diff --git a/CNN_CNN.ipynb b/CNN_CNN.ipynb new file mode 100644 index 0000000..16cc7f9 --- /dev/null +++ b/CNN_CNN.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import torch.utils.data as data_utils\n", + "import os\n", + "import numpy as np\n", + "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import KFold, StratifiedKFold\n", + "import csv\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "import gc\n", + "import h5py\n", + "import pickle as pk\n", + "\n", + "from utils import log_results, SaveBestModel, train_seq, test_seq\n", + "from utils import normalize_mel_sp_slides\n", + "\n", + "from models import cnn_cnn" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set directories" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "dataDir = 'processed'\n", + "resultsDir = 'Results'\n", + "tempDir = 'temp'\n", + "\n", + "if not os.path.exists(resultsDir):\n", + " os.makedirs(resultsDir)\n", + "if not os.path.exists(tempDir):\n", + " os.makedirs(tempDir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "fname = 'birds_cornell_spectr_slide_100_species_sr_32000_len_7_sec_500_250_New.h5'\n", + "fileLoc = os.path.join(dataDir,fname) # 19707 samples per class\n", + "hf = h5py.File(fileLoc, 'r')\n", + "mel_sp = hf.get('mel_spectr')[()]\n", + "metadata_total = pd.read_hdf(fileLoc, 'info')\n", + "hf.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of Species: 100\n" + ] + } + ], + "source": [ + "original_label = list(metadata_total['ebird_code'])\n", + "lb_bin = LabelBinarizer()\n", + "lb_enc = LabelEncoder()\n", + "labels_one_hot = lb_bin.fit_transform(original_label)\n", + "labels_multi_lbl = lb_enc.fit_transform(original_label)\n", + "\n", + "number_of_sample_classes = len(lb_enc.classes_)\n", + "print(\"Number of Species: \", number_of_sample_classes)\n", + "species_id_class_dict_tp = dict()\n", + "for (class_label, species_id) in enumerate(lb_bin.classes_):\n", + " species_id_class_dict_tp[species_id] = class_label" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transform data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "mel_sp_normalized = []\n", + "for i in range(len(mel_sp)):\n", + " xx_ = normalize_mel_sp_slides(mel_sp[i]).astype('float32')\n", + " mel_sp_normalized += [np.expand_dims(xx_, axis=-3)]\n", + "mel_sp_normalized = np.array(mel_sp_normalized)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## configs used in the current paper" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other configs can be generated using similar pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "cfg_cnn = [[32, 'M', 64, 64, 'M', 128, 128, 128, 'M', 128, 128, 128, 'M'],\n", + " [32, 64, 'M', 64, 64, 64, 'M', 128, 128, 128, 'M', 128, 128, 128, 'M', 256, 256, 256, 'M'],\n", + " [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']]\n", + "\n", + "cfg_ts = [[64, 64, 'M', 128, 128, 128, 'M', 128, 128, 128, 'M', 256, 256, 256, 'M'],\n", + " [64, 64, 'M', 128, 128, 128, 'M', 256, 256, 256, 'M', 256, 256, 256, 'M'],\n", + " [64, 64, 'M', 128, 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M']]\n", + "nunits = [256*4, 256*4, 512*4]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16*2\n", + "num_classes=100\n", + "shuffleBatches=True\n", + "num_epoch = 50" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run all configs of CNN+CNN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exp_no = 0\n", + "for ii, cfg1 in enumerate(cfg_cnn):\n", + " for jj, cfg2 in enumerate(cfg_ts):\n", + " exp_no += 1\n", + " \n", + " skf = StratifiedKFold(n_splits=5, random_state=42)\n", + "\n", + " log_file_name = f'100_species_spectr_cnn_p_cnn_7sec_{exp_no}.p'\n", + " store_ = log_results(file_name=log_file_name, results_dir = resultsDir)\n", + "\n", + " exp_ind = 0\n", + " for train_ind, test_ind in skf.split(mel_sp_normalized, labels_multi_lbl): #5-fold resampling\n", + "\n", + " PATH_curr = os.path.join(tempDir, f'currentModel_cnn_p_cnn_{exp_no}_{exp_ind}.pt')\n", + " saveModel = SaveBestModel(PATH=PATH_curr, monitor=-np.inf, verbose=True)\n", + "\n", + " X_train, X_test_p_valid = mel_sp_normalized[train_ind,:], mel_sp_normalized[test_ind,:]\n", + "\n", + " y_train, y_test_p_valid = labels_one_hot[train_ind], labels_one_hot[test_ind]\n", + " y_train_mlbl, y_test_p_valid_mlbl = labels_multi_lbl[train_ind], labels_multi_lbl[test_ind]\n", + " X_valid, X_test, \\\n", + " y_valid, y_test = train_test_split(X_test_p_valid, y_test_p_valid,\n", + " test_size=0.5,\n", + " stratify=y_test_p_valid_mlbl,\n", + " random_state=42)\n", + "\n", + " print('X_train shape: ', X_train.shape)\n", + " print('X_valid shape: ', X_valid.shape)\n", + " print('X_test shape: ', X_test.shape)\n", + "\n", + " X_train, X_valid = torch.from_numpy(X_train).float(), torch.from_numpy(X_valid).float()\n", + " y_train, y_valid = torch.from_numpy(y_train), torch.from_numpy(y_valid)\n", + "\n", + " y_train, y_valid = y_train.float(), y_valid.float()\n", + " train_use = data_utils.TensorDataset(X_train, y_train)\n", + " train_loader = data_utils.DataLoader(train_use, batch_size=batch_size, shuffle=shuffleBatches)\n", + "\n", + " val_use = data_utils.TensorDataset(X_valid, y_valid)\n", + " val_loader = data_utils.DataLoader(val_use, batch_size=32, shuffle=False)\n", + "\n", + " model = cnn_cnn(cfg1, \n", + " cfg2, \n", + " nunits[jj], \n", + " num_classes=100)\n", + " model.to(device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001, weight_decay=1e-7)\n", + "\n", + " val_acc_epochs = []\n", + " val_loss_epochs = []\n", + " for epoch in range(1, num_epoch+1):\n", + " train_loss = train_seq(model, train_loader, optimizer, epoch, \n", + " device,\n", + " verbose=1, loss_fn = 'bceLogit')\n", + " val_loss, val_acc = test_seq(model, val_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " val_acc_epochs.append(val_acc)\n", + " val_loss_epochs.append(val_loss)\n", + " print('val loss = %f, val acc = %f'%(val_loss, val_acc))\n", + " saveModel.check(model, val_acc, comp='max')\n", + "\n", + " # loading best validated model\n", + " model = cnn_cnn(cfg1, \n", + " cfg2, \n", + " nunits[jj], \n", + " num_classes=100)\n", + " model.to(device)\n", + " model.load_state_dict(torch.load(PATH_curr))\n", + "\n", + " X_test, y_test = torch.from_numpy(X_test).float(), torch.from_numpy(y_test).float()\n", + "\n", + " test_use = data_utils.TensorDataset(X_test, y_test)\n", + " test_loader = data_utils.DataLoader(test_use, batch_size=32, shuffle=False)\n", + " test_loss, test_acc = test_seq(model, test_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " print('test loss = %f, test acc = %f'%(test_loss, test_acc))\n", + "\n", + " log_ = dict(\n", + " exp_ind = exp_ind,\n", + " epochs = num_epoch,\n", + " validation_accuracy = val_acc_epochs,\n", + " validation_loss = val_loss_epochs,\n", + " test_loss = test_loss,\n", + " test_accuracy = test_acc,\n", + " X_train_shape = X_train.shape,\n", + " X_valid_shape = X_valid.shape,\n", + " batch_size =batch_size,\n", + " )\n", + " store_.update(log_)\n", + " exp_ind += 1 \n", + " print(f'COMPLETED for configs {ii, jj}...')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CNN_RNN.ipynb b/CNN_RNN.ipynb new file mode 100644 index 0000000..90a8507 --- /dev/null +++ b/CNN_RNN.