0.12.0

🚀 Features

Introduce functions for reading and writing TF Records for segmentation data @vanvalen (#597)

What

Included functions to save datasets as tfrecords and load them into tf.data.Dataset objects

Why

As our training datasets grow, it is becoming difficult to load full datasets into memory. By introducing support for tfrecords, we can load portions of datasets from disk on the fly during training.

🧰 Maintenance

Update models after retraining on deepcell 0.12.0rc @msschwartz21 (#599)

What

• Update models with versions that were trained on tensorflow 2.8 (vanvalenlab/model-registry#17)

Why

• Models should use the same version of tensorflow for predictions as they were trained on

Add option for either batch or layer norm in tracking model @msschwartz21 (#598)

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.

Update TF_VERSION build arg in docker build workflow @msschwartz21 (#596)

The TF_VERSION build arg has to be updated manually

Update Tensorflow to 2.8 @msschwartz21 (#595)

This PR updates tensorflow to 2.8 and drops support for python 3.6. The following changes were necessary to make this upgrade possible:

• Change imports from tensorflow.python.keras to tensorflow.keras which was a change introduced with tensorflow 2.6
• Remove convolutional recurrent layers and their functionality from featurenet and panopticnet. Key functions that were used in the convolutional recurrent layer are no longer available in keras.
• Change imports from tensorflow.keras to keras: keras_parameterized, conv_utils, test_utils
• Drop support for python 3.6

I retrained the nuclear model in the model-registry using this branch of deepcell and the performance was comparable.