import math
import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
super().__init__()
self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
self.negative_slope = negative_slope
self.scale = scale
def forward(self, input):
out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
return out
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
_, minor, in_h, in_w = input.shape
kernel_h, kernel_w = kernel.shape
out = input.view(-1, minor, in_h, 1, in_w, 1)
out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
out = out.view(-1, minor, in_h * up_y, in_w * up_x)
out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ]
out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
out = F.conv2d(out, w)
out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, )
return out[:, :, ::down_y, ::down_x]
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
class MotionPixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=2, keepdim=True) + 1e-8)
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
class Upsample(nn.Module):
def __init__(self, kernel, factor=2):
super().__init__()
self.factor = factor
kernel = make_kernel(kernel) * (factor ** 2)
self.register_buffer('kernel', kernel)
p = kernel.shape[0] - factor
pad0 = (p + 1) // 2 + factor - 1
pad1 = p // 2
self.pad = (pad0, pad1)
def forward(self, input):
return upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
class Downsample(nn.Module):
def __init__(self, kernel, factor=2):
super().__init__()
self.factor = factor
kernel = make_kernel(kernel)
self.register_buffer('kernel', kernel)
p = kernel.shape[0] - factor
pad0 = (p + 1) // 2
pad1 = p // 2
self.pad = (pad0, pad1)
def forward(self, input):
return upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
class Blur(nn.Module):
def __init__(self, kernel, pad, upsample_factor=1):
super().__init__()
kernel = make_kernel(kernel)
if upsample_factor > 1:
kernel = kernel * (upsample_factor ** 2)
self.register_buffer('kernel', kernel)
self.pad = pad
def forward(self, input):
return upfirdn2d(input, self.kernel, pad=self.pad)
class EqualConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size))
self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
self.stride = stride
self.padding = padding
if bias:
self.bias = nn.Parameter(torch.zeros(out_channel))
else:
self.bias = None
def forward(self, input):
return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding, )
def __repr__(self):
return (
f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
)
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
if bias:
self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
else:
self.bias = None
self.activation = activation
self.scale = (1 / math.sqrt(in_dim)) * lr_mul
self.lr_mul = lr_mul
def forward(self, input):
if self.activation:
out = F.linear(input, self.weight * self.scale)
out = fused_leaky_relu(out, self.bias * self.lr_mul)
else:
out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
return out
def __repr__(self):
return (f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})')
class ScaledLeakyReLU(nn.Module):
def __init__(self, negative_slope=0.2):
super().__init__()
self.negative_slope = negative_slope
def forward(self, input):
return F.leaky_relu(input, negative_slope=self.negative_slope)
class ModulatedConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, style_dim, demodulate=True, upsample=False,
downsample=False, blur_kernel=[1, 3, 3, 1], ):
super().__init__()
self.eps = 1e-8
self.kernel_size = kernel_size
self.in_channel = in_channel
self.out_channel = out_channel
self.upsample = upsample
self.downsample = downsample
if upsample:
factor = 2
p = (len(blur_kernel) - factor) - (kernel_size - 1)
pad0 = (p + 1) // 2 + factor - 1
pad1 = p // 2 + 1
self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
if downsample:
factor = 2
p = (len(blur_kernel) - factor) + (kernel_size - 1)
pad0 = (p + 1) // 2
pad1 = p // 2
self.blur = Blur(blur_kernel, pad=(pad0, pad1))
fan_in = in_channel * kernel_size ** 2
self.scale = 1 / math.sqrt(fan_in)
self.padding = kernel_size // 2
self.weight = nn.Parameter(torch.randn(1, out_channel, in_channel, kernel_size, kernel_size))
self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
self.demodulate = demodulate
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
f'upsample={self.upsample}, downsample={self.downsample})'
)
def forward(self, input, style):
batch, in_channel, height, width = input.shape
style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
weight = self.scale * self.weight * style
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
weight = weight.view(batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size)
if self.upsample:
input = input.view(1, batch * in_channel, height, width)
weight = weight.view(batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size)
weight = weight.transpose(1, 2).reshape(batch * in_channel, self.out_channel, self.kernel_size,
self.kernel_size)
out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
out = self.blur(out)
elif self.downsample:
input = self.blur(input)
_, _, height, width = input.shape
input = input.view(1, batch * in_channel, height, width)
out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
else:
input = input.view(1, batch * in_channel, height, width)
out = F.conv2d(input, weight, padding=self.padding, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, self.out_channel, height, width)
return out
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, noise=None):
if noise is None:
