CRTR

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #typescript #llm #golang #go #deep-learning #macos #nodejs #awesome #cli #vue

(★ 58)

Official code for the paper "Contrastive Representations for Temporal Reasoning".

CRTR Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Contrastive Representations for Temporal Reasoning

Official repository of [Contrastive Representations for Temporal Reasoning](TODO_arxiv_link) (CRTR).

## Installation
We recommend using Python v3.10. Set up the repository by running:
```bash
pip install -e .
```

## Datasets
### Example datasets
The example datasets for the Rubik's Cube and Sokoban are stored in `example_datasets/{rubik/sokoban}`. You can use them to verify that the code runs correctly. To replicate the results from the paper, please download the full datasets as described below.

### Downloading Full Datasets
#### Install huggingface-cli:
```pip install -U "huggingface_hub[cli]"```
#### Install git lfs (for Ubuntu):
```bash
sudo apt-get update
sudo apt-get install git-lfs
git lfs install
```

### Rubik's Cube
The dataset for the Rubik's Cube requires `12GB` of available storage. Download it by running:
```bash
huggingface-cli download oolongie/rubik_randomly_shuffled --repo-type dataset  --local-dir training_datasets/rubik
```
You can also generate the dataset by running:
```bash
python rubik_generate_script.py <number_of_shuffles (the paper uses 21)> <save_folder>
```

### Sokoban
The dataset for Sokoban requires `14GB` of available storage. Download it by running:
```bash
huggingface-cli download oolongie/sokoban-12-12-4-trajectories --repo-type dataset  --local-dir training_datasets/sokoban
```

### Eval Boards
Boards that are used for Sokoban evaluation are stored in `example_datasets/sokoban_eval_boards/eval_boards.pkl`.

## Training
To run the training of our method, use the following command:

### Rubik's Cube
For the example dataset:
```bash
python runner.py --config_file configs/train/crtr/rubik.gin
```
For the real dataset:
```bash
python runner.py --config_file configs/train/crtr/rubik.gin --gin_bindings "ContrastiveDataset.path=training_datasets/rubik"
```

### Sokoban
For the example dataset:
```bash
python runner.py --config_file configs/train/crtr/sokoban.gin
```
For the real dataset:
```bash
python runner.py --config_file configs/train/crtr/sokoban.gin --gin_bindings "ContrastiveDataset.path=training_datasets/sokoban/train" "TrainJob.test_path=training_datasets/sokoban/test"
```

## Evaluation
Pretrained checkpoints are provided in the folder `example_checkpoints`.
Evaluation of our method on Sokoban can be done by running:
```bash
python runner.py --config_file configs/solve/search/contrastive/sokoban.gin
```
And on the Rubik's Cube by running:
```bash
python runner.py --config_file configs/solve/search/contrastive/rubik.gin
```

## Logging 
By default, experiment artifacts and results are stored in directory `result_<timestamp>`

CRTR

CRTR Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

CRTR

CRTR Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README