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
|
import numpy as np
import random
import torch
from torch.nn import functional as F
from r_basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from r_basicsr.data.transforms import paired_random_crop
from r_basicsr.models.sr_model import SRModel
from r_basicsr.utils import DiffJPEG, USMSharp
from r_basicsr.utils.img_process_util import filter2D
from r_basicsr.utils.registry import MODEL_REGISTRY
@MODEL_REGISTRY.register(suffix='basicsr')
class RealESRNetModel(SRModel):
"""RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
It is trained without GAN losses.
It mainly performs:
1. randomly synthesize LQ images in GPU tensors
2. optimize the networks with GAN training.
"""
def __init__(self, opt):
super(RealESRNetModel, 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)
# USM sharpen the GT images
if self.opt['gt_usm'] is True:
self.gt = 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, 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.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
# training pair pool
self._dequeue_and_enqueue()
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(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
self.is_train = True
|
