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 environment.yml
    conda activate procc

3. 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

Training

Open World. To train a model, the command is simply:

    python train.py --config CONFIG_FILE 

where CONFIG_FILE is the path to the configuration file of the model. The folder configs contains configuration files for all methods, i.e. CGE in configs/cge, CompCos in configs/compcos, and the other methods in configs/baselines.

To run ProCC on MIT-States, the command is just:

    python train.py --config configs/procc/mit.yml --open_world --fast

On UT-Zappos, the command is:

    python train.py --config configs/procc/utzappos.yml --open_world --fast

Partial Label Setting To train ProCC (in the partial label setting) on MIT-States, run:

    python train.py --config configs/procc/partial/mit.yml --partial --fast

Note: To create a new config, all the available arguments are indicated in params.py.

Test

Open World. To test a model in the open world setting, run:

    python test.py --logpath LOG_DIR --open_world --fast

Partial Label Setting To test a ProCC model on the partial label setting, a similar command can be used:

    python test.py --logpath LOG_DIR --fast --partial