naic_reid

This is Top19's Code for the Person ReID Compitition of NAIC(首届“全国人工智能大赛”（行人重识别 Person ReID 赛项）)

Dependencies

python==3.6
pandas==0.25.1
hickle==3.4.5
tqdm==4.36.1
opencv_python==4.1.1.26
scikit_image==0.15.0
mlconfig==0.0.4
visdom==0.1.8.9
torch==1.0.0
torchvision==0.2.0
yacs==0.1.6
numpy==1.17.4
scipy==1.3.1
apex==0.9.10dev
ipython==7.10.0
Pillow==6.2.1
skimage==0.0

Prerequisites

you can find the compition at https://www.kesci.com/home/competition/5d90401cd8fc4f002da8e7be/content/2

In order to run this project you will need:

Python3 (tested with Python 3.6.0)
PyTorch deep learning framework (tested with version 1.0.0)
rep dataset contains almost 86k train images and 71k test images for test A, 166k for test B, so the training phase requires ~24GB GPU memory. If your GPU doesn't have sufficient memory, please reduce batch size or reduce image train-size.

Team members

Support

Multi-GPU and SyncBN
fp16

Models

Strong Baseline
MGN
MFN(Comming Soon)

Tricks

Due to the characteristics of the dataset, we find Color Jittering can greatly improve model performance.And at the same time we find that using Color Jittering with a 0.5 probability is better.
AQE(average query expansion) can replace rerank as a post-processing method, which can imporve evaluation indicators by 1%~2%.There are two aqe methods in our codes, one is based on cpu and the other is based on gpu, which can speed up about 10 times.About AQE,please follow this paper Fine-tuning CNN Image Retrieval with No Human Annotation
Adabn can improve mfn performance and baseline with arccos, but there is no improvement in baeline with arcface.
Batch GPU ReRanking is modified from the original rerank, using the GPU to calculate the distance, and using sparse matrix storage, which can save memory and time.But due to the limitation of test B submission, this method was not adopted in the end.
Pseudo Label is a trick commonly used in deep learning competitions.We use a trained model to cluster the test set, and the clustering method we choose is dbscan which has two hyperparameters to control the clustering effect.
Multi Triplet-Margine Ensemble means ensemble two models with different triplet loss margin (we choose 0.5 and 0.3). Due to time constraints, we gave up the trick in the test B.

Test A

model	size	backbone	trick	performance
mgn	384x128	resnet50-ibn	adabn + gem	0.77808439
mgn	384x144	resnet50-ibn	adabn + gem	0.78023715
mgn	384x144	resnet50-ibn	adabn + gem + aqe	0.78967123
mgn	384x144	resnet50-ibn	gem + aqe + cj	0.79911998
baseline	384x192	resnet50-ibn	adabn + gem + cosface	0.76778309
baseline	384x192	resnet50-ibn	gem + cosface + cj	0.79208766
baseline	384x192	resnet50-ibn	avg + cosface + cj	0.79478573
baseline	384x192	resnet101-ibn	avg + cosface + cj	0.80346376
baseline	384x192	resnet101-ibn	avg + cosface + cj + all_data	0.80577292
baseline	384x192	resnet101-ibn	avg + cosface + cj05 + all_data	0.80686313
baseline	384x192	resnet101-ibn	avg + arcface + cj05 + all_data	0.819

Test B public

model	size	backbone	trick	performance
baseline	384x192	resnet101-ibn	(avg + arcface + cj05 + all_data) + (avg + arcface + cj05 + all_data + pseudo label) ensemble	0.81488409

