-
-
Notifications
You must be signed in to change notification settings - Fork 330
/
train.py
367 lines (340 loc) · 12.7 KB
/
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
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
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
import logging
import utils.gpu as gpu
from model.build_model import Build_Model
from model.loss.yolo_loss import YoloV4Loss
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import utils.datasets as data
import time
import random
import argparse
from eval.evaluator import *
from utils.tools import *
from tensorboardX import SummaryWriter
import config.yolov4_config as cfg
from utils import cosine_lr_scheduler
from utils.log import Logger
from apex import amp
from eval_coco import *
from eval.cocoapi_evaluator import COCOAPIEvaluator
def detection_collate(batch):
targets = []
imgs = []
for sample in batch:
imgs.append(sample[0])
targets.append(sample[1])
return torch.stack(imgs, 0), targets
class Trainer(object):
def __init__(self, weight_path=None,
resume=False,
gpu_id=0,
accumulate=1,
fp_16=False):
init_seeds(0)
self.fp_16 = fp_16
self.device = gpu.select_device(gpu_id)
self.start_epoch = 0
self.best_mAP = 0.0
self.accumulate = accumulate
self.weight_path = weight_path
self.multi_scale_train = cfg.TRAIN["MULTI_SCALE_TRAIN"]
self.showatt = cfg.TRAIN["showatt"]
if self.multi_scale_train:
print("Using multi scales training")
else:
print("train img size is {}".format(cfg.TRAIN["TRAIN_IMG_SIZE"]))
self.train_dataset = data.Build_Dataset(
anno_file_type="train", img_size=cfg.TRAIN["TRAIN_IMG_SIZE"]
)
self.epochs = (
cfg.TRAIN["YOLO_EPOCHS"]
if cfg.MODEL_TYPE["TYPE"] == "YOLOv4"
else cfg.TRAIN["Mobilenet_YOLO_EPOCHS"]
)
self.eval_epoch = (
30 if cfg.MODEL_TYPE["TYPE"] == "YOLOv4" else 50
)
self.train_dataloader = DataLoader(
self.train_dataset,
batch_size=cfg.TRAIN["BATCH_SIZE"],
num_workers=cfg.TRAIN["NUMBER_WORKERS"],
shuffle=True,
pin_memory=True,
)
self.yolov4 = Build_Model(weight_path=weight_path, resume=resume, showatt=self.showatt).to(
self.device
)
self.optimizer = optim.SGD(
self.yolov4.parameters(),
lr=cfg.TRAIN["LR_INIT"],
momentum=cfg.TRAIN["MOMENTUM"],
weight_decay=cfg.TRAIN["WEIGHT_DECAY"],
)
self.criterion = YoloV4Loss(
anchors=cfg.MODEL["ANCHORS"],
strides=cfg.MODEL["STRIDES"],
iou_threshold_loss=cfg.TRAIN["IOU_THRESHOLD_LOSS"],
)
self.scheduler = cosine_lr_scheduler.CosineDecayLR(
self.optimizer,
T_max=self.epochs * len(self.train_dataloader),
lr_init=cfg.TRAIN["LR_INIT"],
lr_min=cfg.TRAIN["LR_END"],
warmup=cfg.TRAIN["WARMUP_EPOCHS"] * len(self.train_dataloader),
)
if resume:
self.__load_resume_weights(weight_path)
def __load_resume_weights(self, weight_path):
last_weight = os.path.join(os.path.split(weight_path)[0], "last.pt")
chkpt = torch.load(last_weight, map_location=self.device)
self.yolov4.load_state_dict(chkpt["model"])
self.start_epoch = chkpt["epoch"] + 1
if chkpt["optimizer"] is not None:
self.optimizer.load_state_dict(chkpt["optimizer"])
self.best_mAP = chkpt["best_mAP"]
del chkpt
def __save_model_weights(self, epoch, mAP):
if mAP > self.best_mAP:
self.best_mAP = mAP
best_weight = os.path.join(
os.path.split(self.weight_path)[0], "best.pt"
)
last_weight = os.path.join(
os.path.split(self.weight_path)[0], "last.pt"
)
chkpt = {
"epoch": epoch,
"best_mAP": self.best_mAP,
"model": self.yolov4.state_dict(),
"optimizer": self.optimizer.state_dict(),
}
torch.save(chkpt, last_weight)
if self.best_mAP == mAP:
torch.save(chkpt["model"], best_weight)
if epoch > 0 and epoch % 10 == 0:
torch.save(
chkpt,
os.path.join(
os.path.split(self.weight_path)[0],
"backup_epoch%g.pt" % epoch,
),
)
del chkpt
def train(self):
global writer
logger.info(
"Training start,img size is: {:d},batchsize is: {:d},work number is {:d}".format(
cfg.TRAIN["TRAIN_IMG_SIZE"],
cfg.TRAIN["BATCH_SIZE"],
cfg.TRAIN["NUMBER_WORKERS"],
)
)
logger.info(self.yolov4)
logger.info(
"Train datasets number is : {}".format(len(self.train_dataset))
)
def is_valid_number(x):
return not (math.isnan(x) or math.isinf(x) or x > 1e4)
if self.fp_16:
self.yolov4, self.optimizer = amp.initialize(
self.yolov4, self.optimizer, opt_level="O1", verbosity=0
)
logger.info(" ======= start training ====== ")
for epoch in range(self.start_epoch, self.epochs):
start = time.time()
self.yolov4.train()
