tobesuperior / annotation-embedding

LeMA (Leveraging Misleading Answer)

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This is the PyTorch code of the works Focusing valid search space in Open-World Compositional Zero-Shot Learning by leveraging misleading answers. The code provides the implementation of our proposed training method, LeMA, with other baselines (e.g. SymNet, AoP, TMN, LabelEmbed+,RedWine).

Setup

1. Clone the repo

2. We recommend using Anaconda for environment setup. To create the environment and activate it, please run:

conda env create --file anno_environment.yml
conda activate czsl

4. Go to the cloned repo and open a terminal. Download the datasets and embeddings, specifying the desired path (e.g. DATA_ROOT in the example):

bash ./utils/download_data.sh DATA_ROOT
mkdir logs

The word2vec s3 url has stopped working. You can download the data from Kaggle or use this Google Drive link (be careful downloading files from Google Drive).

cd DATA_ROOT/w2v
mv download_location/GoogleNews-vectors-negative300.bin.gz .
gzip -d GoogleNews-vectors-negative300.bin.gz
rm GoogleNews-vectors-negative300.bin.gz

Train

• If you want to apply LeMA during training, enter the "--lema" argument. We provide the LeMA training option for three models (e.g., Co-CGE, CompCos, KG-SP).
• The '--fast_eval' argument helps make the evaluation process more efficient.

train CGQA

python train.py --config configs/model_cfg_path/cgqa.yml (--lema)

train MIT

python train.py --config configs/model_cfg_path/mit.yml (--lema)

train UT-Zappos50k

python train.py --config configs/model_cfg_path/utzappos.yml (--lema)