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Matteo Bortoletto 2024-02-01 15:44:15 +01:00
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@ -24,14 +24,14 @@ If you find our code useful or use it in your own projects, please cite our pape
This code is based on the [original implementation][5] of the BIB benchmark. This code is based on the [original implementation][5] of the BIB benchmark.
## Using `virtualenv` ## Using `virtualenv`
``` ```bash
python -m virtualenv /path/to/env python -m virtualenv /path/to/env
source /path/to/env/bin/activate source /path/to/env/bin/activate
pip install -r requirements.txt pip install -r requirements.txt
``` ```
## Using `conda` ## Using `conda`
``` ```bash
conda create --name <env_name> python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89 conda create --name <env_name> python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89
conda activate <env_name> conda activate <env_name>
pip install -r requirements_conda.txt pip install -r requirements_conda.txt
@ -46,23 +46,23 @@ Run `source bibdgl/bin/activate`.
## Index data ## Index data
This will create the json files with all the indexed frames for each episode in each video. This will create the json files with all the indexed frames for each episode in each video.
``` ```bash
python utils/index_data.py python utils/index_data.py
``` ```
You need to manually set `mode` in the dataset class (in main). You need to manually set `mode` in the dataset class (in main).
## Generate graphs ## Generate graphs
This will generate the graphs from the videos: This will generate the graphs from the videos:
``` ```bash
python /utils/build_graphs.py --mode MODE --cpus NUM_CPUS python /utils/build_graphs.py --mode MODE --cpus NUM_CPUS
``` ```
`MODE` can be `train`, `val` or `test`. NOTE: check `utils/build_graphs.py` to make sure you're loading the correct dataset to generate the graphs you want. `MODE` can be `train`, `val` or `test`. NOTE: check `utils/build_graphs.py` to make sure you're loading the correct dataset to generate the graphs you want.
## Training ## Training
Use `run_train.sh`. Use `CUDA_VISIBLE_DEVICES=0 run_train.sh`.
## Testing ## Testing
Use `run_test.sh`. Use `CUDA_VISIBLE_DEVICES=0 run_test.sh`.
# Hardware setup # Hardware setup
All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU. All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.