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update getting around readme
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@@ -52,75 +52,23 @@ python scripts/run_train.py data/dataset=friends_TBBT-big_living_room
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python scripts/run_train.py data/dataset=friends_TBBT-big_living_room graph.network.far_plane=14
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```
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# Logging and profiling features
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# Getting around the codebase
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We provide many logging functionalities for timing and/or tracking losses during training. All of these loggers are configurable via `configs/logging.yml`
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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.
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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.
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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.
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* Fields (`nerf/fields/`): composed of field modules (`nerf/field_modules/`) and represents the radiance field of the NeRF.
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* 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).
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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.
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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.
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# Setting up Jupyter
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```
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python -m jupyter lab build
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bash environments/run_jupyter.sh
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```
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# Feature Documentation
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## 1. [Hydra config structure](./configs/README.md)
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## 2. [Logging, debugging utilities](./radiance/utils/README.md)
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## 3. [Benchmarking, other tooling](./scripts/README.md)
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# Tooling
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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.
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```
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# Install direnv.
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sudo apt install direnv
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# Add the following line to the bottom of your ~/.bashrc file.
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eval "$(direnv hook bash)"
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# Populate your .envrc with commands you want to run. Then, run the following to allow updates.
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cd /path/to/radiance
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direnv allow .
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```
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2. To run local **github actions**, you can run:
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```
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python scripts/debugging/run_actions.py
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```
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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:
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```
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pip install py-spy
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## for flame graph
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./scripts/debugging/profile.sh -t flame -o flame.svg -p scripts/run_train.py data/dataset=blender_lego
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## for live view of functions
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./scripts/debugging/profile.sh -t top -p scripts/run_train.py data/dataset=blender_lego
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```
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4. For debugging with a debugger.
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```
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ipython --pdb scripts/run_train.py
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```
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5. **Benchmarking** For launching training jobs automatically on blender dataset
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```
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./scripts/benchmarking/launch_train_blender.sh <gpu0> <gpu1> ... <gpu7>
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```
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For testing specific methods, see scripts/benchmarking/run_benchmark.py.
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Modify the `BENCH` variable to specify which jobs ("ckpt_dir") and methods ("method") you want benchmarked. Then run
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```
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python scripts/benchmarking/run_benchmark.py
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```
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# Running other repos with our data
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## 4. Running other repos with our data
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```
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# nerf-pytorch
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@@ -133,7 +81,7 @@ conda activate jaxnerf
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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
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```
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# Speeding up the code
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## 5. Speeding up the code
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Documentation for running the code with CUDA.
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@@ -144,4 +92,12 @@ pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/to
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To run instant-ngp with tcnn, you can do the following. This is with the fox dataset.
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python scripts/run_train.py --config-name=instant_ngp_tcnn.yaml data/dataset=instant_ngp_fox
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```
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## 6. Setting up Jupyter
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```
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python -m jupyter lab build
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bash environments/run_jupyter.sh
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```
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@@ -0,0 +1,7 @@
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# Logging and profiling features
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We provide many logging functionalities for timing and/or tracking losses during training. All of these loggers are configurable via `configs/logging.yml`
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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.
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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.
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@@ -0,0 +1,52 @@
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# Tooling
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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.
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```
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# Install direnv.
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sudo apt install direnv
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# Add the following line to the bottom of your ~/.bashrc file.
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eval "$(direnv hook bash)"
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# Populate your .envrc with commands you want to run. Then, run the following to allow updates.
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cd /path/to/radiance
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direnv allow .
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```
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2. To run local **github actions**, you can run:
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```
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python scripts/debugging/run_actions.py
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```
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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:
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```
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pip install py-spy
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## for flame graph
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./scripts/debugging/profile.sh -t flame -o flame.svg -p scripts/run_train.py data/dataset=blender_lego
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## for live view of functions
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./scripts/debugging/profile.sh -t top -p scripts/run_train.py data/dataset=blender_lego
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```
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4. For debugging with a debugger.
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```
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ipython --pdb scripts/run_train.py
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```
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5. **Benchmarking** For launching training jobs automatically on blender dataset
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```
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./scripts/benchmarking/launch_train_blender.sh <gpu0> <gpu1> ... <gpu7>
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```
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For testing specific methods, see scripts/benchmarking/run_benchmark.py.
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Modify the `BENCH` variable to specify which jobs ("ckpt_dir") and methods ("method") you want benchmarked. Then run
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```
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python scripts/benchmarking/run_benchmark.py
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```
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