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| # 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 |