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!')