diff --git "a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_statistics.py" "b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_statistics.py" deleted file mode 100644--- "a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_statistics.py" +++ /dev/null @@ -1,2034 +0,0 @@ -import os -import sys -import numpy as np -import argparse -import h5py -import time -import _pickle as cPickle -import _pickle -import matplotlib.pyplot as plt -import csv -from sklearn import metrics - -from utilities import (create_folder, get_filename, d_prime) -import config - - -def _load_metrics0(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' - statistics_path = os.path.join(workspace0, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) - bal_map = np.mean(bal_map, axis=-1) - test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) - test_map = np.mean(test_map, axis=-1) - legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) - - # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} - return bal_map, test_map, legend - - -def _load_metrics0_classwise(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' - statistics_path = os.path.join(workspace0, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - return statistics_dict['test'][300]['average_precision'] - - -def _load_metrics0_classwise2(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' - statistics_path = os.path.join(workspace0, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - k = 270 - mAP = np.mean(statistics_dict['test'][k]['average_precision']) - mAUC = np.mean(statistics_dict['test'][k]['auc']) - dprime = d_prime(mAUC) - return mAP, mAUC, dprime - - -def _load_metrics_classwise(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - workspace = '/mnt/cephfs_new_wj/speechsv/kongqiuqiang/workspaces/cvssp/pub_audioset_tagging_cnn' - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - k = 300 - mAP = np.mean(statistics_dict['test'][k]['average_precision']) - mAUC = np.mean(statistics_dict['test'][k]['auc']) - dprime = d_prime(mAUC) - return mAP, mAUC, dprime - - -def plot(args): - - # Arguments & parameters - dataset_dir = args.dataset_dir - workspace = args.workspace - select = args.select - - classes_num = config.classes_num - max_plot_iteration = 1000000 - iterations = np.arange(0, max_plot_iteration, 2000) - - class_labels_indices_path = os.path.join(dataset_dir, 'metadata', - 'class_labels_indices.csv') - - save_out_path = 'results/{}.pdf'.format(select) - create_folder(os.path.dirname(save_out_path)) - - # Read labels - labels = config.labels - - # Plot - fig, ax = plt.subplots(1, 1, figsize=(15, 8)) - lines = [] - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) - bal_map = np.mean(bal_map, axis=-1) - test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) - test_map = np.mean(test_map, axis=-1) - legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) - - # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} - return bal_map, test_map, legend - - bal_alpha = 0.3 - test_alpha = 1.0 - lines = [] - - if select == '1_cnn13': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_no_dropout', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_no_specaug', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_no_specaug', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_no_dropout', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'none', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_no_mixup', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_mixup_in_wave', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='c', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_mixup_in_wave', color='c', alpha=test_alpha) - lines.append(line) - - elif select == '1_pooling': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_gwrp', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_gmpgapgwrp', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_att', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_gmpgapatt', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_resnet': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet18', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='ResNet18', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='resnet34', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet50', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='c', alpha=bal_alpha) - line, = ax.plot(test_map, label='resnet50', color='c', alpha=test_alpha) - lines.append(line) - - elif select == '1_densenet': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'DenseNet121', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='densenet121', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'DenseNet201', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='densenet201', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_cnn9': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn5', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn9', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_hop': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 500, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_hop500', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 640, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_hop640', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 1000, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_hop1000', color='k', alpha=test_alpha) - lines.append(line) - - elif select == '1_emb': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_emb32', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_emb128', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13_emb512', color='k', alpha=test_alpha) - lines.append(line) - - elif select == '1_mobilenet': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='mobilenetv1', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV2', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='mobilenetv2', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_waveform': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_LeeNet', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet18', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_LeeNet18', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_DaiNet', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_DaiNet', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='c', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='c', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet50', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='m', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_ResNet50', color='m', alpha=test_alpha) - lines.append(line) - - elif select == '1_waveform_cnn2d': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_SpAndWav', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_WavCnn2d', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_decision_level': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelMax', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_DecisionLevelMax', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAvg', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_DecisionLevelAvg', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAtt', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_DecisionLevelAtt', color='k', alpha=test_alpha) - lines.append(line) - - elif select == '1_transformer': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer1', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_Transformer1', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer3', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_Transformer3', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_Transformer6', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_Transformer6', color='k', alpha=test_alpha) - lines.append(line) - - elif select == '1_aug': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,mixup', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,none,none', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,none', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup_from_0_epoch', 32) - line, = ax.plot(bal_map, color='m', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,mixup_from_0_epoch', color='m', alpha=test_alpha) - lines.append(line) - - elif select == '1_bal_train_aug': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,mixup', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,none,none', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,none', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup_from_0_epoch', 32) - line, = ax.plot(bal_map, color='m', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,balanced,mixup_from_0_epoch', color='m', alpha=test_alpha) - lines.append(line) - - elif select == '1_sr': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_16k', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_8k', color='b', alpha=test_alpha) - lines.append(line) - - elif select == '1_time_domain': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_time_domain', color='b', alpha=test_alpha) - lines.append(line) - - elif select == '1_partial_full': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.9_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,partial_0.9', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,partial_0.8', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.7_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,partial_0.7', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='m', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,partial_0.5', color='m', alpha=test_alpha) - lines.append(line) - - elif select == '1_window': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 2048, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_win2048', color='b', alpha=test_alpha) - lines.append(line) - - elif select == '1_melbins': - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_mel32', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_mel128', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '1_alternate': - max_plot_iteration = 2000000 - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'alternate', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14_alternate', color='b', alpha=test_alpha) - lines.append(line) - - elif select == '2_all': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='MobileNetV1', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='ResNet34', color='grey', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_WavCnn2d', color='m', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_SpAndWav', color='orange', alpha=test_alpha) - lines.append(line) - - elif select == '2_emb': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_emb32', color='r', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_128', color='k', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='Cnn13_512', color='g', alpha=test_alpha) - lines.append(line) - - elif select == '2_aug': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn13', color='b', alpha=test_alpha) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_no_specaug', 'clip_bce', 'none', 'none', 32) - line, = ax.plot(bal_map, color='c', alpha=bal_alpha) - line, = ax.plot(test_map, label='cnn14,none,none', color='c', alpha=test_alpha) - lines.append(line) - - - - ax.set_ylim(0, 1.) - ax.set_xlim(0, len(iterations)) - ax.xaxis.set_ticks(np.arange(0, len(iterations), 25)) - ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) - ax.yaxis.set_ticks(np.arange(0, 1.01, 0.05)) - ax.yaxis.set_ticklabels(np.around(np.arange(0, 1.01, 0.05), decimals=2)) - ax.grid(color='b', linestyle='solid', linewidth=0.3) - plt.legend(handles=lines, loc=2) - # box = ax.get_position() - # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) - # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) - - plt.savefig(save_out_path) - print('Save figure to {}'.format(save_out_path)) - - -def