242 lines
9.3 KiB
Python
242 lines
9.3 KiB
Python
import functools
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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 models.archs.srflow_orig.module_util as mutil
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from models.archs.arch_util import default_init_weights, ConvGnSilu, ConvGnLelu
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from utils.util import opt_get
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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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# Empirically, 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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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, initial_conv_stride=1, opt=None):
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self.opt = opt
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super(RRDBNet, self).__init__()
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bypass = opt_get(self.opt, ['networks', 'generator', 'rrdb_bypass'])
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if bypass:
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RRDB_block_f = functools.partial(RRDBWithBypass, mid_channels=nf, growth_channels=gc)
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else:
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RRDB_block_f = functools.partial(RRDB, mid_channels=nf, growth_channels=gc)
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self.scale = scale
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if initial_conv_stride == 1:
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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else:
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self.conv_first = nn.Conv2d(in_nc, nf, 7, stride=initial_conv_stride, padding=3, bias=True)
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self.body = mutil.make_layer(RRDB_block_f, nb)
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self.conv_body = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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#### upsampling
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self.conv_up1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 2:
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self.conv_up2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 8:
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self.conv_up3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 16:
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self.conv_up4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 32:
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self.conv_up5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv_hr = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv_last = nn.Conv2d(nf, out_nc, 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, get_steps=False):
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fea = self.conv_first(x)
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block_idxs = opt_get(self.opt, ['networks', 'generator','flow', 'stackRRDB', 'blocks']) or []
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block_results = {}
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for idx, m in enumerate(self.body.children()):
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fea = m(fea)
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for b in block_idxs:
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if b == idx:
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block_results["block_{}".format(idx)] = fea
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trunk = self.conv_body(fea)
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last_lr_fea = fea + trunk
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fea_up2 = self.conv_up1(F.interpolate(last_lr_fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up2)
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fea_up4 = None
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fea_up8 = None
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fea_up16 = None
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fea_up32 = None
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if self.scale >= 4:
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fea_up4 = self.conv_up2(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up4)
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if self.scale >= 8:
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fea_up8 = self.conv_up3(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up8)
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if self.scale >= 16:
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fea_up16 = self.conv_up4(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up16)
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if self.scale >= 32:
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fea_up32 = self.conv_up5(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up32)
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out = self.conv_last(self.lrelu(self.conv_hr(fea)))
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if self.scale >= 4:
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results = {'last_lr_fea': last_lr_fea,
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'fea_up1': last_lr_fea,
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'fea_up2': fea_up2,
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'fea_up4': fea_up4,
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'fea_up8': fea_up8,
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'fea_up16': fea_up16,
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'fea_up32': fea_up32,
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'out': out}
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fea_up0_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up0']) or False
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if fea_up0_en:
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results['fea_up0'] = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
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if fea_upn1_en:
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results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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else:
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raise NotImplementedError
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if get_steps:
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for k, v in block_results.items():
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results[k] = v
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return results
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else:
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return out
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class RRDBLatentWrapper(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, with_bypass, blocks, pretrain_rrdb_path=None, gc=32, scale=4):
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super().__init__()
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self.with_bypass = with_bypass
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self.blocks = blocks
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fake_opt = { 'networks': {'generator': {'flow': {'stackRRDB': {'blocks': blocks}}, 'rrdb_bypass': with_bypass}}}
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self.wrappedRRDB = RRDBNet(in_nc, out_nc, nf, nb, gc, scale, fake_opt)
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if pretrain_rrdb_path is not None:
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rrdb_state_dict = torch.load(pretrain_rrdb_path)
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self.wrappedRRDB.load_state_dict(rrdb_state_dict, strict=True)
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out_dim = nf * (len(blocks) + 1)
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self.postprocess = nn.Sequential(ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=True, norm=True),
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ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=True, norm=True),
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ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=False, norm=False))
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def forward(self, lr):
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rrdbResults = self.wrappedRRDB(lr, get_steps=True)
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blocklist = [rrdbResults["block_{}".format(idx)] for idx in self.blocks]
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blocklist.append(rrdbResults['last_lr_fea'])
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fea = torch.cat(blocklist, dim=1)
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fea = self.postprocess(fea)
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return fea |