-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtrainer.py
271 lines (239 loc) · 10.4 KB
/
trainer.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
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import time
import numpy as np
import torch
import torch.nn.parallel
import torch.utils.data.distributed
from tensorboardX import SummaryWriter
from torch.cuda.amp import GradScaler, autocast
from utils.utils import distributed_all_gather
from monai.data import decollate_batch
import math
import SimpleITK as sitk
def dice(x, y):
intersect = torch.sum(torch.sum(torch.sum(x * y)))
y_sum = torch.sum(torch.sum(torch.sum(y)))
if y_sum == 0:
return 0.0
x_sum = torch.sum(torch.sum(torch.sum(x)))
return 2 * intersect / (x_sum + y_sum)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def train_epoch(model, loader, optimizer, scaler, epoch, loss_func, args):
model.train()
start_time = time.time()
run_loss = AverageMeter()
dice_loss = AverageMeter() #########
nll_loss = AverageMeter()
for idx, batch_data in enumerate(loader):
if isinstance(batch_data, list):
data, target = batch_data
else:
data, target = batch_data["image"], batch_data["label"]
#print(str(args.gpu))
data, target = data.cuda(device = args.gpu), target.cuda(args.gpu)
for param in model.parameters():
param.grad = None
with autocast(enabled=args.amp):
logits, nll_losses = model(data) #到此为止没有做过softmax
#print(logits.shape)
#target[target>0.5] = 1
#target[target<=0.5] = 0
loss,dice,nll = loss_func(logits, target, nll_losses) #########
#print(math.isnan(loss))
if math.isnan(loss):
print('logitis nan?:',torch.isinf(logits).any())
if args.amp:
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
optimizer.step()
if args.distributed:
loss_list = distributed_all_gather([loss], out_numpy=True, is_valid=idx < loader.sampler.valid_length)
run_loss.update(
np.mean(np.mean(np.stack(loss_list, axis=0), axis=0), axis=0), n=args.batch_size * args.world_size
)
else:
run_loss.update(loss.item(), n=args.batch_size)
dice_loss.update(dice.item(), n=args.batch_size) #########
nll_loss.update(nll.item(), n=args.batch_size) #########
if args.rank == 0:
print(
"Epoch {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
"loss: {:.4f}".format(run_loss.avg),
"time {:.2f}s".format(time.time() - start_time),
)
start_time = time.time()
for param in model.parameters():
param.grad = None
return run_loss.avg, dice_loss.avg, nll_loss.avg #########
def val_epoch(model, loader, epoch, acc_func, args, model_inferer=None, post_label=None, post_pred=None):
model.eval()
start_time = time.time()
val_acc = AverageMeter()
with torch.no_grad():
for idx, batch_data in enumerate(loader):
if isinstance(batch_data, list):
data, target = batch_data
else:
data, target = batch_data["image"], batch_data["label"]
data, target = data.cuda(device = args.gpu), target.cuda(device = args.gpu)
#print('data:',data.shape)
#print('target:',target.shape)
with autocast(enabled=args.amp):
if model_inferer is not None:
logits = model_inferer(data,status = 'val')
#print(logits)
else:
logits = model(data)
if not logits.is_cuda:
target = target.cpu()
#target = torch.sigmoid(target)
logits = torch.sigmoid(logits)
val_labels_list = decollate_batch(target)
val_labels_convert = [post_label[1](post_label[0](val_label_tensor)) for val_label_tensor in val_labels_list]
val_outputs_list = decollate_batch(logits)
val_output_convert = [post_pred[1](post_pred[0](val_pred_tensor)) for val_pred_tensor in val_outputs_list]
#print(post_pred[1](post_pred[0](val_outputs_list[0])).shape,val_labels_convert.shape)
acc = acc_func(y_pred=val_output_convert, y=val_labels_convert)
#print(dice(val_labels_convert[0][1],val_output_convert[0][1]),torch.count_nonzero(val_labels_convert[0][0]),val_labels_convert[0][0].shape)
acc = acc.cuda(args.rank)
#print(acc)
