diff --git a/README.md b/README.md index e12ef89b9..761ddd1d1 100644 --- a/README.md +++ b/README.md @@ -52,75 +52,23 @@ python scripts/run_train.py data/dataset=friends_TBBT-big_living_room python scripts/run_train.py data/dataset=friends_TBBT-big_living_room graph.network.far_plane=14 ``` -# Logging and profiling features +# Getting around the codebase -We provide many logging functionalities for timing and/or tracking losses during training. All of these loggers are configurable via `configs/logging.yml` +The entry point for training starts at `scripts/run_train.py`, which spawns instances of our `Trainer()` class (in `nerf/trainer.py`). The `Trainer()` is responsible for setting up the datasets and NeRF graph depending on the config specified. If you are planning on just using our codebase to build a new NeRF method or use an existing implementation, we've abstracted away the training routine in these two files and chances are you will not need to touch them. -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. +The NeRF graph definitions can be found in `nerf/graph/`. Each implementation of NeRF is definined in its own file. For instance, `nerf/graph/instant_ngp.py` contains populates the `NGPGraph()` class with all of the appropriate fields, colliders, and misc. modules. +* Fields (`nerf/fields/`): composed of field modules (`nerf/field_modules/`) and represents the radiance field of the NeRF. +* Misc. Modules (`nerf/misc_modules`- TODO(maybe move to misc_modules? better organization)): any remaining module in the NeRF (e.g. renderers, samplers, losses, and metrics). -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. +To implement any pre-existing NeRF that we have not yet implemented under `nerf/graph/`, create a new graph structure by using provided modules or any new module you define. Then create an associated config with the appropriate parameters defined, with `__target__` pointing to your NeRF class. Then run training as described above. -# Setting up Jupyter -``` -python -m jupyter lab build -bash environments/run_jupyter.sh -``` +# Feature Documentation +## 1. [Hydra config structure](./configs/README.md) +## 2. [Logging, debugging utilities](./radiance/utils/README.md) +## 3. [Benchmarking, other tooling](./scripts/README.md) -# 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 ... -``` - -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 +## 4. Running other repos with our data ``` # nerf-pytorch @@ -133,7 +81,7 @@ 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 +## 5. Speeding up the code Documentation for running the code with CUDA. @@ -144,4 +92,12 @@ pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/to 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 +``` + + +## 6. Setting up Jupyter + +``` +python -m jupyter lab build +bash environments/run_jupyter.sh ``` \ No newline at end of file diff --git a/radiance/utils/README.md b/radiance/utils/README.md new file mode 100644 index 000000000..e47d4e76a --- /dev/null +++ b/radiance/utils/README.md @@ -0,0 +1,7 @@ +# 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. \ No newline at end of file diff --git a/scripts/README.md b/scripts/README.md index e69de29bb..acfba3558 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -0,0 +1,52 @@ +# 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 ... +``` + +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 +``` \ No newline at end of file