Test B private

0.80066173

Implementation

premodels download

you can follow this project https://github.com/XingangPan/IBN-Net
premodels can be download here:https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S

prepare_data

you can put the data in rep_dir, and get the result data in save_dir

split 0.85 train data

train minist2 data (0.85 train data (each pid >=2))
train data (0.85 train data)
train minist4 data (0.85 train data (each pid >=4))

all train data

trainVal2 data (all train data (each pid >= 2))
trainVal data (all train data)

step 1 modify prepare_rep2.py

    root_dir = '/data/Dataset/PReID/'  # dataset root
    rep_dir = root_dir+'dataset2/'  # rep dataset

    save_dir = root_dir+'rep_dataset/' # save path

step 2 run prepare_rep2.py

Train

for baseline model, you can choose arccos baseline or arcface baseline by modifing 'MODEL.BASELINE.COSINE_LOSS_TYPE'

setp1 modify train sh file

 PRETRAIN=resnet101_ibn_a.pth.tar
 DATA_DIR='your data dir'
 SAVE_DIR='your save dir' #(h, w)
 TRAIN_PATH='your train folder'
 QUERY_PATH='your query folder'
 GALLERY_PATH='your gallery folder'
 CUDA_VISIBLE_DEVICES=5 python train.py --config_file='configs/naic/arcface_baseline.yml' \
     SOLVER.BASE_LR '3e-4' SOLVER.WARMUP_EPOCH "10" SOLVER.STEPS "[40, 70]" SOLVER.MAX_EPOCHS "90" SOLVER.START_SAVE_EPOCH "75" SOLVER.EVAL_PERIOD "2" \
     SOLVER.IMS_PER_BATCH "96" DATALOADER.NUM_INSTANCE "6" \
     MODEL.BASELINE.TPL_WEIGHT "1.0" MODEL.BASELINE.CE_WEIGHT "0.33" MODEL.LABEL_SMOOTH "False" MODEL.BASELINE.S "30.0" MODEL.BASELINE.M "0.35" MODEL.BASELINE.COSINE_LOSS_TYPE 'ArcCos' \
     INPUT.SIZE_TRAIN "([384,192])" INPUT.SIZE_TEST "([384,192])" \
     MODEL.NAME "cosine_baseline" MODEL.BACKBONE "('resnet101_ibn_a')" MODEL.BASELINE.POOL_TYPE "avg"\
     DATASETS.DATA_PATH "('${DATA_DIR}')" DATASETS.TRAIN_PATH "('${TRAIN_PATH}')" DATASETS.QUERY_PATH "('${QUERY_PATH}')"        DATASETS.GALLERY_PATH "('${GALLERY_PATH}')"\
     MODEL.PRETRAIN_PATH "('${PRETRAIN}')"  \
     OUTPUT_DIR "('${SAVE_DIR}')"

step 2 run ./shells/rep_train_bl.sh

Test

step1 modify test sh file

DATA_DIR= 'your data dir'
PRETRAIN=resnet101_ibn_a.pth.tar
MODEL_DIR=your model dir #(h, w)
WEIGHT=${MODEL_DIR}cosine_baseline_epoch90.pth
SAVE_DIR=${MODEL_DIR}eval/
 
   --flip \
   --aqe --aqe_k2 7 --aqe_alpha 3.0 \
 CUDA_VISIBLE_DEVICES=2 python test2.py --config_file='configs/naic/arcface_baseline.yml' \
     TEST.IMS_PER_BATCH "128" TEST.RANDOMPERM "5" \
     MODEL.BASELINE.TPL_WEIGHT "1.0" MODEL.BASELINE.CE_WEIGHT "0.33" MODEL.LABEL_SMOOTH "False" MODEL.BASELINE.S "30.0" MODEL.BASELINE.M "0.35" MODEL.BASELINE.COSINE_LOSS_TYPE 'ArcCos' \
     INPUT.SIZE_TRAIN "([384,192])" INPUT.SIZE_TEST "([384,192])" \
     MODEL.NAME "cosine_baseline" MODEL.BACKBONE "('resnet101_ibn_a')" MODEL.BASELINE.POOL_TYPE "avg"\
     DATASETS.DATA_PATH "('${DATA_DIR}')" DATASETS.TRAIN_PATH "your train data folder"\
     MODEL.PRETRAIN_PATH "('${PRETRAIN}')"  \
     OUTPUT_DIR "('${SAVE_DIR}')" \
     TEST.WEIGHT "${WEIGHT}"

step 2 run ./shells/rep_test_bl.sh

pseudo label

step1 modify test sh file for pseudo label

# [20191231] testb pseudo

QUERY_DIR=${ROOT_DIR}dataset2/rep_B/query_b/
GALLERY_DIR=${ROOT_DIR}dataset2/rep_B/gallery_b/
DATA_DIR=${ROOT_DIR}rep_dataset/
PRETRAIN=../weights/resnet101_ibn_a.pth.tar
MODEL_DIR=your model dir #(h, w)
WEIGHT=${MODEL_DIR}cosine_baseline_epoch90.pth
SAVE_DIR=${MODEL_DIR}eval/


