coremltools 4.0b2

What's New

• Improved documentation available at http://coremltools.readme.io.
• New converter path to directly convert PyTorch models without going through ONNX.
• Enhanced TensorFlow 2 conversion support, which now includes support for dynamic control flow and LSTM layers. Support for several popular models and architectures, including Transformers such as GPT and BERT-variants.
• New unified conversion API ct.convert() for converting PyTorch and TensorFlow (including tf.keras) models.
• New Model Intermediate Language (MIL) builder library to either build neural network models directly or implement composite operations.
• New utilities to configure inputs while converting from PyTorch and TensorFlow, using ct.convert() with ct.ImageType(), ct.ClassifierConfig(), etc., see details: https://coremltools.readme.io/docs/neural-network-conversion.
• onnx-coreml converter is now moved under coremltools and can be accessed as ct.converters.onnx.convert().

Deprecations

• Deprecated the following methods

  • NeuralNetworkShaper class.
  • get_allowed_shape_ranges().
  • can_allow_multiple_input_shapes().
  • visualize_spec() method of the MLModel class.
  • quantize_spec_weights(), instead use the quantize_weights() method.
  • get_custom_layer_names(), replace_custom_layer_name(), has_custom_layer(), moved them to internal methods.

• Added deprecation warnings for, will be deprecated in next major release.

  • convert_neural_network_weights_to_fp16(), convert_neural_network_spec_weights_to_fp16(). Instead use the quantize_weights() method. See https://coremltools.readme.io/docs/quantization for details.

Known Issues

• Latest version of Pytorch tested to work with the converter is Torch 1.5.0.
• TensorFlow 2 model conversion is supported for models with 1 concrete function.
• Conversion for TensorFlow and PyTorch models with quantized weights is currently not supported.
• coremltools.utils.rename_feature does not work correctly in renaming the output feature of a model of type neural network classifier
• leaky_relu layer is not added yet to the PyTorch converter, although it's supported in MIL and the Tensorflow converters.