnc\nhE2dN1HTo6ZFbey5vofpvtNRShkr6+LAi6AX7Li2g1v+t0jjmIaaHjVZ03FNjBOjCSEESFIiRILl\nF+DHwI0DWXB8AYOrDmZMnTGkcEhh03Mopaibvy7V3avTZHETmi9pzq7uuyjtWjrGup9v/pwrj65Q\nIEsBQnQIIToEB+VAh5IdaFCgAdXzVpcJzYQQsSJJiRAJzIvAF0w+MJlvd38LwMKWC+lUulOcnjOV\nYypWtFtBrbm1aLSoEXs/2EveTHmjLH/q3in23tjL0jZLeb/E+3EamxAi+ZC1b4RIIEJ0CAuPL6TI\n1CIM3zYc7zLeXOx3Mc4TklAZUmVgXad1pEqRigYLG+AX4Bdl2d8O/0Y252y0KNoiXmITQiQPkpQI\nkUB8s+MbuqzsQoVcFTjd+zSTG00mW9ps8RqDWzo3NnXZxMMXD2nm04zXwa8jlXkZ9JL5x+fTzbOb\nTO8uhLCpZJ2UKKX6KKWuKKVeKKX2K6VifDxgyhT44ANo2hRGjIiPKEVycOzOMcbsGsOIGiNY3nY5\nhbIWslssBbMUZE3HNfxz8x8m7J0Q6fiKMyt4+OIhPcr1sEN0QoikLNkmJUqpdsAEYCRQFjgGbFRK\nuURXb9MmOH0a7t6Fb78Fv6h7uIWwSFBIEB/+9SFFXYoyrMYwe4cDQMVcFRlQeQDf7PyGy48uhzs2\nw3cGtTxqUThrYTtFJ4RIqpJtUgIMAKZrredrrc8CHwMBwAfRVVq9GvbvhzVrQGv488/4CFUkZRP2\nTuDInSPMbj47Qd0O+arWV2RPm50+6/qgtQbg/IPz7Li2g17letk5OiFEUpQskxKllBPgBWwJ3aeN\nT93NQBVL2nB1hZo1YenSuIlRJA/n/M4xcvtIBlYemOAmF0ubMi1TG01lw8UN/HH6D8AY4JolTRZa\nFmtp5+iEEElRskxKABcgBXA3wv67gMVrqbdtC1u3yi0cYZ0QHUKP1T3IkzEPX9f+2t7hmNWsSDPe\nK/oe/Tf0xy/Aj7lH59K1dFeZf0QIESdknpK30KoV9OkDK1dCz572jkYkZCE6hL8v/c2jl494Hfya\nwOBAjtw5wu7ru9nuvR1nJ2d7hxilyQ0nU/yX4tSZV4f7Affp6SVvdiFE3EiuSYkfEAy4RtjvCtyJ\nruKAAQPImPG/ab6zZIHJkzvQs2cHmwcpkoZXQa/ovqo7Pid9wu1PoVLwRdUvLJ7W3V7yZMzDN7W+\nYeCmgVTNU1UW1RNCRMvHxwcfn/Cfd0+ePLGorgodwJbcKKX2Awe01v1NPyvgOjBZa/2DmfLlAF9f\nX1/KlSv3Zv/06dC7N9y5A9mimFIiMDiQ9RfXM/foXG7632S793bSOKWJg6sSsXXq3imevX5GpdyV\n4qT9Jy+f0GppK/Zc38O89+bRpHATnByccErhhINKPHdPg0KC6PFXD7zLeMs6NkKIWDt8+DBeXl4A\nXlrrw1GVSzyfirb3I9BTKdVVKVUUmAY4A3Nj00hL03i/lSsjH7vw4AIDNw4k14+5aLGkBVceX8H3\nli8zfGe8ZejibTx5+YTph6ZT6bdKlPy1JDXn1uT6k+s2P88t/1vUmFsD31u+bOqyiXYl25EuZTpS\nOaZKVAkJgKODI3PfmysJiRAiTiWuT0Yb0lovBQYB3wBHgNJAA631/di0kz071K4d+SmcU/dOUfG3\niiw8vpDOpTtz9KOjHPnoCF3KdGHcnnG8DHppoysRltJaM2DDANwmuNF7XW+yOWdjSeslZEydkZHb\nR9r0XOcfnOedWe/w8MVDdn+wmxp5a9i0fSGESIqS65gSALTWvwC/vG07bdvCJ5/AvXtGknLz6U0a\nLmqIe0Z3dnbbGW6p+aHVhjL/2Hxm+s6kX6V+b3tqEQvTDk1j0oFJjKgxgo/Kf0TO9DkBYzXefuv7\nMbDyQEq5lrLJuXqt7oVTCid2dd1Fnox5bNKmEEIkdcm2p8RaI7eN5M6z8GNhW7YEpWDFCuPWQKNF\njVAo1nVcFy4hASiUtRCdSnVi7J6x0lsSj07fP83ATQPpXb43X9f++k1CAtDTqyf5M+dn6NahNjnX\nrmu72HFtBz/U+0ESEiGEiAVJSmJp1/VdFJ5SmAl7J7xZrCxbNuMWzu9/vKbV0lbceHqD9Z3WkytD\nLrNtDK8xnDvP7jDr8Kz4DD3ZehX0io7LO5I/c37G1x8f6XjKFCkZU2cMa86vYee1nW99vlE7R1Eq\neymaF2n+1m0JIURyIklJLK1stxLvMt58vvlz3Ma7UWVWFbqs7IJzo2/Y7tKe3dd282e7PymRvUSU\nbRTOWpgOJTswds9YXgW9isfok6chW4Zwxu8MPq19onzq6f0S7+OVw4vBmwdjyRNpUZU58O8B/r78\nN1/W+DLRDWYVQgh7k0/NWMqYOiNTGk/h2MfHGFhlIEWyFuHyo8vsDZoKhdbjtm8BZTLFPO/E8BrD\nufn0JrOPzI6xrCQu1tt0aRMT909kXN1xlHYtHWU5B+XAuLrj2P/vfladWxVtm0tOLiHHhBwc+PdA\npGOjdo6imEsxWhdv/daxCyFEciNJiZVKZi/J8BrDmfveXPZ8sIf7n9/jWLtn+O9vS4sW8DKG4SJF\nXYrSvmR7vtv9ndmkIyAwgHlH51F1dlUyjM3A8bvH4+hKEp+bT28y+O/BuI53ZczOMVH2Wlx7fA3v\nP71pUKAB/6v0vxjbfTf/u9QvUJ8hW4YQFBJktoxfgB991/XlyasnNFzUkGN3jr05dvj2YdZeWMuw\n6sOkl0QIIawgn5w2VLpkCtasgX/+gY4dITg4+vJf1viSm/43cfnBhUq/VaL7qu78sOcH+q3rR84J\nOem2qhvOTs5kT5ud73Z/Fz8XkYCdvHeSbn92I99P+ZjmO41q7tUYvm043n96R0rsVp9bTdnpZUnt\nmJq57821OEkY++5YzvmdY/jW4WaPD9o0iBAdwvGPj5M/c37qLajHOb9zgNFLUjBLQdqVbPd2FyqE\nEMmV1lo2CzagHKB9fX11TP76S+sUKbTu1UvrkJDoyx7494D+fvf32nult64wo4JOOyatdhvvpods\nHqIvPbyktdb654M/a4evHfSFBxdiPHdSNeXAFM1X6Nw/5tYT9k7QT14+0Vpr7XPCR6calUpXm11N\n339+X78KeqUHbhio+QrdwqeFfhjwMNbnmrB3guYr9Jwjc8Lt33J5i+Yr9EzfmVprre8/v6+L/1xc\n5/4xt/7r7F+ar9CzD89+62sVQoikxtfXVwMaKKej+a5NttPMx1ZU08xHZc4c+OADY6G+CRMgfXrL\nzhOiQ1AojFnvDS8CX5Dvp3w0L9KcGc2S32ywJ+6eoPzM8vQo24NJDSfhlMIp3PH9/+6nxZIWpEuZ\njmzO2fC97csP9X6gf6X+4V5HS2mt+WjNR8w9OpfNXTdTI28NXga9pPSvpXFL58b2btvf9Lzc9r9N\n9TnVufToEh6ZPDjf93yk+IQQIrmTaebtrHt3Y12cRYugeHH46y/L6jkoh0hfpGmc0jCwykBj7Zyn\nN+Mg2oTrVdArOq3oROGshZnQYILZL/zKuStzoMcBnJ2cufPsDru77+bTyp9alZAAKKX4ufHPVM9b\nnZa/t+Tiw4t8u+tbrj6+yvSm08PdCsqRPgdbum6hjGsZvnv3O0lIhBDiLUhPiYVi21MS6upVY8G+\n9euhdWuYMgVy5Ij9+Z++ekreSXnp7tmdHxv8GPsGEqn/2/R/TD44mYM9DlLGrUy0ZQODA9FoUqZI\naZNzP3rxiMqzKhMUEsSNJzf4otoXfFP7G5u0LYQQyYn0lCQQHh6wdi0sWQK7doGXF7x+Hft2MqTK\nQN8KfZnuO50HAQ9sHqc9PX75mF/++YXT90+H27/96nYm7JvA6NqjY0xIAJxSONksIQHInCYzazqs\n4dGLR3hk8mBoddvM+CqEEMI8SUrigVLQrh2sWwe3b8O+fda1879K/0NrzeQDk20boB1dfHiRKrOq\n0GddH0r8UoLa82qz7NQy7j+/T9eVXamRtwYDqwy0W3yFshbCt5cvW723ktoxtd3iEEKI5ECSknhU\ntiy4uMDff1tXP1vabPTy6sWUg1Pwf+Vv2+DsYMfVHVT6rRIhOoSTn5zEp7UPwSHBtP2jLbkn5ubJ\nqyfMe28eKRxS2DXOfJnzkTtDbrvGIIQQyYEkJfHIwQHq1oVNm6xv47Mqn/Hs9TNmHUnc6+bMOTKH\negvq4enmyf4P91Miewnal2zPzu47OfHJCXqX741Pax/yZspr71CFEELEE0lK4lm9enDoEDx8aF39\nPBnz0KhQI5afWW7bwOJJiA5hyOYhfPDXB3T37M6GThvInCZzuDIls5dkYsOJNC7U2E5RCiGEsAdJ\nSuJZvXqgNWzZYn0bTQo1Ye+NvTx68ch2gcWDV0Gv6LKyC2P3jGV8vfFMazpNHqEVQgjxhlVJiVLK\nQSlVWClVTSlVI+xm6wCTmjx5oGhR68eVADQu1JgQHcLGSxttF1gce/zyMY0WNWL56eUsbbOUz975\nzOp5RIQQQiRNjrGtoJSqDCwG8gIRv1U0YN9RiYlA/fqwapXRY2LN93LuDLkp7VqatRfW0r5ke9sH\naGM3ntyg0aJG3PK/xeaum6nmXs3eIQkhhEiArOkpmQYcAkoCWYDMYbYstgst6apXD65dg4sXrW+j\nSaEmbLi4geCQGFb9s7Onr55SbU41nr1+xt4P90pCIoQQIkrWJCWFgKFa6zNa68da6ydhN1sHmBTV\nrAmOjm9/C8cvwI9/bv1ju8DiwJidY7j//D7bu22nqEtRe4cjhBAiAbMmKTkAFLR1IMlJ+vTwzjtv\nl5RUzl2ZzKkzs+7COtsFZmMXH15k0oFJDK46GI9MHvYORwghRAJnTVIyBZiglOqmlPJSSpUOu9k6\nwKSqXj3YuhWCgqyr7+jgSIOCDVh7Ya1tA7OhQZsG4ZrWlf+r+n/2DkUIIUQiYE1SshwoBswG/gGO\nAkfC/CssUL8+PH0KBw9a30aTQk04fPswt/1v2y4wG9lyeQurzq3i+3rf4+zkbO9whBBCJALWJCX5\nzGz5w/wrLODlBZkzv90tnIYFG6JQrL+43naB2UBQSBCfbvyUqnmq0q5EO3uHI4QQIpGIVVKilHIC\nRgIOWutr5ra4CTPpSZEC6tR5uynnXZxdqJS7UoK7hTPDdwan7p3ip4Y/yVwkQgghLBareUq01oFK\nqdbAqDiKJ1mpXx9694YnTyBjRuvaaFKoCd/v+Z7Xwa9JmSKlbQO0wIOABxy/e5zXwa95Hfyal0Ev\nGbFtBN08u+GV0yve4xFCCJF4xXryNOBP4D1goo1jSXbq1YPgYBgxAsaMgXTpYt9G40KN+XLbl+y+\nvps6+erYPshoPHrxiLLTy3Lj6Y1w+3Oky8G3734br7EIIYRI/KxJSi4AI5RSVQFf4HnYg1rrybYI\nLDnIlw+++cZISJYtg1GjoFs349aOpcq6lSVHuhysu7AuXpMSrTUfrfkI/9f+HOp5CNd0rqRMkRIn\nByfSpUwna9oIIYSINWsGun4IPAa8gF7AgDDbp7YLLXn48ks4exZq1YIePaBsWdi92/L6SikaF2rM\nyrMreRX0Ks7ijGj+sfksO72M6U2n45XTi9wZcpM9bXYyp8ksCYkQQgirxDop0Vrni2aTp2+s4OEB\nixfDgQOQJg00bQq3Y/GUb58Kffj36b/039A/zmIM69LDS/Rd3xfvMt60LdE2Xs4phBAi6bNqlWAR\nNypWhHXrIFUq+N//LK9XNkdZfm78M9N9pzP7yOy4CxDjcd/OKzuTPW12JjeSO3VCCCFsx5pVgqP9\n1tNaf2B9OCJrVvjpJ+jQwVhJuEULy+r1KNeDA/8eoPfa3pR2LU35nOXDHX/++jlKqbeeyGz0ztH8\nc/MfdnXfRYZUGd6qLSGEECIsa3pKMkfYsgN1gFZAJtuFlny1aweNG0OfPsasr5aa0ngKpVxL0Xpp\na/wC/ADjVkv/9f1xm+BGw4UN0VpbHddZv7OM2jmKL2t8SZU8VaxuRwghhDAn1j0lWuuWEfcppRyA\nX4FLtggquVMKfv0ViheHIUPg558tq5faMTXL2y7Ha4YXrZe2JkuaLKw6u4osabLQulhr5h2bx+rz\nq2lepLlVcU0+MJlsztn4otoXVtUXQgghomOTMSVa6xDgR4wncIQNuLvDt98aycmePbGol9Gd39v8\nzp7rezjnd45pTadxfcB15rSYQ518dRi6ZSjBIcGxjufxy8fMOzaPj8t/TCrHVLGuL4QQQsTElgNd\nC2DdvCciCn36GINfe/aEwEDL69XJV4fbn93mZO+T9PLqhbOTM0opvnv3O07dP8WiE4tiHcusw7MI\nDA7k4/Ifx7quEEIIYQlrBrr+GHEXkANoAsyzRVDCkCIF/PgjVK1qrCZctarldbOlzRZpX8VcFWlV\nrBUjto2gXYl2Fvd4BIcEM/WfqbQr2Q63dG6WByGEEELEgjU9JWUjbKVN+z9DJk+zufLlwckJjh2z\nTXuja4/mxtMbTPedbnGd1edXc/XxVf5XMRbPKQshhBCxZM1A19pxEYgwL2VKY8Dr0aO2aa9YtmJ0\n9+zO6J2j6e7ZnfSp0sdYZ/KByVTJXYUKuSrYJgghhBDCjFj3lCiltiqlIj36q5TKoJTaapuwRFie\nnrZLSgBG1hzJ01dP+XFfxDtxkZ24e4JtV7fxv0rSSyKEECJuWXP7phaQ0sz+1ED1t4pGmOXpCSdO\nQFCQbdrLkzEPfSv2Zfy+8Tx68SjaspMPTCZn+py0LtbaNicXQgghomBxUqKUKq2UCh0/Ujz0Z9NW\nFmOhvptxEmUsKKWuKqVCwmzBSqnPI5TJo5Raq5R6rpS6o5T63jTXSoLk6QkvX8L587Zr87Mqn/Ei\n8AWLTyyOssyDgAcsPLGQ3uV7yyJ7Qggh4lxsxpQcBbRpM3eb5gXQzxZBvSUNDAdmYjwZBOAfetCU\nfKwDbgGVgZzAAuC1qV6CU6aM8e/Ro8b4ElvIkT4HTQs3ZdaRWfSp2MdsmRm+M9Ba08url21OKoQQ\nQkQjNr0D+TDmIlFARdPPoVsuIIPWOm5Xg7PcM631fa31PdP2IsyxBkBRoJPW+oTWeiPwJdBHKZUg\n51nJnBny5rXtuBIw1ss5cucIh28fjnTsReALJh2YhHcZb7OPFwshhBC2ZnFSorW+prW+qrV20Fof\nMv0cut3WWsd+mtC484VSyk8pdVgpNUgplSLMscrACa21X5h9G4GMQIl4jTIWPD1t91hwqIYFG5Ij\nXQ5mHZ4V6djsI7PxC/Dj86qfm6kphBBC2J5V4yiUUl2UUnuUUreUUnlN+wYopSxc0zZO/QS0xxiQ\nOw0YCowLc9wNuBuhzt0wxxIkT084cgTeYj29SBwdHOnu2Z1FJxYREBjwZn9gcCA/7P2BtiXaUiBL\nAdudUAghhIiGNY8Ef4Kxzs06jFWBQ3shHhFHk6cppb6LMHg14haslCoMoLWepLXeqbU+qbWeAQwE\n+imlEvVITU9PuH8f7tyxbbsflP2AJ6+esPz08jf7lpxcwrUn1xhSbYhtTyaEEEJEw5oxFP2Anlrr\nP5VSYZeLPQSMt01YkYwH5sRQ5nIU+w9iXKcHcAG4A0ScBczV9G+MX/kDBgwgY8aM4fZ16NCBDh06\nxN59bb8AACAASURBVFT1rYQd7Jojh+3aLZClAHXy1WHWkVl0KdOFEB3Cd7u/o0mhJpR2LR1zA0II\nIUQYPj4++Pj4hNv35MkTi+pak5TkA46Y2f8KSGtFezHSWj8AHlhZvSwQAtwz/bwPGKqUcgkzrqQ+\n8AQ4HVNjEydOpFy5claGYj0PD8iQwUhKGjWybdsflv2QTis6ceHBBU7dP8UZvzPMbDbTticRQgiR\nLJj7Q/3w4cN4eXnFWNeapOQK4Alci7C/IXDGivZsRilVGagEbMN4DPgdjFtNC7TWoWnaJozkY4FS\najDGYoKjgKla61isxRu/lLL9zK6hWhVrRebUmZl1ZBbbrm6jRt4aVHWPxep/QgghhA1Yk5T8CPys\nlEqN6fFgpVQHYAjQw5bBWeEVxiDXkUAqjARqAjAxtIDWOkQp1RT4FdgLPAfmmuokaJ6esGGD7dtN\n7ZiazqU789OBn3gZ9JL1ndbb/iRCCCFEDKxZkO83pdQLYDTgDCzGmIisv9Z6iY3ji21sR4AqFpS7\nATSN+4hsy9MTpkyBZ88gXTrbtt2jXA+mHJxCWbeyNCjQwLaNCyGEEBawarIwrfUiYJFSyhlIp7W+\nF1Md8fY8PY1Hgk+cgCoxpl6xU9q1NJ9V+YxmhZuhlIq5ghBCCGFjb7Xei9Y6IDQhUUqlVkoNsk1Y\nwpzixcHRMW7GlQCMrz+emh4146ZxIYQQIgaxSkqUUtmUUk2VUvVDZ0lVSjkppfoDV4Evom1AvJVU\nqYzEJK6SEiGEEMKeLL59o5SqBqwBMmAsendIKdUd+BMIAv6/vfsOk6o8/z/+vqUqCopGFCWIsYAN\nAXsLYkSxgF0RC0bjN7H3EkuIlcRuflhiFKMCFsSGGjSCbcUCKLGAJYpKEARBlCbs7v37456VYdhl\nd3an7+d1XXPBnPOcc55np5x7njoI+GcW8ihJsjUCR0REJN/SqSm5hpjFdVtiNMuOwBPAH919K3e/\nK2XhO8mC7beH//wHysvznRMREZHMSico2Ra4xt0/JFbVdeAidx+ZlZxJtbbfHpYsgU8/zXdORERE\nMiudoGQdYA5AokZkEfBBNjIlNUuebl5ERKSUpDskeCszq1pJ14AtzWyFqeXd/T8ZyZlUq21b6NAh\nmnCyvNyOiIhITqUblLxEBCNVRif+9cR2Z/mqwZIlm24KX3yR71yIiIhkVjpBSaes5ULSsskm8PHH\n+c6FiIhIZtU5KHH31AX4JE86doQXXsh3LkRERDKrQTO6Sn507AjffAM//ZTvnIiIiGSOgpIi1LFj\n/Pv11/nNh4iISCYpKClCVUHJl2pQExGREqKgpAh16BD/KigREZFSku6Q4BWY2XrAzsQw4Hfc/ZuM\n5EpWqUUL2HBDBSUiIlJa6h2UmNnhwL3AJ0AzYiK10919aKYyJzXr2BGmTct3LkRERDKnzs03ZrZm\nyqY/ATu5+07u3g04Erg2k5mTmnXsqJoSEREpLen0KZloZv2SnpcD6yc9bwcszUiupFYKSkREpNSk\n03yzHzDEzAYCpwNnA4+YWZPEeSqBgZnOoFRvk01g+nSoqIAmmthfRERKQDozuk4DDjSz/sArwO3A\nZolHE2Cquy/JRiZlZR07Qnk5zJixfDSOiIhIMUt7SLC7jwB2BLoCLwOruft7CkhyS3OViIhIqUkr\nKDGzA8zsfGAHdz8FuAgYZmY3mNnqWcmhVEtBiYiIlJp0Rt/cBAwlaknuNrMr3P0VoDuwBHjXzPpk\nJ5uSas01oW1bBSUiIlI60qkpGQgc4O7HEIHJ8QDuvtTdrwAOA/6Y8RxKjTQCR0RESkk6QclCoFPi\n/x2I2pGfuftH7r5npjImtVNQIiIipSSdoORS4AEzm0GMvrkiO1mSulJQIiIipSSdIcHDzOxfwKbA\np+7+ffayJXVRFZS4g1m+cyMiItIwaa194+7fAd9lKS+Spk02gcWLYfZsWH/9WpOLiIgUtLTnKZHC\noWHBIiJSShSUFDEFJSIiUkoUlBSxtm2hVSsFJSIiUhoUlBQxM43AERGR0qGgpMgpKBERkVKhoKTI\nKSgRkWLx00/5zoEUOgUlRU5BiYgUOne45RZYay24555850YKmYKSItexI3z/PfzwQ75zIiKysmXL\n4LTT4LzzYLvt4Pe/h1Gj8p0rKVQKSoqchgWLSKGaPx8OOgj+8Y94vPUWHHEE9O8P48blO3e5MWQI\njByZ3jHujbepS0FJkdtkk/hXQYmIFJIpU2C33eDtt2HMGDj5ZGjSBB54APbaC/r1g3ffXZ5+9mx4\n6KFo5qmszF++M+n22+GMM+C44+LvURcVFdC3b/yN3LObv0KkoKTIbbABNG8O06blOyci0ti5w+uv\nwyGHwNZbx6/98eOhV6/laVq0iOabzp1h//3hsstgxx2hXTs4/vho5vnLX/JXhkx55BE45xw466z4\n8XjCCdGUVZtBg2D06Ajmxo/Pdi4LT1pr30jhWW016NBBNSUikl/PPgtXXx1NNF26RHPNgAERhKRa\nay147jnYZx+44w7o3RtOPz2ClCFD4PLLYYcdYN99M5vH2bOhrCweEyZEPjbeOL5DN944gqovv4wf\neVWLnd533/Jm8roaOzaCkGOPjZqfCROi1uj66+HKK2s+7umn4Zpr4nHvvdEpeLfdGlTk4uPuRfMA\n/giUAQuBuTWk6QA8m0gzE/grsFpKmu2AV4HFwJfAhXW4dnfAJ06c6IWmVy/3I4/Mdy5ESt/Mme47\n7ODerZv73nu7H3qo+0knuT/7bL5zll/33+8O7nvtFX+Lioq6HVdZ6V5evuK28nL3/fZzX3dd92nT\nMpO/UaPct9wy8gjuG2/sfvjh7gce6N61a1yrat/667vvuGN8p3bsGMfNmVP3a02a5L7WWu69e7v/\n9NPy7Zdf7t60qfuECdUf9/HH7q1bx3uqstL9uuvcV1/dfd68BhW9YEycONEBB7r7Ku61xdZ80wx4\nFLizup1mthrwHFEDtAtwIjAQuCopzVrAGOALItC4EBhkZqdkM+PZpGHBIrkxaBB89hnssks0Nyxe\nHNXsffvCY4/lO3f5MWoU/Pa38LvfwcsvwwEHRA1uXZhFP5NkTZrAsGFRi3HEEbBkScPy9/DDcOSR\nsNlmMHx4fFd+/XV0Ph09Gt57D+bMgYUL4zFrVrymjz4KL74Ic+dGZ92FC2u/1ssvQ58+sOWW8Pjj\n0bRe5YorYNttowYltUwLFsBhh8GGG8L998ff5aSTorln2LCGlb/orCpiKdQHEWysVFMC9AGWAesl\nbfs/YB7QNPH8D8CcqueJbdcDH9VyzYKtKRk0KKJ7EcmeKVPcmzRxv/HGFbeXl7sPGBD7Ro7MT97y\n5YUX3Js3dz/66JVrPBpq4kT3Fi3cTzml/ud46CH31VZzP+GE+ufvnXfcW7WKWpWlS6tPs3Ch+1ln\nLa8tmjWr+nTvvx9/r5NPdh8+3H3IEPdrr43a7jXXdP/ooxXTH3qo+7bbRs1JsatrTUneA4z6PFYR\nlPwZmJSybROgEuiaeP5PYFRKmp5ABdBmFdcs2KBk+PB4JUulmk+kEB1ySFTnL1688r5ly9yPOSaq\n50eNynnW8uKNN9zXWMO9T58Vmyky6b774rvttNPcf/ghvWMfeCACkoEDGx4wjRkTr+1JJ60cIJSV\nuW++uXvLlu633lp709XNN/vPTUVNmrivt557587uTz+9ctrnn490b7654vYFC9wPOMD9ttsaVq5c\naqxByd3A8ynbVk8EJfslno8B7kxJ0yURlGy5imsWbFAyaVL1b1wRyYzXXovP2EMP1Zxm2TL3o46K\nm9f997s/84z73/7mfv757v37xw2mJt98E7+Yf/wx83nPtOnT48a69true+4ZtQTZdPvtEfxsvLH7\nU0+tOu2PP7q/9Vb8Lc2iRqKu/Vtq89BD8R5YfXX3tm3dN9zQvVOnCHx22SX6hNTV7NmR19pqQMrL\nIxD+7W+Xb1u61H3//SMvv/hF9gLCTKtrUJL30Tdmdj1w8SqSONDF3T/JUZZW6dxzz6VNmzYrbOvf\nvz/9+/fPU45giy3i36lTYeed85YNkZLkDhdeCN26xaRfNWnaNObZOPZYGDgwtjVrFn2+WrSIvhZX\nXw2XXrpin4vXXoOjjoKZM2Nm5sGD65fPefNieG1lZcyPkdyfoaHmzo2+GQ8/HEN+mzWLeUbuuQfW\nWCNz16nOmWfCwQfHrLD9+sHhh8dInVmz4Kuvon/ItGnw4YfwxRdxjFmkue22uvdvqc2AAdGP6IMP\nYqjzkiXxb8eOcMopK/eNWZX11qtbuiZN4tzXXw833xz9bH77W3jppXiNzzoLnngCjj66fmXKlhEj\nRjBixIgVts2fP79uB68qYsnFA1gX2KKWR9OUY9R8k6JjR/dLLsl3LkSK19Kl7vfcE/1Ckn99jhwZ\nv0r//e+6nae8PGovp09f/iu9osL9T3+K8xx6aDRFVFa633BDVOH37Ol+5pnR3+DTT9PP+8iR7hts\nEKM+mjePkR8LFqR/nup88ol7hw5RA7T//u5Dh+anqbiy0v3hh6P/XFXzR+vW7lttFfk6//zI2zvv\nZL/2JpemT4/3yB13uF9wQdQAPfxw7Ntrr3jvFIPG2nyzPyt3dD2V6OjaLPH890RH1yZJaa6jiDu6\nuscQun798p0LaczKy93/9a/iqU5O9uGH7j16xBd+VbX4+ee7T54c/QX23z8z13nqqQgcOnd279s3\nrnXxxdH0s3Bh3Pz79q3+2CefjBvQKadEs9Crr7pPnRpBDsRxX3/t/tJL0Wly553TG8panfffd2/X\nLvL75ZcNO1em/PBDvF7z5+c7J7nTr18EYBDNWVWq+hNOmZK/vNVVSQYlxBwkXYErgfmJ/3cFWiX2\nrwZMBp4n5iLZD5gFXJ10jtbAjESNyVbA0cAC4ORarl3QQcnZZ8d4epF8WLrU/dhj4xvl4IPdlyzJ\nd47qprw8aitatIgb79tvu3/wgfu55y6fu8IsgpNMmTo1rtWmzcp9JB55JK45ZsyK2//976gB2XFH\n9+23j/9X1Ra0a+f+2GMr9k94553oQLnVVvFLuz4mTIi/QdeuNY8mkdyo6vB62WUrbl+yJF7nc87J\n3rVnzIh+Og0dAVSqQcnQRDNL6mOvpDQdgNGJQGMW8BdWnjxtG+AVYBHwFXBBHa5d0EHJnXdG9WpN\nQ9ZEsmXJkvgl17Sp+6WXxg2+T5/qR6kUiu+/jxv57rtH0HHeee6LFq2YZskS90cfdR82LPPXX7zY\n/bvvVt5eWRmdR7t0Wf5ZnjAhaj722295LdTSpVGLMWqU+9y51V9j6tSoefnlL93ffbfmvLz4ovvp\np0dw9sQTcd6xY+OX+c4713x+ya0pU6oPDC66KDodp75/G+rTT91/97vlAfDWW7vffXf9m8ZKMijJ\n56PQg5Jx47xoqvGkdCxcGP0XWrRwHz06tr34YoxQ2Hff/Lbt//e/7q+8EjfYF1+MpqUbb4yZWJs2\njc/LdttFmkIyaVIESrfeGiM6fvGLCA7q00fkq69i9tmWLWOIbLJly6IfGkSftDXX9J9rX6rm20h3\nGK7k3mefxet1//0NP9ePP8ZIs2OOiVFF7dq5Dx4cNXf9+sX7sm3baHJM972hoKSRBSXffBOv5hNP\n5DsnUooqKqL9+vbb4+b21FMRCO+5Z0ws9dJLK6YfNy6277135jpcpmP8+PhSTb7JQtycDzggJq36\n4ovc56uuTj01mnc6doxak4b0DVm0KObqqJrv46efou/JHntEB8rBg+P1rayM75HXXnN//PHM//KW\n7OndO4YlV6msjCHMnTpFDVhNTS+zZkWT0EEHRdqqz8kmm8RnJPU98N//RtNmq1bRlDh7dt3zqKCk\nkQUllZVRhXfddfnOiZSa//0vaj1gxb4MEDfOsrLqj3vttfj1vdFG7ldfHevG5MLSpe7bbBMdV6dM\niWrozz+PjprFcqP99tv423boELUdDVVZ6X7XXe7NmsXaPeuuG/N+vP56w88t+TdqVHwe33031guq\nmsdkp53i39NPX3kCubKy+Gy2aRPNrRde6P7Pf0Zz4bJlq77epEkxCmrLLeveAfq++xSUNKqgxD0i\n5RNOyHcupJSMHLl8oqiqzpeLF0eAMXVq7b/gp0yJ0SKrrx43xAED4sswm9NmX3dd1ABMmpS9a+TC\nBx9EQJhJb74ZtS99+zZ8ZI4UjqVL3du3j4CzVasIOJ95JvbdfXd8Hvr2jebUysqYCbZp0+hTVd+O\n0J9+GrUrG20Uo6FW5dVX3Zs1U1DS6IKSgQMjMhZpqMWLY0ptcD/ssIbfwL77LvpzbLqp/1w9fPHF\nEThUrRQ7caL7LbfEENeLLqrfdT75JPq3XHhhw/JbykphHRVZ2Z//7D/XiqQOl3722QhWdtopZh2G\naIZp6MCIGTOiX1bbtjXXmH74YdTi9+ihoKTRBSWDB0ePeX3pSEPdeWf8urrvvsy+nyoqov/Jqacu\nH3LbqdPyORhatIghqBD9UtJRWRkLm3XqVFqTZ4nUxbJlq+4nNXHi8gn2Hnssc9edNy/6ljVp4n7N\nNSs2E02fHk2Q227r/vLLdQtKzOOGK7Uws+7AxIkTJ9K9e/d8Z6daTz0FhxwCM2bEEtgi9fWb38QU\n12PGZO8ay5bB2LHwzDOw0Uaw556w444xhfluu8HixTBxYkzfXhf33x/LvY8ZA717Zy/fIsVqzpyY\nGn+jjTJ73mXL4Kqr4Lrr4rP74IOwzjrxmf7+exg/HmbNmkSPHj0Aerj7pJrOlfe1byRzunSJf6dM\nUVAi9Td7Nrz8MtxxR3av06wZ7LdfPFLdfnus43TPPfCHP9R+rm+/hfPPh+OOU0AiUpO6rrmTrmbN\nYl2n3r3h+OOha1fYbLNYl6isLIKgWbPqdq4MLVUkhaBTp3hzTJ2a75xIMXvqqRhbc8gh+cvDTjvF\nonaXXx6Lwa2KO5x6aizCdvPNOcmeiFRjzz1h8uRYQHHKFHj6adhqq/TOoaCkhDRrFtGpghJpiJEj\n4de/hvXXz28+rr8+qoUHDVp1ujvvjEDq3nvhF7/ISdZEpAZt2sRq2fPmRZCSLgUlJaZzZwUlUn/z\n5sWy6Eccke+cwAYbwBVXRDPSBx9Un+b99+G88+CMM2JZexEpDC1a1O84BSUlRkGJNMTTT0NFBRx6\naL5zEs4+GzbdFM45J/KVbNEiOPpo2GILuOGG/ORPRDJLQUmJ6dw5OhctWJDvnEgxGjkSdt+9cDpK\nN28Ot90WtTedO0dTzaJFse/cc2HaNHj4YWjZMq/ZFJEMUVBSYqpG4Hz8cX7zIcVn/nx44YXCaLpJ\n1qcPvP02dO8ezTQdO0Yn2L//PQKWdDvSiUjhUlBSYrbcMv5VE46ka/RoWLoUDjss3zlZ2Y47wiOP\nwKefQv/+8NhjcNRRcMop+c6ZiGSSgpIS07o1tG+voETS9/jjMTdIhw75zknNNt005jCZPRuGD49h\nwCJSOhSUlCB1dpV0LVgAzz9feE03NVljjZhxVkRKi4KSEqSgRNL13HOwZAkcfni+cyIijZmCkhLU\npQt88gmUl+c7J1IMFiyAW26Bbt1iVmARkXxRUFKCOneODovTpuU7J1Lovv8+1qv48MPoqyEikk8K\nSkpQ1RDJO+6Aysr85qWx+uqrmG00HS+9BBddFM0oufDtt7D33jF8/KWXYI89cnNdEZGaKCgpQe3b\nxwyXt94KxxyzfLIpyb4ZM+C002INoh49YoRIbSoq4E9/gn33jdftiCOipiubpk+HvfaCb76BV16J\nIbciIvnWNN8ZkOy44AL41a9iKfeePWPBskKZpbMUzZ4Nf/kLDBkCq68ey3hPmQIDBsSN//zzqz9u\n5kw49tgIDK6+OgKZfv0imHzkkVhkMR1Ll0YNWXl5vO7bbw9NE5/yysqYhGz0aLj//hi98tprsPnm\nDSm5iEjmKCgpYYceGjedgw+OpeBHj4auXfOdq9IzfXoEE4sXw8UXx/TnbdqAe9RaXXBB1KDccAOs\nlqib/PZbGDs21nQxi+aTnj1j36hR8doddxwMG7Y8qKjNhx/C8cdHs1Hz5lFD1rp1rNTZti2MGRPX\nbdsWDjwQrrkGfvnLrPxJRETqRUFJievePX4dH3wwHHAAvPeelnfPpPLyqOlo3hwmT46VbauYwXXX\nRWBy1lkxG2nr1jB+PHz+eaTp3RseeADatVt+3IEHwqOPwpFHwoknwr33rnptl8rKGD1z2WVRO/bW\nW7DNNjBhArz8cjz+85+Ymv2gg2DXXese6IiI5JK5e77zUBTMrDswceLEiXTv3j3f2UnbjBlRS1JV\nY6KZMDPjiivg+uvjxr+qjqIjR0Zg0qFDBAW77gq77BI1FTW9Fo89Fs04zZrFTKt77RWP9u3j9fzf\n/+Lf55+HsrKoobn2Wi1OJyKFZ9KkSfTo0QOgh7tPqimdfi81Eu3bRz+Cgw6KDrDnnpvvHBW/f/87\ngoBrr6195MoRR6Q/W+qRR0aNxwsvwKuvwl13RZNLsvXWi9qRsWOXN/+IiBQrBSWNyIEHwnnnRb+H\nPfeEHXbId46K18yZ0efjN7+Jv2e2dOkSj7PPjj4qU6fCd9/BRhtFoNmiRfauLSKSaxoS3Mhcfz1s\nt100C/zwQ75zE77/Hm66Keb2KAYVFTGqxgwefHB559VsM4sAZY89YuZVBSQiUmoUlDQyzZvDww/H\nKIzf/z5+fefTrFkxgdcFF8TQ1HPOibwVqtdfh913h3HjYmRMcgdVERFpGAUljdBmm8VcFiNGRF+E\nfPnyy/jVP2tWjEi54goYOjSWp7/88rjxP/dcdBJ98EH417/yF0R98kkM091zzxhxM24c9OqVn7yI\niJQqjb6po2IffZPKPUbiNG8ev/5zPRrno49iOGyLFvDiixGIAMydC3/9a6zDsnjxyscddBDcfXf0\np8iFRYsiWLr99rjmdddB//65a7IRESkFdR19o6/WRsoMrroK3ngjRnfk0sSJMbS1bdsIiKoCEoht\ngwfHcNfPPot/582L9WCeegreeSdGpAwblv1akzffjJVzhwyJv9XUqdGXRAGJiEh26Ou1Edt//5gr\n48orc9csMmsW9O0bgcjLL9c89f0668RQ1/btYe21o0alb9+YtbRPnxj5cuihMRIl0376CS69NPqO\nrL12TDh36aUxfbyIiGSPgpJGzCzWW3n7bXj22exfr7w8mj4qKqLWo23b9M+x7rpRS/L441HL0rNn\nrC2TKT/+GBOV3XRTzAlSVgadO2fu/CIiUjMFJY3cPvtE581c1JZcfnlMAvboow1fHPCwwyIomTcv\nmoK+/DIzebzjjujv8tZbUTui6dhFRHJHQUkjV9W35N13o/YiW558MlbRHTw4gohM6Nw5FhysrIxR\nPB9/3LDzLV4MN98ca8R065aRLIqISBoUlAg9e8bw1iuvjBt8pn32WSwsd9hhcP75mT13p04RmFSt\nhjt5cv3Pde+9MGdOdmdoFRGRmikoESBqS95/P2o0MskdjjoqJhkbOjQ7Q4/bt4dXXonF7fbeO0b3\npGvp0hiKfMwx0cFWRERyT0GJADHSpHt3eOKJzJ73v/+NpqGbborajGxZb71YIG+LLaKfzNtvp3f8\nsGHw9dfRj0RERPJDQYn8bJ99YobXTHZ4LSuLf2tbRTcT1l475lzZemvYd9+YJbbK7NkxCVq7dtGM\nNH/+8n0VFdHXpV+/mANFRETyo6iCEjP7o5mVmdlCM5tbQ5rKlEeFmR2VkmY7M3vVzBab2ZdmdmFu\nSlDYevWCGTNiSvVMKSuLIGGddTJ3zlVp3Tqmo+/aNWaMHTkSzjoLOnaEW26JuU7GjoUdd4QPPohj\nHn88ynzZZbnJo4iIVK+oghKgGfAocGct6U4E2gEbABsCP/eUMLO1gDHAF0B34EJgkJmdko0MF5M9\n9oghsJlcD6esLJqGcmmtteD552GHHeDII6Np5uKLY9jwPffAhAnQsmVMHPfIIzF1/L77RqAiIiL5\nU1SzMLj7nwHM7MRaks5399k17DuOCG5OdvdyYIqZdQPOA/6RscwWoTXXjPVwxo2DP/yh4eebOzfm\n/MjHaJZWrWJCuOeei5lr11xz+b7NNoumnVNOiY6tEGUWEZH8KraakroaYmazzewtMzspZd8uwKuJ\ngKTKGGBLM2uTuywWpl694gadiaHBVX06dtut4eeqjzXWgCOOWDEgqdKqFQwfDn/7G5xxBvz617nP\nn4iIrKgUg5IrgKOA3wAjgTvM7Iyk/RsAs1KOmZW0r1Hr1Svm6qjqb9EQb7wB669fuENszSIg+dvf\ncr9KsoiIrCzvQYmZXV9N59TUjqpb1PV87n6tu49398nufgPwF6LfiNTBrrvG4neZ6FdS1Z9EN3wR\nEamLQuhTciMwtJY0nzfg/G8DV5hZM3dfBswkOsEmq3o+s7aTnXvuubRps2IrT//+/enfv38Dslg4\nWraMQGLsWDjnnPqfZ9mymCvk6qszlzcRESl8I0aMYMSIEStsm588D8Mq5D0ocffvgCwsQP+zbsC8\nREACMB64xsyauHtFYltv4GN3r/Wvdsstt9C9e/csZbUw9OoVs5uWl9d/Qbp33421ZHI98kZERPKr\nuh/qkyZNokePHrUem/fmm3SYWQcz6wp0BJqYWdfEo1Vi/0FmdrKZbW1mvzKzPwCXArcnnWY4sBS4\nz8y2MrOjgbOAm3JcnILVqxf88ANMmlT/c5SVRa1LicdvIiKSQXmvKUnTVcAJSc+rbpt7A68Cy4DT\ngZsBAz4DznH3n4f6uvsPZtYbGAJMAOYAg9z93uxnvzjssEOMWBk7NoYIr8pPP0GzZrBaSnhbVhbz\nfjRvnr18iohIaSmqoMTdTwJSh/gm7x9DDO+t7TwfABoEWoNmzWCvvSIoueSSVaft0ycCj+eeWx6Y\nuEdQMnBg1rMqIiIlpKiabyR3evWC11+PmpCafPRRzGkyZgwMGbJ8+xdfwMyZ+ZufREREipOCcyR8\n/wAADtBJREFUEqlWr17RUfWtt2pOM3QorLsunHpqzNpatWZO1SJ8CkpERCQdCkqkWl27xiJ6Nc1X\nsmwZPPggDBgAN98MG20EJ5wQI3bKyqBz5whYRERE6kpBiVRrtdWgZ0944YXq9//rXzBrVvQbadUK\nHngA3nknhhK/8YaGAouISPoUlEiNjjsu1q8ZPXrlfUOHRm1Kt27xfNdd4aKLYNCgmKJeQYmIiKRL\nQYnU6NBDoXdvOPNMWLRo+fbZs+GZZ+CklHFQgwZBly4x+kZBiYiIpEtBidTILEbVfPMNXHvt8u3D\nh8e+AQNWTN+iBTz8cHR63Xzz3OZVRESKn4ISWaXNNou5Sm64AaZOjW1Dh8LBB8N6662cvksXGDxY\ni/CJiEj6FJRIrS65BH75Szj99FjTZvJkTYwmIiKZp6BEatWyZTTjjB0bnV/btYuZXEVERDJJQYnU\nyX77wZFHxiyuxx9f/9WDRUREaqJbi9TZLbfELK+nnZbvnIiISClSUCJ1ttFGMRRYREQkG9R8IyIi\nIgVBQYmIiIgUBAUlIiIiUhAUlIiIiEhBUFAiIiIiBUFBiYiIiBQEBSUiIiJSEBSUiIiISEFQUCIi\nIiIFQUGJiIiIFAQFJSIiIlIQFJSIiIhIQVBQIiIiIgVBQYmIiIgUBAUlIiIiUhAUlIiIiEhBUFAi\nIiIiBUFBiYiIiBQEBSUiIiJSEBSUiIiISEFQUCIiIiIFQUGJiIiIFAQFJSIiIlIQFJSIiIhIQVBQ\nIiIiIgVBQYmIiIgUBAUlIiIiUhAUlIiIiEhBKJqgxMw6mtk/zOxzM1tkZp+a2SAza5aSroOZPWtm\nC81sppn91cxWS0mznZm9amaLzexLM7swt6UpDCNGjMh3FjJOZSp8pVYeUJmKQamVB0qzTEUTlACd\nAQN+B2wFnAv8Hri2KkEi+HgOaArsApwIDASuSkqzFjAG+ALoDlwIDDKzU3JRiEJSim9olanwlVp5\nQGUqBqVWHijNMjXNdwbqyt3HEMFElWlmdiMRmFyU2LYfEbzs7e5zgPfN7ApgsJkNcvdy4DigGXBy\n4vkUM+sGnAf8I0fFERERkRTFVFNSnbWBuUnPdwHeTwQkVcYAbYCtk9K8mghIktNsaWZtMp3B+kSy\nuTrmf//7X9rH1PdahVymXOWtvscVcpkKuTz1Pa6Qy5TL92qplUnvu/pfJ5fvu6INSsxsM+AM4K6k\nzRsAs1KSzkraV9c0GVPIbwAFJfW/joKS+h+jm0P9r6OgpP7H6H1X/+vk8n2X9+YbM7seuHgVSRzo\n4u6fJB2zEfA88Ii735flLFZpCTBlypS0Dpo/fz6TJk0qyGOWLVuW9jH1vVYhlylXeavvcYVcpkIu\nT32PK+Qy5fK9Wmpl0vuu/tfJxDFJ986WqzrO3D2tC2Wama0LrFtLss+rmlvMrD0wDnjD3U9KOdef\ngYPdvXvStk2Az4Fu7j7ZzP4JrOXuhyWl6Qm8BLR19/k15PNYYFh6pRMREZEkA9x9eE07815T4u7f\nAd/VJW2ihmQs8A7w22qSjAf+aGbrJfUr6Q3MBz5KSnONmTVx94qkNB/XFJAkjAEGANOAJXXJr4iI\niABRQ7IJKw5YWUnea0rqKlFD8goxlHcgUBVQ4O6zEmlWA94FZhBNQhsCDwB/d/crEmlaA1OBF4G/\nANsC9wJnu/u9OSqOiIiIpCimoOREILX/iAHu7k2S0nUA7gR6AguB+4FL3b0yKc02wBBgR2AOcLu7\n35jN/IuIiMiqFU1QIiIiIqWtaIcEi4iISGlRUCIiIiIFodEEJWZ2qZm9bWY/mNksM3vCzLaoJt1V\nZjYjsejfi4lJ2pL3tzCzIWY2x8x+NLORZrZ+SprNzexJM5ttZvPN7LXEsONiLlN3M3vBzOYlynW3\nmbUq0PL8zszGJf72lYnOzannWMfMhiXSzEss9pjR8uShTH80s7LEYpRzU/cXW5msjotwFkt5Emme\nslgEdHHiXA+Y2YaZLE+uy5SUtrmZvZdIt12xlsfMpiX2VT0qzOyi1HTFVKZEugPN7M3Eeeaa2ahM\nlykTGk1QAuwJ/A3YGfgNsf7NC2a2elUCM7uYmCX2VGAnoqPsGDNrnnSeW4EDgcOBvYD2wOMp13oW\naEJ0tu0OTAZGW8qNvljKlPjSfBH4JHGO/Ylp++8v0PKsTkyudy0x+V51hgNdgH2Isu8F3J3JwiTk\nskzNgEeJjt7ZlKsy1boIZ5GVB2JKgyOBLYDDgF8Bj2WyMAm5LFOVvwLT65CuPnJZHgcuB9oRs3xv\nmLh2puWsTGZ2ODES9V5ixOluxHdg4XH3RvkA1gMqgT2Sts0Azk163hpYDByV9Pwn4NCkNFsmzrNT\n4vm6iee7J6VZM7GtV5GW6XfANynX2iaRZtNCKk/K8b8mho63TtneOXHebknb9gPKgQ0K7TWqS5lS\n0pwIzM1mOXJdpqS0FwCflVB5Dk6875oUc5mAPsCHSZ+t7Yq1PMS0E2dlM/+5LBPxA/lrYGCuy1Sf\nR2OqKUm1NhFVzgUws05EVPxSVQJ3/wF4C9g1sWkHYsK55DQfA19VpfGYDG4qcIKZrWFmTYE/EOvr\nTMxukbJTJqAFsDTlWlUTyO2R0RKsqD7lqYtdgXnu/m7Stn8nrrVzA/Ncm2yVKZ9yWabURTizISfl\nMbO2xISMZb58IsdsyVqZzKwd8HdiBfbFGcpvbbL9Gl1i0Zw9ycwuMLMmtR/SYNkqU3ei9ptEeWaY\n2XNmtnUtx+VFowxKzMyIJovX3b1qptcNiDdEdYv1VS3U1w5Ymnhj1JQGYF/ijfAj8SE9G9jfVz1j\nbINkuUxjgQ0SH85mZrYOcH3i3BlvD4cGlacuNgC+Td6QuCnMTfM8aclymfIil2Wy6hfhzKhclMfM\nBpvZAmKOpA7AIfXPcZ2ul+0yDQXuSAnysyYH5bkNOIZofr8L+CMx0WbWZLlMmxLNoH8CriKaq+cB\nL5vZ2g3JdzY0yqAEuINooz4mi+efBexOTND2JNGnpF2Wrld1zayUKfEhORE4D1hEVCl+TtzYK1dx\naENk+zXKB5Wpnix3i3Dmojx/BbYnfrxUAA9m8VqQxTKZ2VlE83TVTdsyfY1qZPU1cvdb3f1Vd//A\n3f9OfO+daRnuYJ0im2Wqus9f4+5PJoLHk4iA58gsXK9BGl1QYmb/DzgA6Onu3yTtmkl8oFIDh3aJ\nfVVpmlfTu/nnNGa2T+L8R7v7m+7+nrufQdSYnJjRwiRku0wA7v6wu7cnqgHXBf4M/IIITjKqgeWp\ni5lA6uiiJkDbNM9TZzkoU87lqkwWS0yMJX5F/l89s1uX6+SkPO4+190/c/eXgP7AAWaWlWbDHJRp\nb6Ip4SczWwZ8mtg+wcyG1i/XNcvT5+htool7kwaep1o5KFPVOX9eptfdlxLf3b9MO8NZ1qiCksSL\n3w/Y292/St7n7l8QL/Q+SelbE30M3khsmkh0SktOsyXxwlalWZ2IQFNrECrJwt87y2Uan3o9d5/t\n7ouIiH4xMSqnkMpTF+OBtc2sW9K2fYgvgLfqmfUa5ahMOZWrMiVqSMZR8yKcGZHH16iqr0KLBp5n\nJTkq05lA16RHH+L77yjgsobkP1UeX6NuxPf3t7UlTFeOyjSRGMywZdJ5mhFB1pf1zXvW5Lunba4e\nRPXYPGIYVrukR8ukNBcRKxYfTAybepKI/JunnOcLor2xB1AGvJa0f13izfsYsB2wOXAD0TF022Is\nUyLN6cSHc/PE/xcCpxdoedoRX5CnkOjNnni+TlKa54AJRPPa7sDHwIMF/L6rS5k6JLZdSayMXXWj\naFWMZSJq5T4FXkj8/+drFWl5dkp8droSQX8v4PXEe69ZMZapmut2JAujb3L4Gu1C9AHcDuhEdESe\nBdxX5N8NtxCDF/YlhqP/g6hBaZPpcjX475LvDOSsoPFiVVTzOCEl3SCiz8QiYonlzVL2tyDGls8h\nOrI+BqyfkqY70f49G/ieuMn3LvIy/TNRnsXESszHFnB5/lTDuU5ISrM28BBx854H3AOsUeRlGlrD\ntfYqxjIRzZ2p+yqBiiItzzbESIrZiXP8F/h/wIbF/L5LSd8xsT/TQUmuXqNuRE3qXOKH1wdEYJDR\noDHXrxFRI/dXIhD5PnGeLpkuUyYeWpBPRERECkKj6lMiIiIihUtBiYiIiBQEBSUiIiJSEBSUiIiI\nSEFQUCIiIiIFQUGJiIiIFAQFJSIiIlIQFJSIiIhIQVBQIiIiIgVBQYmIFAwzG2pmlWZWYWZLzWym\nmb1gZieZmaVxnhPNbF428yoimaegREQKzfPABsQ6KvsDY4HbgGfMrK7fWUasVisiRURBiYgUmp/c\nfba7f+Pu77n7YGJ59wOAgQBmdq6Z/cfMFpjZV2Y2xMzWSOz7NXAf0Cap1uXKxL7mZnajmU1PHDs+\nkV5ECoCCEhEpeO4+DpgMHJbYVAGcCWwFnADsTayCCvAGcA7wA7Gs+4bAjYl9Q4CdgaOIpeAfA543\ns19lvxQiUhutEiwiBcPMhgJt3P2wavaNALZ1922q2Xc4cKe7r594fiJwi7u3TUrTAfgc6ODuM5O2\nvwi85e6XZ7xAIpKWpvnOgIhIHf3cT8TMfgNcAnQGWhPfZS3MrKW7L6nh+G2BJsAnKZ1mmwNzspZr\nEakzBSUiUiy6AF+YWUfgGaIp5o/AXGBP4B9EgFFTULImUA50BypT9i3IRoZFJD0KSkSk4JlZL6Km\n4yagB9H0fEHS/mNSDllK1IokezexrZ27l2UxuyJSTwpKRKTQtDCzdiQCCKAP0VTzNPAgEZw0M7Oz\niBqTPYD/SznHNGDNRDAzGVjk7p+a2XDgATO7gAhS1gd6AZPd/fmsl0xEVkmjb0Sk0OwPzAC+IOYs\n+TVwhrsf4uE/wHnARcD7QH8iaPmZu48H7gIeAb4FLkzsGgg8QIzGmQqMAnYAvspukUSkLjT6RkRE\nRAqCakpERESkICgoERERkYKgoEREREQKgoISERERKQgKSkRERKQgKCgRERGRgqCgRERERAqCghIR\nEREpCApKREREpCAoKBEREZGCoKBERERECoKCEhERESkI/x9NT+Qv7veq+gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ac48438>"
+       "<matplotlib.figure.Figure at 0x1c18fac8>"
       ]
      },
      "metadata": {},
@@ -1086,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -1095,7 +1095,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.54\n"
+      "0.49\n"
      ]
     }
    ],

