Spaces:
Running
Running
File size: 4,587 Bytes
c59c099 |
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 |
import torch
from torch import nn as nn
from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import Upsample, make_layer
class ChannelAttention(nn.Module):
"""Channel attention used in RCAN.
Args:
num_feat (int): Channel number of intermediate features.
squeeze_factor (int): Channel squeeze factor. Default: 16.
"""
def __init__(self, num_feat, squeeze_factor=16):
super(ChannelAttention, self).__init__()
self.attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid())
def forward(self, x):
y = self.attention(x)
return x * y
class RCAB(nn.Module):
"""Residual Channel Attention Block (RCAB) used in RCAN.
Args:
num_feat (int): Channel number of intermediate features.
squeeze_factor (int): Channel squeeze factor. Default: 16.
res_scale (float): Scale the residual. Default: 1.
"""
def __init__(self, num_feat, squeeze_factor=16, res_scale=1):
super(RCAB, self).__init__()
self.res_scale = res_scale
self.rcab = nn.Sequential(
nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1),
ChannelAttention(num_feat, squeeze_factor))
def forward(self, x):
res = self.rcab(x) * self.res_scale
return res + x
class ResidualGroup(nn.Module):
"""Residual Group of RCAB.
Args:
num_feat (int): Channel number of intermediate features.
num_block (int): Block number in the body network.
squeeze_factor (int): Channel squeeze factor. Default: 16.
res_scale (float): Scale the residual. Default: 1.
"""
def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1):
super(ResidualGroup, self).__init__()
self.residual_group = make_layer(
RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale)
self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
def forward(self, x):
res = self.conv(self.residual_group(x))
return res + x
@ARCH_REGISTRY.register()
class RCAN(nn.Module):
"""Residual Channel Attention Networks.
``Paper: Image Super-Resolution Using Very Deep Residual Channel Attention Networks``
Reference: https://github.com/yulunzhang/RCAN
Args:
num_in_ch (int): Channel number of inputs.
num_out_ch (int): Channel number of outputs.
num_feat (int): Channel number of intermediate features.
Default: 64.
num_group (int): Number of ResidualGroup. Default: 10.
num_block (int): Number of RCAB in ResidualGroup. Default: 16.
squeeze_factor (int): Channel squeeze factor. Default: 16.
upscale (int): Upsampling factor. Support 2^n and 3.
Default: 4.
res_scale (float): Used to scale the residual in residual block.
Default: 1.
img_range (float): Image range. Default: 255.
rgb_mean (tuple[float]): Image mean in RGB orders.
Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
"""
def __init__(self,
num_in_ch,
num_out_ch,
num_feat=64,
num_group=10,
num_block=16,
squeeze_factor=16,
upscale=4,
res_scale=1,
img_range=255.,
rgb_mean=(0.4488, 0.4371, 0.4040)):
super(RCAN, self).__init__()
self.img_range = img_range
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = make_layer(
ResidualGroup,
num_group,
num_feat=num_feat,
num_block=num_block,
squeeze_factor=squeeze_factor,
res_scale=res_scale)
self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
def forward(self, x):
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
x = self.conv_first(x)
res = self.conv_after_body(self.body(x))
res += x
x = self.conv_last(self.upsample(res))
x = x / self.img_range + self.mean
return x
|