evaluate_rerank.py

import scipy.io
import torch
import numpy as np
import time
from re_ranking import re_ranking


#######################################################################
# Evaluate
def evaluate(score, ql, qc, gl, gc):
    index = np.argsort(score)  # from small to large
    # index = index[::-1]
    # good index
    query_index = np.argwhere(gl == ql)
    camera_index = np.argwhere(gc == qc)

    good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)
    junk_index1 = np.argwhere(gl == -1)
    junk_index2 = np.intersect1d(query_index, camera_index)
    junk_index = np.append(junk_index2, junk_index1)  # .flatten())

    CMC_tmp = compute_mAP(index, good_index, junk_index)
    return CMC_tmp


def compute_mAP(index, good_index, junk_index):
    ap = 0
    cmc = torch.IntTensor(len(index)).zero_()
    if good_index.size == 0:  # if empty
        cmc[0] = -1
        return ap, cmc

    # remove junk_index
    mask = np.in1d(index, junk_index, invert=True)
    index = index[mask]

    # find good_index index
    ngood = len(good_index)
    mask = np.in1d(index, good_index)
    rows_good = np.argwhere(mask == True)
    rows_good = rows_good.flatten()

    cmc[rows_good[0]:] = 1
    for i in range(ngood):
        d_recall = 1.0 / ngood
        precision = (i + 1) * 1.0 / (rows_good[i] + 1)
        if rows_good[i] != 0:
            old_precision = i * 1.0 / rows_good[i]
        else:
            old_precision = 1.0
        ap = ap + d_recall * (old_precision + precision) / 2

    return ap, cmc


######################################################################
result = scipy.io.loadmat('pytorch_result.mat')
query_feature = result['query_f']
query_cam = result['query_cam'][0]
query_label = result['query_label'][0]
gallery_feature = result['gallery_f']
gallery_cam = result['gallery_cam'][0]
gallery_label = result['gallery_label'][0]

CMC = torch.IntTensor(len(gallery_label)).zero_()
ap = 0.0
# re-ranking
print('calculate initial distance')
q_g_dist = np.dot(query_feature, np.transpose(gallery_feature))
q_q_dist = np.dot(query_feature, np.transpose(query_feature))
g_g_dist = np.dot(gallery_feature, np.transpose(gallery_feature))
since = time.time()
re_rank = re_ranking(q_g_dist, q_q_dist, g_g_dist)
time_elapsed = time.time() - since
print('Reranking complete in {:.0f}m {:.0f}s'.format(
    time_elapsed // 60, time_elapsed % 60))
for i in range(len(query_label)):
    ap_tmp, CMC_tmp = evaluate(re_rank[i, :], query_label[i], query_cam[i], gallery_label, gallery_cam)
    if CMC_tmp[0] == -1:
        continue
    CMC = CMC + CMC_tmp
    ap += ap_tmp
    # print(i, CMC_tmp[0])

CMC = CMC.float()
CMC = CMC / len(query_label)  # average CMC
print('top1:%f top5:%f top10:%f mAP:%f' % (CMC[0], CMC[4], CMC[9], ap / len(query_label)))