forked from ccf-ai-infra/GPUCodeForces
42 lines
1014 B
Python
42 lines
1014 B
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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"""
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Sigmoid Derivative算子实现
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"""
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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计算Sigmoid Derivative
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Args:
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x: 输入张量 [任意形状]
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Returns:
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sigmoid'(x) = sigmoid(x) * (1 - sigmoid(x))
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"""
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# 方法1: 直接计算
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sigmoid_x = torch.sigmoid(x)
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return sigmoid_x * (1 - sigmoid_x)
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# 方法2: 数值稳定版本（用于对比）
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# return torch.nn.functional.softplus(-x).exp() * torch.sigmoid(x).square()
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# 适合测试的数据规模
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batch_size = 256
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channels = 64
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height = 32
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width = 32
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def get_inputs():
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# 生成测试数据，包含正负值
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x = torch.randn(batch_size, channels, height, width) * 2.0 # 扩大范围
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return [x]
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def get_init_inputs():
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return [] # 无需初始化
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