-
Notifications
You must be signed in to change notification settings - Fork 40
/
utility.py
278 lines (229 loc) · 8.38 KB
/
utility.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
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
import os
import math
import time
import datetime
from functools import reduce
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.misc as misc
import cv2
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
class timer():
def __init__(self):
self.acc = 0
self.tic()
def tic(self):
self.t0 = time.time()
def toc(self):
return time.time() - self.t0
def hold(self):
self.acc += self.toc()
def release(self):
ret = self.acc
self.acc = 0
return ret
def reset(self):
self.acc = 0
class checkpoint():
def __init__(self, args):
self.args = args
self.ok = True
self.log = torch.Tensor()
now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
if args.load == '.':
if args.save == '.': args.save = now
self.dir = './experiment/' + args.save
else:
self.dir = './experiment/' + args.load
if not os.path.exists(self.dir):
args.load = '.'
else:
self.log = torch.load(self.dir + '/psnr_log.pt')
print('Continue from epoch {}...'.format(len(self.log)))
if args.reset:
os.system('rm -rf ' + self.dir)
args.load = '.'
def _make_dir(path):
if not os.path.exists(path): os.makedirs(path)
_make_dir(self.dir)
_make_dir(self.dir + '/model')
_make_dir(self.dir + '/results')
open_type = 'a' if os.path.exists(self.dir + '/log.txt') else 'w'
self.log_file = open(self.dir + '/log.txt', open_type)
with open(self.dir + '/config.txt', open_type) as f:
f.write(now + '\n\n')
for arg in vars(args):
f.write('{}: {}\n'.format(arg, getattr(args, arg)))
f.write('\n')
def save(self, trainer, epoch, is_best=False):
trainer.model.save(self.dir, epoch, is_best=is_best)
trainer.loss.save(self.dir)
trainer.loss.plot_loss(self.dir, epoch)
self.plot_psnr(epoch)
torch.save(self.log, os.path.join(self.dir, 'psnr_log.pt'))
torch.save(
trainer.optimizer.state_dict(),
os.path.join(self.dir, 'optimizer.pt')
)
def add_log(self, log):
self.log = torch.cat([self.log, log])
def write_log(self, log, refresh=False):
print(log)
self.log_file.write(log + '\n')
if refresh:
self.log_file.close()
self.log_file = open(self.dir + '/log.txt', 'a')
def done(self):
self.log_file.close()
def plot_psnr(self, epoch):
axis = np.linspace(1, epoch, epoch)
label = 'SR on {}'.format(self.args.data_test)
fig = plt.figure()
plt.title(label)
for idx_scale, scale in enumerate(self.args.scale):
plt.plot(
axis,
self.log[:, idx_scale].numpy(),
label='Scale {}'.format(scale)
)
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('PSNR')
plt.grid(True)
plt.savefig('{}/test_{}.pdf'.format(self.dir, self.args.data_test))
plt.close(fig)
def save_results(self, filename, save_list, scale):
filename = '{}/results/{}_x{}_'.format(self.dir, filename, scale)
postfix = ('SR', 'LR', 'HR')
for v, p in zip(save_list, postfix):
normalized = v[0].data.mul(255 / self.args.rgb_range)
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
misc.imsave('{}{}.png'.format(filename, p), ndarr)
def quantize(img, rgb_range):
pixel_range = 255 / rgb_range
return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range)
def calc_psnr(sr, hr, scale, rgb_range, benchmark=False):
diff = (sr - hr).data.div(rgb_range)
if benchmark:
shave = scale
if diff.size(1) > 1:
convert = diff.new(1, 3, 1, 1)
convert[0, 0, 0, 0] = 65.738
convert[0, 1, 0, 0] = 129.057
convert[0, 2, 0, 0] = 25.064
diff.mul_(convert).div_(256)
diff = diff.sum(dim=1, keepdim=True)
else:
if isinstance(scale, list):
shave = [s+6 for s in scale]
else:
shave = scale + 6
if isinstance(shave, list):
shave = [math.ceil(s) for s in shave]
valid = diff[:, :, shave[0]:-shave[0], shave[1]:-shave[1]]
else:
shave = math.ceil(shave)
valid = diff[:, :, shave:-shave, shave:-shave]
mse = valid.pow(2).mean()
return -10 * math.log10(mse)
def calc_ssim(img1, img2, scale=2, benchmark=False):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
if benchmark:
if isinstance(scale, list):
border = [math.ceil(s) for s in scale]
else:
border = math.ceil(scale)
else:
if isinstance(scale, list):
border = [math.ceil(s) + 6 for s in scale]
else:
border = math.ceil(scale) + 6
img1 = img1.data.squeeze().float().clamp(0, 255).round().cpu().numpy()
img1 = np.transpose(img1, (1, 2, 0))
img2 = img2.data.squeeze().cpu().numpy()
img2 = np.transpose(img2, (1, 2, 0))
img1_y = np.dot(img1, [65.738, 129.057, 25.064]) / 255.0 + 16.0
img2_y = np.dot(img2, [65.738, 129.057, 25.064]) / 255.0 + 16.0
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
if isinstance(scale, list):
img1_y = img1_y[border[0]:h - border[0], border[1]:w - border[1]]
img2_y = img2_y[border[0]:h - border[0], border[1]:w - border[1]]
else:
img1_y = img1_y[border:h - border, border:w - border]
img2_y = img2_y[border:h - border, border:w - border]
if img1_y.ndim == 2:
return ssim(img1_y, img2_y)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1, img2))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
def ssim(img1, img2):
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def make_optimizer(args, my_model):
trainable = filter(lambda x: x.requires_grad, my_model.parameters())
if args.optimizer == 'SGD':
optimizer_function = optim.SGD
kwargs = {'momentum': args.momentum}
elif args.optimizer == 'ADAM':
optimizer_function = optim.Adam
kwargs = {
'betas': (args.beta1, args.beta2),
'eps': args.epsilon
}
elif args.optimizer == 'RMSprop':
optimizer_function = optim.RMSprop
kwargs = {'eps': args.epsilon}
kwargs['lr'] = args.lr
kwargs['weight_decay'] = args.weight_decay
return optimizer_function(trainable, **kwargs)
def make_scheduler(args, my_optimizer):
if args.decay_type == 'step':
scheduler = lrs.StepLR(
my_optimizer,
step_size=args.lr_decay,
gamma=args.gamma,
# last_epoch = args.start_epoch
)
elif args.decay_type.find('step') >= 0:
milestones = args.decay_type.split('_')
milestones.pop(0)
milestones = list(map(lambda x: int(x), milestones))
scheduler = lrs.MultiStepLR(
my_optimizer,
milestones=milestones,
gamma=args.gamma
# last_epoch = args.start_epoch
)
scheduler.step(args.start_epoch - 1)
return scheduler