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d4d19cd32b
* Update first_nerf.md Add an example of resuming training, which is a common usage. * Update first_nerf.md Update `load_dir` to make it clearer.
155 lines
4.9 KiB
Markdown
155 lines
4.9 KiB
Markdown
# Training your first model
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## Downloading data
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Download datasets provided by nerfstudio. We support the major datasets and allow users to create their own dataset, described in detail [here](./custom_dataset.md).
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```
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ns-download-data --dataset=blender
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ns-download-data --dataset=nerfstudio --capture=poster
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```
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:::{admonition} Tip
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:class: info
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Use `ns-download-data --help` to view all currently available datasets.
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:::
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The resulting script should download and unpack the dataset as follows:
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```
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|─ nerfstudio/
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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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|- nerfstudio/
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|- poster
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...
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```
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## Training a model
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See which models are available.
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```bash
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ns-train --help
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```
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Run a vanilla nerf model.
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```bash
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ns-train vanilla-nerf
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```
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Run a nerfacto model.
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```bash
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ns-train nerfacto
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```
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Run a nerfacto model with different data and port.
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```
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ns-train nerfacto --vis viewer --data data/nerfstudio/poster --viewer.websocket-port 7007
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```
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Run a nerfacto model and load the latest checkpoint to resume training.
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```
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ns-train nerfacto --vis viewer --data data/nerfstudio/poster --trainer.load_dir outputs/data-nerfstudio-poster/nerfacto/{timestamp}/nerfstudio_models
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```
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:::{admonition} Warning
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:class: warning
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- You may have to change the ports, and be sure to forward the "websocket-port".
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- All data configurations must go at the end. In this case, `nerfstudio-data` and all of its corresponding configurations come at the end after the model and viewer specification.
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:::
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## Intro to nerfstudio CLI and Configs
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Nerfstudio allows customization of training and eval configs from the CLI in a powerful way, but there are some things to understand.
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The most demonstrative and helpful example of the CLI structure is the difference in output between the following commands:
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```bash
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ns-train -h
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```
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```bash
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ns-train nerfacto -h nerfstudio-data
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```
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```bash
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ns-train nerfacto nerfstudio-data -h
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```
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In each of these examples, the -h applies to the previous subcommand (`ns-train`, `nerfacto`, and `nerfstudio-data`).
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In the first example, we get the help menu for the `ns-train` script.
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In the second example, we get the help menu for the `nerfacto` model.
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In the third example, we get the help menu for the `nerfstudio-data` dataparser.
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With our scripts, your arguments will apply to the preceding subcommand in your command, and thus where you put your arguments matters! Any optional arguments you discover from running
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```bash
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ns-train nerfacto -h nerfstudio-data
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```
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need to come directly after the `nerfacto` subcommand since these optional arguments only belong to the `nerfacto` subcommand:
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```bash
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ns-train nerfacto <nerfacto optional args> nerfstudio-data
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```
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Each script will have some other minor quirks (like the training script dataparser subcommand needing to come after the model subcommand), read up on them [here](../reference/cli/index.md).
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## Visualizing training runs
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If you are using a fast NeRF variant (ie. Nerfacto/Instant-NGP), we recommend using our viewer. See our [viewer docs](viewer_quickstart.md) for a tutorial on using the viewer. The viewer will allow interactive, real-time visualization of training.
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For slower methods where the viewer is not recommended, we default to [Wandb](https://wandb.ai/site) to log all training curves, test images, and other stats. We also support logging with [Tensorboard](https://www.tensorflow.org/tensorboard). We pre-specify default `--vis` options depending on the model.
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:::{admonition} Attention
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:class: attention
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- Currently we only support using a single viewer at a time.
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- To toggle between Wandb, Tensorboard, or our Web-based Viewer, you can specify `--vis VIS_OPTION`, where `VIS_OPTION` is one of {viewer,wandb,tensorboard}.
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:::
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#### Rendering videos
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We also provide options to render out the scene of a trained model with a custom trajectory and save the output to a video.
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```bash
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ns-render --load-config={PATH_TO_CONFIG} --traj=spiral --output-path=renders/output.mp4
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```
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While we provide pre-specified trajectory options, `--traj={spiral, interp, filename}` we can also specify a custom trajectory if we specify `--traj=filename --camera-path-filename {PATH}`.
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:::{admonition} Tip
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:class: info
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After running the training, the config path is logged to the terminal under "[base_config.py:263] Saving config to:"
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:::
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:::{admonition} See Also
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:class: seealso
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This quickstart allows you to preform everything in a headless manner.
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We also provide a web-based viewer that allows you to easily monitor training or render out custom trajectories.
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See our [viewer docs](viewer_quickstart.md) for more.
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:::
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## Evaluating Runs
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Calculate the psnr of your trained model and save to a json file.
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```bash
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ns-eval --load-config={PATH_TO_CONFIG} --output-path=output.json
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```
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We also provide a train and evaluation script that allows you to do benchmarking on the classical Blender dataset (see our [benchmarking workflow](../developer_guides/debugging_tools/benchmarking.md)).
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