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A collaboration friendly studio for NeRFs
609fcb04f6
* added skeleton for terminal logger * done skeleton terminal writer logic... todo: actual calls to the write classes * moved lambda to top function * added logic to support timing * prettier printing * prettier printing * lint * separated out timing and other stats variables. printing in dict format * keeping logging history and wiping rest * updated logic to calculate total times instead of average times * made stats tracker configurable * moved stats tracker and profiler to own class file; implemented basic functionality for profiler * lint removing duplicate function |
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| .github/workflows | ||
| .vscode | ||
| configs | ||
| environment | ||
| external | ||
| mattport | ||
| notebooks | ||
| scripts | ||
| tests | ||
| viewer | ||
| .gitignore | ||
| .gitmodules | ||
| pyproject.toml | ||
| README.md | ||
| setup.py | ||
MattPort
Setup the environment
# Clone the repo
git clone --recurse-submodules git@github.com:ethanweber/mattport.git
# Create the python environment
conda create --name mattport python=3.8.13
conda activate mattport
pip install -r environment/requirements.txt
# Install helper library
cd external/goat
python setup.py develop
# then go back, i.e., `cd /path/to/mattport`
# Install mattport as a library
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 the default config
python scripts/run_train.py
# Run with config changes
python scripts/run_train.py machine_config.num_gpus=1
Setting up Jupyter
python -m jupyter lab build
bash environments/run_jupyter.sh