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add person reranking

pull/173/head
KaiyangZhou 2019-05-09 22:47:55 +01:00
parent 6342fb3055
commit 5839a026c3
4 changed files with 138 additions and 21 deletions

20
scripts/default_parser.py
View File

@ -125,13 +125,13 @@ def init_parser():
help='learning rate decay')
# ************************************************************
# Cross entropy loss-specific setting
# Cross entropy loss
# ************************************************************
parser.add_argument('--label-smooth', action='store_true',
help='use label smoothing regularizer in cross entropy loss')
# ************************************************************
# Hard triplet loss-specific setting
# Hard triplet loss
# ************************************************************
parser.add_argument('--margin', type=float, default=0.3,
help='margin for triplet loss')
@ -165,6 +165,13 @@ def init_parser():
help='distance metric')
parser.add_argument('--ranks', type=str, default=[1, 5, 10, 20], nargs='+',
help='cmc ranks')
parser.add_argument('--rerank', action='store_true',
help='use person re-ranking (by Zhong et al. CVPR2017)')
parser.add_argument('--visrank', action='store_true',
help='visualize ranked results, only available in evaluation mode')
parser.add_argument('--visrank-topk', type=int, default=20,
help='visualize topk ranks')
# ************************************************************
# Miscs
@ -182,11 +189,7 @@ def init_parser():
parser.add_argument('--gpu-devices', type=str, default='0',
help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--use-avai-gpus', action='store_true',
help='use available gpus instead of specified devices (useful when using managed clusters)')
parser.add_argument('--visrank', action='store_true',
help='visualize ranked results, only available in evaluation mode')
parser.add_argument('--visrank-topk', type=int, default=20,
help='visualize topk ranks')
help='use available gpus instead of specified devices')
return parser
@ -278,5 +281,6 @@ def engine_run_kwargs(parsed_args):
'visrank': parsed_args.visrank,
'visrank_topk': parsed_args.visrank_topk,
'use_metric_cuhk03': parsed_args.use_metric_cuhk03,
'ranks': parsed_args.ranks
'ranks': parsed_args.ranks,
'rerank': parsed_args.rerank
}

36
torchreid/engine/engine.py
View File

@ -12,13 +12,13 @@ import torch
import torch.nn as nn
import torchreid
from torchreid.utils import AverageMeter, visualize_ranked_results, save_checkpoint
from torchreid.utils import AverageMeter, visualize_ranked_results, save_checkpoint, re_ranking
from torchreid.losses import DeepSupervision
from torchreid import metrics
class Engine(object):
"""A generic base Engine class for both image- and video-reid.
r"""A generic base Engine class for both image- and video-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
@ -43,8 +43,8 @@ class Engine(object):
def run(self, save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None,
start_eval=0, eval_freq=-1, test_only=False, print_freq=10,
dist_metric='euclidean', visrank=False, visrank_topk=20,
use_metric_cuhk03=False, ranks=[1, 5, 10, 20]):
"""A unified pipeline for training and evaluating a model.
use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False):
r"""A unified pipeline for training and evaluating a model.
Args:
save_dir (str): directory to save model.
@ -70,6 +70,8 @@ class Engine(object):
use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.
ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
rerank (bool, optional): use person re-ranking (by Zhong et al. CVPR'17).
Default is False. This is only enabled when test_only=True.
"""
trainloader, testloader = self.datamanager.return_dataloaders()
@ -82,7 +84,8 @@ class Engine(object):
visrank_topk=visrank_topk,
save_dir=save_dir,
use_metric_cuhk03=use_metric_cuhk03,
ranks=ranks
ranks=ranks,
rerank=rerank
)
return
@ -131,7 +134,7 @@ class Engine(object):
print('Elapsed {}'.format(elapsed))
def train(self):
"""Performs training on source datasets for one epoch.
r"""Performs training on source datasets for one epoch.
This will be called every epoch in ``run()``, e.g.
@ -147,8 +150,8 @@ class Engine(object):
raise NotImplementedError
def test(self, epoch, testloader, dist_metric='euclidean', visrank=False, visrank_topk=20,
save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20]):
"""Tests model on target datasets.
save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False):
r"""Tests model on target datasets.
.. note::
@ -176,6 +179,8 @@ class Engine(object):
use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.
ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
rerank (bool, optional): use person re-ranking (by Zhong et al. CVPR'17).
Default is False.
"""
targets = list(testloader.keys())
@ -194,7 +199,8 @@ class Engine(object):
visrank_topk=visrank_topk,
save_dir=save_dir,
use_metric_cuhk03=use_metric_cuhk03,
ranks=ranks
ranks=ranks,
rerank=rerank
)
return rank1
@ -202,13 +208,13 @@ class Engine(object):
@torch.no_grad()
def _evaluate(self, epoch, dataset_name='', queryloader=None, galleryloader=None,
dist_metric='euclidean', visrank=False, visrank_topk=20, save_dir='',
use_metric_cuhk03=False, ranks=[1, 5, 10, 20]):
use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False):
batch_time = AverageMeter()
self.model.eval()
print('Extracting features from query set ...')
qf, q_pids, q_camids = [], [], []
qf, q_pids, q_camids = [], [], [] # query features, query person IDs and query camera IDs
for batch_idx, data in enumerate(queryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
if self.use_gpu:
@ -226,7 +232,7 @@ class Engine(object):
print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = [], [], []
gf, g_pids, g_camids = [], [], [] # gallery features, gallery person IDs and gallery camera IDs
end = time.time()
for batch_idx, data in enumerate(galleryloader):
imgs, pids, camids = self._parse_data_for_eval(data)
@ -249,6 +255,12 @@ class Engine(object):
distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
distmat = distmat.numpy()
if rerank:
print('Applying person re-ranking ...')
distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
distmat = re_ranking(distmat, distmat_qq, distmat_gg)
print('Computing CMC and mAP ...')
cmc, mAP = metrics.evaluate_rank(
distmat,

