Coremltools

* New **documentation** available at [http://coremltools.readme.io](http://coremltools.readme.io/).
* New converters from PyTorch, TensorFlow 1, and TensorFlow 2 available via the new unified converter API, `ct.convert()`
* New **Model Intermediate Language (MIL)** builder library, using which the new converters have been implemented. Using `MIL` its easy to build neural network models directly or [implement composite operations](https://coremltools.readme.io/docs/composite-operators).
* 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.

Highlights of Core ML 4

* Model Deployment
* Model Encryption
* Unified converter API with PyTorch and TensorFlow 2 support in `coremltools` 4
* MIL builder for neural networks and composite ops in `coremltools` 4
* New layers in neural network:
* CumSum
* OneHot
* ClampedReLu
* ArgSort
* SliceBySize
* Convolution3D
* Pool3D
* Bilinear Upsample with align corners and fractional factors
* PixelShuffle
* MatMul with int8 weights and int8 activations
* Concat interleave
* See NeuralNetwork.proto
* Enhanced Xcode model view with interactive previews
* Enhanced Xcode Playground support for Core ML models

* Several bug fixes, including:
* Fix in `rename_feature` API, when used with a neural network model with image inputs
* Bug fixes in conversion of torch ops such as layer norm, flatten, conv transpose, expand, dynamic reshape, slice etc.
* Fixes when converting from PyTorch 1.6.0
* Fixes in supporting `.pth` extension, in addition to `.pt` extension , for torch conversion
* Fixes in TF2 LSTM with dynamic batch size
* Fixes in control flow models with TF 2.3.0
* Fixes in numerical issues with the `inverse` layer, on a few devices, by increasing the lower bound of the output

* Added conversion functions for PyTorch ops such as neg, sum, repeat, where, adaptive_max_pool2d, floordiv etc
* Update Doc strings for several [MIL ops](https://coremltools.readme.io/reference/convertersmilops)
* Support for TF1 models with fake quant ops when used with convolution ops
* Several new MIL optimization passes such as no-op elimination, pad and conv fusion etc.

Whats new

* Support for PyTorch 1.6
* concat with interleave option
* New Torch ops support added
* acos
* acosh
* argsort
* asin
* asinh
* atan
* atan
* atanh
* avg_pool3d
* bmm
* ceil
* cos
* cosh
* cumsum
* elu
* exp
* exp2
* floor
* gather
* hardsigmoid
* is_floating_point
* leaky_relu
* log
* max_pool
* prelu
* reciprocal
* relu6
* round
* rsqrt
* sign
* sin
* sinh
* softplus
* softsign
* sqrt
* square
* tan
* tanh
* threshold
* true_divide
* Improved TF2 test coverage
* MIL definition update
* LSTM activation function moved from TupleInput to individual inputs
* Improvements in MIL infrastructure

Known Issues
* 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.

What's New

* Improved **documentation** available at [http://coremltools.readme.io](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](https://coremltools.readme.io/docs/composite-operators).
* 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](https://github.com/onnx/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.

Whats New

* New **documentation** available at [http://coremltools.readme.io](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](https://coremltools.readme.io/docs/composite-operators).
* 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](https://github.com/onnx/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

* 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 its supported in MIL and the Tensorflow converters.

- Added support for `tf.einsum` op
- Bug fixes in image pre-processing error handling, quantization function for the `embeddingND` layer, conversion of `tf.stack` op
- Updated the transpose removal mlmodel pass
- Fixed import statement to support scikit-learn >=0.21 (sapieneptus )
- Added deprecation warnings for class `NeuralNetworkShaper` and methods `visualize_spec`, `quantize_spec_weights`
- Updated the names of a few functions that were unintentionally exposed to the public API, to internal, by prepending with underscore. The original methods still work but deprecation warnings have been added.