forked from ycwu1997/SS-Net
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtrain_ss_2d.py
More file actions
245 lines (200 loc) · 11.7 KB
/
train_ss_2d.py
File metadata and controls
245 lines (200 loc) · 11.7 KB
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
import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from torch.nn.modules.loss import CrossEntropyLoss
from torchvision import transforms
from tqdm import tqdm
from dataloaders.dataset import (BaseDataSets, RandomGenerator, TwoStreamBatchSampler)
from networks.net_factory import net_factory
from utils import losses, ramps, feature_memory, contrastive_losses, val_2d
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str, default='./data/ACDC', help='Name of Experiment')
parser.add_argument('--exp', type=str, default='SSNet', help='experiment_name')
parser.add_argument('--model', type=str, default='unet', help='model_name')
parser.add_argument('--max_iterations', type=int, default=30000, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int, default=24, help='batch_size per gpu')
parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training')
parser.add_argument('--base_lr', type=float, default=0.01, help='segmentation network learning rate')
parser.add_argument('--patch_size', type=list, default=[256, 256], help='patch size of network input')
parser.add_argument('--seed', type=int, default=1337, help='random seed')
parser.add_argument('--num_classes', type=int, default=4, help='output channel of network')
# label and unlabel
parser.add_argument('--labeled_bs', type=int, default=12, help='labeled_batch_size per gpu')
parser.add_argument('--labelnum', type=int, default=3, help='labeled data')
# costs
parser.add_argument('--gpu', type=str, default='0', help='GPU to use')
parser.add_argument('--consistency', type=float, default=0.1, help='consistency')
parser.add_argument('--consistency_rampup', type=float, default=200.0, help='consistency_rampup')
parser.add_argument('--magnitude', type=float, default='6.0', help='magnitude')
args = parser.parse_args()
def patients_to_slices(dataset, patiens_num):
ref_dict = None
if "ACDC" in dataset:
ref_dict = {"3": 68, "7": 136,
"14": 256, "21": 396, "28": 512, "35": 664, "70": 1312}
elif "Prostate":
ref_dict = {"2": 27, "4": 53, "8": 120,
"12": 179, "16": 256, "21": 312, "42": 623}
else:
print("Error")
return ref_dict[str(patiens_num)]
def get_current_consistency_weight(epoch):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
return args.consistency * ramps.sigmoid_rampup(epoch, args.consistency_rampup)
def train(args, snapshot_path):
base_lr = args.base_lr
num_classes = args.num_classes
max_iterations = args.max_iterations
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
model = net_factory(net_type=args.model, in_chns=1, class_num=num_classes)
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
db_train = BaseDataSets(base_dir=args.root_path,
split="train",
num=None,
transform=transforms.Compose([
RandomGenerator(args.patch_size)
]))
db_val = BaseDataSets(base_dir=args.root_path, split="val")
total_slices = len(db_train)
labeled_slice = patients_to_slices(args.root_path, args.labelnum)
print("Total silices is: {}, labeled slices is: {}".format(total_slices, labeled_slice))
labeled_idxs = list(range(0, labeled_slice))
unlabeled_idxs = list(range(labeled_slice, total_slices))
batch_sampler = TwoStreamBatchSampler(labeled_idxs, unlabeled_idxs, args.batch_size, args.batch_size-args.labeled_bs)
trainloader = DataLoader(db_train, batch_sampler=batch_sampler, num_workers=4, pin_memory=True, worker_init_fn=worker_init_fn)
model.train()
prototype_memory = feature_memory.FeatureMemory(elements_per_class=32, n_classes=num_classes)
valloader = DataLoader(db_val, batch_size=1, shuffle=False, num_workers=1)
optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
ce_loss = CrossEntropyLoss()
dice_loss = losses.DiceLoss(n_classes=num_classes)
adv_loss=losses.VAT2d(epi=args.magnitude)
writer = SummaryWriter(snapshot_path + '/log')
logging.info("{} iterations per epoch".format(len(trainloader)))
iter_num = 0
max_epoch = max_iterations // len(trainloader) + 1
best_performance = 0.0
iterator = tqdm(range(max_epoch), ncols=70)
for _ in iterator:
for _, sampled_batch in enumerate(trainloader):
volume_batch, label_batch = sampled_batch['image'], sampled_batch['label']
volume_batch, label_batch = volume_batch.cuda(), label_batch.cuda()
outputs, embedding= model(volume_batch)
outputs_soft = F.softmax(outputs, dim=1)
labeled_features = embedding[:args.labeled_bs,...]
unlabeled_features = embedding[args.labeled_bs:,...]
y = outputs_soft[:args.labeled_bs]
true_labels = label_batch[:args.labeled_bs]
_, prediction_label = torch.max(y, dim=1)
_, pseudo_label = torch.max(outputs_soft[args.labeled_bs:], dim=1) # Get pseudolabels
mask_prediction_correctly = ((prediction_label == true_labels).float() * (prediction_label > 0).float()).bool()
### select the correct predictions and ignore the background class
# Apply the filter mask to the features and its labels
labeled_features = labeled_features.permute(0, 2, 3, 1)
labels_correct = true_labels[mask_prediction_correctly]
labeled_features_correct = labeled_features[mask_prediction_correctly, ...]
