up

README.md

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