Revert RRDB back to original model

This commit is contained in:
James Betker 2020-10-27 10:25:31 -06:00
parent 1ce863849a
commit 231137ab0a
4 changed files with 155 additions and 271 deletions

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@ -1,293 +1,145 @@
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.archs.arch_util as arch_util
from models.archs.arch_util import PixelUnshuffle
import torchvision
from utils.util import checkpoint
from torch.utils.checkpoint import checkpoint_sequential
from models.archs.arch_util import make_layer, default_init_weights
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True, late_stage_kernel_size=3, late_stage_padding=1):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
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)
# initialization
arch_util.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5],
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'''
"""Residual in Residual Dense Block.
def __init__(self, nf, gc=32):
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_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
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):
out = checkpoint(self.RDB1, x)
out = checkpoint(self.RDB2, out)
out = checkpoint(self.RDB3, out)
"""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 LowDimRRDB(RRDB):
def __init__(self, nf, gc=32, dimensional_adjustment=4):
super(LowDimRRDB, self).__init__(nf * (dimensional_adjustment ** 2), gc * (dimensional_adjustment ** 2))
self.unshuffle = PixelUnshuffle(dimensional_adjustment)
self.shuffle = nn.PixelShuffle(dimensional_adjustment)
class RRDBNet(nn.Module):
"""Networks consisting of Residual in Residual Dense Block, which is used
in ESRGAN.
def forward(self, x):
x = self.unshuffle(x)
x = super(LowDimRRDB, self).forward(x)
return self.shuffle(x)
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.
"""
# Identical to LowDimRRDB but wraps an RRDB rather than inheriting from it. TODO: remove LowDimRRDB when backwards
# compatibility is no longer desired.
class LowDimRRDBWrapper(nn.Module):
# Do not specify nf or gc on the partial_rrdb passed in. That will be done by the wrapper.
def __init__(self, nf, partial_rrdb, gc=32, dimensional_adjustment=4):
super(LowDimRRDBWrapper, self).__init__()
self.rrdb = partial_rrdb(nf=nf * (dimensional_adjustment ** 2), gc=gc * (dimensional_adjustment ** 2))
self.unshuffle = PixelUnshuffle(dimensional_adjustment)
self.shuffle = nn.PixelShuffle(dimensional_adjustment)
def forward(self, x):
x = self.unshuffle(x)
x = self.rrdb(x)
return self.shuffle(x)
# This module performs the majority of the processing done by RRDBNet. It just doesn't have the upsampling at the end.
class RRDBTrunk(nn.Module):
def __init__(self, nf_in, nf_out, nb, gc=32, initial_stride=1, rrdb_block_f=None, conv_first_block=None):
super(RRDBTrunk, self).__init__()
if rrdb_block_f is None:
rrdb_block_f = functools.partial(RRDB, nf=nf_out, gc=gc)
if conv_first_block is None:
self.conv_first = nn.Conv2d(nf_in, nf_out, 7, initial_stride, padding=3, bias=True)
else:
self.conv_first = conv_first_block
self.RRDB_trunk, self.rrdb_layers = arch_util.make_layer(rrdb_block_f, nb, True)
self.trunk_conv = nn.Conv2d(nf_out, nf_out, 3, 1, 1, bias=True)
# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
# convolutions.
def set_temperature(self, temp):
for layer in self.rrdb_layers:
layer.set_temperature(temp)
def forward(self, x):
fea = self.conv_first(x)
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
return fea
# Adds some base methods that all RRDB* classes will use.
class RRDBBase(nn.Module):
def __init__(self):
super(RRDBBase, self).__init__()
# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
# convolutions.
def set_temperature(self, temp):
for trunk in self.trunks:
for layer in trunk.rrdb_layers:
layer.set_temperature(temp)
# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
class RRDBNet(RRDBBase):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1,
rrdb_block_f=None):
def __init__(self,
in_channels,
out_channels,
mid_channels=64,
num_blocks=23,
growth_channels=32):
super(RRDBNet, self).__init__()
# Trunk - does actual processing.
self.trunk = RRDBTrunk(in_nc, nf, nb, gc, initial_stride, rrdb_block_f)
self.trunks = [self.trunk]
# Upsampling
self.scale = scale
self.upconv1 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.body = make_layer(
RRDB,
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)
def forward(self, x):
fea = self.trunk(x)
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return out
def load_state_dict(self, state_dict, strict=True):
# The parameters in self.trunk used to be in this class. To support loading legacy saves, restore them.
t_state = self.trunk.state_dict()
for k in t_state.keys():
if k in state_dict.keys():
state_dict["trunk.%s" % (k,)] = state_dict.pop(k)
super(RRDBNet, self).load_state_dict(state_dict, strict)
# Variant of RRDBNet that is "assisted" by an external pretrained image classifier whose
# intermediate layers have been splayed out, pixel-shuffled, and fed back in.
# TODO: Convert to use new RRDBBase hierarchy.
class AssistedRRDBNet(nn.Module):
# in_nc=number of input channels.
# out_nc=number of output channels.
# nf=internal filter count
# nb=number of additional blocks after the assistance layers.
# gc=growth channel inside of residual blocks
# scale=the number of times the output is doubled in size.
# initial_stride=the stride on the first conv. can be used to downsample the image for processing.
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1):
super(AssistedRRDBNet, self).__init__()
self.scale = scale
self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True)
# Set-up the assist-net, which should do feature extraction for us.
self.assistnet = torchvision.models.wide_resnet50_2(pretrained=True)
self.set_enable_assistnet_training(False)
assist_nf = [4, 8, 16] # Fixed for resnet. Re-evaluate if using other networks.
self.assist2 = RRDB(nf + assist_nf[0], gc)
self.assist3 = RRDB(nf + sum(assist_nf[:2]), gc)
self.assist4 = RRDB(nf + sum(assist_nf), gc)
nf = nf + sum(assist_nf)
# After this, it's just a "standard" RRDB net.
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.RRDB_trunk = arch_util.make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, 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 set_enable_assistnet_training(self, en):
for p in self.assistnet.parameters():
p.requires_grad = en
def res_extract(self, x):
# Width and height must be factors of 16 to use this architecture. Check that here.
(b, f, w, h) = x.shape
assert w % 16 == 0
assert h % 16 == 0
x = self.assistnet.conv1(x)
x = self.assistnet.bn1(x)
x = self.assistnet.relu(x)
x = self.assistnet.maxpool(x)
x = self.assistnet.layer1(x)
l1 = F.pixel_shuffle(x, 4)
x = self.assistnet.layer2(x)
l2 = F.pixel_shuffle(x, 8)
x = self.assistnet.layer3(x)
l3 = F.pixel_shuffle(x, 16)
return l1, l2, l3
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):
# Invoke the assistant net first.
l1, l2, l3 = self.res_extract(x)
"""Forward function.
fea = self.conv_first(x)
fea = self.assist2(torch.cat([fea, l3], dim=1))
fea = self.assist3(torch.cat([fea, l2], dim=1))
fea = self.assist4(torch.cat([fea, l1], dim=1))
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
Returns:
Tensor: Forward results.
"""
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return (out,)
class PixShuffleInitialConv(nn.Module):
def __init__(self, reduction_factor, nf_out):
super(PixShuffleInitialConv, self).__init__()
self.conv = nn.Conv2d(3 * (reduction_factor ** 2), nf_out, 1)
self.unshuffle = PixelUnshuffle(reduction_factor)
def forward(self, x):
(b, f, w, h) = x.shape
# This module can only be applied to input images (with 3 channels)
assert f == 3
x = self.unshuffle(x)
return self.conv(x)
# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
class PixShuffleRRDB(RRDBBase):
def __init__(self, nf, nb, gc=32, scale=2, rrdb_block_f=None):
super(PixShuffleRRDB, self).__init__()
# This class does a 4x pixel shuffle on the filter count inside the trunk, so nf must be divisible by 16.
assert nf % 16 == 0
# Trunk - does actual processing.
self.trunk = RRDBTrunk(3, nf, nb, gc, 1, rrdb_block_f, PixShuffleInitialConv(4, nf))
self.trunks = [self.trunk]
# Upsampling
pix_nf = int(nf/16)
self.scale = scale
self.upconv1 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(pix_nf, 3, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(4)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.trunk(x)
fea = self.pixel_shuffle(fea)
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return (out,)
feat = self.conv_first(x)
body_feat = self.conv_body(checkpoint_sequential(self.body, 5, feat))
feat = feat + body_feat
# upsample
feat = self.lrelu(
self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
feat = self.lrelu(
self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
out = self.conv_last(self.lrelu(self.conv_hr(feat)))
return out