plot_for_paper(args): - - # Arguments & parameters - dataset_dir = args.dataset_dir - workspace = args.workspace - select = args.select - - classes_num = config.classes_num - max_plot_iteration = 1000000 - iterations = np.arange(0, max_plot_iteration, 2000) - - class_labels_indices_path = os.path.join(dataset_dir, 'metadata', - 'class_labels_indices.csv') - - save_out_path = 'results/paper_{}.pdf'.format(select) - create_folder(os.path.dirname(save_out_path)) - - # Read labels - labels = config.labels - - # Plot - fig, ax = plt.subplots(1, 1, figsize=(6, 4)) - lines = [] - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) - bal_map = np.mean(bal_map, axis=-1) - test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) - test_map = np.mean(test_map, axis=-1) - legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) - - # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} - return bal_map, test_map, legend - - bal_alpha = 0.3 - test_alpha = 1.0 - lines = [] - linewidth = 1. - - max_plot_iteration = 540000 - - if select == '2_all': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) - # lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) - # lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - # line, = ax.plot(test_map, label='Wavegram-CNN', color='g', alpha=test_alpha, linewidth=linewidth) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - elif select == '2_emb': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='g', alpha=bal_alpha) - # line, = ax.plot(test_map, label='Cnn13_512', color='g', alpha=test_alpha) - # lines.append(line) - - elif select == '2_bal': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax.plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - elif select == '2_sr': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - elif select == '2_partial': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - # 320, 64, 50, 14000, 'partial_0.9_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - # line, = ax.plot(test_map, label='cnn14,partial_0.9', color='b', alpha=test_alpha, linewidth=linewidth) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - # 320, 64, 50, 14000, 'partial_0.7_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) - # line, = ax.plot(test_map, label='cnn14,partial_0.7', color='k', alpha=test_alpha, linewidth=linewidth) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - elif select == '2_melbins': - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax.plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax.plot(bal_map, color='r', alpha=bal_alpha) - line, = ax.plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax.set_ylim(0, 0.8) - ax.set_xlim(0, len(iterations)) - ax.set_xlabel('Iterations') - ax.set_ylabel('mAP') - ax.xaxis.set_ticks(np.arange(0, len(iterations), 50)) - # ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) - ax.xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k']) - ax.yaxis.set_ticks(np.arange(0, 0.81, 0.05)) - ax.yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3', '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8']) - # ax.yaxis.set_ticklabels(np.around(np.arange(0, 0.81, 0.05), decimals=2)) - ax.yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) - ax.xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) - plt.legend(handles=lines, loc=2) - plt.tight_layout(0, 0, 0) - # box = ax.get_position() - # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) - # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) - - plt.savefig(save_out_path) - print('Save figure to {}'.format(save_out_path)) - - -def plot_for_paper2(args): - - # Arguments & parameters - dataset_dir = args.dataset_dir - workspace = args.workspace - - classes_num = config.classes_num - max_plot_iteration = 1000000 - iterations = np.arange(0, max_plot_iteration, 2000) - - class_labels_indices_path = os.path.join(dataset_dir, 'metadata', - 'class_labels_indices.csv') - - save_out_path = 'results/paper2.pdf' - create_folder(os.path.dirname(save_out_path)) - - # Read labels - labels = config.labels - - # Plot - fig, ax = plt.subplots(2, 3, figsize=(14, 7)) - lines = [] - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) - bal_map = np.mean(bal_map, axis=-1) - test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) - test_map = np.mean(test_map, axis=-1) - legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) - - # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} - return bal_map, test_map, legend - - def _load_metrics0(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size): - workspace0 = '/mnt/cephfs_new_wj/speechsv/qiuqiang.kong/workspaces/pub_audioset_tagging_cnn_transfer' - statistics_path = os.path.join(workspace0, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num) - bal_map = np.mean(bal_map, axis=-1) - test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num) - test_map = np.mean(test_map, axis=-1) - legend = '{}, {}, bal={}, aug={}, bs={}'.format(data_type, model_type, balanced, augmentation, batch_size) - - # return {'bal_map': bal_map, 'test_map': test_map, 'legend': legend} - return bal_map, test_map, legend - - bal_alpha = 0.3 - test_alpha = 1.0 - lines = [] - linewidth = 1. - - max_plot_iteration = 540000 - - if True: - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 0].plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='cnn9', color='r', alpha=test_alpha) - # lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='cnn5', color='g', alpha=test_alpha) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 0].plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='b', alpha=bal_alpha) - # line, = ax.plot(test_map, label='Cnn1d_ResNet34', color='grey', alpha=test_alpha) - # lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax[0, 0].