Deepcell

🧰 Maintenance

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<summary>Swap m2r with m2r2 and update version number to 0.12.3 msschwartz21 (623)</summary>

Bump version number for new release

Also includes a change from `m2r` to `m2r2` for our documentation pipeline. `m2r` is no longer being maintained so it has been replaced with a fork with more active maintenance. https://github.com/CrossNox/m2r2
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<summary>Add support for EfficientNetV2 backbones to the get_backbone utility function msschwartz21 (619)</summary>

What
* Add support for EfficientNetV2 backbones to the get_backbone utility function

Why
* Exposes EfficientNetV2 backbones for use in deepcell model architectures

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🐛 Bug Fixes

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<summary>Add matplotlib to setup.py msschwartz21 (610)</summary>

What
* Add matplotlib requirement to setup.py

Why
* Pip installations used the requirements listed in setup.py so currently matplotlib is not installed when pip installing deepcell
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<summary>Update mesmer post-processing args ngreenwald (609)</summary>

What
Updated the post-processing parameters for the Mesmer model. Also updates the notebook to describe how post-processing can be modified.

Why
The newly retrained model has different parameters that give the best results. In addition, I've gotten questions from a few different people about how to tweak the model output, having it in the notebook will make it easy for people to see the effects.

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🚀 Features

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<summary>Create TFRecords for tracking datasets vanvalen (602)</summary>

What
* Added functionality to create TFRecords for tracking datasets

Why
* As the training datasets grow in size, they are no longer able to fit in memory (as is the case with image generators). Adding functionality for TFRecords will let us train on larger datasets as they are loaded dynamically from disk during training rather than into memory all at once.

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🐛 Bug Fixes

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<summary>Fix tracking model bug that pinned n_filters, encoder_dim and embedding_dim to 64 vanvalen (606)</summary>

What
* Fixed a bug that required the tracking model to have n_filters, encoder_dim, and embedding_dim be pinned to 64

Why
* Model optimization is going to require us to change these parameters to improve performance and reduce model size. This pull request makes that substantially easier by fixing this bug.

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🧰 Maintenance

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<summary>Bump version to 0.12.1 msschwartz21 (605)</summary>


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<summary>Expose option for fixed crops in the Track data object vanvalen (607)</summary>

What
* Modify the Track class so that it allows hooks into get_image_features for crop_mode and norm

Why
* Creating the appearance image feature by cropping and resizing removes information about cell size that the model can use to make more accurate tracking predictions. A previous update to deepcell-tracking (https://github.com/vanvalenlab/deepcell-tracking/pull/98) introduced the crop_mode (either 'fixed' or 'resize') and norm flags to get_image_features. This pull request exposes these flags to the Track class.

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<summary>Bump `deepcell-tracking` to 0.6.0 msschwartz21 (603)</summary>

What
* Bump `deepcell-tracking` to the new minor release
* Update imports to match the reorganization introduced in this release

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<summary>Update the docstring for `format_output_mesmer` curlup (601)</summary>

What
Doc-string for `format_output_mesmer` is now correctly saying "ValueError: if model output list is not len(4)"

Why
Because `format_output_mesmer` code diverged from the doc in what is expected length of model output list
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🧰 Maintenance

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<summary>MAINT: update deprecated code from dependencies and remove warnings filters rossbar (131)</summary>

The current pytest configuration is [suppressing all deprecation warnings](https://github.com/vanvalenlab/deepcell-toolbox/blob/fd398ebf1b21e313870aed745413503dacfb29f2/pytest.ini#L6-L9), which includes warnings from dependency libraries. This makes it much more likely for code to fall out-of-date, as can be seen in [the recent failing CI jobs](https://github.com/vanvalenlab/deepcell-toolbox/actions/runs/3734920418/jobs/6337547681) due to changes in the lastest version of numpy which was just releases (1.24).

This PR addresses the issue in two ways:
1. Updates code to take into account changes in the underlying libraries, and
2. Removes the global warnings filter in favor of fine-grained warnings filtering.

The second bullet involves adding explicit checks for warnings raised by `deepcell-toolbox` itself, to ensure that warnings are being raised correctly in the expected cases.
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<summary>Add Python3.10 support rossbar (128)</summary>

Adds support for Python 3.10. No changes to the underlying library but some dependency updates were required, the main ones being:
- removing the upper-bound on `pytest`, and
- Dropping the `pytest-pep8` extension, which [hasn't been actively maintained in over 8 years](https://pypi.org/project/pytest-pep8/#history)

We can of course re-add code linting using different tooling - I'm happy to do so but would prefer to leave it for a follow-up PR.
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<summary>Add option for either batch or layer norm in tracking model msschwartz21 (598)</summary>

What
* Provide the option to select either `BatchNormalization` or `LayerNormalization` in `GNNTrackingModel`

Why
* This option makes it possible to train the model with a batch size of 1 when layer normalization is enabled.

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🧰 Maintenance

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<summary>Update TF\_VERSION build arg in docker build workflow msschwartz21 (596)</summary>

The TF_VERSION build arg has to be updated manually
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