-
Notifications
You must be signed in to change notification settings - Fork 4
/
train.py
214 lines (173 loc) · 7.56 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
import sys
import os
import torch, numpy as np, torch.utils.data
import datetime
import logging
from tqdm import tqdm
from models import ThreeDFlow
from models import multiScaleLoss
from pathlib import Path
from collections import defaultdict
import transforms
import datasets
import cmd_args
from main_utils import *
def main():
if 'NUMBA_DISABLE_JIT' in os.environ:
del os.environ['NUMBA_DISABLE_JIT']
global args
args = cmd_args.parse_args_from_yaml(sys.argv[1])
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu if args.multi_gpu is None else '0,1,2,3'
'''CREATE DIR'''
experiment_dir = Path('./experiment/')
experiment_dir.mkdir(exist_ok=True)
file_dir = Path(str(experiment_dir) + '/ThreeDFlow%sFlyingthings3d-'%args.model_name + str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')))
file_dir.mkdir(exist_ok=True)
checkpoints_dir = file_dir.joinpath('checkpoints/')
checkpoints_dir.mkdir(exist_ok=True)
log_dir = file_dir.joinpath('logs/')
log_dir.mkdir(exist_ok=True)
os.system('cp %s %s' % ('models.py', log_dir))
os.system('cp %s %s' % ('pointconv_util.py', log_dir))
os.system('cp %s %s' % ('train.py', log_dir))
os.system('cp %s %s' % ('config_train.yaml', log_dir))
'''LOG'''
logger = logging.getLogger(args.model_name)
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler(str(log_dir) + '/train_%s_sceneflow.txt'%args.model_name)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.info('----------------------------------------TRAINING----------------------------------')
logger.info('PARAMETER ...')
logger.info(args)
blue = lambda x: '\033[94m' + x + '\033[0m'
model = ThreeDFlow(args.is_training)
train_dataset = datasets.__dict__[args.dataset](
train=True,
transform=transforms.Augmentation(args.aug_together,
args.aug_pc2,
args.data_process,
args.num_points),
num_points=args.num_points,
data_root = args.data_root,
full=args.full
)
logger.info('train_dataset: ' + str(train_dataset))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)
val_dataset = datasets.__dict__[args.dataset](
train=False,
transform=transforms.ProcessData(args.data_process,
args.num_points,
args.allow_less_points),
num_points=args.num_points,
data_root = args.data_root
)
logger.info('val_dataset: ' + str(val_dataset))
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)
'''GPU selection and multi-GPU'''
if args.multi_gpu is not None:
device_ids = [int(x) for x in args.multi_gpu.split(',')]
torch.backends.cudnn.benchmark = True
model.cuda(device_ids[0])
model = torch.nn.DataParallel(model, device_ids = device_ids)
else:
model.cuda()
if args.pretrain is not None:
model.load_state_dict(torch.load(args.pretrain))
print('load model %s'%args.pretrain)
logger.info('load model %s'%args.pretrain)
else:
print('Training from scratch')
logger.info('Training from scratch')
pretrain = args.pretrain
init_epoch = int(pretrain[-14:-11]) if args.pretrain is not None else 0
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, betas=(0.9, 0.999),
eps=1e-08, weight_decay=args.weight_decay)
optimizer.param_groups[0]['initial_lr'] = args.learning_rate
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=80, gamma=0.5, last_epoch = init_epoch - 1)
LEARNING_RATE_CLIP = 1e-5
history = defaultdict(lambda: list())
best_epe = 1000.0
for epoch in range(init_epoch, args.epochs):
lr = max(optimizer.param_groups[0]['lr'], LEARNING_RATE_CLIP)
print('Learning rate:%f'%lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
total_loss = 0
total_seen = 0
optimizer.zero_grad()
for i, data in tqdm(enumerate(train_loader, 0), total=len(train_loader), smoothing=0.9):
pos1, pos2, norm1, norm2, flow, _ = data
pos1 = pos1.cuda()
pos2 = pos2.cuda()
norm1 = norm1.cuda()
norm2 = norm2.cuda()
flow = flow.cuda()
model = model.train()
pred_flows, gt_flows, pc1, pc2,_,_ = model(pos1, pos2, norm1, norm2,flow)
loss = multiScaleLoss(pred_flows, gt_flows)
history['loss'].append(loss.cpu().data.numpy())
loss.backward()
optimizer.step()
optimizer.zero_grad()
total_loss += loss.cpu().data * args.batch_size
total_seen += args.batch_size
scheduler.step()
train_loss = total_loss / total_seen
str_out = 'EPOCH %d %s mean loss: %f'%(epoch, blue('train'), train_loss)
print(str_out)
logger.info(str_out)
eval_epe3d, eval_loss = eval_sceneflow(model.eval(), val_loader)
str_out = 'EPOCH %d %s mean epe3d: %f mean eval loss: %f'%(epoch, blue('eval'), eval_epe3d, eval_loss)
print(str_out)
logger.info(str_out)
if eval_epe3d < best_epe:
best_epe = eval_epe3d
if args.multi_gpu is not None:
torch.save(model.module.state_dict(), '%s/%s_%.3d_%.4f.pth'%(checkpoints_dir, args.model_name, epoch, best_epe))
else:
torch.save(model.state_dict(), '%s/%s_%.3d_%.4f.pth'%(checkpoints_dir, args.model_name, epoch, best_epe))
logger.info('Save model ...')
print('Save model ...')
print('Best epe loss is: %.5f'%(best_epe))
logger.info('Best epe loss is: %.5f'%(best_epe))
def eval_sceneflow(model, loader):
metrics = defaultdict(lambda:list())
for batch_id, data in tqdm(enumerate(loader), total=len(loader), smoothing=0.9):
pos1, pos2, norm1, norm2, flow, _ = data
#move to cuda
pos1 = pos1.cuda()
pos2 = pos2.cuda()
norm1 = norm1.cuda()
norm2 = norm2.cuda()
flow = flow.cuda()
with torch.no_grad():
pred_flows, gt_flows, pc1, pc2,_,_ = model(pos1, pos2, norm1, norm2,flow)
eval_loss = multiScaleLoss(pred_flows, gt_flows)
epe3d = torch.norm(pred_flows[0].permute(0, 2, 1) - gt_flows[0].permute(0,2,1), dim = 2).mean()
metrics['epe3d_loss'].append(epe3d.cpu().data.numpy())
metrics['eval_loss'].append(eval_loss.cpu().data.numpy())
mean_epe3d = np.mean(metrics['epe3d_loss'])
mean_eval = np.mean(metrics['eval_loss'])
return mean_epe3d, mean_eval
if __name__ == '__main__':
main()