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Zero
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from torch import nn as nn
from torch.nn import functional as F
from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer
@ARCH_REGISTRY.register()
class MSRResNet(nn.Module):
"""Modified SRResNet.
A compacted version modified from SRResNet in
"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
It uses residual blocks without BN, similar to EDSR.
Currently, it supports x2, x3 and x4 upsampling scale factor.
Args:
num_in_ch (int): Channel number of inputs. Default: 3.
num_out_ch (int): Channel number of outputs. Default: 3.
num_feat (int): Channel number of intermediate features. Default: 64.
num_block (int): Block number in the body network. Default: 16.
upscale (int): Upsampling factor. Support x2, x3 and x4. Default: 4.
"""
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4):
super(MSRResNet, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat)
# upsampling
if self.upscale in [2, 3]:
self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(self.upscale)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1)
if self.upscale == 4:
default_init_weights(self.upconv2, 0.1)
def forward(self, x):
feat = self.lrelu(self.conv_first(x))
out = self.body(feat)
if self.upscale == 4:
out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
elif self.upscale in [2, 3]:
out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
out = self.conv_last(self.lrelu(self.conv_hr(out)))
base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
out += base
return out
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