From b1eb518b36f7300b125dd17e58fc1f95ec74f5a2 Mon Sep 17 00:00:00 2001
From: "REDMOND\\sayanpa" <sayanpa@microsoft.com>
Date: Tue, 7 Feb 2017 22:11:28 -0800
Subject: [PATCH 09/31] Fixed the pickle file version

---
 ...nance_Timeseries_Basic_with_Pandas_Numpy.ipynb | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
index e7ee9dfae..917d98c6c 100644
--- a/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
+++ b/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
@@ -124,6 +124,8 @@
     }
    ],
    "source": [
+    "import pickle as  pkl\n",
+    "\n",
     "# We search in cached stock data set with symbol SPY.               \n",
     "# Check for an environment variable defined in CNTK's test infrastructure\n",
     "envvar = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'\n",
@@ -138,7 +140,8 @@
     "        \n",
     "    if not os.path.isfile(data_file):\n",
     "        print(\"Saving\", data_file )\n",
-    "        data.to_pickle(data_file)\n",
+    "        with open(data_file, 'wb') as f:\n",
+    "            pkl.dump(data, f, protocol = 2)\n",
     "    return data\n",
     "\n",
     "data_file = os.path.join(\"data\", \"Stock\", \"stock_SPY.pkl\")\n",
@@ -741,7 +744,7 @@
      "data": {
       "image/png": 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XAUOAHYFJwBhJa9SyyZlAD2Cd7OfXgI+Bu/L2uQtwO3A9sANwD3C3pK2a6Wm0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x996d978>"
+       "<matplotlib.figure.Figure at 0x5c76470>"
       ]
      },
      "metadata": {},
@@ -751,7 +754,7 @@
      "data": {
       "image/png": 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nWt54w2uFDjsMrr668DH77AMvvuhJSseOzRtfqIhK1pxkSU5GAPeZ2bC87ccC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\nApOA24FhwB+BHvjjcmXeOesA+ye3uyt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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a23160>"
+       "<matplotlib.figure.Figure at 0xb2b2cf8>"
       ]
      },
      "metadata": {},
@@ -906,7 +909,7 @@
      "data": {
       "image/png": 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0+O/evHkzl1xyCRdeeCEvvvgiDz/8MOPHj+eLX/xiiz5/55138q1vfYuvfe1r\nTJw4kerqaubNm8eZZ54Zcxvx6NGjGT58OD//+c/ZvHkzX/rSl1i2bBlPPvkk+fn5nHbaaQf7duvW\njYEDB7J69eqYmYTPPfdcPv74Y2pqaposUpq7pNNRLvWAFSkqLFq0KOgQfKElT4C8O/MO3ykNaNmm\neXkdO8+uXbuSlZlF9dvV7GVvYHFkZWbRtWvXpD8vIixatIjbbruNW265hc6dO/OjH/2IWbNmtXgd\nI0eO5M9//jO33norP/vZz+jduzfFxcWUlpYevEOn4buefPJJfvGLX7Bo0SKKi4s59dRTufvuu5uc\ncn7YsGG8/PLLMcVIz5496dOnD++8806TRUpz42lSYZxNS1mRYowxplUyMzPJm5AX+HNz2uIpyCec\ncAKPPvpoq9Zx6aWXNjoDeMkllzTql5GRwd13383dd9992HXOnDmzyWcpvf322032v+qqq7jqqqsa\ntZ933nnU19cf9vtShRUpxhhjWi0zMzMlnz5sOjYrUowxxpjD2L17N3v3HvpSVs+ePX2KRg+7u0eB\nph5Tn4605AlQPKM46BB8oWWbFhfryLMju+GGGzjppJOafZ188slBh5iW7EyKAlpm7dSSJ0D/wTbj\nbDqxGWeDN336dKZPn97s8mnTpvG9733Px4gMWJGiwrhx44IOwRda8gQYdOGgoEPwhZZtOmiQjjw7\nsn79+tGvX7+gw1DHLvcYY4wxJiVZkWKMMcaYlGRFigJlZWVBh+ALLXkCVKyrCDoEX2jZphUVOvI0\nJlE2JkWBWbNmMXTo0KDDaHda8gRYtmAZfc5u/Dj4dKNlmy5bNos+fTpGng0PwDPpLVW2sxUpCixc\nuDDoEHyhJU+Aa++8NugQfKFlm157bernmZ2dTUZGBuPHjw86FOOTjIwMsrOzA43BihQFMjIygg7B\nF1ryBOjStUvQIfhCyzbt0iX188zJyWH9+vVUVVUFHYrxSXZ2Njk5OYHGYEWKMcaYFsnJyQn8l5bR\nxQbOGmOMMSYlWZGiwNSpU4MOwRda8gRYPGdx0CH4Qss2XbxYR55atqeWPP1gRYoCWk7PaskToEfP\nHkGH4Ast27RHDx15atmeWvL0gxUpCkyZMiXoEHyhJU+AC664IOgQfKFlm15wgY48tWxPLXn6wYoU\nY4wxxqQkK1KMMcYYk5KsSFFgw4YNQYfgCy15Amzbsi3oEHyhZZtu26YjTy3bU0uefrAiRYGbb745\n6BB8oSVzuTLTAAAgAElEQVRPgMfmPBZ0CL7Qsk0fe0xHnlq2p5Y8/WBFigLz5s0LOgRfaMkTYNy0\ncUGH4Ast23TcOB15atmeWvL0gxUpCmi5HU5LngA9etktyOnEbkFOL1ry9IMVKcYYY4xJSVakGGOM\nMSYlWZGiwMyZM4MOwRda8gRYWrw06BB8oWWbLl2qI08t21NLnn6wIkWBmpqaoEPwhZY8Aer21QUd\ngi+0bNO6Oh15atmeWvL0Q+BFiohMF5EDca+34vrcLiJbRaRGRJ4RkT5xy48SkSIRqRKRPSKyWERO\n9DeT1DVjxoygQ/CFljwBQteFgg7BF1q2aSikI08t21NLnn4IvEiJ+CfQE+gVeQ1tWCAi04DJQB4w\nCPgYWCYiXaI+Pxu4GLgcOBc4GdAxkYQxxhiTpjoHHUDEfufcjmaW3QDc4Zx7CkBEJgDbgUuBR0Wk\nOzARuMI591ykTy6wXkQGOefWtH/4xhhjjGlrqXIm5Qsi8r6I/EtE/iQipwCIyGl4Z1aebejonNsN\nvAwMiTR9Ba/Yiu6zEaiM6qNaVVVV0CH4QkueAOFd4aBD8IWWbRoO68hTy/bUkqcfUqFIWQ1cDYwE\nfgCcBjwvIkfjFSgO78xJtO2RZeBdJqqLFC/N9VFt4sSJQYfgCy15AiyYsSDoEHyhZZsuWKAjTy3b\nU0uefgi8SHHOLXPOPeac+6dz7hlgFHA8MMaP7x81ahShUCjmNWTIEEpLS2P6LV++nFCo8WDFSZMm\nMX/+/Ji28vJyQqFQo2p6+vTpjW5Nq6ysJBQKNXog1dy5c5k6dWpMW01NDaFQiLKyspj2kpIScnNz\nG8U2duxYSktLKSgoSIs8ojWVR0FBQYfMY/z48Y36PjLzEcpKY9dbuaGSovwiwrvCjL5u9MH2pQuW\n8tLKlwLPoz32q+uuuy4t8mjYHps2bYppX7FiLosXT2X06IKDbXV1NRQVhaioiM1jzZoSFi6ckhJ5\nJLs9CgoKUmp7tNd+dd5556VFHg3bo6Sk5ODvxl69ehEKhcjPz2/0mfYgzjlfvigRIrIGeAZ4APgX\ncLZz7vWo5SuBV51z+SIyHPg/4PjosykisgUodM7NaeY7BgBr165dy4ABA9otF2MOp6qqisIHC8ka\nmEXmcZkJfz68K0z12mryJ+aTnZ3dDhGatlJVVUVh4RKysi4jMzPxbRUOV1FdvYT8/MtsW5tAlZeX\nM3DgQICBzrny9vqewM+kxBORTKAPsNU5txnYBnw9anl3YDDwYqRpLbA/rk9fIAeI/fPSGGOMMR1G\n4Hf3iMivgSeBd4HPADOAT4CFkS6zgVtFpALYAtwBvAc8Dt5AWhGZD9wjIjuBPcC9wAt2Z48xxhjT\ncaXCmZTPAo8AG/AKkx3AOc65agDn3CxgLnAf3l093YCLnHPRU27mA08Bi4GVwFa8OVMMNLrmma60\n5Ak0Gq+SrrRs07IyHXlq2Z5a8vRD4EWKc26cc+6zzrluzrkc59yVkcs80X0KnHMnO+cynHMjnXMV\nccv3OeemOOeynXPHOOe+65z7wN9MUld5ebtdLkwpWvIEqNxYGXQIvtCyTSsrdeSpZXtqydMPgRcp\npv0VFRUFHYIvtOQJcOW0K4MOwRdatumVV+rIU8v21JKnH6xIMcYYY0xKsiLFGGOMMSnJihRjjDHG\npCQrUhRoaqbDdKQlT4CifB3XvLVs06IiHXlq2Z5a8vSDFSkKTJ48OegQfKElT4DhY4cHHYIvtGzT\n4cN15Klle2rJ0w9WpCgwYsSIoEPwhZY8Afqf0z/oEHyhZZv2768jTy3bU0uefgh8xlljjF7btm3j\no48+Suqzxx57LL162YPOjUlnVqQYYwKxbds28n50A9XhPUl9PivzGO6/d44VKsakMbvco0D848/T\nlZY8AdatXBd0CK320UcfUR3eQ9cv9CVr4DlNvuoyj22yvesX+lId3pP0WZhUs26djn1XyzGqJU8/\nWJGiQElJSdAh+EJLngBrlqXPszOP7n483bN6Nfn6YNOmJtuP7n580GG3qTVrdOy7Wo5RLXn6wYoU\nBRYtWhR0CL7QkidA3p15QYfgi2FX6cgzL0/HvqvlGNWSpx+sSDHGGGNMSrIixRhjjDEpyYoUY4wx\nxqQkK1IUyM3NDToEX2jJE6B4RnHQIfjipZLioEPwRXGxjn1XyzGqJU8/WJGigJbZD7XkCdB/sI4Z\nZ0/qqyNPm3E2vWjJ0w9WpCgwbty4oEPwhZY8AQZdOCjoEHxx6gAdeQ4apGPf1XKMasnTD1akGGOM\nMSYlWZFijDHGmJRkRYoCZWVlQYfgCy15AlSsqwg6BF988I6OPCsqdOy7Wo5RLXn6wYoUBWbNmhV0\nCL7QkifAsgXLgg7BF2+t0JHnsmU69l0tx6iWPP1gRYoCCxcuDDoEX2jJE+DaO68NOgRfDJ2gI89r\nr9Wx72o5RrXk6QcrUhTIyMgIOgRfaMkToEvXLkGH4IvOXXTk2aWLjn1XyzGqJU8/WJFijDHGmJRk\nRYoxxhhjUpIVKQpMnTo16BB8oSVPgMVzFgcdgi/Kn9CR5+LFOvZdLceoljz9kFSRIiLfE5GubR2M\naR85OTlBh+ALLXkC9OjZI+gQfHH0cTry7NFDx76r5RjVkqcfkj2TUghsE5H7RETHvNUd2JQpU4IO\nwRda8gS44IoLgg7BF33P1ZHnBRfo2He1HKNa8vRDskXKycC1wGeBF0TknyJyk4ic0HahGWOMMUaz\npIoU51ydc+7PzrmLgRzgIeD7wHsiskRELhYRactAjTHGGKNLqwfOOuf+A/wf8HfAAV8BSoBNIjKs\ntes3rbdhw4agQ/CFljwBtm3ZFnQIvvhou448t23Tse9qOUa15OmHpIsUEckWkRtF5DXgBeBE4FLg\nc8BngFLgj20SpWmVm2++OegQfKElT4DH5jwWdAi+ePVJHXk+9piOfVfLMaolTz8ke3fPX4D3gR/g\nXeo5xTn3XefcUufZA8zCK1gSXfdPReSAiNwT1367iGwVkRoReUZE+sQtP0pEikSkSkT2iMhiETkx\nmfzSzbx584IOwRda8gQYN21c0CH44quX68hz3Dgd+66WY1RLnn5I9kzKbuAbzrl+zrm7nXM7muiz\nA/hCIisVka8CecBrce3TgMmRZYOAj4FlIhI9Z/Zs4GLgcuBcvMG9Ov4MOwwtt8NpyROgRy8dt+Ye\nfbyOPO0W5PSiJU8/JDtw9irn3KrD9HHOuX+1dJ0ikgn8CbgG2BW3+AbgDufcU865fwIT8IqQSyOf\n7Q5MBPKdc885514FcoGv2S3SxhhjTMeU7OWeQhGZ1ET7JBH5TZKxFAFPOudWxK3zNKAX8GxDm3Nu\nN/AyMCTS9BWgc1yfjUBlVB9jjDHGdCDJXu75LvBiE+2rgbGJrkxErgDOBm5pYnEvvLuGtse1b48s\nA+gJ1EWKl+b6qDVz5sygQ/CFljwBlhYvDToEX7z5rI48ly7Vse9qOUa15OmHZIuUbLxxKfE+iixr\nMRH5LN54kv92zn2SZDxJGzVqFKFQKOY1ZMgQSktLY/otX76cUCjU6POTJk1i/vz5MW3l5eWEQiGq\nqqpi2qdPn95o562srCQUCjW6ZW3u3LmNnv9QU1NDKBSirKwspr2kpITc3NxGsY0dO5bS0lJqamrS\nIo9oTeVRU1PTIfMYP358o76PzHyEstLY9VZuqKQov4jwrjB1++oOti9dsJSXVr4UeB6J7lc7d+6M\naX/96ScaFSV7P/qIlQ8UNboVefMrq9kSl1tQeSSyX23atCmmfcWKuSxePJW6uk+P0bq6GoqKQlRU\nxOaxZk0JCxc2nsk0iDyS3a9qampSanu01/FRXl6eFnk0bI+SkpKDvxt79epFKBQiPz+/0Wfagzjn\nEv+QyJtAkXPut3Htk4DJzrkzEljXJcASoB5omADuCLyzJ/VAP6ACONs593rU51YCrzrn8kVkON5c\nLcdHn00RkS1AoXNuThPfOwBYu3btWgYMGNDScI1pc1VVVRQ+WEjWwCwyj8tM+PPhXWGq11aTPzGf\n7OyE/kYI1MaNG5mYn0/WwHPonpXYCc/d1duoXruaBwsL6du3bztF2PaqqqooLFxCVtZlZGYmvq3C\n4Sqqq5eQn39Zh9rWJv2Ul5czcOBAgIHOufLD9U9W5yQ/NxuYLSJZQMMYkq8DNwM/SXBd/wd8Ma6t\nGFgP3OWce0dEtkXW/zocHCg7GG8cC8BaYH+kz18iffrizYYb+yemMcYYYzqEpIoU59z/Rp6C/DNg\nRqT5PeBHzrkHE1zXx8Bb0W0i8jFQ7ZxbH2maDdwqIhXAFuCOyPc9HlnHbhGZD9wjIjuBPcC9wAvO\nuTVJpGiMMcaYgCU946xzbq5z7iS82WV7OOdyEi1QDrX6uO+aBcwF7sO7q6cbcJFzri6qWz7wFLAY\nWAlsxZszRb34a5vpSkue4F3i0aA2rCPPcFjHvqvlGNWSpx/a5Nk9zrn4eU1au84LnHM/jmsrcM6d\n7JzLcM6NdM5VxC3f55yb4pzLds4dE5kB94O2jKujmjhxYtAh+EJLngALZiwIOgRfrF6oI88FC3Ts\nu1qOUS15+iHZeVJOEJE/iEiliNSKSF30q62DNK1TUFAQdAi+0JInwOjrRgcdgi/OGqkjz9GjC4IO\nwRdajlEtefoh2YGzxUBv4NfAf4i7PGNSi5a7l7TkCZDTT8e02z1O0ZFnTo6OfVfLMaolTz8kW6Sc\nC5wbmX7eGGOMMabNJTsm5T3s7Ikxxhhj2lGyRUo+cGdktliT4uJnNExXWvIEGs1Gm64qVuvIs6xM\nx76r5RjVkqcfki1SHgKGA++KyE4R+SD61YbxmTYQP0VzutKSJ0DlxsqgQ/DFh+/pyLOyUse+q+UY\n1ZKnH5Idk/LTNo3CtKuioqLDd0oDWvIEuHLalUGH4ItB39GR55VX6th3tRyjWvL0Q7Izztq5LGOM\nMca0q6QncxORU0WkQEQeEpETI20jRKTFDxc0xhhjjGlOspO5DQPeBM4DxgANj24dCNzeNqEZY4wx\nRrNkz6TMBAqcc8OB6BlmnwXOaXVUpk2FQqGgQ/CFljwBivJ1XPNe+YCOPIuKdOy7Wo5RLXn6Idki\n5Sy8B/nF+wA4IflwTHuYPHly0CH4QkueAMPHDg86BF/0Haojz+HDdey7Wo5RLXn6Idki5SOgVxPt\nXwLeTz4c0x5GjBgRdAi+0JInQP9z+gcdgi9O6qcjz/79dey7Wo5RLXn6IdkiZRFwl4icQGTmWREZ\nDPwG+FMbxWaMMcYYxZItUm4B3gG24g2afQt4EXgFuKNtQjPGGGOMZkkVKc65fc65XOB04FJgIvBf\nzrlxzrn9bRmgab3S0tKgQ/CFljwB1q1cF3QIvvj3GzryXLdOx76r5RjVkqcfkp4nBcA5t9k594Rz\n7hHn3Ia2Csq0rZKSkqBD8IWWPAHWLFsTdAi+2FKuI881a3Tsu1qOUS15+iGpGWdF5P5DLXfO5SUX\njmkPixYtCjoEX2jJEyDvTh2H2LCrdOSZl6dj39VyjGrJ0w/JPrvnpLj3RwL/BRwDPN+qiIwxxhhj\nSP7ZPaPj20SkM/B7vEG0xhhjjDGt0qoxKdEiA2Z/DUxtq3UaY4wxRq82K1IiTsO79GNSSG5ubtAh\n+EJLngDFM4qDDsEXL5UUBx2CL4qLdey7Wo5RLXn6IdmBs7Pim/DGqYSwydxSjpbZD7XkCdB/sI6Z\nWE/qqyNPm3E2vWjJ0w/JDpwdEvf+ALAD+Cnwv62KyLS5cePGBR2CL7TkCTDowkFBh+CLUwfoyHPQ\nIB37rpZjVEuefkh24Oywtg7EGGOMMSZaW49JMcYYY4xpE0kVKSLyioisacmrrQM2iSsrKws6BF9o\nyROgYl1F0CH44oN3dORZUaFj39VyjGrJ0w/Jnkn5O9AXb8Ds6siLSNtKYFnUywRs1qz4cc7pSUue\nAMsW6Di03lqhI89ly3Tsu1qOUS15+iHZgbPHAUXOuZ9FN4rIL4GezrlrWh2ZaTMLFy4MOgRfaMkT\n4No7rw06BF8MnaAjz2uv1bHvajlGteTph2TPpIwB/tBEezHw3aSjMe0iIyMj6BB8oSVPgC5duwQd\ngi86d9GRZ5cuOvZdLceoljz9kGyRsg84p4n2cyLLjDHGGGNaJdnLPfcC94nIl4GGwbGDgWuBO9si\nMGOMMcboltSZFOfcL4FrgK8B90de/w/IiywzKWTqVB2PU9KSJ8DiOYuDDsEX5U/oyHPxYh37rpZj\nVEuefkh6nhTn3CPOucHOue6R12Dn3COJrkdEfiAir4nIR5HXiyJyYVyf20Vkq4jUiMgzItInbvlR\nIlIkIlUiskdEFovIicnmlm5ycnKCDsEXWvIE6NGzR9Ah+OLo43Tk2aOHjn1XyzGqJU8/JF2kiEh3\nEbk6UkAcH2n7koiclOCq/g1MAwYAA4EVwOMickZkndOAyUAeMAj4GFgmItEj6mYDFwOXA+cCJwOP\nJZtbupkyZUrQIfhCS54AF1xxQdAh+KLvuTryvOACHfuulmNUS55+SPYBg2cC/wfUAKfg3dWzExgL\nfAa4qqXrcs79Na7pVhG5Hm8Q7nrgBuAO59xTke+eAGwHLgUeFZHuwETgCufcc5E+ucB6ERnknLMJ\n5YwxxpgOKNkzKYXAI0BvoDaq/a94ZzKSIiKdROQKIAN4UUROA3oBzzb0cc7tBl7m04ccfgWv2Iru\nsxGopPGDEI0xxhjTQSRbpHwV+K1zzsW1vw8kerkHETlTRPbg3b78W+DbkUKjF+DwzpxE2x5ZBtAT\nqIsUL831UW3Dhg1Bh+ALLXkCbNuyLegQfPHRdh15btumY9/VcoxqydMPyRYpnwCZTbT3AaqSWN8G\n4Et4Y05+B/xRRPolGZuJc/PNNwcdgi+05Anw2BwdQ65efVJHno89pmPf1XKMasnTD8kWKU8Ct4lI\nw5gWJyKfAe4CliS6MufcfufcO865V51zPwdewxuLsg3v+UA94z7SM7KMyH+7RMamNNenWaNGjSIU\nCsW8hgwZQmlpaUy/5cuXEwqFGn1+0qRJzJ8/P6atvLycUChEVVVsvTZ9+nRmzpwZ01ZZWUkoFGpU\nec+dO7fRbWw1NTWEQqFGD68qKSkhNze3UWxjx46ltLSUefPmpUUe0ZrKY968eR0yj/Hjxzfq+8jM\nRygrjV1v5YZKivKLCO8KM27auIPtSxcs5aWVLwWeR6L71c6dO2PaX3/6Cd58dmlM23994yJWPlDU\n6IzK5ldWsyUut6DySGS/2rRpU0z7ihVzWbx4KuPGfXqM1tXVUFQUavTQwTVrSli4sPGAzCDySHa/\nmjdvXkptj/Y6PkaPHp0WeTRsj5KSkoO/G3v16kUoFCI/P7/RZ9qDNL5i04IPeXfzLAG+iPccn3/j\n3VHzCnChcy7cqqBEngXedc5NFJGtwK+dc4WRZd3xLuVMcM79OfJ+B97A2b9E+vTFG3R7TnMDZ0Vk\nALB27dq1DBgwoDXhGtMqVVVVFD5YSNbALDKPa+oE5aGFd4WpXltN/sR8srOz2yHC9rFx40Ym5ueT\nNfAcumcldmV2d/U2qteu5sHCQvr27dtOEba9qqoqCguXkJV1GZmZiW+rcLiK6uol5Odf1qG2tUk/\n5eXlDBw4EGCgc668vb4nqbt7nHM7geEich7eZZpMoBxY1sQ4lUMSkV8BT+MNdD0G+G/gPGBEpMts\nvDt+KoAtwB3Ae8DjkVh2i8h84B4R2QnswZsR9wW7s8cYY4zpuBIuUkTkSOApYHLklt/nWhnDicAC\nvAG3HwGvAyOccysAnHOzRCQDuA/vrM0q4CLnXF3UOvKBemAxcBSwFJjUyriMMcYYE6CEx6Q45z7B\nm3Qt8etETa/vGufc551z3ZxzvZxzBwuUqD4FzrmTnXMZzrmRzrmKuOX7nHNTnHPZzrljnHPfdc59\n0BbxpYP465jpSkueAEuLlx6+UxqIH6OSrpYu1bHvajlGteTph2QHzj4MNB5pY1JSTU1N0CH4Qkue\nAHX76g7fKQ3U1+nIs65Ox76r5RjVkqcfkn0KsgMmi8g3gH/gTVX/6ULn7P6rFDJjxoygQ/CFljwB\nQtc1vlMgHZ11kY48QyEd+66WY1RLnn5ItkgZiDd2BOCsuGVtchnIGGOMMbolVKSIyOeBzc65Ye0U\njzHGGGMMkPiYlE3ACQ1vRGSRiMRPtGZSTPykQOlKS57gzY2iQW1YR57hsI59V8sxqiVPPyRapEjc\n+1HA0W0Ui2knEydODDoEX2jJE2DBjAVBh+CL1Qt15LlggY59V8sxqiVPPyR7d4/pQAoKCoIOwRda\n8gQYfd3ow3dKA2eN1JHn6NEFQYfgCy3HqJY8/ZBokeJoPDDWBsqmOC3T/mvJEyCnX07QIfiixyk6\n8szJ0bHvajlGteTph0Tv7hGgWET2Rd53BX4vIvG3IF/WFsEZY4wxRq9Ei5T4C8R/aqtAjDHGGGOi\nJXS5xzmX25JXewVrkhP/KPB0pSVPgLLSssN3SgMVq3XkWVamY9/VcoxqydMPNnBWgfLydnuKdkrR\nkidA5cbKoEPwxYfv6cizslLHvqvlGNWSpx+sSFGgqKgo6BB8oSVPgCunXRl0CL4Y9B0deV55pY59\nV8sxqiVPP1iRYowxxpiUZEWKMcYYY1KSFSnGGGOMSUlWpCgQCml53L2OPAGK8nVc8175gI48i4p0\n7LtajlEtefrBihQFJk+eHHQIvtCSJ8DwscODDsEXfYfqyHP4cB37rpZjVEuefrAiRYERI0YEHYIv\ntOQJ0P+c/kGH4IuT+unIs39/HfuulmNUS55+sCLFGGOMMSnJihRjjDHGpCQrUhQoLS0NOgRfaMkT\nYN3KdUGH4It/v6Ejz3XrdOy7Wo5RLXn6wYoUBUpKSoIOwRda8gRYs2xN0CH4Yku5jjzXrNGx72o5\nRrXk6QcrUhRYtGhR0CH4QkueAHl35gUdgi+GXaUjz7w8HfuulmNUS55+sCLFGGOMMSnJihRjjDHG\npCQrUowxxhiTkqxIUSA3NzfoEHyhJU+A4hnFQYfgi5dKioMOwRfFxTr2XS3HqJY8/WBFigJaZj/U\nkidA/8E6ZmI9qa+OPG3G2fSiJU8/WJGiwLhx44IOwRda8gQYdOGgoEPwxakDdOQ5aJCOfVfLMaol\nTz9YkWKMMcaYlGRFijHGGGNSkhUpCpSVlQUdgi+05AlQsa4i6BB88cE7OvKsqNCx72o5RrXk6YfA\nixQRuUVE1ojIbhHZLiJ/EZHTm+h3u4hsFZEaEXlGRPrELT9KRIpEpEpE9ojIYhE50b9MUtesWbOC\nDsEXWvIEWLZgWdAh+OKtFTryXLZMx76r5RjVkqcfAi9SgGHAXGAw8A3gSGC5iHRr6CAi04DJQB4w\nCPgYWCYiXaLWMxu4GLgcOBc4GXjMjwRS3cKFC4MOwRda8gS49s5rgw7BF0Mn6Mjz2mt17LtajlEt\nefqhc9ABOOdGRb8XkauBD4CBQMM5sxuAO5xzT0X6TAC2A5cCj4pId2AicIVz7rlIn1xgvYgMcs7p\neEpZMzIyMoIOwRda8gTo0rXL4Tulgc5ddOTZpYuOfVfLMaolTz+kwpmUeMcBDvgQQEROA3oBzzZ0\ncM7tBl4GhkSavoJXcEX32QhURvUxxhhjTAeSUkWKiAjeZZsy59xbkeZeeEXL9rju2yPLAHoCdZHi\npbk+xhhjjOlAUqpIAX4L9AeuCDqQdDJ16tSgQ/CFljwBFs9ZHHQIvih/Qkeeixfr2He1HKNa8vRD\nyhQpIjIPGAWc75z7T9SibYDgnS2J1jOyrKFPl8jYlOb6NGnUqFGEQqGY15AhQygtLY3pt3z5ckKh\nUKPPT5o0ifnz58e0lZeXEwqFqKqqimmfPn06M2fOjGmrrKwkFAqxYcOGmPa5c+c22tFramoIhUKN\nbm8rKSlp8lkRY8eOpbS0lJycnLTII1pTeeTk5HTIPMaPH9+o7yMzH6GsNHa9lRsqKcovIrwrTI+e\nPQ62L12wlJdWvhR4HonuVzt37oxpf/3pJ3jz2aUxbZ27HMXKB4r4aHvsYbz5ldVsicstqDwS2a82\nbdoU075ixVwWL55Kjx6fHqN1dTUUFYUa3Za8Zk0JCxdOSYk8kt2vcnJyUmp7tNfxsWvXrrTIo2F7\nlJSUHPzd2KtXL0KhEPn5+Y0+0x7EOefLFx0yCK9AuQQ4zzn3ThPLtwK/ds4VRt53x7uUM8E59+fI\n+x14A2f/EunTF1gPnNPUwFkRGQCsXbt2LQMGDGiv1Iw5rKqqKgofLCRrYBaZx2Um/PnwrjDVa6vJ\nn5hPdnZ2O0TYPjZu3MjE/HyyBp5D96zErsrurt5G9drVPFhYSN++fdspwrZXVVVFYeESsrIuIzMz\n8W0VDldRXb2E/PzLOtS2NumnvLycgQMHAgx0zpW31/cEfnePiPwWGAeEgI9FpOGMyUfOudrI/88G\nbhWRCmALcAfwHvA4eANpRWQ+cI+I7AT2APcCL2i/s8cYY4zpqAIvUoAf4A2MXRnXngv8EcA5N0tE\nMoD78O7+WQVc5Jyri+qfD9QDi4GjgKXApHaN3BhjjDHtJvAxKc65Ts65I5p4/TGuX4Fz7mTnXIZz\nbqRzriJu+T7n3BTnXLZz7hjn3Hedcx/4m01qir9ema605Amwbcshh1qljfixKOlq2zYd+66WY1RL\nnn4IvEgx7e/mm28OOgRfaMkT4LE5OiZTfvVJHXk+9piOfVfLMaolTz9YkaLAvHnzgg7BF1ryBBg3\nbVzQIfjiq5fryHPcOB37rpZjVEuefrAiRYHoW5DTmZY8AXr06nH4Tmng6ON15Bl9C3I603KMasnT\nD1akGGOMMSYlWZFijDHGmJRkRYoC8bMUpisteQIsLV56+E5pIH4G2nS1dKmOfVfLMaolTz9YkaJA\nTU1N0CH4QkueAHX76g7fKQ3U1+nIs65Ox76r5RjVkqcfrEhRYMaMGUGH4AsteQKErmv8HJB0dNZF\nOssVS10AACAASURBVPIMhXTsu1qOUS15+sGKFGOMMcakJCtSjDHGGJOSrEhRIP6R3+lKS57gPflY\ng9qwjjzDYR37rpZjVEuefrAiRYGJEycGHYIvtOQJsGDGgqBD8MXqhTryXLBAx76r5RjVkqcfrEhR\noKCgIOgQfKElT4DR140OOgRfnDVSR56jRxcEHYIvtByjWvL0gxUpCgwYMCDoEHyhJU+AnH46pt3u\ncYqOPHNydOy7Wo5RLXn6wYoUY4wxxqQkK1KMMcYYk5KsSFFg/vz5QYfgCy15ApSVlgUdgi8qVuvI\ns6xMx76r5RjVkqcfrEhRoLy8POgQfKElT4DKjZVBh+CLD9/TkWdlpY59V8sxqiVPP1iRokBRUVHQ\nIfhCS54AV067MugQfDHoOzryvPJKHfuulmNUS55+sCLFGGOMMSnJihRjjDHGpKTOQQdgjOm4wuEw\ntbW1SX12586d1NfXJ/3d9fv3s3PnzqSnIO/atSuZmZlJf78xpv1ZkaJAKBTiiSeeCDqMdqclT4Ci\n/CImFU4KNIZwOMz99z9KdfX+pD5fXb2NbdurOH5/859f+UAR51/TOM/9dfvYvuPfFP+lmKysrKS+\nPyszi7wJeSlRqBQVhZg0Kf33XS3HqJY8/WBFigKTJ08OOgRfaMkTYPjY4UGHQG1tLdXV++nW7QIy\nMo5L+PPhcDn7PynlwCHOpvQd2nSe9fv384nUcVSfo8j6fOJFSs2eGqrfrqa2tjYlipThw3Xsu1qO\nUS15+sGKFAVGjBgRdAi+0JInQP9z+gcdwkEZGceRmZmd8Oe6du1+2D4n9Tt0nl2P7krmcckVGXvZ\nm9Tn2kP//jr2XS3HqJY8/WADZ40xxhiTkqxIMcYYY0xKsiJFgdLS0qBD8IWWPAHWrVwXdAi++Pcb\nOvJct07HvqvlGNWSpx+sSFGgpKQk6BB8oSVPgDXL1gQdgi+2lOvIc80aHfuulmNUS55+sIGzCixa\ntCjoEHyhJU+AvDvzgg7hoH37wkl9rqZmF84dep6UYVc1n6dzjr179xIOJ/794XCYuk/qEv5ce8nL\n07HvajlGteTpBytSjDFJq6ur5aVXH2L/EYlP6Lbzw/f4eF8V9fWfJPzZ+v11hD/+mFdeeZuKLR8m\n/Pm6mlrYUks4HCY7O/E7k4wx/rAixZgorZlBdf/+/XTunPghVV1dnVJ/1Sdi//791NR/yNFfyOLI\nbhkJfXbPuzuo/1c99fWJTwZXX7+fAweEzp0/z9FH90n48wfqdrBn72vs27cv4c8Gra6ulurq6qQ+\nm+w+2sBm6TV+syLFmIhwOMz9f7yf6nDivwDq9tWxcf1G+v5XX7oc2SWhz9Z8XMMbG97g+C8fTybJ\n/QKo21eX9C+utvjFc2S3DLomuI7ORx3Vqu8E6Ny5K0d1TTz2fV2Su0TVINlitrq6mrq65AvSffvC\nvPrqG/z+9/VkZByd0Gfr6mrZuPFN+vb9Il26JLaPNsjK6kxe3hgrVIxvrEhRIDc3lz/84Q9Bh9Hu\nWptnbW0t1eFqup3ejYxjEjsrsOP9HVS/Ws2Rpx5JVq/EZkA98P4B9r6xl/2ftPyMQvGMYq6efjUA\n+/bu49XXXuX39b8nIyOxuCG1poeP91JJMUPGXR10GDFa8ziAmpowb7xRwfHH1xL94y4uzuXqqw+/\n737yyT727j2Cbt2Gk5X12YS+e8eOd6iufosjjxya8GfBG0NUXb2iVbP02r9FJlEpUaSIyDBgKjAQ\nOAm41Dn3RFyf24FrgOOAF4DrnXMVUcuPAu4BxgJHAcuAHzrnPvAliRSmZfbDtsoz45iMhGcxDX/k\n/WXeNTPxGVAbPpuI/oM/nYn1k7pP2HtgL92+0C3hAinVpoePd1Lf1JlZt0FrHgdw4MA77N37Nvvj\nnleU6IyzXbsmPstvOHKGMJnPNtjbykl67d8ik6iUKFKAo4F1wHxgSfxCEZkGTAYmAFuA/wGWicgZ\nzrmGc6ezgYuAy4HdQBHwGDCsvYNPdePGjQs6BF9oyRNg0IWDGrUlUyBBak0PH+/UAY3zTBXJPA4g\n3MylxEGDdOy7Wo5RLXn6ISWKFOfcUmApgIhIE11uAO5wzj0V6TMB2A5cCjwqIt2BicAVzrnnIn1y\ngfUiMsg5p2OyBWOMMSaNpPxkbiJyGtALeLahzTm3G3gZGBJp+gpewRXdZyNQGdXHGGOMMR1Iyhcp\neAWKwztzEm17ZBlAT6AuUrw010etsrKyoEPwhZY8ASrWVRy+Uxr44B0deVZU6Nh3tRyjWvL0Q0co\nUtrVqFGjCIVCMa8hQ4Y0evbC8uXLCYVCjT4/adIk5s+fH9NWXl5OKBSiqqoqpn369OnMnDkzpq2y\nspJQKMSGDRti2ufOncvUqVNj2mpqagiFQo0OgJKSEnJzcxvFNnbsWEpLS5k1a1Za5BGtqTxmzZrV\n6jw+2vkR82+bz7Yt22LaVyxcweI5i2Pa6mrrKMovalQwrFm6huIZxY3yuP+W+xs9c+et1W/xyJ2P\nNOr7yMxHKPv/7d19mFTVfcDx7w9mYdmFCKwvxKpBQyTmRRCMjdGaSBKpJtJWE2OMT1VMjY2xDWk1\naZ8m0fSxiaYJTeImYjQS20JsjBrSGkjR0ESFIC8i4oIgLAvIwu6wuzA7O7Pz8usfd1ZmZ3dh7rze\nuff3eZ59dubOPXfOb8+8/Pbce855avDfp21rG80Lmol0R1jx0xWD6rb3tb2D9j3UfojmBc15xZHo\nT3D99dcX1R4Hdmxj1UPNQ/Zd+/gSdqwZfNxDe9pY9VAz/TlXYb7862VseWb54G3Lf8Wqh5rpOTA4\njr0vbyLeeWTQtmR/P6seah6S2LRuWMvqpYuH1G3bxo2sXLly0DY374+9ezfR3DyPSGTw62rZsq+z\nfPng19WhQ200N8+jo2PnoO3PPvsDHn/8DlasOPoe7e+P0tw8b0jisnbtUp588h+G1O3BBz81ZO2f\nV1/9Dc3NQ+PYufP3rF8/eDbUtrYNecfR03OI66+/vuD3+X333Vf0+xy8/3m1YMECX8Qx0B5Lly59\n87txypQpzJs3b0iM5SKqWpEnypeIpMka3ZM53fM6MFNVX87abxWwUVUXiMilwEpgUnZvioi0AgtV\n9XvDPM8sYP369euZNWtWOUOqumg0WtDQ1FpTbJydnZ0s/MlCmmY3ub4AtX13Oyv+YwVzb5jLlNPc\ndd4VUrY/1s+Y+jFFP3ekO0J4fZgF8xe4nnm1s7OTe+55lM1te5h43umu50nZt2Mz65b/nPM/fh1/\nNPWdw+6T7O8nNMycHvmUPZbD4XbC69fwk4ULmT59uquynZ2dLFz4BE1NV7m+cLa9fRsrVixk7tyv\nMGXK1De39/dHGTPm+K/dkcoX89z5ikQ6CYefYMGCqwqepdc+i/xjw4YNzJ49G2C2qm4o1/N44sLZ\nY1HVXSLSDnwYeBkgc6HsH+OM4AFYDyQz+zyZ2Wc6cAawutJ19hq/v1kGBCVO4M0EpRT6E4VNBFfs\nxGT5GC5BKZVUMklXV9eQ/1iPx4m7sFmJR5JPguIHQXmPBiXOSvBEkiIijcA0YGBkz1kiMgM4pKp7\ncIYX/5OI7MAZgvzPwF7gl+BcSCsiDwPfFZEu4AjwfeB5G9ljzMhi8RgbN7TwQOf/0NDgrifEmZhs\nG6Mme29+leNJ9sc50LGHxU8upqnJ5dwy0SibW/YyefK8gucbMcbkxxNJCs7onN/iXCCrwHcy238K\nzFfV+0SkAViEM5nb74HLs+ZIAVgApIDHcSZzWw7cVpnqG1ObkokkfX1p6usvpqnpLFdl0+mdxGKb\nqE+ly1S78kklkySkn7HTxtJ0lrskRduVvrW9JBK1t+6PMbXGExfOqur/qeooVR2d8zM/a5+7VPVU\nVW1Q1bnZs81mHo+r6u2qeqKqTlDVT9pss47cC6j8KihxAkMufi1GKpkknXafaKTTaRKJWEGrGOdr\nw7LSxZlLVdFR6vyr5uInJSlSqVRJ6/L448F47QblPRqUOCvBKz0ppozOOOOMalehIoISJ8DkUyYP\nup9Kpejt7SUScTfFfle4i/0HdrP61cWMb3PXoxCJhGmPtjCuYwInJ6dCgYsjHkvjxMnH36kAqWQ/\nkd5eXnzxNXa0HnJVNtJ9mDfeOEBXVzvjx7v7m/X2dpFI9BGP9w7aPnlyMF67QXmPBiXOSrAkJQBu\nv/32alehIoISJ8Cca+e8eTsej9PeHmb16hbGT9zn6jhd+zvo7jvM1DNCNLzV3Rdu6nCSUX2jSHTH\nSKXcL7aXj+mXzDn+TgVIpZKk00IodBaNjdNcle3t2k7P4Q2saXmUbe0uR1NFwrzRu5l1ryzllFO+\nQn1mBec5c4Lx2g3KezQocVaCJSnG1LhkMkkykaau7kwaG9/mquyRuq2k05sYVTfG9RDi/lSEUaEQ\nzmVktSkUqmdsvbu4RxEiXZckNHUsDacVkNilRtOX7iKZjFGO3idj/MSSFGN8opAv3NDosWWqjf+F\n6usLS+zqQlB71xobUxWeuHDWlFfubIR+FZQ4gSEzyfpV7kyzftXeHozXblDeo0GJsxIsSQmAO++8\ns9pVqIhajjP7wtd8fh777mNv3o5Go3ht5uhS2firX1S7CmWRSiaIRMJEIp1EIp089tgX37x9rJ/e\n3q6yjqYqt1p+j7oRlDgrwU73BMD9999f7SpURK3GWciFryfPPpuVK52ZqLv2dxDp7Sv5sFgveN/V\nn652FUounUyx/8AWfrfpgTdnmj357dNY+eLC45aNRMIc6Hl1yOigWlGr71G3ghJnJViSEgBBGQ5X\nq3EWcuFrY+PR2wMXv6ZqcFK142mcVJ4hyNWkqTSJUTHqzhxHw0TnwtsG8rsAN34wQmJnnGSyNieS\nq9X3qFtBibMSLEkxxiMKufAV7OLXWlU3zv2Ft6HD9WWqjTHeZNekGGOMMcaTLEkJgHvvvbfaVaiI\noMQJsOWZ5dWuQkVYnP4SlPdoUOKsBEtSAiAajVa7ChURlDgBUv39x9/JByxOfwnKezQocVaCJSkB\ncPfdd1e7ChURlDgBzr18XrWrUBEWp78E5T0alDgrwZIUY4wxxniSje4xvhKJRIjFYgWVDYfD9CeC\n0e1uTCH6+2OEw+GCytbX1zPe5WgmYyxJCYDOzk5OPPHEalej7FpbW3ni6ScIRwr7EI32Rtm8dTOT\nzpvEeI8v/BaLRFwPX61FFqd3xOMRNm7czAMPpGhoaDx+gRxNTSGuumoOU6dOLX3lPCYon7mVYElK\nAMyfP59ly5ZVuxpld+uttzJ7zmzGnT2OhgkNrsun96Xp29xHMpEsQ+1Ka83PfsqHPntbtatRdhan\ndyQScfr6RjNu3KU0NZ3mqmw02k04/Cy33nory5f7fyRTUD5zK8GSlAC46667ql2Firjjjjt4dv2z\nNExoYPxE9/+VRnoiZahVeZw798pqV6EiLE7vqa+fyPjx7nsJ+vqc92gQBOUztxLswtkAmDVrVrWr\nUBEzZsyodhUqZvLpwZh22+L0l6C8R4PymVsJlqQYY4wxxpMsSTHGGGOMJ9k1KQHw8MMPc/PNN1e7\nGnkrdBjxokWL6CcYQ4h3rHmOae+/uNrVKDuL0z/6+2MsWrSIz33uc67L1trw5Vr7zPUyS1ICYMOG\nDTXzholEIjz46IMFDSP+76f+m5POOqkmhhAX69DetmpXoSIsTn8YGL7c0dFCNHqS6/JNTSFuueWa\nmklUaukz1+ssSQmA5ubmalchb7FYjHAkXNAw4iumXMHvnvpdTQwhLtYFn7iu2lWoCIvTHwaGL3/s\nY/cXPHw5FovVTJJSS5+5XmdJivGkQoYR19IQYmOCqJjhyyaYLEkxpkRSqRS9vb1EIu6SpWg0iqqW\nqVbGb9LpFNFoF5FIp6tyvb1dpFKJMtXKmPKwJMWYEojH47S3h1m9uoXxE/e5Ktu1v4NIbx+pVKpM\ntTN+kezvpzcaZt32Jbze+ayrspFImAM9rxKP95apdsaUniUpATBv3ryKT9Fc6AidYhb5W/LNJTSd\n3lRQ2WIlk0mSiTR1dWfS2Pg2V2WP1G0lnd5EKpXOu8yqh5o9P416KVicg6WTSdJ1SUJTx9JwmrvX\nevxghMTOOMlkvNBqFm3Jks/ypS+tdF2umIUNofKjg6rxmetXlqQEwBe+8IWKPl8xI3SKWeTvgssv\n4PVXXnf9nKUUCtUztt5dvUOjx7p+nukXX+q6TC2yOIcXqq93vSBh6HC9q/3L4YIL/tJ1mWIXNoTK\njw6q9Geun1mSEgCXXXZZRZ+vmBE6xSzyN23mtKonKZXy1ne+q9pVqAiL01+mTbvEdZliFjaE6owO\nqvRnrp9ZkmKGVejpGjh6yqZpQpON0DHGlEShI4MAenoKP11UaxPJ+Y0lKWaIYk7XQHGnbIwxppSK\nPV1UaxPJ+Y0lKT420Bvy9NNPc8UVV+RdLhwOs79rPye8+wTXp2uguFM2xWj5Q0tFn6+a9mx+idPf\nO7Pa1Sg7i9NfWlp+w5Qpt1T0OYs5XRSNdrN//9Ps27ePpqb8L1Qe+My1Xpji+S5JEZHbgL8HpgCb\ngNtV9cVq1EVV6erqKrh8fX09DQ3ukwQY3Buy+P7FbGvflnfZgZ6QOefNcX26Bqp3yua5J5/jjPcE\nY8n7Lc8sD8SXmsXpL8899yMuvbSyScqAQk4XFdoLs3jxt9i2LWa9MCXgqyRFRD4FfAe4BVgLLABW\niMjZqupu5qMSWLduHctWLUMpbKKuSfWTuPaqaxk71v3oj+zekImnTaRpdv7/BVSrJ6RYjScUduV/\nLaofP6HaVagIi9NfGhurM0VAoQrthZk48QnGjZtTUC9MtmQySShU2Ne0X3pxfJWk4CQli1T1UQAR\nuRX4GDAfuK/SlTl8+DBdo7o4c8aZrsuG3wiz4lcraD/czpi6Ma7LZ/eGhEIhVz0idvGqMcYc5bYX\nJhQaw+jRoaKuhenvj7Ft2xamT38vY8a4/w7wSy+Ob5IUEakDZgP/MrBNVVVEVgIXVqteo0OjmTDJ\n/X9JPR099KX6qH9HPZOaJrkuX6u9IdWUTqVJp9Mkk0kSCXfTh6eSNlusMeaoYodOd3TsJBx+lbq6\niwOxKONIfJOkACcCo4EDOdsPANMrXx1HKpki0u2+ZyIWdYb/jhs/rqauC6lVyWSStS++xN69B/n9\n719iwqS3uCp/aH8HiaSti2K8rdB1f8DW/ilUoUOnI5nRlUFflNFPSYpb9QAtLeUbEbJv3z762vrY\n0rbFddlYX4xEb4Ldr+yms839B0rXwS4i3RFaN7ey85WdbH1xa0Flu/d3F/XcbssXU3bPa3sYO2Fs\nQWWTySQduw4Sj8QJt3XS2+lufZNI1xH6o3E6du2g71CPq7LdB/eSiicI795BKhLNq0xH6+u0vbKh\n4PLFPPeAWF8PsUOHkQi0b2+he/8bJX/u7DhLVe9iy5fjbzZSnKV87p7wfg537+eZtd9h/Bb3vbOx\n2BE6D7Syfftv6e52d4F6V9c+IpEO9uzZyNatzxRUtrV1Nd3d7idrLKZ8oWV37lzDrl1/qFq9Y7EI\n8XgbmzZtYtIk922dj6zvzrJOZSx+WX01c7onClytqsuyti8GTlDVv8jZ/zrgPytaSWOMMcZfPqOq\nS8p1cN/0pKhqQkTWAx8GlgGIiGTuf3+YIiuAzwCtQGFTqxpjjDHBVA9MxfkuLRvf9KQAiMg1wGLg\nVo4OQf4E8E5V7ahi1Ywxxhjjkm96UgBU9b9E5ETgG8ApwEvAXEtQjDHGmNrjq54UY4wxxvjHqGpX\nwBhjjDFmOJakGGOMMcaTfJOkiMhtIrJLRPpEZI2IvO8Y+14kIs+JSKeIREWkRUS+OMx+n8w81ici\nm0Tk8vJGkZ9SxyoiN4hIWkRSmd9pEXE/+USJuYkzp9xFIpIQkSETT3ixTUsdpx/aU0Q+mFX3dFY8\nJ+fsV9PtmU+cfmjPzP5jROQeEWkVkZiI7BSRG3P2qen2zOx/zDi92p7g+rX7yDBxpEVkc85+xbWp\nqtb8D/ApnGHEfwm8E1gEHAJOHGH/mZky5wBnANcBEeCzWft8AEgAX8KZsfYbQBx4lw9jvQHoAk4C\nTs78nFRLcWaVOwHYAfwa2JDzmOfatExx1nx7Ah8EUsDbs2I42W/tmWecNd+emTK/BF4ALs18Fv0x\ncKGf2jPPOD3XngW+didkv2aBU4FO4KulbNOq/lFK+MddA3wv674Ae4E7XRzjF8BPs+7/DFiWs89q\n4Ic+jPUG4FC127EUcQJLgbuBrzP0y9tzbVqmOGu+PTn65f2WYxyz5tszzzj90J5/mvnCm+jz9swn\nTs+1ZyGxDlP+z4EkcHop27TmT/fI0YUF35xrWZ2/RN4LC4rIeZl9V2VtvjBzjGwr8j1mOZQxVoDx\nme7JNhF5SkTeVZpau1donCJyE3Amzpf3cDzVpmWME3zQnjgfki+JyBsi8hsR+UDO475oT44fJ9R+\ne14JrAO+LCJ7RWSbiHxbRLKnVPdDe+YTJ3ioPaE03y3AfGClqu7J2lZ0m9Z8ksKxFxaccqyCIrJH\nRGI4E781q+ojWQ9PKeSYZVauWLfhvMDm4czCOwp4QUROLVXFXXIdp4i8A2cF7M+oanqE43qtTcsV\nZ823J7Af+BxwNXAVsAdYJSIzs/ap+fYkvzj90J5nAX8CvBvnP+6/xZloszlrHz+0Zz5xeq09oYjv\nFgAReStwOfDjnIeKblNfTeZWgIuB8cD7gXtFZIeqPlblOpXLiLGq6hqcrj4ARGQ10ILz4fn1KtTV\nFREZhbMO09dVdWAlLqlilcoi3zhrvT0BVPU14LWsTWtE5O04s0jfUJ1alV4+cfqhPXG+iNPAdaoa\nARCRLwE/F5HPq2q8qrUrnePG6ZP2zHUjznU2vyz1gf2QpHTinNM9JWf7KUD7sQqq6u7MzS0iMgW4\nCxhIUtoLOWaZlSvW3H2TIrIRmFZUbQvnNs4JwPnATBEZ+I9lFM7yTf3AZaq6Cu+1abniHKQG23Mk\na4GLsu7XenuOJDfOQWq0PfcD+wa+uDNacJLs04DX8Ud75hPnIB5oTyj+tXsT8KiqJnO2F92mNX+6\nR1UTwMDCgsCghQVfcHGo0cDYrPurs4+Z8dHM9qooY6yDZP5jfy/OG67iCojzMPAenJFMMzI/DwBb\nM7f/kNnPU21axjgHqcH2HMlMBsdQ6+05ktw4B6nR9nweOFVEGrK2Tcfpddibue+H9swnzkGq3Z5Q\n3GtXRD6EMzrt4WEeLr5Nq31FcSl+gGuAKIOHToXJDOsCvsng0SyfBz6Ok7lOA24GeoC7s/a5EGeo\n1MDQqbtwhmdVewhyOWL9auaFcyZwHs7IkV6chRlrIs5hyg836sVzbVqmOGu+PXHO5c/D+fB7N/Bv\nOEMZP+Sn9swzTj+0ZyOwG6f39hzgEpxrMx7wWXvmE6fn2rOQWLPK/TvwwgjHLLpNq/YHKcMf+PNA\nK9CHk6Wdn/XYI8CzWfe/AGwGjuCcR1sH3DLMMa/G+S+1D3gZZ7FC38UKfBfYlTneG8CvgHNrKc5h\nyg758vZqm5Y6Tj+0J3AHsD3z4d2BM+rgEr+1Zz5x+qE9M9vOxhnZEcH5Ir8PGOun9swnTq+2Z4Gx\nviUT5/xjHLOoNrUFBo0xxhjjSTV/TYoxxhhj/MmSFGOMMcZ4kiUpxhhjjPEkS1KMMcYY40mWpBhj\njDHGkyxJMcYYY4wnWZJijDHGGE+yJMUYY4wxnmRJijGmIkTkbSKSFpFzizzOIyLyxHH2+a2IfDfr\n/i4R+ZtintcYU3mWpBhjhsgkAmkRSYlIXES2i8hXM4uhFaNaU1yfDzw4cCcT27wq1cUYk6dQtStg\njPGsXwM3AvXA5cAPcRYLuy97p0zioprfGhtS4jrmRVXD1XheY0xxrCfFGDOSuKp2qOoeVX0QWAn8\nmYjcICJdInKliGzBWdX0dHF8TUT2iEhMRDaKyNxhjnuOiDwvIn0isllELhl4QERGichDIrJTRKIi\nsnWk0zSZ5zooIj0i8iMRGfGfruzTPSKyC6dH56lMj8rOzKmolIjMyin3RRFpdfuHM8aUhiUpxph8\nxYAxmdsNwJ3AzcC7gYPAF4EFOMuyvxdnJdhlIvL2nOPcB3wbmImz0uoyEZmUeWwUsAdn5dRzgLuB\ne0TkEznH+AjOcvIfBK4FrsJZETof78Pp0bkBmAK8T1V3A/8L3JSz743AT/I8rjGmxCxJMcYcl4h8\nBJgLPJPZFAL+WlXXqOp2VY0Bfwd8S1V/ntn2FeAlnOQl2w9U9SlV3Qb8NXAYJ9lBVZOqereqblTV\n3aq6FFgMXJNzjDhwk6q2qOqvga8BeV0Yq6qdmZs9qnow61TQw8CnRaQuE/Ms4D2Z5zfGVIElKcaY\nkVwpIkdEJAb8D7AUp2cDoF9VXxnYUUQmAKcCL+Qc43mcHpFsawZuqGoKWJe9j4jcJiLrMqdyjgC3\nAGfkHGOTqsaz7q8GxovI6W6DzPIUkAb+InP/RuC3qtpWxDGNMUWwJMUYM5JngXOBacA4VZ2vqn2Z\nx/pGLlY4EbkW51TQj4GPAjOARzh6mqlsVDUBPArclOlN+TRO74oxpkosSTHGjKRXVXep6l5VTR9r\nR1U9ArwBXJTz0EXAqznb3j9wQ0RGA7Oz9vkA8LyqLlLVTaq6E8i9pgVghoiMzbp/IRBR1T3HjcqR\nAEYPs/0hnOTo85nHn8zzeMaYMrAkxRhTKt8Gviwi14jI2SLyLZyekO/l7HebiPy5iEzHGdY8Eae3\nBGA7cL6IXCYi7xCRb+Bc6JprDPCwiJwjIlcAdwE/cFHXVuDDInKKiEwc2KiqW3FOR90LLMk5pWSM\nqTCbJ8UYUyrfB94C/CtwMk7vyJWq+nrWPgp8JfMzA9iR2edQ5vFFOKN+fpbZdynQjDNPS7ZncBKa\n3+EkLEs4er3MwPNwjPt/B3wH+CtgH3BW1mMP4/TM2KgeY6pM8pt/yRhjgkFEvgpcraozq10XRDhs\nLwAAAIJJREFUY4LOelKMMQYQkUbgTOA24B+rXB1jDHZNijHGDLgfeBFnVNMjx9nXGFMBdrrHGGOM\nMZ5kPSnGGGOM8SRLUowxxhjjSZakGGOMMcaTLEkxxhhjjCdZkmKMMcYYT7IkxRhjjDGeZEmKMcYY\nYzzJkhRjjDHGeJIlKcYYY4zxpP8H4qHiDWIZvp8AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x185c6cf8>"
+       "<matplotlib.figure.Figure at 0xb231ac8>"
       ]
      },
      "metadata": {},
@@ -954,7 +957,7 @@
      "data": {
       "image/png": 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WH17MtTvXeKP6GwD4FPFhT+89fFD/A6oXqc6mrpsI7B3Iq0++SqYMmeKsJ2vG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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x189e96a0>"
+       "<matplotlib.figure.Figure at 0x1ae862e8>"
       ]
      },
      "metadata": {},
@@ -1035,7 +1038,7 @@
      "data": {
       "image/png": 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ppQKUUn8rpQraK97EIG/evPj6+nLq1KlY1w0KCuLGjRtkzZr1zb5KlSrxySef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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c18fac8>"
+       "<matplotlib.figure.Figure at 0x1b6626d8>"
       ]
      },
      "metadata": {},

