Code Base for BioDeepHashing Project

• Hybrid Bio-Training
• HHNet: High-resolution hashing network (arXiv:2403.13747)

Setup

This code base is based on an open-source project: https://github.com/Lucas-Schwengber/h2q

The experiments are split into three phases : Training -> Prediction -> Evaluating

To Intall the environment run:

python -m venv bioenv
source bioenv/bin/activate
pip install -r requirements.txt

And initialize the environment running:

source bioenv/bin/activate

Initializing the directories

To start, create the following directories:

mkdir experiments
mkdir models
mkdir eval

The data directory is the one that will contain our four datasets (CIFAR_10, ImageNet, MS_COCO and NUS_WIDE). See more below.

The experiments directory will contain logs of experiments.

The models directory will contain the trained models weights and metadata.

The eval directory will contain the metrics of each trained model.

Downloading and processing the datasets

The first step is to download and process the datasets. We have 4 datasets: CIFAR_10, ImageNet, MS_COCO and NUS_WIDE. The following code will download the raw datasets, CIFAR_10, MS_COCO and NUS_WIDE:

python src/data/download/CIFAR_10.py
python src/data/download/MS_COCO.py
python src/data/download/NUS_WIDE.py

To pre-process the three datasets above run:

python src/data/process/CIFAR_10.py
python src/data/process/MS_COCO.py
python src/data/process/NUS_WIDE.py

For ImageNet the procedure is slightly different. The download must be done via Kaggle. The instructions are in src/data/download/ImageNet.py. For pre-processing, the raw information containing the splits must be collected from the HashNet repository https://github.com/thuml/HashNet and placed at:

data/raw/ImageNet/hashnet/HashNet/pytorch/data/imagenet/database.txt
data/raw/ImageNet/hashnet/HashNet/pytorch/data/imagenet/train.txt
data/raw/ImageNet/hashnet/HashNet/pytorch/data/imagenet/test.txt

After this, the pre-processing step should be done running:

python src/data/process/ImageNet.py

After running downloading and processing the datasets you can proceed to the experiments.

Running a single experiment

We illustrate the full procedure to train, predict and evaluate a single hyperparameter combination of a given model.

First train running:

python src/models/PenaltyStrategies/train.py -exp bioexp -db CIFAR_10 -bs 128 -ep 20 -arch bio-vgg16 -loss DCH -nbits 32 -mode fa

Then predict running:

python src/models/PenaltyStrategies/predict.py -exp bioexp -db CIFAR_10 -bs 128 -ep 20 -arch bio-vgg16 -loss DCH -nbits 32 -mode fa

Finally evaluate by running:

python src/eval/eval_map.py -p "models/PenaltyStrategies/CIFAR_10/bioexp/-loss=DCH-nbits=32-trf=imagenet-arch=bio-vgg16-seed=0-bs=128-mode=fa-ep=20-pt=10-lr=1e-05-wd=0.0005-optim=adam-penalty=0.01-L2_penalty=0.0-HSWD_penalty=0.0"

Running the experiments

For bio-plausible rules, 3 modes are available:

• Feedback alignment : -mode fa;
• Fixed Random Magnitude Sign-concordant Feedbacks (frSF): -mode frsf;
• Uniform Sign-concordant Feedbacks (uSF) : -mode usf