FuouM-FM_nodes/module_convir/ConvIR.py

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

from .layers import BasicConv, ResBlock


class EBlock(nn.Module):
    def __init__(self, out_channel, num_res, data):
        super(EBlock, self).__init__()

        layers = [ResBlock(out_channel, out_channel, data) for _ in range(num_res - 1)]
        layers.append(ResBlock(out_channel, out_channel, data, filter=True))

        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        return self.layers(x)


class DBlock(nn.Module):
    def __init__(self, channel, num_res, data):
        super(DBlock, self).__init__()

        layers = [ResBlock(channel, channel, data) for _ in range(num_res - 1)]
        layers.append(ResBlock(channel, channel, data, filter=True))
        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        return self.layers(x)


class SCM(nn.Module):
    def __init__(self, out_plane):
        super(SCM, self).__init__()
        self.main = nn.Sequential(
            BasicConv(3, out_plane // 4, kernel_size=3, stride=1, relu=True),
            BasicConv(
                out_plane // 4, out_plane // 2, kernel_size=1, stride=1, relu=True
            ),
            BasicConv(
                out_plane // 2, out_plane // 2, kernel_size=3, stride=1, relu=True
            ),
            BasicConv(out_plane // 2, out_plane, kernel_size=1, stride=1, relu=False),
            nn.InstanceNorm2d(out_plane, affine=True),
        )

    def forward(self, x):
        x = self.main(x)
        return x


class FAM(nn.Module):
    def __init__(self, channel):
        super(FAM, self).__init__()
        self.merge = BasicConv(
            channel * 2, channel, kernel_size=3, stride=1, relu=False
        )

    def forward(self, x1, x2):
        return self.merge(torch.cat([x1, x2], dim=1))


class ConvIR(nn.Module):
    def __init__(self, version, data):
        super(ConvIR, self).__init__()

        if version == "small":
            num_res = 4
        elif version == "base":
            num_res = 8
        elif version == "large":
            num_res = 16

        base_channel = 32

        self.Encoder = nn.ModuleList(
            [
                EBlock(base_channel, num_res, data),
                EBlock(base_channel * 2, num_res, data),
                EBlock(base_channel * 4, num_res, data),
            ]
        )

        self.feat_extract = nn.ModuleList(
            [
                BasicConv(3, base_channel, kernel_size=3, relu=True, stride=1),
                BasicConv(
                    base_channel, base_channel * 2, kernel_size=3, relu=True, stride=2
                ),
                BasicConv(
                    base_channel * 2,
                    base_channel * 4,
                    kernel_size=3,
                    relu=True,
                    stride=2,
                ),
                BasicConv(
                    base_channel * 4,
                    base_channel * 2,
                    kernel_size=4,
                    relu=True,
                    stride=2,
                    transpose=True,
                ),
                BasicConv(
                    base_channel * 2,
                    base_channel,
                    kernel_size=4,
                    relu=True,
                    stride=2,
                    transpose=True,
                ),
                BasicConv(base_channel, 3, kernel_size=3, relu=False, stride=1),
            ]
        )

        self.Decoder = nn.ModuleList(
            [
                DBlock(base_channel * 4, num_res, data),
                DBlock(base_channel * 2, num_res, data),
                DBlock(base_channel, num_res, data),
            ]
        )

        self.Convs = nn.ModuleList(
            [
                BasicConv(
                    base_channel * 4,
                    base_channel * 2,
                    kernel_size=1,
                    relu=True,
                    stride=1,
                ),
                BasicConv(
                    base_channel * 2, base_channel, kernel_size=1, relu=True, stride=1
                ),
            ]
        )

        self.ConvsOut = nn.ModuleList(
            [
                BasicConv(base_channel * 4, 3, kernel_size=3, relu=False, stride=1),
                BasicConv(base_channel * 2, 3, kernel_size=3, relu=False, stride=1),
            ]
        )

        self.FAM1 = FAM(base_channel * 4)
        self.SCM1 = SCM(base_channel * 4)
        self.FAM2 = FAM(base_channel * 2)
        self.SCM2 = SCM(base_channel * 2)

    def forward(self, x):
        x_2 = F.interpolate(x, scale_factor=0.5)
        x_4 = F.interpolate(x_2, scale_factor=0.5)
        z2 = self.SCM2(x_2)
        z4 = self.SCM1(x_4)

        outputs = list()
        # 256
        x_ = self.feat_extract[0](x)
        res1 = self.Encoder[0](x_)
        # 128
        z = self.feat_extract[1](res1)
        z = self.FAM2(z, z2)
        res2 = self.Encoder[1](z)
        # 64
        z = self.feat_extract[2](res2)
        z = self.FAM1(z, z4)
        z = self.Encoder[2](z)

        z = self.Decoder[0](z)
        z_ = self.ConvsOut[0](z)
        # 128
        z = self.feat_extract[3](z)
        outputs.append(z_ + x_4)

        z = torch.cat([z, res2], dim=1)
        z = self.Convs[0](z)
        z = self.Decoder[1](z)
        z_ = self.ConvsOut[1](z)
        # 256
        z = self.feat_extract[4](z)
        outputs.append(z_ + x_2)

        z = torch.cat([z, res1], dim=1)
        z = self.Convs[1](z)
        z = self.Decoder[2](z)
        z = self.feat_extract[5](z)
        outputs.append(z + x)

        return outputs


def build_net(version, data):
    return ConvIR(version, data)