From 6a7eabf23d4a57499ea7982ab690df4bc0b88b52 Mon Sep 17 00:00:00 2001
From: Willi Richert <wilrich@microsoft.com>
Date: Mon, 30 Jan 2017 09:38:12 +0100
Subject: [PATCH 10/31] Require user-defined functions to be wrapped in
 as_composite()

---
 bindings/python/cntk/ops/functions.py         | 26 ++++++++++---
 .../ops/tests/userfunction_complex_test.py    |  3 +-
 .../cntk/ops/tests/userfunction_test.py       | 37 +++++++++++++------
 bindings/python/cntk/utils/__init__.py        |  1 +
 bindings/python/doc/extend.rst                |  7 +++-
 5 files changed, 53 insertions(+), 21 deletions(-)

diff --git a/bindings/python/cntk/ops/functions.py b/bindings/python/cntk/ops/functions.py
index e63b7730b..b180d3329 100644
--- a/bindings/python/cntk/ops/functions.py
+++ b/bindings/python/cntk/ops/functions.py
@@ -647,11 +647,7 @@ class UserFunction(Function):
 
     '''
     def __init__(self, inputs, name=''):
-        # FIXME we need to save a reference here so that the function does not
-        # disappear
-        self.var_inputs = inputs
-
-        super(Function, self).__init__(inputs, name)
+        super(UserFunction, self).__init__(var_inputs, name)
 
         # Memory management for user defined functions has to be controlled by
         # the C++ side. For more information:
@@ -753,11 +749,29 @@ class UserFunction(Function):
         outputs.extend(self.infer_outputs())
 
     def infer_outputs(self):
-        raise NotImplementedError('infer_outputs has to be overridden')
+        '''
+        Returns a list of all output variables this user-defined function
+        outputs.
+
+        Output variables are created by
+        :meth:`~cntk.ops.functions.output_variable`.
+        '''
+        raise NotImplementedError('infer_outputs has to be overwritten')
 
     def op_name(self):
+        '''
+        Returns the operator name.
+        '''
         return 'UserFunction'
 
+    def compose(self):
+        '''
+        Wraps the passed Function to create a composite representing the
+        composite Function graph rooted at the passed root Function.
+        '''
+        from . import as_composite
+        return as_composite(self)
+
 @typemap
 def load_model(filename, device=None):
     '''
diff --git a/bindings/python/cntk/ops/tests/userfunction_complex_test.py b/bindings/python/cntk/ops/tests/userfunction_complex_test.py
index 536ecff1c..0d1dc1cc5 100644
--- a/bindings/python/cntk/ops/tests/userfunction_complex_test.py
+++ b/bindings/python/cntk/ops/tests/userfunction_complex_test.py
@@ -46,7 +46,8 @@ def linear_layer(input_var, output_dim):
 
 def dense_layer(input, output_dim, nonlinearity):
     r = linear_layer(input, output_dim)
-    r = nonlinearity(r)
+    if isinstance(nonlinearity, UserFunction):
+        r = nonlinearity(r).compose()
     return r
 
 def fully_connected_classifier_net(input, num_output_classes, hidden_layer_dim,
diff --git a/bindings/python/cntk/ops/tests/userfunction_test.py b/bindings/python/cntk/ops/tests/userfunction_test.py
index 28332a40f..0e454e6a5 100644
--- a/bindings/python/cntk/ops/tests/userfunction_test.py
+++ b/bindings/python/cntk/ops/tests/userfunction_test.py
@@ -45,7 +45,7 @@ def test_ext_eval_1():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
     i = input_variable(dim, needs_gradient=True, name='i_var')
-    m = MyPlus(i, constant(3))
+    m = MyPlus(i, constant(3)).compose()
     z = m+p
 
     input_data = np.random.rand(dim)
@@ -56,7 +56,7 @@ def test_ext_eval_2_only_param():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
     i = input_variable(dim, needs_gradient=True, name='i_var')
-    m = MyPlus(p, constant(3))
+    m = MyPlus(p, constant(3)).compose()
     # combine does not work
     # z = combine([m.output])
     z = m+i
@@ -68,7 +68,7 @@ def test_ext_eval_2_only_param():
 def test_ext_eval_3_no_input():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
-    m = MyPlus(p, constant(3))
+    m = MyPlus(p, constant(3)).compose()
     z = m+0
 
     result = z.eval()
@@ -79,7 +79,7 @@ def test_ext_eval_4_a_inside_graph():
     dim = 4
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    m = MyPlus(p, constant(3))
+    m = MyPlus(p, constant(3)).compose()
     z = p * m
 
     result = z.eval()
@@ -90,7 +90,7 @@ def test_ext_eval_4_b_inside_graph():
     dim = 4
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    z = p * MyPlus(p, constant(3))
+    z = p * MyPlus(p, constant(3)).compose()
 
     result = z.eval()
     # No batch dimension since we have no input
@@ -100,14 +100,14 @@ def test_ext_eval_5_times():
     dim = 2
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    m = MyPlus(p, constant(3))
+    m = MyPlus(p, constant(3)).compose()
     z = times(m, parameter(shape=(2,50), init=2))
 
     result = z.eval()
     # No batch dimension since we have no input
     assert np.allclose(result, ((p_init*np.ones_like(result))+3)*2*2)
 
-def test_ext_clone():
+def test_ext_eval_6_clone():
     dim = 4
     i = input_variable(dim, needs_gradient=True, name='i_var')
     m = i + 3
@@ -115,20 +115,33 @@ def test_ext_clone():
     p = parameter(shape=(dim,), init=10, name='p')
     z = m + p
     
-    m_udf = MyPlus(i, constant(3))
+    m_udf = MyPlus(i, constant(3)).compose()
     z_clone = z.clone('share', {m : m_udf} );
 
     input_data = np.random.rand(dim)
     result = z_clone.eval([input_data])
     assert np.allclose(result[0][0], input_data+3+10)
 
+def test_ext_eval_7_placeholder():
+    dim = 4
+    p = parameter(shape=(dim,), init=10, name='p')
+    i = input_variable(dim, needs_gradient=True, name='i_var')
+    pl = placeholder_variable()
+    m = MyPlus(pl, constant(3)).compose()
+    m.replace_placeholder(i)
+    z = m+p
+
+    input_data = np.random.rand(dim)
+    result = z.eval([input_data])
+    assert np.allclose(result[0][0], input_data+3+10)
+
 def test_ext_train():
     dim = 4
 
     p = parameter(shape=(dim,), init=10)
     i = input_variable(dim, needs_gradient=True, name='i_var')
     m = MyPlus(i, constant(3))
-    z = m+p
+    z = m.compose()+p
 
     momentum_time_constant = momentum_as_time_constant_schedule(1100)
     lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
@@ -172,7 +185,7 @@ def test_ext_backpropstate(payload):
 
     p = parameter(shape=(dim,), init=10)
     in1 = input_variable(dim, needs_gradient=True, name='i_var')
-    m = TestBackPropState(in1, payload)
+    m = TestBackPropState(in1, payload).compose()
     z = m+p
 
     lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
@@ -222,7 +235,7 @@ def test_ext_lambdafunc():
     k = i*p
     m = LambdaFunc(k,
             when=lambda arg: np.sum(arg)>1,
-            execute=cb.inc)
+            execute=cb.inc).compose()
     z = m+0
 
     momentum_time_constant = momentum_as_time_constant_schedule(1100)
@@ -262,7 +275,7 @@ def test_udf_plus_and_last():
     x = input_variable(shape=(2,))
     y = input_variable(shape=(2,), dynamic_axes=[Axis.default_batch_axis()])
     
-    func = as_composite(PlusAndLast(x, y))
+    func = PlusAndLast(x, y).compose()
 
     dt_precision = np.float32
     operand1 = [AA([[1., 2.], [3., 4.]], dtype=dt_precision)]
diff --git a/bindings/python/cntk/utils/__init__.py b/bindings/python/cntk/utils/__init__.py
index 95cdafdef..30679f91f 100644
--- a/bindings/python/cntk/utils/__init__.py
+++ b/bindings/python/cntk/utils/__init__.py
@@ -112,6 +112,7 @@ def sanitize_input(arg, fallback_dtype=np.float32, reshape=None):
       ``arg`` is a number or NumPy array. Variable otherwise.
     """
 
+    from cntk.ops.functions import UserFunction
     from cntk.ops.variables import Constant, Variable, Parameter
     from cntk.ops.functions import Function
     from cntk.ops import constant
diff --git a/bindings/python/doc/extend.rst b/bindings/python/doc/extend.rst
index deb368376..b4b04ca12 100644
--- a/bindings/python/doc/extend.rst
+++ b/bindings/python/doc/extend.rst
@@ -48,7 +48,10 @@ tuple, strings, etc.)::
 
 This can now be used as a normal operator like::
 
-    s = MySigmoid(prev_node)
+    s = MySigmoid(prev_node).compose()
+
+Note that we cannot pass the `UserFunction` instance directly into the graph, but
+rather its `compose()` ed version.
 
 In case, the operator is initialized with multiple inputs, ``forward()`` 's
 ``argument`` will be a list of those inputs::
@@ -133,7 +136,7 @@ interesting behavior, for instance::
     debug_node = LambdaFunc(node,
             when=lambda arg: np.var(arg)>1,
             execute=lambda arg: pdb.set_trace())
-    # out = ... using debug_node ...
+    # out = ... using debug_node.compose() ...
     # ... training out
 
 Now, if the variance of the input tensor exceeds 1, we will be put into

From 9cb7f392d2c18b492c12ae27878d37b90fd9a03e Mon Sep 17 00:00:00 2001
From: Willi Richert <wilrich@microsoft.com>
Date: Wed, 8 Feb 2017 17:04:17 +0100
Subject: [PATCH 11/31] Change UserFunction.compose() to cntk.user_function()

---
 bindings/python/cntk/core.py                  |  7 +++++
 bindings/python/cntk/ops/functions.py         |  9 +-----
 .../ops/tests/userfunction_complex_test.py    |  2 +-
 .../cntk/ops/tests/userfunction_test.py       | 31 ++++++++++---------
 bindings/python/cntk/utils/__init__.py        |  4 +++
 bindings/python/doc/extend.rst                |  8 ++---
 6 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/bindings/python/cntk/core.py b/bindings/python/cntk/core.py
index 999976bf7..046727cf1 100644
--- a/bindings/python/cntk/core.py
+++ b/bindings/python/cntk/core.py
@@ -292,3 +292,10 @@ class Value(cntk_py.Value):
         '''
         return self.shape[0]
 
+def user_function(user_func):
+    '''
+    Wraps the passed Function to create a composite representing the
+    composite Function graph rooted at the passed root Function.
+    '''
+    from . import as_composite
+    return as_composite(user_func)
diff --git a/bindings/python/cntk/ops/functions.py b/bindings/python/cntk/ops/functions.py
index b180d3329..3e6816c36 100644
--- a/bindings/python/cntk/ops/functions.py
+++ b/bindings/python/cntk/ops/functions.py
@@ -647,7 +647,7 @@ class UserFunction(Function):
 
     '''
     def __init__(self, inputs, name=''):
-        super(UserFunction, self).__init__(var_inputs, name)
+        super(UserFunction, self).__init__(inputs, name)
 
         # Memory management for user defined functions has to be controlled by
         # the C++ side. For more information:
@@ -764,13 +764,6 @@ class UserFunction(Function):
         '''
         return 'UserFunction'
 
-    def compose(self):
-        '''
-        Wraps the passed Function to create a composite representing the
-        composite Function graph rooted at the passed root Function.
-        '''
-        from . import as_composite
-        return as_composite(self)
 
 @typemap
 def load_model(filename, device=None):
diff --git a/bindings/python/cntk/ops/tests/userfunction_complex_test.py b/bindings/python/cntk/ops/tests/userfunction_complex_test.py
index 0d1dc1cc5..8966eea03 100644
--- a/bindings/python/cntk/ops/tests/userfunction_complex_test.py
+++ b/bindings/python/cntk/ops/tests/userfunction_complex_test.py
@@ -47,7 +47,7 @@ def linear_layer(input_var, output_dim):
 def dense_layer(input, output_dim, nonlinearity):
     r = linear_layer(input, output_dim)
     if isinstance(nonlinearity, UserFunction):
-        r = nonlinearity(r).compose()
+        r = user_function(nonlinearity(r))
     return r
 
 def fully_connected_classifier_net(input, num_output_classes, hidden_layer_dim,
diff --git a/bindings/python/cntk/ops/tests/userfunction_test.py b/bindings/python/cntk/ops/tests/userfunction_test.py
index 0e454e6a5..512b46706 100644
--- a/bindings/python/cntk/ops/tests/userfunction_test.py
+++ b/bindings/python/cntk/ops/tests/userfunction_test.py
@@ -25,7 +25,8 @@ class MyPlus(UserFunction):
         self.backward_calls = 0
 
     def infer_outputs(self):
-        return [output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes)]
+        return [output_variable(self.inputs[0].shape,
+            self.inputs[0].dtype, self.inputs[0].dynamic_axes)]
 
     def forward(self, arguments, device=None, outputs_to_retain=None):
         assert len(self.inputs)==2
@@ -45,7 +46,7 @@ def test_ext_eval_1():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
     i = input_variable(dim, needs_gradient=True, name='i_var')
-    m = MyPlus(i, constant(3)).compose()
+    m = user_function(MyPlus(i, constant(3)))
     z = m+p
 
     input_data = np.random.rand(dim)
@@ -56,7 +57,7 @@ def test_ext_eval_2_only_param():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
     i = input_variable(dim, needs_gradient=True, name='i_var')
-    m = MyPlus(p, constant(3)).compose()
+    m = user_function(MyPlus(p, constant(3)))
     # combine does not work
     # z = combine([m.output])
     z = m+i
@@ -68,7 +69,7 @@ def test_ext_eval_2_only_param():
 def test_ext_eval_3_no_input():
     dim = 4
     p = parameter(shape=(dim,), init=10, name='p')
-    m = MyPlus(p, constant(3)).compose()
+    m = user_function(MyPlus(p, constant(3)))
     z = m+0
 
     result = z.eval()
@@ -79,7 +80,7 @@ def test_ext_eval_4_a_inside_graph():
     dim = 4
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    m = MyPlus(p, constant(3)).compose()
+    m = user_function(MyPlus(p, constant(3)))
     z = p * m
 
     result = z.eval()
@@ -90,7 +91,7 @@ def test_ext_eval_4_b_inside_graph():
     dim = 4
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    z = p * MyPlus(p, constant(3)).compose()
+    z = user_function(p * MyPlus(p, constant(3)))
 
     result = z.eval()
     # No batch dimension since we have no input
@@ -100,7 +101,7 @@ def test_ext_eval_5_times():
     dim = 2
     p_init = 10
     p = parameter(shape=(dim,), init=p_init, name='p')
-    m = MyPlus(p, constant(3)).compose()
+    m = user_function(MyPlus(p, constant(3)))
     z = times(m, parameter(shape=(2,50), init=2))
 
     result = z.eval()
@@ -115,7 +116,7 @@ def test_ext_eval_6_clone():
     p = parameter(shape=(dim,), init=10, name='p')
     z = m + p
     
-    m_udf = MyPlus(i, constant(3)).compose()
+    m_udf = user_function(MyPlus(i, constant(3)))
     z_clone = z.clone('share', {m : m_udf} );
 
     input_data = np.random.rand(dim)
@@ -127,9 +128,9 @@ def test_ext_eval_7_placeholder():
     p = parameter(shape=(dim,), init=10, name='p')
     i = input_variable(dim, needs_gradient=True, name='i_var')
     pl = placeholder_variable()
-    m = MyPlus(pl, constant(3)).compose()
-    m.replace_placeholder(i)
+    m = user_function(MyPlus(pl, constant(3)))
     z = m+p
+    z.replace_placeholder(i)
 
     input_data = np.random.rand(dim)
     result = z.eval([input_data])
@@ -141,7 +142,8 @@ def test_ext_train():
     p = parameter(shape=(dim,), init=10)
     i = input_variable(dim, needs_gradient=True, name='i_var')
     m = MyPlus(i, constant(3))
-    z = m.compose()+p
+    # keeping m unwrapped since we need to access its member variables
+    z = user_function(m)+p
 
     momentum_time_constant = momentum_as_time_constant_schedule(1100)
     lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
@@ -185,7 +187,7 @@ def test_ext_backpropstate(payload):
 
     p = parameter(shape=(dim,), init=10)
     in1 = input_variable(dim, needs_gradient=True, name='i_var')
-    m = TestBackPropState(in1, payload).compose()
+    m = user_function(TestBackPropState(in1, payload))
     z = m+p
 
     lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
@@ -235,7 +237,8 @@ def test_ext_lambdafunc():
     k = i*p
     m = LambdaFunc(k,
             when=lambda arg: np.sum(arg)>1,
-            execute=cb.inc).compose()
+            execute=cb.inc)
+    m = user_function(m)
     z = m+0
 
     momentum_time_constant = momentum_as_time_constant_schedule(1100)
@@ -275,7 +278,7 @@ def test_udf_plus_and_last():
     x = input_variable(shape=(2,))
     y = input_variable(shape=(2,), dynamic_axes=[Axis.default_batch_axis()])
     
-    func = PlusAndLast(x, y).compose()
+    func = user_function(PlusAndLast(x, y))
 
     dt_precision = np.float32
     operand1 = [AA([[1., 2.], [3., 4.]], dtype=dt_precision)]
diff --git a/bindings/python/cntk/utils/__init__.py b/bindings/python/cntk/utils/__init__.py
index 30679f91f..16562ea5f 100644
--- a/bindings/python/cntk/utils/__init__.py
+++ b/bindings/python/cntk/utils/__init__.py
@@ -34,6 +34,8 @@ def sanitize_precision(precision):
         return np.float32
     elif precision in [cntk_py.DataType_Double, 'double', 'float64', np.float64]:
         return np.float64
+    elif precision in [cntk_py.DataType_Unknown]:
+        return None
     else:
         raise ValueError('precision value: "%s" is not supported' % precision)
 
@@ -484,6 +486,8 @@ def sanitize_dtype_cntk(dtype):
         return cntk_py.DataType_Float
     elif dtype == np.float64:
         return cntk_py.DataType_Double
+    elif dtype == object:
+        return cntk_py.DataType_Unknown
     else:
         raise ValueError('data type "%s" is not supported' % dtype)
 
diff --git a/bindings/python/doc/extend.rst b/bindings/python/doc/extend.rst
index b4b04ca12..50473f8fb 100644
--- a/bindings/python/doc/extend.rst
+++ b/bindings/python/doc/extend.rst
@@ -48,10 +48,10 @@ tuple, strings, etc.)::
 
 This can now be used as a normal operator like::
 
-    s = MySigmoid(prev_node).compose()
+    s = user_function(MySigmoid(prev_node))
 
-Note that we cannot pass the `UserFunction` instance directly into the graph, but
-rather its `compose()` ed version.
+Note that we cannot pass the `UserFunction` instance directly into the graph. 
+It is representing a pure function, which we have to pass through `user_function()`.
 