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import torch.utils.data as data_utils\n", + "import os\n", + "import numpy as np\n", + "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import KFold, StratifiedKFold\n", + "import csv\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "import gc\n", + "import h5py\n", + "import pickle as pk\n", + "\n", + "from utils import log_results, SaveBestModel, train_seq, test_seq\n", + "from utils import normalize_mel_sp_slides\n", + "\n", + "from models import cnn_rnn" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set directories" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dataDir = 'processed'\n", + "resultsDir = 'Results'\n", + "tempDir = 'temp'\n", + "\n", + "if not os.path.exists(resultsDir):\n", + " os.makedirs(resultsDir)\n", + "if not os.path.exists(tempDir):\n", + " os.makedirs(tempDir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fname = 'birds_cornell_spectr_slide_100_species_sr_32000_len_7_sec_500_250_New.h5'\n", + "fileLoc = os.path.join(dataDir,fname) # 19707 samples per class\n", + "hf = h5py.File(fileLoc, 'r')\n", + "mel_sp = hf.get('mel_spectr')[()]\n", + "metadata_total = pd.read_hdf(fileLoc, 'info')\n", + "hf.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of Species: 100\n" + ] + } + ], + "source": [ + "original_label = list(metadata_total['ebird_code'])\n", + "lb_bin = LabelBinarizer()\n", + "lb_enc = LabelEncoder()\n", + "labels_one_hot = lb_bin.fit_transform(original_label)\n", + "labels_multi_lbl = lb_enc.fit_transform(original_label)\n", + "\n", + "number_of_sample_classes = len(lb_enc.classes_)\n", + "print(\"Number of Species: \", number_of_sample_classes)\n", + "species_id_class_dict_tp = dict()\n", + "for (class_label, species_id) in enumerate(lb_bin.classes_):\n", + " species_id_class_dict_tp[species_id] = class_label" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mel_sp_normalized = []\n", + "for i in range(len(mel_sp)):\n", + " xx_ = normalize_mel_sp_slides(mel_sp[i]).astype('float32')\n", + " mel_sp_normalized += [np.expand_dims(xx_, axis=-3)]\n", + "mel_sp_normalized = np.array(mel_sp_normalized)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16*2\n", + "shuffleBatches=True\n", + "num_epoch = 50" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN configs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cfg_cnn = [32, 'M', 64, 64, 'M', 128, 128, 128, 'M', 128, 128, 128, 'M'] # CNN1\n", + "# n_units = 128*2\n", + "\n", + "cfg_cnn2 = [32, 64, 'M', 64, 64, 64, 'M', 128, 128, 128, 'M', 128, 128, 128, 'M', 256, 256, 256, 'M']\n", + "# n_units = 256*2\n", + "\n", + "cfg_cnn3 = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'] # CNN3\n", + "n_units = 512*2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN configs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For RNN, a list of configs could be provided for testing multiple configurations.\n", + "\n", + "Each configuration element is dictionary with key as 'ordered' name of required RNNS. For example, to have 2 layers of GRUs, use 'GRU_0', 'GRU_1', similarly, for 1 GRU followed by 1 LMU, use 'GRU_0', 'LMU_1', contrary, to use LMU and then GRU, use 'LMU_0', 'GRU_1'. Currently supported RNN cells are LSTM, GRU, and LMU.\n", + "\n", + "Each key has value as another dictionary with entries:\n", + "input_size-> input dimension of this RNN cell\n", + "h_states_ctr-> number of inner states in the RNN cell. For LSTM it is 2, GRU has 1, LMU has 2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hidden_size = 512\n", + "rnnConfigs = [\n", + " {'LSTM_0':{'input_size':n_units, 'h_states_ctr':2},\n", + " 'LSTM_1':{'input_size':hidden_size, 'h_states_ctr':2} # 2 layers of LSTM cell\n", + " },\n", + " {'LMU_0':{'input_size':n_units, 'h_states_ctr':2},\n", + " 'LMU_1':{'input_size':hidden_size, 'h_states_ctr':2}, # 2 layers of LMU cell\n", + " },\n", + " {'GRU_0':{'input_size':n_units, 'h_states_ctr':1},\n", + " 'GRU_1':{'input_size':hidden_size, 'h_states_ctr':1}, # 2 layers of GRU cell\n", + " },\n", + " {'GRU_0':{'input_size':n_units, 'h_states_ctr':1},\n", + " 'LMU_1':{'input_size':hidden_size, 'h_states_ctr':2}, # 1 GRU cell and then 1 LMU cell\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "make sure to assign different exp_no for each experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exp_no_base = 0\n", + "exp_ctr = 0\n", + "for ii, cfg in enumerate(rnnConfigs):\n", + " exp_ctr += 1\n", + "\n", + " exp_no = exp_no_base + exp_ctr\n", + " log_file_name = f'100_species_spectr_cnn_rnn_7sec_h_{hidden_size}_nl_{ii+1}_{exp_no}.p'\n", + " store_ = log_results(file_name=log_file_name, results_dir = resultsDir)\n", + " PATH_curr = os.path.join(tempDir, f'currentModel_cnn_rnn_{exp_no}.pt')\n", + " saveModel = SaveBestModel(PATH=PATH_curr, monitor=-np.inf, verbose=True)\n", + "\n", + " exp_ind = 0\n", + " skf = StratifiedKFold(n_splits=5, random_state=42)\n", + " for train_ind, test_ind in skf.split(mel_sp_normalized, labels_multi_lbl):\n", + "\n", + " PATH_curr = os.path.join(tempDir, f'currentModel_cnn_rnn_{exp_no}_{exp_ind}.pt')\n", + " saveModel = SaveBestModel(PATH=PATH_curr, monitor=-np.inf, verbose=True)\n", + "\n", + " X_train, X_test_p_valid = mel_sp_normalized[train_ind,:], mel_sp_normalized[test_ind,:]\n", + "\n", + " y_train, y_test_p_valid = labels_one_hot[train_ind], labels_one_hot[test_ind]\n", + " y_train_mlbl, y_test_p_valid_mlbl = labels_multi_lbl[train_ind], labels_multi_lbl[test_ind]\n", + " X_valid, X_test, y_valid, y_test = train_test_split(X_test_p_valid, y_test_p_valid,\n", + " test_size=0.5,\n", + " stratify=y_test_p_valid_mlbl,\n", + " random_state=42)\n", + "\n", + " print('X_train shape: ', X_train.shape)\n", + " print('X_valid shape: ', X_valid.shape)\n", + " print('X_test shape: ', X_test.shape)\n", + "\n", + " X_train, X_valid = torch.from_numpy(X_train).float(), torch.from_numpy(X_valid).float()\n", + " y_train, y_valid = torch.from_numpy(y_train), torch.from_numpy(y_valid)\n", + "\n", + " y_train, y_valid = y_train.float(), y_valid.float()\n", + " train_use = data_utils.TensorDataset(X_train, y_train)\n", + " train_loader = data_utils.DataLoader(train_use, batch_size=batch_size, shuffle=shuffleBatches)\n", + "\n", + " val_use = data_utils.TensorDataset(X_valid, y_valid)\n", + " val_loader = data_utils.DataLoader(val_use, batch_size=32, shuffle=False)\n", + "\n", + " model = cnn_rnn(cnnConfig = cfg_cnn3, \n", + " rnnConfig = cfg, \n", + " hidden_size=hidden_size, \n", + " order=order,\n", + " theta=theta,\n", + " num_classes=100)\n", + " model.to(device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001, weight_decay=1e-7)\n", + "\n", + " val_acc_epochs = []\n", + " val_loss_epochs = []\n", + " for epoch in range(1, num_epoch+1):\n", + " train_loss = train_seq(model, train_loader, optimizer, epoch, \n", + " device,\n", + " verbose=1, loss_fn = 'bceLogit')\n", + " val_loss, val_acc = test_seq(model, val_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " val_acc_epochs.append(val_acc)\n", + " val_loss_epochs.append(val_loss)\n", + " print('val loss = %f, val acc = %f'%(val_loss, val_acc))\n", + " saveModel.check(model, val_acc, comp='max')\n", + "\n", + " # loading best validated model\n", + " model = cnn_rnn(cnnConfig = cfg_cnn3, \n", + " rnnConfig = cfg, \n", + " hidden_size=hidden_size, \n", + " order=order,\n", + " theta=theta,\n", + " num_classes=100)\n", + " model.to(device)\n", + " model.load_state_dict(torch.load(PATH_curr))\n", + "\n", + " X_test, y_test = torch.from_numpy(X_test).float(), torch.from_numpy(y_test).float()\n", + "\n", + " test_use = data_utils.TensorDataset(X_test, y_test)\n", + " test_loader = data_utils.DataLoader(test_use, batch_size=32, shuffle=False)\n", + " test_loss, test_acc = test_seq(model, test_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " print('test loss = %f, test acc = %f'%(test_loss, test_acc))\n", + "\n", + " log_ = dict(\n", + " exp_ind = exp_ind,\n", + " epochs = num_epoch,\n", + " validation_accuracy = val_acc_epochs,\n", + " validation_loss = val_loss_epochs,\n", + " test_loss = test_loss,\n", + " test_accuracy = test_acc,\n", + " X_train_shape = X_train.shape,\n", + " X_valid_shape = X_valid.shape,\n", + " batch_size =batch_size,\n", + " )\n", + " store_.update(log_)\n", + " exp_ind += 1 " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md index 5cd7cec..13bb69d 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,23 @@ -# Project +# Recurrent Concolutional Neural networks for sound classification -> This repo has been populated by an initial template to help get you started. Please -> make sure to update the content to build a great experience for community-building. +We present a deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different birdspecies. We use spectrograms constructed on bird audio recordings from the Cornell Bird Challenge (CBC)2020 dataset, which includes recordings of multiple and potentially overlapping bird vocalizations per audio and recordings with background noise. Our experiments show that a hybrid modeling approach that involves a Convolutional Neural Network (CNN) for learning therepresentation for a slice of the spectrogram, and a Recurrent Neural Network (RNN) for the temporal component to combineacross time-points leads to the most accurate model on this dataset. The code has models ranging from stand-alone CNNs to hybrid models of various types obtained by combining CNNs with CNNs or RNNs of the following types:Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU) and Legendre Memory Units (LMU). -As the maintainer of this project, please make a few updates: +## Setup -- Improving this README.MD file to provide a great experience -- Updating SUPPORT.MD with content about this project's support experience -- Understanding the security reporting process in SECURITY.MD -- Remove this section from the README +### Requirements +The code package is developed using Python 3.6 and Pytorch 1.2 with cuda 10.0. For running the experiments first install the required packages using 'requirements.txt' -## Contributing +## Experiments +The data for bird sound classification is downloaded from the Kaggle competition [Cornell birdcall Identification](https://www.kaggle.com/c/birdsong-recognition). -This project welcomes contributions and suggestions. Most contributions require you to agree to a -Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us -the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. +For running the experiments, a data preprocessing pipeline is demostrated in the process_data.ipynb -When you submit a pull request, a CLA bot will automatically determine whether you need to provide -a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions -provided by the bot. You will only need to do this once across all repos using our CLA. +After preprocessing the data, the RCNN models with various combinations of representation/temporal models can be run as follows: -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or -contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. +### CNN + CNN +An example is shown in CNN_CNN.ipynb notebook for the CNN and TCNN configs taken in the paper. In a similar way, a different set of configs could be supplied to the cnn+cnn model. +### CNN + RNN +An exampe for CNN+GRU, CNN+LMU, and CNN+LSTM is shown in CNN_RNN.ipynh notebook. Other variants of RCNNs with different set of parameters can be set as explained in the notebook. ## Trademarks diff --git a/models.py b/models.py new file mode 100644 index 0000000..e002f93 --- /dev/null +++ b/models.py @@ -0,0 +1,715 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. + +import torch +import torch.nn as nn +from torch import Tensor +import torch.nn.functional as F +import torch.optim as optim +from torch.autograd import Variable +import torch.utils.data as data_utils + +import numpy as np +import math + +from typing import List, Tuple +from scipy.special import legendre +from nengolib.signal import Identity, cont2discrete +from nengolib.synapses import LegendreDelay + +# VGG pytorch model is taken from: +# https://pytorch.org/vision/stable/_modules/torchvision/models/vgg.html + +cfg_vgg16 = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'] +class VGG16_pool(nn.Module): + def __init__(self, cfg=cfg_vgg16, num_classes=10, init_weights=True): + super(VGG16_pool, self).__init__() + + self.convBlock = self.make_layers(cfg) + self.avgpool = nn.AdaptiveAvgPool2d((7,7)) + self.Dense1 = nn.Linear(512*7*7, 4096) + self.Dense2 = nn.Linear(4096, 4096) + self.Dense3 = nn.Linear(4096, num_classes) + self.dropout1 = nn.Dropout(0.5) + self.dropout2 = nn.Dropout(0.5) + + if init_weights: + self._initialize_weights() + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.convBlock(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = F.relu(self.Dense1(x)) + x = self.dropout1(x) + x = F.relu(self.Dense2(x)) + x = self.Dense3(x) + return x + + def make_layers(self, cfg): + layers = [] + in_channels = 3 + for layer in cfg: + if layer == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + conv2d = nn.Conv2d(in_channels, layer, kernel_size=3, padding=1) + layers += [conv2d, nn.BatchNorm2d(layer), nn.ReLU(inplace=True)] + in_channels = layer + return nn.Sequential(*layers) + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + +# Resnet pytorch model is taken from: +# https://pytorch.org/vision/stable/_modules/torchvision/models/resnet.html +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + + def forward(self, x): + return self._forward_impl(x) + +def resnet50(**kwargs): + return ResNet(Bottleneck, [3, 4, 6, 3], + **kwargs) + +def resnet18(**kwargs): + return ResNet(Bottleneck, [2, 2, 2, 2], + **kwargs) + + +class cnn(nn.Module): + def __init__(self, cfg, init_weights=True): + super(cnn, self).__init__() + + self.convBlock = self.make_layers(cfg) + self.avgpool = nn.AdaptiveAvgPool2d((2,1)) + + if init_weights: + self._initialize_weights() + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.convBlock(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + + def make_layers(self, cfg): + layers = [] + in_channels = 1 + for layer in cfg: + if layer == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + conv2d = nn.Conv2d(in_channels, layer, kernel_size=3, padding=1) + layers += [conv2d, nn.BatchNorm2d(layer), nn.ReLU(inplace=True)] + in_channels = layer + return nn.Sequential(*layers) + + +class cnn_ts(nn.Module): + def __init__(self, cfg_ts, init_weights=True): + super(cnn_ts, self).__init__() + + self.conv = self.make_layers(cfg_ts) + self.avgpool = nn.AdaptiveAvgPool2d((1,4)) + + if init_weights: + self._initialize_weights() + + def forward(self, x): + x = self.conv(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def make_layers(self, cfg): + layers = [] + in_channels = 1 + for layer in cfg: + if layer == 'M': + layers += [nn.MaxPool2d(kernel_size=(2,3), stride=(2,3))] + else: + conv2d = nn.Conv2d(in_channels, layer, kernel_size=3, padding=1) + layers += [conv2d, nn.BatchNorm2d(layer), nn.ReLU(inplace=True)] + in_channels = layer + return nn.Sequential(*layers) + +class cnn_cnn(nn.Module): + def __init__(self, + cfg, + cfg_ts, + n_units_ts, + num_classes=100): + super(cnn_cnn, self).