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) - # line, = ax[0, 0].plot(test_map, label='ResNet38', color='k', alpha=test_alpha, linewidth=linewidth) - # lines.append(line) - - # (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - # 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32) - # line, = ax.plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - # line, = ax.plot(test_map, label='Wavegram-CNN', color='g', alpha=test_alpha, linewidth=linewidth) - # lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 0].plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[0, 0].legend(handles=lines, loc=2) - ax[0, 0].set_title('(a) Comparison of architectures') - - if True: - lines = [] - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - line, = ax[0, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 1].plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax[0, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - line, = ax[0, 1].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 1].plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[0, 1].legend(handles=lines, loc=2, fontsize=8) - - ax[0, 1].set_title('(b) Comparison of training data and augmentation') - - if True: - lines = [] - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 2].plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 2].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 2].plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics0('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[0, 2].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax[0, 2].plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[0, 2].legend(handles=lines, loc=2) - ax[0, 2].set_title('(c) Comparison of embedding size') - - if True: - lines = [] - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 0].plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 0].plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 0].plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[1, 0].legend(handles=lines, loc=2) - ax[1, 0].set_title('(d) Comparison of amount of training data') - - if True: - lines = [] - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 1].plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 1].plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 1].plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[1, 1].legend(handles=lines, loc=2) - ax[1, 1].set_title('(e) Comparison of sampling rate') - - if True: - lines = [] - iterations = np.arange(0, max_plot_iteration, 2000) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth) - line, = ax[1, 2].plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 2].plot(bal_map, color='b', alpha=bal_alpha) - line, = ax[1, 2].plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - (bal_map, test_map, legend) = _load_metrics('main', 32000, 1024, - 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) - line, = ax[1, 2].plot(bal_map, color='g', alpha=bal_alpha) - line, = ax[1, 2].plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth) - lines.append(line) - - ax[1, 2].legend(handles=lines, loc=2) - ax[1, 2].set_title('(f) Comparison of mel bins number') - - for i in range(2): - for j in range(3): - ax[i, j].set_ylim(0, 0.8) - ax[i, j].set_xlim(0, len(iterations)) - ax[i, j].set_xlabel('Iterations') - ax[i, j].set_ylabel('mAP') - ax[i, j].xaxis.set_ticks(np.arange(0, len(iterations), 50)) - # ax.xaxis.set_ticklabels(np.arange(0, max_plot_iteration, 50000)) - ax[i, j].xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k']) - ax[i, j].yaxis.set_ticks(np.arange(0, 0.81, 0.05)) - ax[i, j].yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3', '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8']) - # ax.yaxis.set_ticklabels(np.around(np.arange(0, 0.81, 0.05), decimals=2)) - ax[i, j].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) - ax[i, j].xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) - - plt.tight_layout(0, 1, 0) - # box = ax.get_position() - # ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) - # ax.legend(handles=lines, bbox_to_anchor=(1.0, 1.0)) - - plt.savefig(save_out_path) - print('Save figure to {}'.format(save_out_path)) - - -def table_values(args): - - # Arguments & parameters - dataset_dir = args.dataset_dir - workspace = args.workspace - select = args.select - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - idx = iteration // 2000 - mAP = np.mean(statistics_dict['test'][idx]['average_precision']) - mAUC = np.mean(statistics_dict['test'][idx]['auc']) - dprime = d_prime(mAUC) - - print('mAP: {:.3f}'.format(mAP)) - print('mAUC: {:.3f}'.format(mAUC)) - print('dprime: {:.3f}'.format(dprime)) - - - if select == 'cnn13': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn5': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn5', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn9': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn9', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_decisionlevelmax': - iteration = 400000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelMax', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_decisionlevelavg': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAvg', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_decisionlevelatt': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_DecisionLevelAtt', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_emb32': - iteration = 560000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_emb128': - iteration = 560000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_emb512': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_emb512', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_hop500': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 500, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_hop640': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 640, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'cnn13_hop1000': - iteration = 540000 - _load_metrics('main', 32000, 1024, - 1000, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'mobilenetv1': - iteration = 560000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'mobilenetv2': - iteration = 560000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV2', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'resnet18': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet18', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'resnet34': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet34', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'resnet50': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'ResNet50', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'dainet': - iteration = 600000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_DaiNet', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'leenet': - iteration = 540000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'leenet18': - iteration = 440000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_LeeNet18', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'resnet34_1d': - iteration = 500000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet34', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'resnet50_1d': - iteration = 500000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn1d_ResNet50', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'waveform_cnn2d': - iteration = 660000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_WavCnn2d', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - elif select == 'waveform_spandwav': - iteration = 700000 - _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - -def crop_label(label): - max_len = 16 - if len(label) <= max_len: - return label - else: - words = label.split(' ') - cropped_label = '' - for w in words: - if len(cropped_label + ' ' + w) > max_len: - break - else: - cropped_label += ' {}'.format(w) - return cropped_label - -def add_comma(integer): - integer = int(integer) - if integer >= 1000: - return str(integer // 1000) + ',' + str(integer % 1000) - else: - return str(integer) - - -def plot_class_iteration(args): - - # Arguments & parameters - workspace = args.workspace - select = args.select - - save_out_path = 'results_map/class_iteration_map.pdf' - create_folder(os.path.dirname(save_out_path)) - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): - statistics_path = os.path.join(workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - return statistics_dict - - iteration = 600000 - statistics_dict = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - mAP_mat = np.array([e['average_precision'] for e in statistics_dict['test']]) - mAP_mat = mAP_mat[0 : 300, :] - sorted_indexes = np.argsort(config.full_samples_per_class)[::-1] - - - fig, axs = plt.subplots(1, 3, figsize=(20, 5)) - ranges = [np.arange(0, 10), np.arange(250, 260), np.arange(517, 527)] - axs[0].set_ylabel('AP') - - for col in range(0, 3): - axs[col].set_ylim(0, 1.) - axs[col].set_xlim(0, 301) - axs[col].set_xlabel('Iterations') - axs[col].set_ylabel('AP') - axs[col].xaxis.set_ticks(np.arange(0, 301, 100)) - axs[col].xaxis.set_ticklabels(['0', '200k', '400k', '600k']) - lines = [] - for _ix in ranges[col]: - _label = crop_label(config.labels[sorted_indexes[_ix]]) + \ - ' ({})'.format(add_comma(config.full_samples_per_class[sorted_indexes[_ix]])) - line, = axs[col].plot(mAP_mat[:, sorted_indexes[_ix]], label=_label) - lines.append(line) - box = axs[col].get_position() - axs[col].set_position([box.x0, box.y0, box.width * 1., box.height]) - axs[col].legend(handles=lines, bbox_to_anchor=(1., 1.)) - axs[col].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3) - - plt.tight_layout(pad=4, w_pad=1, h_pad=1) - plt.savefig(save_out_path) - print(save_out_path) - - -def _load_old_metrics(workspace, filename, iteration, data_type): - - assert data_type in ['train', 'test'] - - stat_name = "stat_{}_iters.p".format(iteration) - - # Load stats - stat_path = os.path.join(workspace, "stats", filename, data_type, stat_name) - try: - stats = cPickle.load(open(stat_path, 'rb')) - except: - stats = cPickle.load(open(stat_path, 'rb'), encoding='latin1') - - precisions = [stat['precisions'] for stat in stats] - recalls = [stat['recalls'] for stat in stats] - maps = np.array([stat['AP'] for stat in stats]) - aucs = np.array([stat['auc'] for stat in stats]) - - return {'average_precision': maps, 'AUC': aucs} - -def _sort(ys): - sorted_idxes = np.argsort(ys) - sorted_idxes = sorted_idxes[::-1] - sorted_ys = ys[sorted_idxes] - sorted_lbs = [config.labels[e] for e in sorted_idxes] - return sorted_ys, sorted_idxes, sorted_lbs - -def load_data(hdf5_path): - with h5py.File(hdf5_path, 'r') as hf: - x = hf['x'][:] - y = hf['y'][:] - video_id_list = list(hf['video_id_list'][:]) - return x, y, video_id_list - -def get_avg_stats(workspace, bgn_iter, fin_iter, interval_iter, filename, data_type): - - assert data_type in ['train', 'test'] - bal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/bal_train.h5" - eval_hdf5 = "/vol/vssp/msos/audioset/packed_features/eval.h5" - unbal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/unbal_train.h5" - - t1 = time.time() - if data_type == 'test': - (te_x, te_y, te_id_list) = load_data(eval_hdf5) - elif data_type == 'train': - (te_x, te_y, te_id_list) = load_data(bal_train_hdf5) - y = te_y - - prob_dir = os.path.join(workspace, "probs", filename, data_type) - names = os.listdir(prob_dir) - - probs = [] - iters = range(bgn_iter, fin_iter, interval_iter) - for iter in iters: - pickle_path = os.path.join(prob_dir, "prob_%d_iters.p" % iter) - try: - prob = cPickle.load(open(pickle_path, 'rb')) - except: - prob = cPickle.load(open(pickle_path, 'rb'), encoding='latin1') - probs.append(prob) - - avg_prob = np.mean(np.array(probs), axis=0) - - n_out = y.shape[1] - stats = [] - for k in range(n_out): # around 7 seconds - (precisions, recalls, thresholds) = metrics.precision_recall_curve(y[:, k], avg_prob[:, k]) - avg_precision = metrics.average_precision_score(y[:, k], avg_prob[:, k], average=None) - (fpr, tpr, thresholds) = metrics.roc_curve(y[:, k], avg_prob[:, k]) - auc = metrics.roc_auc_score(y[:, k], avg_prob[:, k], average=None) - # eer = pp_data.eer(avg_prob[:, k], y[:, k]) - - skip = 1000 - dict = {'precisions': precisions[0::skip], 'recalls': recalls[0::skip], 'AP': avg_precision, - 'fpr': fpr[0::skip], 'fnr': 1. - tpr[0::skip], 'auc': auc} - - stats.append(dict) - - mAPs = np.array([e['AP'] for e in stats]) - aucs = np.array([e['auc'] for e in stats]) - - print("Get avg time: {}".format(time.time() - t1)) - - return {'average_precision': mAPs, 'auc': aucs} - - -def _samples_num_per_class(): - bal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/bal_train.h5" - eval_hdf5 = "/vol/vssp/msos/audioset/packed_features/eval.h5" - unbal_train_hdf5 = "/vol/vssp/msos/audioset/packed_features/unbal_train.h5" - - (x, y, id_list) = load_data(eval_hdf5) - eval_num = np.sum(y, axis=0) - - (x, y, id_list) = load_data(bal_train_hdf5) - bal_num = np.sum(y, axis=0) - - (x, y, id_list) = load_data(unbal_train_hdf5) - unbal_num = np.sum(y, axis=0) - - return bal_num, unbal_num, eval_num - - -def get_label_quality(): - - rate_csv = '/vol/vssp/msos/qk/workspaces/pub_audioset_tagging_cnn_transfer/metadata/qa_true_counts.csv' - - with open(rate_csv, 'r') as f: - reader = csv.reader(f, delimiter=',') - lis = list(reader) - - rates = [] - - for n in range(1, len(lis)): - li = lis[n] - if float(li[1]) == 0: - rate = None - else: - rate = float(li[2]) / float(li[1]) - rates.append(rate) - - return rates - - -def summary_stats(args): - # Arguments & parameters - workspace = args.workspace - - out_stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') - create_folder(os.path.dirname(out_stat_path)) - - # Old workspace - old_workspace = '/vol/vssp/msos/qk/workspaces/audioset_classification' - - # bal_train_metrics = _load_old_metrics(old_workspace, 'tmp127', 20000, 'train') - # eval_metrics = _load_old_metrics(old_workspace, 'tmp127', 20000, 'test') - - bal_train_metrics = get_avg_stats(old_workspace, bgn_iter=10000, fin_iter=50001, interval_iter=5000, filename='tmp127_re', data_type='train') - eval_metrics = get_avg_stats(old_workspace, bgn_iter=10000, fin_iter=50001, interval_iter=5000, filename='tmp127_re', data_type='test') - - maps0te = eval_metrics['average_precision'] - (maps0te, sorted_idxes, sorted_lbs) = _sort(maps0te) - - bal_num, unbal_num, eval_num = _samples_num_per_class() - - output_dict = { - 'labels': config.labels, - 'label_quality': get_label_quality(), - 'sorted_indexes_for_plot': sorted_idxes, - 'official_balanced_trainig_samples': bal_num, - 'official_unbalanced_training_samples': unbal_num, - 'official_eval_samples': eval_num, - 'downloaded_full_training_samples': config.full_samples_per_class, - 'averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations': - {'bal_train': bal_train_metrics, 'eval': eval_metrics} - } - - def _load_metrics(filename, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, data_type, model_type, loss_type, balanced, augmentation, batch_size, iteration): - _workspace = '/vol/vssp/msos/qk/bytedance/workspaces_important/pub_audioset_tagging_cnn_transfer' - statistics_path = os.path.join(_workspace, 'statistics', filename, - 'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format( - sample_rate, window_size, hop_size, mel_bins, fmin, fmax), - 'data_type={}'.format(data_type), model_type, - 'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), - 'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), - 'statistics.pkl') - - statistics_dict = cPickle.load(open(statistics_path, 'rb')) - - _idx = iteration // 2000 - _dict = {'bal_train': {'average_precision': statistics_dict['bal'][_idx]['average_precision'], - 'auc': statistics_dict['bal'][_idx]['auc']}, - 'eval': {'average_precision': statistics_dict['test'][_idx]['average_precision'], - 'auc': statistics_dict['test'][_idx]['auc']}} - return _dict - - iteration = 600000 - output_dict['cnn13_system_iteration60k'] = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - iteration = 560000 - output_dict['mobilenetv1_system_iteration56k'] = _load_metrics('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'MobileNetV1', 'clip_bce', 'balanced', 'mixup', 32, iteration) - - cPickle.dump(output_dict, open(out_stat_path, 'wb')) - print('Write stats for paper to {}'.format(out_stat_path)) - - -def prepare_plot_long_4_rows(sorted_lbs): - N = len(sorted_lbs) - - f,(ax1a, ax2a, ax3a, ax4a) = plt.subplots(4, 1,sharey=False, facecolor='w', figsize=(10, 12)) - - fontsize = 5 - - K = 132 - ax1a.set_xlim(0, K) - ax2a.set_xlim(K, 2 * K) - ax3a.set_xlim(2 * K, 3 * K) - ax4a.set_xlim(3 * K, N) - - truncated_sorted_lbs = [] - for lb in sorted_lbs: - lb = lb[0 : 25] - words = lb.split(' ') - if len(words[-1]) < 3: - lb = ' '.join(words[0:-1]) - truncated_sorted_lbs.append(lb) - - ax1a.grid(which='major', axis='x', linestyle='-', alpha=0.3) - ax2a.grid(which='major', axis='x', linestyle='-', alpha=0.3) - ax3a.grid(which='major', axis='x', linestyle='-', alpha=0.3) - ax4a.grid(which='major', axis='x', linestyle='-', alpha=0.3) - - ax1a.set_yscale('log') - ax2a.set_yscale('log') - ax3a.set_yscale('log') - ax4a.set_yscale('log') - - ax1b = ax1a.twinx() - ax2b = ax2a.twinx() - ax3b = ax3a.twinx() - ax4b = ax4a.twinx() - ax1b.set_ylim(0., 1.) - ax2b.set_ylim(0., 1.) - ax3b.set_ylim(0., 1.) - ax4b.set_ylim(0., 1.) - ax1b.set_ylabel('Average precision') - ax2b.set_ylabel('Average precision') - ax3b.set_ylabel('Average precision') - ax4b.set_ylabel('Average precision') - - ax1b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) - ax2b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) - ax3b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) - ax4b.yaxis.grid(color='grey', linestyle='--', alpha=0.5) - - ax1a.xaxis.set_ticks(np.arange(K)) - ax1a.xaxis.set_ticklabels(truncated_sorted_lbs[0:K], rotation=90, fontsize=fontsize) - ax1a.xaxis.tick_bottom() - ax1a.set_ylabel("Number