-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_weight_loss.py
232 lines (165 loc) · 6.98 KB
/
train_weight_loss.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
from dataset import Ring_Cell_random_crop, collate_fn, Ring_Cell_all
import torch
from torch.utils.data import Dataset, DataLoader
import model_weight_loss as model
import os
from tensorboardX import SummaryWriter
import numpy as np
from tqdm import tqdm
from metric import detection_metric
from lib.nms.pth_nms import pth_nms
def nms(dets, thresh):
"Dispatch to either CPU or GPU NMS implementations.\
Accept dets as tensor"""
return pth_nms(dets, thresh)
def get_lr(optimizer):
return optimizer.param_groups[0]['lr']
def main(params):
if params['writer'] == True:
writer = SummaryWriter(comment='_resnet18 4fold 0; weight loss 10')
retinanet = model.resnet18(num_classes=2, pretrained=True)
retinanet = torch.nn.DataParallel(retinanet).cuda()
if os.path.exists(params['model_path']) and params['resume']:
retinanet.module.load_state_dict(torch.load(params['model_path']))
print('resume training from {}'.format(params['model_path']))
optimizer = torch.optim.Adam(retinanet.parameters(), lr=params['learning_rate'])
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=8, verbose=True, factor=0.9)
train_dataset = Ring_Cell_random_crop(params['train_txt'])
train_dataloader = DataLoader(
train_dataset,
batch_size=params['batch_size'],
num_workers=4,
collate_fn=collate_fn,
shuffle=True
)
test_dataset = Ring_Cell_all(params['test_txt'])
# train
step = 1
best_recall = 0
best_precision = 0
best_ap = 0
for epoch in range(params['max_epoch']):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss_train = []
epoch_loss = []
tq = tqdm(total=len(train_dataloader))
lr = get_lr(optimizer)
tq.set_description('epoch:{}, learning rate:{}'.format(epoch, lr))
for index, (data, label, _) in enumerate(train_dataloader):
optimizer.zero_grad()
classification_loss, regression_loss = retinanet([data.cuda().float(), label])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
tq.update(1)
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
epoch_loss_train.append(float(loss))
epoch_loss.append(float(loss))
if index % 20 == 0 and params['writer'] == True:
writer.add_scalar('loss for train', float(np.mean(epoch_loss)), step)
step += 1
epoch_loss = []
torch.save(retinanet.module.state_dict(), 'ckpt/latest_resnet18_4fold_0_weight_loss_10.pth')
tq.close()
scheduler.step(np.mean(epoch_loss_train))
if params['writer'] == True:
writer.add_scalar('epoch/loss for train', float(np.mean(epoch_loss_train)), epoch)
# test
retinanet.eval()
test_image_size = params['test_image_size']
stride_num = params['test_stride_num']
pred_boxes_total = []
pred_scores_total = []
gt_boxes_total = []
for i, (image, bbox, image_) in enumerate(tqdm(test_dataset)):
h, w = image.size()[1:]
stride_h = (h - test_image_size) / (stride_num - 1)
stride_w = (w - test_image_size) / (stride_num - 1)
pred_boxes = []
pred_scores = []
for h_index in range(stride_num):
for w_index in range(stride_num):
image_patch = image[:, int(h_index * stride_h): int(h_index * stride_h) + test_image_size,
int(w_index * stride_w): int(w_index * stride_w) + test_image_size]
# predict
scores_patch, labels_patch, boxes_patch = retinanet(image_patch.unsqueeze(0).cuda().float())
scores_patch = scores_patch.cpu().detach().numpy() # size -> [num_box]
# labels_patch = labels_patch.cpu().detach().numpy() # size -> [num_box]
boxes_patch = boxes_patch.cpu().detach().numpy() # size -> [num_box, 4]
# change bbox coordinates
if boxes_patch.shape[0] != 0:
start_x = int(w_index * stride_w)
start_y = int(h_index * stride_h)
box_index = (boxes_patch[:, 0] > 5) & (boxes_patch[:, 1] > 5) & (
boxes_patch[:, 2] < test_image_size - 6) \
& (boxes_patch[:, 3] < test_image_size - 6)
boxes_patch = boxes_patch[box_index]
scores_patch = scores_patch[box_index]
boxes_patch[:, 0] = boxes_patch[:, 0] + start_x
boxes_patch[:, 1] = boxes_patch[:, 1] + start_y
boxes_patch[:, 2] = boxes_patch[:, 2] + start_x
boxes_patch[:, 3] = boxes_patch[:, 3] + start_y
boxes_patch = boxes_patch.tolist()
scores_patch = scores_patch.tolist()
pred_boxes.extend(boxes_patch)
pred_scores.extend(scores_patch)
# image = image_.permute(1, 2, 0).numpy()
# for box in pred_boxes:
# image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2)
# nms
if len(pred_boxes) != 0:
pred_boxes = torch.Tensor(pred_boxes).unsqueeze(0) # size -> [1, num_box, 4]
pred_scores = torch.Tensor(pred_scores).unsqueeze(0).unsqueeze(-1) # size -> [1, num_box, 1]
# pred_boxes_w = pred_boxes[0, :, 2] - pred_boxes[0, :, 0]
# pred_boxes_h = pred_boxes[0, :, 3] - pred_boxes[0, :, 1]
# wh_idx = (pred_boxes_w > 15) & (pred_boxes_h > 15)
# pred_boxes = pred_boxes[:, wh_idx, :]
# pred_scores = pred_scores[:, wh_idx, :]
anchors_nms_idx = nms(torch.cat([pred_boxes, pred_scores], dim=2)[0, :, :], 0.5)
pred_boxes = pred_boxes[0, anchors_nms_idx, :]
pred_scores = pred_scores[0, anchors_nms_idx, 0]
pred_boxes = pred_boxes.numpy().tolist()
pred_scores = pred_scores.numpy().tolist()
pred_boxes_total.append(pred_boxes)
pred_scores_total.append(pred_scores)
gt_boxes_total.append(bbox)
else:
pred_boxes_total.append([])
pred_scores_total.append([])
gt_boxes_total.append(bbox)
average_precision, recall, precision = detection_metric(pred_boxes_total, gt_boxes_total, pred_scores_total)
if params['writer'] == True:
writer.add_scalar('epoch/average precision', float(average_precision), epoch)
writer.add_scalar('epoch/recall', float(recall[-1]), epoch)
writer.add_scalar('epoch/precision', float(precision[-1]), epoch)
if float(average_precision) > best_ap:
best_ap = float(average_precision)
torch.save(retinanet.module.state_dict(), 'ckpt/best_ap_resnet18_4fold_0_weight_10.pth')
if float(recall[-1]) > best_recall:
best_recall = float(recall[-1])
torch.save(retinanet.module.state_dict(), 'ckpt/best_recall_resnet18_4fold_0_weight_10.pth')
if float(precision[-1]) > best_precision:
best_precision = float(precision[-1])
torch.save(retinanet.module.state_dict(), 'ckpt/best_precision_resnet18_4fold_0_weight_10.pth')
print('ap: {}, recall: {}, precision: {}'.format(average_precision, recall[-1], precision[-1]))
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '6, 7, 5, 2'
params = {
'learning_rate': 1e-4,
'optim': 'adam',
'max_epoch': 600,
'train_txt': '../train_test_4/train_0.txt',
'test_txt': '../train_test_4/test_0.txt',
'batch_size': 8,
'writer': True,
'model_path': 'ckpt/best_precision_resnet101.pth',
'resume': False,
'test_image_size': 1024,
'test_stride_num': 3
}
main(params)