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Running
on
Zero
| 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 | |
| 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 | |