of audio clips") - - ax2a.xaxis.set_ticks(np.arange(K, 2*K)) - ax2a.xaxis.set_ticklabels(truncated_sorted_lbs[K:2*K], rotation=90, fontsize=fontsize) - ax2a.xaxis.tick_bottom() - # ax2a.tick_params(left='off', which='both') - ax2a.set_ylabel("Number of audio clips") - - ax3a.xaxis.set_ticks(np.arange(2*K, 3*K)) - ax3a.xaxis.set_ticklabels(truncated_sorted_lbs[2*K:3*K], rotation=90, fontsize=fontsize) - ax3a.xaxis.tick_bottom() - ax3a.set_ylabel("Number of audio clips") - - ax4a.xaxis.set_ticks(np.arange(3*K, N)) - ax4a.xaxis.set_ticklabels(truncated_sorted_lbs[3*K:], rotation=90, fontsize=fontsize) - ax4a.xaxis.tick_bottom() - # ax4a.tick_params(left='off', which='both') - ax4a.set_ylabel("Number of audio clips") - - ax1a.spines['right'].set_visible(False) - ax1b.spines['right'].set_visible(False) - ax2a.spines['left'].set_visible(False) - ax2b.spines['left'].set_visible(False) - ax2a.spines['right'].set_visible(False) - ax2b.spines['right'].set_visible(False) - ax3a.spines['left'].set_visible(False) - ax3b.spines['left'].set_visible(False) - ax3a.spines['right'].set_visible(False) - ax3b.spines['right'].set_visible(False) - ax4a.spines['left'].set_visible(False) - ax4b.spines['left'].set_visible(False) - - plt.subplots_adjust(hspace = 0.8) - - return ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b - -def _scatter_4_rows(x, ax, ax2, ax3, ax4, s, c, marker='.', alpha=1.): - N = len(x) - ax.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) - ax2.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) - ax3.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) - ax4.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha) - -def _plot_4_rows(x, ax, ax2, ax3, ax4, c, linewidth=1.0, alpha=1.0, label=""): - N = len(x) - ax.plot(x, c=c, linewidth=linewidth, alpha=alpha) - ax2.plot(x, c=c, linewidth=linewidth, alpha=alpha) - ax3.plot(x, c=c, linewidth=linewidth, alpha=alpha) - line, = ax4.plot(x, c=c, linewidth=linewidth, alpha=alpha, label=label) - return line - -def plot_long_fig(args): - # Arguments & parameters - workspace = args.workspace - - # Paths - stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') - save_out_path = 'results/long_fig.pdf' - create_folder(os.path.dirname(save_out_path)) - - # Stats - stats = cPickle.load(open(stat_path, 'rb')) - - N = len(config.labels) - sorted_indexes = stats['sorted_indexes_for_plot'] - sorted_labels = np.array(config.labels)[sorted_indexes] - audio_clips_per_class = stats['official_balanced_trainig_samples'] + stats['official_unbalanced_training_samples'] - audio_clips_per_class = audio_clips_per_class[sorted_indexes] - - (ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b) = prepare_plot_long_4_rows(sorted_labels) - - # plot the same data on both axes - ax1a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) - ax2a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) - ax3a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) - ax4a.bar(np.arange(N), audio_clips_per_class, alpha=0.3) - - maps_avg_instances = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision'] - maps_avg_instances = maps_avg_instances[sorted_indexes] - - maps_cnn13 = stats['cnn13_system_iteration60k']['eval']['average_precision'] - maps_cnn13 = maps_cnn13[sorted_indexes] - - maps_mobilenetv1 = stats['mobilenetv1_system_iteration56k']['eval']['average_precision'] - maps_mobilenetv1 = maps_mobilenetv1[sorted_indexes] - - maps_logmel_wavegram_cnn = _load_metrics0_classwise('main', 32000, 1024, - 320, 64, 50, 14000, 'full_train', 'Cnn13_SpAndWav', 'clip_bce', 'balanced', 'mixup', 32) - maps_logmel_wavegram_cnn = maps_logmel_wavegram_cnn[sorted_indexes] - - _scatter_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, s=5, c='k') - _scatter_4_rows(maps_cnn13, ax1b, ax2b, ax3b, ax4b, s=5, c='r') - _scatter_4_rows(maps_mobilenetv1, ax1b, ax2b, ax3b, ax4b, s=5, c='b') - _scatter_4_rows(maps_logmel_wavegram_cnn, ax1b, ax2b, ax3b, ax4b, s=5, c='g') - - linewidth = 0.7 - line0te = _plot_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, c='k', linewidth=linewidth, label='AP with averaging instances (baseline)') - line1te = _plot_4_rows(maps_cnn13, ax1b, ax2b, ax3b, ax4b, c='r', linewidth=linewidth, label='AP with CNN14') - line2te = _plot_4_rows(maps_mobilenetv1, ax1b, ax2b, ax3b, ax4b, c='b', linewidth=linewidth, label='AP with MobileNetV1') - line3te = _plot_4_rows(maps_logmel_wavegram_cnn, ax1b, ax2b, ax3b, ax4b, c='g', linewidth=linewidth, label='AP with Wavegram-Logmel-CNN') - - label_quality = stats['label_quality'] - sorted_rate = np.array(label_quality)[sorted_indexes] - for k in range(len(sorted_rate)): - if sorted_rate[k] and sorted_rate[k] == 1: - sorted_rate[k] = 0.99 - - ax1b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') - ax2b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') - ax3b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+') - line_label_quality = ax4b.scatter(np.arange(N)[sorted_rate != None], sorted_rate[sorted_rate != None], s=12, c='r', linewidth=0.8, marker='+', label='Label quality') - ax1b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') - ax2b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') - ax3b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') - ax4b.scatter(np.arange(N)[sorted_rate == None], 0.5 * np.ones(len(np.arange(N)[sorted_rate == None])), s=12, c='r', linewidth=0.8, marker='_') - - plt.legend(handles=[line0te, line1te, line2te, line3te, line_label_quality], fontsize=6, loc=1) - - plt.savefig(save_out_path) - print('Save fig to {}'.format(save_out_path)) - -def plot_flops(args): - - # Arguments & parameters - workspace = args.workspace - - # Paths - save_out_path = 'results_map/flops.pdf' - create_folder(os.path.dirname(save_out_path)) - - plt.figure(figsize=(5, 5)) - fig, ax = plt.subplots(1, 