__init__() + + self.cnn = cnn(cfg) + self.cnn1 = cnn_ts(cfg_ts) + + self.linear1 = nn.Linear(n_units_ts, 512) + self.dropout1 = nn.Dropout(0.5) + self.linear2 = nn.Linear(512, num_classes) + + def forward(self, x, hidden_state=None): + batch_size, timesteps, C, H, W = x.size() + c_in = x.view(batch_size * timesteps, C, H, W) + c_out = self.cnn(c_in) + x = c_out.view(batch_size, timesteps, -1) + x = x.unsqueeze(1) + x = self.cnn1(x) + x = F.relu(self.linear1(x)) + x = self.dropout1(x) + x = self.linear2(x) + return x + + def init_hidden(self, batch_size): + return torch.zeros(1, batch_size, 1) + + +class cnn_lstm(nn.Module): + def __init__(self, n_units, cfg, num_layers=2, num_classes=100): + super(cnn_lstm, self).__init__() + + self.cnn = cnn(cfg) + self.hidden_size = 512 + self.num_layers = num_layers + self.rnn1 = nn.LSTM(input_size=n_units, hidden_size=self.hidden_size, batch_first=True, num_layers=self.num_layers) + self.linear1 = nn.Linear(self.hidden_size, 512) + self.dropout1 = nn.Dropout(0.5) + self.linear2 = nn.Linear(512, num_classes) + + def forward(self, x, hidden_state=None): + batch_size, timesteps, C, H, W = x.size() + c_in = x.view(batch_size * timesteps, C, H, W) + c_out = self.cnn(c_in) + r_in = c_out.view(batch_size, timesteps, -1) + x, _ = self.rnn1(r_in, hidden_state) + x = x.sum(dim=1) + x = self.linear1(x) + # x = self.linear1(x[:, -1, :]) # feeding last ouput of seq to linear layer (OPTIONAL) + x = F.relu(x) + x = self.dropout1(x) + x = self.linear2(x) + return x + + def init_hidden(self, batch_size): + return (torch.zeros(self.num_layers, batch_size, self.hidden_size), + torch.zeros(self.num_layers, batch_size, self.hidden_size)) + + +def Legendre(shape): + if len(shape) != 2: + raise ValueError("Legendre initializer assumes shape is 2D; " + "but shape=%s" % (shape,)) + return np.asarray([legendre(i)(np.linspace(-1, 1, shape[1])) + for i in range(shape[0])]) + + +# LMU cell taken from: https://github.com/nengo/keras-lmu +# and converted to pytorch +class LMUCell(nn.Module): + def __init__(self, + input_dim, + units, + order, + theta, # relative to dt=1 + method='zoh', + realizer=Identity(), # TODO: Deprecate? + factory=LegendreDelay, # TODO: Deprecate? + trainable_input_encoders=True, + trainable_hidden_encoders=True, + trainable_memory_encoders=True, + trainable_input_kernel=True, + trainable_hidden_kernel=True, + trainable_memory_kernel=True, + trainable_A=False, + trainable_B=False, + input_encoders_initializer='lecun_uniform', + input_encoders_initial_val = 0, + hidden_encoders_initializer='lecun_uniform', + hidden_encoders_initial_val = 0, + memory_encoders_initializer='Constant', # 'lecun_uniform', + memory_encoders_initial_val = 0, + input_kernel_initializer='glorot_normal', + input_kernel_initial_val = 0, + hidden_kernel_initializer='glorot_normal', + hidden_kernel_initial_val = 0, + memory_kernel_initializer='glorot_normal', + memory_kernel_initial_val = 0, + hidden_activation='tanh', + **kwargs): + super(LMUCell,self).__init__() + + self.units = units + self.order = order + self.theta = theta + self.method = method + self.realizer = realizer + self.factory = factory + self.trainable_input_encoders = trainable_input_encoders + self.trainable_hidden_encoders = trainable_hidden_encoders + self.trainable_memory_encoders = trainable_memory_encoders + self.trainable_input_kernel = trainable_input_kernel + self.trainable_hidden_kernel = trainable_hidden_kernel + self.trainable_memory_kernel = trainable_memory_kernel + self.trainable_A = trainable_A + self.trainable_B = trainable_B + + self.hidden_activation = hidden_activation + + self._realizer_result = realizer( + factory(theta=theta, order=self.order)) + self._ss = cont2discrete( + self._realizer_result.realization, dt=1., method=method) + self._A = self._ss.A - np.eye(order) # puts into form: x += Ax + self._B = self._ss.B + self._C = self._ss.C + assert np.allclose(self._ss.D, 0) # proper LTI + + self.state_size = (self.units, self.order) + self.output_size = self.units + + def weight_mod(input_dim, output_dim, initialization, + constant_val = 0): + + w = torch.FloatTensor(input_dim, output_dim) + w.requires_grad = True + + if initialization == 'lecun_uniform': + torch.nn.init.kaiming_uniform_(w) + elif initialization == 'glorot_normal': + torch.nn.init.xavier_normal_(w) + elif initialization == 'Constant': + if np.size(constant_val) == 1: + torch.nn.init.constant_(w, constant_val) + else: + w.data = torch.from_numpy(constant_val).float() + elif initialization == 'Legendre': + w.data = torch.from_numpy(Legendre((input_dim, output_dim))).float() + + elif initialization == 'uniform': + stdv = 1.0 / math.sqrt(self.state_size[0]) + torch.nn.init.uniform_(w, -stdv, stdv) + + return w + + self.input_encoders = nn.Parameter(weight_mod(input_dim, 1, + initialization=input_encoders_initializer, + constant_val = input_encoders_initial_val) + ) + if not self.trainable_input_encoders: + self.input_encoders.requires_grad = False + + self.hidden_encoders = nn.Parameter(weight_mod(self.units, 1, + initialization=hidden_encoders_initializer, + constant_val = hidden_encoders_initial_val) + ) + + if not self.trainable_hidden_encoders: + self.hidden_encoders.requires_grad = False + + self.memory_encoders = nn.Parameter(weight_mod(self.order, 1, + initialization='Constant', + constant_val=0) + ) + + if not self.trainable_memory_encoders: + self.memory_encoders.requires_grad = False + + self.input_kernel = nn.Parameter(weight_mod(input_dim, self.units, + initialization=input_kernel_initializer, + constant_val = input_kernel_initial_val) + ) + + if not self.trainable_input_kernel: + self.input_kernel.requires_grad = False + + self.hidden_kernel = nn.Parameter(weight_mod(self.units, self.units, + initialization=hidden_kernel_initializer, + constant_val = hidden_kernel_initial_val) + ) + + if not self.trainable_hidden_kernel: + self.hidden_kernel.requires_grad = False + + self.memory_kernel = nn.Parameter(weight_mod(self.order, self.units, + initialization=memory_kernel_initializer, + constant_val = memory_kernel_initial_val) + ) + + if not self.trainable_memory_kernel: + self.memory_kernel.requires_grad = False + + self.AT = nn.Parameter(weight_mod(self.order, self.order, + initialization='Constant', + constant_val=self._A.T) # transposed + ) + + if not self.trainable_A: + self.AT.requires_grad = False + + self.BT = nn.Parameter(weight_mod(1, self.order, + initialization='Constant', + constant_val=self._B.T) # transposed + ) + if not self.trainable_B: + self.BT.requires_grad = False + + + def forward(self, inputs, states): + h, m = states + + u = torch.mm(inputs, self.input_encoders) \ + + torch.mm(h, self.hidden_encoders) \ + + torch.mm(m, self.memory_encoders) + + m = m + torch.mm(m, self.AT) + torch.mm(u, self.BT) + + if self.hidden_activation == 'tanh': + h = torch.tanh( + torch.mm(inputs, self.input_kernel) + + torch.mm(h, self.hidden_kernel) + + torch.mm(m, self.memory_kernel) + ) + elif self.hidden_activation == 'linear': + h = torch.mm(inputs, self.input_kernel) \ + + torch.mm(h, self.hidden_kernel) \ + + torch.mm(m, self.memory_kernel) + + return h, (h, m) + + +# using https://github.com/pytorch/pytorch/blob/master/benchmarks/fastrnns/custom_lstms.py +# for custom LSTMs +class LMU(nn.Module): + def __init__(self, inp_size = 1, order = 100, theta=100, output_dims = 1): + super(LMU, self).