mirror of
https://github.com/brycedrennan/imaginAIry
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132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
"""Classes for enhanced super-resolution networks"""
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# from https://github.com/XPixelGroup/BasicSR/commit/44672b0c6a607fc2a5aaeea70b9f565d1f3f095b
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# with slight modifications
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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_util 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().__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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# initialization
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default_init_weights(
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[self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1
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)
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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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# Empirically, we use 0.2 to scale the residual for better performance
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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().__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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# Empirically, we use 0.2 to scale the residual for better performance
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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__(
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self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32
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):
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super().__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(
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RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch
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)
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self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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# upsample
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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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# upsample
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feat = self.lrelu(
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self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest"))
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)
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feat = self.lrelu(
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self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest"))
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)
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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