CHPAN

Complemental Human Parsing and Attention Guided Network with Multiple Features for Person Re-Identification is implemented in PyTorch

Prerequisites

• Pytorch 1.1
• cuda 9.0
• python 3.6
• 1GPUs Memory>29G We Recommend Tesla V100

Datasets

To use our code, firstly you should download ReID dataset (Market1501,DukeMTMC-reID,CUHK03-NP and MSMT17) from Here(saqs).

Here we use the CUHK03 dataset as an example for description.

"detected" means the bounding boxes are estimated by pedestrian detector

"labeled" means the bounding boxes are labeled by human

CUHK03_np
│ 
└───Labeled
│   └───bounding_box_test
│   │   │   0003_c1_21.jpg
│   │   │   0003_c1_23.jpg
│   │   │   ...
│   └───bounding_box_train
│   │   │   0001_c1_1.png
│   │   │   0001_c1_2.png
│   │   │   ...
│   └───query
│   │   │   0003_c1_22.png
│   │   │   0003_c2_27.png
│   │   │   ...
└───detected
│   └───bounding_box_test
│   │   │   0003_c1_21.jpg
│   │   │   0003_c1_23.jpg
│   │   │   ...
│   └───bounding_box_train
│   │   │   0001_c1_1.png
│   │   │   0001_c1_2.png
│   │   │   ...
│   └───query
│   │   │   0003_c1_22.png
│   │   │   0003_c2_27.png
│   │   │   ...

Train

First ,you must run the perpare.py to get 'pytorch' folder.

You need prepare your dataset-dir in the 'prepare.py' flie

download_path = '.../dataset/CUHK'

In our train.py,we give you some options,as follows:

parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--gpu_ids',default='1', type=str,help='gpu_ids: e.g. 0  0,1,2  0,2')
parser.add_argument('--name',default='', type=str, help='output model name')
parser.add_argument('--data_dir',default='',type=str, help='training dir path')
parser.add_argument('--batchsize', default=64, type=int, help='batchsize')
parser.add_argument('--erasing_p', default=0, type=float, help='Random Erasing probability, in [0,1]')
parser.add_argument('--warm_epoch', default=10, type=int, help='the first K epoch that needs warm up')
parser.add_argument('--epochnum', default=150, type=int, help='please to select the epoch num')
parser.add_argument('--base_lr', default=0.01, type=float, help='the base_learning rate')
parser.add_argument('--tripletmargin', default=0.3, type=float, help='the tripletmargin')
parser.add_argument('--warmup_begin_lr', default=3e-4, type=float, help='warmup learning rate')
parser.add_argument('--factor', default=0.5, type=float, help='the learning rate decracy')
parser.add_argument('--testing', action='store_true', help='import testing features')
parser.add_argument('--featuresize', default=48, type=int, help='the stage4s feature map size')
opt = parser.parse_args()

Usage

python3 train.py --gpu_ids .. --name .. --data_dir ../cuhk03-np/labeled/pytorch --batchsize --erasing_p  --warm_epoch  --epochnum  --base_lr  --tripletmargin  --warmup_begin_lr  --factor   --featuresize 

python3 train.py --gpu_ids .. --name .. --data_dir ../cuhk03-np/detected/pytorch --batchsize --erasing_p  --warm_epoch  --epochnum  --base_lr  --tripletmargin --warmup_begin_lr  --factor   --featuresize

Test

parser = argparse.ArgumentParser(description='testing')
parser.add_argument('--gpu_ids',default='1', type=str,help='gpu_ids: e.g. 0  0,1,2  0,2')
parser.add_argument('--name',default='', type=str, help='output model name')
parser.add_argument('--test_dir',default='',type=str, help='training dir path')
parser.add_argument('--train_all', action='store_true', help='use all training data' )
parser.add_argument('--batchsize', default=64, type=int, help='batchsize')
parser.add_argument('--featurereid', default=6144, type=int, help='batchsize')
parser.add_argument('--epochnum', default='last', type=str, help='please to select the epoch num')
parser.add_argument('--testing', action='store_true', help='import testing features')
parser.add_argument('--featuresize', default=48, type=int, help='the stage4s feature map size')
opt = parser.parse_args()

python3 test.py --gpu_ids ... --data_dir ../cuhk03-np/labeled/pytorch --name ... --batchsize 64 --epochnum last --train_all --attentionmodel --testing
python3 test.py --gpu_ids ... --data_dir ../cuhk03-np/detected/pytorch  --name ... --batchsize 64 --epochnum last --train_all --attentionmodel --testing

Evaluation

# Using CPU
python3 evaluate.py
# If you want to attain results quickly you can do:
python3 evaluate_gpu.py
# Also, you can use the reranking
python3 evaluate_rerank.py

Datasets	TOP@1	TOP@5	TOP@10	mAP
CUHK_Detected
CUHK_Labeled
DukeMTMC-reID	89.72	94.08	95.42	78.95
Market-1501	95.59	----	----	88.12

Visualization

#you should choose the dateset dir
parser.add_argument('--test_dir',default='...\DukeMTMC-reID\pytorch',type=str, help='./test_data')

python3 demo.py --test_dir ../cuhk03-np/labeled/pytorch 
python3 demo.py --test_dir ../cuhk03-np/detected/pytorch