-
Notifications
You must be signed in to change notification settings - Fork 497
/
train.py
154 lines (110 loc) · 5.49 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
import torch, os, datetime
import numpy as np
from model.model import parsingNet
from data.dataloader import get_train_loader
from utils.dist_utils import dist_print, dist_tqdm, is_main_process, DistSummaryWriter
from utils.factory import get_metric_dict, get_loss_dict, get_optimizer, get_scheduler
from utils.metrics import MultiLabelAcc, AccTopk, Metric_mIoU, update_metrics, reset_metrics
from utils.common import merge_config, save_model, cp_projects
from utils.common import get_work_dir, get_logger
import time
def inference(net, data_label, use_aux):
if use_aux:
img, cls_label, seg_label = data_label
img, cls_label, seg_label = img.cuda(), cls_label.long().cuda(), seg_label.long().cuda()
cls_out, seg_out = net(img)
return {'cls_out': cls_out, 'cls_label': cls_label, 'seg_out':seg_out, 'seg_label': seg_label}
else:
img, cls_label = data_label
img, cls_label = img.cuda(), cls_label.long().cuda()
cls_out = net(img)
return {'cls_out': cls_out, 'cls_label': cls_label}
def resolve_val_data(results, use_aux):
results['cls_out'] = torch.argmax(results['cls_out'], dim=1)
if use_aux:
results['seg_out'] = torch.argmax(results['seg_out'], dim=1)
return results
def calc_loss(loss_dict, results, logger, global_step):
loss = 0
for i in range(len(loss_dict['name'])):
data_src = loss_dict['data_src'][i]
datas = [results[src] for src in data_src]
loss_cur = loss_dict['op'][i](*datas)
if global_step % 20 == 0:
logger.add_scalar('loss/'+loss_dict['name'][i], loss_cur, global_step)
loss += loss_cur * loss_dict['weight'][i]
return loss
def train(net, data_loader, loss_dict, optimizer, scheduler,logger, epoch, metric_dict, use_aux):
net.train()
progress_bar = dist_tqdm(train_loader)
t_data_0 = time.time()
for b_idx, data_label in enumerate(progress_bar):
t_data_1 = time.time()
reset_metrics(metric_dict)
global_step = epoch * len(data_loader) + b_idx
t_net_0 = time.time()
results = inference(net, data_label, use_aux)
loss = calc_loss(loss_dict, results, logger, global_step)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step(global_step)
t_net_1 = time.time()
results = resolve_val_data(results, use_aux)
update_metrics(metric_dict, results)
if global_step % 20 == 0:
for me_name, me_op in zip(metric_dict['name'], metric_dict['op']):
logger.add_scalar('metric/' + me_name, me_op.get(), global_step=global_step)
logger.add_scalar('meta/lr', optimizer.param_groups[0]['lr'], global_step=global_step)
if hasattr(progress_bar,'set_postfix'):
kwargs = {me_name: '%.3f' % me_op.get() for me_name, me_op in zip(metric_dict['name'], metric_dict['op'])}
progress_bar.set_postfix(loss = '%.3f' % float(loss),
data_time = '%.3f' % float(t_data_1 - t_data_0),
net_time = '%.3f' % float(t_net_1 - t_net_0),
**kwargs)
t_data_0 = time.time()
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
args, cfg = merge_config()
work_dir = get_work_dir(cfg)
distributed = False
if 'WORLD_SIZE' in os.environ:
distributed = int(os.environ['WORLD_SIZE']) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
dist_print(datetime.datetime.now().strftime('[%Y/%m/%d %H:%M:%S]') + ' start training...')
dist_print(cfg)
assert cfg.backbone in ['18','34','50','101','152','50next','101next','50wide','101wide']
train_loader, cls_num_per_lane = get_train_loader(cfg.batch_size, cfg.data_root, cfg.griding_num, cfg.dataset, cfg.use_aux, distributed, cfg.num_lanes)
net = parsingNet(pretrained = True, backbone=cfg.backbone,cls_dim = (cfg.griding_num+1,cls_num_per_lane, cfg.num_lanes),use_aux=cfg.use_aux).cuda()
if distributed:
net = torch.nn.parallel.DistributedDataParallel(net, device_ids = [args.local_rank])
optimizer = get_optimizer(net, cfg)
if cfg.finetune is not None:
dist_print('finetune from ', cfg.finetune)
state_all = torch.load(cfg.finetune)['model']
state_clip = {} # only use backbone parameters
for k,v in state_all.items():
if 'model' in k:
state_clip[k] = v
net.load_state_dict(state_clip, strict=False)
if cfg.resume is not None:
dist_print('==> Resume model from ' + cfg.resume)
resume_dict = torch.load(cfg.resume, map_location='cpu')
net.load_state_dict(resume_dict['model'])
if 'optimizer' in resume_dict.keys():
optimizer.load_state_dict(resume_dict['optimizer'])
resume_epoch = int(os.path.split(cfg.resume)[1][2:5]) + 1
else:
resume_epoch = 0
scheduler = get_scheduler(optimizer, cfg, len(train_loader))
dist_print(len(train_loader))
metric_dict = get_metric_dict(cfg)
loss_dict = get_loss_dict(cfg)
logger = get_logger(work_dir, cfg)
cp_projects(args.auto_backup, work_dir)
for epoch in range(resume_epoch, cfg.epoch):
train(net, train_loader, loss_dict, optimizer, scheduler,logger, epoch, metric_dict, cfg.use_aux)
save_model(net, optimizer, epoch ,work_dir, distributed)
logger.close()