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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 numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from saicinpainting.training.modules.base import get_activation, BaseDiscriminator | |
| from saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper | |
| from saicinpainting.training.modules.squeeze_excitation import SELayer | |
| from saicinpainting.utils import get_shape | |
| 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 SeparableFourierUnit(nn.Module): | |
| def __init__(self, in_channels, out_channels, groups=1, kernel_size=3): | |
| # bn_layer not used | |
| super(SeparableFourierUnit, self).__init__() | |
| self.groups = groups | |
| row_out_channels = out_channels // 2 | |
| col_out_channels = out_channels - row_out_channels | |
| self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2, | |
| out_channels=row_out_channels * 2, | |
| kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed | |
| stride=1, padding=(kernel_size // 2, 0), | |
| padding_mode='reflect', | |
| groups=self.groups, bias=False) | |
| self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2, | |
| out_channels=col_out_channels * 2, | |
| kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed | |
| stride=1, padding=(kernel_size // 2, 0), | |
| padding_mode='reflect', | |
| groups=self.groups, bias=False) | |
| self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2) | |
| self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2) | |
| self.relu = torch.nn.ReLU(inplace=True) | |
| def process_branch(self, x, conv, bn): | |
| batch = x.shape[0] | |
| r_size = x.size() | |
| # (batch, c, h, w/2+1, 2) | |
| ffted = torch.fft.rfft(x, norm="ortho") | |
| 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:]) | |
| ffted = self.relu(bn(conv(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]) | |
| output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho") | |
| return output | |
| def forward(self, x): | |
| rowwise = self.process_branch(x, self.row_conv, self.row_bn) | |
| colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2) | |
| out = torch.cat((rowwise, colwise), dim=1) | |
| return out | |
| class SpectralTransform(nn.Module): | |
| def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **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) | |
| ) | |
| fu_class = SeparableFourierUnit if separable_fu else FourierUnit | |
| self.fu = fu_class( | |
| out_channels // 2, out_channels // 2, groups, **fu_kwargs) | |
| if self.enable_lfu: | |
| self.lfu = fu_class( | |
| 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 | |
| #groups_g = 1 if groups == 1 else int(groups * ratio_gout) | |
| #groups_l = 1 if groups == 1 else groups - groups_g | |
| 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 | |
| class FFC_BN_ACT(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, | |
| norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, | |
| padding_type='reflect', | |
| enable_lfu=True, **kwargs): | |
| super(FFC_BN_ACT, self).__init__() | |
| self.ffc = FFC(in_channels, out_channels, kernel_size, | |
| ratio_gin, ratio_gout, stride, padding, dilation, | |
| groups, bias, enable_lfu, padding_type=padding_type, **kwargs) | |
| lnorm = nn.Identity if ratio_gout == 1 else norm_layer | |
| gnorm = nn.Identity if ratio_gout == 0 else norm_layer | |
| global_channels = int(out_channels * ratio_gout) | |
| self.bn_l = lnorm(out_channels - global_channels) | |
| self.bn_g = gnorm(global_channels) | |
| lact = nn.Identity if ratio_gout == 1 else activation_layer | |
| gact = nn.Identity if ratio_gout == 0 else activation_layer | |
| self.act_l = lact(inplace=True) | |
| self.act_g = gact(inplace=True) | |
| def forward(self, x): | |
| x_l, x_g = self.ffc(x) | |
| x_l = self.act_l(self.bn_l(x_l)) | |
| x_g = self.act_g(self.bn_g(x_g)) | |
| return x_l, x_g | |
| class FFCResnetBlock(nn.Module): | |
| def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, | |
| spatial_transform_kwargs=None, inline=False, **conv_kwargs): | |
| super().__init__() | |
| self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, | |
| norm_layer=norm_layer, | |
| activation_layer=activation_layer, | |
| padding_type=padding_type, | |
| **conv_kwargs) | |
| self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, | |
| norm_layer=norm_layer, | |
| activation_layer=activation_layer, | |
| padding_type=padding_type, | |
| **conv_kwargs) | |
| if spatial_transform_kwargs is not None: | |
| self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs) | |
| self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs) | |
| self.inline = inline | |
| def forward(self, x): | |
| if self.inline: | |
| x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:] | |
| else: | |
