-
Notifications
You must be signed in to change notification settings - Fork 0
/
sr_compare.py
executable file
·244 lines (218 loc) · 11 KB
/
sr_compare.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
import torch
import data as Data
import model as Model
import argparse
import logging
import core.logger as Logger
import core.metrics as Metrics
import os
import wandb
import pyiqa
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='config/sr_sr3_16_128.json',
help='JSON file for configuration')
parser.add_argument('-p', '--phase', type=str, choices=['train', 'val'],
help='Run either train(training) or val(generation)', default='train')
parser.add_argument('-gpu', '--gpu_ids', type=str, default=None)
parser.add_argument('-debug', '-d', action='store_true')
parser.add_argument('-enable_wandb', action='store_true')
parser.add_argument('-log_wandb_ckpt', action='store_true')
parser.add_argument('-log_eval', action='store_true')
# parse configs
args = parser.parse_args()
opt = Logger.parse(args)
# Convert to NoneDict, which return None for missing key.
opt = Logger.dict_to_nonedict(opt)
# logging
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
Logger.setup_logger(None, opt['path']['log'],
'train', level=logging.INFO, screen=True)
Logger.setup_logger('val', opt['path']['log'], 'val', level=logging.INFO)
logger = logging.getLogger('base')
logger.info(Logger.dict2str(opt))
# wandb
if opt["enable_wandb"]:
project_name = opt["wandb"]["project"]
print(f"wandb project: {project_name}")
wandb.init(project=project_name)
# dataset
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train' and args.phase != 'val':
train_set = Data.create_dataset(dataset_opt, phase)
train_loader = Data.create_dataloader(
train_set, dataset_opt, phase)
elif phase == 'val':
val_set = Data.create_dataset(dataset_opt, phase)
val_loader = Data.create_dataloader(
val_set, dataset_opt, phase)
logger.info('Initial Dataset Finished')
# model
diffusion = Model.create_model(opt)
logger.info('Initial Model Finished')
# Train
current_step = diffusion.begin_step
current_epoch = diffusion.begin_epoch
n_iter = opt['train']['n_iter']
if opt['path']['resume_state']:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
current_epoch, current_step))
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule'][opt['phase']], schedule_phase=opt['phase'])
if opt['phase'] == 'train':
while current_step < n_iter:
current_epoch += 1
for i, train_data in enumerate(train_loader):
current_step += 1
if current_step > n_iter:
break
diffusion.feed_data(train_data)
diffusion.optimize_parameters()
# log
if current_step % opt['train']['print_freq'] == 0:
logs = diffusion.get_current_log()
message = '<epoch:{:3d}, iter:{:8,d}> '.format(
current_epoch, current_step)
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
#tb_logger.add_scalar(k, v, current_step)
logger.info(message)
wandb.log({
"epoch": current_epoch,
"step": current_step,
**logs})
# validation
if current_step % opt['train']['val_freq'] == 0:
avg_psnr = 0.0
avg_ssim = 0.0
avg_lpips = 0.0
avg_clip_iqa = 0.0
avg_musiq = 0.0
idx = 0
result_path = '{}/{}'.format(opt['path']
['results'], current_epoch)
os.makedirs(result_path, exist_ok=True)
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule']['val'], schedule_phase='val')
for _, val_data in enumerate(val_loader):
idx += 1
diffusion.feed_data(val_data)
diffusion.test(continous=False)
visuals = diffusion.get_current_visuals()
sr_img = Metrics.tensor2img(visuals['SR']) # uint8
hr_img = Metrics.tensor2img(visuals['HR']) # uint8
lr_img = Metrics.tensor2img(visuals['LR']) # uint8
fake_img = Metrics.tensor2img(visuals['INF']) # uint8
# generation
Metrics.save_img(
hr_img, '{}/{}_{}_hr.png'.format(result_path, current_step, idx))
Metrics.save_img(
sr_img, '{}/{}_{}_sr.png'.format(result_path, current_step, idx))
Metrics.save_img(
lr_img, '{}/{}_{}_lr.png'.format(result_path, current_step, idx))
Metrics.save_img(
fake_img, '{}/{}_{}_inf.png'.format(result_path, current_step, idx))
avg_psnr += Metrics.calculate_psnr(
sr_img, hr_img)
avg_ssim += Metrics.calculate_ssim(sr_img, hr_img)
avg_lpips += Metrics.calculate_lpips(sr_img, hr_img)
avg_clip_iqa += Metrics.calculate_clip_iqa(sr_img)
avg_musiq += Metrics.calculate_musiq(sr_img)
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
avg_lpips /= idx
avg_clip_iqa /= idx
avg_musiq /= idx
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule']['train'], schedule_phase='train')
# log
# logger.info('# Validation # PSNR: {:.4e}, SSIM: {:.4e}'.format(avg_psnr, avg_ssim))
logger.info('# Validation # PSNR: {:.4e}, SSIM: {:.4e}, LPIPS: {:.4e}, CLIP IQA: {:.4e}, MUSIQ: {:.4e}'.format(
avg_psnr, avg_ssim, avg_lpips, avg_clip_iqa, avg_musiq))
logger_val = logging.getLogger('val') # validation logger
# logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr: {:.4e}, ssim: {:.4e}'.format(
# current_epoch, current_step, avg_psnr, avg_ssim))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr: {:.4e}, ssim: {:.4e}, lpips: {:.4e}, clip_iqa: {:.4e}, musiq: {:.4e}'.format(
current_epoch, current_step, avg_psnr, avg_ssim, avg_lpips, avg_clip_iqa, avg_musiq))
# Log metrics to wandb
wandb.log({
"epoch": current_epoch,
"step": current_step,
"avg_psnr": avg_psnr,
"avg_ssim": avg_ssim,
"avg_lpips": avg_lpips,
"avg_clip_iqa": avg_clip_iqa,
"avg_musiq": avg_musiq
})
if current_step % opt['train']['save_checkpoint_freq'] == 0:
logger.info('Saving models and training states.')
