| import torch
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| from torch import nn as nn
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| from torch.nn import functional as F
|
|
|
| from basicsr.utils.registry import ARCH_REGISTRY
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| from .arch_util import default_init_weights, make_layer, pixel_unshuffle
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|
|
|
|
| class ResidualDenseBlock(nn.Module):
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| """Residual Dense Block.
|
|
|
| Used in RRDB block in ESRGAN.
|
|
|
| Args:
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| num_feat (int): Channel number of intermediate features.
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| num_grow_ch (int): Channels for each growth.
|
| """
|
|
|
| def __init__(self, num_feat=64, num_grow_ch=32):
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| super(ResidualDenseBlock, self).__init__()
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| self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
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| self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
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| self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
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| self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
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| self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
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|
|
| self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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|
|
|
|
| default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
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|
|
| def forward(self, x):
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| x1 = self.lrelu(self.conv1(x))
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| x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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| x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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| x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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| x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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|
|
| return x5 * 0.2 + x
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|
|
|
|
| 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):
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| super(RRDB, self).__init__()
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| self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
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| self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
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| self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
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|
|
| def forward(self, x):
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| out = self.rdb1(x)
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| out = self.rdb2(out)
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| out = self.rdb3(out)
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|
|
| return out * 0.2 + x
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|
|
|
|
| @ARCH_REGISTRY.register()
|
| class RRDBNet(nn.Module):
|
| """Networks consisting of Residual in Residual Dense Block, which is used
|
| in ESRGAN.
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|
|
| ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
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|
|
| We extend ESRGAN for scale x2 and scale x1.
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| Note: This is one option for scale 1, scale 2 in RRDBNet.
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| We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
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| and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
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|
|
| Args:
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| num_in_ch (int): Channel number of inputs.
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| num_out_ch (int): Channel number of outputs.
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| num_feat (int): Channel number of intermediate features.
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| Default: 64
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| num_block (int): Block number in the trunk network. Defaults: 23
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| 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):
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| super(RRDBNet, self).__init__()
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| self.scale = scale
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| if scale == 2:
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| num_in_ch = num_in_ch * 4
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| elif scale == 1:
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| num_in_ch = num_in_ch * 16
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| self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
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| self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
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| self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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|
|
| self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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| self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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| self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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| self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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|
|
| self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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|
|
| def forward(self, x):
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| if self.scale == 2:
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| feat = pixel_unshuffle(x, scale=2)
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| elif self.scale == 1:
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| feat = pixel_unshuffle(x, scale=4)
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| else:
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| feat = x
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| feat = self.conv_first(feat)
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| body_feat = self.conv_body(self.body(feat))
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| feat = feat + body_feat
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|
|
| feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
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| feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
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| out = self.conv_last(self.lrelu(self.conv_hr(feat)))
|
| return out |