mloss = torch.zeros(4)
logger.info("===Epoch:[{}/{}]===".format(epoch, self.epochs))
for i, (
imgs,
label_sbbox,
label_mbbox,
label_lbbox,
sbboxes,
mbboxes,
lbboxes,
) in enumerate(self.train_dataloader):
self.scheduler.step(
len(self.train_dataloader)
/ (cfg.TRAIN["BATCH_SIZE"])
* epoch
+ i
)
imgs = imgs.to(self.device)
label_sbbox = label_sbbox.to(self.device)
label_mbbox = label_mbbox.to(self.device)
label_lbbox = label_lbbox.to(self.device)
sbboxes = sbboxes.to(self.device)
mbboxes = mbboxes.to(self.device)
lbboxes = lbboxes.to(self.device)
p, p_d = self.yolov4(imgs)
loss, loss_ciou, loss_conf, loss_cls = self.criterion(
p,
p_d,
label_sbbox,
label_mbbox,
label_lbbox,
sbboxes,
mbboxes,
lbboxes,
)
if is_valid_number(loss.item()):
if self.fp_16:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if i % self.accumulate == 0:
self.optimizer.step()
self.optimizer.zero_grad()
# Update running mean of tracked metrics
loss_items = torch.tensor(
[loss_ciou, loss_conf, loss_cls, loss]
)
mloss = (mloss * i + loss_items) / (i + 1)
# Print batch results
if i % 10 == 0:
logger.info(
" === Epoch:[{:3}/{}],step:[{:3}/{}],img_size:[{:3}],total_loss:{:.4f}|loss_ciou:{:.4f}|loss_conf:{:.4f}|loss_cls:{:.4f}|lr:{:.4f}".format(
epoch,
self.epochs,
i,
len(self.train_dataloader) - 1,
self.train_dataset.img_size,
mloss[3],
mloss[0],
mloss[1],
mloss[2],
self.optimizer.param_groups[0]["lr"],
)
)
writer.add_scalar(
"loss_ciou",
mloss[0],
len(self.train_dataloader)
* epoch
+ i,
)
writer.add_scalar(
"loss_conf",
mloss[1],
len(self.train_dataloader)
* epoch
+ i,
)
writer.add_scalar(
"loss_cls",
mloss[2],
len(self.train_dataloader)
* epoch
+ i,
)
writer.add_scalar(
"train_loss",
mloss[3],
len(self.train_dataloader)
* epoch
+ i,
)
# multi-sclae training (320-608 pixels) every 10 batches
if self.multi_scale_train and (i + 1) % 10 == 0:
self.train_dataset.img_size = (
random.choice(range(10, 20)) * 32
)
if (
cfg.TRAIN["DATA_TYPE"] == "VOC"
or cfg.TRAIN["DATA_TYPE"] == "Customer"
):
mAP = 0.0
if epoch >= self.eval_epoch:
logger.info(
"===== Validate =====".format(epoch, self.epochs)
)
logger.info("val img size is {}".format(cfg.VAL["TEST_IMG_SIZE"]))
with torch.no_grad():
APs, inference_time = Evaluator(
self.yolov4, showatt=self.showatt
).APs_voc()
for i in APs:
logger.info("{} --> mAP : {}".format(i, APs[i]))
mAP += APs[i]
mAP = mAP / self.train_dataset.num_classes
logger.info("mAP : {}".format(mAP))
logger.info(
"inference time: {:.2f} ms".format(inference_time)
)
writer.add_scalar("mAP", mAP, epoch)
self.__save_model_weights(epoch, mAP)
logger.info("save weights done")
logger.info(" ===test mAP:{:.3f}".format(mAP))
elif epoch >= 0 and cfg.TRAIN["DATA_TYPE"] == "COCO":
evaluator = COCOAPIEvaluator(
model_type="YOLOv4",
data_dir=cfg.DATA_PATH,
img_size=cfg.VAL["TEST_IMG_SIZE"],
confthre=0.08,
nmsthre=cfg.VAL["NMS_THRESH"],
)
ap50_95, ap50 = evaluator.evaluate(self.yolov4)
logger.info("ap50_95:{}|ap50:{}".format(ap50_95, ap50))
writer.add_scalar("val/COCOAP50", ap50, epoch)
writer.add_scalar("val/COCOAP50_95", ap50_95, epoch)
self.__save_model_weights(epoch, ap50)
print("save weights done")
end = time.time()
logger.info(" ===cost time:{:.4f}s".format(end - start))
logger.info(
"=====Training Finished. best_test_mAP:{:.3f}%====".format(
self.best_mAP
)
)
if __name__ == "__main__":
global logger, writer
parser = argparse.ArgumentParser()
parser.add_argument(
"--weight_path",
type=str,
default="weight/mobilenetv2.pth",
help="weight file path",
) # weight/darknet53_448.weights
parser.add_argument(
"--resume",
action="store_true",
default=False,
help="resume training flag",
)
parser.add_argument(
"--gpu_id",
type=int,
default=-1,
help="whither use GPU(0) or CPU(-1)",
)
parser.add_argument("--log_path", type=str, default="log/", help="log path")
parser.add_argument(
"--accumulate",
type=int,
default=2,
help="batches to accumulate before optimizing",
)
parser.add_argument(
"--fp_16",
type=bool,
default=False,
help="whither to use fp16 precision",
)
opt = parser.parse_args()
writer = SummaryWriter(logdir=opt.log_path + "/event")
logger = Logger(
log_file_name=opt.log_path + "/log.txt",
log_level=logging.DEBUG,
logger_name="YOLOv4",
).get_log()
Trainer(
weight_path=opt.weight_path,
resume=opt.resume,
gpu_id=opt.gpu_id,
accumulate=opt.accumulate,
fp_16=opt.fp_16,
).train()