-
Notifications
You must be signed in to change notification settings - Fork 1
/
train.py
184 lines (152 loc) · 7.66 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
import torch
from config.yolov3 import cfg
import math
import time
from tensorboardX import SummaryWriter
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils
from torch.optim.lr_scheduler import LambdaLR
from utils.build_model import build_model
from utils.loss import build_loss
from utils.post_process import reorginalize_target
from utils.scheduler import adjust_lr_by_wave
from load_data import NewDataset
from torch.utils.data import DataLoader
from utils.nms import non_max_suppression
from plot_curve import plot_map
from plot_curve import plot_loss_and_lr
from plot_curve import ap_per_category
from utils.general import one_cycle
class _Trainer(object):
def __init__(self):
self.device = torch.device(cfg.device)
self.max_epoch = cfg.max_epoch
self.train_dataset = NewDataset(train_set=True)
self.train_dataloader = DataLoader(self.train_dataset,
batch_size=cfg.batch_size,
shuffle=True,
num_workers=cfg.num_worker,
collate_fn=self.train_dataset.collate_fn)
self.val_dataset = NewDataset(train_set=False)
self.val_dataloader = DataLoader(self.val_dataset,
batch_size=1,
shuffle=True,
num_workers=cfg.num_worker,
collate_fn=self.val_dataset.collate_fn)
self.len_train_dataset = len(self.train_dataset)
self.model = build_model(cfg.model)
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=cfg.lr_start, momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
if cfg.linear_lr:
lf = lambda x: (1 - x / (cfg.max_epoch - 1)) * (1.0 - 0.2) + 0.2 # linear
else: # hyp['lrf']==0.2
lf = one_cycle(1, 0.2, cfg.max_epoch) # cosine 1->hyp['lrf']
self.scheduler = LambdaLR(self.optimizer, lr_lambda=lf)
# self.scheduler = adjust_lr_by_wave(self.optimizer, self.max_epoch * self.len_train_dataset, cfg.lr_start,
# cfg.lr_end, cfg.warmup)
# self.scheduler = adjust_lr_by_loss(self.optimizer,cfg.lr_start,cfg.warmup,self.train_dataloader.num_batches)
self.writer = SummaryWriter(cfg.tensorboard_path)
self.iter = 0
self.cocoGt = COCO(cfg.test_json)
def put_log(self, epoch_index, mean_loss, time_per_iter):
print("[epoch:{}|{}] [iter:{}|{}] time:{}s loss:{} giou_loss:{} conf_loss:{} cls_loss:{} lr:{}".format(
epoch_index + 1, self.max_epoch,
self.iter + 1, math.ceil(self.len_train_dataset / cfg.batch_size), round(time_per_iter, 2),
round(mean_loss[0], 4), round(mean_loss[1], 4)
, round(mean_loss[2], 4), round(mean_loss[3], 4),
self.optimizer.param_groups[0]['lr']))
step = epoch_index * math.ceil(self.len_train_dataset / cfg.batch_size) + self.iter
self.writer.add_scalar("loss", mean_loss[0], global_step=step)
self.writer.add_scalar("giou loss", mean_loss[1], global_step=step)
self.writer.add_scalar("conf loss", mean_loss[2], global_step=step)
self.writer.add_scalar("cls loss", mean_loss[3], global_step=step)
self.writer.add_scalar("learning rate", self.optimizer.param_groups[0]['lr'], global_step=step)
def train_one_epoch(self, epoch_index, train_loss=None, train_lr=None):
mean_loss = [0, 0, 0, 0]
self.model.train()
for self.iter, train_data in enumerate(self.train_dataloader):
start_time = time.time()
# self.scheduler.step(epoch_index,
# self.len_train_dataset * epoch_index + self.iter / cfg.batch_size) # 调整学习率
# self.scheduler.step(self.len_train_dataset * epoch_index + self.iter + 1,mean_loss[0])
image, target, _ = train_data
image = image.to(self.device)
output, pred = self.model(image)
# 计算loss
loss, loss_giou, loss_conf, loss_cls = build_loss(output, target)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.scheduler.step()
end_time = time.time()
time_per_iter = end_time - start_time # 每次迭代所花时间
loss_items = [loss.item(), loss_giou.item(), loss_conf.item(), loss_cls.item()]
mean_loss = [(mean_loss[i] * self.iter + loss_items[i]) / (self.iter + 1) for i in range(4)]
self.put_log(epoch_index, mean_loss, time_per_iter)
# 记录训练损失
loss_value = round(mean_loss[0], 4)
if isinstance(train_loss, list):
train_loss.append(loss_value)
now_lr = self.optimizer.param_groups[0]["lr"]
if isinstance(train_lr, list):
train_lr.append(now_lr)
if (epoch_index + 1) % cfg.save_step == 0:
checkpoint = {'epoch': epoch_index,
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict()}
torch.save(self.model.state_dict(),
cfg.checkpoint_save_path + cfg.model + '_' + str(epoch_index + 1) + '.pth')
@torch.no_grad()
def eval(self, epoch_index, mAP_list=None):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(n_threads)
cpu_device = torch.device("cpu")
self.model.eval()
for ann_idx in self.cocoGt.anns:
ann = self.cocoGt.anns[ann_idx]
ann['area'] = maskUtils.area(self.cocoGt.annToRLE(ann))
iou_types = 'segm'
anns = []
for val_data in self.val_dataloader:
image, target, logit = val_data
image = image.to(self.device)
image_size = image.shape[3] # image.shape[2]==image.shape[3]
# resize之后图像的大小
_, pred = self.model(image)
# TODO:当前只支持batch_size=1
pred = pred.unsqueeze(0)
pred = pred[pred[:, :, 8] > cfg.conf_thresh]
if pred.shape[0] == 0:
pass
else:
detections = non_max_suppression(pred.unsqueeze(0), cls_thres=cfg.cls_thresh, nms_thres=cfg.conf_thresh)
anns.extend(reorginalize_target(detections, logit, image_size, self.cocoGt))
for ann in anns:
ann['segmentation'] = self.cocoGt.annToRLE(ann) # 将polygon形式的segmentation转换RLE形式
cocoDt = self.cocoGt.loadRes(anns)
cocoEval = COCOeval(self.cocoGt, cocoDt, iou_types)
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
ap_per_category(self.cocoGt, cocoEval, epoch_index)
print_txt = cocoEval.stats
coco_mAP = print_txt[0]
voc_mAP = print_txt[1]
if isinstance(mAP_list, list):
mAP_list.append(voc_mAP)
if __name__ == '__main__':
train_loss = []
learning_rate = []
val_mAP = []
trainer = _Trainer()
for epoch_index in range(cfg.max_epoch):
trainer.train_one_epoch(epoch_index, train_loss=train_loss, train_lr=learning_rate)
trainer.eval(epoch_index, mAP_list=val_mAP)
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_mAP) != 0:
plot_map(val_mAP)