Changes

• minimum_ios_deployment_target can now only be one of ['12', '11.2'].

• Deprecated conversion support for TensorFlow 2. tfcoreml now only accepts TensorFlow 1 models serialized as frozen graph_def object.

For TensorFlow 2 conversion or when minimum deployment target is iOS13 or later, please use Unified Conversion API introduced in the coremltools 4.0 python package.

Changes:

• Add support for TensorFlow 2.x file format (.h5, SavedModel, and concrete functions).
• Add support for several new ops, such as AddV2, FusedBatchNormV3 for iOS 13+.
• Bug fixes in the TensorFlow converter's op fusion graph pass.
• New minimum_ios_deployment_target flag (replacing previous target_ios which causes confusions). This flag can be utilized for setting minimum targeting iOS, e.g., the model converted with minimum_ios_deployment_target='12' will support iOS 12, iOS 13, and later.

Known Issues:

• tf.keras model conversion supported only with TensorFlow 2
• Currently, there are issues while invoking the TensorFlow 2.x model conversion in Python 2.x.
• Currently, there are issues while converting tf.keras graphs that contain recurrent layers.

This release was focused on adding many new layers and operators which can be found here. The coremltools release 3.0 can be found here.

Examples for model conversions and how to use control flows

• Added the argument target_ios to choose the Core ML spec version that is produced by the converter. target_ios='13' will enable the converter to use all the new layers added in Core ML 3.
• Added custom_conversion_function option where users can provide their own custom conversion function similar to these examples for the custom conversion use case.

Supports new model specification version 4 with target iOS 13 or later, the Core ML model specification can be found in proto or documentation.

Any questions or concerns related to this release can be submitted as an issue and will be review by the team. All comments are welcomed and will be used to improve the existing documentation.

• added support for resizeBilinear and cropAndResize ops
• added support for 8-bit QuantizedConv2D, QuantizedMatMul ops
• some bug fixes

• Adds Python 3 support
• Adds custom layer support
• Miscellaneous bug fixes