1) - - model_types = np.array(['Cnn6', 'Cnn10', 'Cnn14', 'ResNet22', 'ResNet38', 'ResNet54', - 'MobileNetV1', 'MobileNetV2', 'DaiNet', 'LeeNet', 'LeeNet18', - 'Res1dNet30', 'Res1dNet44', 'Wavegram-CNN', 'Wavegram-\nLogmel-CNN']) - flops = np.array([21.986, 21.986, 42.220, 30.081, 48.962, 54.563, 3.614, 2.810, - 30.395, 4.741, 26.369, 32.688, 61.833, 44.234, 53.510]) - mAPs = np.array([0.343, 0.380, 0.431, 0.430, 0.434, 0.429, 0.389, 0.383, 0.295, - 0.266, 0.336, 0.365, 0.355, 0.389, 0.439]) - - sorted_indexes = np.sort(flops) - ax.scatter(flops, mAPs) - - shift = [[1, 0.002], [1, -0.006], [-1, -0.014], [-2, 0.006], [-7, 0.006], - [1, -0.01], [0.5, 0.004], [-1, -0.014], [1, -0.007], [0.8, -0.008], - [1, -0.007], [1, 0.002], [-6, -0.015], [1, -0.008], [0.8, 0]] - - for i, model_type in enumerate(model_types): - ax.annotate(model_type, (flops[i] + shift[i][0], mAPs[i] + shift[i][1])) - - ax.plot(flops[[0, 1, 2]], mAPs[[0, 1, 2]]) - ax.plot(flops[[3, 4, 5]], mAPs[[3, 4, 5]]) - ax.plot(flops[[6, 7]], mAPs[[6, 7]]) - ax.plot(flops[[9, 10]], mAPs[[9, 10]]) - ax.plot(flops[[11, 12]], mAPs[[11, 12]]) - ax.plot(flops[[13, 14]], mAPs[[13, 14]]) - - ax.set_xlim(0, 70) - ax.set_ylim(0.2, 0.5) - ax.set_xlabel('Multi-adds (million)') - ax.set_ylabel('mAP') - - plt.tight_layout(0, 0, 0) - - plt.savefig(save_out_path) - print('Write out figure to {}'.format(save_out_path)) - - -def spearman(args): - - # Arguments & parameters - workspace = args.workspace - - # Paths - stat_path = os.path.join(workspace, 'results', 'stats_for_paper.pkl') - - # Stats - stats = cPickle.load(open(stat_path, 'rb')) - - label_quality = np.array([qu if qu else 0.5 for qu in stats['label_quality']]) - training_samples = np.array(stats['official_balanced_trainig_samples']) + \ - np.array(stats['official_unbalanced_training_samples']) - mAP = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision'] - - import scipy - samples_spearman = scipy.stats.spearmanr(training_samples, mAP)[0] - quality_spearman = scipy.stats.spearmanr(label_quality, mAP)[0] - - print('Training samples spearman: {:.3f}'.format(samples_spearman)) - print('Quality spearman: {:.3f}'.format(quality_spearman)) - - -def print_results(args): - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_mixup_time_domain', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'none', 'none', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'none', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'balanced_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - - # - (mAP, mAUC, dprime) = _load_metrics0_classwise2('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb32', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics0_classwise2('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn13_emb128', 'clip_bce', 'balanced', 'mixup', 32) - - # partial - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.8_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'partial_0.5_full_train', 'Cnn14', 'clip_bce', 'balanced', 'mixup', 32) - - # Sample rate - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_16k', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 64, 50, 14000, 'full_train', 'Cnn14_8k', 'clip_bce', 'balanced', 'mixup', 32) - - # Mel bins - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 128, 50, 14000, 'full_train', 'Cnn14_mel128', 'clip_bce', 'balanced', 'mixup', 32) - - (mAP, mAUC, dprime) = _load_metrics_classwise('main', 32000, 1024, 320, 32, 50, 14000, 'full_train', 'Cnn14_mel32', 'clip_bce', 'balanced', 'mixup', 32) - - import crash - asdf - -if __name__ == '__main__': - - parser = argparse.ArgumentParser(description='') - subparsers = parser.add_subparsers(dest='mode') - - parser_plot = subparsers.add_parser('plot') - parser_plot.add_argument('--dataset_dir', type=str, required=True) - parser_plot.add_argument('--workspace', type=str, required=True) - parser_plot.add_argument('--select', type=str, required=True) - - parser_plot = subparsers.add_parser('plot_for_paper') - parser_plot.add_argument('--dataset_dir', type=str, required=True) - parser_plot.add_argument('--workspace', type=str, required=True) - parser_plot.add_argument('--select', type=str, required=True) - - parser_plot = subparsers.add_parser('plot_for_paper2') - parser_plot.add_argument('--dataset_dir', type=str, required=True) - parser_plot.add_argument('--workspace', type=str, required=True) - - parser_values = subparsers.add_parser('plot_class_iteration') - parser_values.add_argument('--workspace', type=str, required=True) - parser_values.add_argument('--select', type=str, required=True) - - parser_summary_stats = subparsers.add_parser('summary_stats') - parser_summary_stats.add_argument('--workspace', type=str, required=True) - - parser_plot_long = subparsers.add_parser('plot_long_fig') - parser_plot_long.add_argument('--workspace', type=str, required=True) - - parser_plot_flops = subparsers.add_parser('plot_flops') - parser_plot_flops.add_argument('--workspace', type=str, required=True) - - parser_spearman = subparsers.add_parser('spearman') - parser_spearman.add_argument('--workspace', type=str, required=True) - - parser_print = subparsers.add_parser('print') - parser_print.add_argument('--workspace', type=str, required=True) - - args = parser.parse_args() - - if args.mode == 'plot': - plot(args) - - elif args.mode == 'plot_for_paper': - plot_for_paper(args) - - elif args.mode == 'plot_for_paper2': - plot_for_paper2(args) - - elif args.mode == 'table_values': - table_values(args) - - elif args.mode == 'plot_class_iteration': - plot_class_iteration(args) - - elif args.mode == 'summary_stats': - summary_stats(args) - - elif args.mode == 'plot_long_fig': - plot_long_fig(args) - - elif args.mode == 'plot_flops': - plot_flops(args) - - elif args.mode == 'spearman': - spearman(args) - - elif args.mode == 'print': - print_results(args) - - else: - raise Exception('Error argument!') \ No newline at end of file