From ad4de819c43997f2666b5bad95301f5c37f9018e Mon Sep 17 00:00:00 2001
From: victorca25 <victorca25@users.noreply.github.com>
Date: Sun, 9 Oct 2022 13:02:12 +0200
Subject: update ESRGAN architecture and model to support all ESRGAN models in
 the DB, BSRGAN and real-ESRGAN models

---
 modules/esrgan_model_arch.py | 463 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 463 insertions(+)
 create mode 100644 modules/esrgan_model_arch.py

(limited to 'modules/esrgan_model_arch.py')

diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
new file mode 100644
index 00000000..bc9ceb2a
--- /dev/null
+++ b/modules/esrgan_model_arch.py
@@ -0,0 +1,463 @@
+# 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)
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