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115 lines
3.9 KiB
Markdown
115 lines
3.9 KiB
Markdown
# Training First Model
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## Downloading data
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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 TODO].
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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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Use `--help` to view all currently available datasets. 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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|- <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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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 with nerfstudio data. You'll may have to change the ports, and be sure to forward the "websocket-port".
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```
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ns-train nerfacto --vis viewer --viewer.zmq-port 8001 --viewer.websocket-port 8002 nerfstudio-data --pipeline.datamanager.dataparser.data-directory data/nerfstudio/poster --pipeline.datamanager.dataparser.downscale-factor 4
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```
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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](viewer_quickstart.md) for more details. The viewer will allow interactive visualization of training in realtime.
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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>`.
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```bash
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tensorboard --logdir outputs
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```
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## Rendering a Trajectory
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To evaluate the trained NeRF, we provide an evaluation script that allows you to do benchmarking (see our [benchmarking workflow](../developer_guides/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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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](viewer_quickstart.md) for more.
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## FAQ
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- [TinyCUDA installation errors out with cuda mismatch](tiny-cuda-error)
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(tiny-cuda-error)=
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#### TinyCUDA installation errors out with cuda mismatch
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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.`
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**Solution**:
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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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```
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- [Installation errors, File "setup.py" not found](pip-install-error)
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(pip-install-error)=
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#### Installation errors, File "setup.py" not found
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When installing dependencies and nerfstudio with `pip install -e .`, you run into: `ERROR: File "setup.py" not found. Directory cannot be installed in editable mode`
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**Solution**:
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This can be fixed by upgrading pip to the latest version:
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```
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python -m pip install --upgrade pip
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```
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- [Runtime errors, "len(sources) > 0".](cuda-sources-error)
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(cuda-sources-error)=
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#### Runtime errors: "len(sources) > 0", "ctype = \_C.ContractionType(type.value) ; TypeError: 'NoneType' object is not callable".
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When running `train.py `, an error occurs when installing cuda files in the backend code.
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**Solution**:
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This is a problem with not being able to detect the correct CUDA version, and can be fixed by updating the CUDA path environment variables:
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```
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export CUDA_HOME=/usr/local/cuda
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
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export PATH=$PATH:$CUDA_HOME/bin
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```
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