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README.md
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README.md
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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
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This code is based on the [original implementation][5] of the BIB benchmark.
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## Using `virtualenv`
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```
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```bash
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python -m virtualenv /path/to/env
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source /path/to/env/bin/activate
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pip install -r requirements.txt
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```
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## Using `conda`
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```
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```bash
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conda create --name <env_name> python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89
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conda activate <env_name>
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pip install -r requirements_conda.txt
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@ -46,23 +46,23 @@ Run `source bibdgl/bin/activate`.
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## Index data
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This will create the json files with all the indexed frames for each episode in each video.
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```
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```bash
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python utils/index_data.py
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```
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You need to manually set `mode` in the dataset class (in main).
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## Generate graphs
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This will generate the graphs from the videos:
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```
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```bash
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python /utils/build_graphs.py --mode MODE --cpus NUM_CPUS
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```
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`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.
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## Training
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Use `run_train.sh`.
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Use `CUDA_VISIBLE_DEVICES=0 run_train.sh`.
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## Testing
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Use `run_test.sh`.
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Use `CUDA_VISIBLE_DEVICES=0 run_test.sh`.
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# Hardware setup
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All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.
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