File size: 11,781 Bytes
0826939
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F


@MODEL_REGISTRY.register()
class RealESRGANModel(SRGANModel):
    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(RealESRGANModel, self).__init__(opt)
        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
        """It is the training pair pool for increasing the diversity in a batch.

        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

            self.lq = lq_dequeue
            self.gt = gt_dequeue
        else:
            # only do enqueue
            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
            self.queue_ptr = self.queue_ptr + b

    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            self.gt_usm = self.usm_sharpener(self.gt)

            self.kernel1 = data['kernel1'].to(self.device)
            self.kernel2 = data['kernel2'].to(self.device)
            self.sinc_kernel = data['sinc_kernel'].to(self.device)

            ori_h, ori_w = self.gt.size()[2:4]

            # ----------------------- The first degradation process ----------------------- #
            # blur
            out = filter2D(self.gt_usm, self.kernel1)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
            # blur
            if np.random.uniform() < self.opt['second_blur_prob']:
                out = filter2D(out, self.kernel2)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range2'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range2'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)

            # JPEG compression + the final sinc filter
            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
            # as one operation.
            # We consider two orders:
            #   1. [resize back + sinc filter] + JPEG compression
            #   2. JPEG compression + [resize back + sinc filter]
            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
            if np.random.uniform() < 0.5:
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
            else:
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)

            # clamp and round
            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.

            # random crop
            gt_size = self.opt['gt_size']
            (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
                                                                 self.opt['scale'])

            # training pair pool
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True

    def optimize_parameters(self, current_iter):
        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
        if self.opt['l1_gt_usm'] is False:
            l1_gt = self.gt
        if self.opt['percep_gt_usm'] is False:
            percep_gt = self.gt
        if self.opt['gan_gt_usm'] is False:
            gan_gt = self.gt

        # optimize net_g
        for p in self.net_d.parameters():
            p.requires_grad = False

        self.optimizer_g.zero_grad()
        self.output = self.net_g(self.lq)

        l_g_total = 0
        loss_dict = OrderedDict()
        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
            # pixel loss
            if self.cri_pix:
                l_g_pix = self.cri_pix(self.output, l1_gt)
                l_g_total += l_g_pix
                loss_dict['l_g_pix'] = l_g_pix
            # perceptual loss
            if self.cri_perceptual:
                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
                if l_g_percep is not None:
                    l_g_total += l_g_percep
                    loss_dict['l_g_percep'] = l_g_percep
                if l_g_style is not None:
                    l_g_total += l_g_style
                    loss_dict['l_g_style'] = l_g_style
            # gan loss
            fake_g_pred = self.net_d(self.output)
            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
            l_g_total += l_g_gan
            loss_dict['l_g_gan'] = l_g_gan

            l_g_total.backward()
            self.optimizer_g.step()

        # optimize net_d
        for p in self.net_d.parameters():
            p.requires_grad = True

        self.optimizer_d.zero_grad()
        # real
        real_d_pred = self.net_d(gan_gt)
        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
        loss_dict['l_d_real'] = l_d_real
        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
        l_d_real.backward()
        # fake
        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
        loss_dict['l_d_fake'] = l_d_fake
        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
        l_d_fake.backward()
        self.optimizer_d.step()

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

        self.log_dict = self.reduce_loss_dict(loss_dict)