41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
import os
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import torch
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from torch import nn
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from torch.nn import functional as F
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module_path = os.path.dirname(__file__)
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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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rest_dim = [1] * (input.ndim - bias.ndim - 1)
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input = input.cuda()
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if input.ndim == 3:
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return (
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F.leaky_relu(
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input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope
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)
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* scale #增益值，激活函数里的 gain（torch中scale） 是一个增益值，增益值是指的非线性函数稳态时输入幅度与输出幅度的比值，通常被用来乘在激活函数之后使激活函数更加稳定。
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)
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else:
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return (
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F.leaky_relu(
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input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
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)
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* scale
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)
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