/
train.py
159 lines (127 loc) · 5.32 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
import os
import argparse
import numpy as np
from PIL import Image
from datetime import datetime
from skimage.measure import compare_psnr
from skimage.measure import compare_ssim
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from datasets import HelenDataset, CelebDataset
from nets import FSRNet
from utils import write_log, _normalize, _denormalize
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='celeba', type=str)
args = parser.parse_args()
# load dataset
if args.dataset == 'celeba':
trn_dataset = CelebDataset(mode='train')
val_dataset = CelebDataset(mode='test')
elif args.dataset == 'helen':
trn_dataset = HelenDataset(mode='train')
val_dataset = HelenDataset(mode='test')
else:
print('not implemented')
exit()
trn_dloader = torch.utils.data.DataLoader(dataset=trn_dataset, batch_size=14, shuffle=True)
val_dloader = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=1, shuffle=False)
hmaps_ch, pmaps_ch = trn_dataset.num_channels()
# load network
net = FSRNet(hmaps_ch, pmaps_ch)
net = nn.DataParallel(net)
net = net.cuda()
# settings
learning_rate = 2.5e-4
criterion = nn.MSELoss()
optimizer = optim.RMSprop(net.parameters(), lr=learning_rate)
# outputs
output_dir = './outputs_{:%Y-%m-%d-%H-%M-%S}/'.format(datetime.now())
checkp_dir = os.path.join(output_dir, '_checkpoints')
logtxt_dir = os.path.join(output_dir, 'log.txt')
os.makedirs(output_dir, exist_ok=True)
os.makedirs(checkp_dir, exist_ok=True)
# train & valid
num_epoch = 100
for epoch_idx in range(1, num_epoch + 1):
# train
loss1s = []; loss2s = []; loss3s = []; losses = []
for batch_idx, (image_lr, image_hr, hmaps, pmaps) in enumerate(trn_dloader, start=1):
image_lr = torch.from_numpy(_normalize(image_lr)).float().cuda()
image_hr = torch.from_numpy(_normalize(image_hr)).float().cuda()
b1 = (len(np.array(hmaps)[0]) != 1)
b2 = (len(np.array(pmaps)[0]) != 1)
if b1 and b2:
hmaps = torch.from_numpy(_normalize(hmaps)).float().cuda()
pmaps = torch.from_numpy(_normalize(pmaps)).float().cuda()
image_pr = torch.cat((hmaps, pmaps), 1)
elif b1:
image_pr = torch.from_numpy(_normalize(hmaps)).float().cuda()
elif b2:
image_pr = torch.from_numpy(_normalize(pmaps)).float().cuda()
y_c, prs, out = net(image_lr)
loss1 = criterion(y_c, image_hr)
loss2 = criterion(out, image_hr)
loss3 = criterion(prs, image_pr)
loss = loss1 + loss2 + loss3
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss1s.append(loss1.data)
loss2s.append(loss2.data)
loss3s.append(loss3.data)
losses.append(loss.data)
if batch_idx % 300 == 0:
_loss = sum(losses) / len(losses)
_loss1 = sum(loss1s) / len(loss1s)
_loss2 = sum(loss2s) / len(loss2s)
_loss3 = sum(loss3s) / len(loss3s)
log_str = ''
log_str += '[%3d/%3d]' % (epoch_idx, num_epoch)
log_str += '[%5d/%5d]' % (batch_idx, len(trn_dloader))
log_str += '\t%.4f\t%.4f\t%.4f\t%.4f' % (_loss, _loss1, _loss2, _loss3)
write_log(logtxt_dir, log_str)
print(log_str)
loss1s = []; loss2s = []; loss3s = []; losses = []
# valid
PSNRs = []; SSIMs = []
for batch_idx, (image_lr, image_hr, _, _) in enumerate(val_dloader, start=1):
image_lr = torch.from_numpy(_normalize(image_lr)).float().cuda()
image_hr = torch.from_numpy(_normalize(image_hr)).float().cuda()
with torch.no_grad():
y_c, prs, out = net(image_lr)
real = _denormalize(image_hr)[0].astype('uint8')
pred = _denormalize(out)[0].astype('uint8')
psnr = compare_psnr(real, pred)
ssim = compare_ssim(real, pred, multichannel=True)
PSNRs.append(psnr)
SSIMs.append(ssim)
if batch_idx == 1:
_dir = os.path.join(output_dir, '%03d' % epoch_idx)
os.makedirs(_dir, exist_ok=True)
y_c = _denormalize(y_c)[0].astype('uint8')
out = _denormalize(out)[0].astype('uint8')
prs = _denormalize(prs)[0].astype('uint8')
y_c_img = Image.fromarray(y_c)
out_img = Image.fromarray(out)
pms_img = Image.new('RGB', (64*pmaps_ch, 64))
for i in range(pmaps_ch):
pms_img.paste(Image.fromarray(prs[:,:,hmaps_ch+i].astype('uint8')), (64*i, 0))
y_c_img.save(os.path.join(_dir, '%d_lrimg_pred.jpg' % batch_idx))
out_img.save(os.path.join(_dir, '%d_hrimg_pred.jpg' % batch_idx))
pms_img.save(os.path.join(_dir, '%d_pmaps_pred.jpg' % batch_idx))
mean_psnr = sum(PSNRs) / len(PSNRs)
mean_ssim = sum(SSIMs) / len(SSIMs)
log_str = '* Mean PSNR = %.4f, Mean SSIM = %.4f' % (mean_psnr, mean_ssim)
write_log(logtxt_dir, log_str)
print(log_str)
file_name = os.path.join(checkp_dir, '%03d_psnr%.2f.pth' % (epoch_idx, mean_psnr))
torch.save({'epoch': epoch_idx,
'hmaps_ch': hmaps_ch,
'pmaps_ch': pmaps_ch,
'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict()}, file_name)