forked from ccf-ai-infra/GPUCodeForces
46 lines
1016 B
Python
46 lines
1016 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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N, C, H, W = 32, 64, 56, 56
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EPS = 1e-6
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class HellingerDistance(nn.Module):
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def __init__(self, eps=1e-6):
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super().__init__()
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self.eps = eps
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def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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x = torch.relu(x)
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y = torch.relu(y)
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sqrt_x = torch.sqrt(x + self.eps)
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sqrt_y = torch.sqrt(y + self.eps)
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diff_sq = torch.square(sqrt_x - sqrt_y)
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sum_sq = torch.sum(diff_sq, dim=[1, 2, 3])
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return torch.sqrt(sum_sq + self.eps) / 1.41421356 # 1/sqrt(2)
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.op = HellingerDistance(EPS)
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def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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return self.op(x, y)
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def get_inputs():
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x = torch.rand(N, C, H, W, dtype=torch.float32)
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y = torch.rand(N, C, H, W, dtype=torch.float32)
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return [x, y]
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def get_init_inputs():
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return []
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