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https://github.com/nerfstudio-project/nerfstudio.git
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119 lines
3.2 KiB
Markdown
119 lines
3.2 KiB
Markdown
# Installation
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This repository is tested with CUDA 11.3. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html#linux-installers) before preceding.
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<details>
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<summary>Installing Conda</summary>
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This step is fairly self-explanatory, but here are the basic steps. You can also find countless tutorials online.
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```bash
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cd /path/to/install/miniconda
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mkdir -p miniconda3
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3/miniconda.sh
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bash miniconda3/miniconda.sh -b -u -p miniconda3
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rm -rf miniconda/miniconda.sh
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```
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</details>
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Create the python environment
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```bash
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conda create --name nerfactory python=3.8.13
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conda activate nerfactory
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```
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Clone the repo
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```bash
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git clone git@github.com:plenoptix/nerfactory.git
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```
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Install dependencies
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```bash
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cd nerfactory
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pip install -r environment/requirements.txt
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```
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Install nerfactory as a library
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```bash
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pip install -e .
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```
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Install library with CUDA development support. Change setup.py to `USE_CUDA = True` and then
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```bash
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python setup.py develop
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```
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Install tiny-cuda-nn (tcnn) to use with the graph_instant_ngp.yaml config
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```bash
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pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
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```
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# Downloading data
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Download the original [NeRF dataset](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) and unfold it in the following format. This is for the blender dataset type. We support the major datasets and allow users to create their own dataset, described in detail [here](data/creating_dataset.md).
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```
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|─ nerfactory/
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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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|- <dataset_format>/
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|- <scene>
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...
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```
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# Training a model
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To run with all the defaults, e.g. vanilla nerf method with the blender lego image
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Run a vanilla nerf model.
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```bash
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python scripts/run_train.py
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```
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Run a faster version with instant ngp using tcnn (without the viewer).
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```bash
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python scripts/run_train.py --config-name=graph_instant_ngp.yaml
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```
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Run with the viewer. However, you'll have to start the viewer server first. (See the docs.)
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```bash
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python scripts/run_train.py --config-name=graph_instant_ngp.yaml viewer.enable=true
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```
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With support for [Hydra](https://hydra.cc/), you can run with other configurations by changing appropriate configs defined in `configs/` or by setting flags via command-line arguments.
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# Visualizing training runs
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If you run everything with the default configuration, by default, we use [TensorBoard](https://www.tensorflow.org/tensorboard) to log all training curves, test images, and other stats. Once the job is launched, you will be able to track training by launching the tensorboard in `outputs/blender_lego/vanilla_nerf/<timestamp>/<events.tfevents>`.
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```bash
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tensorboard --logdir outputs
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```
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Or the following:
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```bash
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export TENSORBOARD_PORT=<port>
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bash environment/run_tensorboard.sh
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```
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# Rendering a Trajectory
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```bash
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python scripts/run_eval.py --method=traj --traj=spiral --output-filename=output.mp4 --config-name=graph_instant_ngp.yaml trainer.resume_train.load_dir=outputs/blender_lego/instant_ngp/2022-07-07_230905/checkpoints
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```
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