__init__() + + self.units = output_dims + self.order = order + self.output_dims = output_dims + self.lmu_cell = LMUCell( + input_dim = inp_size, + units=output_dims, + order=order, + theta = theta, + input_encoders_initializer='uniform', + hidden_encoders_initializer='uniform', + memory_encoders_initializer='uniform', # 'lecun_uniform', + input_kernel_initializer='uniform', + hidden_kernel_initializer='uniform', + memory_kernel_initializer='uniform', + ) + + def forward(self, x, state): + x = x.unbind(1) + outputs = torch.jit.annotate(List[Tensor], []) + for i in range(len(x)): + out, state = self.lmu_cell(x[i], state) + outputs += [out] + + return torch.stack(outputs).permute(1, 0, 2), state # axes permuted to make output of shape B, seq_len, num_outputs + + def init_hidden(self, batch_size): + return (torch.zeros(batch_size, self.units), + torch.zeros(batch_size, self.order)) + +# using https://github.com/pytorch/pytorch/blob/master/benchmarks/fastrnns/custom_lstms.py +# for custom LSTMs +class LMUGate(nn.Module): + def __init__(self, inp_size = 1, order = 100, theta=100, output_dims = 1): + super(LMUGate, self).__init__() + + self.units = output_dims + self.order = order + self.output_dims = output_dims + self.lmu_cell = LMUCellGate( + input_dim = inp_size, + units=output_dims, + order=order, + theta = theta, + ) + + def forward(self, x, state): + x = x.unbind(1) + outputs = torch.jit.annotate(List[Tensor], []) + for i in range(len(x)): + out, state = self.lmu_cell(x[i], state) + outputs += [out] + + return torch.stack(outputs).permute(1, 0, 2), state # axes permuted to make output of shape B, seq_len, num_outputs + + def init_hidden(self, batch_size): + return (torch.zeros(batch_size, self.units), + torch.zeros(batch_size, self.order)) + + +class cnn_rnn(nn.Module): + def __init__(self, + theta=500, + num_classes=100, + order=100, + hidden_size = 512, + cnnConfig = cfg_vgg16, + rnnConfig = { + 'LMU_0': + {'input_size':1024, 'h_states_ctr':1} + } + ): + super(cnn_rnn, self).__init__() + + # sample: GRU -> LMU + # rnnConfig = { + # 'GRU_0': + # {'input_size':n_units, 'h_states_ctr':1}, + # 'LMU_1': + # {'input_size':self.hidden_size, 'h_states_ctr':2}, + # } + + self.cnn = cnn(cnnConfig) + self.hidden_size = hidden_size + self.order = order + self.theta = theta + self.rnnConfig = rnnConfig + self.rnnBlock = self.make_rnn() + self.linear1 = nn.Linear(self.hidden_size, 512) + self.dropout1 = nn.Dropout(0.5) + self.linear2 = nn.Linear(512, num_classes) + + def forward(self, x, hidden_state): + batch_size, timesteps, C, H, W = x.size() + c_in = x.view(batch_size * timesteps, C, H, W) + c_out = self.cnn(c_in) + x = c_out.view(batch_size, timesteps, -1) + + ctr = 0 + for (rnn_name, config_), rnn in zip(self.rnnConfig.items(), self.rnnBlock): + if config_['h_states_ctr']==1: + h_s = hidden_state[ctr] + else: + h_s = tuple([hidden_state[ctr+i] for i in range(config_['h_states_ctr'])]) + ctr += config_['h_states_ctr'] + x, _ = rnn(x, h_s) + + x = x.sum(dim=1) + x = self.linear1(x) + x = F.relu(x) + x = self.dropout1(x) + x = self.linear2(x) + return x + + def make_rnn(self): + layers = [] + for rnn_name, config_ in self.rnnConfig.items(): + if rnn_name.split('_')[0] == 'LMU': + layers += [LMU(inp_size=config_['input_size'], + order=self.order, theta=self.theta, + output_dims=self.hidden_size)] + elif rnn_name.split('_')[0] == 'GRU': + layers += [nn.GRU(input_size=config_['input_size'], + hidden_size=self.hidden_size, batch_first=True)] + elif rnn_name.split('_')[0] == 'LSTM': + layers += [nn.LSTM(input_size=config_['input_size'], + hidden_size=self.hidden_size, batch_first=True)] + + return nn.ModuleList(layers) + + + def init_hidden(self, batch_size): + h_s = [] + for rnn_name in self.rnnConfig.keys(): + if rnn_name.split('_')[0] == 'LMU': + h_s += [torch.zeros(batch_size, self.hidden_size)] + h_s += [torch.zeros(batch_size, self.order)] + elif rnn_name.split('_')[0] == 'GRU': + h_s += [torch.zeros(1, batch_size, self.hidden_size)] + elif rnn_name.split('_')[0] == 'LSTM': + h_s += [torch.zeros(1, batch_size, self.hidden_size)] + h_s += [torch.zeros(1, batch_size, self.hidden_size)] + return tuple(h_s) \ No newline at end of file diff --git a/process_data.ipynb b/process_data.ipynb new file mode 100644 index 0000000..697025a --- /dev/null +++ b/process_data.ipynb @@ -0,0 +1,626 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import glob\n", + "import os\n", + "import librosa\n", + "import librosa.display\n", + "import soundfile as sf\n", + "import audioread\n", + "import wave\n", + "import re\n", + "from collections import Counter\n", + "import cv2\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "import math\n", + "from joblib import Parallel, delayed\n", + "import multiprocessing\n", + "import gc\n", + "import random\n", + "\n", + "import pickle as pk\n", + "import h5py\n", + "from fastprogress.fastprogress import progress_bar\n", + "\n", + "import IPython.display as ipd\n", + "from IPython.display import Audio, display\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.path.join('train_audio')\n", + "extended_data_dir = os.path.join('train_extended') # extended data downloaded from Kaggle competition\n", + "dataDir = 'data'\n", + "resultsDir = 'Results'\n", + "\n", + "# sampling_rate = 44100 # original\n", + "sampling_rate = 32000\n", + "\n", + "if not os.path.exists(dataDir):\n", + " os.makedirs(dataDir)\n", + "if not os.path.exists(resultsDir):\n", + " os.makedirs(resultsDir)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "train_metadata = pd.read_csv(os.path.join('train.csv'))\n", + "train_extended_metadata = pd.read_csv('train_extended.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "birds_species = [x.split('/')[-1] for x in glob.glob('train_audio/*')]\n", + "data_labels = {birds_species[i]:i for i in range(len(birds_species))}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_audio(fname, sr):\n", + " try:\n", + " sx, sr = librosa.load(fname, sr=sr)\n", + " print('ok')\n", + " return sx\n", + " except ZeroDivisionError as zer:\n", + " print('0')\n", + " return []\n", + " except:\n", + " print('d')\n", + " return []\n", + " \n", + "raw_audio_next = []\n", + "labels_next = []\n", + "num_cores = multiprocessing.cpu_count()\n", + "ctr_ = 0\n", + "meta_data = []\n", + "for specie, label in progress_bar(data_labels.items()):\n", + " \n", + " data_location = os.path.join(data_dir, specie)\n", + " idx_use = train_metadata['ebird_code'] == specie\n", + " metadata_specie = train_metadata.loc[idx_use]\n", + " filenames = list(metadata_specie['filename'])\n", + " samples_loc_list = [os.path.join(data_location, file) for file in filenames]\n", + "\n", + " if len(samples_loc_list)>=100:\n", + " audio_ = Parallel(n_jobs=num_cores)(\n", + " delayed(get_audio)(fname, sampling_rate) \n", + " for fname in samples_loc_list\n", + " )\n", + " raw_audio_next.extend(audio_)\n", + " labels_next.extend([specie for _ in range(len(samples_loc_list))]) \n", + " meta_data.append(metadata_specie)\n", + " print(f'taking {specie}...')\n", + " ctr_ += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "meta_data = pd.concat(meta_data, sort=False).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "labels_freq = {a:labels_next.count(a) for a in labels_next}" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "labels_extended = list(train_extended_metadata['ebird_code'])\n", + "labels_freq_extended = {a:labels_extended.count(a) for a in labels_extended}" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "labels_freq_total = dict(labels_freq)\n", + "for key, val in labels_freq_extended.items():\n", + " labels_freq_total[key] += val" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1778" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(labels_freq_total.