model/HWMNet.py

import torch
import torch.nn as nn
from WT.transform import DWT, IWT

##---------- Basic Layers ----------
def conv3x3(in_chn, out_chn, bias=True):
    layer = nn.Conv2d(in_chn, out_chn, kernel_size=3, stride=1, padding=1, bias=bias)
    return layer

def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
    return nn.Conv2d(
        in_channels, out_channels, kernel_size,
        padding=(kernel_size // 2), bias=bias, stride=stride)

def bili_resize(factor):
    return nn.Upsample(scale_factor=factor, mode='bilinear', align_corners=False)

##---------- Basic Blocks ----------
class UNetConvBlock(nn.Module):
    def __init__(self, in_size, out_size, downsample):
        super(UNetConvBlock, self).__init__()
        self.downsample = downsample
        self.body = [HWB(n_feat=in_size, o_feat=in_size, kernel_size=3, reduction=16, bias=False, act=nn.PReLU())]# for _ in range(wab)]
        self.body = nn.Sequential(*self.body)

        if downsample:
            self.downsample = PS_down(out_size, out_size, downscale=2)

        self.tail = nn.Conv2d(in_size, out_size, kernel_size=1)

    def forward(self, x):
        out = self.body(x)
        out = self.tail(out)
        if self.downsample:
            out_down = self.downsample(out)
            return out_down, out
        else:
            return out

class UNetUpBlock(nn.Module):
    def __init__(self, in_size, out_size):
        super(UNetUpBlock, self).__init__()
        self.up = PS_up(in_size, out_size, upscale=2)
        self.conv_block = UNetConvBlock(in_size, out_size, downsample=False)

    def forward(self, x, bridge):
        up = self.up(x)
        out = torch.cat([up, bridge], dim=1)
        out = self.conv_block(out)
        return out

##---------- Resizing Modules (Pixel(Un)Shuffle) ----------
class PS_down(nn.Module):
    def __init__(self, in_size, out_size, downscale):
        super(PS_down, self).__init__()
        self.UnPS = nn.PixelUnshuffle(downscale)
        self.conv1 = nn.Conv2d((downscale**2) * in_size, out_size, 1, 1, 0)

    def forward(self, x):
        x = self.UnPS(x)  # h/2, w/2, 4*c
        x = self.conv1(x)
        return x

class PS_up(nn.Module):
    def __init__(self, in_size, out_size, upscale):
        super(PS_up, self).__init__()

        self.PS = nn.PixelShuffle(upscale)
        self.conv1 = nn.Conv2d(in_size//(upscale**2), out_size, 1, 1, 0)

    def forward(self, x):
        x = self.PS(x)  # h/2, w/2, 4*c
        x = self.conv1(x)
        return x

##---------- Selective Kernel Feature Fusion (SKFF) ----------
class SKFF(nn.Module):
    def __init__(self, in_channels, height=3, reduction=8, bias=False):
        super(SKFF, self).__init__()

        self.height = height
        d = max(int(in_channels / reduction), 4)

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_du = nn.Sequential(nn.Conv2d(in_channels, d, 1, padding=0, bias=bias), nn.PReLU())

        self.fcs = nn.ModuleList([])
        for i in range(self.height):
            self.fcs.append(nn.Conv2d(d, in_channels, kernel_size=1, stride=1, bias=bias))

        self.softmax = nn.Softmax(dim=1)

    def forward(self, inp_feats):
        batch_size, n_feats, H, W = inp_feats[1].shape

        inp_feats = torch.cat(inp_feats, dim=1)
        inp_feats = inp_feats.view(batch_size, self.height, n_feats, inp_feats.shape[2], inp_feats.shape[3])

        feats_U = torch.sum(inp_feats, dim=1)
        feats_S = self.avg_pool(feats_U)
        feats_Z = self.conv_du(feats_S)

        attention_vectors = [fc(feats_Z) for fc in self.fcs]
        attention_vectors = torch.cat(attention_vectors, dim=1)
        attention_vectors = attention_vectors.view(batch_size, self.height, n_feats, 1, 1)

        attention_vectors = self.softmax(attention_vectors)
        feats_V = torch.sum(inp_feats * attention_vectors, dim=1)

        return feats_V


##########################################################################
# Spatial Attention Layer
class SALayer(nn.Module):
    def __init__(self, kernel_size=5, bias=False):
        super(SALayer, self).__init__()
        self.conv_du = nn.Sequential(
            nn.Conv2d(2, 1, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias),
            nn.Sigmoid()
        )

    def forward(self, x):
        # torch.max will output 2 things, and we want the 1st one
        max_pool, _ = torch.max(x, dim=1, keepdim=True)
        avg_pool = torch.mean(x, 1, keepdim=True)
        channel_pool = torch.cat([max_pool, avg_pool], dim=1)  # [N,2,H,W]  could add 1x1 conv -> [N,3,H,W]
        y = self.conv_du(channel_pool)

        return x * y

##########################################################################
# Channel Attention Layer
class CALayer(nn.Module):
    def __init__(self, channel, reduction=16, bias=False):
        super(CALayer, self).__init__()
        # global average pooling: feature --> point
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        # feature channel downscale and upscale --> channel weight
        self.conv_du = nn.Sequential(
            nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
            nn.ReLU(inplace=True),
            nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),
            nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv_du(y)
        return x * y