# get projected features
with torch.no_grad():
model.eval()
proj_labeled_features_correct = model.projection_head(labeled_features_correct)
model.train()
# updated memory bank
prototype_memory.add_features_from_sample_learned(model, proj_labeled_features_correct, labels_correct)
labeled_features_all = labeled_features.reshape(-1, labeled_features.size()[-1])
labeled_labels = true_labels.reshape(-1)
# get predicted features
proj_labeled_features_all = model.projection_head(labeled_features_all)
pred_labeled_features_all = model.prediction_head(proj_labeled_features_all)
# Apply contrastive learning loss
loss_contr_labeled = contrastive_losses.contrastive_class_to_class_learned_memory(model, pred_labeled_features_all, labeled_labels, num_classes, prototype_memory.memory)
unlabeled_features = unlabeled_features.permute(0, 2, 3, 1).reshape(-1, labeled_features.size()[-1])
pseudo_label = pseudo_label.reshape(-1)
# get predicted features
proj_feat_unlabeled = model.projection_head(unlabeled_features)
pred_feat_unlabeled = model.prediction_head(proj_feat_unlabeled)
# Apply contrastive learning loss
loss_contr_unlabeled = contrastive_losses.contrastive_class_to_class_learned_memory(model, pred_feat_unlabeled, pseudo_label, num_classes, prototype_memory.memory)
loss_seg_ce = ce_loss(outputs[:args.labeled_bs], true_labels[:].long())
loss_seg_dice = dice_loss(y, true_labels.unsqueeze(1))
loss_lds =adv_loss(model, volume_batch)
consistency_weight = get_current_consistency_weight(iter_num//150)
loss = loss_seg_dice + consistency_weight * (loss_lds + 0.1 * (loss_contr_labeled + loss_contr_unlabeled))
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_num = iter_num + 1
writer.add_scalar('info/total_loss', loss, iter_num)
writer.add_scalar('info/loss_ce', loss_seg_ce, iter_num)
writer.add_scalar('info/loss_dice', loss_seg_dice, iter_num)
writer.add_scalar('info/loss_vat', loss_lds, iter_num)
writer.add_scalar('info/loss_cl_l', loss_contr_labeled, iter_num)
writer.add_scalar('info/loss_cl_u', loss_contr_unlabeled, iter_num)
writer.add_scalar('info/consistency_weight', consistency_weight, iter_num)
logging.info('iteration %d : loss : %f, loss_ce: %f, loss_dice: %f,loss_vat: %f, loss_cl_l: %f,loss_cl_u: %f' %(iter_num, loss, loss_seg_ce, loss_seg_dice, loss_lds, loss_contr_labeled, loss_contr_unlabeled))
if iter_num % 20 == 0:
image = volume_batch[1, 0:1, :, :]
writer.add_image('train/Image', image, iter_num)
outputs = torch.argmax(torch.softmax(outputs, dim=1), dim=1, keepdim=True)
writer.add_image('train/Prediction', outputs[1, ...] * 50, iter_num)
labs = label_batch[1, ...].unsqueeze(0) * 50
writer.add_image('train/GroundTruth', labs, iter_num)
if iter_num > 0 and iter_num % 200 == 0:
model.eval()
metric_list = 0.0
for _, sampled_batch in enumerate(valloader):
metric_i = val_2d.test_single_volume(sampled_batch["image"], sampled_batch["label"], model, classes=num_classes)
metric_list += np.array(metric_i)
metric_list = metric_list / len(db_val)
for class_i in range(num_classes-1):
writer.add_scalar('info/val_{}_dice'.format(class_i+1), metric_list[class_i, 0], iter_num)
writer.add_scalar('info/val_{}_hd95'.format(class_i+1), metric_list[class_i, 1], iter_num)
performance = np.mean(metric_list, axis=0)[0]
mean_hd95 = np.mean(metric_list, axis=0)[1]
writer.add_scalar('info/val_mean_dice', performance, iter_num)
writer.add_scalar('info/val_mean_hd95', mean_hd95, iter_num)
if performance > best_performance:
best_performance = performance
save_mode_path = os.path.join(snapshot_path, 'iter_{}_dice_{}.pth'.format(iter_num, round(best_performance, 4)))
save_best_path = os.path.join(snapshot_path,'{}_best_model.pth'.format(args.model))
torch.save(model.state_dict(), save_mode_path)
torch.save(model.state_dict(), save_best_path)
logging.info('iteration %d : mean_dice : %f mean_hd95 : %f' % (iter_num, performance, mean_hd95))
model.train()
if iter_num >= max_iterations:
break
if iter_num >= max_iterations:
iterator.close()
break
writer.close()
return "Training Finished!"
if __name__ == "__main__":
if args.deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
snapshot_path = "./model/ACDC_{}_{}_labeled/{}".format(args.exp, args.labelnum, args.model)
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
if os.path.exists(snapshot_path + '/code'):
shutil.rmtree(snapshot_path + '/code')
shutil.copytree('./code/', snapshot_path + '/code',shutil.ignore_patterns(['.git', '__pycache__']))
logging.basicConfig(filename=snapshot_path+"/log.txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
train(args, snapshot_path)