SuperQuality/evaluate.py at master · fenghansen/SuperQuality · 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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import time
import yaml
import sys
from tqdm import tqdm
from torch.optim import lr_scheduler
from torchvision.utils import make_grid
from torchvision import transforms
from torchsummary import summary
from base_trainer import BaseTrainer
from losses import *
from models import *
from base_parser import BaseParser
from dataloader import *

class KinD_noDecom_Trainer(BaseTrainer):
    @no_grad
    def test(self, epoch=-1, plot_dir='./images/samples-KinD'):
        self.model.eval()
        self.model.to(device=self.device)
        if 'decom_net' in model._modules:
            for L_low_tensor, L_high_tensor, name in self.dataloader_test:
                L_low = L_low_tensor.to(self.device)
                L_high = L_high_tensor.to(self.device)

                with torch.no_grad():
                    R_low, I_low = self.model.decom_net(L_low)
                    R_high, I_high = self.model.decom_net(L_high)
                    I_low_3 = torch.cat([I_low, I_low, I_low], dim=1)
                    I_high_3 = torch.cat([I_high, I_high, I_high], dim=1)

                    output_low = I_low_3 * R_low
                    output_high = I_high_3 * R_high

                    ratio = torch.mean(I_high) / 0.5
                    R_final, I_final, output_final = self.model(L_low, ratio)

                R_final_np = R_final.detach().cpu().numpy()[0]
                I_final_np = I_final.detach().cpu().numpy()[0]
                R_low_np = R_low.detach().cpu().numpy()[0]
                I_low_np = I_low.detach().cpu().numpy()[0]
                R_high_np = R_high.detach().cpu().numpy()[0]
                I_high_np = I_high.detach().cpu().numpy()[0]
                output_final_np = output_final.detach().cpu().numpy()[0]
                output_low_np = output_low.detach().cpu().numpy()[0]
                output_high_np = output_high.detach().cpu().numpy()[0]
                # ratio_map_np = ratio_map.detach().cpu().numpy()[0]
                L_low_np = L_low_tensor.numpy()[0]
                L_high_np = L_high_tensor.numpy()[0]

                sample_imgs = np.concatenate( (R_low_np, I_low_np, output_low_np, L_low_np,
                                            R_high_np, I_high_np, output_high_np, L_high_np,
                                            R_final_np, I_final_np, output_final_np, L_high_np), axis=0 )

                filepath = os.path.join(plot_dir, f'{name[0]}_epoch_{epoch}.png')
                split_point = [0, 3, 4, 7, 10, 13, 14, 17, 20, 23, 24, 27, 30]
                img_dim = I_high_np.shape[1:]
                # sample(sample_imgs, split=split_point, figure_size=(3, 4),
                #             img_dim=img_dim, path=filepath, num=epoch)
                sample(output_final_np, path=os.path.join(plot_dir, f'{name[0]}.png'))
        else:
            for R_low_tensor, I_low_tensor, R_high_tensor, I_high_tensor, name in self.dataloader_test:
                R_low = R_low_tensor.to(self.device)
                R_high = R_high_tensor.to(self.device)
                I_low = I_low_tensor.to(self.device)
                I_high = I_high_tensor.to(self.device)
                I_high_3 = torch.cat([I_high, I_high, I_high], dim=1)
                output_high = I_high_3 * R_high

                # while True:
                #     b = float(input('请输入增强水平：'))
                #     if b <= 0: break
                # b = 0.6; w = 0.5
                # bright_low = torch.mean(I_low)
                # bright_high = torch.ones_like(bright_low) * b + bright_low * w
                # ratio = torch.div(bright_high, bright_low)
                # print(bright_high, ratio)
                # ratio_map = torch.ones_like(I_low) * ratio
                ratio = 1
                R_final, I_final, output_final = self.model(R_low, I_low, ratio)

                R_final_np = R_final.detach().cpu().numpy()[0]
                I_final_np = I_final.detach().cpu().numpy()[0]
                output_final_np = output_final.detach().cpu().numpy()[0]
                output_high_np = output_high.detach().cpu().numpy()[0]
                # ratio_map_np = ratio_map.detach().cpu().numpy()[0]
                I_high_np = I_high_tensor.numpy()[0]
                R_high_np = R_high_tensor.numpy()[0]

                sample_imgs = np.concatenate( (R_high_np, I_high_np, output_high_np,
                                            R_final_np, I_final_np, output_final_np), axis=0 )

                filepath = os.path.join(plot_dir, f'{name[0]}_epoch_{epoch}.png')
                split_point = [0, 3, 4, 7, 10, 11, 14]
                img_dim = I_high_np.shape[1:]
                sample(sample_imgs, split=split_point, figure_size=(2, 3),
                            img_dim=img_dim, path=filepath, num=epoch)


if __name__ == "__main__":
    criterion = None
    parser = BaseParser()
    args = parser.parse()
    # args.noDecom = True
    with open(args.config) as f:
        config = yaml.load(f)
    if config['noDecom'] is True:
        model = KinD_noDecom()
    else:
        model = KinD(use_MaskMul=True)

    if args.checkpoint is not None:
        if config['noDecom'] is False:
            pretrain_decom = torch.load('./weights/decom_net_normal.pth')
            model.decom_net.load_state_dict(pretrain_decom)
            log('Model loaded from decom_net.pth')
        pretrain_resotre = torch.load('./weights/restore_GAN_mask_best.pth')
        model.restore_net.load_state_dict(pretrain_resotre)
        log('Model loaded from restore_net.pth')
        pretrain_illum = torch.load('./weights/illum_net_custom_final.pth')
        model.illum_net.load_state_dict(pretrain_illum)
        log('Model loaded from illum_net.pth')

    if config['noDecom'] is True:
        root_path_test = r'H:\datasets\Low-Light Dataset\LOLdataset_decom\eval15'
        list_path_test = os.path.join(root_path_test, 'pair_list.csv')

        log("Buliding LOL Dataset (noDecom)...")
        # transform = transforms.Compose([transforms.ToTensor()])
        dst_test = LOLDataset_Decom(root_path_test, list_path_test,
                                crop_size=config['length'], to_RAM=True, training=False)
    else:
        root_path_test = r'C:\DeepLearning\KinD_plus-master\LOLdataset\eval15'
        list_path_test = os.path.join(root_path_test, 'pair_list.csv')

        log("Buliding LOL Dataset...")
        # transform = transforms.Compose([transforms.ToTensor()])
        dst_test = LOLDataset(root_path_test, list_path_test,
                                crop_size=config['length'], to_RAM=True, training=False)

    test_loader = DataLoader(dst_test, batch_size=1)

    KinD = KinD_noDecom_Trainer(config, None, criterion, model, dataloader_test=test_loader)

    # Please change your output direction here
    output_dir = './images/compare-lr-custom-best'
    KinD.test(plot_dir=output_dir)