 In case, the operator is initialized with multiple inputs, ``forward()`` 's
 ``argument`` will be a list of those inputs::
@@ -136,7 +136,7 @@ interesting behavior, for instance::
     debug_node = LambdaFunc(node,
             when=lambda arg: np.var(arg)>1,
             execute=lambda arg: pdb.set_trace())
-    # out = ... using debug_node.compose() ...
+    # out = ... using user_function(debug_node) ...
     # ... training out
 
 Now, if the variance of the input tensor exceeds 1, we will be put into

From c89f9a5ad3e8ddb9d12841cae42d779973a4f809 Mon Sep 17 00:00:00 2001
From: Willi Richert <wilrich@microsoft.com>
Date: Wed, 8 Feb 2017 18:40:19 +0100
Subject: [PATCH 12/31] Add Variable.as_constant/parameter() and do the proper
 conversion in graph's DFS

---
 bindings/python/cntk/graph.py            |  5 ++++
 bindings/python/cntk/ops/variables.py    | 20 +++++++++++++++
 bindings/python/cntk/tests/graph_test.py | 31 +++++++++++++++---------
 bindings/python/doc/extend.rst           |  2 +-
 4 files changed, 46 insertions(+), 12 deletions(-)

diff --git a/bindings/python/cntk/graph.py b/bindings/python/cntk/graph.py
index bd3db3980..014792680 100644
--- a/bindings/python/cntk/graph.py
+++ b/bindings/python/cntk/graph.py
@@ -42,6 +42,11 @@ def depth_first_search(node, visitor):
                 pass
 
         if visitor(node):
+            if node.is_parameter:
+                node = node.as_parameter()
+            elif node.is_constant:
+                node = node.as_constant()
+
             accum.append(node)
 
         visited.add(node)
diff --git a/bindings/python/cntk/ops/variables.py b/bindings/python/cntk/ops/variables.py
index dacf27f64..7d831f673 100644
--- a/bindings/python/cntk/ops/variables.py
+++ b/bindings/python/cntk/ops/variables.py
@@ -132,6 +132,26 @@ class Variable(VariableMixin, TensorOpsMixin, cntk_py.Variable):
         super(Variable, self).__init__(shape, is_sparse, dtype, needs_gradient, name,
                          dynamic_axes)
 
+    @typemap
+    def as_parameter(self):
+        '''
+        Converts this instance into a :class:`Parameter`
+        '''
+        if not self.is_parameter:
+            raise TypeError('cannot be converted into a Parameter')
+
+        return cntk_py.Parameter(self)
+
+    @typemap
+    def as_constant(self):
+        '''
+        Converts this instance into a :class:`Constant`
+        '''
+        if not self.is_constant:
+            raise TypeError('cannot be converted into a Constant')
+
+        return cntk_py.Constant(self)
+
 
 class Parameter(VariableMixin, TensorOpsMixin, cntk_py.Parameter):
     '''
diff --git a/bindings/python/cntk/tests/graph_test.py b/bindings/python/cntk/tests/graph_test.py
index 38571678f..83bdbb9f0 100644
--- a/bindings/python/cntk/tests/graph_test.py
+++ b/bindings/python/cntk/tests/graph_test.py
@@ -20,15 +20,13 @@ def _graph_dict():
 
     d['i1'] = input_variable(
         shape=(2, 3), dynamic_axes=input_dynamic_axes, name='i1')
-    d['i2'] = input_variable(
-        shape=(2, 3), dynamic_axes=input_dynamic_axes, name='i2')
+    d['c1'] = constant(shape=(2, 3), value=6, name='c1')
+    d['p1'] = parameter(shape=(3, 2), init=7, name='p1')
 
-    d['p1'] = parameter(shape=(3, 2), name='p1')
-
-    d['op1'] = plus(d['i1'], d['i2'], name='op1')
+    d['op1'] = plus(d['i1'], d['c1'], name='op1')
     d['op2'] = times(d['op1'], d['p1'], name='op2')
 
-    #d['slice'] = slice(d['i2'], Axis.default_dynamic_axis(), 0, 3)
+    #d['slice'] = slice(d['c1'], Axis.default_dynamic_axis(), 0, 3)
     #label_sentence_start = sequence.first(raw_labels)
 
     # no name
@@ -49,9 +47,9 @@ def _simple_dict():
     d = {}
 
     d['i1'] = input_variable(shape=(2, 3), name='i1')
-    d['i2'] = input_variable(shape=(2, 3), name='i2')
-    d['p1'] = parameter(shape=(3, 2), name='p1')
-    d['op1'] = plus(d['i1'], d['i2'], name='op1')
+    d['c1'] = constant(shape=(2, 3), value=6, name='c1')
+    d['p1'] = parameter(shape=(3, 2), init=7, name='p1')
+    d['op1'] = plus(d['i1'], d['c1'], name='op1')
     d['op2'] = times(d['op1'], d['p1'], name='op2')
     d['root'] = d['op2']
 
@@ -64,7 +62,7 @@ def _simple_dict():
 def test_find_nodes():
     d = _graph_dict()
 
-    for name in ['i1', 'i2', 'p1', 'op1', 'op2', 'past']:
+    for name in ['i1', 'c1', 'p1', 'op1', 'op2', 'past']:
         n = find_all_with_name(d['root'], name)
         assert len(n) == 1, name
         assert n[0].name == name, name
@@ -89,6 +87,17 @@ def test_find_nodes():
 
     assert find_by_name(d['root'], 'none') is None
 
+def test_find_nodes_returning_proper_types():
+    d = _graph_dict()
+
+    c1 = find_by_name(d['root'], 'c1')
+    assert isinstance(c1, Constant)
+    assert np.allclose(c1.value, np.zeros((2,3))+6)
+
+    p1 = find_by_name(d['root'], 'p1')
+    assert isinstance(p1, Parameter)
+    assert np.allclose(p1.value, np.zeros((3,2))+7)
+
 
 def test_plot():
     d = _simple_dict()
@@ -107,4 +116,4 @@ def test_depth_first_search():
 
     found = depth_first_search(d['op2'], lambda x:True)
     found_names = [v.name for v in found]
-    assert found_names == ['op2', 'op1', 'i1', 'i2', 'p1']
+    assert found_names == ['op2', 'op1', 'i1', 'c1', 'p1']
diff --git a/bindings/python/doc/extend.rst b/bindings/python/doc/extend.rst
index 50473f8fb..48d2d7945 100644
--- a/bindings/python/doc/extend.rst
+++ b/bindings/python/doc/extend.rst
@@ -51,7 +51,7 @@ This can now be used as a normal operator like::
     s = user_function(MySigmoid(prev_node))
 
 Note that we cannot pass the `UserFunction` instance directly into the graph. 
-It is representing a pure function, which we have to pass through `user_function()`.
+It is representing a primitive function, which we have to pass through `user_function()`.
 
 In case, the operator is initialized with multiple inputs, ``forward()`` 's
 ``argument`` will be a list of those inputs::

From 6beb7cdb31d70467ed667133ef3854a3b112f64d Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Fri, 27 Jan 2017 15:48:01 -0800
Subject: [PATCH 13/31] Adding preliminary version of VGG 16 and 19 in BS.

---
 .../BrainScript/ResNet101_ImageNet1K.cntk     |   2 +-
 .../ResNet/BrainScript/ResNet110_CIFAR10.cntk |   2 +-
 .../BrainScript/ResNet152_ImageNet1K.cntk     |   2 +-
 .../ResNet/BrainScript/ResNet20_CIFAR10.cntk  |   2 +-
 .../BrainScript/ResNet50_ImageNet1K.cntk      |   2 +-
 .../VGG/BrainScript/VGG16_ImageNet.cntk       | 177 +++++++++++++++++
 .../VGG/BrainScript/VGG19_ImageNet.cntk       | 180 ++++++++++++++++++
 7 files changed, 362 insertions(+), 5 deletions(-)
 create mode 100644 Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
 create mode 100644 Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk

diff --git a/Examples/Image/Classification/ResNet/BrainScript/ResNet101_ImageNet1K.cntk b/Examples/Image/Classification/ResNet/BrainScript/ResNet101_ImageNet1K.cntk
index 626df070c..a97d60615 100644
--- a/Examples/Image/Classification/ResNet/BrainScript/ResNet101_ImageNet1K.cntk
+++ b/Examples/Image/Classification/ResNet/BrainScript/ResNet101_ImageNet1K.cntk
@@ -1,4 +1,4 @@
-# Node: ResNet-50 with ImageNet -- 101 layers bottleneck ResNet for image classification
+# ResNet-101 with ImageNet -- 101 layers bottleneck ResNet for image classification
 # Reference: "Deep Residual Learning for Image Recognition" https://arxiv.org/abs/1512.03385
 
 command = TrainNetwork:BNStatistics:Eval
diff --git a/Examples/Image/Classification/ResNet/BrainScript/ResNet110_CIFAR10.cntk b/Examples/Image/Classification/ResNet/BrainScript/ResNet110_CIFAR10.cntk
index ca0e09a84..18cffd463 100644
--- a/Examples/Image/Classification/ResNet/BrainScript/ResNet110_CIFAR10.cntk
+++ b/Examples/Image/Classification/ResNet/BrainScript/ResNet110_CIFAR10.cntk
@@ -1,4 +1,4 @@
-# ConvNet applied on CIFAR-10 dataset, with data augmentation (translation and flipping).
+# ResNet110 applied on CIFAR-10 dataset, with data augmentation (translation and flipping).
 
 command = TrainConvNet:Eval
 
diff --git a/Examples/Image/Classification/ResNet/BrainScript/ResNet152_ImageNet1K.cntk b/Examples/Image/Classification/ResNet/BrainScript/ResNet152_ImageNet1K.cntk
index d92d7ec9f..e72499af7 100644
--- a/Examples/Image/Classification/ResNet/BrainScript/ResNet152_ImageNet1K.cntk
+++ b/Examples/Image/Classification/ResNet/BrainScript/ResNet152_ImageNet1K.cntk
@@ -1,4 +1,4 @@
-# Node: ResNet-50 with ImageNet -- 152 layers bottleneck ResNet for image classification
+# ResNet-152 with ImageNet -- 152 layers bottleneck ResNet for image classification
 # Reference: "Deep Residual Learning for Image Recognition" https://arxiv.org/abs/1512.03385
 
 command = TrainNetwork:BNStatistics:Eval
diff --git a/Examples/Image/Classification/ResNet/BrainScript/ResNet20_CIFAR10.cntk b/Examples/Image/Classification/ResNet/BrainScript/ResNet20_CIFAR10.cntk
index c3b638e21..353675a4f 100644
--- a/Examples/Image/Classification/ResNet/BrainScript/ResNet20_CIFAR10.cntk
+++ b/Examples/Image/Classification/ResNet/BrainScript/ResNet20_CIFAR10.cntk
@@ -1,4 +1,4 @@
-# ConvNet applied on CIFAR-10 dataset, with data augmentation (translation and flipping).
+# ResNet20 applied on CIFAR-10 dataset, with data augmentation (translation and flipping).
 
 command = TrainConvNet:Eval
 
diff --git a/Examples/Image/Classification/ResNet/BrainScript/ResNet50_ImageNet1K.cntk b/Examples/Image/Classification/ResNet/BrainScript/ResNet50_ImageNet1K.cntk
index ccf3e6467..91c6e4e93 100644
--- a/Examples/Image/Classification/ResNet/BrainScript/ResNet50_ImageNet1K.cntk
+++ b/Examples/Image/Classification/ResNet/BrainScript/ResNet50_ImageNet1K.cntk
@@ -1,4 +1,4 @@
-# Node: ResNet-50 with ImageNet -- 50 layers bottleneck ResNet for image classification
+# ResNet-50 with ImageNet -- 50 layers bottleneck ResNet for image classification
 # Reference: "Deep Residual Learning for Image Recognition" https://arxiv.org/abs/1512.03385
 
 command = TrainNetwork:BNStatistics:Eval
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
new file mode 100644
index 000000000..7bbe9ac61
--- /dev/null
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -0,0 +1,177 @@
+# VGG16 with ImageNet -- 16 layers ConvNet for image classification
+# Reference: "Very Deep Convolutional Networks for Large-Scale Image Recognition" https://arxiv.org/abs/1409.1556
+
+RootDir = "."
+
+ConfigDir = "$RootDir$"
+DataDir = "$RootDir$"
+OutputDir = "$RootDir$/Output"
+ModelDir = "$OutputDir$/Models"
+
+ndlMacros="$ConfigDir$/Macros.ndl"
+
+precision = "float"
+deviceId = "Auto"
+
+command = Train:Test
+
+parallelTrain = "true"
+traceLevel = 1
+numMBsToShowResult = 500
+
+modelPath = "$ModelDir$/VGG16"
+stderr = "$OutputDir$/VGG16"
+
+################################
+Train = {
+    action = "train"
+
+    BrainScriptNetworkBuilder = {
+        imageShape = 224:224:3
+        labelDim = 1000
+                
+        model = Sequential (
+            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            DenseLayer         {4096, activation=ReLU} : Dropout : 
+            DenseLayer         {4096, activation=ReLU} : Dropout :
+            LinearLayer        {labelDim}
+        )
+
+        # inputs
+        features = Input {imageShape}
+        featNorm = features - Splice(Constant(103.939):Constant(116.779):Constant(123.68), axis=3)
+        labels = Input {labelDim}
+
+        # apply model to features
+        z = model (featNorm)
+
+        # loss and error computation
+        ce       = CrossEntropyWithSoftmax  (labels, z)
+        errs     = ClassificationError      (labels, z)
+        top5Errs = ClassificationError      (labels, z, topN=5)  # only used in Eval action
+
+        # declare special nodes
+        featureNodes    = (features)
+        labelNodes      = (labels)
+        criterionNodes  = (ce)
+        evaluationNodes = (errs)
+        outputNodes     = (z)
+    }
+    
+    SGD = {
+        epochSize = 0
+        minibatchSize = 256
+        # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
+        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001
+        momentumPerMB = 0.9
+        maxEpochs = 74
+        gradUpdateType = None
+        L2RegWeight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
+        dropoutRate = 0.5
+        
+        # TODO: try less bits?
+        ParallelTrain = {
+            parallelizationMethod = "DataParallelSGD"
+            distributedMBReading = "true"
+            parallelizationStartEpoch = 1
+            DataParallelSGD = {
+                gradientBits = 32
+            }
+        }
+        
+        numMBsToShowResult = 100
+    }
+    
+    # Reader
+    reader = {
+        verbosity = 0
+        randomize = true
+        randomizationWindow = 1
+
+        deserializers = (
+        {   
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file = "$DataDir$/train_map.txt"
+            input = {
+                features = {
+                    transforms = (
+                        {
+                            type = "Crop"
+                            cropType = "RandomSide"
+                            sideRatio = 0.875
+                            jitterType = "UniRatio"
+                        }:{
+                            type = "Scale"
+                            width = 224
+                            height = 224
+                            channels = 3
+                            interpolations = "linear"
+                        }:{
+                            type = "Transpose"
+                        }
+                    )
+                }
+                labels = {
+                    labelDim = 1000
+                }
+            }
+        })
+    }    
+}
+
+################################
+Test = {
+    action=test
+    minibatchSize=128
+    evalNodeNames = errs:top5Errs  # also test top-5 error rate
+    
+    # Reader
+    reader = {
+        verbosity = 0
+        randomize = false
+
+        deserializers = (
+        {
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file="$DataDir$/val_map.txt"
+            input = {
+                features = {
+                    transforms = (
+                        {
+                            type = "Crop"
+                            cropType = "center"
+                            sideRatio = 0.875
+                        }:{
+                            type = "Scale"
+                            width = 224
+                            height = 224
+                            channels = 3
+                        }:{
+                            type = "Transpose"
+                        }
+                    )
+                }
+                labels = {
+                    labelDim = 1000
+                }
+            }
+        })
+    }        
+}
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
new file mode 100644
index 000000000..03a5137cf
--- /dev/null
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -0,0 +1,180 @@
+# VGG19 with ImageNet -- 19 layers ConvNet for image classification
+# Reference: "Very Deep Convolutional Networks for Large-Scale Image Recognition" https://arxiv.org/abs/1409.1556
+
+RootDir = "."
+
+ConfigDir = "$RootDir$"
+DataDir = "$RootDir$"
+OutputDir = "$RootDir$/Output"
+ModelDir = "$OutputDir$/Models"
+
+ndlMacros="$ConfigDir$/Macros.ndl"
+
+precision = "float"
+deviceId = "Auto"
+
+command = Train:Test
+
+parallelTrain = "true"
+traceLevel = 1
+numMBsToShowResult = 500
+
+modelPath = "$ModelDir$/VGG19"
+stderr = "$OutputDir$/VGG19"
+
+################################
+Train = {
+    action = "train"
+
+    BrainScriptNetworkBuilder = {
+        imageShape = 224:224:3
+        labelDim = 1000
+                
+        model = Sequential (
+            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            MaxPoolingLayer    {(2:2), stride=(2:2)} :
+            DenseLayer         {4096, activation=ReLU} : Dropout : 
+            DenseLayer         {4096, activation=ReLU} : Dropout :
+            LinearLayer        {labelDim}
+        )
+
+        # inputs
+        features = Input {imageShape}
+        featNorm = features - Splice(Constant(103.939):Constant(116.779):Constant(123.68), axis=3)
+        labels = Input {labelDim}
+
+        # apply model to features
+        z = model (featNorm)
+
+        # loss and error computation
+        ce       = CrossEntropyWithSoftmax  (labels, z)
+        errs     = ClassificationError      (labels, z)
+        top5Errs = ClassificationError      (labels, z, topN=5)  # only used in Eval action
+
+        # declare special nodes
+        featureNodes    = (features)
+        labelNodes      = (labels)
+        criterionNodes  = (ce)
+        evaluationNodes = (errs)
+        outputNodes     = (z)
+    }
+    
+    SGD = {
+        epochSize = 0
+        minibatchSize = 256
+        # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
+        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001
+        momentumPerMB = 0.9
+        maxEpochs = 74
+        gradUpdateType = None
+        L2RegWeight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
+        dropoutRate = 0.5
+        
+        # TODO: try less bits?
+        ParallelTrain = {
+            parallelizationMethod = "DataParallelSGD"
+            distributedMBReading = "true"
+            parallelizationStartEpoch = 1
+            DataParallelSGD = {
+                gradientBits = 32
+            }
+        }
+        
+        numMBsToShowResult = 100
+    }
+    
+    # Reader
+    reader = {
+        verbosity = 0
+        randomize = true
+        randomizationWindow = 1
+
+        deserializers = (
+        {   
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file = "$DataDir$/train_map.txt"
+            input = {
+                features = {
+                    transforms = (
+                        {
+                            type = "Crop"
+                            cropType = "RandomSide"
+                            sideRatio = 0.875
+                            jitterType = "UniRatio"
+                        }:{
+                            type = "Scale"
+                            width = 224
+                            height = 224
+                            channels = 3
+                            interpolations = "linear"
+                        }:{
+                            type = "Transpose"
+                        }
+                    )
+                }
+                labels = {
+                    labelDim = 1000
+                }
+            }
+        })
+    }    
+}
+
+################################
+Test = {
+    action=test
+    minibatchSize=128
+    evalNodeNames = errs:top5Errs  # also test top-5 error rate
+    
+    # Reader
+    reader = {
+        verbosity = 0
+        randomize = false
+
+        deserializers = (
+        {
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file="$DataDir$/val_map.txt"
+            input = {
+                features = {
+                    transforms = (
+                        {
+                            type = "Crop"
+                            cropType = "center"
+                            sideRatio = 0.875
+                        }:{
+                            type = "Scale"
+                            width = 224
+                            height = 224
+                            channels = 3
+                        }:{
+                            type = "Transpose"
+                        }
+                    )
+                }
+                labels = {
+                    labelDim = 1000
+                }
+            }
+        })
+    }        
+}

From 330376ff894f34641fb63064b32006e0b2162d86 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Fri, 27 Jan 2017 16:44:26 -0800
Subject: [PATCH 14/31] Working version, but haven't verify accuracy.

---
 .../AlexNet/BrainScript/AlexNet_ImageNet.cntk |   2 -
 .../VGG/BrainScript/VGG16_ImageNet.cntk       | 115 ++++++++---------
 .../VGG/BrainScript/VGG19_ImageNet.cntk       | 121 ++++++++----------
 3 files changed, 105 insertions(+), 133 deletions(-)

diff --git a/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk b/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
index 7ce5f9a69..51c57e41d 100644
--- a/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
+++ b/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
@@ -6,8 +6,6 @@ DataDir = "$RootDir$"
 OutputDir = "$RootDir$/Output"
 ModelDir = "$OutputDir$/Models"
 
-ndlMacros="$ConfigDir$/Macros.ndl"
-
 precision = "float"
 deviceId = "Auto"
 
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
index 7bbe9ac61..2bc9b4212 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -8,8 +8,6 @@ DataDir = "$RootDir$"
 OutputDir = "$RootDir$/Output"
 ModelDir = "$OutputDir$/Models"
 
-ndlMacros="$ConfigDir$/Macros.ndl"
-
 precision = "float"
 deviceId = "Auto"
 
@@ -22,32 +20,37 @@ numMBsToShowResult = 500
 modelPath = "$ModelDir$/VGG16"
 stderr = "$OutputDir$/VGG16"
 
+ImageH    = 224
+ImageW    = 224
+ImageC    = 3
+NumLabels = 1000
+
 ################################
 Train = {
     action = "train"
 
     BrainScriptNetworkBuilder = {
-        imageShape = 224:224:3
-        labelDim = 1000
+        imageShape  = $ImageH$:$ImageW$:$ImageC$
+        labelDim    = $NumLabels$
                 
         model = Sequential (
-            ConvolutionalLayer {64, (3:3)} : ReLU : 
-            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            ConvolutionalLayer {64, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {64, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {128, (3:3)} : ReLU : 
-            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            ConvolutionalLayer {128, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {128, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
             DenseLayer         {4096, activation=ReLU} : Dropout : 
             DenseLayer         {4096, activation=ReLU} : Dropout :
@@ -56,7 +59,7 @@ Train = {
 
         # inputs
         features = Input {imageShape}
-        featNorm = features - Splice(Constant(103.939):Constant(116.779):Constant(123.68), axis=3)
+        featNorm = features - Splice(Constant(104):Constant(117):Constant(124), axis=3)
         labels = Input {labelDim}
 
         # apply model to features
@@ -101,36 +104,33 @@ Train = {
     
     # Reader
     reader = {
-        verbosity = 0
-        randomize = true
-        randomizationWindow = 1
-
-        deserializers = (
-        {   
+        verbosity = 0 ; randomize = true
+        deserializers = ({
             type = "ImageDeserializer" ; module = "ImageReader"
             file = "$DataDir$/train_map.txt"
             input = {
-                features = {
-                    transforms = (
-                        {
-                            type = "Crop"
-                            cropType = "RandomSide"
-                            sideRatio = 0.875
-                            jitterType = "UniRatio"
-                        }:{
-                            type = "Scale"
-                            width = 224
-                            height = 224
-                            channels = 3
-                            interpolations = "linear"
-                        }:{
-                            type = "Transpose"
-                        }
-                    )
-                }
-                labels = {
-                    labelDim = 1000
-                }
+                features = { transforms = (
+                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.875 ; jitterType = "uniRatio" } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = $NumLabels$ }
+            }
+        })
+    }
+
+    cvreader = {
+        verbosity = 0 ; randomize = false
+        deserializers = ({
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file = "$DataDir$/val_map.txt"
+            input = {
+                features = { transforms = (
+                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.875 } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = $NumLabels$ }
             }
         })
     }    
@@ -152,25 +152,12 @@ Test = {
             type = "ImageDeserializer" ; module = "ImageReader"
             file="$DataDir$/val_map.txt"
             input = {
-                features = {
-                    transforms = (
-                        {
-                            type = "Crop"
-                            cropType = "center"
-                            sideRatio = 0.875
-                        }:{
-                            type = "Scale"
-                            width = 224
-                            height = 224
-                            channels = 3
-                        }:{
-                            type = "Transpose"
-                        }
-                    )
-                }
-                labels = {
-                    labelDim = 1000
-                }
+                features = { transforms = (
+                    { type = "Crop"; cropType = "center"; sideRatio = 0.875 } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = 1000}
             }
         })
     }        
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
index 03a5137cf..56a949940 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -8,8 +8,6 @@ DataDir = "$RootDir$"
 OutputDir = "$RootDir$/Output"
 ModelDir = "$OutputDir$/Models"
 
-ndlMacros="$ConfigDir$/Macros.ndl"
-
 precision = "float"
 deviceId = "Auto"
 
@@ -22,35 +20,40 @@ numMBsToShowResult = 500
 modelPath = "$ModelDir$/VGG19"
 stderr = "$OutputDir$/VGG19"
 
+ImageH    = 224
+ImageW    = 224
+ImageC    = 3
+NumLabels = 1000
+
 ################################
 Train = {
     action = "train"
 
     BrainScriptNetworkBuilder = {
-        imageShape = 224:224:3
-        labelDim = 1000
+        imageShape  = $ImageH$:$ImageW$:$ImageC$
+        labelDim    = $NumLabels$
                 
         model = Sequential (
-            ConvolutionalLayer {64, (3:3)} : ReLU : 
-            ConvolutionalLayer {64, (3:3)} : ReLU : 
+            ConvolutionalLayer {64, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {64, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {128, (3:3)} : ReLU : 
-            ConvolutionalLayer {128, (3:3)} : ReLU : 
+            ConvolutionalLayer {128, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {128, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
-            ConvolutionalLayer {256, (3:3)} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {256, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
-            ConvolutionalLayer {512, (3:3)} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
+            ConvolutionalLayer {512, (3:3), pad = true} : ReLU : 
             MaxPoolingLayer    {(2:2), stride=(2:2)} :
             DenseLayer         {4096, activation=ReLU} : Dropout : 
             DenseLayer         {4096, activation=ReLU} : Dropout :
@@ -59,7 +62,7 @@ Train = {
 
         # inputs
         features = Input {imageShape}
-        featNorm = features - Splice(Constant(103.939):Constant(116.779):Constant(123.68), axis=3)
+        featNorm = features - Splice(Constant(104):Constant(117):Constant(124), axis=3)
         labels = Input {labelDim}
 
         # apply model to features
@@ -104,36 +107,33 @@ Train = {
     
     # Reader
     reader = {
-        verbosity = 0
-        randomize = true
-        randomizationWindow = 1
-
-        deserializers = (
-        {   
+        verbosity = 0 ; randomize = true
+        deserializers = ({
             type = "ImageDeserializer" ; module = "ImageReader"
             file = "$DataDir$/train_map.txt"
             input = {
-                features = {
-                    transforms = (
-                        {
-                            type = "Crop"
-                            cropType = "RandomSide"
-                            sideRatio = 0.875
-                            jitterType = "UniRatio"
-                        }:{
-                            type = "Scale"
-                            width = 224
-                            height = 224
-                            channels = 3
-                            interpolations = "linear"
-                        }:{
-                            type = "Transpose"
-                        }
-                    )
-                }
-                labels = {
-                    labelDim = 1000
-                }
+                features = { transforms = (
+                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.875 ; jitterType = "uniRatio" } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = $NumLabels$ }
+            }
+        })
+    }
+
+    cvreader = {
+        verbosity = 0 ; randomize = false
+        deserializers = ({
+            type = "ImageDeserializer" ; module = "ImageReader"
+            file = "$DataDir$/val_map.txt"
+            input = {
+                features = { transforms = (
+                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.875 } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = $NumLabels$ }
             }
         })
     }    
@@ -155,25 +155,12 @@ Test = {
             type = "ImageDeserializer" ; module = "ImageReader"
             file="$DataDir$/val_map.txt"
             input = {
-                features = {
-                    transforms = (
-                        {
-                            type = "Crop"
-                            cropType = "center"
-                            sideRatio = 0.875
-                        }:{
-                            type = "Scale"
-                            width = 224
-                            height = 224
-                            channels = 3
-                        }:{
-                            type = "Transpose"
-                        }
-                    )
-                }
-                labels = {
-                    labelDim = 1000
-                }
+                features = { transforms = (
+                    { type = "Crop"; cropType = "center"; sideRatio = 0.875 } :
+                    { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
+                    { type = "Transpose" }
+                )}
+                labels = { labelDim = 1000}
             }
         })
     }        

From b896648497044311ba451201ce0f330a236365ec Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Fri, 27 Jan 2017 15:48:01 -0800
Subject: [PATCH 15/31] Adding preliminary version of VGG 16 and 19 in BS.

---
 .../Classification/VGG/BrainScript/VGG16_ImageNet.cntk      | 6 +++++-
 .../Classification/VGG/BrainScript/VGG19_ImageNet.cntk      | 1 -
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
index 2bc9b4212..2a429ef24 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -8,6 +8,11 @@ DataDir = "$RootDir$"
 OutputDir = "$RootDir$/Output"
 ModelDir = "$OutputDir$/Models"
 
+<<<<<<< HEAD
+=======
+ndlMacros="$ConfigDir$/Macros.ndl"
+
+>>>>>>> Adding preliminary version of VGG 16 and 19 in BS.
 precision = "float"
 deviceId = "Auto"
 
@@ -25,7 +30,6 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
-################################
 Train = {
     action = "train"
 
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
index 56a949940..4635c7837 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -25,7 +25,6 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
-################################
 Train = {
     action = "train"
 

From 0685829a2bac9bc895f8991d4c84860e91e06261 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Fri, 27 Jan 2017 16:44:26 -0800
Subject: [PATCH 16/31] Working version, but haven't verify accuracy.

---
 .../Classification/VGG/BrainScript/VGG16_ImageNet.cntk      | 6 +-----
 .../Classification/VGG/BrainScript/VGG19_ImageNet.cntk      | 1 +
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
index 2a429ef24..2bc9b4212 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -8,11 +8,6 @@ DataDir = "$RootDir$"
 OutputDir = "$RootDir$/Output"
 ModelDir = "$OutputDir$/Models"
 
-<<<<<<< HEAD
-=======
-ndlMacros="$ConfigDir$/Macros.ndl"
-
->>>>>>> Adding preliminary version of VGG 16 and 19 in BS.
 precision = "float"
 deviceId = "Auto"
 
@@ -30,6 +25,7 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
+################################
 Train = {
     action = "train"
 
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
index 4635c7837..56a949940 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -25,6 +25,7 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
+################################
 Train = {
     action = "train"
 

From 065480ed463e4e15c8c17332fa5aa1c4f2a9b681 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Tue, 31 Jan 2017 07:46:25 -0800
Subject: [PATCH 17/31] Revision of VGG model to train on Philly. Clean up the
 directory.

---
 .../Python/AlexNet_ImageNet_Distributed.py    |    15 +-
 .../VGG/BrainScript/VGG16_ImageNet.cntk       |    17 +-
 .../VGG/BrainScript/VGG19_ImageNet.cntk       |    17 +-
 .../Classification/VGG/CreateEvalModel.mel    |     7 -
 .../Classification/VGG/ImageNet1K_mean.xml    | 37643 ----------------
 Examples/Image/Classification/VGG/Macros.ndl  |    55 -
 .../VGG/Python/VGG16_ImageNet_Distributed.py  |   234 +
 Examples/Image/Classification/VGG/VGG_A.ndl   |    76 -
 .../VGG/VGG_A_ndl_deprecated.cntk             |   109 -
 Examples/Image/Classification/VGG/VGG_E.ndl   |    84 -
 .../Image/Classification/VGG/VGG_E_BN.ndl     |    85 -
 .../VGG/VGG_E_BN_ndl_deprecated.cntk          |   118 -
 .../VGG/VGG_E_ndl_deprecated.cntk             |   118 -
 13 files changed, 262 insertions(+), 38316 deletions(-)
 delete mode 100644 Examples/Image/Classification/VGG/CreateEvalModel.mel
 delete mode 100644 Examples/Image/Classification/VGG/ImageNet1K_mean.xml
 delete mode 100644 Examples/Image/Classification/VGG/Macros.ndl
 create mode 100644 Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
 delete mode 100644 Examples/Image/Classification/VGG/VGG_A.ndl
 delete mode 100644 Examples/Image/Classification/VGG/VGG_A_ndl_deprecated.cntk
 delete mode 100644 Examples/Image/Classification/VGG/VGG_E.ndl
 delete mode 100644 Examples/Image/Classification/VGG/VGG_E_BN.ndl
 delete mode 100644 Examples/Image/Classification/VGG/VGG_E_BN_ndl_deprecated.cntk
 delete mode 100644 Examples/Image/Classification/VGG/VGG_E_ndl_deprecated.cntk

diff --git a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
index b2439538b..75a80e2a5 100644
--- a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
@@ -17,7 +17,7 @@ from cntk.ops import *
 from cntk.distributed import data_parallel_distributed_learner, Communicator
 from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
 from cntk.blocks import Placeholder, Block
-from cntk.layers import Convolution, Activation, MaxPooling, Dense, Dropout, default_options
+from cntk.layers import Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options
 from cntk.models import Sequential
 from cntk.initializer import normal
 
@@ -32,6 +32,7 @@ image_height = 227
 image_width  = 227
 num_channels = 3  # RGB
 num_classes  = 1000
+model_name   = "AlexNet.model"
 
 # Create a minibatch source.
 def create_image_mb_source(map_file, is_training, total_number_of_samples):
@@ -95,21 +96,21 @@ def create_alexnet():
     with default_options(activation=None, pad=True, bias=True):
         z = Sequential([
             # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU) 
-            Convolution((11,11), 96, init=normal(0.01), pad=False, strides=(4,4), name='conv1'), 
+            Convolution2D((11,11), 96, init=normal(0.01), pad=False, strides=(4,4), name='conv1'), 
             Activation(activation=relu, name='relu1'), 
             LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm1'),
             MaxPooling((3,3), (2,2), name='pool1'),
             
-            Convolution((5,5), 192, init=normal(0.01), init_bias=0.1, name='conv2'), 
+            Convolution2D((5,5), 192, init=normal(0.01), init_bias=0.1, name='conv2'), 
             Activation(activation=relu, name='relu2'), 
             LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm2'),
             MaxPooling((3,3), (2,2), name='pool2'),
             
-            Convolution((3,3), 384, init=normal(0.01), name='conv3'), 
+            Convolution2D((3,3), 384, init=normal(0.01), name='conv3'), 
             Activation(activation=relu, name='relu3'), 
-            Convolution((3,3), 384, init=normal(0.01), init_bias=0.1, name='conv4'), 
+            Convolution2D((3,3), 384, init=normal(0.01), init_bias=0.1, name='conv4'), 
             Activation(activation=relu, name='relu4'), 
-            Convolution((3,3), 256, init=normal(0.01), init_bias=0.1, name='conv5'), 
+            Convolution2D((3,3), 256, init=normal(0.01), init_bias=0.1, name='conv5'), 
             Activation(activation=relu, name='relu5'), 
             MaxPooling((3,3), (2,2), name='pool5'), 
             
@@ -164,7 +165,7 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
     }
 
     training_session = cntk.training_session(train_source, trainer,
-        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, "AlexNet_"), epoch_size)
+        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
     training_session.train()
 
     # process minibatches and evaluate the model
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
index 2bc9b4212..4aceeea8c 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -25,6 +25,9 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
+parallelTrain = true
+hyperCompressMemory = true
+
 ################################
 Train = {
     action = "train"
@@ -80,11 +83,11 @@ Train = {
     
     SGD = {
         epochSize = 0
-        minibatchSize = 256
+        minibatchSize = 128
         # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
-        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001
+        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001*10:0.00001
         momentumPerMB = 0.9
-        maxEpochs = 74
+        maxEpochs = 80
         gradUpdateType = None
         L2RegWeight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
         dropoutRate = 0.5
@@ -99,7 +102,7 @@ Train = {
             }
         }
         
-        numMBsToShowResult = 100
+        numMBsToShowResult = 250
     }
     
     # Reader
@@ -110,7 +113,7 @@ Train = {
             file = "$DataDir$/train_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.875 ; jitterType = "uniRatio" } :
+                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.4375:0.875 ; jitterType = "uniRatio" } :  # [256, 512] jitter in scale 
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
@@ -126,7 +129,7 @@ Train = {
             file = "$DataDir$/val_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.875 } :
+                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.5833333 } :   # 384 crop to 224 
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
@@ -153,7 +156,7 @@ Test = {
             file="$DataDir$/val_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop"; cropType = "center"; sideRatio = 0.875 } :
+                    { type = "Crop"; cropType = "center"; sideRatio = 0.5833333 } :     # 384 crop to 224
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
index 56a949940..b345aef42 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -25,6 +25,9 @@ ImageW    = 224
 ImageC    = 3
 NumLabels = 1000
 
+parallelTrain = true
+hyperCompressMemory = true
+
 ################################
 Train = {
     action = "train"
@@ -83,11 +86,11 @@ Train = {
     
     SGD = {
         epochSize = 0
-        minibatchSize = 256
+        minibatchSize = 128
         # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
-        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001
+        learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001*10:0.00001
         momentumPerMB = 0.9
-        maxEpochs = 74
+        maxEpochs = 80
         gradUpdateType = None
         L2RegWeight = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
         dropoutRate = 0.5
@@ -102,7 +105,7 @@ Train = {
             }
         }
         
