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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)