##########################################################################
# Half Wavelet Dual Attention Block (HWB)
class HWB(nn.Module):
    def __init__(self, n_feat, o_feat, kernel_size, reduction, bias, act):
        super(HWB, self).__init__()
        self.dwt = DWT()
        self.iwt = IWT()

        modules_body = \
            [
                conv(n_feat*2, n_feat, kernel_size, bias=bias),
                act,
                conv(n_feat, n_feat*2, kernel_size, bias=bias)
            ]
        self.body = nn.Sequential(*modules_body)

        self.WSA = SALayer()
        self.WCA = CALayer(n_feat*2, reduction, bias=bias)

        self.conv1x1 = nn.Conv2d(n_feat*4, n_feat*2, kernel_size=1, bias=bias)
        self.conv3x3 = nn.Conv2d(n_feat, o_feat, kernel_size=3, padding=1, bias=bias)
        self.activate = act
        self.conv1x1_final = nn.Conv2d(n_feat, o_feat, kernel_size=1, bias=bias)

    def forward(self, x):
        residual = x

        # Split 2 part
        wavelet_path_in, identity_path = torch.chunk(x, 2, dim=1)

        # Wavelet domain (Dual attention)
        x_dwt = self.dwt(wavelet_path_in)
        res = self.body(x_dwt)
        branch_sa = self.WSA(res)
        branch_ca = self.WCA(res)
        res = torch.cat([branch_sa, branch_ca], dim=1)
        res = self.conv1x1(res) + x_dwt
        wavelet_path = self.iwt(res)

        out = torch.cat([wavelet_path, identity_path], dim=1)
        out = self.activate(self.conv3x3(out))
        out += self.conv1x1_final(residual)

        return out


##########################################################################
##---------- HWMNet-LOL ----------
class HWMNet(nn.Module):
    def __init__(self, in_chn=3, wf=64, depth=4):
        super(HWMNet, self).__init__()
        self.depth = depth
        self.down_path = nn.ModuleList()
        self.bili_down = bili_resize(0.5)
        self.conv_01 = nn.Conv2d(in_chn, wf, 3, 1, 1)

        # encoder of UNet-64
        prev_channels = 0
        for i in range(depth):  # 0,1,2,3
            downsample = True if (i + 1) < depth else False
            self.down_path.append(UNetConvBlock(prev_channels + wf, (2 ** i) * wf, downsample))
            prev_channels = (2 ** i) * wf

        # decoder of UNet-64
        self.up_path = nn.ModuleList()
        self.skip_conv = nn.ModuleList()
        self.conv_up = nn.ModuleList()
        self.bottom_conv = nn.Conv2d(prev_channels, wf, 3, 1, 1)
        self.bottom_up = bili_resize(2 ** (depth-1))

        for i in reversed(range(depth - 1)):
            self.up_path.append(UNetUpBlock(prev_channels, (2 ** i) * wf))
            self.skip_conv.append(nn.Conv2d((2 ** i) * wf, (2 ** i) * wf, 3, 1, 1))
            self.conv_up.append(nn.Sequential(*[bili_resize(2 ** i), nn.Conv2d((2 ** i) * wf, wf, 3, 1, 1)]))
            prev_channels = (2 ** i) * wf

        self.final_ff = SKFF(in_channels=wf, height=depth)
        self.last = conv3x3(prev_channels, in_chn, bias=True)

    def forward(self, x):
        img = x
        scale_img = img

        ##### shallow conv #####
        x1 = self.conv_01(img)
        encs = []
        ######## UNet-64 ########
        # Down-path (Encoder)
        for i, down in enumerate(self.down_path):
            if i == 0:
                x1, x1_up = down(x1)
                encs.append(x1_up)
            elif (i + 1) < self.depth:
                scale_img = self.bili_down(scale_img)
                left_bar = self.conv_01(scale_img)
                x1 = torch.cat([x1, left_bar], dim=1)
                x1, x1_up = down(x1)
                encs.append(x1_up)
            else:
                scale_img = self.bili_down(scale_img)
                left_bar = self.conv_01(scale_img)
                x1 = torch.cat([x1, left_bar], dim=1)
                x1 = down(x1)

        # Up-path (Decoder)
        ms_result = [self.bottom_up(self.bottom_conv(x1))]
        for i, up in enumerate(self.up_path):
            x1 = up(x1, self.skip_conv[i](encs[-i - 1]))
            ms_result.append(self.conv_up[i](x1))
        # Multi-scale selective feature fusion
        msff_result = self.final_ff(ms_result)

        ##### Reconstruct #####
        out_1 = self.last(msff_result) + img

        return out_1

if __name__ == "__main__":
    input = torch.ones(1, 3, 400, 592, dtype=torch.float, requires_grad=False).cuda()

    model = HWMNet(in_chn=3, wf=96, depth=4).cuda()
    out = model(input)
    flops, params = profile(model, inputs=(input,))

    # RDBlayer = SK_RDB(in_channels=64, growth_rate=64, num_layers=3)
    # print(RDBlayer)
    # out = RDBlayer(input)
    # flops, params = profile(RDBlayer, inputs=(input,))
    print('input shape:', input.shape)
    print('parameters:', params/1e6)
    print('flops', flops/1e9)
    print('output shape', out.shape)