forked from ccf-ai-infra/GPUCodeForces
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
class Model(nn.Module):
|
|
"""
|
|
Chebyshev Distance implementation.
|
|
Computes the Chebyshev distance (maximum absolute difference) between two sets of vectors.
|
|
"""
|
|
def __init__(self):
|
|
super(Model, self).__init__()
|
|
|
|
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Compute Chebyshev distance between x and y.
|
|
|
|
Args:
|
|
x (torch.Tensor): First set of vectors [batch_size, feature_dim]
|
|
y (torch.Tensor): Second set of vectors [batch_size, feature_dim]
|
|
|
|
Returns:
|
|
torch.Tensor: Chebyshev distances [batch_size]
|
|
"""
|
|
# Input validation
|
|
if x.shape != y.shape:
|
|
raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
|
|
|
|
if x.dim() != 2:
|
|
raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
|
|
|
|
# Compute Chebyshev distance: max(|x_i - y_i|)
|
|
chebyshev_dist = torch.max(torch.abs(x - y), dim=1)[0]
|
|
|
|
return chebyshev_dist
|
|
|
|
batch_size = 256
|
|
feature_dim = 512
|
|
|
|
def get_inputs():
|
|
# Generate two sets of vectors
|
|
x = torch.randn(batch_size, feature_dim)
|
|
y = torch.randn(batch_size, feature_dim)
|
|
return [x, y]
|
|
|
|
def get_init_inputs():
|
|
return [] # No special initialization inputs needed
|