# Radiance

# Quickstart

## Installation: Setup the environment

```
# Clone the repo
git clone --recurse-submodules git@github.com:ethanweber/radiance.git

# Create the python environment
conda create --name radiance python=3.8.13
conda activate radiance
pip install -r environment/requirements.txt

# Install radiance as a library
pip install -e .

# Install library with CUDA support. Change setup.py to `USE_CUDA = True` and then
python setup.py develop

# Running the test cases
pytest tests
```

## Getting the data

Download the original NeRF dataset and put it in the following format.

```
- data/
    - blender/
        - fern/
        - lego/
```

## Training a model

```
# Run with default config
python scripts/run_train.py

# Run with config changes
python scripts/run_train.py machine_config.num_gpus=1
python scripts/run_train.py data.dataset.downscale_factor=1

# Run with different datasets
python scripts/run_train.py data/dataset=blender_lego
python scripts/run_train.py data/dataset=friends_TBBT-big_living_room

# Run with different datasets and config changes
python scripts/run_train.py data/dataset=friends_TBBT-big_living_room graph.network.far_plane=14
```

# Logging and profiling features

We provide many logging functionalities for timing and/or tracking losses during training. All of these loggers are configurable via `configs/logging.yml`

1. **Writer**: Logs losses and generated images during training to a specified output stream. Specify the type of writer (Tensorboard, Local Writer, Weights and Biases), and how often to log in the config.

2. **Profiler**: Computes the average total time of execution for any function with the `@profiler.time_function` decorator. Prints out the full profile at the termination of training or the program.

# Setting up Jupyter

```
python -m jupyter lab build
bash environments/run_jupyter.sh
```

# Tooling

1. One can use [autoenv](https://github.com/hyperupcall/autoenv) to make setting up the environment and environment variables easier. This will run the `.envrc` file upon entering the `/path/to/radiance` folder. It will also remove the environment parameters upon leaving.

```
# Install direnv.
sudo apt install direnv

# Add the following line to the bottom of your ~/.bashrc file.
eval "$(direnv hook bash)"

# Populate your .envrc with commands you want to run. Then, run the following to allow updates.
cd /path/to/radiance
direnv allow .
```

2. To run local **github actions**, you can run:

```
python scripts/debugging/run_actions.py
```

3. To run local **profiling** to get a flame graph, make sure [pyspy](https://github.com/benfred/py-spy) is installed and you can run:

```
pip install py-spy

## for flame graph
./scripts/debugging/profile.sh -t flame -o flame.svg -p scripts/run_train.py data/dataset=blender_lego

## for live view of functions
./scripts/debugging/profile.sh -t top -p scripts/run_train.py data/dataset=blender_lego
```

4. For debugging with a debugger.

```
ipython --pdb scripts/run_train.py
```

5. **Benchmarking** For launching training jobs automatically on blender dataset

```
./scripts/benchmarking/launch_train_blender.sh <gpu0> <gpu1> ... <gpu7>
```

For testing specific methods, see scripts/benchmarking/run_benchmark.py.
Modify the `BENCH` variable to specify which jobs ("ckpt_dir") and methods ("method") you want benchmarked. Then run

```
python scripts/benchmarking/run_benchmark.py
```

# Running other repos with our data

```
# nerf-pytorch
cd external
python run_nerf.py --config configs/chair.txt --datadir /path/to/radiance/data/blender/chair

# jaxnerf
cd external
conda activate jaxnerf
python -m jaxnerf.train --data_dir=/path/to/radiance/data/blender/chair --train_dir=/path/to/radiance/outputs/blender_chair_jaxnerf --config=/path/to/radiance/external/jaxnerf/configs/demo --render_every 100
```

# Speeding up the code

Documentation for running the code with CUDA.

```
This is how to install tiny-cuda-nn.
# https://github.com/NVlabs/tiny-cuda-nn
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

To run instant-ngp with tcnn, you can do the following. This is with the fox dataset.
python scripts/run_train.py --config-name=instant_ngp_tcnn.yaml data/dataset=instant_ngp_fox
```