463 lines
18 KiB
Python
463 lines
18 KiB
Python
# this file is adapted from https://github.com/victorca25/iNNfer
|
|
|
|
import math
|
|
import functools
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
####################
|
|
# RRDBNet Generator
|
|
####################
|
|
|
|
class RRDBNet(nn.Module):
|
|
def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
|
|
act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
|
|
finalact=None, gaussian_noise=False, plus=False):
|
|
super(RRDBNet, self).__init__()
|
|
n_upscale = int(math.log(upscale, 2))
|
|
if upscale == 3:
|
|
n_upscale = 1
|
|
|
|
self.resrgan_scale = 0
|
|
if in_nc % 16 == 0:
|
|
self.resrgan_scale = 1
|
|
elif in_nc != 4 and in_nc % 4 == 0:
|
|
self.resrgan_scale = 2
|
|
|
|
fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
|
|
rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
|
norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
|
|
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
|
|
LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
|
|
|
|
if upsample_mode == 'upconv':
|
|
upsample_block = upconv_block
|
|
elif upsample_mode == 'pixelshuffle':
|
|
upsample_block = pixelshuffle_block
|
|
else:
|
|
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
|
|
if upscale == 3:
|
|
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
|
|
else:
|
|
upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
|
|
HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
|
|
HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
|
|
|
|
outact = act(finalact) if finalact else None
|
|
|
|
self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
|
|
*upsampler, HR_conv0, HR_conv1, outact)
|
|
|
|
def forward(self, x, outm=None):
|
|
if self.resrgan_scale == 1:
|
|
feat = pixel_unshuffle(x, scale=4)
|
|
elif self.resrgan_scale == 2:
|
|
feat = pixel_unshuffle(x, scale=2)
|
|
else:
|
|
feat = x
|
|
|
|
return self.model(feat)
|
|
|
|
|
|
class RRDB(nn.Module):
|
|
"""
|
|
Residual in Residual Dense Block
|
|
(ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
|
|
"""
|
|
|
|
def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
|
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
|
|
spectral_norm=False, gaussian_noise=False, plus=False):
|
|
super(RRDB, self).__init__()
|
|
# This is for backwards compatibility with existing models
|
|
if nr == 3:
|
|
self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
|
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
|
gaussian_noise=gaussian_noise, plus=plus)
|
|
self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
|
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
|
gaussian_noise=gaussian_noise, plus=plus)
|
|
self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
|
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
|
gaussian_noise=gaussian_noise, plus=plus)
|
|
else:
|
|
RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
|
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
|
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
|
|
self.RDBs = nn.Sequential(*RDB_list)
|
|
|
|
def forward(self, x):
|
|
if hasattr(self, 'RDB1'):
|
|
out = self.RDB1(x)
|
|
out = self.RDB2(out)
|
|
out = self.RDB3(out)
|
|
else:
|
|
out = self.RDBs(x)
|
|
return out * 0.2 + x
|
|
|
|
|
|
class ResidualDenseBlock_5C(nn.Module):
|
|
"""
|
|
Residual Dense Block
|
|
The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
|
|
Modified options that can be used:
|
|
- "Partial Convolution based Padding" arXiv:1811.11718
|
|
- "Spectral normalization" arXiv:1802.05957
|
|
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
|
|
{Rakotonirina} and A. {Rasoanaivo}
|
|
"""
|
|
|
|
def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
|
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
|
|
spectral_norm=False, gaussian_noise=False, plus=False):
|
|
super(ResidualDenseBlock_5C, self).__init__()
|
|
|
|
self.noise = GaussianNoise() if gaussian_noise else None
|
|
self.conv1x1 = conv1x1(nf, gc) if plus else None
|
|
|
|
self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
|
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
|
spectral_norm=spectral_norm)
|
|
self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
|
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
|
spectral_norm=spectral_norm)
|
|
self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
|
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
|
spectral_norm=spectral_norm)
|
|
self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
|
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
|
spectral_norm=spectral_norm)
|
|
if mode == 'CNA':
|
|
last_act = None
|
|
else:
|
|
last_act = act_type
|
|
self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
|
|
norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
|
|
spectral_norm=spectral_norm)
|
|
|
|
def forward(self, x):
|
|
x1 = self.conv1(x)
|
|
x2 = self.conv2(torch.cat((x, x1), 1))
|
|
if self.conv1x1:
|
|
x2 = x2 + self.conv1x1(x)
|
|
x3 = self.conv3(torch.cat((x, x1, x2), 1))
|
|
x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
|
|
if self.conv1x1:
|
|
x4 = x4 + x2
|
|
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
|
|
if self.noise:
|
|
return self.noise(x5.mul(0.2) + x)
|
|
else:
|
|
return x5 * 0.2 + x
|
|
|
|
|
|
####################
|
|
# ESRGANplus
|
|
####################
|
|
|
|
class GaussianNoise(nn.Module):
|
|
def __init__(self, sigma=0.1, is_relative_detach=False):
|
|
super().__init__()
|
|
self.sigma = sigma
|
|
self.is_relative_detach = is_relative_detach
|
|
self.noise = torch.tensor(0, dtype=torch.float)
|
|
|
|
def forward(self, x):
|
|
if self.training and self.sigma != 0:
|
|
self.noise = self.noise.to(x.device)
|
|
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
|
|
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
|
|
x = x + sampled_noise
|
|
return x
|
|
|
|
def conv1x1(in_planes, out_planes, stride=1):
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
|
|
|
|
|
####################
|
|
# SRVGGNetCompact
|
|
####################
|
|
|
|
class SRVGGNetCompact(nn.Module):
|
|
"""A compact VGG-style network structure for super-resolution.