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@ -5,6 +5,22 @@ import torch.nn.functional as F
import torch.nn.utils.spectral_norm as SpectralNorm
from math import sqrt
def kaiming_init(module,
a=0,
mode='fan_out',
nonlinearity='relu',
bias=0,
distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.kaiming_uniform_(
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
else:
nn.init.kaiming_normal_(
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def pixel_norm(x, epsilon=1e-8):
return x * torch.rsqrt(torch.mean(torch.pow(x, 2), dim=1, keepdims=True) + epsilon)
@ -28,14 +44,34 @@ def initialize_weights(net_l, scale=1):
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers, return_layers=False):
def make_layer(block, num_blocks, **kwarg):
"""Make layers by stacking the same blocks.
Args:
block (nn.module): nn.module class for basic block.
num_blocks (int): number of blocks.
Returns:
nn.Sequential: Stacked blocks in nn.Sequential.
"""
layers = []
for _ in range(n_layers):
layers.append(block())
if return_layers:
return nn.Sequential(*layers), layers
else:
return nn.Sequential(*layers)
for _ in range(num_blocks):
layers.append(block(**kwarg))
return nn.Sequential(*layers)
def default_init_weights(module, scale=1):
"""Initialize network weights.
Args:
modules (nn.Module): Modules to be initialized.
scale (float): Scale initialized weights, especially for residual
blocks.
"""
for m in module.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m, a=0, mode='fan_in', bias=0)
m.weight.data *= scale
elif isinstance(m, nn.Linear):
kaiming_init(m, a=0, mode='fan_in', bias=0)
m.weight.data *= scale
class ResidualBlock(nn.Module):

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@ -110,6 +110,8 @@ class BaseModel():
for k, v in load_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
if k.startswith('generator'): # Hack to fix ESRGAN pretrained model.
load_net_clean[k[10:]] = v
else:
load_net_clean[k] = v
network.load_state_dict(load_net_clean, strict=strict)

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@ -36,14 +36,8 @@ def define_G(opt, net_key='network_G', scale=None):
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
# RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
initial_stride = 1 if 'initial_stride' not in opt_net else opt_net['initial_stride']
assert initial_stride == 1 or initial_stride == 2
# Need to adjust the scale the generator sees by the stride since the stride causes a down-sample.
gen_scale = scale * initial_stride
netG = RRDBNet_arch.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'] if 'scale' in opt_net.keys() else gen_scale,
initial_stride=initial_stride)
netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'])
elif which_model == 'rcan':
#args: n_resgroups, n_resblocks, res_scale, reduction, scale, n_feats
opt_net['rgb_range'] = 255