#sitk_img = sitk.GetImageFromArray(val_labels_convert[0][1].cpu().numpy(), isVector=False)
#sitk.WriteImage(sitk_img, os.path.join('/local/scratch/v_jiayin_sun/UNETR/BTCV/dataset/dataset_0/labelsTr_thres','fuck.nii.gz'))
if args.distributed:
acc_list = distributed_all_gather([acc], out_numpy=True, is_valid=idx < loader.sampler.valid_length)
avg_acc = np.mean([np.nanmean(l) for l in acc_list])
else:
acc_list = acc.detach().cpu().numpy()
avg_acc = np.mean([np.nanmean(l) for l in acc_list])
if args.rank == 0:
print(
"Val {}/{} {}/{}".format(epoch, args.max_epochs, idx, len(loader)),
"acc",
avg_acc,
"time {:.2f}s".format(time.time() - start_time),
)
#print(avg_acc)
val_acc.update(avg_acc)
start_time = time.time()
#print(val_acc.avg)
return val_acc.avg
def save_checkpoint(model, epoch, args, filename="model.pt", best_acc=0, optimizer=None, scheduler=None):
state_dict = model.state_dict() if not args.distributed else model.module.state_dict()
save_dict = {"epoch": epoch, "best_acc": best_acc, "state_dict": state_dict}
if optimizer is not None:
save_dict["optimizer"] = optimizer.state_dict()
if scheduler is not None:
save_dict["scheduler"] = scheduler.state_dict()
filename = os.path.join(args.logdir, filename)
torch.save(save_dict, filename)
print("Saving checkpoint", filename)
def run_training(
model,
train_loader,
val_loader,
optimizer,
loss_func,
acc_func,
args,
model_inferer=None,
scheduler=None,
start_epoch=0,
post_label=None,
post_pred=None,
wandb_writer = None
):
writer = None
if args.logdir is not None and args.rank == 0: #默认test
writer = SummaryWriter(log_dir=args.logdir)
if args.rank == 0:
print("Writing Tensorboard logs to ", args.logdir)
scaler = None
if args.amp:
scaler = GradScaler()
val_acc_max = 0.0
for epoch in range(start_epoch, args.max_epochs):
if args.distributed:
train_loader.sampler.set_epoch(epoch)
torch.distributed.barrier()
print(args.rank, time.ctime(), "Epoch:", epoch)
epoch_time = time.time()
train_loss, dice_loss, nll_loss = train_epoch( #训练函数
model, train_loader, optimizer, scaler=scaler, epoch=epoch, loss_func=loss_func, args=args
)
if args.rank == 0:
print(
"Final training {}/{}".format(epoch, args.max_epochs - 1),
"loss: {:.4f}".format(train_loss),
"time {:.2f}s".format(time.time() - epoch_time),
)
if args.rank == 0 and writer is not None:
writer.add_scalar("train_loss", train_loss, epoch)
wandb_writer.log({'loss': train_loss, 'epoch': epoch, 'diceloss': dice_loss, 'nllloss': nll_loss})
b_new_best = False
if (epoch + 1) % args.val_every == 0: #默认每100个epoch做一次validation
if args.distributed:
torch.distributed.barrier()
epoch_time = time.time()
val_avg_acc = val_epoch(
model,
val_loader,
epoch=epoch,
acc_func=acc_func,
model_inferer=model_inferer,
args=args,
post_label=post_label,
post_pred=post_pred,
)
if args.rank == 0:
print(
"Final validation {}/{}".format(epoch, args.max_epochs - 1),
"acc",
val_avg_acc,
"time {:.2f}s".format(time.time() - epoch_time),
)
wandb_writer.log({'val_avg_acc': val_avg_acc, 'epoch': epoch})
if writer is not None:
writer.add_scalar("val_acc", val_avg_acc, epoch)
if val_avg_acc > val_acc_max:
print("new best ({:.6f} --> {:.6f}). ".format(val_acc_max, val_avg_acc))
val_acc_max = val_avg_acc
b_new_best = True
if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
save_checkpoint(
model, epoch, args, best_acc=val_acc_max, optimizer=optimizer, scheduler=scheduler
)
if args.rank == 0 and args.logdir is not None and args.save_checkpoint:
save_checkpoint(model, epoch, args, best_acc=val_acc_max, filename="model_final.pt")
if b_new_best:
print("Copying to model.pt new best model!!!!")
shutil.copyfile(os.path.join(args.logdir, "model_final.pt"), os.path.join(args.logdir, "model.pt"))
if scheduler is not None:
scheduler.step()
print("Training Finished !, Best Accuracy: ", val_acc_max)
return val_acc_max