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# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import numpy as np
import torch
import torch.nn as nn
class AffineMod(nn.Module):
r"""Learning affine modulation of activation.
Args:
in_features (int): Number of input features.
style_features (int): Number of style features.
mod_bias (bool): Whether to modulate bias.
"""
def __init__(self,
in_features,
style_features,
mod_bias=True
):
super().__init__()
self.weight_alpha = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_alpha = nn.Parameter(torch.full([in_features], 1, dtype=torch.float)) # init to 1
self.weight_beta = None
self.bias_beta = None
self.mod_bias = mod_bias
if mod_bias:
self.weight_beta = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_beta = nn.Parameter(torch.full([in_features], 0, dtype=torch.float))
@staticmethod
def _linear_f(x, w, b):
w = w.to(x.dtype)
x_shape = x.shape
x = x.reshape(-1, x_shape[-1])
if b is not None:
b = b.to(x.dtype)
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = x.reshape(*x_shape[:-1], -1)
return x
# x: B, ... , Cin
# z: B, 1, 1, , Cz
def forward(self, x, z):
x_shape = x.shape
z_shape = z.shape
x = x.reshape(x_shape[0], -1, x_shape[-1])
z = z.reshape(z_shape[0], 1, z_shape[-1])
alpha = self._linear_f(z, self.weight_alpha, self.bias_alpha) # [B, ..., I]
x = x * alpha
if self.mod_bias:
beta = self._linear_f(z, self.weight_beta, self.bias_beta) # [B, ..., I]
x = x + beta
x = x.reshape(*x_shape[:-1], x.shape[-1])
return x
class ModLinear(nn.Module):
r"""Linear layer with affine modulation (Based on StyleGAN2 mod demod).
Equivalent to affine modulation following linear, but faster when the same modulation parameters are shared across
multiple inputs.
Args:
in_features (int): Number of input features.
out_features (int): Number of output features.
style_features (int): Number of style features.
bias (bool): Apply additive bias before the activation function?
mod_bias (bool): Whether to modulate bias.
output_mode (bool): If True, modulate output instead of input.
weight_gain (float): Initialization gain
"""
def __init__(self,
in_features,
out_features,
style_features,
bias=True,
mod_bias=True,
output_mode=False,
weight_gain=1,
bias_init=0
):
super().__init__()
weight_gain = weight_gain / np.sqrt(in_features)
self.weight = nn.Parameter(torch.randn([out_features, in_features]) * weight_gain)
self.bias = nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
self.weight_alpha = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_alpha = nn.Parameter(torch.full([in_features], 1, dtype=torch.float)) # init to 1
self.weight_beta = None
self.bias_beta = None
self.mod_bias = mod_bias
self.output_mode = output_mode
if mod_bias:
if output_mode:
mod_bias_dims = out_features
else:
mod_bias_dims = in_features
self.weight_beta = nn.Parameter(torch.randn([mod_bias_dims, style_features]) / np.sqrt(style_features))
self.bias_beta = nn.Parameter(torch.full([mod_bias_dims], 0, dtype=torch.float))
@staticmethod
def _linear_f(x, w, b):
w = w.to(x.dtype)
x_shape = x.shape
x = x.reshape(-1, x_shape[-1])
if b is not None:
b = b.to(x.dtype)
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = x.reshape(*x_shape[:-1], -1)
return x
# x: B, ... , Cin
# z: B, 1, 1, , Cz
def forward(self, x, z):
x_shape = x.shape
z_shape = z.shape
x = x.reshape(x_shape[0], -1, x_shape[-1])
z = z.reshape(z_shape[0], 1, z_shape[-1])
alpha = self._linear_f(z, self.weight_alpha, self.bias_alpha) # [B, ..., I]
w = self.weight.to(x.dtype) # [O I]
w = w.unsqueeze(0) * alpha # [1 O I] * [B 1 I] = [B O I]
if self.mod_bias:
beta = self._linear_f(z, self.weight_beta, self.bias_beta) # [B, ..., I]
if not self.output_mode:
x = x + beta
b = self.bias
if b is not None:
b = b.to(x.dtype)[None, None, :]
if self.mod_bias and self.output_mode:
if b is None:
b = beta
else:
b = b + beta
# [B ? I] @ [B I O] = [B ? O]
if b is not None:
x = torch.baddbmm(b, x, w.transpose(1, 2))
else:
x = x.bmm(w.transpose(1, 2))
x = x.reshape(*x_shape[:-1], x.shape[-1])
return x
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