-        numMBsToShowResult = 100
+        numMBsToShowResult = 250
     }
     
     # Reader
@@ -113,7 +116,7 @@ Train = {
             file = "$DataDir$/train_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.875 ; jitterType = "uniRatio" } :
+                    { type = "Crop" ; cropType = "randomSide" ; sideRatio = 0.4375:0.875 ; jitterType = "uniRatio" } :  # [256, 512] jitter in scale 
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
@@ -129,7 +132,7 @@ Train = {
             file = "$DataDir$/val_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.875 } :
+                    { type = "Crop" ; cropType = "Center" ; sideRatio = 0.5833333 } :   # 384 crop to 224 
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
@@ -156,7 +159,7 @@ Test = {
             file="$DataDir$/val_map.txt"
             input = {
                 features = { transforms = (
-                    { type = "Crop"; cropType = "center"; sideRatio = 0.875 } :
+                    { type = "Crop"; cropType = "center"; sideRatio = 0.5833333 } :     # 384 crop to 224
                     { type = "Scale" ; width = $ImageW$ ; height = $ImageH$ ; channels = $ImageC$ ; interpolations = "linear" } :
                     { type = "Transpose" }
                 )}
diff --git a/Examples/Image/Classification/VGG/CreateEvalModel.mel b/Examples/Image/Classification/VGG/CreateEvalModel.mel
deleted file mode 100644
index 256730e23..000000000
--- a/Examples/Image/Classification/VGG/CreateEvalModel.mel
+++ /dev/null
@@ -1,7 +0,0 @@
-m1=LoadModel($CurModel$, format=cntk)
-SetDefaultModel(m1)
-
-# Add top-5 error prediction node.
-ErrTop5 = ClassificationError(labels, OutputNodes.z, Const(5), tag = "eval")
-
-SaveModel(m1, $NewModel$, format=cntk)
\ No newline at end of file
diff --git a/Examples/Image/Classification/VGG/ImageNet1K_mean.xml b/Examples/Image/Classification/VGG/ImageNet1K_mean.xml
deleted file mode 100644
index d0559fe23..000000000
--- a/Examples/Image/Classification/VGG/ImageNet1K_mean.xml
+++ /dev/null
@@ -1,37643 +0,0 @@
-<?xml version="1.0"?>
-<opencv_storage>
-<Channel>3</Channel>
-<Row>224</Row>
-<Col>224</Col>
-<MeanImg type_id="opencv-matrix">
-  <rows>1</rows>
-  <cols>150528</cols>
-  <dt>f</dt>
-  <data>
-    1.14421082e+002 1.23886177e+002 1.25102142e+002 1.14528633e+002
-    1.24029129e+002 1.25247543e+002 1.14600151e+002 1.24108231e+002
-    1.25336716e+002 1.14648720e+002 1.24191330e+002 1.25423950e+002
-    1.14682617e+002 1.24233604e+002 1.25500443e+002 1.14694283e+002
-    1.24279190e+002 1.25554878e+002 1.14731667e+002 1.24340919e+002
-    1.25643700e+002 1.14758606e+002 1.24405815e+002 1.25709961e+002
-    1.14816902e+002 1.24470901e+002 1.25805214e+002 1.14776230e+002
-    1.24462746e+002 1.25810654e+002 1.14790581e+002 1.24475845e+002
-    1.25868042e+002 1.14831467e+002 1.24545944e+002 1.25945343e+002
-    1.14857819e+002 1.24574120e+002 1.26008453e+002 1.14855255e+002
-    1.24607857e+002 1.26050598e+002 1.14903374e+002 1.24656021e+002
-    1.26115402e+002 1.14899757e+002 1.24695023e+002 1.26160515e+002
-    1.14957581e+002 1.24753906e+002 1.26274422e+002 1.14925034e+002
-    1.24765938e+002 1.26284309e+002 1.14946121e+002 1.24781296e+002
-    1.26331642e+002 1.14964256e+002 1.24815979e+002 1.26380722e+002
-    1.15015709e+002 1.24861694e+002 1.26462387e+002 1.15021767e+002
-    1.24893906e+002 1.26503578e+002 1.15043251e+002 1.24921928e+002
-    1.26555542e+002 1.15047134e+002 1.24949043e+002 1.26598473e+002
-    1.15108376e+002 1.25010010e+002 1.26689507e+002 1.15107399e+002
-    1.25025932e+002 1.26725960e+002 1.15116783e+002 1.25023125e+002
-    1.26767548e+002 1.15140007e+002 1.25069969e+002 1.26828133e+002
-    1.15157341e+002 1.25078773e+002 1.26881332e+002 1.15173828e+002
-    1.25121361e+002 1.26927711e+002 1.15191513e+002 1.25142616e+002
-    1.26980255e+002 1.15197319e+002 1.25186951e+002 1.27016609e+002
-    1.15247276e+002 1.25220169e+002 1.27117088e+002 1.15215782e+002
-    1.25234184e+002 1.27127335e+002 1.15241806e+002 1.25251144e+002
-    1.27173012e+002 1.15276436e+002 1.25304382e+002 1.27236809e+002
-    1.15316666e+002 1.25330147e+002 1.27308357e+002 1.15324089e+002
-    1.25363441e+002 1.27354347e+002 1.15330986e+002 1.25375389e+002
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-    1.00548782e+002 1.14023079e+002 1.20719757e+002 1.00596756e+002
-    1.14033722e+002 1.20726219e+002 1.00572113e+002 1.14040154e+002
-    1.20688828e+002 1.00603363e+002 1.14065468e+002 1.20698692e+002
-    1.00618820e+002 1.14091057e+002 1.20694122e+002 1.00642349e+002
-    1.14091652e+002 1.20691338e+002 1.00633263e+002 1.14101830e+002
-    1.20668358e+002 1.00638840e+002 1.14094551e+002 1.20643005e+002
-    1.00645348e+002 1.14111473e+002 1.20630981e+002 1.00668846e+002
-    1.14121277e+002 1.20630630e+002 1.00661743e+002 1.14120529e+002
-    1.20607239e+002 1.00700348e+002 1.14130768e+002 1.20600571e+002
-    1.00730980e+002 1.14174278e+002 1.20611710e+002 1.00749092e+002
-    1.14173401e+002 1.20597672e+002 1.00758713e+002 1.14196587e+002
-    1.20590393e+002 1.00769157e+002 1.14189987e+002 1.20574242e+002
-    1.00777702e+002 1.14215889e+002 1.20551125e+002 1.00825989e+002
-    1.14218674e+002 1.20546005e+002 1.00823921e+002 1.14248085e+002
-    1.20530449e+002 1.00848663e+002 1.14264206e+002 1.20536690e+002
-    1.00893364e+002 1.14320557e+002 1.20563156e+002 1.00943642e+002
-    1.14342522e+002 1.20576118e+002 1.00941833e+002 1.14357208e+002
-    1.20555771e+002 1.00944557e+002 1.14346001e+002 1.20525063e+002
-    1.00964958e+002 1.14373932e+002 1.20518791e+002 1.00983307e+002
-    1.14374512e+002 1.20503929e+002 1.00993034e+002 1.14402618e+002
-    1.20496902e+002 1.00999138e+002 1.14383286e+002 1.20464478e+002
-    1.01019974e+002 1.14412727e+002 1.20461823e+002 1.01061073e+002
-    1.14439560e+002 1.20468567e+002 1.01086983e+002 1.14483925e+002
-    1.20469780e+002 1.01128021e+002 1.14506813e+002 1.20483154e+002
-    1.01146492e+002 1.14541534e+002 1.20468666e+002 1.01214256e+002
-    1.14560776e+002 1.20483604e+002 1.01216026e+002 1.14584114e+002
-    1.20459290e+002 1.01240555e+002 1.14588036e+002 1.20452629e+002
-    1.01238113e+002 1.14597420e+002 1.20422005e+002 1.01246544e+002
-    1.14572487e+002 1.20387161e+002 1.01214958e+002 1.14544518e+002
-    1.20327484e+002 1.01254845e+002 1.14568672e+002 1.20319977e+002
-    1.01293129e+002 1.14607735e+002 1.20328003e+002 1.01308327e+002
-    1.14594193e+002 1.20299347e+002 1.01333870e+002 1.14622322e+002
-    1.20294449e+002 1.01384399e+002 1.14637131e+002 1.20283470e+002
-    1.01403511e+002 1.14668663e+002 1.20273804e+002 1.01427376e+002
-    1.14667328e+002 1.20257431e+002 1.01410439e+002 1.14665970e+002
-    1.20208176e+002 1.01434898e+002 1.14665184e+002 1.20193993e+002
-    1.01456245e+002 1.14697701e+002 1.20172256e+002 1.01500648e+002
-    1.14683968e+002 1.20138718e+002 1.01494019e+002 1.14698891e+002
-    1.20101997e+002 1.01515282e+002 1.14693008e+002 1.20083527e+002
-    1.01535484e+002 1.14718666e+002 1.20072426e+002 1.01567917e+002
-    1.14715683e+002 1.20056450e+002 1.01582764e+002 1.14735489e+002
-    1.20030876e+002 1.01608948e+002 1.14740341e+002 1.20007637e+002
-    1.01644653e+002 1.14771683e+002 1.20003387e+002 1.01660095e+002
-    1.14751404e+002 1.19962799e+002 1.01658463e+002 1.14747169e+002
-    1.19927658e+002 1.01700439e+002 1.14726875e+002 1.19893051e+002
-    1.01679253e+002 1.14697578e+002 1.19823166e+002 1.01696503e+002
-    1.14664757e+002 1.19776810e+002 1.01686310e+002 1.14649246e+002
-    1.19725540e+002 1.01679588e+002 1.14606056e+002 1.19670380e+002
-    1.01607971e+002 1.14524063e+002 1.19560692e+002</data></MeanImg>
-</opencv_storage>
diff --git a/Examples/Image/Classification/VGG/Macros.ndl b/Examples/Image/Classification/VGG/Macros.ndl
deleted file mode 100644
index 949bf29e5..000000000
--- a/Examples/Image/Classification/VGG/Macros.ndl
+++ /dev/null
@@ -1,55 +0,0 @@
-# Fully-connected layer with ReLU activation.
-DnnReLULayer(inDim, outDim, x, wScale, bValue)
-[
-    W = Parameter(outDim, inDim, init = Gaussian, initValueScale = wScale) 
-    b = Parameter(outDim, init = fixedValue, value = bValue) 
-    t = Times(W, x)
-    z = Plus(t, b)
-    y = RectifiedLinear(z)
-]
-
-# Fully-connected layer with batch normalization and ReLU activation.
-DnnBNReLULayer(inDim, outDim, x, wScale, bValue)
-[
-    W = Parameter(outDim, inDim, init = Gaussian, initValueScale = wScale) 
-    b = Parameter(outDim, 1, init = fixedValue, value = bValue) 
-    sc = Parameter(outDim, 1, init = Gaussian, initValueScale = 0.01)
-    m = Parameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
-    v = Parameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
-    t = Times(W, x)
-    bn = BatchNormalization(t, sc, b, m, v, spatial = false)
-    y = RectifiedLinear(bn)
-]
-
-# Fully-connected layer.
-DnnLayer(inDim, outDim, x, wScale, bValue)
-[
-    W = Parameter(outDim, inDim, init = Gaussian, initValueScale = wScale)
-    b = Parameter(outDim, init = fixedValue, value = bValue)
-    t = Times(W, x)
-    z = Plus(t, b)
-]
-
-# Convolutional layer with ReLU activation.
-ConvReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue)
-[
-    W = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
-    b = ImageParameter(1, 1, outMap, init = fixedValue, value = bValue, imageLayout = "cudnn")
-    c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = "cudnn")
-    z = Plus(c, b);
-    y = RectifiedLinear(z);
-]
-
-# Convolutional layer with batch normalization and ReLU activation.
-ConvBNReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scValue)
-[
-    W = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
-    b = Parameter(outMap, 1, init = fixedValue, value = bValue)
-    sc = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
-    m = Parameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
-    v = Parameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
-    
-    c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = "cudnn")
-    bn = BatchNormalization(c, sc, b, m, v, spatial = true, imageLayout = "cudnn")
-    y = RectifiedLinear(bn);
-]
diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
new file mode 100644
index 000000000..ecfba291e
--- /dev/null
+++ b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
@@ -0,0 +1,234 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+from __future__ import print_function
+import os
+import math
+import argparse
+import numpy as np
+import cntk
+import _cntk_py
+
+from cntk.utils import *
+from cntk.ops import *
+from cntk.distributed import data_parallel_distributed_learner, Communicator
+from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
+from cntk.blocks import Placeholder, Block
+from cntk.layers import Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options
+from cntk.models import Sequential, LayerStack
+from cntk.initializer import normal
+
+# default Paths relative to current python file.
+abs_path   = os.path.dirname(os.path.abspath(__file__))
+data_path  = os.path.join(abs_path, "..", "..", "..", "DataSets", "ImageNet")
+model_path = os.path.join(abs_path, "Models")
+log_dir = None
+
+# model dimensions
+image_height = 224
+image_width  = 224
+num_channels = 3  # RGB
+num_classes  = 1000
+model_name   = "VGG16.model"
+
+# Create a minibatch source.
+def create_image_mb_source(map_file, is_training, total_number_of_samples):
+    if not os.path.exists(map_file):
+        raise RuntimeError("File '%s' does not exist." %map_file)
+
+    # transformation pipeline for the features has jitter/crop only when training
+    transforms = []
+    if is_training:
+        transforms += [
+            ImageDeserializer.crop(crop_type='randomside', side_ratio=(0.4375,0.875), jitter_type='uniratio') # train uses jitter
+        ]
+    else: 
+        transforms += [
+            ImageDeserializer.crop(crop_type='center', side_ratio=0.5833333) # test has no jitter
+        ]
+
+    transforms += [
+        ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'),
+    ]
+
+    # deserializer
+    return MinibatchSource(
+        ImageDeserializer(map_file, StreamDefs(
+            features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
+            labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
+        epoch_size=total_number_of_samples,
+        multithreaded_deserializer = True)
+
+# Create the network.
+def create_vgg16():
+
+    # Input variables denoting the features and label data
+    feature_var = input_variable((num_channels, image_height, image_width))
+    label_var = input_variable((num_classes))
+
+    # apply model to input
+    # remove mean value 
+    input = minus(feature_var, constant([[[104]], [[117]], [[124]]]), name='mean_removed_input')
+    
+    with default_options(activation=None, pad=True, bias=True):
+        z = Sequential([
+            # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU) 
+            LayerStack(2, lambda i: [
+                Convolution2D((3,3), 64, name='conv1_{}'.format(i)), 
+                Activation(activation=relu, name='relu1_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool1'),
+
+            LayerStack(2, lambda i: [
+                Convolution2D((3,3), 128, name='conv2_{}'.format(i)), 
+                Activation(activation=relu, name='relu2_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool2'),
+
+            LayerStack(3, lambda i: [
+                Convolution2D((3,3), 256, name='conv3_{}'.format(i)), 
+                Activation(activation=relu, name='relu3_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool3'),
+
+            LayerStack(3, lambda i: [
+                Convolution2D((3,3), 512, name='conv4_{}'.format(i)), 
+                Activation(activation=relu, name='relu4_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool4'),
+
+            LayerStack(3, lambda i: [
+                Convolution2D((3,3), 512, name='conv5_{}'.format(i)), 
+                Activation(activation=relu, name='relu5_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool5'),
+
+            Dense(4096, name='fc6'), 
+            Activation(activation=relu, name='relu6'), 
+            Dropout(0.5, name='drop6'), 
+            Dense(4096, name='fc7'), 
+            Activation(activation=relu, name='relu7'), 
+            Dropout(0.5, name='drop7'),
+            Dense(num_classes, name='fc8')
+            ])(input)
+
+    # loss and metric
+    ce = cross_entropy_with_softmax(z, label_var)
+    pe = classification_error(z, label_var)
+
+    log_number_of_parameters(z) ; print()
+
+    return {
+        'feature': feature_var,
+        'label': label_var,
+        'ce' : ce,
+        'pe' : pe,
+        'output': z
+    }
+
+# Create trainer
+def create_trainer(network, epoch_size, num_quantization_bits):
+    # Set learning parameters
+    lr_per_mb         = [0.01]*20 + [0.001]*20 + [0.0001]*20 + [0.00001]*10 + [0.000001]
+    lr_schedule       = cntk.learning_rate_schedule(lr_per_mb, unit=cntk.learner.UnitType.minibatch, epoch_size=epoch_size)
+    mm_schedule       = cntk.learner.momentum_schedule(0.9)
+    l2_reg_weight     = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
+    
+    # Create learner
+    # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency 
+    parameter_learner = data_parallel_distributed_learner(
+        cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight),
+        num_quantization_bits=num_quantization_bits,
+        distributed_after=0)
+
+    # Create trainer
+    return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
+
+# Train and test
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size):
+
+    # define mapping from intput streams to network inputs
+    input_map = {
+        network['feature']: train_source.streams.features,
+        network['label']: train_source.streams.labels
+    }
+
+    training_session = cntk.training_session(train_source, trainer,
+        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
+    training_session.train()
+
+    # process minibatches and evaluate the model
+    metric_numer    = 0
+    metric_denom    = 0
+    minibatch_index = 0
+
+    while True:
+        data = test_source.next_minibatch(minibatch_size, input_map=input_map)
+        if not data: break
+        local_mb_samples=data[network['label']].num_samples
+        metric_numer += trainer.test_minibatch(data) * local_mb_samples
+        metric_denom += local_mb_samples
+        minibatch_index += 1
+
+    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
+    progress_printer.end_progress_print(fin_msg)
+
+    print("")
+    print(fin_msg)
+    print("")
+
+    return metric_numer/metric_denom
+
+
+# Train and evaluate the network.
+def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, 
+                           log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
+    _cntk_py.set_computation_network_trace_level(0)
+
+    progress_printer = ProgressPrinter(
+        freq=num_mbs_per_log,
+        tag='Training',
+        log_to_file=log_to_file,
+        rank=Communicator.rank(),
+        gen_heartbeat=gen_heartbeat,
+        num_epochs=max_epochs)
+
+    network = create_vgg16()
+    trainer = create_trainer(network, epoch_size, num_quantization_bits)
+    train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
+    test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size)
+ 
+
+if __name__=='__main__':
+    
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('-datadir', help='specify the location of your data');
+    parser.add_argument('-logdir', help='specify where the training log will be saved');
+    parser.add_argument('-outputdir',  help='specify where the output model/checkpoint files shall be saved');
+
+    args = vars(parser.parse_args())
+
+    if args['datadir'] != None:
+        data_path = args['datadir']
+
+    if args['logdir'] != None:
+        log_dir = args['logdir']
+
+    if args['outputdir'] != None:
+        model_path = args['outputdir'] + "/models"
+
+    train_data=os.path.join(data_path, 'train_map.txt')
+    test_data=os.path.join(data_path, 'val_map.txt')
+
+    vgg16_train_and_eval(train_data, test_data, 
+                         num_quantization_bits=32, 
+                         max_epochs=80, 
+                         log_to_file=log_dir, 
+                         num_mbs_per_log=500, 
+                         gen_heartbeat=True)
+    Communicator.finalize()
diff --git a/Examples/Image/Classification/VGG/VGG_A.ndl b/Examples/Image/Classification/VGG/VGG_A.ndl
deleted file mode 100644
index 6f5c5f450..000000000
--- a/Examples/Image/Classification/VGG/VGG_A.ndl
+++ /dev/null
@@ -1,76 +0,0 @@
-load=ndlMacros
-run=DNN
-
-ndlMacros = [
-    ImageW = 224
-    ImageH = 224
-    ImageC = 3
-    LabelDim = 1000
-
-    features = ImageInput(ImageW, ImageH, ImageC, tag = feature, imageLayout = "cudnn")
-    labels = Input(LabelDim, tag = label)
-    
-    # Kernels width and height.
-    kW = 3
-    kH = 3
-    # Kernel stride.
-    hs = 1
-    vs = 1
-    
-    # Pooling settings.
-    poolW = 2
-    poolH = 2
-    poolhs = 2
-    poolvs = 2
-    
-    # Initial parameter values.
-    convWScale = 0.01 #7
-    convBValue = 0
-    fc1WScale = 0.01 #8
-    fc1BValue = 0
-    fc2WScale = 0.01 #3.2
-    fc2BValue = 0
-    fc3WScale = 0.01 #3.2
-    fc3BValue = 0
-]
-
-DNN=[
-    cMap1 = 64
-    conv1 = ConvReLULayer(features, cMap1, 27, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool1 = MaxPooling(conv1, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap2 = 128
-    conv2 = ConvReLULayer(pool1, cMap2, 576, kW, kH, hs, vs, convWScale, convBValue)
-   
-    pool2 = MaxPooling(conv2, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap3 = 256
-    conv3 = ConvReLULayer(pool2, cMap3, 1152, kW, kH, hs, vs, convWScale, convBValue)
-    conv4 = ConvReLULayer(conv3, cMap3, 2304, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool3 = MaxPooling(conv4, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap5 = 512
-    conv5 = ConvReLULayer(pool3, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue)
-    conv6 = ConvReLULayer(conv5, cMap5, 4608, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool4 = MaxPooling(conv6, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap6 = 512
-    conv7 = ConvReLULayer(pool4, cMap6, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv8 = ConvReLULayer(conv7, cMap6, 4608, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool5 = MaxPooling(conv8, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    hiddenDim = 4096
-    h1 = DnnReLULayer(25088, hiddenDim, pool5, fc1WScale, fc1BValue)
-    h1_d = Dropout(h1)
-    h2 = DnnReLULayer(hiddenDim, hiddenDim, h1_d, fc2WScale, fc2BValue)
-    h2_d = Dropout(h2)
-    ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
-    
-    CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
-    Err = ClassificationError(labels, ol, tag = Eval)
-    OutputNodes = ol
-]
diff --git a/Examples/Image/Classification/VGG/VGG_A_ndl_deprecated.cntk b/Examples/Image/Classification/VGG/VGG_A_ndl_deprecated.cntk
deleted file mode 100644
index e2775ba16..000000000
--- a/Examples/Image/Classification/VGG/VGG_A_ndl_deprecated.cntk
+++ /dev/null
@@ -1,109 +0,0 @@
-# Note: This sample uses the deprecated NdlNetworkBuilder.
-#       An updated version using BrainScript is coming soon.
-#       Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
-#
-RootDir = "."
-
-ConfigDir = "$RootDir$"
-DataDir = "$RootDir$"
-OutputDir = "$RootDir$/Output"
-ModelDir = "$OutputDir$/Models"
-
-ndlMacros="$ConfigDir$/Macros.ndl"
-
-precision="float"
-deviceId="Auto"
-
-command=Train:AddTop5Eval:Test
-
-stderr="$OutputDir$/VGG_A"
-traceLevel=1
-numMBsToShowResult=500
-
-Train=[
-    action="train"
-    modelPath="$ModelDir$/VGG_A"
-    traceLevel=1
-
-     NDLNetworkBuilder=[
-        networkDescription="$ConfigDir$/VGG_A.ndl"
-    ]
-    
-    SGD=[
-        epochSize=0
-        minibatchSize=32
-        learningRatesPerMB=0.01*20:0.003*12:0.001*28:0.0003
-        momentumPerMB=0.9
-        maxEpochs=70
-        gradUpdateType="None"
-        L2RegWeight=0.0005
-        dropoutRate=0*5:0.5
-        
-        numMBsToShowResult=10
-    ]
-    
-    reader=[
-        readerType="ImageReader"
-        # Map file which maps images to labels using the following format:
-        # <full path to image><tab><numerical label (0-based class id)>
-        # Example:
-        # C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG<tab>0
-        file="$ConfigDir$/train_map.txt"
-        # Randomize images before every epoch. Possible values: None, Auto. Default: Auto.
-        randomize="Auto"
-        features=[
-            # Below are the required parameters.
-            width=224
-            height=224
-            channels=3
-            # Below are the optional parameters.
-            # Possible values: Center, Random. Default: Center
-            cropType="RandomSide"
-            # Horizontal random flip, will be enabled by default because cropType=RandomSide
-            #hflip="true"
-            # Crop scale side ratio. Examples: sideRatio=0.9, sideRatio=0.7:0.9.
-            sideRatio=0.875
-            # Crop scale ratio jitter type.
-            # Possible values: None, UniRatio. Default: None
-            jitterType="UniRatio"
-            # Interpolation to use when scaling image to width x height size.
-            # Possible values: nearest, linear, cubic, lanczos. Default: linear.
-            interpolations="Linear"
-            # Stores mean values for each pixel in OpenCV matrix XML format.
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]
-
-AddTop5Eval=[    
-    action="edit"
-    CurModel="$ModelDir$/VGG_A"
-    NewModel="$ModelDir$/VGG_A.Top5"
-    editPath="$ConfigDir$/CreateEvalModel.mel"
-]
-
-Test=[
-    action="test"
-    modelPath="$ModelDir$/VGG_A.Top5"
-    # Set minibatch size for testing.
-    minibatchSize=32
-
-    reader=[
-        readerType="ImageReader"
-        file="$ConfigDir$/val_map.txt"
-        randomize="None"
-        features=[
-            width=224
-            height=224
-            channels=3
-            cropType="Center"
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]
diff --git a/Examples/Image/Classification/VGG/VGG_E.ndl b/Examples/Image/Classification/VGG/VGG_E.ndl
deleted file mode 100644
index 70e05b52b..000000000
--- a/Examples/Image/Classification/VGG/VGG_E.ndl
+++ /dev/null
@@ -1,84 +0,0 @@
-load=ndlMacros
-run=DNN
-
-ndlMacros = [
-    ImageW = 224
-    ImageH = 224
-    ImageC = 3
-    LabelDim = 1000
-
-    features = ImageInput(ImageW, ImageH, ImageC, tag = feature, imageLayout = "cudnn")
-    labels = Input(LabelDim, tag = label)
-    
-    # Kernels width and height.
-    kW = 3
-    kH = 3
-    # Kernel stride.
-    hs = 1
-    vs = 1
-    
-    # Pooling settings.
-    poolW = 2
-    poolH = 2
-    poolhs = 2
-    poolvs = 2
-    
-    # Initial parameter values.
-    convWScale = 7.07
-    convBValue = 0
-    fc1WScale = 3.0
-    fc1BValue = 1
-    fc2WScale = 3.0
-    fc2BValue = 1
-    fc3WScale = 1.0
-    fc3BValue = 1
-]
-
-DNN=[
-    cMap1 = 64
-    conv1 = ConvReLULayer(features, cMap1, 27, kW, kH, hs, vs, convWScale, convBValue)
-    conv2 = ConvReLULayer(conv1, cMap1, 576, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool1 = MaxPooling(conv2, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap3 = 128
-    conv3 = ConvReLULayer(pool1, cMap3, 576, kW, kH, hs, vs, convWScale, convBValue)
-    conv4 = ConvReLULayer(conv3, cMap3, 1152, kW, kH, hs, vs, convWScale, convBValue)
-   
-    pool2 = MaxPooling(conv4, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap5 = 256
-    conv5 = ConvReLULayer(pool2, cMap5, 1152, kW, kH, hs, vs, convWScale, convBValue)
-    conv6 = ConvReLULayer(conv5, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue)
-    conv7 = ConvReLULayer(conv6, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue)
-    conv8 = ConvReLULayer(conv7, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool3 = MaxPooling(conv8, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap9 = 512
-    conv9 = ConvReLULayer(pool3, cMap9, 2304, kW, kH, hs, vs, convWScale, convBValue)
-    conv10 = ConvReLULayer(conv9, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv11 = ConvReLULayer(conv10, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv12 = ConvReLULayer(conv11, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool4 = MaxPooling(conv12, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap13 = 512
-    conv13 = ConvReLULayer(pool4, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv14 = ConvReLULayer(conv13, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv15 = ConvReLULayer(conv14, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue)
-    conv16 = ConvReLULayer(conv15, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue)
-
-    pool5 = MaxPooling(conv16, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    hiddenDim = 4096
-    h1 = DnnReLULayer(25088, hiddenDim, pool5, fc1WScale, fc1BValue)
-    h1_d = Dropout(h1)
-    h2 = DnnReLULayer(hiddenDim, hiddenDim, h1_d, fc2WScale, fc2BValue)
-    h2_d = Dropout(h2)
-    ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
-    
-    CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
-    Err = ClassificationError(labels, ol, tag = Eval)
-    OutputNodes = ol
-]
diff --git a/Examples/Image/Classification/VGG/VGG_E_BN.ndl b/Examples/Image/Classification/VGG/VGG_E_BN.ndl
deleted file mode 100644
index 11741fab4..000000000
--- a/Examples/Image/Classification/VGG/VGG_E_BN.ndl
+++ /dev/null
@@ -1,85 +0,0 @@
-load=ndlMacros
-run=DNN
-
-ndlMacros = [
-    ImageW = 224
-    ImageH = 224
-    ImageC = 3
-    LabelDim = 1000
-
-    features = ImageInput(ImageW, ImageH, ImageC, tag = feature, imageLayout = "cudnn")
-    labels = Input(LabelDim, tag = label)
-    
-    # Kernels width and height.
-    kW = 3
-    kH = 3
-    # Kernel stride.
-    hs = 1
-    vs = 1
-    
-    # Pooling settings.
-    poolW = 2
-    poolH = 2
-    poolhs = 2
-    poolvs = 2
-    
-    # Initial parameter values.
-    convWScale = 7.07
-    convBValue = 0
-    scValue = 0.03
-    fc1WScale = 3.0
-    fc1BValue = 1
-    fc2WScale = 3.0
-    fc2BValue = 1
-    fc3WScale = 1.0
-    fc3BValue = 1
-]
-
-DNN=[
-    cMap1 = 64
-    conv1 = ConvBNReLULayer(features, cMap1, 27, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv2 = ConvBNReLULayer(conv1, cMap1, 576, kW, kH, hs, vs, convWScale, convBValue, scValue)
-
-    pool1 = MaxPooling(conv2, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap3 = 128
-    conv3 = ConvBNReLULayer(pool1, cMap3, 576, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv4 = ConvBNReLULayer(conv3, cMap3, 1152, kW, kH, hs, vs, convWScale, convBValue, scValue)
-   
-    pool2 = MaxPooling(conv4, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap5 = 256
-    conv5 = ConvBNReLULayer(pool2, cMap5, 1152, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv6 = ConvBNReLULayer(conv5, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv7 = ConvBNReLULayer(conv6, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv8 = ConvBNReLULayer(conv7, cMap5, 2304, kW, kH, hs, vs, convWScale, convBValue, scValue)
-
-    pool3 = MaxPooling(conv8, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap9 = 512
-    conv9 = ConvBNReLULayer(pool3, cMap9, 2304, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv10 = ConvBNReLULayer(conv9, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv11 = ConvBNReLULayer(conv10, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv12 = ConvBNReLULayer(conv11, cMap9, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-
-    pool4 = MaxPooling(conv12, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    cMap13 = 512
-    conv13 = ConvBNReLULayer(pool4, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv14 = ConvBNReLULayer(conv13, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv15 = ConvBNReLULayer(conv14, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-    conv16 = ConvBNReLULayer(conv15, cMap13, 4608, kW, kH, hs, vs, convWScale, convBValue, scValue)
-
-    pool5 = MaxPooling(conv16, poolW, poolH, poolhs, poolvs, imageLayout = "cudnn")
-
-    hiddenDim = 4096
-    h1 = DnnBNReLULayer(25088, hiddenDim, pool5, fc1WScale, fc1BValue)
-    h1_d = Dropout(h1)
-    h2 = DnnBNReLULayer(hiddenDim, hiddenDim, h1_d, fc2WScale, fc2BValue)
-    h2_d = Dropout(h2)
-    ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
-    
-    CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
-    Err = ClassificationError(labels, ol, tag = Eval)
-    OutputNodes = ol
-]
diff --git a/Examples/Image/Classification/VGG/VGG_E_BN_ndl_deprecated.cntk b/Examples/Image/Classification/VGG/VGG_E_BN_ndl_deprecated.cntk
deleted file mode 100644
index 4f9a374d0..000000000
--- a/Examples/Image/Classification/VGG/VGG_E_BN_ndl_deprecated.cntk
+++ /dev/null
@@ -1,118 +0,0 @@
-# Note: This sample uses the deprecated NdlNetworkBuilder.
-#       An updated version using BrainScript is coming soon.
-#       Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
-#
-RootDir = "."
-
-ConfigDir = "$RootDir$"
-DataDir = "$RootDir$"
-OutputDir = "$RootDir$/Output"
-ModelDir = "$OutputDir$/Models"
-
-ndlMacros="$ConfigDir$/Macros.ndl"
-
-precision="float"
-deviceId="Auto"
-
-command=Train:AddTop5Eval:Test
-
-parallelTrain="false"
-
-stderr="$OutputDir$/VGG_E_BN"
-traceLevel=1
-
-Train=[
-    action="train"
-    modelPath="$ModelDir$/VGG_E_BN"
-
-     NDLNetworkBuilder=[
-        networkDescription="$ConfigDir$/VGG_E_BN.ndl"
-    ]
-    
-    SGD=[
-        epochSize=0
-        minibatchSize=16
-        learningRatesPerMB=0.01*20:0.003*12:0.001*28:0.0003
-        momentumPerMB=0.9
-        maxEpochs=70
-        gradUpdateType="None"
-        L2RegWeight=0.0005
-        dropoutRate=0*5:0.5
-        
-        ParallelTrain=[
-            parallelizationMethod="DataParallelSGD"
-            distributedMBReading="true"
-            parallelizationStartEpoch=1
-            DataParallelSGD=[
-                gradientBits=32
-            ]
-        ]
-        
-        numMBsToShowResult=10
-    ]
-    
-    reader=[
-        readerType="ImageReader"
-        # Map file which maps images to labels using the following format:
-        # <full path to image><tab><numerical label (0-based class id)>
-        # Example:
-        # C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG<tab>0
-        file="$DataDir$/train_map.txt"
-        # Randomize images before every epoch. Possible values: None, Auto. Default: Auto.
-        randomize="Auto"
-        features=[
-            # Below are the required parameters.
-            width=224
-            height=224
-            channels=3
-            # Below are the optional parameters.
-            # Possible values: Center, Random. Default: Center
-            cropType="RandomSide"
-            # Horizontal random flip, will be enabled because cropType=RandomSide
-            #hflip="true"
-            # Crop scale side ratio. Examples: sideRatio=0.9, sideRatio=0.7:0.9.
-            sideRatio=0.875
-            # Crop scale ratio jitter type.
-            # Possible values: None, UniRatio. Default: None
-            jitterType="UniRatio"
-            # Interpolation to use when scaling image to width x height size.
-            # Possible values: nearest, linear, cubic, lanczos. Default: linear.
-            interpolations="Linear"
-            # Stores mean values for each pixel in OpenCV matrix XML format.
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]
-
-AddTop5Eval=[    
-    action="edit"
-    CurModel="$ModelDir$/VGG_E_BN"
-    NewModel="$ModelDir$/VGG_E_BN.Top5"
-    editPath="$ConfigDir$/CreateEvalModel.mel"
-]
-
-Test=[
-    action="test"
-    modelPath=$ModelDir$/VGG_E_BN.Top5
-    # Set minibatch size for testing.
-    minibatchSize=16
-
-    reader=[
-        readerType="ImageReader"
-        file="$DataDir$/val_map.txt"
-        randomize="None"
-        features=[
-            width=224
-            height=224
-            channels=3
-            cropType="Center"
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]
diff --git a/Examples/Image/Classification/VGG/VGG_E_ndl_deprecated.cntk b/Examples/Image/Classification/VGG/VGG_E_ndl_deprecated.cntk
deleted file mode 100644
index 14ae0a9d2..000000000
--- a/Examples/Image/Classification/VGG/VGG_E_ndl_deprecated.cntk
+++ /dev/null
@@ -1,118 +0,0 @@
-# Note: This sample uses the deprecated NdlNetworkBuilder.
-#       An updated version using BrainScript is coming soon.
-#       Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
-#
-RootDir = "."
-
-ConfigDir = "$RootDir$"
-DataDir = "$RootDir$"
-OutputDir = "$RootDir$/Output"
-ModelDir = "$OutputDir$/Models"
-
-ndlMacros="$ConfigDir$/Macros.ndl"
-
-precision="float"
-deviceId="Auto"
-
-command=Train:AddTop5Eval:Test
-
-parallelTrain="false"
-
-stderr="$OutputDir$/VGG_E"
-traceLevel=1
-
-Train=[
-    action="train"
-    modelPath="$ModelDir$/VGG_E"
-
-     NDLNetworkBuilder=[
-        networkDescription="$ConfigDir$/VGG_E.ndl"
-    ]
-    
-    SGD=[
-        epochSize=0
-        minibatchSize=16
-        learningRatesPerMB=0.01*20:0.003*12:0.001*28:0.0003
-        momentumPerMB=0.9
-        maxEpochs=70
-        gradUpdateType="None"
-        L2RegWeight=0.0005
-        dropoutRate=0*5:0.5
-        
-        ParallelTrain=[
-            parallelizationMethod="DataParallelSGD"
-            distributedMBReading="true"
-            parallelizationStartEpoch=1
-            DataParallelSGD=[
-                gradientBits=32
-            ]
-        ]
-        
-        numMBsToShowResult=10
-    ]
-    
-    reader=[
-        readerType="ImageReader"
-        # Map file which maps images to labels using the following format:
-        # <full path to image><tab><numerical label (0-based class id)>
-        # Example:
-        # C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG<tab>0
-        file="$DataDir$/train_map.txt"
-        # Randomize images before every epoch. Possible values: None, Auto. Default: Auto.
-        randomize="Auto"
-        features=[
-            # Below are the required parameters.
-            width=224
-            height=224
-            channels=3
-            # Below are the optional parameters.
-            # Possible values: Center, Random. Default: Center
-            cropType="RandomSide"
-            # Horizontal random flip, will be enabled because cropType=RandomSide
-            #hflip="true"
-            # Crop scale side ratio. Examples: sideRatio=0.9, sideRatio=0.7:0.9.
-            sideRatio=0.875
-            # Crop scale ratio jitter type.
-            # Possible values: None, UniRatio. Default: None
-            jitterType="UniRatio"
-            # Interpolation to use when scaling image to width x height size.
-            # Possible values: nearest, linear, cubic, lanczos. Default: linear.
-            interpolations="Linear"
-            # Stores mean values for each pixel in OpenCV matrix XML format.
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]
-
-AddTop5Eval=[    
-    action="edit"
-    CurModel="$ModelDir$/VGG_E"
-    NewModel="$ModelDir$/VGG_E.Top5"
-    editPath="$ConfigDir$/CreateEvalModel.mel"
-]
-
-Test=[
-    action="test"
-    modelPath="$ModelDir$/VGG_E.Top5"
-    # Set minibatch size for testing.
-    minibatchSize=16
-
-    reader=[
-        readerType="ImageReader"
-        file="$DataDir$/val_map.txt"
-        randomize="None"
-        features=[
-            width=224
-            height=224
-            channels=3
-            cropType="Center"
-            meanFile="$ConfigDir$/ImageNet1K_mean.xml"
-        ]
-        labels=[
-            labelDim=1000
-        ]
-    ]    
-]

From a7f2f4d47831942965bd9540907ff227262b3989 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Tue, 31 Jan 2017 14:40:31 -0800
Subject: [PATCH 18/31] Add randomize to VGG distributed training.

---
 .../Classification/VGG/Python/VGG16_ImageNet_Distributed.py  | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
index ecfba291e..f425bbfdc 100644
--- a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
@@ -39,11 +39,13 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
     if not os.path.exists(map_file):
         raise RuntimeError("File '%s' does not exist." %map_file)
 
+    randomize = False
     # transformation pipeline for the features has jitter/crop only when training
     transforms = []
     if is_training:
+        randomize = True
         transforms += [
-            ImageDeserializer.crop(crop_type='randomside', side_ratio=(0.4375,0.875), jitter_type='uniratio') # train uses jitter
+            ImageDeserializer.crop(crop_type='randomside', side_ratio='0.4375:0.875', jitter_type='uniratio') # train uses jitter
         ]
     else: 
         transforms += [
@@ -59,6 +61,7 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
         ImageDeserializer(map_file, StreamDefs(
             features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
             labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
+        randomize = randomize, 
         epoch_size=total_number_of_samples,
         multithreaded_deserializer = True)
 

From 21a019a09d92deb76613b7fb89f7bf0c06f60d2a Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 1 Feb 2017 08:45:21 -0800
Subject: [PATCH 19/31] Add hyper memory compress.

---
 .../Classification/VGG/Python/VGG16_ImageNet_Distributed.py     | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
index f425bbfdc..8bf21667b 100644
--- a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
@@ -34,6 +34,8 @@ num_channels = 3  # RGB
 num_classes  = 1000
 model_name   = "VGG16.model"
 
+cntk.cntk_py.enable_hyper_memory_compress()
+
 # Create a minibatch source.
 def create_image_mb_source(map_file, is_training, total_number_of_samples):
     if not os.path.exists(map_file):

From 6cc9801cfd280614671b6e29bdc1fb1196d983f1 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 1 Feb 2017 08:48:22 -0800
Subject: [PATCH 20/31] Add VGG19 Python model.

---
 .../VGG/Python/VGG19_ImageNet_Distributed.py  | 239 ++++++++++++++++++
 1 file changed, 239 insertions(+)
 create mode 100644 Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py

diff --git a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
new file mode 100644
index 000000000..4f02459c0
--- /dev/null
+++ b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
@@ -0,0 +1,239 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+from __future__ import print_function
+import os
+import math
+import argparse
+import numpy as np
+import cntk
+import _cntk_py
+
+from cntk.utils import *
+from cntk.ops import *
+from cntk.distributed import data_parallel_distributed_learner, Communicator
+from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
+from cntk.blocks import Placeholder, Block
+from cntk.layers import Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options
+from cntk.models import Sequential, LayerStack
+from cntk.initializer import normal
+
+# default Paths relative to current python file.
+abs_path   = os.path.dirname(os.path.abspath(__file__))
+data_path  = os.path.join(abs_path, "..", "..", "..", "DataSets", "ImageNet")
+model_path = os.path.join(abs_path, "Models")
+log_dir = None
+
+# model dimensions
+image_height = 224
+image_width  = 224
+num_channels = 3  # RGB
+num_classes  = 1000
+model_name   = "VGG16.model"
+
+cntk.cntk_py.enable_hyper_memory_compress()
+
+# Create a minibatch source.
+def create_image_mb_source(map_file, is_training, total_number_of_samples):
+    if not os.path.exists(map_file):
+        raise RuntimeError("File '%s' does not exist." %map_file)
+
+    randomize = False
+    # transformation pipeline for the features has jitter/crop only when training
+    transforms = []
+    if is_training:
+        randomize = True
+        transforms += [
+            ImageDeserializer.crop(crop_type='randomside', side_ratio='0.4375:0.875', jitter_type='uniratio') # train uses jitter
+        ]
+    else: 
+        transforms += [
+            ImageDeserializer.crop(crop_type='center', side_ratio=0.5833333) # test has no jitter
+        ]
+
+    transforms += [
+        ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'),
+    ]
+
+    # deserializer
+    return MinibatchSource(
+        ImageDeserializer(map_file, StreamDefs(
+            features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
+            labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
+        randomize = randomize, 
+        epoch_size=total_number_of_samples,
+        multithreaded_deserializer = True)
+
+# Create the network.
+def create_vgg16():
+
+    # Input variables denoting the features and label data
+    feature_var = input_variable((num_channels, image_height, image_width))
+    label_var = input_variable((num_classes))
+
+    # apply model to input
+    # remove mean value 
+    input = minus(feature_var, constant([[[104]], [[117]], [[124]]]), name='mean_removed_input')
+    
+    with default_options(activation=None, pad=True, bias=True):
+        z = Sequential([
+            # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU) 
+            LayerStack(2, lambda i: [
+                Convolution2D((3,3), 64, name='conv1_{}'.format(i)), 
+                Activation(activation=relu, name='relu1_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool1'),
+
+            LayerStack(2, lambda i: [
+                Convolution2D((3,3), 128, name='conv2_{}'.format(i)), 
+                Activation(activation=relu, name='relu2_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool2'),
+
+            LayerStack(4, lambda i: [
+                Convolution2D((3,3), 256, name='conv3_{}'.format(i)), 
+                Activation(activation=relu, name='relu3_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool3'),
+
+            LayerStack(4, lambda i: [
+                Convolution2D((3,3), 512, name='conv4_{}'.format(i)), 
+                Activation(activation=relu, name='relu4_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool4'),
+
+            LayerStack(4, lambda i: [
+                Convolution2D((3,3), 512, name='conv5_{}'.format(i)), 
+                Activation(activation=relu, name='relu5_{}'.format(i)), 
+            ]),
+            MaxPooling((2,2), (2,2), name='pool5'),
+
+            Dense(4096, name='fc6'), 
+            Activation(activation=relu, name='relu6'), 
+            Dropout(0.5, name='drop6'), 
+            Dense(4096, name='fc7'), 
+            Activation(activation=relu, name='relu7'), 
+            Dropout(0.5, name='drop7'),
+            Dense(num_classes, name='fc8')
+            ])(input)
+
+    # loss and metric
+    ce = cross_entropy_with_softmax(z, label_var)
+    pe = classification_error(z, label_var)
+
+    log_number_of_parameters(z) ; print()
+
+    return {
+        'feature': feature_var,
+        'label': label_var,
+        'ce' : ce,
+        'pe' : pe,
+        'output': z
+    }
+
+# Create trainer
+def create_trainer(network, epoch_size, num_quantization_bits):
+    # Set learning parameters
+    lr_per_mb         = [0.01]*20 + [0.001]*20 + [0.0001]*20 + [0.00001]*10 + [0.000001]
+    lr_schedule       = cntk.learning_rate_schedule(lr_per_mb, unit=cntk.learner.UnitType.minibatch, epoch_size=epoch_size)
+    mm_schedule       = cntk.learner.momentum_schedule(0.9)
+    l2_reg_weight     = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
+    
+    # Create learner
+    # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency 
+    parameter_learner = data_parallel_distributed_learner(
+        cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight),
+        num_quantization_bits=num_quantization_bits,
+        distributed_after=0)
+
+    # Create trainer
+    return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
+
+# Train and test
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size):
+
+    # define mapping from intput streams to network inputs
+    input_map = {
+        network['feature']: train_source.streams.features,
+        network['label']: train_source.streams.labels
+    }
+
+    training_session = cntk.training_session(train_source, trainer,
+        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
+    training_session.train()
+
+    # process minibatches and evaluate the model
+    metric_numer    = 0
+    metric_denom    = 0
+    minibatch_index = 0
+
+    while True:
+        data = test_source.next_minibatch(minibatch_size, input_map=input_map)
+        if not data: break
+        local_mb_samples=data[network['label']].num_samples
+        metric_numer += trainer.test_minibatch(data) * local_mb_samples
+        metric_denom += local_mb_samples
+        minibatch_index += 1
+
+    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
+    progress_printer.end_progress_print(fin_msg)
+
+    print("")
+    print(fin_msg)
+    print("")
+
+    return metric_numer/metric_denom
+
+
+# Train and evaluate the network.
+def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, 
+                           log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
+    _cntk_py.set_computation_network_trace_level(0)
+
+    progress_printer = ProgressPrinter(
+        freq=num_mbs_per_log,
+        tag='Training',
+        log_to_file=log_to_file,
+        rank=Communicator.rank(),
+        gen_heartbeat=gen_heartbeat,
+        num_epochs=max_epochs)
+
+    network = create_vgg16()
+    trainer = create_trainer(network, epoch_size, num_quantization_bits)
+    train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
+    test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size)
+ 
+
+if __name__=='__main__':
+    
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('-datadir', help='specify the location of your data');
+    parser.add_argument('-logdir', help='specify where the training log will be saved');
+    parser.add_argument('-outputdir',  help='specify where the output model/checkpoint files shall be saved');
+
+    args = vars(parser.parse_args())
+
+    if args['datadir'] != None:
+        data_path = args['datadir']
+
+    if args['logdir'] != None:
+        log_dir = args['logdir']
+
+    if args['outputdir'] != None:
+        model_path = args['outputdir'] + "/models"
+
+    train_data=os.path.join(data_path, 'train_map.txt')
+    test_data=os.path.join(data_path, 'val_map.txt')
+
+    vgg16_train_and_eval(train_data, test_data, 
+                         num_quantization_bits=32, 
+                         max_epochs=80, 
+                         log_to_file=log_dir, 
+                         num_mbs_per_log=500, 
+                         gen_heartbeat=True)
+    Communicator.finalize()

From 74ec3ab2d36f2fb6f7ded44a2fe7486eda6d6d42 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 1 Feb 2017 08:50:30 -0800
Subject: [PATCH 21/31] Add test for VGG16.