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(labels_freq.values())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_audio(fname, sr):\n", + " try:\n", + " sx, sr = librosa.load(fname, sr=sr)\n", + " return sx\n", + " except ZeroDivisionError as zer:\n", + " return []\n", + " except:\n", + " return []\n", + " \n", + "raw_audio_extended = []\n", + "labels_extended = []\n", + "metadata_extended = []\n", + "num_cores = multiprocessing.cpu_count()\n", + "ctr_ = 0\n", + "\n", + "for specie in progress_bar(labels_freq.keys()):\n", + " data_location = os.path.join(extended_data_dir, specie)\n", + " idx_use = train_extended_metadata['ebird_code'] == specie\n", + " metadata_specie = train_extended_metadata.loc[idx_use]\n", + " filenames = list(metadata_specie['filename'])\n", + " samples_loc_list = [os.path.join(data_location, file) for file in filenames]\n", + " \n", + " audio_ = Parallel(n_jobs=num_cores)(\n", + " delayed(get_audio)(fname, sampling_rate) for fname in samples_loc_list\n", + " )\n", + " raw_audio_extended.extend(audio_)\n", + " labels_extended.extend([specie for _ in range(len(samples_loc_list))])\n", + " metadata_extended.append(metadata_specie)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metadata_extended = pd.concat(metadata_extended, sort=False).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# possible empty data in the extended dataset\n", + "idx_pop = []\n", + "for ii, audio in enumerate(raw_audio_extended):\n", + " if len(audio)==0:\n", + " idx_pop.append(ii)\n", + "\n", + "raw_audio_extended = [audio for ii, audio in enumerate(raw_audio_extended) if ii not in idx_pop]\n", + "labels_extended = [lbl for ii, lbl in enumerate(labels_extended) if ii not in idx_pop]\n", + "metadata_extended = metadata_extended.drop(idx_pop).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "raw_audio_next.extend(raw_audio_extended)\n", + "labels_next.extend(labels_extended)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "metadata_total = pd.concat([meta_data, metadata_extended], sort=False).reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Savecloseaudio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = 'birds_cornell_audio_100_species_sr_32000.h5'\n", + "hf = h5py.File(os.path.join(dataDir,\n", + " file_name), 'w')\n", + "grp = hf.create_group('raw_audio')\n", + "for ii, audio in enumerate(raw_audio_next):\n", + " grp.create_dataset(np.str(ii), data = audio)\n", + "\n", + "hf.create_dataset('sr', data=sampling_rate)\n", + "hf.close()\n", + "metadata_total.to_hdf(os.path.join(dataDir,\n", + " file_name), key='info')" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "librosa.display.waveplot(raw_audio_next[0][:7*sampling_rate], sr=sampling_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spectrograms" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "labels = np.unique(metadata_extended['ebird_code'])\n", + "spectr_time_len = 7\n", + "idx_use = []\n", + "ctr = 0\n", + "for lbl in labels:\n", + " idx_label = metadata_extended.loc[metadata_extended['ebird_code'] == lbl].index\n", + " useful = []\n", + " for ii in idx_label:\n", + " if len(raw_audio_extended[ii])>=spectr_time_len*sampling_rate:\n", + " useful.append(ii)\n", + " if len(useful)>20:\n", + " idx_use.extend(useful)\n", + " ctr += 1\n", + " if ctr == 100:\n", + " break\n", + "metadata_clipped = metadata_extended.loc[idx_use].reset_index(drop=True)\n", + "clipped_audio = []\n", + "for idx in idx_use:\n", + " clipped_audio.append(raw_audio_extended[idx][:spectr_time_len*sampling_rate])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "S = librosa.feature.melspectrogram(\n", + " y = clipped_audio[1],\n", + " sr = sampling_rate,\n", + " n_mels=128,\n", + " )\n", + " # flip is done across vertically to make lower frequencies in the bottom part, as done\n", + " # by the liborsa specshow\n", + "librosa.display.specshow(librosa.power_to_db(S, ref=np.max), x_axis='time', y_axis='hz', sr = sampling_rate)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# get spectrograms" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def get_spectr(audio, sampling_rate):\n", + " S = librosa.feature.melspectrogram(\n", + " y = audio,\n", + " sr = sampling_rate,\n", + " n_mels=128,\n", + " )\n", + " # flip is done across vertically to make lower frequencies in the bottom part, as done\n", + " # by the liborsa specshow\n", + " return np.flip(librosa.power_to_db(S, ref=np.max).astype('float32'), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "num_cores = multiprocessing.cpu_count()\n", + "spectr_clipped = Parallel(n_jobs=num_cores)(\n", + " delayed(get_spectr)(audio, sampling_rate)\n", + " for audio in clipped_audio\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "spectr_clipped = np.array(spectr_clipped)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = f'birds_cornell_spectr_100_species_sr_32000_len_{spectr_time_len}_sec_New.h5'\n", + "hf = h5py.File(os.path.join(dataDir,\n", + " file_name), 'w')\n", + "grp = hf.create_dataset('mel_spectr', data=spectr_clipped)\n", + "hf.close()\n", + "metadata_clipped.to_hdf(os.path.join(dataDir,\n", + " file_name), key='info')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sliding window spectrograms" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "win_len = 500 # in ms\n", + "win_slide_len = 250 # in ms" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "def get_sliding_spectr(full_audio, win_len, win_slide_len, sampling_rate):\n", + " num_windows_max_possible = int((7000-win_len)/win_slide_len)\n", + "\n", + " win_ind = 0\n", + " spectr_try = get_spectr(full_audio[win_ind*int(win_slide_len/1000*sampling_rate):win_ind*int(win_slide_len/1000*sampling_rate)+int(win_len/1000 * sampling_rate)],\n", + " sampling_rate)\n", + " spectr_slides = np.empty((num_windows_max_possible, np.shape(spectr_try)[0], np.shape(spectr_try)[1]))\n", + " for win_ind in range(num_windows_max_possible):\n", + " spectr_slides[win_ind,:] = get_spectr(full_audio[win_ind*int(win_slide_len/1000*sampling_rate):win_ind*int(win_slide_len/1000*sampling_rate)+int(win_len/1000 * sampling_rate)],\n", + " sampling_rate)\n", + " return spectr_slides\n", + " \n", + "num_cores = multiprocessing.cpu_count()\n", + "\n", + "spectr_clipped_slide = Parallel(n_jobs=num_cores)(\n", + " delayed(get_sliding_spectr)(audio, win_len, win_slide_len, sampling_rate) \n", + " for audio in clipped_audio)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "spectr_clipped_slide = np.array(spectr_clipped_slide)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = f'birds_cornell_spectr_slide_100_species_sr_32000_len_{spectr_time_len}_sec_{win_len}_{win_slide_len}_New.h5' \n", + "hf = h5py.File(os.path.join(dataDir,\n", + " file_name), 'w')\n", + "grp = hf.create_dataset('mel_spectr', data=spectr_clipped_slide)\n", + "hf.close()\n", + "metadata_clipped.to_hdf(os.path.join(dataDir,\n", + " file_name), key='info')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "idx_spectr = 10\n", + "plt.figure(figsize=(20, 5))\n", + "S = np.flipud(spectr_clipped[idx_spectr])\n", + "librosa.display.specshow(S, x_axis='time', y_axis='hz', sr = sampling_rate)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_windows = 46\n", + "max_plt_per_row = 6\n", + "plt.figure(figsize=(int(2.5*num_windows), 10*(num_windows//max_plt_per_row +1))) \n", + "for i in range(num_windows):\n", + " plt.subplot(num_windows//max_plt_per_row +1, max_plt_per_row, i+1)\n", + " win_ind = i\n", + " S = spectr_clipped_slide[idx_spectr][i,:,:]\n", + " librosa.display.specshow(np.flipud(S))\n", + " plt.tight_layout()\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..074db67 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +pytorch == 1.2.0 +librosa == 0.8.0 +pandas == 1.1.1 +nengolib == 0.5.2 +nengo == 2.8.0 +h5py == 2.10.0 +fastprogress == 1.0.0 +audioread == 2.1.8 +scikit-learn == 0.21.3 \ No newline at end of file diff --git a/spectr_vgg16.ipynb b/spectr_vgg16.ipynb new file mode 100644 index 0000000..6efbc25 --- /dev/null +++ b/spectr_vgg16.