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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 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(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
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