import torch from torch import nn as nn from torch.nn import functional as F from torch.nn import init as init from torch.nn.modules.batchnorm import _BatchNorm def pixel_unshuffle(x, scale): """Pixel unshuffle. Args: x (Tensor): Input feature with shape (b, c, hh, hw). scale (int): Downsample ratio. Returns: Tensor: the pixel unshuffled feature. """ b, c, hh, hw = x.size() out_channel = c * (scale**2) assert hh % scale == 0 and hw % scale == 0 h = hh // scale w = hw // scale x_view = x.view(b, c, h, scale, w, scale) return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) @torch.no_grad() def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): """Initialize network weights. Args: module_list (list[nn.Module] | nn.Module): Modules to be initialized. scale (float): Scale initialized weights, especially for residual blocks. Default: 1. bias_fill (float): The value to fill bias. Default: 0 kwargs (dict): Other arguments for initialization function. """ if not isinstance(module_list, list): module_list = [module_list] for module in module_list: for m in module.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, **kwargs) m.weight.data *= scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, **kwargs) m.weight.data *= scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, _BatchNorm): init.constant_(m.weight, 1) if m.bias is not None: m.bias.data.fill_(bias_fill) def make_layer(basic_block, num_basic_block, **kwarg): """Make layers by stacking the same blocks. Args: basic_block (nn.module): nn.module class for basic block. num_basic_block (int): number of blocks. Returns: nn.Sequential: Stacked blocks in nn.Sequential. """ layers = [] for _ in range(num_basic_block): layers.append(basic_block(**kwarg)) return nn.Sequential(*layers) class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat=64, num_grow_ch=32): super(ResidualDenseBlock, self).__init__() self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) # initialization default_init_weights( [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1 ) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) # Empirically, we use 0.2 to scale the residual for better performance return x5 * 0.2 + x class RRDB(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat, num_grow_ch=32): super(RRDB, self).__init__() self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) def forward(self, x): out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) # Empirically, we use 0.2 to scale the residual for better performance return out * 0.2 + x class RRDBNet(nn.Module): """Networks consisting of Residual in Residual Dense Block, which is used in ESRGAN. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. We extend ESRGAN for scale x2 and scale x1. Note: This is one option for scale 1, scale 2 in RRDBNet. We first employ the pixel-unshuffle an inverse operation of pixelshuffle to reduce the spatial size and enlarge the channel size before feeding inputs into the main ESRGAN architecture. Args: num_in_ch (int): Channel number of inputs. num_out_ch (int): Channel number of outputs. num_feat (int): Channel number of intermediate features. Default: 64 num_block (int): Block number in the trunk network. Defaults: 23 num_grow_ch (int): Channels for each growth. Default: 32. """ def __init__( self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32 ): super(RRDBNet, self).__init__() self.scale = scale if scale == 2: num_in_ch = num_in_ch * 4 elif scale == 1: num_in_ch = num_in_ch * 16 self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.body = make_layer( RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch ) self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) # upsample self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) 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) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): if self.scale == 2: feat = pixel_unshuffle(x, scale=2) elif self.scale == 1: feat = pixel_unshuffle(x, scale=4) else: feat = x feat = self.conv_first(feat) body_feat = self.conv_body(self.body(feat)) feat = feat + body_feat # upsample feat = self.lrelu( self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")) ) feat = self.lrelu( self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")) ) out = self.conv_last(self.lrelu(self.conv_hr(feat))) return out