import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torch.utils.checkpoint import checkpoint_sequential from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels=64, growth_channels=32): super(ResidualDenseBlock, self).__init__() for i in range(5): out_channels = mid_channels if i == 4 else growth_channels self.add_module( f'conv{i+1}', nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3, 1, 1)) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) for i in range(5): default_init_weights(getattr(self, f'conv{i+1}'), 0.1) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ 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)) # Emperically, 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: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels, growth_channels=32): super(RRDB, self).__init__() self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) # Emperically, we use 0.2 to scale the residual for better performance return out * 0.2 + x class RRDBWithBypass(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels, growth_channels=32): super(RRDBWithBypass, self).__init__() self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True), ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False), ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False), nn.Sigmoid()) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) bypass = self.bypass(torch.cat([x, out], dim=1)) self.bypass_map = bypass.detach().clone() # Empirically, we use 0.2 to scale the residual for better performance return out * 0.2 * bypass + 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. Currently, it supports x4 upsampling scale factor. Args: in_channels (int): Channel number of inputs. out_channels (int): Channel number of outputs. mid_channels (int): Channel number of intermediate features. Default: 64 num_blocks (int): Block number in the trunk network. Defaults: 23 growth_channels (int): Channels for each growth. Default: 32. """ def __init__(self, in_channels, out_channels, mid_channels=64, num_blocks=23, growth_channels=32, body_block=RRDB, blocks_per_checkpoint=4, scale=4): super(RRDBNet, self).__init__() self.num_blocks = num_blocks self.blocks_per_checkpoint = blocks_per_checkpoint self.scale = scale self.in_channels = in_channels first_conv_stride = 1 if in_channels <= 4 else scale first_conv_ksize = 3 if first_conv_stride == 1 else 7 first_conv_padding = 1 if first_conv_stride == 1 else 3 self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding) self.body = make_layer( body_block, num_blocks, mid_channels=mid_channels, growth_channels=growth_channels) self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) # upsample self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) for m in [ self.conv_first, self.conv_body, self.conv_up1, self.conv_up2, self.conv_hr, self.conv_last ]: default_init_weights(m, 0.1) def forward(self, x, ref=None): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ if self.in_channels > 4: x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic") if ref is None: ref = torch.zeros_like(x_lg) x_lg = torch.cat([x_lg, ref], dim=1) else: x_lg = x feat = self.conv_first(x_lg) body_feat = self.conv_body(checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)) feat = feat + body_feat # upsample feat = self.lrelu( self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) if self.scale == 4: feat = self.lrelu( self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) else: feat = self.lrelu(self.conv_up2(feat)) out = self.conv_last(self.lrelu(self.conv_hr(feat))) return out def visual_dbg(self, step, path): for i, bm in enumerate(self.body): if hasattr(bm, 'bypass_map'): torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))