return image
else:
return image + self.weight * noise
class ConstantInput(nn.Module):
def __init__(self, channel, size=4):
super().__init__()
self.input = nn.Parameter(torch.randn(1, channel, size, size))
def forward(self, input):
batch = input.shape[0]
out = self.input.repeat(batch, 1, 1, 1)
return out
class StyledConv(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, style_dim, upsample=False, blur_kernel=[1, 3, 3, 1],
demodulate=True):
super().__init__()
self.conv = ModulatedConv2d(
in_channel,
out_channel,
kernel_size,
style_dim,
upsample=upsample,
blur_kernel=blur_kernel,
demodulate=demodulate,
)
self.noise = NoiseInjection()
self.activate = FusedLeakyReLU(out_channel)
def forward(self, input, style, noise=None):
out = self.conv(input, style)
out = self.noise(out, noise=noise)
out = self.activate(out)
return out
class ConvLayer(nn.Sequential):
def __init__(
self,
in_channel,
out_channel,
kernel_size,
downsample=False,
blur_kernel=[1, 3, 3, 1],
bias=True,
activate=True,
):
layers = []
if downsample:
factor = 2
p = (len(blur_kernel) - factor) + (kernel_size - 1)
pad0 = (p + 1) // 2
pad1 = p // 2
layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
stride = 2
self.padding = 0
else:
stride = 1
self.padding = kernel_size // 2
layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride,
bias=bias and not activate))
if activate:
if bias:
layers.append(FusedLeakyReLU(out_channel))
else:
layers.append(ScaledLeakyReLU(0.2))
super().__init__(*layers)
class ToRGB(nn.Module):
def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
super().__init__()
if upsample:
self.upsample = Upsample(blur_kernel)
self.conv = ConvLayer(in_channel, 3, 1)
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
def forward(self, input, skip=None):
out = self.conv(input)
out = out + self.bias
if skip is not None:
skip = self.upsample(skip)
out = out + skip
return out
class ToFlow(nn.Module):
def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
super().__init__()
if upsample:
self.upsample = Upsample(blur_kernel)
self.style_dim = style_dim
self.in_channel = in_channel
self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
def forward(self, input, style, feat, skip=None): # input 是来自上一层的 feature， style 是 512 的 condition， feat 是来自于 unet 的跳层
out = self.conv(input, style)
out = out + self.bias
# warping
xs = np.linspace(-1, 1, input.size(2))
xs = np.meshgrid(xs, xs)
xs = np.stack(xs, 2)
xs = torch.tensor(xs, requires_grad=False).float().unsqueeze(0).repeat(input.size(0), 1, 1, 1).to(input.device)
# import pdb;pdb.set_trace()
if skip is not None:
skip = self.upsample(skip)
out = out + skip
sampler = torch.tanh(out[:, 0:2, :, :])
mask = torch.sigmoid(out[:, 2:3, :, :])
flow = sampler.permute(0, 2, 3, 1) + xs # xs在这里相当于一个 location 的位置
feat_warp = F.grid_sample(feat, flow) * mask
# import pdb;pdb.set_trace()
return feat_warp, feat_warp + input * (1.0 - mask), out
class Direction(nn.Module):
def __init__(self, motion_dim):
super(Direction, self).__init__()
self.weight = nn.Parameter(torch.randn(512, motion_dim))
def forward(self, input):
# input: (bs*t) x 512
weight = self.weight + 1e-8
Q, R = torch.qr(weight) # get eignvector, orthogonal [n1, n2, n3, n4]
if input is None:
return Q
else:
input_diag = torch.diag_embed(input) # alpha, diagonal matrix
out = torch.matmul(input_diag, Q.T)
out = torch.sum(out, dim=1)
return out
class Synthesis(nn.Module):
def __init__(self, size, style_dim, motion_dim, blur_kernel=[1, 3, 3, 1], channel_multiplier=1):
super(Synthesis, self).__init__()
self.size = size
self.style_dim = style_dim
self.motion_dim = motion_dim
self.direction = Direction(motion_dim) # Linear Motion Decomposition (LMD) from LIA
self.channels = {
4: 512,
8: 512,
16: 512,
32: 512,
64: 256 * channel_multiplier,
128: 128 * channel_multiplier,
256: 64 * channel_multiplier,
512: 32 * channel_multiplier,
1024: 16 * channel_multiplier,
}
self.input = ConstantInput(self.channels[4])
self.conv1 = StyledConv(self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel)
self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
self.log_size = int(math.log(size, 2))
self.num_layers = (self.log_size - 2) * 2 + 1
self.convs = nn.ModuleList()
self.upsamples = nn.ModuleList()
self.to_rgbs = nn.ModuleList()
self.to_flows = nn.ModuleList()
in_channel = self.channels[4]
for i in range(3, self.log_size + 1):
out_channel = self.channels[2 ** i]
self.convs.append(StyledConv(in_channel, out_channel, 3, style_dim, upsample=True,
blur_kernel=blur_kernel))
self.convs.append(StyledConv(out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel))
self.to_rgbs.append(ToRGB(out_channel, style_dim))
self.to_flows.append(ToFlow(out_channel, style_dim))
in_channel = out_channel
self.n_latent = self.log_size * 2 - 2
def forward(self, source_before_decoupling, target_motion, feats):
directions = self.direction(target_motion)
latent = source_before_decoupling + directions # wa + directions
inject_index = self.n_latent
latent = latent.unsqueeze(1).repeat(1, inject_index, 1)
out = self.input(latent)
out = self.conv1(out, latent[:, 0])
i = 1
for conv1, conv2, to_rgb, to_flow, feat in zip(self.convs[::2], self.convs[1::2], self.to_rgbs,
self.to_flows, feats):
out = conv1(out, latent[:, i])
out = conv2(out, latent[:, i + 1])
if out.size(2) == 8:
out_warp, out, skip_flow = to_flow(out, latent[:, i + 2], feat)
skip = to_rgb(out_warp)
else:
out_warp, out, skip_flow = to_flow(out, latent[:, i + 2], feat, skip_flow)
skip = to_rgb(out_warp, skip)
i += 2
img = skip
return img