ffc.py

# Fast Fourier Convolution NeurIPS 2020
# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf

import torch
import torch.nn as nn
import torch.nn.functional as F
# from models.modules.squeeze_excitation import SELayer
import torch.fft

class SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        res = x * y.expand_as(x)
        return res


class FFCSE_block(nn.Module):
    def __init__(self, channels, ratio_g):
        super(FFCSE_block, self).__init__()
        in_cg = int(channels * ratio_g)
        in_cl = channels - in_cg
        r = 16

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.conv1 = nn.Conv2d(channels, channels // r,
                               kernel_size=1, bias=True)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
            channels // r, in_cl, kernel_size=1, bias=True)
        self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
            channels // r, in_cg, kernel_size=1, bias=True)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = x if type(x) is tuple else (x, 0)
        id_l, id_g = x

        x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
        x = self.avgpool(x)
        x = self.relu1(self.conv1(x))

        x_l = 0 if self.conv_a2l is None else id_l * \
            self.sigmoid(self.conv_a2l(x))
        x_g = 0 if self.conv_a2g is None else id_g * \
            self.sigmoid(self.conv_a2g(x))
        return x_l, x_g


class FourierUnit(nn.Module):

    def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
                 spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
        # bn_layer not used
        super(FourierUnit, self).__init__()
        self.groups = groups

        self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
                                          out_channels=out_channels * 2,
                                          kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
        self.bn = torch.nn.BatchNorm2d(out_channels * 2)
        self.relu = torch.nn.ReLU(inplace=True)

        # squeeze and excitation block
        self.use_se = use_se
        if use_se:
            if se_kwargs is None:
                se_kwargs = {}
            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)

        self.spatial_scale_factor = spatial_scale_factor
        self.spatial_scale_mode = spatial_scale_mode
        self.spectral_pos_encoding = spectral_pos_encoding
        self.ffc3d = ffc3d
        self.fft_norm = fft_norm

    def forward(self, x):
        batch = x.shape[0]

        if self.spatial_scale_factor is not None:
            orig_size = x.shape[-2:]
            x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)

        r_size = x.size()
        # (batch, c, h, w/2+1, 2)
        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
        ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
        ffted = ffted.view((batch, -1,) + ffted.size()[3:])

        if self.spectral_pos_encoding:
            height, width = ffted.shape[-2:]
            coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
            coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)

        if self.use_se:
            ffted = self.se(ffted)

        ffted = self.conv_layer(ffted)  # (batch, c*2, h, w/2+1)
        ffted = self.relu(self.bn(ffted))

        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)
        ffted = torch.complex(ffted[..., 0], ffted[..., 1])

        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
        output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)

        if self.spatial_scale_factor is not None:
            output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)

        return output


class SpectralTransform(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs):
        # bn_layer not used
        super(SpectralTransform, self).__init__()
        self.enable_lfu = enable_lfu
        if stride == 2:
            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        else:
            self.downsample = nn.Identity()

        self.stride = stride
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels //
                      2, kernel_size=1, groups=groups, bias=False),
            nn.BatchNorm2d(out_channels // 2),
            nn.ReLU(inplace=True)
        )
        self.fu = FourierUnit(
            out_channels // 2, out_channels // 2, groups, **fu_kwargs)
        if self.enable_lfu:
            self.lfu = FourierUnit(
                out_channels // 2, out_channels // 2, groups)
        self.conv2 = torch.nn.Conv2d(
            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)

    def forward(self, x):
        x = self.downsample(x)
        x = self.conv1(x)
        output = self.fu(x)

        if self.enable_lfu:
            n, c, h, w = x.shape
            split_no = 2
            split_s = h // split_no
            xs = torch.cat(torch.split(
                x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
            xs = torch.cat(torch.split(xs, split_s, dim=-1),
                           dim=1).contiguous()
            xs = self.lfu(xs)
            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
        else:
            xs = 0

        output = self.conv2(x + output + xs)
        return output


class FFC(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size,
                 ratio_gin, ratio_gout, stride=1, padding=0,
                 dilation=1, groups=1, bias=False, enable_lfu=True,
                 padding_type='reflect', gated=False, **spectral_kwargs):
        super(FFC, self).__init__()

        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
        self.stride = stride

        in_cg = int(in_channels * ratio_gin)
        in_cl = in_channels - in_cg
        out_cg = int(out_channels * ratio_gout)
        out_cl = out_channels - out_cg

        self.ratio_gin = ratio_gin
        self.ratio_gout = ratio_gout
        self.global_in_num = in_cg

        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
        self.convl2l = module(in_cl, out_cl, kernel_size,
                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
        self.convl2g = module(in_cl, out_cg, kernel_size,
                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
        self.convg2l = module(in_cg, out_cl, kernel_size,
                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
        self.convg2g = module(
            in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)

        self.gated = gated
        module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
        self.gate = module(in_channels, 2, 1)

    def forward(self, x):
        x_l, x_g = x if type(x) is tuple else (x, 0)
        out_xl, out_xg = 0, 0

        if self.gated:
            total_input_parts = [x_l]
            if torch.is_tensor(x_g):
                total_input_parts.append(x_g)
            total_input = torch.cat(total_input_parts, dim=1)

            gates = torch.sigmoid(self.gate(total_input))
            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
        else:
            g2l_gate, l2g_gate = 1, 1

        if self.ratio_gout != 1:
            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
        if self.ratio_gout != 0:
            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)

        return out_xl, out_xg