mirror of
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More renaming (#590)
This commit is contained in:
Matthew Tancik
GitHub
parent
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commit
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86
README.md
86
README.md
@ -60,13 +60,11 @@ We hope nerfstudio enables you to build faster :hammer: learn together :books: a
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The quickstart will help you get started with the default vanilla nerf trained on the classic blender lego scene.
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For more complex changes (e.g. running with your own data/ setting up a new NeRF graph, please refer to our [references](#learn-more).
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#### 1. Installation: Setup the environment
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## 1. Installation: Setup the environment
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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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### Create environment
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</details>
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Create the python environment
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We reccomend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before preceding.
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```
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conda create --name nerfstudio python=3.8.13
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@ -74,36 +72,46 @@ conda activate nerfstudio
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python -m pip install --upgrade pip
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```
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Clone the repo
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### Dependencies
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```
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git clone git@github.com:plenoptix/nerfstudio.git
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```
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Install dependencies and nerfstudio as a library
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```
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cd nerfstudio
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pip install -e .
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```
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Install tiny-cuda-nn (tcnn) to run instant_ngp
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Install pytorch with CUDA (this repo has been tested with CUDA 11.3) and [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn)
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```
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pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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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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Install tab completion for nerfstudio (bash & zsh)
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### Installing nerfstudio
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Easy option:
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```
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nf-install-cli
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pip install nerfstudio
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```
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Run the test cases
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If you would want the latest and greatest:
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```
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pytest tests
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git clone git@github.com:plenoptix/nerfstudio.git
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cd nerfstudio
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pip install -e .
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```
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### Optional Installs
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**Tab completion (bash & zsh)**
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This needs to be rerun when the CLI changes, for example if nerfstudio is updated.
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```bash
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ns-install-cli
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```
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**Development packages**
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```bash
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pip install -e.[dev]
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pip install -e.[docs]
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```
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#### 2. Getting the data
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@ -111,7 +119,7 @@ pytest tests
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Download the original NeRF Blender dataset. We support the major datasets and allow users to create their own dataset, described in detail [here](https://plenoptix-nerfstudio.readthedocs-hosted.com/en/latest/tutorials/data/index.html).
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```
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python scripts/downloads/download_data.py --dataset=blender
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ns-download-data --dataset=blender
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```
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Use `--help` to view all currently available datasets. The resulting script should download and unpack the dataset as follows:
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@ -128,40 +136,34 @@ Use `--help` to view all currently available datasets. The resulting script shou
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...
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```
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#### 3. Training a model
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## 3. 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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```
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# To see what models are available.
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python scripts/train.py --help
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ns-train --help
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# Run a vanilla nerf model.
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python scripts/train.py vanilla_nerf
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ns-train vanilla-nerf
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# Run a faster version with instant ngp using tcnn (without the viewer).
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python scripts/train.py instant_ngp
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#
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ns-train nerfacto
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```
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#### 3.x Training a model with the viewer
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### 3.x Training a model with the viewer
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Make sure to forward a port for the websocket to localhost. The default port is 7007, which you should be expose to localhost:7007.
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```bash
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# with the default port
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python scripts/train.py instant_ngp --viewer.enable
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ns-train nerfacto --vis viewer
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# with a specified websocket port
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python scripts/train.py instant_ngp --viewer.enable --viewer.websocket-port=7008
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# with the viewer bridge server as a separate process
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# in one terminal, start the bridge server:
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viewer-bridge-server # or equivalently, python scripts/run_viewer_bridge_server.py
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# in another terminal, start training:
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python scripts/train.py instant_ngp --viewer.enable --viewer.no-launch-bridge-server
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ns-train nerfacto --vis viewer --viewer.websocket-port=7008
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```
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#### 4. Visualizing training runs
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### 4. Visualizing training runs
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We support multiple methods to visualize training, the default configuration uses Tensorboard. More information on logging can be found [here](https://plenoptix-nerfactory.readthedocs-hosted.com/en/latest/tooling/logging_profiling.html).
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@ -170,7 +172,7 @@ We support multiple methods to visualize training, the default configuration use
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We have developed our own Real-time web viewer, more information can be found [here](https://plenoptix-nerfactory.readthedocs-hosted.com/en/latest/tutorials/viewer/index.html). This viewer runs during training and is designed to work with models that have fast rendering pipelines.
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To turn on the viewer, simply add the flag `--viewer.enable`.
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To turn on the viewer, simply add the flag `--vis viewer`.
