🚀 Features
<details>
<summary>Add new GCNN based model architecture for tracking with new `tf.data.Dataset` MekWarrior (506)</summary>
What
* End-to-End updates for DeepCell's approach to the tracking problem in live-cell imaging. A dataset builder module was added to to more closely follow TensorFlow's preferred style (with tracking dataset object being the first example). The previous LSTM and Siamese Neural Network (SNN) architecture was moved from `featurenet.py` in the `model_zoo` to a new 'tracking.py,` where a graph-based architecture for classification was also installed. This new model required 2 new "merge" layers and an additional loss function compatible with masking out portions of the adjacency matrix. The Tracking Application has been updated to take advantage of this new model and approach.
Why
* These updates represent the natural evolution of the repo's approach to tracking. It addresses key needs to further enable adoption of TF2 and dramatically improves tracking speed.
</details>
<details>
<summary>InferenceTimer callback to measure inference time during training. willgraf (508)</summary>
What
Add new `InferenceTimer` callback to measure inference time for a pre-defined number of samples:
Average inference speed per sample for 100 total samples: 0.00204s ± 0.00158s.
Why
* Fixes 275
</details>
<details>
<summary>Allow preprocessing_fn and postprocessing_fn to be overridden. willgraf (521)</summary>
What
* Set `preprocessing_fn` and `postprocessing_fn` as arguments to `init` to allow them to be overridden when creating applications.
Why
* This allows the applications to be more flexible and prevents users from subclassing the Application when they use a different preprocessing function.
</details>
<details>
<summary>Add pad_mode as an argument to CytoplasmSegmentation.predict. willgraf (524)</summary>
Enables overriding the default `pad_mode` to use "reflect" by default.
What
* Add `pad_mode` as a new argument to `CytoplasmSegmentation.predict` with default value `"reflect"`.
Why
* Allows overriding the padding mode for tiling the input. Prevents edge effects for models not trained on zero-padded data.
</details>
<details>
<summary>Support individual parameter passing to Mesmer app ngreenwald (545)</summary>
What
Previously, the default values for the Mesmer post-processing kwargs were configured such that if the user passed any args to the app, all of the other defaults would be reset. This PR modifies the behavior so that only the kwargs specified by the user are changed, the other defaults remain unchanged
Why
Removes confusing behavior where users would think they're only overriding a single arg, when in fact they would be resetting all of the args back to the `deep_watershed` defaults, which are not the same as the `Mesmer` defaults
</details>
<details>
<summary>Remove fully convolutional layers from the GNNTrackingModel. willgraf (549)</summary>
What
* Remove the Fully Connected layers from the tracking decoder
* Enable multiple graph convolutional layer types with new `graph_layer` argument, currently supports GCN and GCS.
Why
* Improve model performance and ease of use.
</details>
🐛 Bug Fixes
<details>
<summary>Set `include_top` to `True` by default in `__create_semantic_head` willgraf (528)</summary>
What
* Set `include_top` to `True` by default in `__create_semantic_head`
* Remove `include_top` from example notebook to avoid confusion
Why
* Fixes 515
</details>
<details>
<summary>Add Semantic data generators to `data_generators.__all__`. willgraf (514)</summary>
What
* Add Semantic data generators to `data_generators.__all__`.
Why
* Allow sphinx to build the docs, fixes 513.
</details>
<details>
<summary>Application batch prediction ngreenwald (540)</summary>
What
Modifies the applications to use internal batch prediction function rather than the default model.predict batching functionality. Closes 538
Why
The default model.predict batch function creates a tf.dataset object with all images, not just the specified batch size. This leads to memory issues on the GPU when batch processing tiles from large images.
</details>
<details>
<summary>Fix `sed` commands to only replace `tensorflow` not `tensorflow-addons`. willgraf (548)</summary>
What
* Use `sed` to replace `tensorflow~=` with `tensorflow-cpu~=`. Including the `~` prevents other deps that start with the word `tensorflow` from getting replaced.
* Bump version to 0.10.0-rc.2
Why
* Fixes 547
</details>
🧰 Maintenance
<details>
<summary>Bump TensorFlow to 2.5.1 willgraf (543)</summary>
What
* Update TensorFlow to the latest 2.5.x release.
Why
* Fixes several [CVEs](https://github.com/tensorflow/tensorflow/releases/tag/v2.5.1).
</details>
<details>
<summary>Add template for release-drafter workflow willgraf (517)</summary>
What
* Add template file for `release-drafter` workflow.
Why
* Creates a template to build release drafts with `release-drafter`.
* The workflow will be added as a separate PR because it needs this template in the default branch.
</details>
<details>
<summary>Add release-drafter workflow file to automatically release drafts. willgraf (518)</summary>
What
* Add `release-drafter` workflow YAML file.
Why
* Automatically update release drafts on commits to master.
</details>
<details>
<summary>Install pydot and graphviz to use tf.keras.utils.plot_model willgraf (525)</summary>
What
* Add `pydot>=1.4.2,<2` to `setup.py` and `requirements.txt`
* Install `graphviz` in the `Dockerfile`
Why
* Enable use of `tf.keras.utils.plot_model` out of the box
* Fixes 504
</details>
<details>
<summary>Update Nuclear and Cytoplasm Segmentation models. willgraf (527)</summary>
What
* Update version of both Nuclear and Cytoplasm Segmentation models.
Why
* These new models have been trained using the data registry and are the latest models available.
</details>
<details>
<summary>Update deepcell-toolbox to 0.10.0 willgraf (529)</summary>
What
* Bump `deepcell-toolbox` version to 0.10.0
* Update `deepcell.metrics` to remove old metrics imports
* Update `Mesmer` to use new combined `deep_watershed`
Why
* Update to latest version of the toolbox
</details>
<details>
<summary>Support Python 3.9 willgraf (531)</summary>
What
* Include Python 3.9 in the GitHub Actions testing workflow.
* Update setup.py to include support for Python 3.9.
Why
* TensorFlow 2.5.0 supports Python3.9 and so should DeepCell.
</details>
<details>
<summary>Cache the entire Python environment to speed up build times. willgraf (535)</summary>
What
* Cache the entire Python environment in the testing GitHub Action workflow.
Why
* [Drastically speed up build times](https://medium.com/ai2-blog/python-caching-in-github-actions-e9452698e98d).
</details>
<details>
<summary>Bump version to 0.10.0. willgraf (530)</summary>
What
* Update version to 0.10.0
Why
* Get ready for the next release
</details>
<details>
<summary>Update model in CellTracking application. willgraf (550)</summary>
What
* Update neighborhood encoder and inference model hashes for the newly trained model
Why
* New model reflects architecture changes and has been trained on diverse data.
</details>