-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_asd.py
273 lines (242 loc) · 8.58 KB
/
train_asd.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
import argparse
import warnings
warnings.filterwarnings('ignore')
import os
import random
import numpy as np
import pandas as pd
import yaml
import torch
from utils.GPU import auto_gpu
auto_gpu()
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from dataset import ASDDataset
from label import dev_section_label_dict, dev_eval_section_label_dict
from nnet.model import MobileFaceNet, SimpleMobileFaceNet
from nnet.arcface import ArcMarginProduct
from nnet.center_loss import CenterLoss
from trainer import ASDTask
def main(
config,
log_dir,
gpus,
fast_dev_run=False,
test_state_dict=None,
):
config.update({'log_dir': log_dir})
if config["training"]["use_eval"]:
dev_train_df = pd.read_csv(config["data"]["dev_train_csv"])
eval_train_df = pd.read_csv(config["data"]["eval_train_csv"])
train_df = pd.concat([dev_train_df, eval_train_df])
train_dataset = ASDDataset(
audio_folder=config["data"]["audio_folder"],
csv_entries=train_df,
class_label_dict=dev_eval_section_label_dict,
pad_to=config["data"]["audio_max_len"],
dir_name='train',
return_class_label=True,
return_domain_label=config["represent"]["domain_represent"]
)
num_class = len(dev_eval_section_label_dict.keys())
else:
train_df = pd.read_csv(config["data"]["dev_train_csv"])
train_dataset = ASDDataset(
audio_folder=config["data"]["audio_folder"],
csv_entries=train_df,
class_label_dict=dev_section_label_dict,
pad_to=config["data"]["audio_max_len"],
dir_name='train',
return_class_label=True,
return_domain_label=config["represent"]["domain_represent"]
)
num_class = len(dev_section_label_dict)
test_df = pd.read_csv(config["data"]["dev_test_csv"])
test_dataset = ASDDataset(
audio_folder=config["data"]["audio_folder"],
csv_entries=test_df,
class_label_dict=dev_section_label_dict,
pad_to=config["data"]["audio_max_len"],
dir_name='test',
return_class_label=True,
return_anomaly_label=True,
return_domain_label=True,
return_filename=True
)
########################
# define arcface
########################
if config["training"]["arcface"]:
arcface = ArcMarginProduct(config["net"]["embedding_size"],
config["net"]["num_class"],
m=config["net"]["margin"],
s=config["net"]["scale"])
else:
arcface = None
########################
# define model
########################
model = MobileFaceNet(num_class=config["net"]["num_class"],
arcface=arcface,
embedding_size=config["net"]["embedding_size"])
########################
# define center loss
########################
if config["training"]["center_loss"]:
criterion_cent = CenterLoss(num_classes=config["net"]["num_class"],
feat_dim=config["net"]["embedding_size"])
opt_center_loss = torch.optim.Adam(criterion_cent.parameters(), config["opt"]["center_loss_lr"])
else:
criterion_cent = None
opt_center_loss = None
if test_state_dict is None:
valid_df = pd.read_csv(config["data"]["dev_test_csv"])
valid_dataset = ASDDataset(
audio_folder=config["data"]["audio_folder"],
csv_entries=valid_df,
class_label_dict=dev_section_label_dict,
pad_to=config["data"]["audio_max_len"],
dir_name='test',
return_class_label=True,
return_anomaly_label=True,
return_domain_label=config["represent"]["domain_represent"]
)
opt = torch.optim.Adam(model.parameters(), config["opt"]["lr"], betas=(0.9, 0.999))
scheduler = {
"scheduler": torch.optim.lr_scheduler.ExponentialLR(opt, gamma=0.99),
"interval": "epoch"
}
logger = TensorBoardLogger(
os.path.dirname(config["log_dir"]), config["log_dir"].split("/")[-1],
version=config["version"]
)
callbacks = [
EarlyStopping(
monitor="valid/auc",
patience=config["training"]["early_stop_patience"],
verbose=True,
mode="max",
),
ModelCheckpoint(
logger.log_dir,
monitor="valid/auc",
save_top_k=1,
mode="max",
save_last=True,
),
]
else:
valid_dataset = None
batch_sampler = None
opt = None
scheduler = None
logger = False
callbacks = None
ASD_training = ASDTask(
config,
model=model,
opt=opt,
train_data=train_dataset,
valid_data=valid_dataset,
test_data=test_dataset,
scheduler=scheduler,
fast_dev_run=fast_dev_run,
center_loss=criterion_cent,
opt_center_loss=opt_center_loss
)
if fast_dev_run:
log_every_n_steps = 1
limit_train_batches = 2
limit_val_batches = 2
limit_test_batches = 1.0
n_epochs = 1
else:
log_every_n_steps = 40
limit_train_batches = 1.0
limit_val_batches = 1.0
limit_test_batches = 1.0
n_epochs = config["training"]["n_epochs"]
trainer = pl.Trainer(
accelerator='gpu',
devices=gpus,
precision=config["training"]["precision"],
max_epochs=n_epochs,
callbacks=callbacks,
logger=logger,
gradient_clip_val=config["training"]["gradient_clip"],
check_val_every_n_epoch=config["training"]["validation_interval"],
num_sanity_val_steps=0,
log_every_n_steps=log_every_n_steps,
limit_train_batches=limit_train_batches,
limit_val_batches=limit_val_batches,
limit_test_batches=limit_test_batches,
)
if test_state_dict is None:
trainer.fit(ASD_training)
best_path = trainer.checkpoint_callback.best_model_path
print(f"best_model_path: {best_path}")
test_state_dict = torch.load(best_path)["state_dict"]
ASD_training.load_state_dict(test_state_dict)
trainer.test(ASD_training)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Training a ASD system")
parser.add_argument(
"--conf_file",
default="./confs/default.yaml",
help="The configuration file with all the experiment parameters.",
)
parser.add_argument(
"--log_dir",
default="./exp",
help="Directory where to save tensorboard logs, saved models, etc.",
)
parser.add_argument(
"--version",
default="test",
help="Record your experimental details to distinguish others experiment",
)
parser.add_argument(
"--test_from_checkpoint", default=None, help="Test the model specified"
)
parser.add_argument(
"--gpus",
default="1",
help="The number of GPUs to train on, or the gpu to use, default='0', "
"so uses one GPU",
)
parser.add_argument(
"--fast_dev_run",
action="store_true",
default=False,
help="Use this option to make a 'fake' run which is useful for development and debugging. "
"It uses very few batches and epochs so it won't give any meaningful result.",
)
args = parser.parse_args()
with open(args.conf_file, "r") as f:
configs = yaml.safe_load(f)
test_from_checkpoint = args.test_from_checkpoint
test_model_state_dict = None
if test_from_checkpoint is not None:
checkpoint = torch.load(test_from_checkpoint)
configs_ckpt = checkpoint["hyper_parameters"]
print(f"loaded model from: {test_from_checkpoint}")
test_model_state_dict = checkpoint["state_dict"]
seed = configs["training"]["seed"]
configs.update({'version': args.version})
if seed:
torch.manual_seed(seed)
torch.random.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
pl.seed_everything(seed)
torch.backends.cudnn.deterministic = True
main(
configs,
args.log_dir,
args.gpus,
args.fast_dev_run,
test_model_state_dict
)