diffusion.save_network(current_epoch, current_step)
# save model
logger.info('End of training.')
else:
logger.info('Begin Model Evaluation.')
avg_psnr = 0.0
avg_ssim = 0.0
avg_lpips = 0.0
avg_clip_iqa = 0.0
avg_musiq = 0.0
idx = 0
result_path = '{}'.format(opt['path']['results'])
os.makedirs(result_path, exist_ok=True)
for _, val_data in enumerate(val_loader):
idx += 1
diffusion.feed_data(val_data)
diffusion.test(continous=True)
visuals = diffusion.get_current_visuals()
hr_img = Metrics.tensor2img(visuals['HR']) # uint8
lr_img = Metrics.tensor2img(visuals['LR']) # uint8
fake_img = Metrics.tensor2img(visuals['INF']) # uint8
sr_img_mode = 'grid'
if sr_img_mode == 'single':
# single img series
sr_img = visuals['SR'] # uint8
sample_num = sr_img.shape[0]
for iter in range(0, sample_num):
Metrics.save_img(
Metrics.tensor2img(sr_img[iter]), '{}/{}_{}_sr_{}.png'.format(result_path, current_step, idx, iter))
else:
# grid img
sr_img = Metrics.tensor2img(visuals['SR']) # uint8
Metrics.save_img(
sr_img, '{}/{}_{}_sr_process.png'.format(result_path, current_step, idx))
Metrics.save_img(
Metrics.tensor2img(visuals['SR'][-1]), '{}/{}_{}_sr.png'.format(result_path, current_step, idx))
Metrics.save_img(
hr_img, '{}/{}_{}_hr.png'.format(result_path, current_step, idx))
Metrics.save_img(
lr_img, '{}/{}_{}_lr.png'.format(result_path, current_step, idx))
Metrics.save_img(
fake_img, '{}/{}_{}_inf.png'.format(result_path, current_step, idx))
# generation
eval_psnr = Metrics.calculate_psnr(Metrics.tensor2img(visuals['SR'][-1]), hr_img)
eval_ssim = Metrics.calculate_ssim(Metrics.tensor2img(visuals['SR'][-1]), hr_img)
eval_lpips = Metrics.calculate_lpips(Metrics.tensor2img(visuals['SR'][-1]), hr_img)
eval_clip_iqa = Metrics.calculate_clip_iqa(Metrics.tensor2img(visuals['SR'][-1]))
eval_musiq = Metrics.calculate_musiq(Metrics.tensor2img(visuals['SR'][-1]))
avg_psnr += eval_psnr
avg_ssim += eval_ssim
avg_lpips += eval_lpips
avg_clip_iqa += eval_clip_iqa
avg_musiq += eval_musiq
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
avg_lpips /= idx
avg_clip_iqa /= idx
avg_musiq /= idx
# log
logger.info('# Validation # PSNR: {:.4e}'.format(avg_psnr))
logger.info('# Validation # SSIM: {:.4e}'.format(avg_ssim))
logger.info('# Validation # LPIPS: {:.4e}'.format(avg_lpips))
logger.info('# Validation # CLIP IQA: {:.4e}'.format(avg_clip_iqa))
logger.info('# Validation # MUSIQ: {:.4e}'.format(avg_musiq))
logger_val = logging.getLogger('val') # validation logger
# logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr: {:.4e}, ssim：{:.4e}'.format(
# current_epoch, current_step, avg_psnr, avg_ssim))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr: {:.4e}, ssim: {:.4e}, lpips: {:.4e}, clip_iqa: {:.4e}, musiq: {:.4e}'.format(
current_epoch, current_step, avg_psnr, avg_ssim, avg_lpips, avg_clip_iqa, avg_musiq))