mindspore/model_zoo/research/cv/MaskedFaceRecognition/utils/distance.py

# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Numpy version of euclidean distance, etc."""
import numpy as np
from utils.metric import cmc, mean_ap


def normalize(nparray, order=2, axis=0):
    """Normalize a N-D numpy array along the specified axis."""
    norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
    return nparray / (norm + np.finfo(np.float32).eps)


def compute_dist(array1, array2, dis_type='euclidean'):
    """Compute the euclidean or cosine distance of all pairs.
    Args:
        array1: numpy array with shape [m1, n]
        array2: numpy array with shape [m2, n]
    type:
        one of ['cosine', 'euclidean']
    Returns:
        numpy array with shape [m1, m2]
  """
    assert dis_type in ['cosine', 'euclidean']
    if dis_type == 'cosine':
        array1 = normalize(array1, axis=1)
        array2 = normalize(array2, axis=1)
        dist = np.matmul(array1, array2.T)
        return -1*dist

    # shape [m1, 1]
    square1 = np.sum(np.square(array1), axis=1)[..., np.newaxis]
    # shape [1, m2]
    square2 = np.sum(np.square(array2), axis=1)[np.newaxis, ...]
    squared_dist = - 2 * np.matmul(array1, array2.T) + square1 + square2
    squared_dist[squared_dist < 0] = 0
    dist = np.sqrt(squared_dist)
    return dist


def compute_score(dist_mat, query_ids, gallery_ids):
    mAP = mean_ap(distmat=dist_mat, query_ids=query_ids, gallery_ids=gallery_ids)
    cmc_scores, _ = cmc(distmat=dist_mat, query_ids=query_ids, gallery_ids=gallery_ids, topk=10)
    return mAP, cmc_scores