mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
c46ff4f75f
* cleaning up requirements.txt and adding versions * need to update github actions, docs, and make optional dependencies work * updated documentation * set directory * new functorch version * updated caching * fixed hash
125 lines
4.2 KiB
Markdown
125 lines
4.2 KiB
Markdown
# Installation
|
|
|
|
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.
|
|
|
|
<details>
|
|
<summary>Installing Conda</summary>
|
|
|
|
This step is fairly self-explanatory, but here are the basic steps. You can also find countless tutorials online.
|
|
|
|
```bash
|
|
cd /path/to/install/miniconda
|
|
|
|
mkdir -p miniconda3
|
|
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3/miniconda.sh
|
|
bash miniconda3/miniconda.sh -b -u -p miniconda3
|
|
rm -rf miniconda/miniconda.sh
|
|
```
|
|
|
|
</details>
|
|
|
|
Create the python environment
|
|
|
|
```bash
|
|
conda create --name nerfactory python=3.8.13
|
|
conda activate nerfactory
|
|
```
|
|
|
|
Clone the repo
|
|
|
|
```bash
|
|
git clone git@github.com:plenoptix/nerfactory.git
|
|
```
|
|
|
|
Install dependencies and nerfactory as a library
|
|
|
|
```bash
|
|
pip install -e .
|
|
```
|
|
|
|
Install library with CUDA development support. Change setup.py to `USE_CUDA = True` and then
|
|
|
|
```bash
|
|
python setup.py develop
|
|
```
|
|
|
|
Install tiny-cuda-nn (tcnn) to use with the graph_instant_ngp.yaml config
|
|
|
|
```bash
|
|
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
|
|
```
|
|
|
|
# Downloading data
|
|
|
|
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).
|
|
|
|
```
|
|
|─ nerfactory/
|
|
├─ data/
|
|
| ├─ blender/
|
|
| ├─ fern/
|
|
| ├─ lego/
|
|
...
|
|
|- <dataset_format>/
|
|
|- <scene>
|
|
...
|
|
```
|
|
|
|
# Training a model
|
|
|
|
To run with all the defaults, e.g. vanilla nerf method with the blender lego image
|
|
|
|
Run a vanilla nerf model.
|
|
|
|
```bash
|
|
python scripts/run_train.py
|
|
```
|
|
|
|
Run a faster version with instant ngp using tcnn (without the viewer).
|
|
|
|
```bash
|
|
python scripts/run_train.py --config-name=graph_instant_ngp.yaml
|
|
```
|
|
|
|
Run with the viewer. However, you'll have to start the viewer server first. (See [viewer docs](../tutorials/viewer/viewer_quickstart.md))
|
|
|
|
```bash
|
|
python scripts/run_train.py --config-name=graph_instant_ngp.yaml viewer.enable=true
|
|
```
|
|
|
|
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.
|
|
|
|
# Visualizing training runs
|
|
|
|
If you using a fast NeRF variant (ie. Instant-NGP), we reccomend using our viewer. See our [viewer docs](../tutorials/viewer/viewer_quickstart.md) for more details. The viewer will allow interactive visualization of training in realtime.
|
|
|
|
Additionally, 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>`.
|
|
|
|
```bash
|
|
tensorboard --logdir outputs
|
|
```
|
|
|
|
# Rendering a Trajectory
|
|
|
|
To evaluate the trained NeRF, we provide an evaluation script that allows you to do benchmarking (see our [benchmarking workflow](../tooling/benchmarking.md)) or to render out the scene with a custom trajectory and save the output to a video.
|
|
|
|
```bash
|
|
python scripts/run_eval.py render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml --traj=spiral --output-path=output.mp4
|
|
```
|
|
|
|
Please note, this quickstart allows you to preform everything in a headless manner. We also provide a web-based visualizer that allows you to easily monitor training or render out trajectories. See our [viewer docs](../tutorials/viewer/viewer_quickstart.md) for more.
|
|
|
|
# FAQ
|
|
|
|
* [TinyCUDA installation errors out with cuda mismatch](tiny-cuda-error)
|
|
|
|
(tiny-cuda-error)=
|
|
#### TinyCUDA installation errors out with cuda mismatch
|
|
|
|
While installing tiny-cuda, you run into: `The detected CUDA version mismatches the version that was used to compile PyTorch (10.2). Please make sure to use the same CUDA versions.`
|
|
|
|
**Solution**:
|
|
```
|
|
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
|
|
```
|