Skip to content

coremltools 4.0b2

Pre-release
Pre-release
Compare
Choose a tag to compare
@1duo 1duo released this 27 Jul 21:22
4.0b2
37e619d
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

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.

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.
Assets 27
Loading