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75 lines
2.7 KiB
ReStructuredText
75 lines
2.7 KiB
ReStructuredText
.. _quickstart:
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Quickstart
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============
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Installation
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-------------------------------
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Clone the repo::
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git clone --recurse-submodules git@github.com:ethanweber/pyrad.git
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Create the python environment::
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conda create --name pyrad python=3.8.13
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conda activate pyrad
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pip install -r environment/requirements.txt
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Install pyrad 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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Field Modules
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Getting the data
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-------------------------------
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Download the original NeRF dataset and put it in the following format.::
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- data/
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- blender/
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- fern/
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- lego/
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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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Getting around the codebase
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-------------------------------
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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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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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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 making sure ``__target__`` points to your NeRF class (see [here](./configs/README.md) for more info on how to create the config). Then run training as described above. |