# 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 from typing import List, Tuple import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import abc import random from kornia.geometry.transform import rotate from .inpainting_lama_mpe import LamaMPEInpainter # Currently not used class LamaInpainter(LamaMPEInpainter): _MODEL_MAPPING = { 'model': { 'url': '', 'hash': '', 'file': '.', }, } async def _load(self, device: str): model = get_generator() sd = torch.load(self._get_file_path('inpainting_lama.ckpt'), map_location='cpu') model.load_state_dict(sd['model'] if 'model' in sd else sd) self.model.eval() self.device = device if device.startswith('cuda') or device == 'mps': self.model.to(device) class DepthWiseSeparableConv(nn.Module): def __init__(self, in_dim, out_dim, *args, **kwargs): super().__init__() if 'groups' in kwargs: # ignoring groups for Depthwise Sep Conv del kwargs['groups'] self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs) self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1) def forward(self, x): out = self.depthwise(x) out = self.pointwise(out) return out class MultidilatedConv(nn.Module): def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True, shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs): super().__init__() convs = [] self.equal_dim = equal_dim assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode if comb_mode in ('cat_out', 'cat_both'): self.cat_out = True if equal_dim: assert out_dim % dilation_num == 0 out_dims = [out_dim // dilation_num] * dilation_num self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], []) else: out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] out_dims.append(out_dim - sum(out_dims)) index = [] starts = [0] + out_dims[:-1] lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)] for i in range(out_dims[-1]): for j in range(dilation_num): index += list(range(starts[j], starts[j] + lengths[j])) starts[j] += lengths[j] self.index = index assert(len(index) == out_dim) self.out_dims = out_dims else: self.cat_out = False self.out_dims = [out_dim] * dilation_num if comb_mode in ('cat_in', 'cat_both'): if equal_dim: assert in_dim % dilation_num == 0 in_dims = [in_dim // dilation_num] * dilation_num else: in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] in_dims.append(in_dim - sum(in_dims)) self.in_dims = in_dims self.cat_in = True else: self.cat_in = False self.in_dims = [in_dim] * dilation_num conv_type = DepthWiseSeparableConv if use_depthwise else nn.Conv2d dilation = min_dilation for i in range(dilation_num): if isinstance(padding, int): cur_padding = padding * dilation else: cur_padding = padding[i] convs.append(conv_type( self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs )) if i > 0 and shared_weights: convs[-1].weight = convs[0].weight convs[-1].bias = convs[0].bias dilation *= 2 self.convs = nn.ModuleList(convs) self.shuffle_in_channels = shuffle_in_channels if self.shuffle_in_channels: # shuffle list as shuffling of tensors is nondeterministic in_channels_permute = list(range(in_dim)) random.shuffle(in_channels_permute) # save as buffer so it is saved and loaded with checkpoint self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute)) def forward(self, x): if self.shuffle_in_channels: x = x[:, self.in_channels_permute] outs = [] if self.cat_in: if self.equal_dim: x = x.chunk(len(self.convs), dim=1) else: new_x = [] start = 0 for dim in self.in_dims: new_x.append(x[:, start:start+dim]) start += dim x = new_x for i, conv in enumerate(self.convs): if self.cat_in: input = x[i] else: input = x outs.append(conv(input)) if self.cat_out: out = torch.cat(outs, dim=1)[:, self.index] else: out = sum(outs) return out class BaseDiscriminator(nn.Module): @abc.abstractmethod def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]: """ Predict scores and get intermediate activations. Useful for feature matching loss :return tuple (scores, list of intermediate activations) """ raise NotImplemented() def get_conv_block_ctor(kind='default'): if not isinstance(kind, str): return kind if kind == 'default': return nn.Conv2d if kind == 'depthwise': return DepthWiseSeparableConv if kind == 'multidilated': return MultidilatedConv raise ValueError(f'Unknown convolutional block kind {kind}') def get_norm_layer(kind='bn'): if not isinstance(kind, str): return kind if kind == 'bn': return nn.BatchNorm2d if kind == 'in': return nn.InstanceNorm2d raise ValueError(f'Unknown norm block kind {kind}') def get_activation(kind='tanh'): if kind == 'tanh': return nn.Tanh() if kind == 'sigmoid': return nn.Sigmoid() if kind is False: return nn.Identity() raise ValueError(f'Unknown activation kind {kind}') class LearnableSpatialTransformWrapper(nn.Module): def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): super().__init__() self.impl = impl self.angle = torch.rand(1) * angle_init_range if train_angle: self.angle = nn.Parameter(self.angle, requires_grad=True) self.pad_coef = pad_coef def forward(self, x): if torch.is_tensor(x): return self.inverse_transform(self.impl(self.transform(x)), x) elif isinstance(x, tuple): x_trans = tuple(self.transform(elem) for elem in x) y_trans = self.impl(x_trans) return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)) else: raise ValueError(f'Unexpected input type {type(x)}') def transform(self, x): height, width = x.shape[2:] pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect') x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded)) return x_padded_rotated def inverse_transform(self, y_padded_rotated, orig_x): height, width = orig_x.shape[2:] pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated)) y_height, y_width = y_padded.shape[2:] y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] return y 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 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 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 #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(1), FFC_BN_ACT(input_nc, ngf, kernel_size=3, 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=4, 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=4, stride=2, padding=1, output_padding=0), 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(1), nn.Conv2d(ngf, output_nc, kernel_size=3, 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, mask = None): if mask is not None: input = torch.cat([mask, input], dim = 1) 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 def get_generator(n_blocks: int = 9): init_conv_kwargs = { 'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False, } downsample_conv_kwargs = { 'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False, } resnet_conv_kwargs = { 'ratio_gin': 0.75, 'ratio_gout': 0.75, 'enable_lfu': False, } return FFCResNetGenerator(4, 3, ngf=64, n_blocks=n_blocks,add_out_act=False,init_conv_kwargs=init_conv_kwargs,downsample_conv_kwargs=downsample_conv_kwargs,resnet_conv_kwargs=resnet_conv_kwargs) def get_discriminator(): init_conv_kwargs = { 'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False, } conv_kwargs = { 'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False, } return FFCNLayerDiscriminator(3, norm_layer = nn.Identity, init_conv_kwargs = init_conv_kwargs, conv_kwargs = conv_kwargs) from torchsummary import summary def test_model(): dis = get_generator() image = torch.randn((1, 4, 640, 640)) final = dis(image) breakpoint() print(final.shape) if __name__ == '__main__': test_model()