-
Notifications
You must be signed in to change notification settings - Fork 557
/
predict.py
159 lines (141 loc) · 7.79 KB
/
predict.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 cog
import tempfile
from pathlib import Path
import argparse
import shutil
import os
import cv2
import glob
import torch
from collections import OrderedDict
import numpy as np
from main_test_swinir import define_model, setup, get_image_pair
class Predictor(cog.Predictor):
def setup(self):
model_dir = 'experiments/pretrained_models'
self.model_zoo = {
'real_sr': {
4: os.path.join(model_dir, '003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth')
},
'gray_dn': {
15: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth')
},
'color_dn': {
15: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth')
},
'jpeg_car': {
10: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth'),
20: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth'),
30: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth'),
40: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth')
}
}
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='real_sr', help='classical_sr, lightweight_sr, real_sr, '
'gray_dn, color_dn, jpeg_car')
parser.add_argument('--scale', type=int, default=1, help='scale factor: 1, 2, 3, 4, 8') # 1 for dn and jpeg car
parser.add_argument('--noise', type=int, default=15, help='noise level: 15, 25, 50')
parser.add_argument('--jpeg', type=int, default=40, help='scale factor: 10, 20, 30, 40')
parser.add_argument('--training_patch_size', type=int, default=128, help='patch size used in training SwinIR. '
'Just used to differentiate two different settings in Table 2 of the paper. '
'Images are NOT tested patch by patch.')
parser.add_argument('--large_model', action='store_true',
help='use large model, only provided for real image sr')
parser.add_argument('--model_path', type=str,
default=self.model_zoo['real_sr'][4])
parser.add_argument('--folder_lq', type=str, default=None, help='input low-quality test image folder')
parser.add_argument('--folder_gt', type=str, default=None, help='input ground-truth test image folder')
self.args = parser.parse_args('')
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.tasks = {
'Real-World Image Super-Resolution': 'real_sr',
'Grayscale Image Denoising': 'gray_dn',
'Color Image Denoising': 'color_dn',
'JPEG Compression Artifact Reduction': 'jpeg_car'
}
@cog.input("image", type=Path, help="input image")
@cog.input("task_type", type=str, default='Real-World Image Super-Resolution',
options=['Real-World Image Super-Resolution', 'Grayscale Image Denoising', 'Color Image Denoising',
'JPEG Compression Artifact Reduction'],
help="image restoration task type")
@cog.input("noise", type=int, default=15, options=[15, 25, 50],
help='noise level, activated for Grayscale Image Denoising and Color Image Denoising. '
'Leave it as default or arbitrary if other tasks are selected')
@cog.input("jpeg", type=int, default=40, options=[10, 20, 30, 40],
help='scale factor, activated for JPEG Compression Artifact Reduction. '
'Leave it as default or arbitrary if other tasks are selected')
def predict(self, image, task_type='Real-World Image Super-Resolution', jpeg=40, noise=15):
self.args.task = self.tasks[task_type]
self.args.noise = noise
self.args.jpeg = jpeg
# set model path
if self.args.task == 'real_sr':
self.args.scale = 4
self.args.model_path = self.model_zoo[self.args.task][4]
elif self.args.task in ['gray_dn', 'color_dn']:
self.args.model_path = self.model_zoo[self.args.task][noise]
else:
self.args.model_path = self.model_zoo[self.args.task][jpeg]
try:
# set input folder
input_dir = 'input_cog_temp'
os.makedirs(input_dir, exist_ok=True)
input_path = os.path.join(input_dir, os.path.basename(image))
shutil.copy(str(image), input_path)
if self.args.task == 'real_sr':
self.args.folder_lq = input_dir
else:
self.args.folder_gt = input_dir
model = define_model(self.args)
model.eval()
model = model.to(self.device)
# setup folder and path
folder, save_dir, border, window_size = setup(self.args)
os.makedirs(save_dir, exist_ok=True)
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
test_results['psnr_b'] = []
# psnr, ssim, psnr_y, ssim_y, psnr_b = 0, 0, 0, 0, 0
out_path = Path(tempfile.mkdtemp()) / "out.png"
for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, '*')))):
# read image
imgname, img_lq, img_gt = get_image_pair(self.args, path) # image to HWC-BGR, float32
img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]],
(2, 0, 1)) # HCW-BGR to CHW-RGB
img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(self.device) # CHW-RGB to NCHW-RGB
# inference
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
output = model(img_lq)
output = output[..., :h_old * self.args.scale, :w_old * self.args.scale]
# save image
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
cv2.imwrite(str(out_path), output)
finally:
clean_folder(input_dir)
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))