forked from mrq/DL-Art-School
211 lines
7.4 KiB
Python
211 lines
7.4 KiB
Python
import os
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchvision
|
|
from torch.utils.checkpoint import checkpoint_sequential
|
|
|
|
from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu
|
|
|
|
|
|
class ResidualDenseBlock(nn.Module):
|
|
"""Residual Dense Block.
|
|
|
|
Used in RRDB block in ESRGAN.
|
|
|
|
Args:
|
|
mid_channels (int): Channel number of intermediate features.
|
|
growth_channels (int): Channels for each growth.
|
|
"""
|
|
|
|
def __init__(self, mid_channels=64, growth_channels=32):
|
|
super(ResidualDenseBlock, self).__init__()
|
|
for i in range(5):
|
|
out_channels = mid_channels if i == 4 else growth_channels
|
|
self.add_module(
|
|
f'conv{i+1}',
|
|
nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
|
|
1, 1))
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
for i in range(5):
|
|
default_init_weights(getattr(self, f'conv{i+1}'), 0.1)
|
|
|
|
|
|
def forward(self, x):
|
|
"""Forward function.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor with shape (n, c, h, w).
|
|
|
|
Returns:
|
|
Tensor: Forward results.
|
|
"""
|
|
x1 = self.lrelu(self.conv1(x))
|
|
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
|
|
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
|
|
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
|
|
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
|
|
# Emperically, we use 0.2 to scale the residual for better performance
|
|
return x5 * 0.2 + x
|
|
|
|
|
|
class RRDB(nn.Module):
|
|
"""Residual in Residual Dense Block.
|
|
|
|
Used in RRDB-Net in ESRGAN.
|
|
|
|
Args:
|
|
mid_channels (int): Channel number of intermediate features.
|
|
growth_channels (int): Channels for each growth.
|
|
"""
|
|
|
|
def __init__(self, mid_channels, growth_channels=32):
|
|
super(RRDB, self).__init__()
|
|
self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
|
|
def forward(self, x):
|
|
"""Forward function.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor with shape (n, c, h, w).
|
|
|
|
Returns:
|
|
Tensor: Forward results.
|
|
"""
|
|
out = self.rdb1(x)
|
|
out = self.rdb2(out)
|
|
out = self.rdb3(out)
|
|
# Emperically, we use 0.2 to scale the residual for better performance
|
|
return out * 0.2 + x
|
|
|
|
|
|
class RRDBWithBypass(nn.Module):
|
|
"""Residual in Residual Dense Block.
|
|
|
|
Used in RRDB-Net in ESRGAN.
|
|
|
|
Args:
|
|
mid_channels (int): Channel number of intermediate features.
|
|
growth_channels (int): Channels for each growth.
|
|
"""
|
|
|
|
def __init__(self, mid_channels, growth_channels=32):
|
|
super(RRDBWithBypass, self).__init__()
|
|
self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
|
|
self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True),
|
|
ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False),
|
|
ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False),
|
|
nn.Sigmoid())
|
|
|
|
def forward(self, x):
|
|
"""Forward function.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor with shape (n, c, h, w).
|
|
|
|
Returns:
|
|
Tensor: Forward results.
|
|
"""
|
|
out = self.rdb1(x)
|
|
out = self.rdb2(out)
|
|
out = self.rdb3(out)
|
|
bypass = self.bypass(torch.cat([x, out], dim=1))
|
|
self.bypass_map = bypass.detach().clone()
|
|
# Emperically, we use 0.2 to scale the residual for better performance
|
|
return out * 0.2 * bypass + x
|
|
|
|
|
|
class RRDBNet(nn.Module):
|
|
"""Networks consisting of Residual in Residual Dense Block, which is used
|
|
in ESRGAN.
|
|
|
|
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
|
|
Currently, it supports x4 upsampling scale factor.
|
|
|
|
Args:
|
|
in_channels (int): Channel number of inputs.
|
|
out_channels (int): Channel number of outputs.
|
|
mid_channels (int): Channel number of intermediate features.
|
|
Default: 64
|
|
num_blocks (int): Block number in the trunk network. Defaults: 23
|
|
growth_channels (int): Channels for each growth. Default: 32.
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
mid_channels=64,
|
|
num_blocks=23,
|
|
growth_channels=32,
|
|
body_block=RRDB,
|
|
blocks_per_checkpoint=4,
|
|
scale=4):
|
|
super(RRDBNet, self).__init__()
|
|
self.num_blocks = num_blocks
|
|
self.blocks_per_checkpoint = blocks_per_checkpoint
|
|
self.scale = scale
|
|
self.in_channels = in_channels
|
|
first_conv_stride = 1 if in_channels <= 4 else scale
|
|
first_conv_ksize = 3 if first_conv_stride == 1 else 7
|
|
first_conv_padding = 1 if first_conv_stride == 1 else 3
|
|
self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
|
|
self.body = make_layer(
|
|
body_block,
|
|
num_blocks,
|
|
mid_channels=mid_channels,
|
|
growth_channels=growth_channels)
|
|
self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
|
|
# upsample
|
|
self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
|
|
self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
|
|
self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
|
|
self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
|
|
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
for m in [
|
|
self.conv_first, self.conv_body, self.conv_up1,
|
|
self.conv_up2, self.conv_hr, self.conv_last
|
|
]:
|
|
default_init_weights(m, 0.1)
|
|
|
|
def forward(self, x, ref=None):
|
|
"""Forward function.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor with shape (n, c, h, w).
|
|
|
|
Returns:
|
|
Tensor: Forward results.
|
|
"""
|
|
if self.in_channels > 4:
|
|
x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic")
|
|
if ref is None:
|
|
ref = torch.zeros_like(x_lg)
|
|
x_lg = torch.cat([x_lg, ref], dim=1)
|
|
else:
|
|
x_lg = x
|
|
feat = self.conv_first(x_lg)
|
|
body_feat = self.conv_body(checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat))
|
|
feat = feat + body_feat
|
|
# upsample
|
|
feat = self.lrelu(
|
|
self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
|
|
if self.scale == 4:
|
|
feat = self.lrelu(
|
|
self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
|
|
else:
|
|
feat = self.lrelu(self.conv_up2(feat))
|
|
out = self.conv_last(self.lrelu(self.conv_hr(feat)))
|
|
return out
|
|
|
|
def visual_dbg(self, step, path):
|
|
for i, bm in enumerate(self.body):
|
|
torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
|
|
|