Running using Napari
============================

This section goes over how to use the napari
to train and predict on a custom dataset.

.. admonition:: Note

   This guide assumes you have already installed CryoViT and setup napari by following the instructions in :ref:`Installing CryoViT <installing cryovit>` and :ref:`Setting up napari <setting up napari>`.
   If you have not done so, please do that first.

   You also need a valid ``.dset`` file, which specifies the training dataset. See :ref:`Setting Up a Dataset <using napari>` for instructions on how to set one up.

   A GPU is also highly recommended.

=========================
The Training Manager
=========================

The Training Manager covers the same functionality as the ``cryovit train`` and
``cryovit evaluate`` commands in the
:ref:`CLI <running from the command line>`.

First, open the Training Manager from the "Plugins" menu under "CryoViT". Below
is what your napari window should look like after opening the Training Manager:

.. figure:: ../_static/napari/train.png
    :align: center
    :width: 95%

    The CryoViT Training Manager in napari.

First, we need to load in a dataset file.
This depends on the model you want to train:

    1. When using the **CRYOVIT** model, DINOv2 features need to be
       calculated before training.

       Use the "Feature Extraction" section **(1)** to create a new dataset
       containing the features. This will automatically be selected when
       "Calculate Image Features"
       is clicked.
    2. When using any other model (e.g., **3D U-Net**, **SAM2**), you can
       directly load your dataset in the "Load Existing Dataset" section
       **(2)**.

Then, select the model you want to train in the "Model Selection"
section **(3)**. This involves choosing a name, the model architecture
(e.g., CRYOVIT, 3D U-Net, SAM2), and the label you want to train on.

Finally, click "Start Training" in the "Training Parameters" section **(4)**.

.. note::

    For monitoring training progress in more detail, you can enable the
    "Log Training" option, which will print an additional command in the
    terminal that you can run to monitor training with **Tensorboard**.

    This is similar to the ``--log-training`` option in the
    :ref:`CLI <monitoring training>`.

When training is complete, you only need to specify an additional
"Evaluation Result Directory" to run evaluation on the same dataset.
This step is optional, and can be skipped if you just want to use
the model for inference.

.. note::

    The "Visualize Results" option will save the evaluation predictions,
    similar to the ``--visualize`` option in the :ref:`CLI <evaluating the model>`

=========================
The Inference Manager
=========================

The Inference Manager covers the same functionality as the ``cryovit infer``
command in the :ref:`CLI <running from the command line>`.

First, open the Inference Manager from the “Plugins” menu under “CryoViT”.
Below is what your napari window should look like after opening
the Inference Manager:

.. figure:: ../_static/napari/infer.png
    :align: center
    :width: 95%

    The CryoViT Inference Manager in napari.

The Inference Manager assumes you have imported data into napari,
which can then be selected in the "Data" section **(1)**, and
have your pre-trained models saved in a folder, which can be specified
as the "Model Directory" **(3)**.

The plugin will read all ``.model`` files in the model directory,
and you can then select which model to use.

The "Model Info" section will display basic information about the model
that you specified during training.

.. note::

    If using a **CRYOVIT** model, then DINOv2 features will be
    automatically computed for the input data before running inference.

    These are saved in a temporary folder **(1)**, which is then deleted
    after inference is complete.


.. figure:: ../_static/napari/infer_labels.png
    :align: center
    :width: 95%

    Inference results on an example tomogram using the pre-trained
    mitochondria model in the :ref:`quick start guide <quick start guide>`.
    The results are visualized in 3D mode **(2)**.

.. tip::

    Similar to the :ref:`CLI <using a pre-trained model>`, adjust the
    threshold slider **(3)** to change the confidence threshold for
    generating the binary segmentation mask.

.. note::

    If you plan on doing inference for a large number of files,
    it is recommended to use the CLI instead of the napari plugin,
    since the plugin has an additional overhead for computing
    DINOv2 features for each file.