""" The code is based on https://github.com/apple/ml-gsn/ with adaption. """
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.torch_utils.ops.native_ops import (
FusedLeakyReLU,
fused_leaky_relu,
upfirdn2d,
)
class DiscriminatorHead(nn.Module):
def __init__(self, in_channel, disc_stddev=False):
super().__init__()
self.disc_stddev = disc_stddev
stddev_dim = 1 if disc_stddev else 0
self.conv_stddev = ConvLayer2d(
in_channel=in_channel + stddev_dim,
out_channel=in_channel,
kernel_size=3,
activate=True
)
self.final_linear = nn.Sequential(
nn.Flatten(),
EqualLinear(in_channel=in_channel * 4 * 4, out_channel=in_channel, activate=True),
EqualLinear(in_channel=in_channel, out_channel=1),
)
def cat_stddev(self, x, stddev_group=4, stddev_feat=1):
perm = torch.randperm(len(x))
inv_perm = torch.argsort(perm)
batch, channel, height, width = x.shape
x = x[perm
] # shuffle inputs so that all views in a single trajectory don't get put together
group = min(batch, stddev_group)
stddev = x.view(group, -1, stddev_feat, channel // stddev_feat, height, width)
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
stddev = stddev.repeat(group, 1, height, width)
stddev = stddev[inv_perm] # reorder inputs
x = x[inv_perm]
out = torch.cat([x, stddev], 1)
return out
def forward(self, x):
if self.disc_stddev:
x = self.cat_stddev(x)
x = self.conv_stddev(x)
out = self.final_linear(x)
return out
class ConvDecoder(nn.Module):
def __init__(self, in_channel, out_channel, in_res, out_res):
super().__init__()
log_size_in = int(math.log(in_res, 2))
log_size_out = int(math.log(out_res, 2))
self.layers = []
in_ch = in_channel
for i in range(log_size_in, log_size_out):
out_ch = in_ch // 2
self.layers.append(
ConvLayer2d(
in_channel=in_ch,
out_channel=out_ch,
kernel_size=3,
upsample=True,
bias=True,
activate=True
)
)
in_ch = out_ch
self.layers.append(
ConvLayer2d(
in_channel=in_ch, out_channel=out_channel, kernel_size=3, bias=True, activate=False
)
)
self.layers = nn.Sequential(*self.layers)
def forward(self, x):
return self.layers(x)
class StyleDiscriminator(nn.Module):
def __init__(self, in_channel, in_res, ch_mul=64, ch_max=512, **kwargs):
super().__init__()
log_size_in = int(math.log(in_res, 2))
log_size_out = int(math.log(4, 2))
self.conv_in = ConvLayer2d(in_channel=in_channel, out_channel=ch_mul, kernel_size=3)
# each resblock will half the resolution and double the number of features (until a maximum of ch_max)
self.layers = []
in_channels = ch_mul
for i in range(log_size_in, log_size_out, -1):
out_channels = int(min(in_channels * 2, ch_max))
self.layers.append(
ConvResBlock2d(in_channel=in_channels, out_channel=out_channels, downsample=True)
)
in_channels = out_channels
self.layers = nn.Sequential(*self.layers)
self.disc_out = DiscriminatorHead(in_channel=in_channels, disc_stddev=True)
def forward(self, x):
x = self.conv_in(x)
x = self.layers(x)
out = self.disc_out(x)
return out
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 Blur(nn.Module):
"""Blur layer.
Applies a blur kernel to input image using finite impulse response filter. Blurring feature maps after
convolutional upsampling or before convolutional downsampling helps produces models that are more robust to
shifting inputs (https://richzhang.github.io/antialiased-cnns/). In the context of GANs, this can provide
cleaner gradients, and therefore more stable training.
Args:
----
kernel: list, int
A list of integers representing a blur kernel. For exmaple: [1, 3, 3, 1].
pad: tuple, int
A tuple of integers representing the number of rows/columns of padding to be added to the top/left and
the bottom/right respectively.
upsample_factor: int
Upsample factor.
"""
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):
out = upfirdn2d(input, self.kernel, pad=self.pad)
return out
class Upsample(nn.Module):
"""Upsampling layer.
Perform upsampling using a blur kernel.
Args:
----
kernel: list, int
A list of integers representing a blur kernel. For exmaple: [1, 3, 3, 1].
factor: int
Upsampling factor.
"""
def __init__(self, kernel=[1, 3, 3, 1], 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):
out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
return out
class Downsample(nn.Module):
"""Downsampling layer.
Perform downsampling using a blur kernel.
Args:
----
kernel: list, int
A list of integers representing a blur kernel. For exmaple: [1, 3, 3, 1].
factor: int
Downsampling factor.
"""
def __init__(self, kernel=[1, 3, 3, 1], 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):
out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
return out
class EqualLinear(nn.Module):
"""Linear layer with equalized learning rate.
During the forward pass the weights are scaled by the inverse of the He constant (i.e. sqrt(in_dim)) to
prevent vanishing gradients and accelerate training. This constant only works for ReLU or LeakyReLU
activation functions.
Args:
----
in_channel: int
Input channels.
out_channel: int
Output channels.
bias: bool
Use bias term.
bias_init: float
Initial value for the bias.
lr_mul: float
Learning rate multiplier. By scaling weights and the bias we can proportionally scale the magnitude of
the gradients, effectively increasing/decreasing the learning rate for this layer.
activate: bool
Apply leakyReLU activation.
