mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-14 03:44:02 +08:00
Ethan Weber
7d8beaaed4
* correcting implementation for instant ngp * instant ngp with tcnn * black and pylint * merged master
147 lines
4.2 KiB
Markdown
147 lines
4.2 KiB
Markdown
# MattPort
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# Quickstart
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## Installation: Setup the environment
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```
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# Clone the repo
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git clone --recurse-submodules git@github.com:ethanweber/mattport.git
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# Create the python environment
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conda create --name mattport python=3.8.13
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conda activate mattport
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pip install -r environment/requirements.txt
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# Install mattport as a library
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pip install -e .
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# Install library with CUDA support. Change setup.py to `USE_CUDA = True` and then
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python setup.py develop
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# Running the test cases
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pytest tests
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```
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## Getting the data
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Download the original NeRF dataset and put it in the following format.
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```
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- data/
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- blender/
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- fern/
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- lego/
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```
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## Training a model
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```
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# Run with default config
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python scripts/run_train.py
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# Run with config changes
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python scripts/run_train.py machine_config.num_gpus=1
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python scripts/run_train.py data.dataset.downscale_factor=1
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# Run with different datasets
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python scripts/run_train.py data/dataset=blender_lego
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python scripts/run_train.py data/dataset=friends_TBBT-big_living_room
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# Run with different datasets and config changes
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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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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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# 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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# 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/mattport` 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/mattport
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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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```
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# nerf-pytorch
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cd external
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python run_nerf.py --config configs/chair.txt --datadir /path/to/mattport/data/blender/chair
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# jaxnerf
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cd external
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conda activate jaxnerf
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python -m jaxnerf.train --data_dir=/path/to/mattport/data/blender/chair --train_dir=/path/to/mattport/outputs/blender_chair_jaxnerf --config=/path/to/mattport/external/jaxnerf/configs/demo --render_every 100
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```
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# Speeding up the code
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Documentation for running the code with CUDA.
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```
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This is how to install tiny-cuda-nn.
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# https://github.com/NVlabs/tiny-cuda-nn
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pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
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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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``` |