diff options
Diffstat (limited to 'modules/esrgan_model_arch.py')
| -rw-r--r-- | modules/esrgan_model_arch.py | 487 |
1 files changed, 435 insertions, 52 deletions
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py index e413d36e..bc9ceb2a 100644 --- a/modules/esrgan_model_arch.py +++ b/modules/esrgan_model_arch.py @@ -1,80 +1,463 @@ -# this file is taken from https://github.com/xinntao/ESRGAN
+# 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
-def make_layer(block, n_layers):
- layers = []
- for _ in range(n_layers):
- layers.append(block())
- return nn.Sequential(*layers)
+####################
+# 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
-class ResidualDenseBlock_5C(nn.Module):
- def __init__(self, nf=64, gc=32, bias=True):
- 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, 3, 1, 1, bias=bias)
- self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
- self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
- self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ 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
- # initialization
- # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+ 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)
- def forward(self, x):
- 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))
- return x5 * 0.2 + x
+ 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'''
+ """
+ Residual in Residual Dense Block
+ (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
+ """
- def __init__(self, nf, gc=32):
+ 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__()
- self.RDB1 = ResidualDenseBlock_5C(nf, gc)
- self.RDB2 = ResidualDenseBlock_5C(nf, gc)
- self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+ # 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):
- out = self.RDB1(x)
- out = self.RDB2(out)
- out = self.RDB3(out)
+ 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 RRDBNet(nn.Module):
- def __init__(self, in_nc, out_nc, nf, nb, gc=32):
- super(RRDBNet, self).__init__()
- RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+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.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
- self.RRDB_trunk = 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, 3, 1, 1, bias=True)
- self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
- self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
- self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+ self.noise = GaussianNoise() if gaussian_noise else None
+ self.conv1x1 = conv1x1(nf, gc) if plus else None
- self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ 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):
- fea = self.conv_first(x)
- trunk = self.trunk_conv(self.RRDB_trunk(fea))
- fea = fea + trunk
+ 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
+
- fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
- fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
- out = self.conv_last(self.lrelu(self.HRconv(fea)))
+####################
+# 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)
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