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import torch
from torch.nn import functional as F
import math
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162
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])
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81
class FusedLeakyReLU(torch.nn.Module):
def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
super().__init__()
self.bias = torch.nn.Parameter(torch.zeros(1, channel, 1, 1))
self.negative_slope = negative_slope
self.scale = scale
def forward(self, input):
return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class Blur(torch.nn.Module):
def __init__(self, kernel, pad):
super().__init__()
kernel = torch.tensor(kernel, dtype=torch.float32)
kernel = kernel[None, :] * kernel[:, None]
kernel = kernel / kernel.sum()
self.register_buffer('kernel', kernel)
self.pad = pad
def forward(self, input):
return upfirdn2d(input, self.kernel, pad=self.pad)
#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590
class ScaledLeakyReLU(torch.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)
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605
class EqualConv2d(torch.nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
super().__init__()
self.weight = torch.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
self.bias = torch.nn.Parameter(torch.zeros(out_channel)) if bias else None
def forward(self, input):
return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding)
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134
class EqualLinear(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
super().__init__()
self.weight = torch.nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
self.bias = torch.nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) if bias else 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)
return fused_leaky_relu(out, self.bias * self.lr_mul)
return F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654
class ConvLayer(torch.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)
layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))
stride, padding = 2, 0
else:
stride, padding = 1, kernel_size // 2
layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate))
if activate:
layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))
super().__init__(*layers)
# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704
class ResBlock(torch.nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.conv1 = ConvLayer(in_channel, in_channel, 3)
self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False)
def forward(self, input):
out = self.conv2(self.conv1(input))
skip = self.skip(input)
return (out + skip) / math.sqrt(2)
class AppearanceEncoder(torch.nn.Module):
def __init__(self, w_dim=512):
super().__init__()
self.convs = torch.nn.ModuleList([
ConvLayer(3, 32, 1), ResBlock(32, 64),
ResBlock(64, 128), ResBlock(128, 256),
ResBlock(256, 512), ResBlock(512, 512),
ResBlock(512, 512), ResBlock(512, 512),
EqualConv2d(512, w_dim, 4, padding=0, bias=False)
])
def forward(self, x):
for conv in self.convs:
x = conv(x)
return x.squeeze((-2, -1))
class MotionEncoder(torch.nn.Module):
def __init__(self, dim=512, motion_dim=20):
super().__init__()
self.net_app = AppearanceEncoder(dim)
self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim) for _ in range(4)] + [EqualLinear(dim, motion_dim)])
def encode_motion(self, x):
return self.fc(self.net_app(x))
class MotionProjector(torch.nn.Module):
def __init__(self, m_dim):
super().__init__()
self.weight = torch.nn.Parameter(torch.randn(512, m_dim))
self.motion_dim = m_dim
def forward(self, input):
stabilized_weight = self.weight + 1e-8 * torch.eye(512, self.motion_dim, device=self.weight.device, dtype=self.weight.dtype)
Q, _ = torch.linalg.qr(stabilized_weight)
if input is None:
return Q
return torch.sum(input.unsqueeze(-1) * Q.T, dim=1)
class MotionDecoder(torch.nn.Module):
def __init__(self, m_dim):
super().__init__()
self.direction = MotionProjector(m_dim)
class MotionExtractor(torch.nn.Module):
def __init__(self, s_dim=512, m_dim=20):
super().__init__()
self.enc = MotionEncoder(s_dim, m_dim)
self.dec = MotionDecoder(m_dim)
def forward(self, img):
motion_feat = self.enc.encode_motion(img)
return self.dec.direction(motion_feat) |