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</details>
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@ -192,13 +194,13 @@ We support logging to weights and biases. To enable wandb logging, add the flag
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</details>
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#### 5. Rendering a trajectories during inference
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## 5. Rendering a trajectories during inference
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```
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python scripts/eval.py render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml--traj=spiral --output-path=output.mp4
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ns-eval render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml--traj=spiral --output-path=output.mp4
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```
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#### 6. In-depth guide
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## 6. In-depth guide
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For a more in-depth tutorial on how to modify/implement your own NeRF Graph, please see our [walk-through](https://plenoptix-nerfactory.readthedocs-hosted.com/en/latest/tutorials/pipelines/index.html).
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@ -1,10 +1,8 @@
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# 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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### Create environment
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</details>
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Create the python environment
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We reccomend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before preceding.
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```bash
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conda create --name nerfstudio python=3.8.13
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@ -12,31 +10,46 @@ conda activate nerfstudio
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python -m pip install --upgrade pip
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```
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Clone the repo
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### Dependencies
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```bash
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git clone git@github.com:plenoptix/nerfstudio.git
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```
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Install dependencies and nerfstudio as a library
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```bash
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cd nerfstudio
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pip install -e .
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```
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Install tiny-cuda-nn (tcnn) to run instant_ngp
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Install pytorch with CUDA (this repo has been tested with CUDA 11.3) and [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn)
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```bash
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pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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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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Install tab completion for nerfstudio (bash and zsh)
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### Installing nerfstudio
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Easy option:
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```bash
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# If the CLI changes, we can also re-install to synchronize completions
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nf-install-cli
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pip install nerfstudio
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```
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If you would want the latest and greatest:
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```bash
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git clone git@github.com:plenoptix/nerfstudio.git
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cd nerfstudio
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pip install -e .
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```
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### Optional Installs
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#### Tab completion (bash & zsh)
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This needs to be rerun when the CLI changes, for example if nerfstudio is updated.
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```bash
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ns-install-cli
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```
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### Development packages
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```bash
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pip install -e.[dev]
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pip install -e.[docs]
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```
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# Downloading data
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@ -44,7 +57,7 @@ nf-install-cli
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Download the original NeRF Blender dataset. We support the major datasets and allow users to create their own dataset, described in detail [here](https://plenoptix-nerfactory.readthedocs-hosted.com/en/latest/tutorials/data/index.html).
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```
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python scripts/downloads/download_data.py --dataset=blender
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ns-download-data --dataset=blender
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```
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Use `--help` to view all currently available datasets. The resulting script should download and unpack the dataset as follows:
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@ -68,23 +81,15 @@ To run with all the defaults, e.g. vanilla nerf method with the blender lego ima
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Run a vanilla nerf model.
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```bash
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python scripts/train.py
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ns-train vanilla-nerf
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```
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Run a faster version with instant ngp using tcnn (without the viewer).
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Run a vanilla nerf model.
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```bash
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python scripts/train.py --config-name=graph_instant_ngp.yaml
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ns-train nerfacto
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```
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Run with the viewer. However, you'll have to start the viewer server first. (See [viewer docs](../tutorials/viewer/viewer_quickstart.md))
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```bash
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python scripts/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 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.
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@ -100,7 +105,7 @@ tensorboard --logdir outputs
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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.
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```bash
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python scripts/eval.py render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml --traj=spiral --output-path=output.mp4
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ns-eval render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml --traj=spiral --output-path=output.mp4
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```
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Please note, this quickstart allows you to preform everything in a headless manner. We also provide a web-based viewer that allows you to easily monitor training or render out trajectories. See our [viewer docs](../tutorials/viewer/viewer_quickstart.md) for more.
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@ -40,8 +40,8 @@ from nerfstudio.models.base import VanillaModelConfig
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from nerfstudio.models.instant_ngp import InstantNGPModelConfig
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from nerfstudio.models.mipnerf import MipNerfModel
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from nerfstudio.models.mipnerf_360 import MipNerf360Model
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from nerfstudio.models.nerfacto import NerfactoModelConfig
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from nerfstudio.models.nerfw import NerfWModelConfig
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from nerfstudio.models.proposal import ProposalModelConfig
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from nerfstudio.models.semantic_nerf import SemanticNerfModel
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from nerfstudio.models.tensorf import TensoRFModelConfig
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from nerfstudio.models.vanilla_nerf import NeRFModel
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@ -50,14 +50,14 @@ from nerfstudio.pipelines.dynamic_batch import DynamicBatchPipelineConfig
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base_configs: Dict[str, Config] = {}
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base_configs["proposal"] = Config(
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method_name="proposal",
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base_configs["nerfacto"] = Config(
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method_name="nerfacto",
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trainer=TrainerConfig(steps_per_eval_batch=500, steps_per_save=2000, mixed_precision=True),
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pipeline=VanillaPipelineConfig(
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datamanager=VanillaDataManagerConfig(
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dataparser=BlenderDataParserConfig(), train_num_rays_per_batch=4096, eval_num_rays_per_batch=8192
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),
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model=ProposalModelConfig(eval_num_rays_per_chunk=1 << 16),
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model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 16),
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),
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optimizers={
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"proposal_networks": {
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@ -13,7 +13,7 @@
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# limitations under the License.
