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import torch |
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from torch import nn as nn |
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from torch.nn import functional as F |
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from .arch_utils import default_init_weights, make_layer, pixel_unshuffle |
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class ResidualDenseBlock(nn.Module): |
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"""Residual Dense Block. |
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Used in RRDB block in ESRGAN. |
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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. |
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""" |
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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): |
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"""Residual in Residual Dense Block. |
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Used in RRDB-Net in ESRGAN. |
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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. |
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""" |
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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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class RRDBNet(nn.Module): |
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"""Networks consisting of Residual in Residual Dense Block, which is used |
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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. |
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""" |
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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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if scale == 8: |
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self.conv_up3 = 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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if self.scale == 8: |
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feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest'))) |
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out = self.conv_last(self.lrelu(self.conv_hr(feat))) |
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return out |
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