---
 .../VGG16_ImageNet_Distributed_test.py        | 56 +++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py

diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
new file mode 100644
index 000000000..9e2550c57
--- /dev/null
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
@@ -0,0 +1,56 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+import numpy as np
+import os
+import sys
+from cntk.ops.tests.ops_test_utils import cntk_device
+from cntk.cntk_py import DeviceKind_GPU
+from cntk.device import set_default_device
+from cntk.io import ReaderConfig, ImageDeserializer
+from cntk import distributed
+import pytest
+
+abs_path = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(abs_path)
+sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "VGG", "Python"))
+from prepare_test_data import prepare_ImageNet_data
+from VGG16_ImageNet_Distributed import vgg16_train_and_eval
+
+#TOLERANCE_ABSOLUTE = 2E-1
+
+def test_vgg16_error(device_id):
+    if cntk_device(device_id).type() != DeviceKind_GPU:
+        pytest.skip('test only runs on GPU')
+    set_default_device(cntk_device(device_id))
+
+    base_path = prepare_ImageNet_data()
+    # change dir to locate data.zip correctly
+    os.chdir(base_path)
+
+    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
+    set_computation_network_trace_level(1)
+    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
+    #force_deterministic_algorithms()
+    # TODO: do the above; they lead to slightly different results, so not doing it for now
+
+    # for test purpose we train and test on same data 
+    train_data=os.path.join(base_path, 'val1024_map.txt')
+    test_data=os.path.join(base_path, 'val1024_map.txt')    
+    
+    test_error = vgg16_train_and_eval(train_data, test_data, 
+                                      num_quantization_bits=32, 
+                                      minibatch_size=2,
+                                      epoch_size=4, 
+                                      max_epochs=2)
+    distributed.Communicator.finalize()
+#    expected_test_error = 0.0
+
+# We are removing tolerance in error because running small epoch size has huge variance in accuracy. Will add
+# tolerance back once convolution operator is determinsitic. 
+
+#    assert np.allclose(test_error, expected_test_error,
+#                       atol=TOLERANCE_ABSOLUTE)

From e7ade66b5565946ce6a1cee8865f69d8197d66ba Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 1 Feb 2017 18:55:57 -0800
Subject: [PATCH 22/31] Add readme files and edit samples.json.

---
 .../Classification/VGG/BrainScript/README.md  | 19 +++++++++++++
 .../Image/Classification/VGG/Python/README.md | 27 +++++++++++++++++++
 Examples/Image/Classification/VGG/README.md   | 25 +++++++++++++++++
 Tools/samples.json                            |  2 +-
 4 files changed, 72 insertions(+), 1 deletion(-)
 create mode 100644 Examples/Image/Classification/VGG/BrainScript/README.md
 create mode 100644 Examples/Image/Classification/VGG/Python/README.md
 create mode 100644 Examples/Image/Classification/VGG/README.md

diff --git a/Examples/Image/Classification/VGG/BrainScript/README.md b/Examples/Image/Classification/VGG/BrainScript/README.md
new file mode 100644
index 000000000..efba060dd
--- /dev/null
+++ b/Examples/Image/Classification/VGG/BrainScript/README.md
@@ -0,0 +1,19 @@
+# CNTK Examples: Image/Classification/VGG
+
+## BrainScript
+
+### VGG16_ImageNet.cntk
+
+This is the VGG model that contains 16 layers, which was referred as `ConvNet configuration D` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
+
+Run the example from the current folder using:
+
+`cntk configFile=VGG16_ImageNet.cntk`
+
+### VGG19_ImageNet.cntk
+
+This is the VGG model that contains 19 layers, which was referred as `ConvNet configuration E` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
+
+Run the example from the current folder using:
+
+`cntk configFile=VGG19_ImageNet.cntk` 
diff --git a/Examples/Image/Classification/VGG/Python/README.md b/Examples/Image/Classification/VGG/Python/README.md
new file mode 100644
index 000000000..839500972
--- /dev/null
+++ b/Examples/Image/Classification/VGG/Python/README.md
@@ -0,0 +1,27 @@
+# CNTK Examples: Image/Classification/VGG
+
+## Python
+
+### VGG16_ImageNet_Distributed.py
+
+This is the VGG model that contains 16 layers, which was referred as `ConvNet configuration D` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
+
+Run the example from the current folder using:
+
+`python VGG16_ImageNet_Distributed.py`
+
+To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is:
+
+`mpiexec -n <#workers> python VGG16_ImageNet_Distributed.py`
+
+### VGG19_ImageNet_Distributed.py
+
+This is the VGG model that contains 19 layers, which was referred as `ConvNet configuration E` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
+
+Run the example from the current folder using:
+
+`python VGG19_ImageNet_Distributed.py`
+
+To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is:
+
+`mpiexec -n <#workers> python VGG19_ImageNet_Distributed.py` 
diff --git a/Examples/Image/Classification/VGG/README.md b/Examples/Image/Classification/VGG/README.md
new file mode 100644
index 000000000..040d58cea
--- /dev/null
+++ b/Examples/Image/Classification/VGG/README.md
@@ -0,0 +1,25 @@
+# CNTK Examples: Image/Classification/VGG
+
+## Overview
+
+|Data:     |The ILSVRC2012 dataset (http://www.image-net.org/challenges/LSVRC/2012/) for image classification.
+|:---------|:---
+|Purpose   |This folder contains examples that demonstrate how to use CNTK to define VGG network (https://arxiv.org/abs/1409.1556) for image classification.
+|Network   |VGG.
+|Training  |Stochastic gradient descent with momentum.
+|Comments  |See below.
+
+## Running the example
+
+### Getting the data
+We use the ILSVRC2012 datasets to demonstrate how to train the VGG model which was developed by the [Visual Geometry Group in University of Oxford](http://www.robots.ox.ac.uk/~vgg/research/very_deep/). It won the second place in the ILSVRC-2014 challenge. VGG has been a very popular model for its simple architect and high accuracy. 
+
+ILSVRC2012 datasets are not included in the CNTK distribution. You may obtain it through http://image-net.org.
+
+## Details
+
+We give examples for both Python and BrainScript.
+
+### [Python](./Python)
+
+### [BrainScript](./BrainScript)
diff --git a/Tools/samples.json b/Tools/samples.json
index 83276f97c..ce0022cf6 100644
--- a/Tools/samples.json
+++ b/Tools/samples.json
@@ -204,7 +204,7 @@
         "name":  "VGG",
         "url":  "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/VGG",
         "description":  "Deep CNN from University of Oxford. This was the winning model for the ILSVRC2014 localization task.",
-        "language":  ["BrainScript"],
+        "language":  ["Python", "BrainScript"],
         "type":  ["Recipe"]
     },
     {

From 39e7dd2e7fb60d485c857f5f624130939f5ffc2e Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Thu, 2 Feb 2017 07:40:55 -0800
Subject: [PATCH 23/31] Revision based on CR.

---
 .../Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk    | 2 +-
 .../Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk    | 2 +-
 .../Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk    | 2 +-
 Tools/samples.json                                              | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk b/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
index 51c57e41d..74b5ef03c 100644
--- a/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
+++ b/Examples/Image/Classification/AlexNet/BrainScript/AlexNet_ImageNet.cntk
@@ -86,7 +86,7 @@ Train = {
     SGD = {
         epochSize = 0
         minibatchSize = 256
-        # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
+        # CNTK weights new gradient by (1-momentum) for unit gain, thus we divide Caffe's learning rate by (1-momentum)
         learningRatesPerMB = 0.1*25:0.01*25:0.001*25:0.0001*25:0.00001
         momentumPerMB = 0.9
         maxEpochs = 112
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
index 4aceeea8c..23720be41 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG16_ImageNet.cntk
@@ -84,7 +84,7 @@ Train = {
     SGD = {
         epochSize = 0
         minibatchSize = 128
-        # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
+        # CNTK weights new gradient by (1-momentum) for unit gain, thus we divide Caffe's learning rate by (1-momentum)
         learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001*10:0.00001
         momentumPerMB = 0.9
         maxEpochs = 80
diff --git a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
index b345aef42..ac98b3d46 100644
--- a/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
+++ b/Examples/Image/Classification/VGG/BrainScript/VGG19_ImageNet.cntk
@@ -87,7 +87,7 @@ Train = {
     SGD = {
         epochSize = 0
         minibatchSize = 128
-        # CNTK weights new gradient by (1-momentum) for unit gain, thus we multiply Caffe's learning rate by (1-momentum)
+        # CNTK weights new gradient by (1-momentum) for unit gain, thus we divide Caffe's learning rate by (1-momentum)
         learningRatesPerMB = 0.1*20:0.01*20:0.001*20:0.0001*10:0.00001
         momentumPerMB = 0.9
         maxEpochs = 80
diff --git a/Tools/samples.json b/Tools/samples.json
index ce0022cf6..8361b35f3 100644
--- a/Tools/samples.json
+++ b/Tools/samples.json
@@ -76,7 +76,7 @@
         "name":  "Fast R-CNN",
         "url":  "https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN",
         "description":  "Train object detection from images by adapting pre-trained classification models on arbitrarily sized regions of interest using ROI pooling.",
-        "language":  ["BrainScript"],
+        "language":  ["Python", "BrainScript"],
         "type":  ["Tutorial", "Recipe"]
     },
     {

From f9052ea5f34eae143c0e24128cd17ec9b405caf8 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 8 Feb 2017 15:28:30 -0800
Subject: [PATCH 24/31] Revision due to changes in train_session. Also rewrote
 tests due to failure caused by single thread running of AlexNet test and VGG
 test. Bug fix in ReshapingNodes.h.

---
 .../Python/AlexNet_ImageNet_Distributed.py    | 99 ++++++++++---------
 .../ConvNet_CIFAR10_DataAug_Distributed.py    | 38 ++++---
 .../VGG/Python/VGG16_ImageNet_Distributed.py  | 98 +++++++++---------
 Source/ComputationNetworkLib/ReshapingNodes.h |  2 +
 .../AlexNet_ImageNet_Distributed_test.py      | 56 ++++-------
 ...onvNet_CIFAR10_DataAug_Distributed_test.py | 49 +++++----
 .../Examples/ConvNet_CIFAR10_DataAug_test.py  |  3 +-
 .../TrainResNet_CIFAR10_Distributed_test.py   |  9 +-
 .../VGG16_ImageNet_Distributed_test.py        | 56 ++++-------
 .../Examples/prepare_test_data.py             |  9 +-
 ...run_ConvNet_CIFAR10_DataAug_Distributed.py | 43 --------
 11 files changed, 194 insertions(+), 268 deletions(-)
 delete mode 100644 Tests/EndToEndTests/CNTKv2Python/Examples/run_ConvNet_CIFAR10_DataAug_Distributed.py

diff --git a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
index 75a80e2a5..04a33399c 100644
--- a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
@@ -59,6 +59,7 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
         ImageDeserializer(map_file, StreamDefs(
             features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
             labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
+        randomize = is_training, 
         epoch_size=total_number_of_samples,
         multithreaded_deserializer = True)
 
@@ -124,8 +125,9 @@ def create_alexnet():
             ])(input)
 
     # loss and metric
-    ce = cross_entropy_with_softmax(z, label_var)
-    pe = classification_error(z, label_var)
+    ce  = cross_entropy_with_softmax(z, label_var)
+    pe  = classification_error(z, label_var)
+    pe5 = classification_error(z, label_var, topN=5)
 
     log_number_of_parameters(z) ; print()
 
@@ -134,6 +136,7 @@ def create_alexnet():
         'label': label_var,
         'ce' : ce,
         'pe' : pe,
+        'pe5': pe5, 
         'output': z
     }
 
@@ -146,9 +149,10 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     l2_reg_weight     = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
     
     # Create learner
+    local_learner = cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight)
     # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency 
     parameter_learner = data_parallel_distributed_learner(
-        cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight),
+        local_learner,
         num_quantization_bits=num_quantization_bits,
         distributed_after=0)
 
@@ -156,7 +160,7 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
 
 # Train and test
-def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size):
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore):
 
     # define mapping from intput streams to network inputs
     input_map = {
@@ -164,36 +168,24 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         network['label']: train_source.streams.labels
     }
 
-    training_session = cntk.training_session(train_source, trainer,
-        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
+    training_session = cntk.training_session(
+        training_minibatch_source = train_source, 
+        trainer = trainer,
+        model_inputs_to_mb_source_mapping = input_map, 
+        mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
+        progress_printer = progress_printer, 
+        checkpoint_filename = os.path.join(model_path, model_name), 
+        progress_frequency = epoch_size, 
+        cv_source = test_source, 
+        cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+        restore = restore)
+
+    # Train all minibatches 
     training_session.train()
 
-    # process minibatches and evaluate the model
-    metric_numer    = 0
-    metric_denom    = 0
-    minibatch_index = 0
-
-    while True:
-        data = test_source.next_minibatch(minibatch_size, input_map=input_map)
-        if not data: break
-        local_mb_samples=data[network['label']].num_samples
-        metric_numer += trainer.test_minibatch(data) * local_mb_samples
-        metric_denom += local_mb_samples
-        minibatch_index += 1
-
-    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
-    progress_printer.end_progress_print(fin_msg)
-
-    print("")
-    print(fin_msg)
-    print("")
-
-    return metric_numer/metric_denom
-
-
 # Train and evaluate the network.
 def alexnet_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=256, epoch_size = 1281167, max_epochs=112, 
-                           log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
+                           restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=True):
     _cntk_py.set_computation_network_trace_level(0)
 
     progress_printer = ProgressPrinter(
@@ -208,35 +200,46 @@ def alexnet_train_and_eval(train_data, test_data, num_quantization_bits=32, mini
     trainer = create_trainer(network, epoch_size, num_quantization_bits)
     train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
     test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
-    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore)
  
 
 if __name__=='__main__':
     
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('-datadir', help='specify the location of your data');
-    parser.add_argument('-logdir', help='specify where the training log will be saved');
-    parser.add_argument('-outputdir',  help='specify where the output model/checkpoint files shall be saved');
+    parser.add_argument('-datadir', '--datadir', help='Data directory where the ImageNet dataset is located', required=False, default=data_path)
+    parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None)
+    parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None)
+    parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='112')
+    parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='256')
+    parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='1281167')
+    parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32')
+    parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true')
+    parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None)
 
     args = vars(parser.parse_args())
 
-    if args['datadir'] != None:
-        data_path = args['datadir']
-
-    if args['logdir'] != None:
-        log_dir = args['logdir']
-
-    if args['outputdir'] != None:
+    if args['outputdir'] is not None:
         model_path = args['outputdir'] + "/models"
+    if args['datadir'] is not None:
+        data_path = args['datadir']
+    if args['logdir'] is not None:
+        log_dir = args['logdir']
+    if args['device'] is not None:
+        cntk.device.set_default_device(cntk.device.gpu(args['device']))
 
     train_data=os.path.join(data_path, 'train_map.txt')
     test_data=os.path.join(data_path, 'val_map.txt')
 
-    alexnet_train_and_eval(train_data, test_data, 
-                           num_quantization_bits=32, 
-                           max_epochs=112, 
-                           log_to_file=log_dir, 
-                           num_mbs_per_log=500, 
-                           gen_heartbeat=True)
-    Communicator.finalize()
+    try:
+        alexnet_train_and_eval(train_data, test_data, 
+                               minibatch_size=args['minibatch_size'], 
+                               epoch_size=args['epoch_size'],
+                               num_quantization_bits=args['quantized_bits'],
+                               max_epochs=args['num_epochs'],
+                               restore=not args['restart'],
+                               log_to_file=args['logdir'],
+                               num_mbs_per_log=200,
+                               gen_heartbeat=True)
+    finally:
+        cntk.distributed.Communicator.finalize()    
diff --git a/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py b/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
index 197cd6095..6cb8abddf 100644
--- a/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
+++ b/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
@@ -45,6 +45,7 @@ def create_image_mb_source(map_file, mean_file, train, total_number_of_samples):
         cntk.io.ImageDeserializer(map_file, cntk.io.StreamDefs(
             features = cntk.io.StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
             labels   = cntk.io.StreamDef(field='label', shape=num_classes))),   # and second as 'label'
+        randomize=train, 
         epoch_size=total_number_of_samples,
         multithreaded_deserializer = True)
 
@@ -105,15 +106,15 @@ def create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_
                                               l2_regularization_weight=l2_reg_weight)
 
     if block_size != None:
-        learner = cntk.distributed.block_momentum_distributed_learner(local_learner, block_size=block_size)
+        parameter_learner = cntk.distributed.block_momentum_distributed_learner(local_learner, block_size=block_size)
     else:
-        learner = cntk.distributed.data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)
+        parameter_learner = cntk.distributed.data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)
 
     # Create trainer
-    return cntk.Trainer(network['output'], network['ce'], network['pe'], learner)
+    return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
 
 # Train and test
-def train_and_test(network, trainer, train_source, test_source, progress_printer, epoch_size):
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore):
 
     # define mapping from intput streams to network inputs
     input_map = {
@@ -125,20 +126,20 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         training_minibatch_source = train_source,
         trainer = trainer,
         model_inputs_to_mb_source_mapping = input_map, 
-        mb_size_schedule = cntk.minibatch_size_schedule(64),
+        mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
         progress_printer = progress_printer, 
         checkpoint_filename = os.path.join(model_path, "ConvNet_CIFAR10_DataAug"),
         progress_frequency=epoch_size,
         cv_source = test_source,
-        cv_mb_size_schedule=cntk.minibatch_size_schedule(16),
-        restore=False)
+        cv_mb_size_schedule=cntk.minibatch_size_schedule(minibatch_size),
+        restore=restore)
 
     # Train all minibatches 
     training_session.train()
 
 # Train and evaluate the network.
-def convnet_cifar10_dataaug(train_data, test_data, mean_data, epoch_size=50000, num_quantization_bits=32, 
-                            block_size=3200, warm_up=0, max_epochs=2, log_to_file=None, 
+def convnet_cifar10_dataaug(train_data, test_data, mean_data, minibatch_size=64, epoch_size=50000, num_quantization_bits=32, 
+                            block_size=3200, warm_up=0, max_epochs=2, restore=False, log_to_file=None, 
                             num_mbs_per_log=None, gen_heartbeat=False):
     _cntk_py.set_computation_network_trace_level(0)
 
@@ -154,7 +155,7 @@ def convnet_cifar10_dataaug(train_data, test_data, mean_data, epoch_size=50000,
     trainer = create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_up)
     train_source = create_image_mb_source(train_data, mean_data, train=True, total_number_of_samples=max_epochs * epoch_size)
     test_source = create_image_mb_source(test_data, mean_data, train=False, total_number_of_samples=cntk.io.FULL_DATA_SWEEP)
-    train_and_test(network, trainer, train_source, test_source, progress_printer, epoch_size)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore)
  
 
 if __name__=='__main__':
@@ -165,20 +166,25 @@ if __name__=='__main__':
     parser.add_argument('-datadir', '--datadir', help='Data directory where the CIFAR dataset is located', required=False, default=data_path)
     parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None)
     parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None)
-    parser.add_argument('-e', '--epochs', help='Total number of epochs to train', type=int, required=False, default='160')
+    parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='160')
+    parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='64')
+    parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='50000')
     parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32')
     parser.add_argument('-a', '--distributed_after', help='Number of samples to train with before running distributed', type=int, required=False, default='0')
     parser.add_argument('-b', '--block_samples', type=int, help="Number of samples per block for block momentum (BM) distributed learner (if 0 BM learner is not used)", required=False, default=None)
+    parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true')
     parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None)
 
     args = vars(parser.parse_args())
 
     if args['outputdir'] is not None:
         model_path = args['outputdir'] + "/models"
-    if args['device'] is not None:
-        cntk.device.set_default_device(cntk.device.gpu(args['device']))
     if args['datadir'] is not None:
         data_path = args['datadir']
+    if args['logdir'] is not None:
+        log_dir = args['logdir']
+    if args['device'] is not None:
+        cntk.device.set_default_device(cntk.device.gpu(args['device']))
 
     mean_data=os.path.join(data_path, 'CIFAR-10_mean.xml')
     train_data=os.path.join(data_path, 'train_map.txt')
@@ -186,11 +192,13 @@ if __name__=='__main__':
 
     try:
         convnet_cifar10_dataaug(train_data, test_data, mean_data, 
-                                epoch_size=50000,
+                                minibatch_size=args['minibatch_size'], 
+                                epoch_size=args['epoch_size'],
                                 num_quantization_bits=args['quantized_bits'],
                                 block_size=args['block_samples'],
                                 warm_up=args['distributed_after'],
-                                max_epochs=args['epochs'],
+                                max_epochs=args['num_epochs'],
+                                restore=not args['restart'],
                                 log_to_file=args['logdir'],
                                 num_mbs_per_log=10,
                                 gen_heartbeat=True)
diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
index 8bf21667b..894e4c214 100644
--- a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
@@ -41,11 +41,9 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
     if not os.path.exists(map_file):
         raise RuntimeError("File '%s' does not exist." %map_file)
 
-    randomize = False
     # transformation pipeline for the features has jitter/crop only when training
     transforms = []
     if is_training:
-        randomize = True
         transforms += [
             ImageDeserializer.crop(crop_type='randomside', side_ratio='0.4375:0.875', jitter_type='uniratio') # train uses jitter
         ]
@@ -63,7 +61,7 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
         ImageDeserializer(map_file, StreamDefs(
             features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
             labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
-        randomize = randomize, 
+        randomize = is_training, 
         epoch_size=total_number_of_samples,
         multithreaded_deserializer = True)
 
@@ -123,6 +121,7 @@ def create_vgg16():
     # loss and metric
     ce = cross_entropy_with_softmax(z, label_var)
     pe = classification_error(z, label_var)
+    pe5 = classification_error(z, label_var, topN=5)
 
     log_number_of_parameters(z) ; print()
 
@@ -131,6 +130,7 @@ def create_vgg16():
         'label': label_var,
         'ce' : ce,
         'pe' : pe,
+        'pe5': pe5, 
         'output': z
     }
 
@@ -143,9 +143,10 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     l2_reg_weight     = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
     
     # Create learner
+    local_learner = cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight)
     # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency 
     parameter_learner = data_parallel_distributed_learner(
-        cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight),
+        local_learner, 
         num_quantization_bits=num_quantization_bits,
         distributed_after=0)
 
@@ -153,7 +154,7 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
 
 # Train and test
-def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size):
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore):
 
     # define mapping from intput streams to network inputs
     input_map = {
@@ -161,36 +162,24 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         network['label']: train_source.streams.labels
     }
 
-    training_session = cntk.training_session(train_source, trainer,
-        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
+    training_session = cntk.training_session(
+        training_minibatch_source = train_source, 
+        trainer = trainer,
+        model_inputs_to_mb_source_mapping = input_map, 
+        mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
+        progress_printer = progress_printer, 
+        checkpoint_filename = os.path.join(model_path, model_name), 
+        progress_frequency = epoch_size, 
+        cv_source = test_source, 
+        cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+        restore = restore)
+
+    # Train all minibatches 
     training_session.train()
 
-    # process minibatches and evaluate the model
-    metric_numer    = 0
-    metric_denom    = 0
-    minibatch_index = 0
-
-    while True:
-        data = test_source.next_minibatch(minibatch_size, input_map=input_map)
-        if not data: break
-        local_mb_samples=data[network['label']].num_samples
-        metric_numer += trainer.test_minibatch(data) * local_mb_samples
-        metric_denom += local_mb_samples
-        minibatch_index += 1
-
-    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
-    progress_printer.end_progress_print(fin_msg)
-
-    print("")
-    print(fin_msg)
-    print("")
-
-    return metric_numer/metric_denom
-
-
 # Train and evaluate the network.
 def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, 
-                           log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
+                         restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
     _cntk_py.set_computation_network_trace_level(0)
 
     progress_printer = ProgressPrinter(
@@ -205,35 +194,46 @@ def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, miniba
     trainer = create_trainer(network, epoch_size, num_quantization_bits)
     train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
     test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
-    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore)
  
 
 if __name__=='__main__':
     
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('-datadir', help='specify the location of your data');
-    parser.add_argument('-logdir', help='specify where the training log will be saved');
-    parser.add_argument('-outputdir',  help='specify where the output model/checkpoint files shall be saved');
+    parser.add_argument('-datadir', '--datadir', help='Data directory where the ImageNet dataset is located', required=False, default=data_path)
+    parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None)
+    parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None)
+    parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='80')
+    parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='128')
+    parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='1281167')
+    parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32')
+    parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true')
+    parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None)
 
     args = vars(parser.parse_args())
 
-    if args['datadir'] != None:
-        data_path = args['datadir']
-
-    if args['logdir'] != None:
-        log_dir = args['logdir']
-
-    if args['outputdir'] != None:
+    if args['outputdir'] is not None:
         model_path = args['outputdir'] + "/models"
+    if args['datadir'] is not None:
+        data_path = args['datadir']
+    if args['logdir'] is not None:
+        log_dir = args['logdir']
+    if args['device'] is not None:
+        cntk.device.set_default_device(cntk.device.gpu(args['device']))
 
     train_data=os.path.join(data_path, 'train_map.txt')
     test_data=os.path.join(data_path, 'val_map.txt')
 
-    vgg16_train_and_eval(train_data, test_data, 
-                         num_quantization_bits=32, 
-                         max_epochs=80, 
-                         log_to_file=log_dir, 
-                         num_mbs_per_log=500, 
-                         gen_heartbeat=True)
-    Communicator.finalize()
+    try:
+        vgg16_train_and_eval(train_data, test_data, 
+                             minibatch_size=args['minibatch_size'], 
+                             epoch_size=args['epoch_size'],
+                             num_quantization_bits=args['quantized_bits'],
+                             max_epochs=args['num_epochs'],
+                             restore=not args['restart'],
+                             log_to_file=args['logdir'],
+                             num_mbs_per_log=200,
+                             gen_heartbeat=True)
+    finally:
+        cntk.distributed.Communicator.finalize()    
diff --git a/Source/ComputationNetworkLib/ReshapingNodes.h b/Source/ComputationNetworkLib/ReshapingNodes.h
index 19abc13d0..27da43de0 100644
--- a/Source/ComputationNetworkLib/ReshapingNodes.h
+++ b/Source/ComputationNetworkLib/ReshapingNodes.h
@@ -69,6 +69,8 @@ public:
         if (flags & CopyNodeFlags::copyNodeValue)
         {
             auto node = dynamic_pointer_cast<ReshapeNode<ElemType>>(nodeP);
+            node->m_beginDimParameter = m_beginDimParameter;
+            node->m_endDimParameter = m_endDimParameter;
             node->m_replacementSampleLayout = m_replacementSampleLayout;
         }
     }
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/AlexNet_ImageNet_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/AlexNet_ImageNet_Distributed_test.py
index 1dc931aef..fe39cca6f 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/AlexNet_ImageNet_Distributed_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/AlexNet_ImageNet_Distributed_test.py
@@ -7,50 +7,28 @@
 import numpy as np
 import os
 import sys
+import signal
+import subprocess
+import re
+import pytest
 from cntk.ops.tests.ops_test_utils import cntk_device
 from cntk.cntk_py import DeviceKind_GPU
 from cntk.device import set_default_device
-from cntk.io import ReaderConfig, ImageDeserializer
-from cntk import distributed
-import pytest
 
 abs_path = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(abs_path)
-sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "AlexNet", "Python"))
+example_dir = os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "AlexNet", "Python")
+sys.path.append(example_dir)
 from prepare_test_data import prepare_ImageNet_data
-from AlexNet_ImageNet_Distributed import alexnet_train_and_eval
+from ConvNet_CIFAR10_DataAug_Distributed_test import mpiexec_test
+script_under_test = os.path.join(example_dir, "AlexNet_ImageNet_Distributed.py")
 
-#TOLERANCE_ABSOLUTE = 2E-1
-
-def test_alexnet_error(device_id):
-    if cntk_device(device_id).type() != DeviceKind_GPU:
-        pytest.skip('test only runs on GPU')
-    set_default_device(cntk_device(device_id))
-
-    base_path = prepare_ImageNet_data()
-    # change dir to locate data.zip correctly
-    os.chdir(base_path)
-
-    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
-    set_computation_network_trace_level(1)
-    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
-    #force_deterministic_algorithms()
-    # TODO: do the above; they lead to slightly different results, so not doing it for now
-
-    # for test purpose we train and test on same data 
-    train_data=os.path.join(base_path, 'val1024_map.txt')
-    test_data=os.path.join(base_path, 'val1024_map.txt')    
-    
-    test_error = alexnet_train_and_eval(train_data, test_data, 
-                                        num_quantization_bits=32, 
-                                        minibatch_size=16,
-                                        epoch_size=64, 
-                                        max_epochs=2)
-    distributed.Communicator.finalize()
-#    expected_test_error = 0.0
-
-# We are removing tolerance in error because running small epoch size has huge variance in accuracy. Will add
-# tolerance back once convolution operator is determinsitic. 
-
-#    assert np.allclose(test_error, expected_test_error,
-#                       atol=TOLERANCE_ABSOLUTE)
+def test_alexnet_imagenet_distributed(device_id):
+    params = [ "-n", "2",
+               "-m", "8", 
+               "-e", "16",
+               "-datadir", prepare_ImageNet_data(),
+               "-q", "32",
+               "-r", 
+               "-device", "0" ]
+    mpiexec_test(device_id, script_under_test, params, 0.99, True)
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_Distributed_test.py
index 883cc26ef..9adeb46e4 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_Distributed_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_Distributed_test.py
@@ -16,28 +16,15 @@ from cntk.cntk_py import DeviceKind_GPU
 from cntk.device import set_default_device
 
 abs_path = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(abs_path)
 example_dir = os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "ConvNet", "Python")
-script_under_test = os.path.join(example_dir, "ConvNet_CIFAR10_DataAug_Distributed.py")
-
 sys.path.append(example_dir)
+from prepare_test_data import prepare_CIFAR10_data
+script_under_test = os.path.join(example_dir, "ConvNet_CIFAR10_DataAug_Distributed.py")
 
 TOLERANCE_ABSOLUTE = 2E-1
 TIMEOUT_SECONDS = 300
 
-def data_set_directory():
-    try:
-        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
-                                *"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
-        # N.B. CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY has {train,test}_map.txt
-        #      and CIFAR-10_mean.xml in the base_path.
-    except KeyError:
-        base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                                *"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))
-
-    base_path = os.path.normpath(base_path)
-    os.chdir(os.path.join(base_path, '..'))
-    return base_path
-
 def mpiexec_test(device_id, script, params, expected_test_error, match_exactly=True, per_minibatch_tolerance=TOLERANCE_ABSOLUTE, error_tolerance=TOLERANCE_ABSOLUTE):
     if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
@@ -56,6 +43,7 @@ def mpiexec_test(device_id, script, params, expected_test_error, match_exactly=T
     results = re.findall("Cross Validation \[.+?\]: Minibatch\[.+?\]: errs = (.+?)%", str_out)
 
     assert len(results) == 2
+    print(results)
 
     if match_exactly:
         assert results[0] == results[1]
@@ -65,23 +53,32 @@ def mpiexec_test(device_id, script, params, expected_test_error, match_exactly=T
     assert np.allclose(float(results[0])/100, expected_test_error, atol=error_tolerance)
 
 def test_cifar_convnet_distributed(device_id):
-    params = [ "-e", "2",
-               "-datadir", data_set_directory(),
+    params = [ "-n", "2",
+               "-m", "64", 
+               "-e", "3200",
+               "-datadir", prepare_CIFAR10_data(),
                "-q", "32",
+               "-r",
                "-device", "0" ]
-    mpiexec_test(device_id, script_under_test, params, 0.617)
+    mpiexec_test(device_id, script_under_test, params, 0.75, True)
 
 def test_cifar_convnet_distributed_1bitsgd(device_id):
-    params = [ "-e", "2",
-               "-datadir", data_set_directory(),
+    params = [ "-n", "2",
+               "-m", "64", 
+               "-e", "3200", 
+               "-datadir", prepare_CIFAR10_data(),
                "-q", "1",
+               "-r",
                "-device", "0" ]
-    mpiexec_test(device_id, script_under_test, params, 0.617)
+    mpiexec_test(device_id, script_under_test, params, 0.75, True)
 
 
 def test_cifar_convnet_distributed_block_momentum(device_id):
-    params = [ "-e", "2",
-               "-datadir", data_set_directory(),
-               "-b", "3200",
+    params = [ "-n", "2",
+               "-m", "64", 
+               "-e", "3200",
+               "-datadir", prepare_CIFAR10_data(),
+               "-b", "1600",
+               "-r",
                "-device", "0" ]
-    mpiexec_test(device_id, script_under_test, params, 0.6457, False, 10)
+    mpiexec_test(device_id, script_under_test, params, 0.78, False, 10)
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_test.py
index 8eea34d75..5110693c1 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/ConvNet_CIFAR10_DataAug_test.py
@@ -30,8 +30,7 @@ def test_cifar_convnet_error(device_id):
     # change dir to locate data.zip correctly
     os.chdir(base_path)
 
-    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
-    set_computation_network_trace_level(1)
+    from _cntk_py import set_fixed_random_seed, force_deterministic_algorithms
     set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
     #force_deterministic_algorithms()
     # TODO: do the above; they lead to slightly different results, so not doing it for now
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/TrainResNet_CIFAR10_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/TrainResNet_CIFAR10_Distributed_test.py
index 3b9ddc9c9..f06e33f23 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/TrainResNet_CIFAR10_Distributed_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/TrainResNet_CIFAR10_Distributed_test.py
@@ -15,13 +15,14 @@ example_dir = os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image"
 sys.path.append(example_dir)
 sys.path.append(abs_path)
 
-from ConvNet_CIFAR10_DataAug_Distributed_test import mpiexec_test, data_set_directory
+from prepare_test_data import prepare_CIFAR10_data
+from ConvNet_CIFAR10_DataAug_Distributed_test import mpiexec_test
 
 script_under_test = os.path.join(example_dir, "TrainResNet_CIFAR10_Distributed.py")
 
 def test_cifar_resnet_distributed(device_id):
     params = [ "-e", "2",
-               "-datadir", data_set_directory(),
+               "-datadir", prepare_CIFAR10_data(),
                "-q", "32",
                "-es", "512",
                "-device", "0" ]
@@ -29,7 +30,7 @@ def test_cifar_resnet_distributed(device_id):
 
 def test_cifar_resnet_distributed_1bitsgd(device_id):
     params = [ "-e", "2",
-               "-datadir", data_set_directory(),
+               "-datadir", prepare_CIFAR10_data(),
                "-q", "1",
                "-es", "512",
                "-device", "0" ]
@@ -38,7 +39,7 @@ def test_cifar_resnet_distributed_1bitsgd(device_id):
 
 def test_cifar_resnet_distributed_block_momentum(device_id):
     params = [ "-e", "2",
-               "-datadir", data_set_directory(),
+               "-datadir", prepare_CIFAR10_data(),
                "-b", "3200",
                "-es", "512",
                "-device", "0" ]
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
index 9e2550c57..2dded57b2 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
@@ -7,50 +7,28 @@
 import numpy as np
 import os
 import sys
+import signal
+import subprocess
+import re
+import pytest
 from cntk.ops.tests.ops_test_utils import cntk_device
 from cntk.cntk_py import DeviceKind_GPU
 from cntk.device import set_default_device
-from cntk.io import ReaderConfig, ImageDeserializer
-from cntk import distributed
-import pytest
 
 abs_path = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(abs_path)
-sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "VGG", "Python"))
+example_dir = os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "VGG", "Python")
+sys.path.append(example_dir)
 from prepare_test_data import prepare_ImageNet_data
-from VGG16_ImageNet_Distributed import vgg16_train_and_eval
+from ConvNet_CIFAR10_DataAug_Distributed_test import mpiexec_test
+script_under_test = os.path.join(example_dir, "VGG16_ImageNet_Distributed.py")
 
-#TOLERANCE_ABSOLUTE = 2E-1
-
-def test_vgg16_error(device_id):
-    if cntk_device(device_id).type() != DeviceKind_GPU:
-        pytest.skip('test only runs on GPU')
-    set_default_device(cntk_device(device_id))
-
-    base_path = prepare_ImageNet_data()
-    # change dir to locate data.zip correctly
-    os.chdir(base_path)
-
-    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
-    set_computation_network_trace_level(1)
-    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
-    #force_deterministic_algorithms()
-    # TODO: do the above; they lead to slightly different results, so not doing it for now
-
-    # for test purpose we train and test on same data 
-    train_data=os.path.join(base_path, 'val1024_map.txt')
-    test_data=os.path.join(base_path, 'val1024_map.txt')    
-    
-    test_error = vgg16_train_and_eval(train_data, test_data, 
-                                      num_quantization_bits=32, 
-                                      minibatch_size=2,
-                                      epoch_size=4, 
-                                      max_epochs=2)
-    distributed.Communicator.finalize()
-#    expected_test_error = 0.0
-
-# We are removing tolerance in error because running small epoch size has huge variance in accuracy. Will add
-# tolerance back once convolution operator is determinsitic. 
-
-#    assert np.allclose(test_error, expected_test_error,
-#                       atol=TOLERANCE_ABSOLUTE)
+def test_alexnet_imagenet_distributed(device_id):
+    params = [ "-n", "2",
+               "-m", "1", 
+               "-e", "2",
+               "-datadir", prepare_ImageNet_data(),
+               "-q", "32",
+               "-r", 
+               "-device", "0" ]
+    mpiexec_test(device_id, script_under_test, params, 0.99, True)
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/prepare_test_data.py b/Tests/EndToEndTests/CNTKv2Python/Examples/prepare_test_data.py
index 6df54a26e..ee18e5652 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/prepare_test_data.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/prepare_test_data.py
@@ -29,16 +29,19 @@ def prepare_CIFAR10_data():
     
 def prepare_ImageNet_data(): 
     base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                             *"../../../../Examples/Image/DataSets/ImageNet".split("/"))
+                             *"../../../../Examples/Image/DataSets/ImageNet/test_data".split("/"))
     base_path = os.path.normpath(base_path)
+    if not os.path.isdir(base_path): 
+        os.mkdir(base_path)
     
     # If val1024_map.txt don't exist locally, copy to local location
-    if not os.path.isfile(os.path.join(base_path, 'val1024_map.txt')): 
+    if not (os.path.isfile(os.path.join(base_path, 'train_map.txt')) and os.path.isfile(os.path.join(base_path, 'val_map.txt'))):
         # copy from backup location 
         base_path_bak = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                      *"Image/ImageNet/2012/v0".split("/"))
         base_path_bak = os.path.normpath(base_path_bak)
         
-        copyfile(os.path.join(base_path_bak, 'val1024_map.txt'), os.path.join(base_path, 'val1024_map.txt'))
+        copyfile(os.path.join(base_path_bak, 'val1024_map.txt'), os.path.join(base_path, 'train_map.txt'))
+        copyfile(os.path.join(base_path_bak, 'val1024_map.txt'), os.path.join(base_path, 'val_map.txt'))
         copyfile(os.path.join(base_path_bak, 'val1024.zip'), os.path.join(base_path, 'val1024.zip'))
     return base_path
\ No newline at end of file
diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/run_ConvNet_CIFAR10_DataAug_Distributed.py b/Tests/EndToEndTests/CNTKv2Python/Examples/run_ConvNet_CIFAR10_DataAug_Distributed.py
deleted file mode 100644
index 6184aecd2..000000000
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/run_ConvNet_CIFAR10_DataAug_Distributed.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-# Licensed under the MIT license. See LICENSE.md file in the project root
-# for full license information.
-# ==============================================================================
-
-import numpy as np
-import os
-import sys
-import platform
-from cntk.io import ReaderConfig, ImageDeserializer, FULL_DATA_SWEEP
-from cntk import distributed
-from cntk.device import set_default_device, gpu
-
-abs_path = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(abs_path)
-sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Image", "Classification", "ConvNet", "Python"))
-from prepare_test_data import prepare_CIFAR10_data
-from ConvNet_CIFAR10_DataAug_Distributed import convnet_cifar10_dataaug
-
-def run_cifar_convnet_distributed():
-    base_path = prepare_CIFAR10_data()
-    # change dir to locate data.zip correctly
-    os.chdir(base_path)
-
-    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
-    set_computation_network_trace_level(1) 
-    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
-    #force_deterministic_algorithms()
-    # TODO: do the above; they lead to slightly different results, so not doing it for now
-
-    train_data = os.path.join(base_path, 'train_map.txt')
-    mean_data = os.path.join(base_path, 'CIFAR-10_mean.xml')
-    test_data = os.path.join(base_path, 'test_map.txt')
-
-    num_quantization_bits = 32
-    return convnet_cifar10_dataaug(train_data, test_data, mean_data, num_quantization_bits, epoch_size=512, max_epochs=2)
-
-if __name__=='__main__':
-    assert distributed.Communicator.rank() < distributed.Communicator.num_workers()
-    set_default_device(gpu(0)) # force using GPU-0 in test for speed
-    run_cifar_convnet_distributed()
-    distributed.Communicator.finalize()

From 2fff01f57cc8eaca88efa5d1ea6fe05553ec0fb3 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 8 Feb 2017 15:41:51 -0800
Subject: [PATCH 25/31] Update VGG19.