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copyright (c) Microsoft Corporation. All rights reserved.\n", + "Licensed under the MIT License." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import torch.utils.data as data_utils\n", + "import os\n", + "import numpy as np\n", + "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import KFold, StratifiedKFold\n", + "import csv\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "import gc\n", + "import h5py\n", + "import pickle as pk\n", + "\n", + "from utils import log_results, SaveBestModel, train, test\n", + "from utils import mel_sp_to_image\n", + "\n", + "from models import VGG16_pool" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set directories" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataDir = 'processed'\n", + "resultsDir = 'Results'\n", + "tempDir = 'temp'\n", + "\n", + "if not os.path.exists(resultsDir):\n", + " os.makedirs(resultsDir)\n", + "if not os.path.exists(tempDir):\n", + " os.makedirs(tempDir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fname = 'birds_cornell_spectr_100_species_sr_32000_len_7_sec_New.h5'\n", + "fileLoc = os.path.join(dataDir,fname) # 19707 samples per class\n", + "hf = h5py.File(fileLoc, 'r')\n", + "mel_sp = hf.get('mel_spectr')[()]\n", + "metadata_total = pd.read_hdf(fileLoc, 'info')\n", + "hf.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "original_label = list(metadata_total['ebird_code'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lb_bin = LabelBinarizer()\n", + "lb_enc = LabelEncoder()\n", + "labels_one_hot = lb_bin.fit_transform(original_label)\n", + "labels_multi_lbl = lb_enc.fit_transform(original_label)\n", + "\n", + "number_of_sample_classes = len(lb_enc.classes_)\n", + "print(\"Number of Species: \", number_of_sample_classes)\n", + "species_id_class_dict_tp = dict()\n", + "for (class_label, species_id) in enumerate(lb_bin.classes_):\n", + " species_id_class_dict_tp[species_id] = class_label" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "mel_sp_normalized = []\n", + "for i in range(len(mel_sp)):\n", + " xx_ = mel_sp_to_image(mel_sp[i]).astype('float32')\n", + " mel_sp_normalized += [np.rollaxis(xx_, 2, 0)]\n", + "mel_sp_normalized = np.array(mel_sp_normalized)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16*2\n", + "num_classes=100\n", + "shuffleBatches=True\n", + "num_epoch = 50" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "skf = StratifiedKFold(n_splits=5, random_state=42)\n", + "exp_no = 0\n", + "log_file_name = f'100_species_spectr_vgg16_7sec_{exp_no}.p'\n", + "store_ = log_results(file_name=log_file_name, results_dir = resultsDir)\n", + "\n", + "exp_ind = 0\n", + "for train_ind, test_ind in skf.split(mel_sp_normalized, labels_multi_lbl):\n", + " \n", + " PATH_curr = os.path.join(tempDir, f'currentModel_vgg16_{exp_no}_{exp_ind}.pt')\n", + " saveModel = SaveBestModel(PATH=PATH_curr, monitor=-np.inf, verbose=True)\n", + "\n", + " X_train, X_test_p_valid = mel_sp_normalized[train_ind,:], mel_sp_normalized[test_ind,:]\n", + " \n", + " y_train, y_test_p_valid = labels_one_hot[train_ind], labels_one_hot[test_ind]\n", + " y_train_mlbl, y_test_p_valid_mlbl = labels_multi_lbl[train_ind], labels_multi_lbl[test_ind]\n", + " X_valid, X_test, y_valid, y_test = train_test_split(X_test_p_valid, y_test_p_valid,\n", + " test_size=0.5,\n", + " stratify=y_test_p_valid_mlbl,\n", + " random_state=42)\n", + "\n", + " print('X_train shape: ', X_train.shape)\n", + " print('X_valid shape: ', X_valid.shape)\n", + " print('X_test shape: ', X_test.shape)\n", + "\n", + " X_train, X_valid = torch.from_numpy(X_train).float(), torch.from_numpy(X_valid).float()\n", + " y_train, y_valid = torch.from_numpy(y_train), torch.from_numpy(y_valid)\n", + " \n", + " y_train, y_valid = y_train.float(), y_valid.float()\n", + " train_use = data_utils.TensorDataset(X_train, y_train)\n", + " train_loader = data_utils.DataLoader(train_use, batch_size=batch_size, shuffle=shuffleBatches)\n", + "\n", + " val_use = data_utils.TensorDataset(X_valid, y_valid)\n", + " val_loader = data_utils.DataLoader(val_use, batch_size=32, shuffle=False)\n", + " \n", + " model = VGG16_pool(num_classes=100)\n", + " model.to(device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001, weight_decay=1e-7)\n", + "\n", + " val_acc_epochs = []\n", + " val_loss_epochs = []\n", + " for epoch in range(1, num_epoch+1):\n", + " train_loss = train(model, train_loader, optimizer, epoch, \n", + " device,\n", + " verbose=1, loss_fn = 'bceLogit')\n", + " val_loss, val_acc = test(model, val_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " val_acc_epochs.append(val_acc)\n", + " val_loss_epochs.append(val_loss)\n", + " print('val loss = %f, val acc = %f'%(val_loss, val_acc))\n", + " saveModel.check(model, val_acc, comp='max')\n", + " \n", + " # loading best validated model\n", + " model = VGG16_pool(num_classes=100)\n", + " model.to(device)\n", + " model.load_state_dict(torch.load(PATH_curr))\n", + "\n", + " X_test, y_test = torch.from_numpy(X_test).float(), torch.from_numpy(y_test).float()\n", + "\n", + " test_use = data_utils.TensorDataset(X_test, y_test)\n", + " test_loader = data_utils.DataLoader(test_use, batch_size=32, shuffle=False)\n", + " test_loss, test_acc = test(model, test_loader,\n", + " device,\n", + " loss_fn = 'bceLogit')\n", + " print('test loss = %f, test acc = %f'%(test_loss, test_acc))\n", + " \n", + " log_ = dict(\n", + " exp_ind = exp_ind,\n", + " epochs = num_epoch,\n", + " validation_accuracy = val_acc_epochs,\n", + " validation_loss = val_loss_epochs,\n", + " test_loss = test_loss,\n", + " test_accuracy = test_acc,\n", + " X_train_shape = X_train.shape,\n", + " X_valid_shape = X_valid.shape,\n", + " batch_size =batch_size,\n", + " )\n", + " store_.update(log_)\n", + " exp_ind += 1 " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..c3380f3 --- /dev/null +++ b/utils.py @@ -0,0 +1,325 @@ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. + +import torch +import torch.nn as nn +from torch import Tensor +import torch.nn.functional as F +import torch.optim as optim +import torch.utils.data as data_utils +import numpy as np +import librosa +from joblib import Parallel, delayed +import multiprocessing +import h5py +import pickle as pk +import cv2 +import os +import matplotlib.pyplot as plt +from fastprogress.fastprogress import progress_bar + + +class log_results(object): + def __init__(self, file_name = 'log', results_dir = 'Results'): + self.results_dir = results_dir + self.fname = file_name + + if not os.path.exists(self.results_dir): + os.makedirs(results_dir) + + def update(self, log): + file_path = os.path.join(self.results_dir, self.fname) + if isinstance(log, dict): + pk.dump(log, open(file_path, 'ab')) + else: + print('log has to be in dictionary format') + + +class SaveBestModel(object): + + def __init__(self, monitor = np.inf, PATH = './currTorchModel.pt', + verbose=False): + + self.monitor = monitor + self.PATH = PATH + self.verbose = verbose + + def check(self, model, currVal, comp='min'): + if comp is 'min': + if currVal < self.monitor: + self.monitor = currVal + torch.save(model.state_dict(), self.PATH) + if self.verbose: + print('saving best model...') + elif comp is 'max': + if currVal > self.monitor: + self.monitor = currVal + torch.save(model.state_dict(), self.PATH) + if