269 lines
9.8 KiB
Python
269 lines
9.8 KiB
Python
# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
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# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
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# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
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# Augmentation (ADA)
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# =======================================================================
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# 1. Definitions
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# "Licensor" means any person or entity that distributes its Work.
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# "Software" means the original work of authorship made available under
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# this License.
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# "Work" means the Software and any additions to or derivative works of
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# the Software that are made available under this License.
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# The terms "reproduce," "reproduction," "derivative works," and
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# "distribution" have the meaning as provided under U.S. copyright law;
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# provided, however, that for the purposes of this License, derivative
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# works shall not include works that remain separable from, or merely
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# link (or bind by name) to the interfaces of, the Work.
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# Works, including the Software, are "made available" under this License
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# by including in or with the Work either (a) a copyright notice
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# referencing the applicability of this License to the Work, or (b) a
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# copy of this License.
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# 2. License Grants
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# 2.1 Copyright Grant. Subject to the terms and conditions of this
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# License, each Licensor grants to you a perpetual, worldwide,
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# non-exclusive, royalty-free, copyright license to reproduce,
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# 3. Limitations
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# 3.1 Redistribution. You may reproduce or distribute the Work only
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# 3.2 Derivative Works. You may specify that additional or different
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# works that are subject to Your Terms. Notwithstanding Your Terms,
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# this License (including the redistribution requirements in Section
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# 3.3 Use Limitation. The Work and any derivative works thereof only
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# THIS LICENSE.
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# 5. Limitation of Liability.
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# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
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# THE POSSIBILITY OF SUCH DAMAGES.
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# =======================================================================
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import torch
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import torch.nn.functional as F
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from torch import nn
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from torch.autograd import Function
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from ..utils import ext_loader
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ext_module = ext_loader.load_ext('_ext', ['fused_bias_leakyrelu'])
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class FusedBiasLeakyReLUFunctionBackward(Function):
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"""Calculate second order deviation.
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This function is to compute the second order deviation for the fused leaky
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relu operation.
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"""
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@staticmethod
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def forward(ctx, grad_output, out, negative_slope, scale):
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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empty = grad_output.new_empty(0)
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grad_input = ext_module.fused_bias_leakyrelu(
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grad_output,
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empty,
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out,
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act=3,
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grad=1,
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alpha=negative_slope,
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scale=scale)
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dim = [0]
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if grad_input.ndim > 2:
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dim += list(range(2, grad_input.ndim))
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grad_bias = grad_input.sum(dim).detach()
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return grad_input, grad_bias
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@staticmethod
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def backward(ctx, gradgrad_input, gradgrad_bias):
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out, = ctx.saved_tensors
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# The second order deviation, in fact, contains two parts, while the
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# the first part is zero. Thus, we direct consider the second part
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# which is similar with the first order deviation in implementation.
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gradgrad_out = ext_module.fused_bias_leakyrelu(
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gradgrad_input,
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gradgrad_bias.to(out.dtype),
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out,
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act=3,
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grad=1,
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alpha=ctx.negative_slope,
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scale=ctx.scale)
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return gradgrad_out, None, None, None
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class FusedBiasLeakyReLUFunction(Function):
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@staticmethod
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def forward(ctx, input, bias, negative_slope, scale):
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empty = input.new_empty(0)
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out = ext_module.fused_bias_leakyrelu(
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input,
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bias,
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empty,
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act=3,
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grad=0,
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alpha=negative_slope,
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scale=scale)
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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return out
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@staticmethod
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def backward(ctx, grad_output):
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out, = ctx.saved_tensors
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grad_input, grad_bias = FusedBiasLeakyReLUFunctionBackward.apply(
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grad_output, out, ctx.negative_slope, ctx.scale)
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return grad_input, grad_bias, None, None
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class FusedBiasLeakyReLU(nn.Module):
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"""Fused bias leaky ReLU.
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This function is introduced in the StyleGAN2:
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http://arxiv.org/abs/1912.04958
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The bias term comes from the convolution operation. In addition, to keep
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the variance of the feature map or gradients unchanged, they also adopt a
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scale similarly with Kaiming initialization. However, since the
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:math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
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final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
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your own scale.
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TODO: Implement the CPU version.
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Args:
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channel (int): The channel number of the feature map.
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negative_slope (float, optional): Same as nn.LeakyRelu.
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Defaults to 0.2.
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scale (float, optional): A scalar to adjust the variance of the feature
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map. Defaults to 2**0.5.
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"""
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def __init__(self, num_channels, negative_slope=0.2, scale=2**0.5):
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super(FusedBiasLeakyReLU, self).__init__()
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self.bias = nn.Parameter(torch.zeros(num_channels))
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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return fused_bias_leakyrelu(input, self.bias, self.negative_slope,
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self.scale)
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def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5):
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"""Fused bias leaky ReLU function.
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This function is introduced in the StyleGAN2:
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http://arxiv.org/abs/1912.04958
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The bias term comes from the convolution operation. In addition, to keep
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the variance of the feature map or gradients unchanged, they also adopt a
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scale similarly with Kaiming initialization. However, since the
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:math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
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final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
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your own scale.
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Args:
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input (torch.Tensor): Input feature map.
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bias (nn.Parameter): The bias from convolution operation.
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negative_slope (float, optional): Same as nn.LeakyRelu.
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Defaults to 0.2.
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scale (float, optional): A scalar to adjust the variance of the feature
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map. Defaults to 2**0.5.
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Returns:
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torch.Tensor: Feature map after non-linear activation.
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"""
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if not input.is_cuda:
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return bias_leakyrelu_ref(input, bias, negative_slope, scale)
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return FusedBiasLeakyReLUFunction.apply(input, bias.to(input.dtype),
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negative_slope, scale)
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def bias_leakyrelu_ref(x, bias, negative_slope=0.2, scale=2**0.5):
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if bias is not None:
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assert bias.ndim == 1
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assert bias.shape[0] == x.shape[1]
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x = x + bias.reshape([-1 if i == 1 else 1 for i in range(x.ndim)])
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x = F.leaky_relu(x, negative_slope)
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if scale != 1:
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x = x * scale
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return x
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