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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class FFCSE_block(nn.Module): |
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def __init__(self, channels, ratio_g): |
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super(FFCSE_block, self).__init__() |
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in_cg = int(channels * ratio_g) |
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in_cl = channels - in_cg |
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r = 16 |
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
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self.conv1 = nn.Conv2d(channels, channels // r, |
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kernel_size=1, bias=True) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.conv_a2l = None if in_cl == 0 else nn.Conv2d( |
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channels // r, in_cl, kernel_size=1, bias=True) |
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self.conv_a2g = None if in_cg == 0 else nn.Conv2d( |
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channels // r, in_cg, kernel_size=1, bias=True) |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x): |
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x = x if type(x) is tuple else (x, 0) |
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id_l, id_g = x |
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x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1) |
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x = self.avgpool(x) |
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x = self.relu1(self.conv1(x)) |
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x_l = 0 if self.conv_a2l is None else id_l * \ |
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self.sigmoid(self.conv_a2l(x)) |
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x_g = 0 if self.conv_a2g is None else id_g * \ |
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self.sigmoid(self.conv_a2g(x)) |
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return x_l, x_g |
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FFT_OP_SUPPORT = True |
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class FourierUnit(nn.Module): |
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def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear', |
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spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'): |
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super(FourierUnit, self).__init__() |
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self.groups = groups |
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self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), |
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out_channels=out_channels * 2, |
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kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False) |
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self.bn = torch.nn.BatchNorm2d(out_channels * 2) |
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self.relu = torch.nn.ReLU(inplace=True) |
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self.use_se = use_se |
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self.spatial_scale_factor = spatial_scale_factor |
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self.spatial_scale_mode = spatial_scale_mode |
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self.spectral_pos_encoding = spectral_pos_encoding |
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self.ffc3d = ffc3d |
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self.fft_norm = fft_norm |
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def forward(self, x): |
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batch = x.shape[0] |
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input_dtype = x.dtype |
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if self.spatial_scale_factor is not None: |
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orig_size = x.shape[-2:] |
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x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, |
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align_corners=False) |
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r_size = x.size() |
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fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) |
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if x.dtype in (torch.float16, torch.bfloat16): |
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x = x.type(torch.float32) |
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global FFT_OP_SUPPORT |
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if FFT_OP_SUPPORT: |
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try: |
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ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) |
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except: |
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FFT_OP_SUPPORT = False |
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print(f'FFT OP not supported with this card, try run it with cpu...') |
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if not FFT_OP_SUPPORT: |
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ffted = torch.fft.rfftn(x.to(device='cpu', dtype=torch.float32), dim=fft_dim, norm=self.fft_norm).to( |
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device=x.device) |
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ffted = torch.stack((ffted.real, ffted.imag), dim=-1) |
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ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() |
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ffted = ffted.view((batch, -1,) + ffted.size()[3:]) |
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if self.spectral_pos_encoding: |
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height, width = ffted.shape[-2:] |
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coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted) |
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coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted) |
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ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) |
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if self.use_se: |
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ffted = self.se(ffted) |
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ffted = self.conv_layer(ffted) |
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ffted = self.relu(self.bn(ffted)) |
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ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute( |
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0, 1, 3, 4, 2).contiguous() |
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if ffted.dtype in (torch.float16, torch.bfloat16): |
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ffted = ffted.type(torch.float32) |
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ffted = torch.complex(ffted[..., 0], ffted[..., 1]) |
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ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] |
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if FFT_OP_SUPPORT: |
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output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm) |
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else: |
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output = torch.fft.irfftn(ffted.to(device='cpu', dtype=torch.float32), s=ifft_shape_slice, dim=fft_dim, |
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norm=self.fft_norm).to(device=ffted.device, dtype=input_dtype) |
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if self.spatial_scale_factor is not None: |
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output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False) |
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return output |
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class SpectralTransform(nn.Module): |
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def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs): |
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super(SpectralTransform, self).__init__() |
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self.enable_lfu = enable_lfu |
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if stride == 2: |
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self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) |
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else: |
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self.downsample = nn.Identity() |
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self.stride = stride |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(in_channels, out_channels // |
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2, kernel_size=1, groups=groups, bias=False), |
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nn.BatchNorm2d(out_channels // 2), |
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nn.ReLU(inplace=True) |
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) |
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self.fu = FourierUnit( |
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out_channels // 2, out_channels // 2, groups, **fu_kwargs) |
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if self.enable_lfu: |
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self.lfu = FourierUnit( |
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out_channels // 2, out_channels // 2, groups) |
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self.conv2 = torch.nn.Conv2d( |
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out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False) |
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def forward(self, x): |
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x = self.downsample(x) |
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x = self.conv1(x) |
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output = self.fu(x) |