self.verbose: + print('saving best model...') + + +def normalize_mel_sp_slides(X, eps=1e-6): + mean = X.mean() + X = X - mean + std = X.std() + Xstd = X / (std + eps) + _min, _max = Xstd.min(), Xstd.max() + norm_max = _max + norm_min = _min + if (_max - _min) > eps: + # Normalize to [0, 255] + V = Xstd + V[V < norm_min] = norm_min + V[V > norm_max] = norm_max + V = (V - norm_min) / (norm_max - norm_min) + else: + V = np.zeros_like(X, dtype=np.uint8) + return V + + +def mel_sp_slides_to_image(X, eps=1e-6, resize=False, nrow=224, ncol=224): + mean = X.mean() + X = X - mean + std = X.std() + Xstd = X / (std + eps) + # cmap = plt.cm.jet + cmap = plt.cm.viridis + norm = plt.Normalize(vmin=Xstd.min(), vmax=Xstd.max()) + + # map the normalized data to colors + # image is now RGBA (nrowxncolx4) + # last channel is alpha value for transparency, set to 1 + image = cmap(norm(Xstd)) + if resize: + return cv2.resize( + image[:,:,:3], (nrow, ncol), + interpolation=cv2.INTER_LINEAR + ) + else: + return image[:,:,:,:3] + + +def mel_sp_to_image(X, eps=1e-6, nrow=224, ncol=224): + mean = X.mean() + X = X - mean + std = X.std() + Xstd = X / (std + eps) + # cmap = plt.cm.jet + cmap = plt.cm.viridis + norm = plt.Normalize(vmin=Xstd.min(), vmax=Xstd.max()) + + # map the normalized data to colors + # image is now RGBA (nrowxncolx4) + # last channel is alpha value for transparency, set to 1 + image = cmap(norm(Xstd)) + return cv2.resize(image[:,:,:3], (nrow, ncol), + interpolation=cv2.INTER_LINEAR + ) + + + +def train_seq(model, train_loader, optimizer, epoch, device, verbose = 0, + lr_schedule = None, weight = None, loss_fn = 'crossEnt'): + """Training""" + if lr_schedule is not None: + optimizer = lr_schedule(optimizer, epoch) + + model.train() + for batch_idx, (data, target) in enumerate(progress_bar(train_loader)): + + h_s = model.init_hidden(len(data)) + if isinstance(h_s, tuple): + h_s = tuple([x.to(device) for x in h_s]) + else: + h_s = h_s.to(device) + + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + + output = model(data, h_s) + if loss_fn == 'crossEnt': + criteria = nn.CrossEntropyLoss().cuda() + elif loss_fn == 'bceLogit': + criteria = nn.BCEWithLogitsLoss().cuda() + + loss = criteria(output, target) + + loss.backward() + optimizer.step() + + if verbose>0: + print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch, batch_idx * len(data), len(train_loader.dataset), + 100. * batch_idx / len(train_loader), loss.item())) + return loss.item() + + +def evalModel_seq(data_loader, model, device, verbose=0, stochastic_pass = True, + compute_metrics=True, activationName = None, + loss_fn = 'crossEnt'): + + if stochastic_pass: + model.train() + else: + model.eval() + + test_loss = 0 + predictions = [] + activations = [] + correct = 0 + with torch.no_grad(): + for data, target in data_loader: + + data, target = data.to(device), target.to(device) + + h_s = model.init_hidden(len(data)) + if isinstance(h_s, tuple): + h_s = tuple([x.to(device) for x in h_s]) + else: + h_s =h_s.to(device) + + output = model(data, h_s) + + if compute_metrics: + predictionClasses = output.argmax(dim=1, keepdim=True) + if loss_fn == 'crossEnt': + criteria = nn.CrossEntropyLoss().cuda() + correct += predictionClasses.eq(target.view_as(predictionClasses)).sum().item() + elif loss_fn == 'bceLogit': + criteria = nn.BCEWithLogitsLoss().cuda() + correct += predictionClasses.eq(target.argmax(dim=1).view_as(predictionClasses)).sum().item() + + test_loss += criteria(output, target).sum().item() + else: + softmaxed = F.softmax(output.cpu(), dim=1) + predictions.extend(softmaxed.data.numpy()) + + if compute_metrics: + return test_loss, correct + else: + return predictions, activations + + +def test_seq(model, test_loader, device, verbose=0, activationName = None, + loss_fn = 'crossEnt'): + """Testing""" + model.eval() + test_loss = 0 + correct = 0 + + total_test_loss, total_corrections = evalModel_seq(test_loader, model, device=device, + verbose = verbose, + stochastic_pass = False, compute_metrics = True, + activationName = activationName, loss_fn = loss_fn) + + test_loss = total_test_loss/ len(test_loader) # loss function already averages over batch size + test_acc = total_corrections / len(test_loader.dataset) + if verbose>0: + print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( + test_loss, correct, len(test_loader.dataset), + 100. * correct / len(test_loader.dataset))) + print('{{"metric": "Eval - cross entropy Loss", "value": {}, "epoch": {}}}'.format( + test_loss, epoch)) + print('{{"metric": "Eval - Accuracy", "value": {}, "epoch": {}}}'.format( + 100. * correct / len(test_loader.dataset), epoch)) + + return test_loss, test_acc + + +def train(model, train_loader, optimizer, epoch, device, verbose = 0, + lr_schedule = None, weight = None, loss_fn = 'crossEnt'): + """Training""" + if lr_schedule is not None: + optimizer = lr_schedule(optimizer, epoch) + + model.train() + for batch_idx, (data, target) in enumerate(progress_bar(train_loader)): + + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + + output = model(data) + if loss_fn == 'crossEnt': + criteria = nn.CrossEntropyLoss().cuda() + elif loss_fn == 'bceLogit': + criteria = nn.BCEWithLogitsLoss().cuda() + + loss = criteria(output, target) + + loss.backward() + optimizer.step() + + if verbose>0: + print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch, batch_idx * len(data), len(train_loader.dataset), + 100. * batch_idx / len(train_loader), loss.item())) + return loss.item() + + +def evalModel(data_loader, model, device, verbose=0, stochastic_pass = True, + compute_metrics=True, activationName = None, + loss_fn = 'crossEnt'): + + if stochastic_pass: + model.train() + else: + model.eval() + + test_loss = 0 + predictions = [] + activations = [] + correct = 0 + with torch.no_grad(): + for data, target in data_loader: + + data, target = data.to(device), target.to(device) + + output = model(data) + + if compute_metrics: + predictionClasses = output.argmax(dim=1, keepdim=True) + + if loss_fn == 'crossEnt': + criteria = nn.CrossEntropyLoss().cuda() + correct += predictionClasses.eq(target.view_as(predictionClasses)).sum().item() + elif loss_fn == 'bceLogit': + criteria = nn.BCEWithLogitsLoss().cuda() + correct += predictionClasses.eq(target.argmax(dim=1).view_as(predictionClasses)).sum().item() + + test_loss += criteria(output, target).sum().item() + else: + softmaxed = F.softmax(output.cpu(), dim=1) + predictions.extend(softmaxed.data.numpy()) + + if compute_metrics: + return test_loss, correct + else: + return predictions, activations + + +def test(model, test_loader, device, verbose=0, activationName = None, + loss_fn = 'crossEnt'): + """Testing""" + model.eval() + test_loss = 0 + correct = 0 + + total_test_loss, total_corrections = evalModel(test_loader, model, device=device, + verbose = verbose, + stochastic_pass = False, compute_metrics = True, + activationName = activationName, + loss_fn=loss_fn) + + test_loss = total_test_loss/ len(test_loader) # loss function already averages over batch size + test_acc = total_corrections / len(test_loader.dataset) + if verbose>0: + print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( + test_loss, correct, len(test_loader.dataset), + 100. * correct / len(test_loader.dataset))) + print('{{"metric": "Eval - cross entropy Loss", "value": {}, "epoch": {}}}'.format( + test_loss, epoch)) + print('{{"metric": "Eval - Accuracy", "value": {}, "epoch": {}}}'.format( + 100. * correct / len(test_loader.dataset), epoch)) + + return test_loss, test_acc \ No newline at end of file