onnx-web/api/onnx_web/models/rrdb.py

# from https://github.com/cszn/BSRGAN/blob/main/models/network_rrdbnet.py

from logging import getLogger

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init

logger = getLogger(__name__)


def initialize_weights(net_l, scale=1):
    if not isinstance(net_l, list):
        net_l = [net_l]
    for net in net_l:
        for m in net.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, a=0, mode="fan_in")
                m.weight.data *= scale  # for residual block
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, a=0, mode="fan_in")
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias.data, 0.0)


def make_layer(block, n_layers, **kwarg):
    layers = []
    for _ in range(n_layers):
        layers.append(block(**kwarg))
    return nn.Sequential(*layers)


def pixel_unshuffle(x, scale):
    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)


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)

        # initialization
        initialize_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))
        return x5 * 0.2 + x


class RRDB(nn.Module):
    """Residual in Residual Dense Block"""

    def __init__(self, nf, gc=32):
        super(RRDB, self).__init__()
        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
        self.RDB3 = ResidualDenseBlock_5C(nf, gc)

    def forward(self, x):
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out * 0.2 + x


class RRDBNet(nn.Module):
    def __init__(
        self,
        num_in_ch=3,
        num_out_ch=3,
        num_feat=64,
        num_block=23,
        num_grow_ch=32,
        scale=4,
    ):
        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

        logger.trace(
            "RRDBNet params: %s",
            [num_in_ch, num_out_ch, num_feat, num_block, num_grow_ch, scale],
        )

        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
        self.body = make_layer(RRDB, num_block, nf=num_feat, gc=num_grow_ch)
        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)

        # upsampling
        if self.scale > 1:
            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)

        if self.scale == 4:
            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)

        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1, bias=True)

        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)
        trunk = self.conv_body(self.body(feat))
        feat = feat + trunk

        if self.scale > 1:
            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"))
            )

        out = self.conv_last(self.lrelu(self.conv_hr(feat)))

        return out