|
|
This class is copied from https://github.com/xinntao/Real-ESRGAN
|
|
"""
|
|
|
|
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
|
|
super(SRVGGNetCompact, self).__init__()
|
|
self.num_in_ch = num_in_ch
|
|
self.num_out_ch = num_out_ch
|
|
self.num_feat = num_feat
|
|
self.num_conv = num_conv
|
|
self.upscale = upscale
|
|
self.act_type = act_type
|
|
|
|
self.body = nn.ModuleList()
|
|
# the first conv
|
|
self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
|
|
# the first activation
|
|
if act_type == 'relu':
|
|
activation = nn.ReLU(inplace=True)
|
|
elif act_type == 'prelu':
|
|
activation = nn.PReLU(num_parameters=num_feat)
|
|
elif act_type == 'leakyrelu':
|
|
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
|
self.body.append(activation)
|
|
|
|
# the body structure
|
|
for _ in range(num_conv):
|
|
self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
|
|
# activation
|
|
if act_type == 'relu':
|
|
activation = nn.ReLU(inplace=True)
|
|
elif act_type == 'prelu':
|
|
activation = nn.PReLU(num_parameters=num_feat)
|
|
elif act_type == 'leakyrelu':
|
|
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
|
self.body.append(activation)
|
|
|
|
# the last conv
|
|
self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
|
|
# upsample
|
|
self.upsampler = nn.PixelShuffle(upscale)
|
|
|
|
def forward(self, x):
|
|
out = x
|
|
for i in range(0, len(self.body)):
|
|
out = self.body[i](out)
|
|
|
|
out = self.upsampler(out)
|
|
# add the nearest upsampled image, so that the network learns the residual
|
|
base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
|
|
out += base
|
|
return out
|
|
|
|
|
|
####################
|
|
# Upsampler
|
|
####################
|
|
|
|
class Upsample(nn.Module):
|
|
r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
|
|
The input data is assumed to be of the form
|
|
`minibatch x channels x [optional depth] x [optional height] x width`.
|
|
"""
|
|
|
|
def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
|
|
super(Upsample, self).__init__()
|
|
if isinstance(scale_factor, tuple):
|
|
self.scale_factor = tuple(float(factor) for factor in scale_factor)
|
|
else:
|
|
self.scale_factor = float(scale_factor) if scale_factor else None
|
|
self.mode = mode
|
|
self.size = size
|
|
self.align_corners = align_corners
|
|
|
|
def forward(self, x):
|
|
return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
|
|
|
|
def extra_repr(self):
|
|
if self.scale_factor is not None:
|
|
info = 'scale_factor=' + str(self.scale_factor)
|
|
else:
|
|
info = 'size=' + str(self.size)
|
|
info += ', mode=' + self.mode
|
|
return info
|
|
|
|
|
|
def pixel_unshuffle(x, scale):
|
|
""" Pixel unshuffle.
|
|
Args:
|
|
x (Tensor): Input feature with shape (b, c, hh, hw).
|
|
scale (int): Downsample ratio.
|
|
Returns:
|
|
Tensor: the pixel unshuffled feature.
|
|
"""
|
|
b, c, hh, hw = x.size()
|
|
out_channel = c * (scale**2)
|
|
assert hh % scale == 0 and hw % scale == 0
|
|
h = hh // scale
|
|
w = hw // scale
|
|
x_view = x.view(b, c, h, scale, w, scale)
|
|
return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
|
|
|
|
|
|
def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
|
|
pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
|
|
"""
|
|
Pixel shuffle layer
|
|
(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
|
|
Neural Network, CVPR17)
|
|
"""
|
|
conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
|
|
pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
|
|
pixel_shuffle = nn.PixelShuffle(upscale_factor)
|
|
|
|
n = norm(norm_type, out_nc) if norm_type else None
|
|
a = act(act_type) if act_type else None
|
|
return sequential(conv, pixel_shuffle, n, a)
|
|
|
|
|
|
def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
|
|
pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
|
|
""" Upconv layer """
|
|
upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
|
|
upsample = Upsample(scale_factor=upscale_factor, mode=mode)
|
|
conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
|
|
pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
|
|
return sequential(upsample, conv)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
####################
|
|
# Basic blocks
|
|
####################
|
|
|
|
|
|
def make_layer(basic_block, num_basic_block, **kwarg):
|
|
"""Make layers by stacking the same blocks.
