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82 lines
2.3 KiB
82 lines
2.3 KiB
2 years ago
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# pylint: skip-file
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# type: ignore
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# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
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import torch
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from torch import nn
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from torch.autograd import Function
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fused_act_ext = None
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class FusedLeakyReLUFunctionBackward(Function):
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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 = fused_act_ext.fused_bias_act(
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grad_output, empty, out, 3, 1, negative_slope, scale
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)
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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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gradgrad_out = fused_act_ext.fused_bias_act(
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gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
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)
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return gradgrad_out, None, None, None
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class FusedLeakyReLUFunction(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 = fused_act_ext.fused_bias_act(
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input, bias, empty, 3, 0, negative_slope, scale
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)
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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 = FusedLeakyReLUFunctionBackward.apply(
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grad_output, out, ctx.negative_slope, ctx.scale
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)
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return grad_input, grad_bias, None, None
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(channel))
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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_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
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return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
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