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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
Ref git repo: 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
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