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| from basicsr.utils.registry import ARCH_REGISTRY | |
| from torch import nn as nn | |
| from torch.nn import functional as F | |
| class SRVGGNetCompact(nn.Module): | |
| """A compact VGG-style network structure for super-resolution. | |
| It is a compact network structure, which performs upsampling in the last layer and no convolution is | |
| conducted on the HR feature space. | |
| 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_conv (int): Number of convolution layers in the body network. Default: 16. | |
| upscale (int): Upsampling factor. Default: 4. | |
| act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. | |
| """ | |
| def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'): | |
| super(SRVGGNetCompact, self).__init__() | |
| self.num_in_ch = num_in_ch | |
| self.num_out_ch = num_out_ch | |
| self.num_feat = num_feat | |
| self.num_conv = num_conv | |
| self.upscale = upscale | |
| self.act_type = act_type | |
| self.body = nn.ModuleList() | |
| # the first conv | |
| self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)) | |
| # the first activation | |
| if act_type == 'relu': | |
| activation = nn.ReLU(inplace=True) | |
| elif act_type == 'prelu': | |
| activation = nn.PReLU(num_parameters=num_feat) | |
| elif act_type == 'leakyrelu': | |
| activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) | |
| self.body.append(activation) | |
| # the body structure | |
| for _ in range(num_conv): | |
| self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1)) | |
| # activation | |
| if act_type == 'relu': | |
| activation = nn.ReLU(inplace=True) | |
| elif act_type == 'prelu': | |
| activation = nn.PReLU(num_parameters=num_feat) | |
| elif act_type == 'leakyrelu': | |
| activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) | |
| self.body.append(activation) | |
| # the last conv | |
| self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1)) | |
| # upsample | |
| self.upsampler = nn.PixelShuffle(upscale) | |
| def forward(self, x): | |
| out = x | |
| for i in range(0, len(self.body)): | |
| out = self.body[i](out) | |
| out = self.upsampler(out) | |
| # add the nearest upsampled image, so that the network learns the residual | |
| base = F.interpolate(x, scale_factor=self.upscale, mode='nearest') | |
| out += base | |
| return out | |