Spaces:
Running
on
Zero
Running
on
Zero
| 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 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) | |
| # TODO: may write a cpp file | |
| 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) | |
| 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 |