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AudioGPT/audio_detection/audio_infer/utils/plot_statistics.py
yangdongchao 322ed8cbb2 detection and extraction
2023-04-06 00:11:23 +08:00

2034 lines
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Python
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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!')
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