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import torch | |
from torch import nn | |
from torch.nn import functional as F | |
class FusedLeakyReLU(nn.Module): | |
def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5): | |
super().__init__() | |
if bias: | |
self.bias = nn.Parameter(torch.zeros(channel)) | |
else: | |
self.bias = None | |
self.negative_slope = negative_slope | |
self.scale = scale | |
def forward(self, inputs): | |
return fused_leaky_relu(inputs, self.bias, self.negative_slope, self.scale) | |
def fused_leaky_relu(inputs, bias=None, negative_slope=0.2, scale=2 ** 0.5): | |
if bias is not None: | |
rest_dim = [1] * (inputs.ndim - bias.ndim - 1) | |
return ( | |
F.leaky_relu( | |
inputs + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope | |
) | |
* scale | |
) | |
else: | |
return F.leaky_relu(inputs, negative_slope=negative_slope) * scale |