|
|
Args:
|
|
basic_block (nn.module): nn.module class for basic block. (block)
|
|
num_basic_block (int): number of blocks. (n_layers)
|
|
Returns:
|
|
nn.Sequential: Stacked blocks in nn.Sequential.
|
|
"""
|
|
layers = []
|
|
for _ in range(num_basic_block):
|
|
layers.append(basic_block(**kwarg))
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
|
|
""" activation helper """
|
|
act_type = act_type.lower()
|
|
if act_type == 'relu':
|
|
layer = nn.ReLU(inplace)
|
|
elif act_type in ('leakyrelu', 'lrelu'):
|
|
layer = nn.LeakyReLU(neg_slope, inplace)
|
|
elif act_type == 'prelu':
|
|
layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
|
|
elif act_type == 'tanh': # [-1, 1] range output
|
|
layer = nn.Tanh()
|
|
elif act_type == 'sigmoid': # [0, 1] range output
|
|
layer = nn.Sigmoid()
|
|
else:
|
|
raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
|
|
return layer
|
|
|
|
|
|
class Identity(nn.Module):
|
|
def __init__(self, *kwargs):
|
|
super(Identity, self).__init__()
|
|
|
|
def forward(self, x, *kwargs):
|
|
return x
|
|
|
|
|
|
def norm(norm_type, nc):
|
|
""" Return a normalization layer """
|
|
norm_type = norm_type.lower()
|
|
if norm_type == 'batch':
|
|
layer = nn.BatchNorm2d(nc, affine=True)
|
|
elif norm_type == 'instance':
|
|
layer = nn.InstanceNorm2d(nc, affine=False)
|
|
elif norm_type == 'none':
|
|
def norm_layer(x): return Identity()
|
|
else:
|
|
raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
|
|
return layer
|
|
|
|
|
|
def pad(pad_type, padding):
|
|
""" padding layer helper """
|
|
pad_type = pad_type.lower()
|
|
if padding == 0:
|
|
return None
|
|
if pad_type == 'reflect':
|
|
layer = nn.ReflectionPad2d(padding)
|
|
elif pad_type == 'replicate':
|
|
layer = nn.ReplicationPad2d(padding)
|
|
elif pad_type == 'zero':
|
|
layer = nn.ZeroPad2d(padding)
|
|
else:
|
|
raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
|
|
return layer
|
|
|
|
|
|
def get_valid_padding(kernel_size, dilation):
|
|
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
|
|
padding = (kernel_size - 1) // 2
|
|
return padding
|
|
|
|
|
|
class ShortcutBlock(nn.Module):
|
|
""" Elementwise sum the output of a submodule to its input """
|
|
def __init__(self, submodule):
|
|
super(ShortcutBlock, self).__init__()
|
|
self.sub = submodule
|
|
|
|
def forward(self, x):
|
|
output = x + self.sub(x)
|
|
return output
|
|
|
|
def __repr__(self):
|
|
return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
|
|
|
|
|
|
def sequential(*args):
|
|
""" Flatten Sequential. It unwraps nn.Sequential. """
|
|
if len(args) == 1:
|
|
if isinstance(args[0], OrderedDict):
|
|
raise NotImplementedError('sequential does not support OrderedDict input.')
|
|
return args[0] # No sequential is needed.
|
|
modules = []
|
|
for module in args:
|
|
if isinstance(module, nn.Sequential):
|
|
for submodule in module.children():
|
|
modules.append(submodule)
|
|
elif isinstance(module, nn.Module):
|
|
modules.append(module)
|
|
return nn.Sequential(*modules)
|
|
|
|
|
|
def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
|
|
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
|
|
spectral_norm=False):
|
|
""" Conv layer with padding, normalization, activation """
|
|
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
|
|
padding = get_valid_padding(kernel_size, dilation)
|
|
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
|
|
padding = padding if pad_type == 'zero' else 0
|
|
|
|
if convtype=='PartialConv2D':
|
|
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
|
dilation=dilation, bias=bias, groups=groups)
|
|
elif convtype=='DeformConv2D':
|
|
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
|
dilation=dilation, bias=bias, groups=groups)
|
|
elif convtype=='Conv3D':
|
|
c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
|
dilation=dilation, bias=bias, groups=groups)
|
|
else:
|
|
c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
|
dilation=dilation, bias=bias, groups=groups)
|
|
|
|
if spectral_norm:
|
|
c = nn.utils.spectral_norm(c)
|
|
|
|
a = act(act_type) if act_type else None
|
|
if 'CNA' in mode:
|
|
n = norm(norm_type, out_nc) if norm_type else None
|
|
return sequential(p, c, n, a)
|
|
elif mode == 'NAC':
|
|
if norm_type is None and act_type is not None:
|
|
a = act(act_type, inplace=False)
|
|
n = norm(norm_type, in_nc) if norm_type else None
|
|
return sequential(n, a, p, c)
|