PNP Loss in PyTorch
What's New
- 2023-04-05: Major updates include:
- Added PNPLoss to PML(Pytorch Metric Learning).
What can I find here?
This repository contains all code and implementations used in:
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
accepted to AAAI 2022
Requirements:
- PyTorch 1.2.0+ & Faiss-Gpu
- Python 3.6+
- pretrainedmodels, torchvision 0.3.0+
An exemplary setup of a virtual environment containing everything needed:
(1) wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
(2) bash Miniconda3-latest-Linux-x86_64.sh (say yes to append path to bashrc)
(3) source .bashrc
(4) conda create -n DL python=3.6
(5) conda activate DL
(6) conda install matplotlib scipy scikit-learn scikit-image tqdm pandas pillow
(7) conda install pytorch torchvision faiss-gpu cudatoolkit=10.0 -c pytorch
(8) pip install wandb pretrainedmodels
(9) Run the scripts!
Datasets:
Data for
-
Stanford Online Products (http://cvgl.stanford.edu/projects/lifted_struct/)
-
For Stanford Online Products:
online_products
└───images
| └───bicycle_final
| │ 111085122871_0.jpg
| ...
|
└───Info_Files
| │ bicycle.txt
| │ ...
Assuming your folder is placed in e.g. <$datapath/sop>, pass $datapath as input to --source.
Training:
Training is done by using main.py and setting the respective flags, all of which are listed and explained in parameters.py.
A basic sample run using the best parameters would like this:
python main.py --loss PNP --seed 0 --bs 384 --data_sampler class_random --samples_per_class 4 --arch resnet50_frozen_normalize --source ../retrieval_dataset --n_epochs 400 --lr 1e-5 --embed_dim 512 --evaluate_on_gpu --dataset online_products --variant PNP-D_q --alpha 4
Paper
If you find this work useful, please consider citing:
@article{Li_Min_Song_Zhu_Kang_Wei_Wei_Jiang_2022,
title={Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough},
volume={36},
url={https://ojs.aaai.org/index.php/AAAI/article/view/20042},
DOI={10.1609/aaai.v36i2.20042},
number={2},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Li, Zhuo and Min, Weiqing and Song, Jiajun and Zhu, Yaohui and Kang, Liping and Wei, Xiaoming and Wei, Xiaolin and Jiang, Shuqiang},
year={2022},
month={Jun.},
pages={1518-1526}
}