DL-Art-School/codes/models/archs/RRDBNet_arch.py

import os

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.utils.checkpoint import checkpoint_sequential

from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu


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)


    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.

    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(mid_channels, growth_channels)
        self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
        self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)

    def forward(self, x):
        """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 RRDBWithBypass(nn.Module):
    """Residual in Residual Dense Block.

    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(RRDBWithBypass, self).__init__()
        self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
        self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
        self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
        self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True),
                                    ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False),
                                    ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False),
                                    nn.Sigmoid())

    def forward(self, x):
        """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)
        bypass = self.bypass(torch.cat([x, out], dim=1))
        self.bypass_map = bypass.detach().clone()
        # Empirically, we use 0.2 to scale the residual for better performance
        return out * 0.2 * bypass + 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.
    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.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 mid_channels=64,
                 num_blocks=23,
                 growth_channels=32,
                 body_block=RRDB,
                 blocks_per_checkpoint=4,
                 scale=4):
        super(RRDBNet, self).__init__()
        self.num_blocks = num_blocks
        self.blocks_per_checkpoint = blocks_per_checkpoint
        self.scale = scale
        self.in_channels = in_channels
        first_conv_stride = 1 if in_channels <= 4 else scale
        first_conv_ksize = 3 if first_conv_stride == 1 else 7
        first_conv_padding = 1 if first_conv_stride == 1 else 3
        self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
        self.body = make_layer(
            body_block,
            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)

        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, ref=None):
        """Forward function.

        Args:
            x (Tensor): Input tensor with shape (n, c, h, w).

        Returns:
            Tensor: Forward results.
        """
        if self.in_channels > 4:
            x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic")
            if ref is None:
                ref = torch.zeros_like(x_lg)
            x_lg = torch.cat([x_lg, ref], dim=1)
        else:
            x_lg = x
        feat = self.conv_first(x_lg)
        body_feat = self.conv_body(checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat))
        feat = feat + body_feat
        # upsample
        feat = self.lrelu(
            self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
        if self.scale == 4:
            feat = self.lrelu(
                self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
        else:
            feat = self.lrelu(self.conv_up2(feat))
        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
        return out

    def visual_dbg(self, step, path):
        for i, bm in enumerate(self.body):
            torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))