CRESNet/main_test_color.py at main · yiyunchen/CRESNet · GitHub

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
import os.path
import logging
import numpy as np
from datetime import datetime
from collections import OrderedDict
import torch
import cv2
from utils import utils_logger
from utils import utils_image as util
import requests
# ----------------------------------------
# load model
# ----------------------------------------
from CEESDB_arch import CEESDBNet as net
# from CEESDB_arch import CEESDBNet2 as net

def main():

    quality_factor_list = [50, 70]
    testset_name = 'urban100'
    n_channels = 3  # set 1 for grayscale image, set 3 for color image
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    model_name = 'CEESDB_rbqe'
    model_path = './pretrain_model/CEESDBNet_rbqe.pth'
    model = net(in_nc=3, out_nc=3, nf=64, cond_dim=1, ca_type='CE', order=6)

    # model_name = 'CEESDB_fbcnn'
    # model_path = './pretrain_model/CEESDB_fbcnn.pth'
    # model = net(order=5)

    show_img = False                 # default: False
    testsets = '/data/dataset'
    results = '../results'

    result_name = testset_name + '_' + model_name
    util.mkdir(os.path.join(results, result_name))
    logger_name = result_name
    utils_logger.logger_info(logger_name, log_path=os.path.join(results, result_name, logger_name + '.log'))
    logger = logging.getLogger(logger_name)

    model.load_state_dict(torch.load(model_path), strict=True)
    model.eval()
    for k, v in model.named_parameters():
        v.requires_grad = False
    model = model.to(device)
    logger.info('Model path: {:s}'.format(model_path))

    logger.info('Model params: {}'.format(sum(map(lambda x: x.numel(), model.parameters()))))

    for quality_factor in quality_factor_list:

        H_path = os.path.join(testsets, testset_name)
        E_path = os.path.join(results, result_name, str(quality_factor))   # E_path, for Estimated images
        util.mkdir(E_path)

        logger.info('--------------- quality factor: {:d} ---------------'.format(quality_factor))
        border = 0

        test_results = OrderedDict()
        test_results['psnr'] = []
        test_results['ssim'] = []
        test_results['psnrb'] = []
        test_results['psnrlq'] = []
        test_results['ssimlq'] = []
        test_results['psnrblq'] = []
        H_paths = util.get_image_paths(H_path)
        for idx, img in enumerate(H_paths):

            # ------------------------------------
            # (1) img_L
            # ------------------------------------
            img_name, ext = os.path.splitext(os.path.basename(img))
            logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))

            img_L = util.imread_uint(img, n_channels=n_channels)
            if n_channels == 3:
                img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2BGR)
            _, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
            img_L = cv2.imdecode(encimg, 0) if n_channels == 1 else cv2.imdecode(encimg, 1)
            if n_channels == 3:
                img_L = cv2.cvtColor(img_L, cv2.COLOR_BGR2RGB)

            img_L = util.uint2tensor4(img_L)
            img_L = img_L.to(device)

            # ------------------------------------
            # (2) img_E
            # ------------------------------------

            img_E, QF = model(img_L, mode='val')
            QF = 1 - QF
            img_E = util.tensor2single(img_E)
            img_E = util.single2uint(img_E)
            img_H = util.imread_uint(H_paths[idx], n_channels=n_channels).squeeze()
            # --------------------------------
            # PSNR and SSIM, PSNRB
            # --------------------------------
            img_L = util.tensor2single(img_L)
            img_L = util.single2uint(img_L)
            psnr = util.calculate_psnr(img_L, img_H, border=border)
            ssim = util.calculate_ssim(img_L, img_H, border=border)
            psnrb = util.calculate_psnrb(img_H, img_L, border=border)
            test_results['psnrlq'].append(psnr)
            test_results['ssimlq'].append(ssim)
            test_results['psnrblq'].append(psnrb)
            logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.3f}; PSNRB: {:.2f} dB.'.format(img_name + ext, psnr, ssim, psnrb))
            psnr = util.calculate_psnr(img_E, img_H, border=border)
            ssim = util.calculate_ssim(img_E, img_H, border=border)
            psnrb = util.calculate_psnrb(img_H, img_E, border=border)

            test_results['psnr'].append(psnr)
            test_results['ssim'].append(ssim)
            test_results['psnrb'].append(psnrb)

            logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.3f}; PSNRB: {:.2f} dB.'.format(img_name+ext, psnr, ssim, psnrb))
            logger.info('predicted quality factor: {:d}'.format(round(float(QF*100))))

            util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
            util.imsave(img_E, os.path.join(E_path, img_name+'.png'))

        ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
        ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
        ave_psnrb = sum(test_results['psnrb']) / len(test_results['psnrb'])

        logger.info(
             'Average PSNR/SSIM/PSNRB - {} -: {:.2f}$\\vert${:.4f}$\\vert${:.2f}.'.format(result_name+'_'+str(quality_factor), ave_psnr, ave_ssim, ave_psnrb))


if __name__ == '__main__':
    main()