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