architecture/rrdb.py

# -*- coding: utf-8 -*-

# Paper Github Repository: https://github.com/xinntao/Real-ESRGAN
# Code snippet from: https://github.com/XPixelGroup/BasicSR/blob/master/basicsr/archs/rrdbnet_arch.py
# Paper: https://arxiv.org/pdf/2107.10833.pdf

import os, sys
import torch
from torch import nn as nn
from torch.nn import functional as F
from itertools import repeat
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm


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 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.
        num_basic_block (int): number of blocks.

    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 default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
    """Initialize network weights.

    Args:
        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
        scale (float): Scale initialized weights, especially for residual
            blocks. Default: 1.
        bias_fill (float): The value to fill bias. Default: 0
        kwargs (dict): Other arguments for initialization function.
    """
    if not isinstance(module_list, list):
        module_list = [module_list]
    for module in module_list:
        for m in module.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, _BatchNorm):
                init.constant_(m.weight, 1)
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)

class ResidualDenseBlock(nn.Module):
    """Residual Dense Block.

    Used in RRDB block in ESRGAN.

    Args:
        num_feat (int): Channel number of intermediate features.
        num_grow_ch (int): Channels for each growth.
    """

    def __init__(self, num_feat=64, num_grow_ch=32):
        super(ResidualDenseBlock, self).__init__()
        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

        # initialization
        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)

    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))
        # Empirically, 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:
        num_feat (int): Channel number of intermediate features.
        num_grow_ch (int): Channels for each growth.
    """

    def __init__(self, num_feat, num_grow_ch=32):
        super(RRDB, self).__init__()
        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)

    def forward(self, x):
        out = self.rdb1(x)
        out = self.rdb2(out)
        out = self.rdb3(out)
        # Empirically, we use 0.2 to scale the residual for better performance
        return out * 0.2 + x



class RRDBNet(nn.Module):
    """Networks consisting of Residual in Residual Dense Block, which is used
    in ESRGAN.

    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.

    We extend ESRGAN for scale x2 and scale x1.
    Note: This is one option for scale 1, scale 2 in RRDBNet.
    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.

    Args:
        num_in_ch (int): Channel number of inputs.
        num_out_ch (int): Channel number of outputs.
        num_feat (int): Channel number of intermediate features.
            Default: 64
        num_block (int): Block number in the trunk network. Defaults: 6 for our Anime training cases
        num_grow_ch (int): Channels for each growth. Default: 32.
    """

    def __init__(self, num_in_ch, num_out_ch, scale, num_feat=64, num_block=6, num_grow_ch=32):

        super(RRDBNet, self).__init__()
        self.scale = scale
        if scale == 2:
            num_in_ch = num_in_ch * 4
        elif scale == 1:
            num_in_ch = num_in_ch * 16
        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        # upsample
        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        if self.scale == 2:
            feat = pixel_unshuffle(x, scale=2)
        elif self.scale == 1:
            feat = pixel_unshuffle(x, scale=4)
        else:
            feat = x
        feat = self.conv_first(feat)
        body_feat = self.conv_body(self.body(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



def main():
    root_path = os.path.abspath('.')
    sys.path.append(root_path)

    from opt import opt                 # Manage GPU to choose
    from pthflops import count_ops
    from torchsummary import summary
    import time
    
    # We use RRDB 6Blocks by default.
    model = RRDBNet(3, 3).cuda()
    pytorch_total_params = sum(p.numel() for p in model.parameters())
    print(f"RRDB has param {pytorch_total_params//1000} K params")


    # Count the number of FLOPs to double check
    x = torch.randn((1, 3, 180, 180)).cuda()
    start = time.time()
    x = model(x)
    print("output size is ", x.shape)
    total = time.time() - start
    print(total)


if __name__ == "__main__":
    main()