-
Notifications
You must be signed in to change notification settings - Fork 39
/
demo_2x.py
58 lines (46 loc) · 1.65 KB
/
demo_2x.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
import cv2
import math
import sys
import torch
import numpy as np
import argparse
from imageio import mimsave
'''==========import from our code=========='''
sys.path.append('.')
import config as cfg
from Trainer import Model
from benchmark.utils.padder import InputPadder
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='ours', type=str)
args = parser.parse_args()
assert args.model in ['ours', 'ours_small'], 'Model not exists!'
'''==========Model setting=========='''
TTA = True
if args.model == 'ours_small':
TTA = False
cfg.MODEL_CONFIG['LOGNAME'] = 'ours_small'
cfg.MODEL_CONFIG['MODEL_ARCH'] = cfg.init_model_config(
F = 16,
depth = [2, 2, 2, 2, 2]
)
else:
cfg.MODEL_CONFIG['LOGNAME'] = 'ours'
cfg.MODEL_CONFIG['MODEL_ARCH'] = cfg.init_model_config(
F = 32,
depth = [2, 2, 2, 4, 4]
)
model = Model(-1)
model.load_model()
model.eval()
model.device()
print(f'=========================Start Generating=========================')
I0 = cv2.imread('example/img1.jpg')
I2 = cv2.imread('example/img2.jpg')
I0_ = (torch.tensor(I0.transpose(2, 0, 1)).cuda() / 255.).unsqueeze(0)
I2_ = (torch.tensor(I2.transpose(2, 0, 1)).cuda() / 255.).unsqueeze(0)
padder = InputPadder(I0_.shape, divisor=32)
I0_, I2_ = padder.pad(I0_, I2_)
mid = (padder.unpad(model.inference(I0_, I2_, TTA=TTA, fast_TTA=TTA))[0].detach().cpu().numpy().transpose(1, 2, 0) * 255.0).astype(np.uint8)
images = [I0[:, :, ::-1], mid[:, :, ::-1], I2[:, :, ::-1]]
mimsave('example/out_2x.gif', images, fps=3)
print(f'=========================Done=========================')