3
torchreid/utils/__init__.py
View File

@ -4,4 +4,5 @@ from .avgmeter import *
from .loggers import *
from .tools import *
from .reidtools import *
from .torchtools import *
from .torchtools import *
from .rerank import re_ranking

100
torchreid/utils/rerank.py 100644
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@ -0,0 +1,100 @@
#!/usr/bin/env python2/python3
# -*- coding: utf-8 -*-
"""
Source: https://github.com/zhunzhong07/person-re-ranking
Created on Mon Jun 26 14:46:56 2017
@author: luohao
Modified by Houjing Huang, 2017-12-22.
- This version accepts distance matrix instead of raw features.
- The difference of `/` division between python 2 and 3 is handled.
- numpy.float16 is replaced by numpy.float32 for numerical precision.
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
q_g_dist: query-gallery distance matrix, numpy array, shape [num_query, num_gallery]
q_q_dist: query-query distance matrix, numpy array, shape [num_query, num_query]
g_g_dist: gallery-gallery distance matrix, numpy array, shape [num_gallery, num_gallery]
k1, k2, lambda_value: parameters, the original paper is (k1=20, k2=6, lambda_value=0.3)
Returns:
final_dist: re-ranked distance, numpy array, shape [num_query, num_gallery]
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
__all__ = ['re_ranking']
import numpy as np
def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
# The following naming, e.g. gallery_num, is different from outer scope.
# Don't care about it.
original_dist = np.concatenate(
[np.concatenate([q_q_dist, q_g_dist], axis=1),
np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
axis=0)
original_dist = np.power(original_dist, 2).astype(np.float32)
original_dist = np.transpose(1. * original_dist/np.max(original_dist,axis = 0))
V = np.zeros_like(original_dist).astype(np.float32)
initial_rank = np.argsort(original_dist).astype(np.int32)
query_num = q_g_dist.shape[0]
gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
all_num = gallery_num
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] = 1.*weight/np.sum(weight)
original_dist = original_dist[:query_num,]
if k2 != 1:
V_qe = np.zeros_like(V,dtype=np.float32)
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.float32)
for i in range(query_num):
temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float32)
indNonZero = np.where(V[i,:] != 0)[0]
indImages = []
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