File size: 6,039 Bytes
522606a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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

from torch import optim
from torch.utils.data import DataLoader
from torchvision.utils import save_image
from tqdm import trange

from Dataloader import *
from .utils import image_quality
from .utils.cls import CyclicLR
from .utils.prepare_images import *

train_folder = './dataset/train'
test_folder = "./dataset/test"

img_dataset = ImageDBData(db_file='dataset/images.db', db_table="train_images_size_128_noise_1_rgb", max_images=24)
img_data = DataLoader(img_dataset, batch_size=6, shuffle=True, num_workers=6)

total_batch = len(img_data)
print(len(img_dataset))

test_dataset = ImageDBData(db_file='dataset/test2.db', db_table="test_images_size_128_noise_1_rgb", max_images=None)
num_test = len(test_dataset)
test_data = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=1)

criteria = nn.L1Loss()

model = CARN_V2(color_channels=3, mid_channels=64, conv=nn.Conv2d,
                single_conv_size=3, single_conv_group=1,
                scale=2, activation=nn.LeakyReLU(0.1),
                SEBlock=True, repeat_blocks=3, atrous=(1, 1, 1))

model.total_parameters()


# model.initialize_weights_xavier_uniform()

# fp16 training is available in GPU only
model = network_to_half(model)
model = model.cuda()
model.load_state_dict(torch.load("CARN_model_checkpoint.pt"))

learning_rate = 1e-4
weight_decay = 1e-6
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay, amsgrad=True)
# optimizer = optim.SGD(model.parameters(), momentum=0.9, nesterov=True, weight_decay=weight_decay, lr=learning_rate)

# optimizer = FP16_Optimizer(optimizer, static_loss_scale=128.0, verbose=False)
# optimizer.load_state_dict(torch.load("CARN_adam_checkpoint.pt"))

last_iter = -1  # torch.load("CARN_scheduler_last_iter")
scheduler = CyclicLR(optimizer, base_lr=1e-4, max_lr=1e-4,
                     step_size=3 * total_batch, mode="triangular",
                     last_batch_iteration=last_iter)
train_loss = []
train_ssim = []
train_psnr = []

test_loss = []
test_ssim = []
test_psnr = []

# train_loss = torch.load("train_loss.pt")
# train_ssim = torch.load("train_ssim.pt")
# train_psnr = torch.load("train_psnr.pt")
#
# test_loss = torch.load("test_loss.pt")
# test_ssim = torch.load("test_ssim.pt")
# test_psnr = torch.load("test_psnr.pt")


counter = 0
iteration = 2
ibar = trange(iteration, ascii=True, maxinterval=1, postfix={"avg_loss": 0, "train_ssim": 0, "test_ssim": 0})
for i in ibar:
    # batch_loss = []
    # insample_ssim = []
    # insample_psnr = []
    for index, batch in enumerate(img_data):
        scheduler.batch_step()
        lr_img, hr_img = batch
        lr_img = lr_img.cuda().half()
        hr_img = hr_img.cuda()

        # model.zero_grad()
        optimizer.zero_grad()
        outputs = model.forward(lr_img)
        outputs = outputs.float()
        loss = criteria(outputs, hr_img)
        # loss.backward()
        optimizer.backward(loss)
        # nn.utils.clip_grad_norm_(model.parameters(), 5)
        optimizer.step()

        counter += 1
        # train_loss.append(loss.item())

        ssim = image_quality.msssim(outputs, hr_img).item()
        psnr = image_quality.psnr(outputs, hr_img).item()

        ibar.set_postfix(ratio=index / total_batch, loss=loss.item(),
                         ssim=ssim, batch=index,
                         psnr=psnr,
                         lr=scheduler.current_lr
                         )
        train_loss.append(loss.item())
        train_ssim.append(ssim)
        train_psnr.append(psnr)

        # +++++++++++++++++++++++++++++++++++++
        #      save checkpoints by iterations
        # -------------------------------------

        if (counter + 1) % 500 == 0:
            torch.save(model.state_dict(), 'CARN_model_checkpoint.pt')
            torch.save(optimizer.state_dict(), 'CARN_adam_checkpoint.pt')
            torch.save(train_loss, 'train_loss.pt')
            torch.save(train_ssim, "train_ssim.pt")
            torch.save(train_psnr, 'train_psnr.pt')
            torch.save(scheduler.last_batch_iteration, "CARN_scheduler_last_iter.pt")

    # +++++++++++++++++++++++++++++++++++++
    #           End of One Epoch      
    # -------------------------------------

    # one_ite_loss = np.mean(batch_loss)
    # one_ite_ssim = np.mean(insample_ssim)
    # one_ite_psnr = np.mean(insample_psnr)

    # print(f"One iteration loss {one_ite_loss}, ssim {one_ite_ssim}, psnr {one_ite_psnr}")
    # train_loss.append(one_ite_loss)
    # train_ssim.append(one_ite_ssim)
    # train_psnr.append(one_ite_psnr)

    torch.save(model.state_dict(), 'CARN_model_checkpoint.pt')
    # torch.save(scheduler, "CARN_scheduler_optim.pt")
    torch.save(optimizer.state_dict(), 'CARN_adam_checkpoint.pt')
    torch.save(train_loss, 'train_loss.pt')
    torch.save(train_ssim, "train_ssim.pt")
    torch.save(train_psnr, 'train_psnr.pt')
    # torch.save(scheduler.last_batch_iteration, "CARN_scheduler_last_iter.pt")

    # +++++++++++++++++++++++++++++++++++++
    #           Test
    # -------------------------------------

    with torch.no_grad():
        ssim = []
        batch_loss = []
        psnr = []
        for index, test_batch in enumerate(test_data):
            lr_img, hr_img = test_batch
            lr_img = lr_img.cuda()
            hr_img = hr_img.cuda()

            lr_img_up = model(lr_img)
            lr_img_up = lr_img_up.float()
            loss = criteria(lr_img_up, hr_img)

            save_image([lr_img_up[0], hr_img[0]], f"check_test_imgs/{index}.png")
            batch_loss.append(loss.item())
            ssim.append(image_quality.msssim(lr_img_up, hr_img).item())
            psnr.append(image_quality.psnr(lr_img_up, hr_img).item())

        test_ssim.append(np.mean(ssim))
        test_loss.append(np.mean(batch_loss))
        test_psnr.append(np.mean(psnr))

        torch.save(test_loss, 'test_loss.pt')
        torch.save(test_ssim, "test_ssim.pt")
        torch.save(test_psnr, "test_psnr.pt")

# import subprocess

# subprocess.call(["shutdown", "/s"])