Índice de evaluación del modelo
Prefacio
Al introducir la reidentificación de peatones, mencioné que los indicadores de evaluación más utilizados son mAP, cmc, rerank, etc., por lo que este blog es principalmente un análisis de esta parte del código.
Para conocer algunos indicadores de evaluación de uso común, puede leer este blog:
Indicadores de evaluación comunes para la reidentificación de peatones
Evaluación mAP y CMC
El siguiente código está en el archivo eval_metrics.py del paquete utils:
from __future__ import print_function, absolute_import
import numpy as np
import copy
from collections import defaultdict
import sys
# distmat.shape [3368,15913]
def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
"""Evaluation with market1501 metric
Key: for each query identity, its gallery images from the same camera view are discarded.
"""
# 获得query gallery图片（特征）的数目
num_q, num_g = distmat.shape
# 判断 如果gallery的数目小于rank 则吧gallery的数目给rank
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
# 将dismat中的元素从小到大排列，提取其对应的index(索引)，然后输出到indexs
indices = np.argsort(distmat, axis=1)
# 进行匹配，如果g_pids[indices]等于q_pids[:, np.newaxis]身份ID，则被置1。
# matches[3368，15913]，排列之后的结果类似如下：
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
# compute cmc curve for each query
all_cmc = []
all_AP = [] # 记录query每张图像的AP
num_valid_q = 0. # number of valid query 记录有效的query数量
# 对每一个query中的图片进行处理
for q_idx in range(num_q):
# get query pid and camid
q_pid = q_pids[q_idx]
q_camid = q_camids[q_idx]
# remove gallery samples that have the same pid and camid with query
# 取出当前第q_idx张图片，在gallery中查询过后的排序结果
# [3368,15913]-->[15913,]
order = indices[q_idx]
# 删除与查询具有相同pid和camid的gallery样本,也就是删除query和gallery中相同图片的结果
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
keep = np.invert(remove)
# compute cmc curve
# 二进制向量，值为1的位置是正确的匹配
orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
if not np.any(orig_cmc):
# 当查询标识未出现在图库中时，此条件为真
# this condition is true when query identity does not appear in gallery
continue
# 计算一行中的累加值，如一个数组为[0,0,1,1,0,2,0]
# 通过cumsum得到[0,0,1,2,2,4,4]
cmc = orig_cmc.cumsum()
# cmc > 1的位置，表示都预测正确了
cmc[cmc > 1] = 1
# 根据max_rank，添cmc到all_cmc之中
all_cmc.append(cmc[:max_rank])
num_valid_q += 1.
# compute average precision 平均精度
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
num_rel = orig_cmc.sum()
tmp_cmc = orig_cmc.cumsum()
tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
AP = tmp_cmc.sum() / num_rel
all_AP.append(AP)
# # 所有查询身份没有出现在图库（gallery）中则报错
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
# 计算平均cmc精度
all_cmc = np.asarray(all_cmc).astype(np.float32)
all_cmc = all_cmc.sum(0) / num_valid_q
mAP = np.mean(all_AP)
return all_cmc, mAP
# 测试代码
def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
volver a clasificar la evaluación
Este es el código de re-clasificación directamente. Tengo tiempo para volver atrás y leer el documento correspondiente. No entiendo el proceso de implementación. Este código se coloca en el archivo de re-clasificación en utils.
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri, 25 May 2018 20:29:09
@author: luohao
"""
"""
CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
Matlab version: https://github.com/zhunzhong07/person-re-ranking
"""
"""
API
probFea: all feature vectors of the query set (torch tensor)
probFea: all feature vectors of the gallery set (torch tensor)
k1,k2,lambda: parameters, the original paper is (k1=20,k2=6,lambda=0.3)
MemorySave: set to 'True' when using MemorySave mode
Minibatch: avaliable when 'MemorySave' is 'True'
"""
import numpy as np
import torch
def re_ranking(probFea, galFea, k1, k2, lambda_value, local_distmat = None, only_local = False):
# if feature vector is numpy, you should use 'torch.tensor' transform it to tensor
# 原图像特征 3368? 对应query中图片数目
query_num = probFea.size(0)
# query+gally总共数目
all_num = query_num + galFea.size(0)
if only_local:
original_dist = local_distmat
else:
# 拼接
feat = torch.cat([probFea,galFea])
# 计算距离
print('using GPU to compute original distance')
distmat = torch.pow(feat,2).sum(dim=1, keepdim=True).expand(all_num,all_num) + \
torch.pow(feat, 2).sum(dim=1, keepdim=True).expand(all_num, all_num).t()
distmat.addmm_(1,-2,feat,feat.t())
original_dist = distmat.numpy()
# 删除变量feat，解除对数据的引用
del feat
if not local_distmat is None:
original_dist = original_dist + local_distmat
gallery_num = original_dist.shape[0]
original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
V = np.zeros_like(original_dist).astype(np.float16)
initial_rank = np.argsort(original_dist).astype(np.int32)
print('starting re_ranking')
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[i, :k1 + 1]
backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
fi = np.where(backward_k_neigh_index == i)[0]
k_reciprocal_index = forward_k_neigh_index[fi]
k_reciprocal_expansion_index = k_reciprocal_index
for j in range(len(k_reciprocal_index)):
candidate = k_reciprocal_index[j]
candidate_forward_k_neigh_index = initial_rank[candidate, :int(np.around(k1 / 2)) + 1]
candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
:int(np.around(k1 / 2)) + 1]
fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2 / 3 * len(
candidate_k_reciprocal_index):
k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)
k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
original_dist = original_dist[:query_num, ]
if k2 != 1:
V_qe = np.zeros_like(V, dtype=np.float16)
for i in range(all_num):
V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
V = V_qe
del V_qe
del initial_rank
invIndex = []
for i in range(gallery_num):
invIndex.append(np.where(V[:, i] != 0)[0])
jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
for i in range(query_num):
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
indNonZero = np.where(V[i, :] != 0)[0]
indImages = [invIndex[ind] for ind in indNonZero]
for j in range(len(indNonZero)):
temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
V[indImages[j], indNonZero[j]])
jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value
del original_dist
del V
del jaccard_dist
final_dist = final_dist[:query_num, query_num:]
return final_dist