56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
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import functools
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import torch.nn as nn
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import torch.nn.functional as F
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import models.archs.arch_util as arch_util
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class MSRResNet(nn.Module):
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''' modified SRResNet'''
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def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, upscale=4):
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super(MSRResNet, self).__init__()
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self.upscale = upscale
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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basic_block = functools.partial(arch_util.ResidualBlock_noBN, nf=nf)
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self.recon_trunk = arch_util.make_layer(basic_block, nb)
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# upsampling
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if self.upscale == 2:
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self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
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self.pixel_shuffle = nn.PixelShuffle(2)
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elif self.upscale == 3:
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self.upconv1 = nn.Conv2d(nf, nf * 9, 3, 1, 1, bias=True)
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self.pixel_shuffle = nn.PixelShuffle(3)
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elif self.upscale == 4:
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self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
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self.upconv2 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
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self.pixel_shuffle = nn.PixelShuffle(2)
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self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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# initialization
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arch_util.initialize_weights([self.conv_first, self.upconv1, self.HRconv, self.conv_last],
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0.1)
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if self.upscale == 4:
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arch_util.initialize_weights(self.upconv2, 0.1)
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def forward(self, x):
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fea = self.lrelu(self.conv_first(x))
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out = self.recon_trunk(fea)
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if self.upscale == 4:
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out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
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out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
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elif self.upscale == 3 or self.upscale == 2:
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out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
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out = self.conv_last(self.lrelu(self.HRconv(out)))
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base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
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out += base
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return out
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