import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.srflow.module_util as mutil
from models.arch_util import default_init_weights, ConvGnSilu, ConvGnLelu
from trainer.networks import register_model
from utils.util import opt_get
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()
# Empirically, we use 0.2 to scale the residual for better performance
return out * 0.2 * bypass + x
class RRDBNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, initial_conv_stride=1, opt=None):
self.opt = opt
super(RRDBNet, self).__init__()
bypass = opt_get(self.opt, ['networks', 'generator', 'rrdb_bypass'])
if bypass:
RRDB_block_f = functools.partial(RRDBWithBypass, mid_channels=nf, growth_channels=gc)
else:
RRDB_block_f = functools.partial(RRDB, mid_channels=nf, growth_channels=gc)
self.scale = scale
if initial_conv_stride == 1:
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
else:
self.conv_first = nn.Conv2d(in_nc, nf, 7, stride=initial_conv_stride, padding=3, bias=True)
self.body = mutil.make_layer(RRDB_block_f, nb)
self.conv_body = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.conv_up1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 2:
self.conv_up2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 8:
self.conv_up3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 16:
self.conv_up4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.scale >= 32:
self.conv_up5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_hr = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x, get_steps=False):
fea = self.conv_first(x)
block_idxs = opt_get(self.opt, ['networks', 'generator','flow', 'stackRRDB', 'blocks']) or []
block_results = {}
for idx, m in enumerate(self.body.children()):
fea = m(fea)
for b in block_idxs:
if b == idx:
block_results["block_{}".format(idx)] = fea
trunk = self.conv_body(fea)
last_lr_fea = fea + trunk
fea_up2 = self.conv_up1(F.interpolate(last_lr_fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up2)
fea_up4 = None
fea_up8 = None
fea_up16 = None
fea_up32 = None
if self.scale >= 4:
fea_up4 = self.conv_up2(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up4)
if self.scale >= 8:
fea_up8 = self.conv_up3(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up8)
if self.scale >= 16:
fea_up16 = self.conv_up4(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up16)
if self.scale >= 32:
fea_up32 = self.conv_up5(F.interpolate(fea, scale_factor=2, mode='nearest'))
fea = self.lrelu(fea_up32)
out = self.conv_last(self.lrelu(self.conv_hr(fea)))
if self.scale >= 4:
results = {'last_lr_fea': last_lr_fea,
'fea_up1': last_lr_fea,
'fea_up2': fea_up2,
'fea_up4': fea_up4,
'fea_up8': fea_up8,
'fea_up16': fea_up16,
'fea_up32': fea_up32,
'out': out}
fea_up0_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up0']) or False
if fea_up0_en:
results['fea_up0'] = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
if fea_upn1_en:
results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
else:
raise NotImplementedError
if get_steps:
for k, v in block_results.items():
results[k] = v
return results
else:
return out
class RRDBLatentWrapper(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, with_bypass, blocks, pretrain_rrdb_path=None, gc=32, scale=4):
super().__init__()
self.with_bypass = with_bypass
self.blocks = blocks
fake_opt = { 'networks': {'generator': {'flow': {'stackRRDB': {'blocks': blocks}}, 'rrdb_bypass': with_bypass}}}
self.wrappedRRDB = RRDBNet(in_nc, out_nc, nf, nb, gc, scale, fake_opt)
if pretrain_rrdb_path is not None:
rrdb_state_dict = torch.load(pretrain_rrdb_path)
self.wrappedRRDB.load_state_dict(rrdb_state_dict, strict=True)
out_dim = nf * (len(blocks) + 1)
self.postprocess = nn.Sequential(ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=True, norm=True),
ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=True, norm=True),
ConvGnLelu(out_dim, out_dim, kernel_size=1, bias=True, activation=False, norm=False))
def forward(self, lr):
rrdbResults = self.wrappedRRDB(lr, get_steps=True)
blocklist = [rrdbResults["block_{}".format(idx)] for idx in self.blocks]
blocklist.append(rrdbResults['last_lr_fea'])
fea = torch.cat(blocklist, dim=1)
fea = self.postprocess(fea)
return fea
@register_model
def register_rrdb_latent_wrapper(opt_net, opt):
return RRDBLatentWrapper(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], with_bypass=opt_net['with_bypass'],
blocks=opt_net['blocks_for_latent'], scale=opt_net['scale'],
pretrain_rrdb_path=opt_net['pretrain_path'])
@register_model
def register_rrdb_srflow(opt_net, opt):
return RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
initial_conv_stride=opt_net['initial_stride'])