pymetric

pymetric tries to build a metric learning and retrieval codebase based on PyTorch. It refers from pycls On Network Design Spaces for Visual Recognition project and fast-reid from (https://github.com/JDAI-CV/fast-reid).

Introduction

2nd place in Google Landmark Retrieval 2020 Competition. (https://drive.google.com/file/d/1XnzxMOHhzua9tjrAjo-X55ieKVJzRJw_/view?usp=sharing)
Includes pycls (https://github.com/facebookresearch/pycls), refer to pycls.md.
pymetric is written by DistributedDataParallel which is different from fast-reid. Now mainly includes features such as arcface loss and circle loss, ongoing.
Add multicards feature extraction, searching topk and computing mAP.

Installation

Requirements:

NVIDIA GPU, Linux, Python3(tested on 3.6.10)
PyTorch, various Python packages; Instructions for installing these dependencies are found below

Notes:

pymetric does not currently support running on CPU; a GPU system is required
pymetric has been tested with CUDA 10.2 and cuDNN 7.1

PyTorch

To install PyTorch with CUDA support, follow the installation instructions from the PyTorch website.

pymetric

Clone the pymetric repository:

# PYMETRIC=/path/to/clone/pymetric
git clone https://github.com/feymanpriv/pymetric $PYMETRIC

Install Python dependencies:

pip install -r $PYMETRIC/requirements.txt

Set PYTHONPATH:

cd $PYMETRIC && export PYTHONPATH=`pwd`:$PYTHONPATH

Datasets

Same with pycls, pymetric finds datasets via symlinks from metric/datasets/data to the actual locations where the dataset images and annotations are stored. Refer to DATA.md.

Getting Started

Training a metric model:

python tools/train_metric.py \
    --cfg configs/metric/R-50-1x64d_step_8gpu.yaml \
    OUT_DIR ./output \
    PORT 12001 \
    TRAIN.WEIGHTS path/to/pretrainedmodel

Resume training:

python tools/train_metric.py \
    --cfg configs/metric/R-50-1x64d_step_8gpu.yaml \
    OUT_DIR ./output \
    PORT 12001 \
    TRAIN.AUTO_RESUME True

Extracting features(labels) and evaluation

set ${total_num} = n*(gpu_cards)
sh tools/metric/eval/infer.sh
python search.py search_gpu ${queryfea_path}, ${referfea_path}, ${output}

Convert to tensorflow2.3 (please refer onnx and onnx-tensorflow)
examples: tools/convert/torch2onnx.py tools/convert/onnx2tf.py

Pretrained weights

-resnet50 (c3l3)

-resnet101 (t3ln)

-resnest269 (3c5a)

Results

2nd place on Google Landmark Retrieval Challenge 2020

Backbone	Scale	Margin	Size	Public Score	Private Score
ResNeSt269	30	0.15	224/224	0.35129	0.30819
ResNeSt269	30	0.15	448/448	0.36972	0.33015
ResNeSt269	30	0.15	640/448	0.39040	0.34718

License

pymetric is released under the MIT license. Please see the LICENSE file for more information.