 SAVE_DIR=${MODEL_DIR}sub/
 CUDA_VISIBLE_DEVICES=2 python test2.py --config_file='configs/naic/arcface_baseline.yml' \
     --sub \
     --pseudo --pseudo_hist --pseudo_visual --pseudo_algorithm "auto" --pseudo_eps 0.55 --pseudo_minpoints 3 --pseudo_maxpoints 100 --pseudo_savepath '../rep_work_dirs/testb_pseudo_hist_065_080/'\
     --query_dir ${QUERY_DIR}\
     --gallery_dir ${GALLERY_DIR}\
     MODEL.BASELINE.S "30.0" MODEL.BASELINE.M "0.35" \
     MODEL.BASELINE.COSINE_LOSS_TYPE 'ArcCos' \
     INPUT.SIZE_TRAIN "([384,192])" INPUT.SIZE_TEST "([384,192])" \
     MODEL.NAME "cosine_baseline" MODEL.BACKBONE "('resnet101_ibn_a')" MODEL.BASELINE.POOL_TYPE "avg"\
     DATASETS.DATA_PATH "('${DATA_DIR}')" DATASETS.TRAIN_PATH "rep_trainVal2"\
     MODEL.PRETRAIN_PATH "('${PRETRAIN}')"  \
     OUTPUT_DIR "('${SAVE_DIR}')" \
     TEST.WEIGHT "${WEIGHT}"

then you can get pseudo data in pseudo_savepath

step2 copy pseudo data to trainVal data

modify prepare_pseudo.py

    root_dir = '/data/Dataset/PReID/'  # root of dataset
    
    origin_path = root_dir+'dataset2/'+'pid_dataset/' # original trainval data
    pseudo_path = '../rep_work_dirs/testb_pseudo_hist_065_080/' # pseudo data

    save_dir = root_dir+'testb_pseudo_hist_065_080_dataset/' # save path

run prepare_pseudo.py

step3 train the model again using mix dataset(original trainval data and pseudo data)

Model Ensemble

step1 modify dist_ensemble.py

 save_dir = '../rep_work_dirs/testb_ensembles/'  # save path
 query_dir='/data/Dataset/PReID/dataset2/query_/' # query path
 gallery_dir='/data/Dataset/PReID/dataset2/gallery_/' # gallery path
 save_fname = 'ensemble1.json' # submit filename
 dist_fnames = [  ## distance matrix
    #     'origin_tpl03_e90_flip_sub_aqe.pkl',
    #     'origin_tpl03_e80_flip_sub_aqe.pkl',
    #     'finetune_tpl05_e16_flip_sub_aqe.pkl',
    #     'finetune_tpl05_e14_flip_sub_aqe.pkl'

    # ]

step2 run dist_ensemble.py

Notes:

1.Due to time constraints, the best solution we eventually adopted was to use a baseline model and a pseudo-labeled baseline model for fusion.

2.We also trained MGN to a better effect, but the final effect of mgn is not as good as the baseline + arcface + cj.

3.The MFN network comes from one of my teammates, and he plans to use the model in a paper. So it cannot be open source now. If you are interested, you can follow his Github：https://github.com/douzi0248/Re-ID

4.The codes are expanded on a ReID-baseline , which is open sourced by Hao Luo.(Thanks for Hao Luo and DTennant, our baseline model comes from https://github.com/michuanhaohao/reid-strong-baseline and https://github.com/DTennant/reid_baseline_with_syncbn)

ghw2016z / naic_reid