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"""
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Implementation of Instant NGP.
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NeRF implementation that combines many recent advancements.
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"""
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from __future__ import annotations
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@ -51,10 +51,10 @@ from nerfstudio.utils.callbacks import (
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@dataclass
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class ProposalModelConfig(ModelConfig):
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"""Proposal Model Config"""
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class NerfactoModelConfig(ModelConfig):
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"""Nerfacto Model Config"""
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_target: Type = field(default_factory=lambda: ProposalModel)
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_target: Type = field(default_factory=lambda: NerfactoModel)
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near_plane: float = 0.05
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"""How far along the ray to start sampling."""
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far_plane: float = 1000.0
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@ -87,14 +87,14 @@ class ProposalModelConfig(ModelConfig):
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"""Whether use single jitter or not for first proposal network."""
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class ProposalModel(Model):
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"""Instant NGP model
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class NerfactoModel(Model):
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"""Nerfacto model
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Args:
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config: instant NGP configuration to instantiate model
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config: Nerfacto configuration to instantiate model
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"""
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config: ProposalModelConfig
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config: NerfactoModelConfig
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def populate_modules(self):
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"""Set the fields and modules."""
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@ -74,10 +74,12 @@ docs = [
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]
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[project.scripts]
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nf-install-cli = "scripts.completions.install:entrypoint"
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nf-process-data = "scripts.process_data:entrypoint"
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nf-train = "scripts.train:entrypoint"
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nf-eval = "scripts.eval:entrypoint"
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ns-install-cli = "scripts.completions.install:entrypoint"
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ns-process-data = "scripts.process_data:entrypoint"
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ns-download-data = "scripts.downloads.download_data:entrypoint"
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ns-train = "scripts.train:entrypoint"
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ns-eval = "scripts.eval:entrypoint"
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ns-dev-test = "scripts.debugging.run_actions:entrypoint"
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[options]
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# equivalent to using --extra-index-url with pip, which is needed for specifying the CUDA version torch and torchvision
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@ -224,7 +224,7 @@ def main(
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.split("\n")
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)
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entry_points = list(
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# nf-train = "..." ==> nf-train
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# ns-train = "..." ==> ns-train
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e.partition("=")[0].strip()
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for e in entry_points
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)
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@ -86,6 +86,11 @@ def run_code_checks(continue_on_fail: bool = False):
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# run_github_actions_file(filename=".github/workflows/viewer_build_deploy.yml", continue_on_fail=continue_on_fail)
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if __name__ == "__main__":
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def entrypoint():
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"""Entrypoint for use with pyproject scripts."""
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dcargs.extras.set_accent_color("bright_yellow")
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dcargs.cli(run_code_checks)
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if __name__ == "__main__":
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entrypoint()
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@ -100,11 +100,11 @@ def main(
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download_nerfactory(dataset_name)
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if __name__ == "__main__":
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try:
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# Requires dcargs>=0.3.3.
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dcargs.extras.set_accent_color("bright_yellow")
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except AttributeError:
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pass
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def entrypoint():
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"""Entrypoint for use with pyproject scripts."""
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dcargs.extras.set_accent_color("bright_yellow")
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dcargs.cli(main)
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if __name__ == "__main__":
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entrypoint()
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@ -14,7 +14,7 @@ from nerfstudio.configs.base_configs import base_configs
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from nerfstudio.datamanagers.dataparsers.blender_parser import BlenderDataParserConfig
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from nerfstudio.engine.trainer import train_loop
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BLACKLIST = ["base", "semantic-nerf", "mipnerf-360", "instant-ngp", "proposal"]
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BLACKLIST = ["base", "semantic-nerf", "mipnerf-360", "instant-ngp", "nerfacto"]
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def set_reduced_config(config: Config):
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