promote power_wrapper to 'detection' submodule

python/caffe/detection/detector.py

@@ -1,6 +1,6 @@
 """
-Classify a number of images at once, optionally using the selective
-search window proposal method.
+Do windowed detection by classifying a number of images/crops at once,
+optionally using the selective search window proposal method.
 
 This implementation follows
   Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
@@ -300,7 +300,7 @@ if __name__ == "__main__":
     "images_dim", 256, "Canonical dimension of (square) images.")
   gflags.DEFINE_string(
     "images_mean_file",
-    os.path.join(os.path.dirname(__file__), 'ilsvrc_2012_mean.npy'),
+    os.path.join(os.path.dirname(__file__), '../imagenet/ilsvrc_2012_mean.npy'),
     "Data set image mean (numpy array).")
   FLAGS = gflags.FLAGS
   FLAGS(sys.argv)

python/caffe/detection/selective_search_demo.ipynb

@@ -19,14 +19,14 @@
       "\n",
       "    wget http://farm1.static.flickr.com/220/512450093_7717fb8ce8.jpg\n",
       "    echo `pwd`/\"512450093_7717fb8ce8.jpg\" > image_cat.txt\n",
-      "    python power_wrapper.py --images_file=image_cat.txt --crop_mode=selective_search --model_def=<path to imagenet_deploy.prototxt> --pretrained_model=<path to alexnet_train_iter_470000> --output=selective_cat.h5\n",
+      "    python detector.py --images_file=image_cat.txt --crop_mode=selective_search --model_def=<path to imagenet_deploy.prototxt> --pretrained_model=<path to alexnet_train_iter_470000> --output=selective_cat.h5\n",
       "    \n",
       "    \n",
       "Running this outputs an HDF5 file with the filenames, selected windows, and their ImageNet scores.\n",
       "Of course, we only ran on one image, so the filenames will all be the same.\n",
       "\n",
-      "In general, `power_wrapper` is most efficient when running on a lot of images: it first extracts window proposals for all of them, then batches the windows for efficient GPU processing, and then outputs the results.\n",
-      "Simply list an image per line in the `images_file`, and `power_wrapper` will process all of them."
+      "In general, `detector` is most efficient when running on a lot of images: it first extracts window proposals for all of them, then batches the windows for efficient GPU processing, and then outputs the results.\n",
+      "Simply list an image per line in the `images_file`, and `detector` will process all of them."
      ]
     },
     {