82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
|
# pylint: skip-file
|
||
|
|
# type: ignore
|
||
|
|
# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch.autograd import Function
|
||
|
|
|
||
|
|
fused_act_ext = None
|
||
|
|
|
||
|
|
|
||
|
|
class FusedLeakyReLUFunctionBackward(Function):
|
||
|
|
@staticmethod
|
||
|
|
def forward(ctx, grad_output, out, negative_slope, scale):
|
||
|
|
ctx.save_for_backward(out)
|
||
|
|
ctx.negative_slope = negative_slope
|
||
|
|
ctx.scale = scale
|
||
|
|
|
||
|
|
empty = grad_output.new_empty(0)
|
||
|
|
|
||
|
|
grad_input = fused_act_ext.fused_bias_act(
|
||
|
|
grad_output, empty, out, 3, 1, negative_slope, scale
|
||
|
|
)
|
||
|
|
|
||
|
|
dim = [0]
|
||
|
|
|
||
|
|
if grad_input.ndim > 2:
|
||
|
|
dim += list(range(2, grad_input.ndim))
|
||
|
|
|
||
|
|
grad_bias = grad_input.sum(dim).detach()
|
||
|
|
|
||
|
|
return grad_input, grad_bias
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def backward(ctx, gradgrad_input, gradgrad_bias):
|
||
|
|
(out,) = ctx.saved_tensors
|
||
|
|
gradgrad_out = fused_act_ext.fused_bias_act(
|
||
|
|
gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
|
||
|
|
)
|
||
|
|
|
||
|
|
return gradgrad_out, None, None, None
|
||
|
|
|
||
|
|
|
||
|
|
class FusedLeakyReLUFunction(Function):
|
||
|
|
@staticmethod
|
||
|
|
def forward(ctx, input, bias, negative_slope, scale):
|
||
|
|
empty = input.new_empty(0)
|
||
|
|
out = fused_act_ext.fused_bias_act(
|
||
|
|
input, bias, empty, 3, 0, negative_slope, scale
|
||
|
|
)
|
||
|
|
ctx.save_for_backward(out)
|
||
|
|
ctx.negative_slope = negative_slope
|
||
|
|
ctx.scale = scale
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def backward(ctx, grad_output):
|
||
|
|
(out,) = ctx.saved_tensors
|
||
|
|
|
||
|
|
grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
|
||
|
|
grad_output, out, ctx.negative_slope, ctx.scale
|
||
|
|
)
|
||
|
|
|
||
|
|
return grad_input, grad_bias, None, None
|
||
|
|
|
||
|
|
|
||
|
|
class FusedLeakyReLU(nn.Module):
|
||
|
|
def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.bias = nn.Parameter(torch.zeros(channel))
|
||
|
|
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 FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
|