auto-reid-fast's Introduction

Implementation of Paper Auto-Reid

This is a fast re-implementation of paper Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification.

Features & Introductions.

Please note that this repo is based on Cuda 8.0+ and Cudnn related to them.
This model is tested on Cuda 9.0 9.1 and 10.0 and related cudnn libs on ubuntu server 14 and 16.
However, please do not use a batch size smaller than 2*your_gpus, because that will cause bn failure. At the same time, check that your PyTorch > = 1.0 and slurm is installed on your server. Also, please note that if sub batch size (batch size // world_size) is compared small, it may have a decreased final performance. A preferred setting is batch size 128-512. However, for DARTS searching structures, which may require a higher single card memory, batch_size 16-64 have been tested successfully to get the correct result.
The training could be visualized through TensorboardX. All the checkpoints and searched graph structures are automatically saved into subfolder based on the task_name.

Update August 2th: integrate DARTS genotypes into tensorboard, you could see how structure changes during training right now!

Features to add

Add apex support for distributed training.
Improve the paper
Install as a lib

Dependency & Install

conda create -n auto-reid python=3.6
conda install pip
conda install -c pytorch torch torchvision
conda install pyyaml
pip install graphviz
pip install tensorboardX
pip install apex



python setup.py install

Run Training and Searching

bash scripts/prepare_datasets/prepare_market.py
srun -n your_node_nums --gres gpu:gpunums -p your_partition python train_baseline_search_triplet.py --distributed True --config configs/Retrieval_classification_DARTS_distributed_triplet.yaml