214 lines
6.7 KiB
Python
214 lines
6.7 KiB
Python
# from https://github.com/cszn/BSRGAN/blob/main/models/network_rrdbnet.py
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from logging import getLogger
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.init as init
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logger = getLogger(__name__)
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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net_l = [net_l]
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for net in net_l:
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for m in net.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, a=0, mode="fan_in")
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m.weight.data *= scale # for residual block
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, a=0, mode="fan_in")
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias.data, 0.0)
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def make_layer(block, n_layers, **kwarg):
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layers = []
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for _ in range(n_layers):
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layers.append(block(**kwarg))
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return nn.Sequential(*layers)
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def pixel_unshuffle(x, scale):
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b, c, hh, hw = x.size()
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out_channel = c * (scale**2)
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assert hh % scale == 0 and hw % scale == 0
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h = hh // scale
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w = hw // scale
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x_view = x.view(b, c, h, scale, w, scale)
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return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
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class ResidualDenseBlock_5C(nn.Module):
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def __init__(self, nf=64, gc=32, bias=True):
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super(ResidualDenseBlock_5C, self).__init__()
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# gc: growth channel, i.e. intermediate channels
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self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
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self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
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self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
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self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
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self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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# initialization
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initialize_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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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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def __init__(self, nf, gc=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock_5C(nf, gc)
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self.rdb2 = ResidualDenseBlock_5C(nf, gc)
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self.rdb3 = ResidualDenseBlock_5C(nf, gc)
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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 RRDBNetRescale(nn.Module):
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"""
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RRDBNet with variable input channels based on scale.
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This is the format expected by the official models.
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In this architecture, the modules stay the same but input channels change.
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"""
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def __init__(
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self,
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=4,
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):
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super(RRDBNetRescale, 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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logger.trace(
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"RRDBNetRescale params: %s",
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[num_in_ch, num_out_ch, num_feat, num_block, num_grow_ch, scale],
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)
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self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
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self.body = make_layer(RRDB, num_block, nf=num_feat, gc=num_grow_ch)
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self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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# upsampling
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self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1, bias=True)
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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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trunk = self.conv_body(self.body(feat))
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feat = feat + trunk
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if self.scale > 1:
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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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if self.scale == 4:
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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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class RRDBNetFixed(nn.Module):
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"""
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RRDBNet with fixed input channels regardless of scale.
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This is the format expected by many third-party models.
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In this architecture, the modules come and go based on scale, but the input channels stay the same.
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"""
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def __init__(
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self,
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=4,
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):
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super(RRDBNetFixed, self).__init__()
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self.scale = scale
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logger.trace(
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"RRDBNetFixed params: %s",
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[num_in_ch, num_out_ch, num_feat, num_block, num_grow_ch, scale],
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)
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self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
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self.body = make_layer(RRDB, num_block, nf=num_feat, gc=num_grow_ch)
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self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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# upsampling
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if self.scale > 1:
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self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1, bias=True)
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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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feat = self.conv_first(x)
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trunk = self.conv_body(self.body(feat))
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feat = feat + trunk
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if self.scale > 1:
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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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