| x_l, x_g = x if type(x) is tuple else (x, 0) | |
| id_l, id_g = x_l, x_g | |
| x_l, x_g = self.conv1((x_l, x_g)) | |
| x_l, x_g = self.conv2((x_l, x_g)) | |
| x_l, x_g = id_l + x_l, id_g + x_g | |
| out = x_l, x_g | |
| if self.inline: | |
| out = torch.cat(out, dim=1) | |
| return out | |
| class ConcatTupleLayer(nn.Module): | |
| def forward(self, x): | |
| assert isinstance(x, tuple) | |
| x_l, x_g = x | |
| assert torch.is_tensor(x_l) or torch.is_tensor(x_g) | |
| if not torch.is_tensor(x_g): | |
| return x_l | |
| return torch.cat(x, dim=1) | |
| class FFCResNetGenerator(nn.Module): | |
| def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, | |
| padding_type='reflect', activation_layer=nn.ReLU, | |
| up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), | |
| init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={}, | |
| spatial_transform_layers=None, spatial_transform_kwargs={}, | |
| add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}): | |
| assert (n_blocks >= 0) | |
| super().__init__() | |
| model = [nn.ReflectionPad2d(3), | |
| FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer, | |
| activation_layer=activation_layer, **init_conv_kwargs)] | |
| ### downsample | |
| for i in range(n_downsampling): | |
| mult = 2 ** i | |
| if i == n_downsampling - 1: | |
| cur_conv_kwargs = dict(downsample_conv_kwargs) | |
| cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0) | |
| else: | |
| cur_conv_kwargs = downsample_conv_kwargs | |
| model += [FFC_BN_ACT(min(max_features, ngf * mult), | |
| min(max_features, ngf * mult * 2), | |
| kernel_size=3, stride=2, padding=1, | |
| norm_layer=norm_layer, | |
| activation_layer=activation_layer, | |
| **cur_conv_kwargs)] | |
| mult = 2 ** n_downsampling | |
| feats_num_bottleneck = min(max_features, ngf * mult) | |
| ### resnet blocks | |
| for i in range(n_blocks): | |
| cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer, | |
| norm_layer=norm_layer, **resnet_conv_kwargs) | |
| if spatial_transform_layers is not None and i in spatial_transform_layers: | |
| cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs) | |
| model += [cur_resblock] | |
| model += [ConcatTupleLayer()] | |
| ### upsample | |
| for i in range(n_downsampling): | |
| mult = 2 ** (n_downsampling - i) | |
| model += [nn.ConvTranspose2d(min(max_features, ngf * mult), | |
| min(max_features, int(ngf * mult / 2)), | |
| kernel_size=3, stride=2, padding=1, output_padding=1), | |
| up_norm_layer(min(max_features, int(ngf * mult / 2))), | |
| up_activation] | |
| if out_ffc: | |
| model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer, | |
| norm_layer=norm_layer, inline=True, **out_ffc_kwargs)] | |
| model += [nn.ReflectionPad2d(3), | |
| nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] | |
| if add_out_act: | |
| model.append(get_activation('tanh' if add_out_act is True else add_out_act)) | |
| self.model = nn.Sequential(*model) | |
| def forward(self, input): | |
| return self.model(input) | |
| class FFCNLayerDiscriminator(BaseDiscriminator): | |
| def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512, | |
| init_conv_kwargs={}, conv_kwargs={}): | |
| super().__init__() | |
| self.n_layers = n_layers | |
| def _act_ctor(inplace=True): | |
| return nn.LeakyReLU(negative_slope=0.2, inplace=inplace) | |
| kw = 3 | |
| padw = int(np.ceil((kw-1.0)/2)) | |
| sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer, | |
| activation_layer=_act_ctor, **init_conv_kwargs)]] | |
| nf = ndf | |
| for n in range(1, n_layers): | |
| nf_prev = nf | |
| nf = min(nf * 2, max_features) | |
| cur_model = [ | |
| FFC_BN_ACT(nf_prev, nf, | |
| kernel_size=kw, stride=2, padding=padw, | |
| norm_layer=norm_layer, | |
| activation_layer=_act_ctor, | |
| **conv_kwargs) | |
| ] | |
| sequence.append(cur_model) | |
| nf_prev = nf | |
| nf = min(nf * 2, 512) | |
| cur_model = [ | |
| FFC_BN_ACT(nf_prev, nf, | |
| kernel_size=kw, stride=1, padding=padw, | |
| norm_layer=norm_layer, | |
| activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs), | |
| **conv_kwargs), | |
| ConcatTupleLayer() | |
| ] | |
| sequence.append(cur_model) | |
| sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] | |
| for n in range(len(sequence)): | |
| setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) | |
| def get_all_activations(self, x): | |
| res = [x] | |
| for n in range(self.n_layers + 2): | |
| model = getattr(self, 'model' + str(n)) | |
| res.append(model(res[-1])) | |
| return res[1:] | |
| def forward(self, x): | |
| act = self.get_all_activations(x) | |
| feats = [] | |
| for out in act[:-1]: | |
| if isinstance(out, tuple): | |
| if torch.is_tensor(out[1]): | |
| out = torch.cat(out, dim=1) | |
| else: | |
| out = out[0] | |
| feats.append(out) | |
| return act[-1], feats | |