deep-person-reid/torchreid/metrics/distance.py

from __future__ import division, print_function, absolute_import
import torch
from torch.nn import functional as F


def compute_distance_matrix(input1, input2, metric='euclidean'):
    """A wrapper function for computing distance matrix.

    Args:
        input1 (torch.Tensor): 2-D feature matrix.
        input2 (torch.Tensor): 2-D feature matrix.
        metric (str, optional): "euclidean" or "cosine".
            Default is "euclidean".

    Returns:
        torch.Tensor: distance matrix.

    Examples::
       >>> from torchreid import metrics
       >>> input1 = torch.rand(10, 2048)
       >>> input2 = torch.rand(100, 2048)
       >>> distmat = metrics.compute_distance_matrix(input1, input2)
       >>> distmat.size() # (10, 100)
    """
    # check input
    assert isinstance(input1, torch.Tensor)
    assert isinstance(input2, torch.Tensor)
    assert input1.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
        input1.dim()
    )
    assert input2.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(
        input2.dim()
    )
    assert input1.size(1) == input2.size(1)

    if metric == 'euclidean':
        distmat = euclidean_squared_distance(input1, input2)
    elif metric == 'cosine':
        distmat = cosine_distance(input1, input2)
    else:
        raise ValueError(
            'Unknown distance metric: {}. '
            'Please choose either "euclidean" or "cosine"'.format(metric)
        )

    return distmat


def euclidean_squared_distance(input1, input2):
    """Computes euclidean squared distance.

    Args:
        input1 (torch.Tensor): 2-D feature matrix.
        input2 (torch.Tensor): 2-D feature matrix.

    Returns:
        torch.Tensor: distance matrix.
    """
    m, n = input1.size(0), input2.size(0)
    mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)
    mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    distmat = mat1 + mat2
    distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)
    return distmat


def cosine_distance(input1, input2):
    """Computes cosine distance.

    Args:
        input1 (torch.Tensor): 2-D feature matrix.
        input2 (torch.Tensor): 2-D feature matrix.

    Returns:
        torch.Tensor: distance matrix.
    """
    input1_normed = F.normalize(input1, p=2, dim=1)
    input2_normed = F.normalize(input2, p=2, dim=1)
    distmat = 1 - torch.mm(input1_normed, input2_normed.t())
    return distmat