-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain.py
183 lines (159 loc) · 6.92 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
import argparse
from pathlib import Path
import torch
import torch.nn.functional as F
from torch import optim
import torch.utils.data
import torch.utils.tensorboard
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
from config import cfg
from model import Net
from utils import get_logger
class Trainer:
def __init__(self, cfg):
self.cfg = cfg
self.init_env()
self.init_device()
self.init_data()
self.init_model()
self.init_optimizer()
def init_env(self):
self.exp_dir = Path(
self.cfg.train_log_root).expanduser().joinpath(self.cfg.exp_id)
self.exp_dir.mkdir(parents=True, exist_ok=True)
self.log_dir = self.exp_dir.joinpath(self.cfg.log_subdir)
self.tb_dir = self.exp_dir.joinpath(self.cfg.tb_subdir)
self.ckpt_dir = self.exp_dir.joinpath(self.cfg.ckpt_subdir)
self.logger = get_logger(__name__, self.log_dir)
self.tb = SummaryWriter(self.tb_dir)
torch.manual_seed(self.cfg.seed)
self.epoch = 0
self.acc = 0.
self.logger.info('Train log location: {}'.format(self.exp_dir))
def init_device(self):
self.use_cuda = not self.cfg.no_cuda and torch.cuda.is_available()
if self.use_cuda:
self.device = torch.device('cuda')
self.logger.info('Using gpu')
else:
self.device = torch.device('cpu')
self.logger.info('Using cpu')
def init_data(self):
self.logger.info('Initializing data loader...')
kwargs = {
'num_workers': 1, 'pin_memory': True} if self.use_cuda else {}
self.train_loader = torch.utils.data.DataLoader(
datasets.MNIST(
self.cfg.data_root, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=self.cfg.batch_size, shuffle=True, **kwargs)
self.logger.info('Train loader has been initialized.')
self.val_loader = torch.utils.data.DataLoader(
datasets.MNIST(
self.cfg.data_root, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=self.cfg.val_batch_size, shuffle=True, **kwargs)
self.logger.info('Test loader has been initialized.')
def init_model(self):
self.model = Net()
data, target = next(iter(self.train_loader))
self.tb.add_graph(self.model, data)
self.model = self.model.to(self.device)
self.logger.info('Model has been initialized.')
def init_optimizer(self):
cfg_optim = self.cfg.optim
optim_func = getattr(optim, cfg_optim.type)
self.optimizer = optim_func(
self.model.parameters(), **dict(self.cfg.optim.args))
self.logger.info('Optimizer has been initialized.')
def train(self):
self.model.train()
for batch_idx, (data, target) in enumerate(self.train_loader):
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output = self.model(data)
loss = F.nll_loss(output, target)
loss.backward()
self.optimizer.step()
self.train_loss = loss.item()
if batch_idx % self.cfg.log_interval == 0:
self.logger.info(
'{:2d}, {}/{} loss: {:.6f}, test acc: {:.2f}%'.format(
self.epoch, batch_idx * len(data),
len(self.train_loader.dataset), loss.item(), self.acc))
total_iter = self.epoch * len(self.train_loader) + batch_idx
self.tb.add_scalar('train/loss', loss.item(), total_iter)
def test(self):
self.model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in self.val_loader:
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(self.val_loader.dataset)
self.acc = 100. * correct / len(self.val_loader.dataset)
self.logger.info(
'{:2d}, test loss: {:.4f}, test acc: {}/{} ({:.2f}%)'.format(
self.epoch, test_loss, correct, len(self.val_loader.dataset),
self.acc))
self.tb.add_scalar('test/acc', self.acc, self.epoch)
self.tb.add_scalar('test/loss', test_loss, self.epoch)
def load(self, for_resuming_training=True, label='latest'):
ckpt_path = self.ckpt_dir.joinpath('{}.pt'.format(label))
if ckpt_path.is_file():
self.logger.info('Loading model from {}'.format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location=self.device)
self.model.load_state_dict(ckpt['model_state_dict'])
if for_resuming_training:
self.optimizer.load_state_dict(ckpt['optimizer_state_dict'])
self.epoch = ckpt['epoch'] + 1
self.acc = ckpt['acc']
self.logger.info(
'Model of epoch {} loaded.'.format(ckpt['epoch']))
else:
self.logger.info('No checkpoint found.')
def save(self, label='latest'):
self.logger.info('Saving model...')
self.ckpt_dir.mkdir(exist_ok=True, parents=True)
ckpt_path = self.ckpt_dir.joinpath('{}.pt'.format(label))
torch.save({
'epoch': self.epoch,
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'train_loss': self.train_loss,
'acc': self.acc
}, ckpt_path)
self.logger.info('Model saved to {}.'.format(ckpt_path))
def start(self):
self.load(for_resuming_training=True)
if self.epoch > 0:
self.logger.info('Training start from epoch {}'.format(self.epoch))
try:
for self.epoch in range(self.epoch, self.cfg.epochs):
self.train()
self.test()
self.logger.info('Training is finished.')
except KeyboardInterrupt:
self.logger.warning('Keyboard Interrupted.')
except Exception as e:
self.logger.exception(repr(e))
finally:
if self.epoch > 0:
self.save()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument(
'--exp', default='exps/default.yaml', help='Override parameters.')
args = parser.parse_args()
cfg.merge_from_file(args.exp)
trainer = Trainer(cfg)
trainer.start()