forked from ccf-ai-infra/GPUCodeForces
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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"""
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Canberra Distance implementation.
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Computes the Canberra distance between two sets of vectors.
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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, y: torch.Tensor) -> torch.Tensor:
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"""
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Compute Canberra distance between x and y.
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Args:
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x (torch.Tensor): First set of vectors [batch_size, feature_dim]
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y (torch.Tensor): Second set of vectors [batch_size, feature_dim]
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Returns:
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torch.Tensor: Canberra distances [batch_size]
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"""
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# Input validation
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if x.shape != y.shape:
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raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
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if x.dim() != 2:
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raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
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# Compute Canberra distance: Σ(|x_i - y_i| / (|x_i| + |y_i|))
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# Step 1: Compute absolute differences
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diff = torch.abs(x - y)
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# Step 2: Compute denominator (sum of absolute values)
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denom = torch.abs(x) + torch.abs(y)
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# Step 3: Handle division by zero (where denominator is 0)
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# When both x_i and y_i are 0, the term is defined as 0
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ratio = torch.where(denom > 0, diff / denom, torch.zeros_like(diff))
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# Step 4: Sum along feature dimension
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distance = torch.sum(ratio, dim=1)
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return distance
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batch_size = 256
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feature_dim = 1024
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def get_inputs():
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# Generate two sets of positive vectors (to avoid sign issues)
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x = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
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y = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
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return [x, y]
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def get_init_inputs():
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return [] # No special initialization inputs needed
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