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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 | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from kornia.geometry.transform import rotate | |
| import torch.fft as fft | |
| from icecream import ic | |
| import PIL | |
| def save_image_grid(feats, fname, gridsize): | |
| gw, gh = gridsize | |
| idx = gw * gh | |
| max_num = torch.max(feats[:idx]).item() | |
| min_num = torch.min(feats[:idx]).item() | |
| feats = feats[:idx].cpu() * 255 / (max_num - min_num) | |
| feats = np.asarray(feats, dtype=np.float32) | |
| feats = np.rint(feats).clip(0, 255).astype(np.uint8) | |
| C, H, W = feats.shape | |
| feats = feats.reshape(gh, gw, 1, H, W) | |
| feats = feats.transpose(0, 3, 1, 4, 2) | |
| feats = feats.reshape(gh * H, gw * W, 1) | |
| feats = np.stack([feats]*3, axis=2).squeeze() * 10 | |
| feats = np.rint(feats).clip(0, 255).astype(np.uint8) | |
| from icecream import ic | |
| ic(feats.shape) | |
| feats = PIL.Image.fromarray(feats) | |
| feats.save(fname + '.png') | |
| def _conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): | |
| return F.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) | |
| 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 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=False), | |
| 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.relu = torch.nn.ReLU(inplace=False) | |
| # 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 = 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(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, fname=None): | |
| 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 | |
| # for i in range(x_g.shape[0]): | |
| # c, h, w = x_g[i].shape | |
| # gh = 3 | |
| # gw = 3 | |
| # save_image_grid(x_g[i].detach(), f'vis/{fname}_xg_{h}', (gh, gw)) | |
| # for i in range(x_l.shape[0]): | |
| # c, h, w = x_l[i].shape | |
| # gh = 3 | |
| # gw = 3 | |
| # save_image_grid(x_l[i].detach(), f'vis/{fname}_xl_{h}', (gh, gw)) | |
| spec_x = self.convg2g(x_g) | |
| # for i in range(spec_x.shape[0]): | |
| # c, h, w = spec_x[i].shape | |
| # gh = 3 | |
| # gw = 3 | |
| # save_image_grid(spec_x[i].detach(), f'vis/{fname}_spec_x_{h}', (gh, gw)) | |
| 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 + spec_x | |
| # for i in range(out_xg.shape[0]): | |
| # c, h, w = out_xg[i].shape | |
| # gh = 3 | |
| # gw = 3 | |
| # save_image_grid(out_xg[i].detach(), f'vis/{fname}_outg_{h}', (gh, gw)) | |
| # for i in range(out_xl.shape[0]): | |
| # c, h, w = out_xl[i].shape | |
| # gh = 3 | |
| # gw = 3 | |
| # save_image_grid(out_xl[i].detach(), f'vis/{fname}_outl_{h}', (gh, gw)) | |
| 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.SyncBatchNorm, 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, fname=None): | |
| x_l, x_g = self.ffc(x, fname=fname,) | |
| x_l = self.act_l(x_l) | |
| x_g = self.act_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, ratio_gin=0.75, ratio_gout=0.75): | |
| 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, | |
| ratio_gin=ratio_gin, ratio_gout=ratio_gout) | |
| 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, | |
| ratio_gin=ratio_gin, ratio_gout=ratio_gout) | |
| 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, fname=None): | |
| 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), fname=fname) | |
| x_l, x_g = self.conv2((x_l, x_g), fname=fname) | |
| 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) | |