forked from mrq/DL-Art-School
201 lines
6.9 KiB
Python
201 lines
6.9 KiB
Python
import os
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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 torchvision
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from torch.utils.checkpoint import checkpoint_sequential
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from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu
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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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels=64, growth_channels=32):
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super(ResidualDenseBlock, self).__init__()
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for i in range(5):
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out_channels = mid_channels if i == 4 else growth_channels
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self.add_module(
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f'conv{i+1}',
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nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
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1, 1))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for i in range(5):
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default_init_weights(getattr(self, f'conv{i+1}'), 0.1)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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# Emperically, 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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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# Emperically, 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 RRDBWithBypass(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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=32):
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super(RRDBWithBypass, self).__init__()
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True),
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ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False),
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ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False),
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nn.Sigmoid())
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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bypass = self.bypass(torch.cat([x, out], dim=1))
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self.bypass_map = bypass.detach().clone()
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# Emperically, we use 0.2 to scale the residual for better performance
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return out * 0.2 * bypass + 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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Currently, it supports x4 upsampling scale factor.
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Args:
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in_channels (int): Channel number of inputs.
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out_channels (int): Channel number of outputs.
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mid_channels (int): Channel number of intermediate features.
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Default: 64
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num_blocks (int): Block number in the trunk network. Defaults: 23
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growth_channels (int): Channels for each growth. Default: 32.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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mid_channels=64,
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num_blocks=23,
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growth_channels=32,
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body_block=RRDB,
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blocks_per_checkpoint=4,
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scale=4):
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super(RRDBNet, self).__init__()
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self.num_blocks = num_blocks
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self.blocks_per_checkpoint = blocks_per_checkpoint
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self.scale = scale
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self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
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self.body = make_layer(
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body_block,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels)
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self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for m in [
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self.conv_first, self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_hr, self.conv_last
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]:
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default_init_weights(m, 0.1)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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feat = self.conv_first(x)
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body_feat = self.conv_body(checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, 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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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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else:
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feat = self.lrelu(self.conv_up2(feat))
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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def visual_dbg(self, step, path):
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for i, bm in enumerate(self.body):
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torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
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