"""
def __init__(self, in_channel, out_channel, bias=True, bias_init=0, lr_mul=1, activate=False):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_channel, in_channel).div_(lr_mul))
if bias:
self.bias = nn.Parameter(torch.zeros(out_channel).fill_(bias_init))
else:
self.bias = None
self.activate = activate
self.scale = (1 / math.sqrt(in_channel)) * lr_mul
self.lr_mul = lr_mul
def forward(self, input):
if self.activate:
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 EqualConv2d(nn.Module):
"""2D convolution layer with equalized learning rate.
During the forward pass the weights are scaled by the inverse of the He constant (i.e. sqrt(in_dim)) to
prevent vanishing gradients and accelerate training. This constant only works for ReLU or LeakyReLU
activation functions.
Args:
----
in_channel: int
Input channels.
out_channel: int
Output channels.
kernel_size: int
Kernel size.
stride: int
Stride of convolutional kernel across the input.
padding: int
Amount of zero padding applied to both sides of the input.
bias: bool
Use bias term.
"""
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):
out = F.conv2d(
input,
self.weight * self.scale,
bias=self.bias,
stride=self.stride,
padding=self.padding
)
return out
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 EqualConvTranspose2d(nn.Module):
"""2D transpose convolution layer with equalized learning rate.
During the forward pass the weights are scaled by the inverse of the He constant (i.e. sqrt(in_dim)) to
prevent vanishing gradients and accelerate training. This constant only works for ReLU or LeakyReLU
activation functions.
Args:
----
in_channel: int
Input channels.
out_channel: int
Output channels.
kernel_size: int
Kernel size.
stride: int
Stride of convolutional kernel across the input.
padding: int
Amount of zero padding applied to both sides of the input.
output_padding: int
Extra padding added to input to achieve the desired output size.
bias: bool
Use bias term.
"""
def __init__(
self,
in_channel,
out_channel,
kernel_size,
stride=1,
padding=0,
output_padding=0,
bias=True
):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_channel, out_channel, kernel_size, kernel_size))
self.scale = 1 / math.sqrt(in_channel * kernel_size**2)
self.stride = stride
self.padding = padding
self.output_padding = output_padding
if bias:
self.bias = nn.Parameter(torch.zeros(out_channel))
else:
self.bias = None
def forward(self, input):
out = F.conv_transpose2d(
input,
self.weight * self.scale,
bias=self.bias,
stride=self.stride,
padding=self.padding,
output_padding=self.output_padding,
)
return out
def __repr__(self):
return (
f'{self.__class__.__name__}({self.weight.shape[0]}, {self.weight.shape[1]},'
f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
)
class ConvLayer2d(nn.Sequential):
def __init__(
self,
in_channel,
out_channel,
kernel_size=3,
upsample=False,
downsample=False,
blur_kernel=[1, 3, 3, 1],
bias=True,
activate=True,
):
assert not (upsample and downsample), 'Cannot upsample and downsample simultaneously'
layers = []
if upsample:
factor = 2
p = (len(blur_kernel) - factor) - (kernel_size - 1)
pad0 = (p + 1) // 2 + factor - 1
pad1 = p // 2 + 1
layers.append(
EqualConvTranspose2d(
in_channel,
out_channel,
kernel_size,
padding=0,
stride=2,
bias=bias and not activate
)
)
layers.append(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
layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
layers.append(
EqualConv2d(
in_channel,
out_channel,
kernel_size,
padding=0,
stride=2,
bias=bias and not activate
)
)
if (not downsample) and (not upsample):
padding = kernel_size // 2
layers.append(
EqualConv2d(
in_channel,
out_channel,
kernel_size,
padding=padding,
stride=1,
bias=bias and not activate
)
)
if activate:
layers.append(FusedLeakyReLU(out_channel, bias=bias))
super().__init__(*layers)
class ConvResBlock2d(nn.Module):
"""2D convolutional residual block with equalized learning rate.
Residual block composed of 3x3 convolutions and leaky ReLUs.
Args:
----
in_channel: int
Input channels.
out_channel: int
Output channels.
upsample: bool
Apply upsampling via strided convolution in the first conv.
downsample: bool
Apply downsampling via strided convolution in the second conv.
"""
def __init__(self, in_channel, out_channel, upsample=False, downsample=False):
super().__init__()
assert not (upsample and downsample), 'Cannot upsample and downsample simultaneously'
mid_ch = in_channel if downsample else out_channel
self.conv1 = ConvLayer2d(in_channel, mid_ch, upsample=upsample, kernel_size=3)
self.conv2 = ConvLayer2d(mid_ch, out_channel, downsample=downsample, kernel_size=3)
if (in_channel != out_channel) or upsample or downsample:
self.skip = ConvLayer2d(
in_channel,
out_channel,
upsample=upsample,
downsample=downsample,
kernel_size=1,
activate=False,
bias=False,
)
def forward(self, input):
out = self.conv1(input)
out = self.conv2(out)
if hasattr(self, 'skip'):
skip = self.skip(input)
out = (out + skip) / math.sqrt(2)
else:
out = (out + input) / math.sqrt(2)
return out