---
 .../VGG/Python/VGG19_ImageNet_Distributed.py  | 106 +++++++++---------
 1 file changed, 53 insertions(+), 53 deletions(-)

diff --git a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
index 4f02459c0..327a291a6 100644
--- a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
@@ -32,7 +32,7 @@ image_height = 224
 image_width  = 224
 num_channels = 3  # RGB
 num_classes  = 1000
-model_name   = "VGG16.model"
+model_name   = "VGG19.model"
 
 cntk.cntk_py.enable_hyper_memory_compress()
 
@@ -41,11 +41,9 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
     if not os.path.exists(map_file):
         raise RuntimeError("File '%s' does not exist." %map_file)
 
-    randomize = False
     # transformation pipeline for the features has jitter/crop only when training
     transforms = []
     if is_training:
-        randomize = True
         transforms += [
             ImageDeserializer.crop(crop_type='randomside', side_ratio='0.4375:0.875', jitter_type='uniratio') # train uses jitter
         ]
@@ -63,12 +61,12 @@ def create_image_mb_source(map_file, is_training, total_number_of_samples):
         ImageDeserializer(map_file, StreamDefs(
             features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
             labels   = StreamDef(field='label', shape=num_classes))),   # and second as 'label'
-        randomize = randomize, 
+        randomize = is_training, 
         epoch_size=total_number_of_samples,
         multithreaded_deserializer = True)
 
 # Create the network.
-def create_vgg16():
+def create_vgg19():
 
     # Input variables denoting the features and label data
     feature_var = input_variable((num_channels, image_height, image_width))
@@ -123,6 +121,7 @@ def create_vgg16():
     # loss and metric
     ce = cross_entropy_with_softmax(z, label_var)
     pe = classification_error(z, label_var)
+    pe5 = classification_error(z, label_var, topN=5)
 
     log_number_of_parameters(z) ; print()
 
@@ -131,6 +130,7 @@ def create_vgg16():
         'label': label_var,
         'ce' : ce,
         'pe' : pe,
+        'pe5': pe5, 
         'output': z
     }
 
@@ -143,9 +143,10 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     l2_reg_weight     = 0.0005 # CNTK L2 regularization is per sample, thus same as Caffe
     
     # Create learner
+    local_learner = cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight)
     # Since we reuse parameter settings (learning rate, momentum) from Caffe, we set unit_gain to False to ensure consistency 
     parameter_learner = data_parallel_distributed_learner(
-        cntk.learner.momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule, unit_gain=False, l2_regularization_weight=l2_reg_weight),
+        local_learner, 
         num_quantization_bits=num_quantization_bits,
         distributed_after=0)
 
@@ -153,7 +154,7 @@ def create_trainer(network, epoch_size, num_quantization_bits):
     return cntk.Trainer(network['output'], network['ce'], network['pe'], parameter_learner)
 
 # Train and test
-def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size):
+def train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore):
 
     # define mapping from intput streams to network inputs
     input_map = {
@@ -161,36 +162,24 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         network['label']: train_source.streams.labels
     }
 
-    training_session = cntk.training_session(train_source, trainer,
-        cntk.minibatch_size_schedule(minibatch_size), progress_printer, input_map, os.path.join(model_path, model_name), epoch_size)
+    training_session = cntk.training_session(
+        training_minibatch_source = train_source, 
+        trainer = trainer,
+        model_inputs_to_mb_source_mapping = input_map, 
+        mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
+        progress_printer = progress_printer, 
+        checkpoint_filename = os.path.join(model_path, model_name), 
+        progress_frequency = epoch_size, 
+        cv_source = test_source, 
+        cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+        restore = restore)
+
+    # Train all minibatches 
     training_session.train()
 
-    # process minibatches and evaluate the model
-    metric_numer    = 0
-    metric_denom    = 0
-    minibatch_index = 0
-
-    while True:
-        data = test_source.next_minibatch(minibatch_size, input_map=input_map)
-        if not data: break
-        local_mb_samples=data[network['label']].num_samples
-        metric_numer += trainer.test_minibatch(data) * local_mb_samples
-        metric_denom += local_mb_samples
-        minibatch_index += 1
-
-    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
-    progress_printer.end_progress_print(fin_msg)
-
-    print("")
-    print(fin_msg)
-    print("")
-
-    return metric_numer/metric_denom
-
-
 # Train and evaluate the network.
-def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, 
-                           log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
+def vgg19_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80, 
+                         restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
     _cntk_py.set_computation_network_trace_level(0)
 
     progress_printer = ProgressPrinter(
@@ -201,39 +190,50 @@ def vgg16_train_and_eval(train_data, test_data, num_quantization_bits=32, miniba
         gen_heartbeat=gen_heartbeat,
         num_epochs=max_epochs)
 
-    network = create_vgg16()
+    network = create_vgg19()
     trainer = create_trainer(network, epoch_size, num_quantization_bits)
     train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
     test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
-    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size)
+    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, restore)
  
 
 if __name__=='__main__':
     
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('-datadir', help='specify the location of your data');
-    parser.add_argument('-logdir', help='specify where the training log will be saved');
-    parser.add_argument('-outputdir',  help='specify where the output model/checkpoint files shall be saved');
+    parser.add_argument('-datadir', '--datadir', help='Data directory where the ImageNet dataset is located', required=False, default=data_path)
+    parser.add_argument('-outputdir', '--outputdir', help='Output directory for checkpoints and models', required=False, default=None)
+    parser.add_argument('-logdir', '--logdir', help='Log file', required=False, default=None)
+    parser.add_argument('-n', '--num_epochs', help='Total number of epochs to train', type=int, required=False, default='80')
+    parser.add_argument('-m', '--minibatch_size', help='Minibatch size', type=int, required=False, default='128')
+    parser.add_argument('-e', '--epoch_size', help='Epoch size', type=int, required=False, default='1281167')
+    parser.add_argument('-q', '--quantized_bits', help='Number of quantized bits used for gradient aggregation', type=int, required=False, default='32')
+    parser.add_argument('-r', '--restart', help='Indicating whether to restart from scratch (instead of restart from checkpoint file by default)', action='store_true')
+    parser.add_argument('-device', '--device', type=int, help="Force to run the script on a specified device", required=False, default=None)
 
     args = vars(parser.parse_args())
 
-    if args['datadir'] != None:
-        data_path = args['datadir']
-
-    if args['logdir'] != None:
-        log_dir = args['logdir']
-
-    if args['outputdir'] != None:
+    if args['outputdir'] is not None:
         model_path = args['outputdir'] + "/models"
+    if args['datadir'] is not None:
+        data_path = args['datadir']
+    if args['logdir'] is not None:
+        log_dir = args['logdir']
+    if args['device'] is not None:
+        cntk.device.set_default_device(cntk.device.gpu(args['device']))
 
     train_data=os.path.join(data_path, 'train_map.txt')
     test_data=os.path.join(data_path, 'val_map.txt')
 
-    vgg16_train_and_eval(train_data, test_data, 
-                         num_quantization_bits=32, 
-                         max_epochs=80, 
-                         log_to_file=log_dir, 
-                         num_mbs_per_log=500, 
-                         gen_heartbeat=True)
-    Communicator.finalize()
+    try:
+        vgg19_train_and_eval(train_data, test_data, 
+                             minibatch_size=args['minibatch_size'], 
+                             epoch_size=args['epoch_size'],
+                             num_quantization_bits=args['quantized_bits'],
+                             max_epochs=args['num_epochs'],
+                             restore=not args['restart'],
+                             log_to_file=args['logdir'],
+                             num_mbs_per_log=200,
+                             gen_heartbeat=True)
+    finally:
+        cntk.distributed.Communicator.finalize()    

From 6829622172423268c618713dc7074f89b7f3fff7 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 8 Feb 2017 17:05:47 -0800
Subject: [PATCH 26/31] Disable test for VGG16. Jenkins ran out of memory. Will
 enable it once we finish memory optimization.

---
 .../VGG16_ImageNet_Distributed_test.py         | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
index 2dded57b2..4c7406eb9 100644
--- a/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
+++ b/Tests/EndToEndTests/CNTKv2Python/Examples/VGG16_ImageNet_Distributed_test.py
@@ -23,12 +23,12 @@ from prepare_test_data import prepare_ImageNet_data
 from ConvNet_CIFAR10_DataAug_Distributed_test import mpiexec_test
 script_under_test = os.path.join(example_dir, "VGG16_ImageNet_Distributed.py")
 
-def test_alexnet_imagenet_distributed(device_id):
-    params = [ "-n", "2",
-               "-m", "1", 
-               "-e", "2",
-               "-datadir", prepare_ImageNet_data(),
-               "-q", "32",
-               "-r", 
-               "-device", "0" ]
-    mpiexec_test(device_id, script_under_test, params, 0.99, True)
+# def test_alexnet_imagenet_distributed(device_id):
+    # params = [ "-n", "2",
+               # "-m", "1", 
+               # "-e", "2",
+               # "-datadir", prepare_ImageNet_data(),
+               # "-q", "32",
+               # "-r", 
+               # "-device", "0" ]
+    # mpiexec_test(device_id, script_under_test, params, 0.99, True)

From cd972fc42d340a1f9b0271557dc1c764954ec9c2 Mon Sep 17 00:00:00 2001
From: Cha Zhang <chazhang@microsoft.com>
Date: Wed, 8 Feb 2017 21:26:01 -0800
Subject: [PATCH 27/31] Added a few comments to avoid confusion of using
 checkpoint and cross validation.

---
 .../AlexNet/Python/AlexNet_ImageNet_Distributed.py         | 3 +++
 .../ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py  | 7 +++++--
 .../VGG/Python/VGG16_ImageNet_Distributed.py               | 3 +++
 .../VGG/Python/VGG19_ImageNet_Distributed.py               | 3 +++
 4 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
index 04a33399c..f2906b7ef 100644
--- a/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py
@@ -174,10 +174,13 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         model_inputs_to_mb_source_mapping = input_map, 
         mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
         progress_printer = progress_printer, 
+#        checkpoint_frequency = epoch_size,
         checkpoint_filename = os.path.join(model_path, model_name), 
+#        save_all_checkpoints = True,
         progress_frequency = epoch_size, 
         cv_source = test_source, 
         cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+#        cv_frequency = epoch_size,
         restore = restore)
 
     # Train all minibatches 
diff --git a/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py b/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
index 6cb8abddf..94c7a73b0 100644
--- a/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
+++ b/Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug_Distributed.py
@@ -128,10 +128,13 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         model_inputs_to_mb_source_mapping = input_map, 
         mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
         progress_printer = progress_printer, 
+#        checkpoint_frequency = epoch_size, 
         checkpoint_filename = os.path.join(model_path, "ConvNet_CIFAR10_DataAug"),
+#        save_all_checkpoints = False, 
         progress_frequency=epoch_size,
         cv_source = test_source,
         cv_mb_size_schedule=cntk.minibatch_size_schedule(minibatch_size),
+#        cv_frequency = epoch_size,
         restore=restore)
 
     # Train all minibatches 
@@ -200,8 +203,8 @@ if __name__=='__main__':
                                 max_epochs=args['num_epochs'],
                                 restore=not args['restart'],
                                 log_to_file=args['logdir'],
-                                num_mbs_per_log=10,
-                                gen_heartbeat=True)
+                                num_mbs_per_log=100,
+                                gen_heartbeat=False)
     finally:
         cntk.distributed.Communicator.finalize()
 
diff --git a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
index 894e4c214..9021030fe 100644
--- a/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG16_ImageNet_Distributed.py
@@ -168,10 +168,13 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         model_inputs_to_mb_source_mapping = input_map, 
         mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
         progress_printer = progress_printer, 
+#        checkpoint_frequency = epoch_size,
         checkpoint_filename = os.path.join(model_path, model_name), 
+#        save_all_checkpoints = True,
         progress_frequency = epoch_size, 
         cv_source = test_source, 
         cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+#        cv_frequency = epoch_size,
         restore = restore)
 
     # Train all minibatches 
diff --git a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
index 327a291a6..df9eadd07 100644
--- a/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
+++ b/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
@@ -168,10 +168,13 @@ def train_and_test(network, trainer, train_source, test_source, progress_printer
         model_inputs_to_mb_source_mapping = input_map, 
         mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size), 
         progress_printer = progress_printer, 
+#        checkpoint_frequency = epoch_size,
         checkpoint_filename = os.path.join(model_path, model_name), 
+#        save_all_checkpoints = True,
         progress_frequency = epoch_size, 
         cv_source = test_source, 
         cv_mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
+#        cv_frequency = epoch_size,
         restore = restore)
 
     # Train all minibatches 

From a7206d728207393168ada3b5867c47eb2f11bbaa Mon Sep 17 00:00:00 2001
From: Amit Agarwal <amitaga@microsoft.com>
Date: Wed, 8 Feb 2017 16:48:54 -0800
Subject: [PATCH 28/31] CNTK v2 library: Add scatter/gather support for sparse
 operands, enabling a bunch of sequence ops on sparse operands

---
 .../ComputationNetworkLib/ReshapingNodes.cpp  | 28 +++++++++++++-
 Source/Math/GPUSparseMatrix.cu                |  1 +
 Source/Math/Matrix.cpp                        | 24 ++++++++++--
 .../python/cntk/ops/tests/reshaping_test.py   | 38 ++++++++++++++++++-
 4 files changed, 86 insertions(+), 5 deletions(-)

diff --git a/Source/ComputationNetworkLib/ReshapingNodes.cpp b/Source/ComputationNetworkLib/ReshapingNodes.cpp
index e5653c0e4..cfe208fc3 100644
--- a/Source/ComputationNetworkLib/ReshapingNodes.cpp
+++ b/Source/ComputationNetworkLib/ReshapingNodes.cpp
@@ -432,7 +432,20 @@ template <class ElemType>
     InputRef(INDEXDATA).MaskMissingValueColumnsTo(FrameRange(InputRef(INDEXDATA).GetMBLayout()), -1); // indicates an invalid column to Gather/Scatter
     let&  index  = InputRef(INDEXDATA) .Value(); // column indices to copy from
     let&  source = InputRef(SOURCEDATA).Value(); // source data to copy
-    auto& output =                      Value(); // output goes here
+
+#ifdef _MSC_VER
+    auto& outputValuePtrRef = ValuePtrRef();
+#else
+    auto& outputValuePtrRef = this->template ValuePtrRef();
+#endif
+    if ((source.GetMatrixType() == SPARSE) && (outputValuePtrRef->GetMatrixType() != SPARSE))
+        outputValuePtrRef = std::make_shared<Matrix<ElemType>>(outputValuePtrRef->GetNumRows(),
+                                                               outputValuePtrRef->GetNumCols(),
+                                                               outputValuePtrRef->GetPreferredDeviceId(),
+                                                               source.GetMatrixType(),
+                                                               source.GetFormat());
+
+    auto& output = Value(); // output goes here
     output.DoGatherColumnsOf(/*beta=*/0, index, source, /*alpha=*/1);
 }
 
@@ -493,6 +506,19 @@ template <class ElemType>
     InputRef(INDEXDATA).MaskMissingValueColumnsTo(FrameRange(InputRef(INDEXDATA).GetMBLayout()), -1); // indicates an invalid column to Gather/Scatter
     let&  index  = InputRef(INDEXDATA) .Value(); // column indices to copy from
     let&  source = InputRef(SOURCEDATA).Value(); // source data to copy
+
+#ifdef _MSC_VER
+    auto& outputValuePtrRef = ValuePtrRef();
+#else
+    auto& outputValuePtrRef = this->template ValuePtrRef();
+#endif
+    if ((source.GetMatrixType() == SPARSE) && (outputValuePtrRef->GetMatrixType() != SPARSE))
+        outputValuePtrRef = std::make_shared<Matrix<ElemType>>(outputValuePtrRef->GetNumRows(),
+                                                               outputValuePtrRef->GetNumCols(),
+                                                               outputValuePtrRef->GetPreferredDeviceId(),
+                                                               source.GetMatrixType(),
+                                                               source.GetFormat());
+
     auto& output =                      Value(); // output goes here
     output.DoScatterColumnsOf(/*beta=*/0, index, source, /*alpha=*/1);
 }
diff --git a/Source/Math/GPUSparseMatrix.cu b/Source/Math/GPUSparseMatrix.cu
index 7e05d5d4a..143d27d98 100644
--- a/Source/Math/GPUSparseMatrix.cu
+++ b/Source/Math/GPUSparseMatrix.cu
@@ -1148,6 +1148,7 @@ void GPUSparseMatrix<ElemType>::ConvolveAndWeightedAdd(ElemType alpha, const GPU
             {
                 RuntimeError("Only support c += alpha * a operation");
             }
+
             int blocksPerGrid = (int) ceil(1.0 * cRows / GridDim::maxThreadsPerBlock);
             SyncGuard syncGuard;
             for (int rowInB = 0; rowInB < l; rowInB++)
diff --git a/Source/Math/Matrix.cpp b/Source/Math/Matrix.cpp
index dfa93a7af..18ee92f5d 100644
--- a/Source/Math/Matrix.cpp
+++ b/Source/Math/Matrix.cpp
@@ -1089,6 +1089,9 @@ Matrix<ElemType>& Matrix<ElemType>::DoGatherColumnsOf(ElemType beta, const Matri
 {
     DecideAndMoveToRightDevice(*this, idx, a); // TODO: only move target if beta != 0
 
+    if (a.GetMatrixType() != this->GetMatrixType())
+        RuntimeError("Matrix::DoGatherColumnsOf: The source and target matrices must have same storage type (SPARSE/DENSE).");
+
     DISPATCH_MATRIX_ON_FLAG(&a, this,
         { m_CPUMatrix->DoGatherColumnsOf(beta, *idx.m_CPUMatrix, *a.m_CPUMatrix, alpha); },
         { m_GPUMatrix->DoGatherColumnsOf(beta, *idx.m_GPUMatrix, *a.m_GPUMatrix, alpha); },
@@ -1101,8 +1104,7 @@ Matrix<ElemType>& Matrix<ElemType>::DoGatherColumnsOf(ElemType beta, const Matri
             CPUSparseMatrix<ElemType> tempA(a.GetFormat(), a.GetNumRows(), a.GetNumCols(), a.m_GPUSparseMatrix->GetNumNZElements());
             a.m_GPUSparseMatrix->CopyToCPUSparseMatrix(tempA);
 
-            CPUSparseMatrix<ElemType> tempThis(m_GPUSparseMatrix->GetFormat(), m_GPUSparseMatrix->GetNumRows(), m_GPUSparseMatrix->GetNumCols(),
-                m_GPUSparseMatrix->GetNumNZElements());
+            CPUSparseMatrix<ElemType> tempThis(m_GPUSparseMatrix->GetFormat(), m_GPUSparseMatrix->GetNumRows(), m_GPUSparseMatrix->GetNumCols(), m_GPUSparseMatrix->GetNumNZElements());
             m_GPUSparseMatrix->CopyToCPUSparseMatrix(tempThis);
 
             tempThis.DoGatherColumnsOf(beta, *tempIdx.m_CPUMatrix, tempA, alpha);
@@ -1121,11 +1123,27 @@ Matrix<ElemType>& Matrix<ElemType>::DoScatterColumnsOf(ElemType beta, const Matr
 {
     DecideAndMoveToRightDevice(*this, idx, a); // TODO: only move target if beta != 0
 
+    if (a.GetMatrixType() != this->GetMatrixType())
+        RuntimeError("Matrix::DoScatterColumnsOf: The source and target matrices must have same storage type (SPARSE/DENSE).");
+
     DISPATCH_MATRIX_ON_FLAG(&a, this,
         { m_CPUMatrix->DoScatterColumnsOf(beta, *idx.m_CPUMatrix, *a.m_CPUMatrix, alpha); },
         { m_GPUMatrix->DoScatterColumnsOf(beta, *idx.m_GPUMatrix, *a.m_GPUMatrix, alpha); },
         { m_CPUSparseMatrix->DoScatterColumnsOf(beta, *idx.m_CPUMatrix, *a.m_CPUSparseMatrix, alpha); },
-        { NOT_IMPLEMENTED; });
+        { 
+            // TODO replace by more performant version directly on GPU that does not require the round-trip over CPU.
+
+            Matrix<ElemType> tempIdx(CPUDEVICE); tempIdx.AssignValuesOf(idx);
+
+            CPUSparseMatrix<ElemType> tempA(a.GetFormat(), a.GetNumRows(), a.GetNumCols(), a.m_GPUSparseMatrix->GetNumNZElements());
+            a.m_GPUSparseMatrix->CopyToCPUSparseMatrix(tempA);
+
+            CPUSparseMatrix<ElemType> tempThis(m_GPUSparseMatrix->GetFormat(), m_GPUSparseMatrix->GetNumRows(), m_GPUSparseMatrix->GetNumCols(), m_GPUSparseMatrix->GetNumNZElements());
+            m_GPUSparseMatrix->CopyToCPUSparseMatrix(tempThis);
+
+            tempThis.DoScatterColumnsOf(beta, *tempIdx.m_CPUMatrix, tempA, alpha);
+            m_GPUSparseMatrix->SetValue(tempThis);
+        });
 
     return *this;
 }
diff --git a/bindings/python/cntk/ops/tests/reshaping_test.py b/bindings/python/cntk/ops/tests/reshaping_test.py
index 00f466026..972d8486c 100644
--- a/bindings/python/cntk/ops/tests/reshaping_test.py
+++ b/bindings/python/cntk/ops/tests/reshaping_test.py
@@ -14,7 +14,7 @@ import pytest
 from .ops_test_utils import unittest_helper, _test_unary_op, _test_binary_op, AA, I, precision, PRECISION_TO_TYPE, cntk_device
 import cntk as C
 from cntk.axis import Axis
-from ...utils import sanitize_dtype_cntk
+from ...utils import sanitize_dtype_cntk, one_hot
 from .. import constant
 
 EPS_IN_LOG = 1e-37        # 1e-37 is the highest guaranteed precision
@@ -395,3 +395,39 @@ def test_op_gather_derived_dynamic_axes_equivalence(device_id, precision):
     res = z.eval({a: input_data1, b: input_data2})
     expected_forward = [[[3.]]]
     assert np.array_equal(res, expected_forward)
+
+
+def test_op_gather_sparse(device_id):
+    from .. import sequence, times
+
+    input_sparse_indices = [[1, 3, 5], [2, 4]]
+    vocab_size = 6
+    input_data = one_hot(input_sparse_indices, vocab_size)
+
+    a = I(shape=(vocab_size,), is_sparse=True, name='a')
+
+    a_last = sequence.last(a)
+    a_last_dense = times(a_last, np.eye(vocab_size))
+    res = a_last_dense.eval({a : input_data})
+    assert np.array_equal(res, [[[0, 0, 0, 0, 0, 1]], [[0, 0, 0, 0, 1, 0]]])
+
+    a_last_2 = sequence.slice(a, -2, 0)
+    a_last_2_dense = times(a_last_2, np.eye(vocab_size))
+    res = a_last_2_dense.eval({a : input_data})
+    assert np.array_equal(res, [[[0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1]], [[0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0]]])
+
+
+def test_op_scatter_sparse(device_id):
+    from .. import sequence, times
+
+    input_sparse_indices = [[1, 3, 5], [2, 4]]
+    vocab_size = 6
+    input_data = one_hot(input_sparse_indices, vocab_size)
+
+    a = I(shape=(vocab_size,), is_sparse=True, name='a')
+
+    a_last_scatter = sequence.scatter(sequence.last(a), sequence.is_first(a))
+    a_last_scatter_dense = times(a_last_scatter, np.eye(vocab_size))
+    res = a_last_scatter_dense.eval({a : input_data})
+    assert np.array_equal(res[0], np.asarray([[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]))
+    assert np.array_equal(res[1], np.asarray([[0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0]]))

From 063e1964dd0b61b78abeea9e0bd49a563cd6e481 Mon Sep 17 00:00:00 2001
From: PengchengHe <penhe@microsoft.com>
Date: Fri, 13 Jan 2017 09:41:44 +0000
Subject: [PATCH 29/31] 1. Fix dynamicaxis of output of cosine_distance. 2. Fix
 Recurrencee in python layers.py

---
 Source/CNTKv2LibraryDll/PrimitiveFunction.cpp | 1 -
 bindings/python/cntk/layers.py                | 5 ++++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/Source/CNTKv2LibraryDll/PrimitiveFunction.cpp b/Source/CNTKv2LibraryDll/PrimitiveFunction.cpp
index 4a09c3bbd..a8a0f9625 100644
--- a/Source/CNTKv2LibraryDll/PrimitiveFunction.cpp
+++ b/Source/CNTKv2LibraryDll/PrimitiveFunction.cpp
@@ -137,7 +137,6 @@ namespace CNTK
             (op == PrimitiveOpType::CrossEntropyWithSoftmax) ||
             (op == PrimitiveOpType::ClassificationError) ||
             (op == PrimitiveOpType::Logistic) ||
-            (op == PrimitiveOpType::CosDistance) || 
             (op == PrimitiveOpType::LambdaRank) ||
             (op == PrimitiveOpType::NDCG))
         {
diff --git a/bindings/python/cntk/layers.py b/bindings/python/cntk/layers.py
index 8349c8f0e..e1725397a 100644
--- a/bindings/python/cntk/layers.py
+++ b/bindings/python/cntk/layers.py
@@ -349,7 +349,10 @@ def Recurrence(over, go_backwards=False, initial_state=initial_state_default_or_
     f_x_h_c = over(x, prev_state) # apply the recurrent over
     # this returns a Function (x, (h_prev, c_prev)) -> (h, c)
     h_c = f_x_h_c.outputs
-    replacements = { value_forward: value for (value_forward, value) in zip(list(_as_tuple(state_forward)), h_c) }
+    if type(state_forward) is tuple and len(state_forward) > 1: 
+      replacements = { value_forward: value for (value_forward, value) in zip(list(_as_tuple(state_forward)), h_c) }
+    else:
+      replacements = {(state_forward,)[0] : h_c[0] }
     f_x_h_c.replace_placeholders(replacements)  # resolves state_forward := h_c
     h = f_x_h_c.outputs[0]  # 'h' is a Variable (the output of a Function that computed it)
     if _trace_layers:

From 04db23b5f82b8d77a64264d8b43107d70ac0405b Mon Sep 17 00:00:00 2001
From: PengchengHe <penhe@microsoft.com>
Date: Thu, 26 Jan 2017 11:32:49 +0000
Subject: [PATCH 30/31] Add Adam Learner as the original paper

---
 .gitignore                                    |  1 +
 Source/CNTKv2LibraryDll/API/CNTKLibrary.h     |  4 +-
 Source/CNTKv2LibraryDll/Learner.cpp           | 53 +++++++++++++++-
 Source/CNTKv2LibraryDll/Learner.h             | 30 ++++++++++
 Source/Math/CPUMatrix.cpp                     | 35 +++++++++++
 Source/Math/CPUMatrix.h                       |  5 +-
 Source/Math/GPUMatrix.cu                      | 25 ++++++++
 Source/Math/GPUMatrix.h                       |  7 ++-
 Source/Math/GPUMatrixCUDAKernels.cuh          | 60 +++++++++++++++++++
 Source/Math/GPUSparseMatrix.cu                | 33 ++++++++++
 Source/Math/GPUSparseMatrix.h                 |  1 +
 Source/Math/Matrix.cpp                        | 34 +++++++++++
 Source/Math/Matrix.h                          |  4 ++
 Source/Math/NoGPU.cpp                         | 12 ++++
 Tests/UnitTests/MathTests/CPUMatrixTests.cpp  | 30 ++++++++++
 Tests/UnitTests/MathTests/GPUMatrixTests.cpp  | 28 +++++++++
 .../UnitTests/V2LibraryTests/LearnerTests.cpp | 13 +++-
 .../V2LibraryTests/SerializationTests.cpp     |  0
 bindings/csharp/Swig/cntk_cs.i                |  0
 bindings/python/cntk/learner.py               |  3 -
 .../cntk/ops/tests/cosine_distance_test.py    | 29 +++++++++
 bindings/python/cntk/tests/layers_test.py     | 46 ++++++++++++++
 22 files changed, 442 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 Source/CNTKv2LibraryDll/API/CNTKLibrary.h
 mode change 100644 => 100755 Source/CNTKv2LibraryDll/Learner.cpp
 mode change 100644 => 100755 Source/CNTKv2LibraryDll/Learner.h
 mode change 100644 => 100755 Source/Math/CPUMatrix.cpp
 mode change 100644 => 100755 Source/Math/CPUMatrix.h
 mode change 100644 => 100755 Source/Math/GPUMatrix.cu
 mode change 100644 => 100755 Source/Math/GPUMatrix.h
 mode change 100644 => 100755 Source/Math/GPUMatrixCUDAKernels.cuh
 mode change 100644 => 100755 Source/Math/GPUSparseMatrix.cu
 mode change 100644 => 100755 Source/Math/GPUSparseMatrix.h
 mode change 100644 => 100755 Source/Math/Matrix.cpp
 mode change 100644 => 100755 Source/Math/Matrix.h
 mode change 100644 => 100755 Source/Math/NoGPU.cpp
 mode change 100644 => 100755 Tests/UnitTests/MathTests/CPUMatrixTests.cpp
 mode change 100644 => 100755 Tests/UnitTests/MathTests/GPUMatrixTests.cpp
 mode change 100644 => 100755 Tests/UnitTests/V2LibraryTests/LearnerTests.cpp
 mode change 100644 => 100755 Tests/UnitTests/V2LibraryTests/SerializationTests.cpp
 mode change 100644 => 100755 bindings/csharp/Swig/cntk_cs.i
 create mode 100644 bindings/python/cntk/ops/tests/cosine_distance_test.py

diff --git a/.gitignore b/.gitignore
index 816fcd182..db69af62a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -269,3 +269,4 @@ Tutorials/slots.wl
 /packages
 /CNTK.VC.db
 /CNTK.VC.VC.opendb
+.cache
diff --git a/Source/CNTKv2LibraryDll/API/CNTKLibrary.h b/Source/CNTKv2LibraryDll/API/CNTKLibrary.h
old mode 100644
new mode 100755
index 68b5b634e..848cdc10f
--- a/Source/CNTKv2LibraryDll/API/CNTKLibrary.h
+++ b/Source/CNTKv2LibraryDll/API/CNTKLibrary.h
@@ -3880,7 +3880,9 @@ namespace CNTK
     static MomentumSchedule DefaultVarianceMomentum = MomentumAsTimeConstantSchedule(2 * 3600 * 100);
 
     ///
-    /// Create an instance of the CNTK built-in Adam learner (only the low-memory variant is supported at the moment).
+    /// Create an instance of Adam learner as the original paper.
+    /// Due to history reason, the legacy implementation of AdamLearner is FSAdaGrad. To keep compitability on the interface, we
+    /// will switch to the original Adam only when lowMemory = false, while keep the legacy logic when it leaves default, aka. true.
     ///
     CNTK_API LearnerPtr AdamLearner(const std::vector<Parameter>& parameters,
                                     const LearningRateSchedule& learningRateSchedule,
diff --git a/Source/CNTKv2LibraryDll/Learner.cpp b/Source/CNTKv2LibraryDll/Learner.cpp
old mode 100644
new mode 100755
index 563c3a061..2bcf3cd0d
--- a/Source/CNTKv2LibraryDll/Learner.cpp
+++ b/Source/CNTKv2LibraryDll/Learner.cpp
@@ -543,6 +543,48 @@ namespace CNTK
                                                 s_targetAdagradAvDenom, momentum, varMomentum, UseUnitGainMomentum());
     }
 
+    LearnerAdam::LearnerAdam(const vector<Parameter>& parameters,
+        const LearningRateSchedule& learningRateSchedule,
+        const MomentumSchedule& momentumSchedule,
+        bool unitGain,
+        const MomentumSchedule& varianceMomentumSchedule,
+        AdditionalLearningOptions additionalOptions)
+        : LearnerMomentumSGD(parameters, learningRateSchedule, momentumSchedule,
+            unitGain, additionalOptions, /*allocateSmoothGradients*/ false),
+        m_varianceMomentumSchedule(varianceMomentumSchedule)
+    {
+        for (const auto& parameter : parameters)
+        {
+            const auto shape = GetMatrixShape(parameter);
+            NDArrayViewPtr view = AllocateNDArrayView(parameter, { shape[0], 2 * shape[1] });
+            m_smoothedGradientValues.emplace(parameter, view);
+            m_smoothedCounts.emplace(parameter, 0.0);
+        }
+    }
+
+    /*virtual*/ void LearnerAdam::Update(const Parameter& parameter, const NDArrayViewPtr& gradientValue,
+        const NDArrayViewPtr& smoothedGradientValue, size_t trainingSampleCount) const /*override*/
+    {
+        DISPATCH_TO_TYPED_UPDATE_FUNCTION;
+    }
+
+    template <typename ElementType>
+    void LearnerAdam::Update(const Parameter& parameter, const NDArrayViewPtr& gradientValue,
+        const NDArrayViewPtr& smoothedGradientValue, size_t trainingSampleCount) const
+    {
+        GET_WRITABLE_MATRICES;
+
+        const auto learningRate = LearningRate(trainingSampleCount);
+        const auto momentum = MomentumValueForMB(trainingSampleCount);
+
+        const auto varMomentum = VarianceMomentumValueForMB(trainingSampleCount);
+
+        double& smoothedCount = m_smoothedCounts.at(parameter);
+
+        smoothedGradientMatrix->AdamUpdate(*gradientMatrix, *parameterMatrix, smoothedCount, learningRate,
+            momentum, varMomentum, UseUnitGainMomentum());
+    }
+
     LearnerRMSProp::LearnerRMSProp(const vector<Parameter>& parameters,
                                    const LearningRateSchedule& learningRateSchedule,
                                    double gamma, double inc, double dec, double max, double min,
@@ -623,16 +665,21 @@ namespace CNTK
     LearnerPtr AdamLearner(const vector<Parameter>& parameters,
                            const LearningRateSchedule& learningRateSchedule,
                            const MomentumSchedule& momentumSchedule,
-                           bool unitGain,
+                           bool unitGain, /*=true*/
                            const MomentumSchedule& varianceMomentumSchedule, /*= MomentumAsTimeConstantSchedulePerSample(2 * 3600 * 100)*/
                            bool lowMemory, /*= true*/
                            AdditionalLearningOptions additionalOptions /*= AdditionalLearningOptions()*/)
     {
+        // TODO: Due to history reason, the legacy AdamLearner using FSAdaGrad implementation instead of the original paper implementation.
+        //      To keep interface backward compatible, the new adam will be enabled only when lowMemory is false.
         if (!lowMemory)
         {
-            LogicError("AdamLearner: only the low-memory variant is supported at the moment.");
+            return MakeSharedObject<LearnerAdam>(parameters, learningRateSchedule, momentumSchedule, unitGain, varianceMomentumSchedule, additionalOptions);
+        }
+        else
+        {
+            return MakeSharedObject<LearnerFSAdaGrad>(parameters, learningRateSchedule, momentumSchedule, unitGain, varianceMomentumSchedule, additionalOptions);
         }
-        return MakeSharedObject<LearnerFSAdaGrad>(parameters, learningRateSchedule, momentumSchedule, unitGain, varianceMomentumSchedule, additionalOptions);
     }
 
     LearnerPtr AdaGradLearner(const vector<Parameter>& parameters,
diff --git a/Source/CNTKv2LibraryDll/Learner.h b/Source/CNTKv2LibraryDll/Learner.h
old mode 100644
new mode 100755
index 38f8e6c06..db1f8cfc0
--- a/Source/CNTKv2LibraryDll/Learner.h
+++ b/Source/CNTKv2LibraryDll/Learner.h
@@ -234,6 +234,36 @@ namespace CNTK
         MomentumSchedule m_varianceMomentumSchedule;
     };
 
+    class LearnerAdam : public LearnerMomentumSGD
+    {
+    public:
+
+        LearnerAdam(const std::vector<Parameter>& parameters,
+            const LearningRateSchedule& learningRateSchedule,
+            const MomentumSchedule& momentumSchedule,
+            bool unitGain,
+            const MomentumSchedule& varianceMomentumSchedule,
+            AdditionalLearningOptions additionalOptions);
+
+    protected:
+
+        virtual void Update(const Parameter& parameter, const NDArrayViewPtr& gradientValue, const NDArrayViewPtr& smoothedGradientValue, size_t trainingSampleCount) const override;
+
+        template <typename ElementType>
+        void Update(const Parameter& parameter, const NDArrayViewPtr& gradientValue, const NDArrayViewPtr& smoothedGradientValue, size_t trainingSampleCount) const;
+
+    private:
+
+        // returns current per-minibatch variance momentum value.
+        double VarianceMomentumValueForMB(size_t minibatchSize) const
+        {
+            return MomentumValueForMB(m_varianceMomentumSchedule, minibatchSize);
+        }
+
+        mutable std::unordered_map<Parameter, double> m_smoothedCounts;
+        MomentumSchedule m_varianceMomentumSchedule;
+    };
+
     class LearnerRMSProp : public LearnerBase
     {
     public:
diff --git a/Source/Math/CPUMatrix.cpp b/Source/Math/CPUMatrix.cpp
old mode 100644
new mode 100755
index 103ef49c1..4370f8baf
--- a/Source/Math/CPUMatrix.cpp
+++ b/Source/Math/CPUMatrix.cpp
@@ -1246,6 +1246,41 @@ void CPUMatrix<ElemType>::FSAdagrad(CPUMatrix<ElemType>& gradients,
     }
 }
 
+template <class ElemType>
+void CPUMatrix<ElemType>::Adam(CPUMatrix<ElemType>& gradients, CPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample,
+    ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum)
+{
+    size_t numColsNeeded = 2 * gradients.GetNumCols();
+    auto unitGainFactor = ElemType(unitGainMomentum ? (1.0 - momentum) : 1.0);
+
+    if (IsEmpty() || (GetNumCols() < numColsNeeded))
+    {
+        RequireSize(gradients.GetNumRows(), numColsNeeded);
+        SetValue(0.0);
+    }
+
+    assert((GetNumRows() == gradients.GetNumRows()) && (GetNumCols() == numColsNeeded));
+
+    size_t n = gradients.GetNumElements();
+    ElemType* grad = gradients.Data();
+    ElemType* smoothAda = Data();
+    ElemType* smoothMom = Data() + n;
+    ElemType* val = functionValues.Data();
+#pragma omp parallel for
+    // TODO: Unroll 4-times for better performance leveraging vectorization
+    for (long i = 0; i < n; i++)
+    {
+        ElemType g = grad[i];
+        ElemType adaSqr = adaWeight * smoothAda[i] + (1.0f - adaWeight) * g * g;
+        smoothAda[i] = adaSqr;
+        ElemType ada = sqrt(adaSqr);
+        ElemType w = adaMul * (ElemType)( 1.0 / (ada + 1e-8));
+        g = momentum * smoothMom[i] + unitGainFactor * g;
+        smoothMom[i] = g;
+        val[i] -= g * w * learnRatePerSample;
+    }
+}
+
 template <class ElemType>
 ElemType CPUMatrix<ElemType>::RmsProp(CPUMatrix<ElemType>& gradients,
                                       ElemType RMS_GAMMA,
diff --git a/Source/Math/CPUMatrix.h b/Source/Math/CPUMatrix.h
old mode 100644
new mode 100755
index b680acac6..3903cba51
--- a/Source/Math/CPUMatrix.h
+++ b/Source/Math/CPUMatrix.h
@@ -95,7 +95,10 @@ public:
     
     void FSAdagrad(CPUMatrix<ElemType>& gradients, CPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample, 
                    ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
-    
+
+    void Adam(CPUMatrix<ElemType>& gradients, CPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample,
+        ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
+
     ElemType RmsProp(CPUMatrix<ElemType>& gradients,
                      ElemType RMS_GAMMA,
                      ElemType RMS_WGT_INC,
diff --git a/Source/Math/GPUMatrix.cu b/Source/Math/GPUMatrix.cu
old mode 100644
new mode 100755
index 64216b043..04a33a5b9
--- a/Source/Math/GPUMatrix.cu
+++ b/Source/Math/GPUMatrix.cu
@@ -1413,6 +1413,31 @@ void GPUMatrix<ElemType>::FSAdagrad(GPUMatrix<ElemType>& gradients,
                                                                          learnRatePerSample, momentum, adaWeight, adaMul, unitGainMomentum);
 }
 
+template <class ElemType>
+void GPUMatrix<ElemType>::Adam(GPUMatrix<ElemType>& gradients,
+    GPUMatrix<ElemType>& functionValues,
+    ElemType learnRatePerSample,
+    ElemType momentum,
+    ElemType adaWeight,
+    ElemType adaMul,
+    bool unitGainMomentum)
+{
+    size_t numColsNeeded = 2 * gradients.GetNumCols();
+
+    if (IsEmpty() || (GetNumCols() < numColsNeeded))
+    {
+        RequireSize(gradients.GetNumRows(), numColsNeeded);
+        SetValue(0.0);
+    }
+
+    assert((GetNumRows() == gradients.GetNumRows()) && (GetNumCols() == numColsNeeded));
+
+    size_t n = gradients.GetNumElements();
+    int blocksPerGrid = (n + GridDim::maxThreadsPerBlock - 1) / GridDim::maxThreadsPerBlock;
+    _adam<ElemType> << <blocksPerGrid, GridDim::maxThreadsPerBlock >> >(n, gradients.Data(), Data(), Data() + n, functionValues.Data(),
+        learnRatePerSample, momentum, adaWeight, adaMul, unitGainMomentum);
+}
+
 template <class ElemType>
 ElemType GPUMatrix<ElemType>::RmsProp(GPUMatrix<ElemType>& gradients,
                                       ElemType RMS_GAMMA,
diff --git a/Source/Math/GPUMatrix.h b/Source/Math/GPUMatrix.h
old mode 100644
new mode 100755
index 3724fbc7f..c918d9a52
--- a/Source/Math/GPUMatrix.h
+++ b/Source/Math/GPUMatrix.h
@@ -224,10 +224,13 @@ public:
     }
 
     ElemType Adagrad(GPUMatrix<ElemType>& gradients, const bool needAveMultiplier);
-    
-    void FSAdagrad(GPUMatrix<ElemType>& gradients, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample, 
+
+    void FSAdagrad(GPUMatrix<ElemType>& gradients, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample,
                    ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
 
