-
Notifications
You must be signed in to change notification settings - Fork 1
/
lightning_train.py
204 lines (161 loc) · 6.99 KB
/
lightning_train.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
# Copyright (C) 2024 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
import os
import random
from argparse import Namespace
import fast_bss_eval
import numpy as np
import pytorch_lightning as pl
import torch
import torch.utils.data as data
from loguru import logger
from pesq import pesq
from pytorch_lightning.utilities import rank_zero_only
from torch import optim
from datasets.dataset_creator import dataset_creator
from loss_functions.ras_loss import RASLoss
from nets.build_model import build_model
from utils.audio_utils import istft_4dim
from utils.collate import collate_seq, collate_seq_eras
class RASTrainingModule(pl.LightningModule):
def __init__(self, hparams, data_path):
super().__init__()
if not isinstance(hparams, Namespace):
hparams = Namespace(hparams.model_name, **hparams.model_conf)
self.data_path = data_path
self.save_hyperparameters(hparams)
self.model = build_model(hparams.model_name, hparams.model_conf)
self.loss = RASLoss(**hparams.eras_loss_conf)
self.current_step = 0 # used for learning-rate warmup
def load_pretrained_weight(self):
if self.hparams.pretrained_model_path is not None:
if torch.cuda.is_available():
state_dict = torch.load(self.hparams.pretrained_model_path)
else:
state_dict = torch.load(self.hparams.pretrained_model_path, map_location=torch.device("cpu"))
try:
state_dict = state_dict["state_dict"]
except KeyError:
print("No key named state_dict. Directly loading from model.")
state_dict = {k.replace("model.", ""): v for k, v in state_dict.items()}
self.model.load_state_dict(state_dict)
logger.info("Loaded weights from " + self.hparams.pretrained_model_path)
def on_batch_end(self):
# learning rate warmup
self.warmup_lr()
@rank_zero_only
def _symlink_logger(self):
# Keep track of which log file goes with which tensorboard log folder
tensorboard_log_dir = self.trainer.logger.log_dir
logger.info(f"Tensorboard logs: {tensorboard_log_dir}")
if os.path.exists(self.hparams.log_file):
_, log_name = os.path.split(self.hparams.log_file)
new_log_path = os.path.join(tensorboard_log_dir, log_name)
# when resuming training, symlink already exists
if not os.path.islink(new_log_path):
os.symlink(os.path.abspath(self.hparams.log_file), new_log_path)
def warmup_lr(self):
# get initial learning rate at step 0
if self.current_step == 0:
for param_group in self.optimizers().optimizer.param_groups:
self.peak_lr = param_group["lr"]
self.current_step += 1
if getattr(self.hparams, "warmup_steps", 0) >= self.current_step:
for param_group in self.optimizers().optimizer.param_groups:
param_group["lr"] = self.peak_lr * self.current_step / self.hparams.warmup_steps
def on_train_start(self):
self._symlink_logger()
self.load_pretrained_weight()
def forward(self, x):
return self.model(x)
def _step(self, batch):
input_features, target_dict = batch
input_features, lens = input_features
y = self.forward(input_features) # (batch, frame, freq) -> (batch, frame, freq, num_src)
loss = self.loss(y, target_dict, device=self.device, training=self.model.training)
return loss
def training_step(self, batch, batch_idx):
loss = self._step(batch)
loss_for_logging = {}
for k, v in loss.items():
loss_for_logging[f"train/{k}"] = v
self.log_dict(loss_for_logging, on_step=True, on_epoch=True, sync_dist=True)
self.on_batch_end()
return loss["loss"]
def validation_step(self, batch, batch_idx):
loss = self._step(batch)
loss_for_logging = {}
for k, v in loss.items():
loss_for_logging[f"val/{k}"] = v
self.log_dict(loss_for_logging, on_epoch=True, sync_dist=True)
return loss["loss"]
def test_step(self, batch, batch_idx):
input_features, target_dict = batch
input_features, lens = input_features
sample_rate = self.hparams.dataloading_conf["sr"]
est = self.forward(input_features) # (batch, frame, freq) -> (batch, frame, freq, num_src)
# apply FCP
est = self.loss.filtering_func(est, input_features)
est = est[..., self.loss.ref_channel, :]
# TF-domain -> time-domain by iSTFT
est = istft_4dim(est, **self.loss.stft_conf)[0].T
# reference signal
ref = target_dict["y_srcs"]["reverb"][0][0, ..., self.loss.ref_channel, :].T
# compute metrics
m = min(ref.shape[-1], est.shape[-1])
sisnr, perm = fast_bss_eval.si_sdr(ref[..., :m], est[..., :m], return_perm=True)
sisnr = sisnr.mean().cpu().numpy()
perm = perm.cpu().numpy()
sdr = fast_bss_eval.sdr(ref, est).mean().cpu().numpy()
ref, est = ref.cpu().numpy(), est.cpu().numpy()
pesq_score = 0.0
for i, p in enumerate(perm):
pesq_score += pesq(sample_rate, ref[i], est[p], mode="nb")
pesq_score /= i + 1
result = {
"test/sisnr": float(sisnr),
"test/sdr": float(sdr),
"test/pesq": float(pesq_score),
}
self.log_dict(result, on_epoch=True)
return result
def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(), **self.hparams.optimizer_conf)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, **self.hparams.scheduler_conf)
return {
"optimizer": optimizer,
"lr_scheduler": scheduler,
"monitor": "val/loss",
}
def _init_fn(self, worker_id):
random.seed(self.hparams.seed + worker_id)
np.random.seed(self.hparams.seed + worker_id)
torch.manual_seed(self.hparams.seed + worker_id)
def _get_data_loader(self, partition):
shuffle = self.hparams.shuffle if partition == "tr" else None
if partition == "tr":
batch_size = self.hparams.batch_size
elif partition == "cv":
batch_size = self.hparams.val_batch_size
else:
batch_size = 1
d = dataset_creator(self.hparams, self.data_path, partition)
if getattr(d, "running_eras", False):
collate_fn = collate_seq_eras
else:
collate_fn = collate_seq
return data.DataLoader(
d,
batch_size,
collate_fn=collate_fn,
shuffle=shuffle,
num_workers=self.hparams.num_workers,
worker_init_fn=self._init_fn,
)
def train_dataloader(self):
return self._get_data_loader("tr")
def val_dataloader(self):
return self._get_data_loader("cv")
def test_dataloader(self):
return self._get_data_loader("tt")