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Offline Regional Prompt Learning (RPL) of PromptDet

method overview

Prerequisites

# Clone this repo
git clone https://github.com/fcjian/RPL.git
cd RPL/Dassl.pytorch/

# Create a conda environment
conda create -y -n dassl python=3.8

# Activate the environment
conda activate dassl

# Install torch (requires version >= 1.8.1) and torchvision
# Please refer to https://pytorch.org/ if you need a different cuda version
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

# Install dependencies
pip install -r requirements.txt

# Install this library (no need to re-build if the source code is modified)
python setup.py develop

# assume that you are under the root directory of this project
# and then install a few more packages required by CLIP
pip install -r requirements.txt

Data preparation

# stege-I: generate the object crops from LVIS v1.0
# training set
python tools/promptdet/save_object_crops.py --file-path data/lvis_v1/annotations/lvis_v1_train_seen.json --img-root data/lvis_v1 --save-root data/lvis_train_object_crops --num-thread 10
# val set
python tools/promptdet/save_object_crops.py --file-path data/lvis_v1/annotations/lvis_v1_val_seen.json --img-root data/lvis_v1 --save-root data/lvis_val_object_crops --num-thread 10
# correct the category names
mv data/lvis_train_object_crops/speaker_\(stero_equipment\) data/lvis_train_object_crops/speaker_\(stereo_equipment\)
mv data/lvis_val_object_crops/speaker_\(stero_equipment\) data/lvis_valhi_object_crops/speaker_\(stereo_equipment\)
mv data/lvis_train_object_crops/monitor_\(computer_equipment\)\ computer_monitor data/lvis_train_object_crops/monitor_\(computer_equipment\)_computer_monitor
mv data/lvis_val_object_crops/monitor_\(computer_equipment\)\ computer_monitor data/lvis_val_object_crops/monitor_\(computer_equipment\)_computer_monitor

# stege-II: random sample the image crops using symbolic links
# training set
python tools/promptdet/sample_image.py --source-root data/lvis_train_object_crops/ --target-root lvis_and_laion_data/imagenet/images/train --random-sample --num-images 200
# val set
python tools/promptdet/sample_images.py --source-root data/lvis_val_object_crops/ --target-root lvis_and_laion_data/imagenet/images/val --random-sample --num-images 200
# align the categories of the val set with the one of the training set
python tools/promptdet/align_val_with_train.py --train-root lvis_and_laion_data/imagenet/images/train/ --val-root lvis_and_laion_data/imagenet/images/val/

[0] Annotation file of base categories: lvis_v1_train_seen.json and lvis_v1_val_seen.json.


After training the prompt vectors, you can source the LAION images and update the training data iteratively:

# stege-I: generate the category embeddings
python tools/promptdet/gen_category_embedding.py --model-file output/imagenet/RPL/vit_b32_ep6_promptdet_600shots/nctx1_csc_ctp/seed3/prompt_learner/model.pth.tar-6 --name-file promptdet_resources/lvis_category_and_description.txt --out-file promptdet_resources/lvis_category_embeddings.pt

# stege-II: install the dependencies, download the laion400m 64GB image.index and metadata.hdf5 (https://the-eye.eu/public/AI/cah/), and then retrival the LAION images
pip install faiss-cpu==1.7.2 img2dataset==1.12.0 fire==0.4.0 h5py==3.6.0
python tools/promptdet/retrieval_laion_image.py --indice-folder [laion400m-64GB-index] --metadata [metadata.hdf5] --text-features promptdet_resources/lvis_category_embeddings.pt --base-category --output-folder data/sourced_data --num-images 300

# stege-III: download the LAION images
python tools/promptdet/download_laion_image.py --base-category --output-folder data/sourced_data --num-thread 10

# stege-IV: sample the top-K sourced images using symbolic links
# need to delete the 'preprocessed.pkl' and 'split_fewshot' in 'lvis_and_laion_data/imagenet/' if they exist
python tools/promptdet/sample_image.py --source-root data/sourced_data --target-root lvis_and_laion_data/imagenet/images/train --laion-image --num-images 200


The directory structure should look like:

lvis_and_laion_data
|-- imagenet/
|   |-- classnames.txt
|   |-- images/
|   |   |-- train/
|   |   |-- val/

Training

cd scripts
sh main.sh imagenet vit_b32_ep6 lvis_and_laion_data 1 600 # the last number shuold be greater than or equal to the maximum number of the training images of the category

For your convenience, we also provide the learned prompt vectors and the category embeddings from the second iteration.

Acknowledgement

Thanks CoOp and Dassl.pytorch for the wonderful open source project!

Citation

If you find PromptDet or RPL useful in your research, please consider citing:

@inproceedings{feng2022promptdet,
    title={PromptDet: Towards Open-vocabulary Detection using Uncurated Images},
    author={Feng, Chengjian and Zhong, Yujie and Jie, Zequn and Chu, Xiangxiang and Ren, Haibing and Wei, Xiaolin and Xie, Weidi and Ma, Lin},
    journal={Proceedings of the European Conference on Computer Vision},
    year={2022}
}