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if self.enable_lfu: |
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n, c, h, w = x.shape |
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split_no = 2 |
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split_s = h // split_no |
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xs = torch.cat(torch.split( |
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x[:, :c // 4], split_s, dim=-2), dim=1).contiguous() |
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xs = torch.cat(torch.split(xs, split_s, dim=-1), |
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dim=1).contiguous() |
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xs = self.lfu(xs) |
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xs = xs.repeat(1, 1, split_no, split_no).contiguous() |
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else: |
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xs = 0 |
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output = self.conv2(x + output + xs) |
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return output |
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class FFC(nn.Module): |
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def __init__(self, in_channels, out_channels, kernel_size, |
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ratio_gin, ratio_gout, stride=1, padding=0, |
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dilation=1, groups=1, bias=False, enable_lfu=True, |
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padding_type='reflect', gated=False, **spectral_kwargs): |
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super(FFC, self).__init__() |
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assert stride == 1 or stride == 2, "Stride should be 1 or 2." |
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self.stride = stride |
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in_cg = int(in_channels * ratio_gin) |
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in_cl = in_channels - in_cg |
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out_cg = int(out_channels * ratio_gout) |
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out_cl = out_channels - out_cg |
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self.ratio_gin = ratio_gin |
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self.ratio_gout = ratio_gout |
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self.global_in_num = in_cg |
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module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d |
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self.convl2l = module(in_cl, out_cl, kernel_size, |
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stride, padding, dilation, groups, bias, padding_mode=padding_type) |
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module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d |
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self.convl2g = module(in_cl, out_cg, kernel_size, |
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stride, padding, dilation, groups, bias, padding_mode=padding_type) |
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module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d |
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self.convg2l = module(in_cg, out_cl, kernel_size, |
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stride, padding, dilation, groups, bias, padding_mode=padding_type) |
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module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform |
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self.convg2g = module( |
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in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs) |
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self.gated = gated |
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module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d |
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self.gate = module(in_channels, 2, 1) |
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def forward(self, x): |
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x_l, x_g = x if type(x) is tuple else (x, 0) |
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out_xl, out_xg = 0, 0 |
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if self.gated: |
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total_input_parts = [x_l] |
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if torch.is_tensor(x_g): |
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total_input_parts.append(x_g) |
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total_input = torch.cat(total_input_parts, dim=1) |
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gates = torch.sigmoid(self.gate(total_input)) |
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g2l_gate, l2g_gate = gates.chunk(2, dim=1) |
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else: |
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g2l_gate, l2g_gate = 1, 1 |
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if self.ratio_gout != 1: |
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out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate |
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if self.ratio_gout != 0: |
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out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) |
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return out_xl, out_xg |
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class FFC_BN_ACT(nn.Module): |
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def __init__(self, in_channels, out_channels, |
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kernel_size, ratio_gin, ratio_gout, |
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stride=1, padding=0, dilation=1, groups=1, bias=False, |
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norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, |
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padding_type='reflect', |
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enable_lfu=True, **kwargs): |
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super(FFC_BN_ACT, self).__init__() |
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self.ffc = FFC(in_channels, out_channels, kernel_size, |
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ratio_gin, ratio_gout, stride, padding, dilation, |
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groups, bias, enable_lfu, padding_type=padding_type, **kwargs) |
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lnorm = nn.Identity if ratio_gout == 1 else norm_layer |
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gnorm = nn.Identity if ratio_gout == 0 else norm_layer |
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global_channels = int(out_channels * ratio_gout) |
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self.bn_l = lnorm(out_channels - global_channels) |
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self.bn_g = gnorm(global_channels) |
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lact = nn.Identity if ratio_gout == 1 else activation_layer |
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gact = nn.Identity if ratio_gout == 0 else activation_layer |
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self.act_l = lact(inplace=True) |
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self.act_g = gact(inplace=True) |
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def forward(self, x): |
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x_l, x_g = self.ffc(x) |
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x_l = self.act_l(self.bn_l(x_l)) |
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x_g = self.act_g(self.bn_g(x_g)) |
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return x_l, x_g |
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class FFCResnetBlock(nn.Module): |
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def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, |
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spatial_transform_kwargs=None, inline=False, **conv_kwargs): |
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super().__init__() |
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self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, |
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norm_layer=norm_layer, |
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activation_layer=activation_layer, |
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padding_type=padding_type, |
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**conv_kwargs) |
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self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, |
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norm_layer=norm_layer, |
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activation_layer=activation_layer, |
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padding_type=padding_type, |
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**conv_kwargs) |
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self.inline = inline |
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def forward(self, x): |
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if self.inline: |
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x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:] |
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else: |
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x_l, x_g = x if type(x) is tuple else (x, 0) |
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id_l, id_g = x_l, x_g |
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x_l, x_g = self.conv1((x_l, x_g)) |
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x_l, x_g = self.conv2((x_l, x_g)) |
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x_l, x_g = id_l + x_l, id_g + x_g |
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out = x_l, x_g |
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if self.inline: |
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out = torch.cat(out, dim=1) |
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return out |
