-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathtest.py
182 lines (152 loc) · 6.71 KB
/
test.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
import os
import math
import argparse
import random
import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from data.data_sampler import DistIterSampler
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models import create_model
import numpy as np
def init_dist(backend='nccl', **kwargs):
''' initialization for distributed training'''
# if mp.get_start_method(allow_none=True) is None:
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn')
rank = int(os.environ['RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(rank % num_gpus)
dist.init_process_group(backend=backend, **kwargs)
def main():
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YMAL file.')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
#### distributed training settings
if args.launcher == 'none': # disabled distributed training
opt['dist'] = False
rank = -1
print('Disabled distributed training.')
else:
opt['dist'] = True
init_dist()
world_size = torch.distributed.get_world_size()
rank = torch.distributed.get_rank()
#### loading resume state if exists
if opt['path'].get('resume_state', None):
# distributed resuming: all load into default GPU
device_id = torch.cuda.current_device()
resume_state = torch.load(opt['path']['resume_state'],
map_location=lambda storage, loc: storage.cuda(device_id))
option.check_resume(opt, resume_state['iter']) # check resume options
else:
resume_state = None
#### mkdir and loggers
if rank <= 0: # normal training (rank -1) OR distributed training (rank 0)
if resume_state is None:
util.mkdir_and_rename(
opt['path']['experiments_root']) # rename experiment folder if exists
util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
# config loggers. Before it, the log will not work
util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
util.setup_logger('val', opt['path']['log'], 'val_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
# tensorboard logger
if opt['use_tb_logger'] and 'debug' not in opt['name']:
version = float(torch.__version__[0:3])
if version >= 1.1: # PyTorch 1.1
from tensorboardX import SummaryWriter
else:
logger.info(
'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
from tensorboardX import SummaryWriter
tb_logger = SummaryWriter(log_dir='./tb_logger/' + opt['name'])
else:
util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
logger = logging.getLogger('base')
# convert to NoneDict, which returns None for missing keys
opt = option.dict_to_nonedict(opt)
#### random seed
seed = opt['train']['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
if rank <= 0:
logger.info('Random seed: {}'.format(seed))
util.set_random_seed(seed)
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
#### create train and val dataloader
dataset_ratio = 200 # enlarge the size of each epoch
for phase, dataset_opt in opt['datasets'].items():
if phase == 'val':
val_set = create_dataset(dataset_opt)
val_loader = create_dataloader(val_set, dataset_opt, opt, None)
if rank <= 0:
logger.info('Number of val images in [{:s}]: {:d}'.format(
dataset_opt['name'], len(val_set)))
else:
raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
#### create model
model = create_model(opt)
# #### resume training
# if resume_state:
# logger.info('Resuming training from epoch: {}, iter: {}.'.format(
# resume_state['epoch'], resume_state['iter']))
#
# start_epoch = resume_state['epoch']
# current_step = resume_state['iter']
# model.resume_training(resume_state) # handle optimizers and schedulers
# else:
# current_step = 0
# start_epoch = 0
#### test
avg_psnr = 0.0
idx = 0
for val_data in val_loader:
idx += 1
# img_name = os.path.splitext(os.path.basename(val_data['LQ_path'][0]))[0]
# img_dir = os.path.join(opt['path']['val_images'], img_name)
# util.mkdir(img_dir)
model.feed_data_test(val_data)
model.test()
visuals = model.get_current_visuals()
img_input = visuals['img_input'].numpy()
img_pred = visuals['img_pred'].numpy()
img_gt = visuals['img_gt'].numpy()
########################## save images for visualization###################
img_input = img_input[::-1,:,:]
img_pred1 = img_pred[::-1,:,:]
img_gt1 = img_gt[::-1,:,:]
img_input = img_input.transpose(1,2,0)
img_pred1 = img_pred1.transpose(1,2,0)
img_gt1 = img_gt1.transpose(1,2,0)
from PIL import Image
img_pred1 = np.clip(img_pred1,0,1)
Image.fromarray((img_pred1*255).astype(np.uint8)).save(os.path.join(opt['path']['val_images'], '%03d.png'%idx))
img_input = np.clip(img_input,0,1)
Image.fromarray((img_input*255).astype(np.uint8)).save(os.path.join(opt['path']['val_images'], '%03d_i.png'%idx))
img_gt1 = np.clip(img_gt1,0,1)
Image.fromarray((img_gt1*255).astype(np.uint8)).save(os.path.join(opt['path']['val_images'], '%03d_t.png'%idx))
def compute_psnr(img_orig, img_out, peak):
mse = np.mean(np.square(img_orig - img_out))
psnr = 10 * np.log10(peak * peak / mse)
return psnr
curr_psnr = compute_psnr(img_pred, img_gt, 1)
avg_psnr += curr_psnr
print('idx', idx, curr_psnr)
avg_psnr = avg_psnr / idx
logger.info('# Validation # PSNR: {:.4e}.'.format(avg_psnr))
if __name__ == '__main__':
main()