+    void Adam(GPUMatrix<ElemType>& gradients, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample,
+                  ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
+
     ElemType RmsProp(GPUMatrix<ElemType>& gradients, 
                      ElemType RMS_GAMMA, 
                      ElemType RMS_WGT_INC, 
diff --git a/Source/Math/GPUMatrixCUDAKernels.cuh b/Source/Math/GPUMatrixCUDAKernels.cuh
old mode 100644
new mode 100755
index 310a90598..522e14cc7
--- a/Source/Math/GPUMatrixCUDAKernels.cuh
+++ b/Source/Math/GPUMatrixCUDAKernels.cuh
@@ -5132,6 +5132,66 @@ __global__ void _maskColumnsValue(ElemType* a, const char* columnsMask, CUDA_LON
         a[IDX2C(rowIdx, colIdx, numRows)] = val;
     }
 }
+
+template <class ElemType>
+__global__ void _adam(CUDA_LONG size, ElemType* grad, ElemType* smoothAda, ElemType* smoothMom, ElemType* val,
+    ElemType lr, ElemType mom, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum)
+{
+    const ElemType unitGainFactor = unitGainMomentum ? (1.0 - mom) : 1.0;
+    CUDA_LONG idx = blockIdx.x * blockDim.x + threadIdx.x;
+    CUDA_LONG stride = blockDim.x * gridDim.x;
+    for (; idx < size; idx += stride)
+    {
+        ElemType g = grad[idx];
+        ElemType adaSqr = adaWeight * smoothAda[idx] + (1.0f - adaWeight) * g * g;
+        smoothAda[idx] = adaSqr;
+        ElemType w;
+        if (sizeof(ElemType) == sizeof(double))
+        {
+            w = adaMul * rsqrt(adaSqr + 1e-8);
+        }
+        else
+        {
+            w = adaMul * rsqrtf(adaSqr + 1e-8);
+        }
+
+        g = mom * smoothMom[idx] + unitGainFactor * g;
+        smoothMom[idx] = g;
+        g = lr*g*w;
+        val[idx] -= g;
+    }
+}
+
+template <class ElemType>
+__global__ void _adam4BlockSparseCol(CUDA_LONG size,
+    ElemType* grad_bsc, const GPUSPARSE_INDEX_TYPE* colOrRow2blockId, const size_t len,
+    ElemType* smoothAda, ElemType* smoothMom, ElemType* val,
+    ElemType lr, ElemType mom, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum)
+{
+    const ElemType unitGainFactor = unitGainMomentum ? (1.0 - mom) : 1.0;
+    CUDA_LONG idx = blockIdx.x * blockDim.x + threadIdx.x;
+    CUDA_LONG stride = blockDim.x * gridDim.x;
+    for (; idx < size; idx += stride)
+    {
+        ElemType g = _getvalue4BlockSparseCol(grad_bsc, colOrRow2blockId, len, idx);
+        ElemType adaSqr = adaWeight * smoothAda[idx] + (1.0f - adaWeight) * g * g;
+        smoothAda[idx] = adaSqr;
+        ElemType w;
+        if (sizeof(ElemType) == sizeof(double))
+        {
+            w = adaMul * rsqrt(adaSqr + 1e-8);
+        }
+        else
+        {
+            w = adaMul * rsqrtf(adaSqr + 1e-8);
+        }
+
+        g = mom * smoothMom[idx] + unitGainFactor * g;
+        smoothMom[idx] = g;
+        g = lr*g*w;
+        val[idx] -= g;
+    }
+}
 }
 }
 }
diff --git a/Source/Math/GPUSparseMatrix.cu b/Source/Math/GPUSparseMatrix.cu
old mode 100644
new mode 100755
index 7e05d5d4a..2be070949
--- a/Source/Math/GPUSparseMatrix.cu
+++ b/Source/Math/GPUSparseMatrix.cu
@@ -1546,6 +1546,39 @@ void GPUSparseMatrix<ElemType>::FSAdagrad(
         learnRatePerSample, momentum, adaWeight, adaMul, unitGainMomentum);
 }
 
+template <class ElemType>
+void GPUSparseMatrix<ElemType>::Adam(
+    GPUMatrix<ElemType>& c,
+    GPUMatrix<ElemType>& functionValues,
+    ElemType learnRatePerSample,
+    ElemType momentum,
+    ElemType adaWeight,
+    ElemType adaMul,
+    bool unitGainMomentum)
+{
+    if (GetFormat() != MatrixFormat::matrixFormatSparseBlockCol)
+    {
+        NOT_IMPLEMENTED;
+    }
+
+    size_t numColsNeeded = 2 * GetNumCols();
+
+    if (c.IsEmpty() || (c.GetNumCols() < numColsNeeded))
+    {
+        c.RequireSize(GetNumRows(), numColsNeeded);
+        c.SetValue(0.0);
+    }
+
+    assert((c.GetNumRows() == GetNumRows()) && (c.GetNumCols() == numColsNeeded));
+
+    size_t n = GetNumElements();
+    int blocksPerGrid = (n + GridDim::maxThreadsPerBlock - 1) / GridDim::maxThreadsPerBlock;
+    _adam4BlockSparseCol<ElemType> << <blocksPerGrid, GridDim::maxThreadsPerBlock >> >(
+        n, Data(), ColOrRow2BlockId(), GetNumRows(),
+        c.Data(), c.Data() + n, functionValues.Data(),
+        learnRatePerSample, momentum, adaWeight, adaMul, unitGainMomentum);
+}
+
 template <class ElemType>
 ElemType GPUSparseMatrix<ElemType>::RmsProp(GPUMatrix<ElemType>& c,
     ElemType RMS_GAMMA,
diff --git a/Source/Math/GPUSparseMatrix.h b/Source/Math/GPUSparseMatrix.h
old mode 100644
new mode 100755
index d2ad53057..32a349adc
--- a/Source/Math/GPUSparseMatrix.h
+++ b/Source/Math/GPUSparseMatrix.h
@@ -412,6 +412,7 @@ public:
     ElemType Adagrad(GPUMatrix<ElemType>& c, const bool needAveMultiplier);
     void FSAdagrad(GPUMatrix<ElemType>& c, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
     ElemType RmsProp(GPUMatrix<ElemType>& c, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier);
+    void Adam(GPUMatrix<ElemType>& c, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum);
 
     static void Multiply(const GPUSparseMatrix<ElemType>& S, const GPUMatrix<ElemType>& D, GPUMatrix<ElemType>& C);
     static void Multiply(const GPUMatrix<ElemType>& D, const GPUSparseMatrix<ElemType>& S, GPUMatrix<ElemType>& C);
diff --git a/Source/Math/Matrix.cpp b/Source/Math/Matrix.cpp
old mode 100644
new mode 100755
index dfa93a7af..dd941a30d
--- a/Source/Math/Matrix.cpp
+++ b/Source/Math/Matrix.cpp
@@ -1672,6 +1672,40 @@ void Matrix<ElemType>::FSAdagradUpdate(size_t mbSize,
     // Note: Since both 'this' and gradients are changed, we must call SetDataLocation() on 'this' as well.
 }
 
+///
+// Implement the original adam algorithm according to the paper
+// Ref: ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION, https://arxiv.org/pdf/1412.6980.pdf
+///
+template <class ElemType>
+void Matrix<ElemType>::AdamUpdate(Matrix<ElemType>& gradients, Matrix<ElemType>& functionValues, double& smoothedCount,
+    const double learnRatePerSample, const double meanMomentum, const double varMomentum, bool unitGainMomentum)
+{
+    smoothedCount++;
+    // Bias correction
+    let biasCorrection = (ElemType)(sqrt(1- pow(varMomentum, smoothedCount))/(1- pow(meanMomentum, smoothedCount)));
+
+    DISPATCH_MATRIX_ON_FLAG(&gradients, &gradients,
+    {
+        m_CPUMatrix->Adam(*gradients.m_CPUMatrix, *functionValues.m_CPUMatrix,
+        (ElemType)learnRatePerSample, (ElemType)meanMomentum, (ElemType)varMomentum,
+        biasCorrection, unitGainMomentum);
+        SetDataLocation(CPU);
+    },
+    {
+        m_GPUMatrix->Adam(*gradients.m_GPUMatrix, *functionValues.m_GPUMatrix,
+        (ElemType)learnRatePerSample, (ElemType)meanMomentum, (ElemType)varMomentum,
+        biasCorrection, unitGainMomentum);
+        SetDataLocation(GPU);
+    },
+    { NOT_IMPLEMENTED; },
+    { gradients.m_GPUSparseMatrix->Adam(*m_GPUMatrix, *functionValues.m_GPUMatrix, 
+        (ElemType)learnRatePerSample, (ElemType)meanMomentum, 
+        (ElemType)varMomentum, biasCorrection, unitGainMomentum); 
+        SetDataLocation(GPU); });
+
+    // Note: Since both 'this' and gradients are changed, we must call SetDataLocation() on 'this' as well.
+}
+
 template <class ElemType>
 ElemType Matrix<ElemType>::RmsProp(Matrix<ElemType>& gradients,
                                    ElemType RMS_GAMMA,
diff --git a/Source/Math/Matrix.h b/Source/Math/Matrix.h
old mode 100644
new mode 100755
index f9c01f729..afa44d78d
--- a/Source/Math/Matrix.h
+++ b/Source/Math/Matrix.h
@@ -215,6 +215,10 @@ public:
                          Matrix<ElemType>& gradients, Matrix<ElemType>& functionValues, double& smoothedCount,
                          const double learnRatePerSample, const double targetAdagradAvDenom,
                          const double meanMomentum, const double varMomentum, bool unitGainMomentum = true);
+
+    void AdamUpdate(Matrix<ElemType>& gradients, Matrix<ElemType>& functionValues, double& smoothedCount,
+        const double learnRatePerSample, const double meanMomentum, const double varMomentum, bool unitGainMomentum = true);
+
     ElemType RmsProp(Matrix<ElemType>& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier);
 
     void Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve = 10000, bool growOnly = true); // by default we only reallocate if need to grow
diff --git a/Source/Math/NoGPU.cpp b/Source/Math/NoGPU.cpp
old mode 100644
new mode 100755
index 3d8dccfd6..4b84c5f40
--- a/Source/Math/NoGPU.cpp
+++ b/Source/Math/NoGPU.cpp
@@ -261,6 +261,11 @@ void GPUSparseMatrix<ElemType>::FSAdagrad(GPUMatrix<ElemType>&, GPUMatrix<ElemTy
 {
 }
 
+template<class ElemType>
+void GPUSparseMatrix<ElemType>::Adam(GPUMatrix<ElemType>& c, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum)
+{
+}
+
 template<class ElemType>
 ElemType GPUSparseMatrix<ElemType>::RmsProp(GPUMatrix<ElemType>&, ElemType, ElemType, ElemType, ElemType, ElemType, const bool)
 {
@@ -1077,6 +1082,13 @@ void GPUMatrix<ElemType>::FSAdagrad(GPUMatrix<ElemType>& gradients, GPUMatrix<El
 {
 }
 
+template <class ElemType>
+void GPUMatrix<ElemType>::Adam(GPUMatrix<ElemType>& gradients, GPUMatrix<ElemType>& functionValues, ElemType learnRatePerSample,
+    ElemType momentum, ElemType adaWeight, ElemType adaMul, bool unitGainMomentum)
+{
+
+}
+
 template <class ElemType>
 ElemType GPUMatrix<ElemType>::RmsProp(GPUMatrix<ElemType>& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier)
 {
diff --git a/Tests/UnitTests/MathTests/CPUMatrixTests.cpp b/Tests/UnitTests/MathTests/CPUMatrixTests.cpp
old mode 100644
new mode 100755
index 97a638221..22d5dbf12
--- a/Tests/UnitTests/MathTests/CPUMatrixTests.cpp
+++ b/Tests/UnitTests/MathTests/CPUMatrixTests.cpp
@@ -898,6 +898,36 @@ BOOST_FIXTURE_TEST_CASE(CPUMatrixSeedingDouble, RandomSeedFixture)
     BOOST_CHECK(m1.IsEqualTo(m2));
 }
 
+BOOST_FIXTURE_TEST_CASE(CPUMatrixAdam, RandomSeedFixture)
+{
+    CPUMatrix<double> adamMatrix;
+    CPUMatrix<double> gradients(2, 1);
+    CPUMatrix<double> parameters(2, 1);
+    CPUMatrix<double> expectedParameters(2, 1);
+    CPUMatrix<double> expectedStates(2, 2);
+    double gradientValues[] = { 0.1, -0.1 };
+    double paramValues[] = { 0.1, 0.1 };
+    double expectedValues[] = { -0.05811338, 0.25811338 };
+    double expectedStateValues[] = {1e-5, 0.01, 1e-5, -0.01};
+    gradients.SetValue(2, 1, gradientValues, matrixFormatRowMajor);
+    parameters.SetValue(2, 1, paramValues, matrixFormatRowMajor);
+    expectedParameters.SetValue(2, 1, expectedValues, matrixFormatRowMajor);
+    expectedStates.SetValue(2, 2, expectedStateValues, matrixFormatRowMajor);
+    adamMatrix.Adam(gradients, parameters, 0.1, 0.9, 0.999, 0.5, true);
+
+    BOOST_CHECK(parameters.IsEqualTo(expectedParameters, 1e-6));
+    BOOST_CHECK(adamMatrix.IsEqualTo(expectedStates, 1e-6));
+
+    double expectedValues2[] = { -0.27059249, 0.47059249 };
+    double expectedStateValues2[] = { 2e-05, 0.019, 2e-05, -0.019 };
+    expectedParameters.SetValue(2, 1, expectedValues2, matrixFormatRowMajor);
+    expectedStates.SetValue(2, 2, expectedStateValues2, matrixFormatRowMajor);
+    adamMatrix.Adam(gradients, parameters, 0.1, 0.9, 0.999, 0.5, true);
+
+    BOOST_CHECK(parameters.IsEqualTo(expectedParameters, 1e-6));
+    BOOST_CHECK(adamMatrix.IsEqualTo(expectedStates, 1e-6));
+}
+
 BOOST_AUTO_TEST_SUITE_END()
 }
 } } }
diff --git a/Tests/UnitTests/MathTests/GPUMatrixTests.cpp b/Tests/UnitTests/MathTests/GPUMatrixTests.cpp
old mode 100644
new mode 100755
index 2ba9daa62..cb8cbade0
--- a/Tests/UnitTests/MathTests/GPUMatrixTests.cpp
+++ b/Tests/UnitTests/MathTests/GPUMatrixTests.cpp
@@ -537,6 +537,34 @@ BOOST_FIXTURE_TEST_CASE(GPUMatrixCurandSeedingDouble, RandomSeedFixture)
     BOOST_CHECK(m1.IsEqualTo(m2));
 }
 
+BOOST_FIXTURE_TEST_CASE(GPUMatrixAdam, RandomSeedFixture)
+{
+    GPUMatrix<double> adamMatrix(c_deviceIdZero);
+    GPUMatrix<double> gradients(2, 1, c_deviceIdZero);
+    GPUMatrix<double> parameters(2, 1, c_deviceIdZero);
+    GPUMatrix<double> expectedParameters(2, 1, c_deviceIdZero);
+    GPUMatrix<double> expectedStates(2, 2, c_deviceIdZero);
+    double gradientValues[] = { 0.1, -0.1 };
+    double paramValues[] = { 0.1, 0.1 };
+    double expectedValues[] = { -0.05803489, 0.25803488 };
+    double expectedStateValues[] = { 1e-5, 0.01, 1e-5, -0.01 };
+    gradients.SetValue(2, 1, c_deviceIdZero, gradientValues, matrixFormatRowMajor);
+    parameters.SetValue(2, 1, c_deviceIdZero, paramValues, matrixFormatRowMajor);
+    expectedParameters.SetValue(2, 1, c_deviceIdZero, expectedValues, matrixFormatRowMajor);
+    expectedStates.SetValue(2, 2, c_deviceIdZero, expectedStateValues, matrixFormatRowMajor);
+    adamMatrix.Adam(gradients, parameters, 0.1, 0.9, 0.999, 0.5, true);
+    BOOST_CHECK(parameters.IsEqualTo(expectedParameters, 1e-6));
+    BOOST_CHECK(adamMatrix.IsEqualTo(expectedStates, 1e-6));
+
+    double expectedValues2[] = { -0.27046135, 0.47046134 };
+    double expectedStateValues2[] = { 2e-05, 0.019, 2e-05, -0.019 };
+    expectedParameters.SetValue(2, 1, c_deviceIdZero, expectedValues2, matrixFormatRowMajor);
+    expectedStates.SetValue(2, 2, c_deviceIdZero, expectedStateValues2, matrixFormatRowMajor);
+    adamMatrix.Adam(gradients, parameters, 0.1, 0.9, 0.999, 0.5, true);
+    BOOST_CHECK(parameters.IsEqualTo(expectedParameters, 1e-6));
+    BOOST_CHECK(adamMatrix.IsEqualTo(expectedStates, 1e-6));
+}
+
 #if 0 // Temporarily disabling
 BOOST_FIXTURE_TEST_CASE(GPUMatrixLargeInequality, RandomSeedFixture)
 {
diff --git a/Tests/UnitTests/V2LibraryTests/LearnerTests.cpp b/Tests/UnitTests/V2LibraryTests/LearnerTests.cpp
old mode 100644
new mode 100755
index 88ba2cd83..91a664db3
--- a/Tests/UnitTests/V2LibraryTests/LearnerTests.cpp
+++ b/Tests/UnitTests/V2LibraryTests/LearnerTests.cpp
@@ -95,6 +95,15 @@ void TestFSAdaGradLearner(size_t numParameters, size_t numMinibatches, bool unit
     TestUpdate<ElementType>(learner, shape, numMinibatches, device);
 }
 
+template <typename ElementType>
+void TestAdamLearner(size_t numParameters, size_t numMinibatches, bool unitGainMomentum, const DeviceDescriptor& device)
+{
+    NDShape shape = CreateShape(rng() % maxNumAxes + 1, maxDimSize);
+    auto parameters = CreateParameters<ElementType>(shape, numParameters, device);
+    auto learner = AdamLearner(parameters, LearningRatePerSampleSchedule({ 0.5 }), MomentumAsTimeConstantSchedule({ 10.0, 100.0, 1000.0 }), unitGainMomentum, MomentumPerSampleSchedule(0.99), false);
+    TestUpdate<ElementType>(learner, shape, numMinibatches, device);
+}
+
 template <typename ElementType>
 void TestRMSPropLearner(size_t numParameters, size_t numMinibatches, const DeviceDescriptor& device)
 {
@@ -335,6 +344,8 @@ void LearnerTests()
             TestMomentumSGDLearner<float>(numParameters, numMinibatches, unitGain, device);
             TestNesterovLearner<float>(numParameters, numMinibatches, unitGain, device);
             TestFSAdaGradLearner<double>(numParameters, numMinibatches, unitGain, device);
+            TestAdamLearner<float>(numParameters, numMinibatches, unitGain, device);
+            TestAdamLearner<double>(numParameters, numMinibatches, unitGain, device);
         }
     }
-}
\ No newline at end of file
+}
diff --git a/Tests/UnitTests/V2LibraryTests/SerializationTests.cpp b/Tests/UnitTests/V2LibraryTests/SerializationTests.cpp
old mode 100644
new mode 100755
diff --git a/bindings/csharp/Swig/cntk_cs.i b/bindings/csharp/Swig/cntk_cs.i
old mode 100644
new mode 100755
diff --git a/bindings/python/cntk/learner.py b/bindings/python/cntk/learner.py
index 085e66e05..023b2ef99 100644
--- a/bindings/python/cntk/learner.py
+++ b/bindings/python/cntk/learner.py
@@ -564,9 +564,6 @@ def adam_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
         <http://arxiv.org/abs/1412.6980>`_. International Conference for
         Learning Representations, 2015. 
     '''
-    if not low_memory:
-        raise NotImplementedError('adam: low_memory=True currently required')
-
     _verify_learning_rate_type(lr)
     _verify_momentum_type(momentum)
     _verify_momentum_type(variance_momentum)
diff --git a/bindings/python/cntk/ops/tests/cosine_distance_test.py b/bindings/python/cntk/ops/tests/cosine_distance_test.py
new file mode 100644
index 000000000..68d5a3660
--- /dev/null
+++ b/bindings/python/cntk/ops/tests/cosine_distance_test.py
@@ -0,0 +1,29 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+"""
+Unit tests for the cosine distance class.
+"""
+
+import numpy as np
+import pytest
+from .. import *
+from ...axis import Axis
+from ... import sequence
+
+def test_cosine_distance():
+  a = np.reshape(np.arange(25.0, dtype = np.float32), (5,5))
+  b = np.reshape(np.arange(0, 5, dtype=np.float32), (1,5))
+  
+  src = input_variable(shape=(5), dynamic_axes=[ Axis.default_batch_axis(), Axis("Seq")])
+  tgt = input_variable(shape=(5))
+  tgt_br = sequence.broadcast_as(tgt, src)
+  cos_seq = cosine_distance(src, tgt_br)
+  assert len(cos_seq.dynamic_axes)==2
+  assert cos_seq.dynamic_axes[1].name=="Seq"
+  val = cos_seq.eval({src:[a], tgt:[b]})
+  expected = [[ 1.,        0.914659,  0.878459,  0.86155,   0.851852]] 
+  print(np.allclose(val, expected))
diff --git a/bindings/python/cntk/tests/layers_test.py b/bindings/python/cntk/tests/layers_test.py
index df7c57e30..9caa7c952 100644
--- a/bindings/python/cntk/tests/layers_test.py
+++ b/bindings/python/cntk/tests/layers_test.py
@@ -8,6 +8,10 @@ import numpy as np
 import pytest
 
 from ..layers import *
+from ..blocks import  init_default_or_glorot_uniform, Parameter, _INFERRED, Placeholder
+from ..utils import _as_tuple
+from ..ops import  sigmoid, times, tanh, element_times, plus, combine, input_variable
+from ..axis import Axis
 
 def test_layers_name(device_id): 
     from cntk import placeholder_variable, combine
@@ -19,3 +23,45 @@ def test_layers_name(device_id):
     q = Convolution((3,3), 3, name='conv33')(I)
     assert(q.root_function.name == 'conv33')
 
+def gru_cell(shape, init=init_default_or_glorot_uniform, name=''): # (x, (h,c))
+  shape = _as_tuple(shape)
+
+  if len(shape) != 1 :
+    raise ValueError("gru_cell: shape must be vectors (rank-1 tensors)")
+
+  # determine stacking dimensions
+  cell_shape_stacked = shape * 2  # patched dims with stack_axis duplicated 4 times
+
+  # parameters
+  Wz = Parameter(cell_shape_stacked, init = init, name='Wz')
+  Wr = Parameter(cell_shape_stacked, init = init, name='Wr')
+  Wh = Parameter(cell_shape_stacked, init = init, name='Wh')
+  Uz = Parameter( _INFERRED + shape, init = init, name = 'Uz')
+  Ur = Parameter( _INFERRED + shape, init = init, name = 'Ur')
+  Uh = Parameter( _INFERRED + shape, init = init, name = 'Uh')
+
+  def create_s_placeholder():
+    # we pass the known dimensions here, which makes dimension inference easier
+    return Placeholder(shape=shape, name='S') # (h, c)
+
+  # parameters to model function
+  x = Placeholder(name='gru_block_arg')
+  prev_status = create_s_placeholder()
+
+  # formula of model function
+  Sn_1 = prev_status
+
+  z = sigmoid(times(x, Uz, name='x*Uz') + times(Sn_1, Wz, name='Sprev*Wz'), name='z')
+  r = sigmoid(times(x, Ur, name='x*Ur') + times(Sn_1, Wr, name='Sprev*Wr'), name='r')
+  h = tanh(times(x, Uh, name='x*Uh') + times(element_times(Sn_1, r, name='Sprev*r'), Wh), name='h')
+  s = plus(element_times((1-z), h, name='(1-z)*h'), element_times(z, Sn_1, name='z*SPrev'), name=name)
+  apply_x_s = combine([s])
+  apply_x_s.create_placeholder = create_s_placeholder
+  return apply_x_s
+
+def test_recurrence():
+  r = Recurrence(gru_cell(5), go_backwards=False)
+  a = input_variable(shape=(5,), dynamic_axes=[Axis.default_batch_axis(), Axis('Seq')])
+  x = np.reshape(np.arange(0,25, dtype=np.float32), (1,5,5))
+  rt = r(a).eval({a:x})
+  print(rt)

From 236fa8ac9f2c884e97db24759228fd2ae288d65d Mon Sep 17 00:00:00 2001
From: Mark Hillebrand <mahilleb@microsoft.com>
Date: Thu, 9 Feb 2017 10:53:43 +0100
Subject: [PATCH 31/31] Tests/EndToEndTests/UnitTests/MathTests/baseline.txt:
 missing baseline update

---
 .../UnitTests/MathTests/baseline.txt          | 6113 +----------------
 1 file changed, 153 insertions(+), 5960 deletions(-)

diff --git a/Tests/EndToEndTests/UnitTests/MathTests/baseline.txt b/Tests/EndToEndTests/UnitTests/MathTests/baseline.txt
index fea177fab..bd48fb5a9 100644
--- a/Tests/EndToEndTests/UnitTests/MathTests/baseline.txt
+++ b/Tests/EndToEndTests/UnitTests/MathTests/baseline.txt
@@ -1,5840 +1,13 @@
 CPU info:
-    CPU Model Name: Intel(R) Xeon(R) CPU W3565 @ 3.20GHz
-    Hardware threads: 8
-    Total Memory: 12580436 kB
+    CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
+    Hardware threads: 24
+    Total Memory: 264172964 kB
 -------------------------------------------------------------------
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
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-Using cuDNN batch normalization engine.
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-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-Using cuDNN batch normalization engine.
-Using CNTK batch normalization engine.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 5, Kernel: 1 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 1, Kernel: 1 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 5, Kernel: 1 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 5, Kernel: 1 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 1, Kernel: 1 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 5, Kernel: 1 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 1 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 1 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 1 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 5, Kernel: 3 x 3 x 1, Map: 5, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 1, Kernel: 3 x 3 x 3, Map: 1, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 5, Kernel: 3 x 3 x 3, Map: 5, Stride: 2 x 2 x 3, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 3 x 3 x 1, Output: 3 x 3 x 2, Kernel: 3 x 3 x 1, Map: 2, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 5 x 2, Output: 3 x 3 x 3 x 2, Kernel: 3 x 3 x 3 x 2, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 5 x 5 x 3 x 1, Output: 3 x 3 x 2 x 2, Kernel: 3 x 3 x 2 x 1, Map: 2, Stride: 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 1, Output: 16 x 8 x 8, Kernel: 3 x 3 x 1, Map: 8, Stride: 1 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using GEMM convolution engine for geometry: Input: 16 x 16 x 2, Output: 8 x 8 x 1, Kernel: 1 x 1 x 2, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0 x 0 x 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using cuDNN convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 1, Output: 1 x 2 x 1, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 2 x 3, Output: 1 x 2 x 3, Kernel: 1 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 1, Output: 1 x 3 x 1, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 3 x 3, Output: 1 x 3 x 3, Kernel: 1 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 1, Output: 1 x 4 x 1, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 1 x 4 x 3, Output: 1 x 4 x 3, Kernel: 1 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 1, Output: 1 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 3 x 3, Output: 1 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 2 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 1, Output: 1 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 2 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 2 x 4 x 3, Output: 1 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 4 x 5 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 1, Output: 2 x 3 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 4 x 5 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 5 x 3, Output: 2 x 3 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 2 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 1 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 3 x 4 x 3, Output: 2 x 2 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 6 x 7 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 1, Output: 3 x 4 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 6 x 7 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 6 x 7 x 3, Output: 3 x 4 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 5 x 6 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 1, Output: 3 x 3 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 5 x 6 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 5 x 6 x 3, Output: 3 x 3 x 3, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, OuRunning 138 test cases...
+===== Tensor test 'elementwise addition'
+Running 150 test cases...
  [512 x 256] [512 x 256] -> [512 x 256] 	// GPU
     [512 x 256] [512 x 256] -> [512 x 256] 	// CPU
-    [3 x 2] [3 x 1] -> [3 x 2] 	// GPU
-    [3 x 2] [3 x 1] -> [3 x 2] 	// CPU
-    [28 x 28 x 128 x 32] [1 x 1 x 128] -> [28 x 28 x 128 x 32] 	// GPU
-    [28 x 28 x 128 x 32] [1 x 1 x 128] -> [28 x 28 x 128 x 32] 	// CPU
-    [256 x 256 x 64 x 32] [1 x 1 x 64] -> [256 x 256 x 64 x 32] 	// GPU
-    [256 x 256 x 64 x 32] [1 x 1 x 64] -> [256 x 256 x 64 x 32] 	// CPU
-    [2048 x 1024] -> [2048] 	// GPU
-    [2048 x 1024] -> [2048] 	// CPU
-    [256 x 256 x 64 x 32] -> [1 x 1 x 64] 	// GPU
-    [256 x 256 x 64 x 32] -> [1 x 1 x 64] 	// CPU
-   tput: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-using reference convolution engine for geometry: Input: 4 x 4 x 1, Output: 2 x 2 x 1, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
-RandomSeedFixture::RandomSeedFixture (GPU): creating curand object with seed 42, sizeof(ElemType)==4
-RandomSeedFixture::RandomSeedFixture (GPU): creating curand object with seed 42, sizeof(ElemType)==8
-WARNING: The same matrix with dim [2, 3] has been transferred between different devices for 20 times.
-===== Tensor test 'elementwise addition'
-
+   
 ###### GPU result (131072, 1) [0:7, 0:9] ######
 
 -0.2939662337	
@@ -5859,7 +32,9 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 0.9675793052	
 ...
 ===== Tensor test 'addition wth simple broadcasting'
-
+ [3 x 2] [3 x 1] -> [3 x 2] 	// GPU
+    [3 x 2] [3 x 1] -> [3 x 2] 	// CPU
+   
 ###### GPU result (6, 1) [0:7, 0:9] ######
 
 -0.2939662337	
@@ -5878,7 +53,9 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 -1.6296070814	
 -1.6918985844	
 ===== Tensor test 'bias addition (broadcasting)'
-
+ [28 x 28 x 128 x 32] [1 x 1 x 128] -> [28 x 28 x 128 x 32] 	// GPU
+    [28 x 28 x 128 x 32] [1 x 1 x 128] -> [28 x 28 x 128 x 32] 	// CPU
+   
 ###### GPU result (3211264, 1) [0:7, 0:9] ######
 
 -0.2939662337	
@@ -5903,7 +80,9 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 0.8700708747	
 ...
 ===== Tensor test 'bias addition (broadcasting)'
-
+ [256 x 256 x 64 x 32] [1 x 1 x 64] -> [256 x 256 x 64 x 32] 	// GPU
+    [256 x 256 x 64 x 32] [1 x 1 x 64] -> [256 x 256 x 64 x 32] 	// CPU
+   
 ###### GPU result (134217728, 1) [0:7, 0:9] ######
 
 -0.2939662337	
@@ -5928,17 +107,19 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 0.8700708747	
 ...
 ===== Tensor test 'bias gradient (reduction)'
-
+ [2048 x 1024] -> [2048] 	// GPU
+    [2048 x 1024] -> [2048] 	// CPU
+   
 ###### GPU result (2048, 1) [0:7, 0:9] ######
 
-1.2924220562	
-20.1920471191	
-31.9372062683	
+1.2924218178	
+20.1920490265	
+31.9372329712	
 13.5184125900	
-3.1232621670	
--14.3535261154	
--4.3421869278	
--15.4718856812	
+3.1232664585	
+-14.3535432816	
+-4.3421840668	
+-15.4718914032	
 ...
 
 ###### CPU result (2048, 1) [0:7, 0:9] ######
@@ -5953,7 +134,9 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 -15.4718885422	
 ...
 ===== Tensor test 'bias gradient (reduction)'
-
+ [256 x 256 x 64 x 32] -> [1 x 1 x 64] 	// GPU
+    [256 x 256 x 64 x 32] -> [1 x 1 x 64] 	// CPU
+   
 ###### GPU result (64, 1) [0:7, 0:9] ######
 
 351.1564941406	
@@ -5972,23 +155,23 @@ WARNING: The same matrix with dim [2, 3] has been transferred between different
 -124.9017105103	
 221.3192443848	
 -120.1618041992	
-1640.0551757813	
+1640.0551757812	
 -266.5106201172	
 371.6577148438	
 -903.1638183594	
 ...
-MultiplyAndAdd Directly:  0.000000 seconds
-MultiplyAndAdd With ColumnSlice:  0.035000 seconds
-MultiplyAndAdd With ColumnSlice&:  0.034000 seconds
-MultiplyAndAdd With AssignColumnSlice:  0.035000 seconds
-AssignSigmoidOf With ColumnSlice:  0.005000 seconds
-AssignSigmoidOf With AssignColumnSlice:  0.006000 seconds
-testRnnForwardPropSRP:  0.007000 seconds
-TestOldRnnForwardPropSRP:  0.020000 seconds
+MultiplyAndAdd Directly:  0.000040 seconds
+MultiplyAndAdd With ColumnSlice:  0.002050 seconds
+MultiplyAndAdd With ColumnSlice&:  0.002118 seconds
+MultiplyAndAdd With AssignColumnSlice:  0.002191 seconds
+AssignSigmoidOf With ColumnSlice:  0.002206 seconds
+AssignSigmoidOf With AssignColumnSlice:  0.015601 seconds
+testRnnForwardPropSRP:  0.001586 seconds
+TestOldRnnForwardPropSRP:  0.008699 seconds
 
 Test module "MathTests" has passed with:
-  138 test cases out of 138 passed
-  10554474 assertions out of 10554474 passed
+  150 test cases out of 150 passed
+  13210418 assertions out of 13210418 passed
 
   Test suite "BatchNormalizationSuite" has passed with:
     2 test cases out of 2 passed
@@ -6051,47 +234,8 @@ Test module "MathTests" has passed with:
       770 assertions out of 770 passed
 
   Test suite "CPUMatrixSuite" has passed with:
-    31 test cases out of 31 passed
-    5248662 assertions out of 5248662 passed
-
-    Test case "CPUMatrixSuite/CPUSparseMatrixColumnSlice" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "CPUMatrixSuite/CPUSparseMatrixCopyColumnSliceToDense" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "CPUMatrixSuite/CPUSparseMatrixMultiplyAndAdd" has passed with:
-      20000 assertions out of 20000 passed
-
-    Test case "CPUMatrixSuite/MatrixMultiplyTest" has passed with:
-      1484 assertions out of 1484 passed
-
-    Test case "CPUMatrixSuite/MatrixMultiplyAndPlusAndMinus" has passed with:
-      1050120 assertions out of 1050120 passed
-
-    Test case "CPUMatrixSuite/MatrixScaleAndAdd" has passed with:
-      1572864 assertions out of 1572864 passed
-
-    Test case "CPUMatrixSuite/MatrixScaleAndAdd_double" has passed with:
-      1572864 assertions out of 1572864 passed
-
-    Test case "CPUMatrixSuite/MatrixNorms" has passed with:
-      14 assertions out of 14 passed
-
-    Test case "CPUMatrixSuite/MatrixInnerProductOfMatrices" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "CPUMatrixSuite/CPUMatrixFileWriteRead" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "CPUMatrixSuite/MatrixFileWriteRead" has passed with:
-      3 assertions out of 3 passed
-
-    Test case "CPUMatrixSuite/CPUMatrix1BitQuantizeFloat" has passed with:
-      481229 assertions out of 481229 passed
-
-    Test case "CPUMatrixSuite/CPUMatrix1BitQuantizeDouble" has passed with:
-      549976 assertions out of 549976 passed
+    32 test cases out of 32 passed
+    5248666 assertions out of 5248666 passed
 
     Test case "CPUMatrixSuite/CPUMatrixConstructorNoFlags" has passed with:
       8 assertions out of 8 passed
@@ -6147,9 +291,96 @@ Test module "MathTests" has passed with:
     Test case "CPUMatrixSuite/CPUMatrixSeedingDouble" has passed with:
       1 assertion out of 1 passed
 
+    Test case "CPUMatrixSuite/CPUMatrixAdam" has passed with:
+      4 assertions out of 4 passed
+
+    Test case "CPUMatrixSuite/CPUSparseMatrixColumnSlice" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "CPUMatrixSuite/CPUSparseMatrixCopyColumnSliceToDense" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "CPUMatrixSuite/CPUSparseMatrixMultiplyAndAdd" has passed with:
+      20000 assertions out of 20000 passed
+
+    Test case "CPUMatrixSuite/MatrixMultiplyTest" has passed with:
+      1484 assertions out of 1484 passed
+
+    Test case "CPUMatrixSuite/MatrixMultiplyAndPlusAndMinus" has passed with:
+      1050120 assertions out of 1050120 passed
+
+    Test case "CPUMatrixSuite/MatrixScaleAndAdd" has passed with:
+      1572864 assertions out of 1572864 passed
+
+    Test case "CPUMatrixSuite/MatrixScaleAndAdd_double" has passed with:
+      1572864 assertions out of 1572864 passed
+
+    Test case "CPUMatrixSuite/MatrixNorms" has passed with:
+      14 assertions out of 14 passed
+
+    Test case "CPUMatrixSuite/MatrixInnerProductOfMatrices" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "CPUMatrixSuite/CPUMatrixFileWriteRead" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "CPUMatrixSuite/MatrixFileWriteRead" has passed with:
+      3 assertions out of 3 passed
+
+    Test case "CPUMatrixSuite/CPUMatrix1BitQuantizeFloat" has passed with:
+      481229 assertions out of 481229 passed
+
+    Test case "CPUMatrixSuite/CPUMatrix1BitQuantizeDouble" has passed with:
+      549976 assertions out of 549976 passed
+
+  Test suite "QuantizersUnitTests" has passed with:
+    2 test cases out of 2 passed
+    12 assertions out of 12 passed
+
+    Test case "QuantizersUnitTests/FloatToShort" has passed with:
+      6 assertions out of 6 passed
+
+    Test case "QuantizersUnitTests/QuantizeZeros" has passed with:
+      6 assertions out of 6 passed
+
+  Test suite "QuantizedOperationsUnitTests" has passed with:
+    1 test case out of 1 passed
+    65 assertions out of 65 passed
+
+    Test case "QuantizedOperationsUnitTests/MultiplyIntToShort" has passed with:
+      65 assertions out of 65 passed
+
+  Test suite "MathTensorTests" has passed with:
+    9 test cases out of 9 passed
+    6 assertions out of 6 passed
+
+    Test case "MathTensorTests/ElementwiseAddition" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/AdditionWithSimpleBroadcasting" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/BiasAddition" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/BiasAddition2" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/BiasGradient" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/BiasGradient2" has passed with:
+      1 assertion out of 1 passed
+
+    Test case "MathTensorTests/ColumnSliceMultAndAdd" has passed
+
+    Test case "MathTensorTests/RnnForwardProp" has passed
+
+    Test case "MathTensorTests/OldRnnForwardProp" has passed
+
   Test suite "GPUMatrixSuite" has passed with:
-    56 test cases out of 56 passed
-    1263762 assertions out of 1263762 passed
+    57 test cases out of 57 passed
+    1263769 assertions out of 1263769 passed
 
     Test case "GPUMatrixSuite/GPUBlasMultiplyAndWeightedAdd" has passed with:
       1 assertion out of 1 passed
@@ -6214,6 +445,9 @@ Test module "MathTests" has passed with:
     Test case "GPUMatrixSuite/GPUMatrixCurandSeedingDouble" has passed with:
       1 assertion out of 1 passed
 
+    Test case "GPUMatrixSuite/GPUMatrixAdam" has passed with:
+      4 assertions out of 4 passed
+
     Test case "GPUMatrixSuite/GPUSparseMatrixConstructorsAndInitializers" has passed with:
       10 assertions out of 10 passed
 
@@ -6248,7 +482,7 @@ Test module "MathTests" has passed with:
       4 assertions out of 4 passed
 
     Test case "GPUMatrixSuite/GPUSparseMatrixInnerProduct" has passed with:
-      2 assertions out of 2 passed
+      5 assertions out of 5 passed
 
     Test case "GPUMatrixSuite/GPUSparseMatrixColumnSlice" has passed with:
       4 assertions out of 4 passed
@@ -6318,19 +552,9 @@ Test module "MathTests" has passed with:
     Test case "GPUMatrixSuite/MatrixSparseElementWisePower" has passed with:
       1 assertion out of 1 passed
 
-  Test suite "MatrixLearnerSuite" has passed with:
-      2 test case out of 2 passed
-      4 assertions out of 4 passed
- 
-    Test case "MatrixLearnerSuite/FSAdagradSparse" has passed with:
-      2 assertions out of 2 passed
-
-    Test case "MatrixLearnerSuite/RmsPropSparse" has passed with:
-      2 assertions out of 2 passed
-
   Test suite "MatrixUnitTests" has passed with:
-    26 test cases out of 26 passed
-    4048755 assertions out of 4048755 passed
+    31 test cases out of 31 passed
+    6684617 assertions out of 6684617 passed
 
     Test case "MatrixUnitTests/MatrixConstructors" has passed with:
       10 assertions out of 10 passed
@@ -6384,7 +608,7 @@ Test module "MathTests" has passed with:
       431 assertions out of 431 passed
 
     Test case "MatrixUnitTests/MatrixAssignXOf" has passed with:
-      3076088 assertions out of 3076088 passed
+      5711864 assertions out of 5711864 passed
 
     Test case "MatrixUnitTests/MatrixSumOfElements" has passed with:
       4 assertions out of 4 passed
@@ -6409,59 +633,28 @@ Test module "MathTests" has passed with:
 
     Test case "MatrixUnitTests/MatrixAssignNumOfDiff" has passed with:
       2 assertions out of 2 passed
-      
-    Test case "MatrixUnitTests/MatrixScale" has passed
 
-    Test case "MatrixUnitTests/MatrixSGDUpdate" has passed
+    Test case "MatrixUnitTests/MatrixScale" has passed with:
+      1 assertion out of 1 passed
 
-    Test case "MatrixUnitTests/MatrixMomentumSGDUpdate_WithAndWithout_UnitGain" has passed
+    Test case "MatrixUnitTests/MatrixSGDUpdate" has passed with:
+      21 assertions out of 21 passed
 
-    Test case "MatrixUnitTests/MatrixNesterovAcceleratedMomentumSGDUpdate_WithAndWithout_UnitGain" has passed     
+    Test case "MatrixUnitTests/MatrixMomentumSGDUpdate_WithAndWithout_UnitGain" has passed with:
+      32 assertions out of 32 passed
+
+    Test case "MatrixUnitTests/MatrixNesterovAcceleratedMomentumSGDUpdate_WithAndWithout_UnitGain" has passed with:
+      32 assertions out of 32 passed
 
     Test case "MatrixUnitTests/MatrixFSAdagradUpdate_WithAndWithout_UnitGain" has passed
-    
-  Test suite "QuantizersUnitTests" has passed with:
+
+  Test suite "MatrixLearnerSuite" has passed with:
     2 test cases out of 2 passed
-    12 assertions out of 12 passed
+    4 assertions out of 4 passed
 
-    Test case "QuantizersUnitTests/FloatToShort" has passed with:
-      6 assertions out of 6 passed
+    Test case "MatrixLearnerSuite/FSAdagradSparse" has passed with:
+      2 assertions out of 2 passed
 
-    Test case "QuantizersUnitTests/QuantizeZeros" has passed with:
-      6 assertions out of 6 passed
-
-  Test suite "QuantizedOperationsUnitTests" has passed with:
-    1 test case out of 1 passed
-    65 assertions out of 65 passed
-
-    Test case "QuantizedOperationsUnitTests/MultiplyIntToShort" has passed with:
-      65 assertions out of 65 passed
-
-  Test suite "MathTensorTests" has passed with:
-    9 test cases out of 9 passed
-    6 assertions out of 6 passed
-
-    Test case "MathTensorTests/ElementwiseAddition" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/AdditionWithSimpleBroadcasting" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/BiasAddition" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/BiasAddition2" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/BiasGradient" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/BiasGradient2" has passed with:
-      1 assertion out of 1 passed
-
-    Test case "MathTensorTests/ColumnSliceMultAndAdd" has passed
-
-    Test case "MathTensorTests/RnnForwardProp" has passed
-
-    Test case "MathTensorTests/OldRnnForwardProp" has passed
+    Test case "MatrixLearnerSuite/RmsPropSparse" has passed with:
+      2 assertions out of 2 passed