forked from xxgege/StableNet
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmain.py
192 lines (153 loc) · 6.73 KB
/
main.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
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
import models
from ops.config import parser
from training.schedule import lr_setter
from training.train import train
from training.validate import validate
from utilis.meters import AverageMeter
from utilis.saving import save_checkpoint
from dataset import MyDataset
best_loss1 = 0
def main():
args = parser.parse_args()
args.log_path = os.path.join(args.log_base, args.dataset, args.arch, 'mae_' + str(args.cv) +'_log.txt')
if not os.path.exists(os.path.dirname(args.log_path)):
os.makedirs(os.path.dirname(args.log_path))
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
main_worker(ngpus_per_node, args)
def main_worker(ngpus_per_node, args):
global best_loss1
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
cv = args.cv
trainpath = os.path.join(args.datapath, 'u%d.base' % cv)
testpath = os.path.join(args.datapath, 'u%d.test' % cv)
if args.dataset == 'DoubanMusic':
train_dataset = MyDataset(trainpath)
test_dataset = MyDataset(testpath)
elif args.dataset == 'ml-100k':
train_dataset = MyDataset(trainpath, timestamp=True)
test_dataset = MyDataset(testpath, timestamp=True)
if args.distributed:
print("initializing distributed sampler")
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](train_loader, args=args, biased=args.biased)
print("=> finished creating model '{}'".format(args.arch))
if args.distributed:
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.L1Loss()
criterion_train = nn.L1Loss(reduction='none').cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
log_dir = os.path.dirname(args.log_path)
print('tensorboard dir {}'.format(log_dir))
tensor_writer = SummaryWriter(log_dir)
if args.evaluate:
validate(test_loader, model, criterion, 0, True, args, tensor_writer)
return
front_loss = validate(test_loader, model, criterion, -1, True, args, tensor_writer)
early_stop = 0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
lr_setter(optimizer, epoch, args)
train(train_loader, model, criterion_train, optimizer, epoch, args, tensor_writer)
loss1 = validate(test_loader, model, criterion, epoch, True, args, tensor_writer)
if loss1 > front_loss:
early_stop = early_stop + 1
else:
early_stop = 0
if early_stop == args.early_stop:
break
else:
front_loss = loss1
is_best = loss1 < best_loss1
best_loss1 = min(loss1, best_loss1)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
pass
# save_checkpoint({
# 'epoch': epoch + 1,
# 'arch': args.arch,
# 'state_dict': model.state_dict(),
# 'best_acc1': best_acc1,
# 'optimizer' : optimizer.state_dict(),
# }, is_best, args.log_path, epoch)
if __name__ == '__main__':
main()