coremltools 7.0b2

• The default neural network backend is now mlprogram for iOS15/macOS12. Previously calling coremltools.convert() without providing the convert_to or the minimum_deployment_target arguments, used the lowest deployment target (iOS11/macOS10.13) and the neuralnetwork backend. Now the conversion process will default to iOS15/macOS12 and the mlprogram backend. You can change this behavior by providing a minimum_deployment_target or convert_to value.
• Changes default I/O type for Neural Networks to FP16 for iOS16/macOS13 or later when mlprogram backend is used.
• Changes upper input range behavior when backend is mlprogram:
  • If RangeDim is used and no upper-bound is set (with a positive number), an exception will be raised.
  • If the user does not use the inputs parameter but there are undetermined dim in input shape (for example, TF with "None" in input placeholder), it will be sanitized to a finite number (default_size + 1) and raise a warning.
• New utility method for getting weight metadata: coremltools.optimize.coreml.get_weights_metadata. This information can be used to customize optimization across ops when using coremltools.optimize.coreml APIs.
• Support for new PyTorch ops: repeat_interleave and unflatten.
• New and updated iOS17/macOS14 ops: batch_norm, conv, conv_transpose, expand_dims, gru, instance_norm, inverse, l2_norm, layer_norm, linear, local_response_norm, log, lstm, matmul, reshape_like, resample, resize, reverse, reverse_sequence, rnn, rsqrt, slice_by_index, slice_by_size, sliding_windows, squeeze, transpose.
• Various other bug fixes, enhancements, clean ups and optimizations.

Special thanks to our external contributors for this release: @fukatani, @pcuenca, @KWiecko, @comeweber and @sercand

coremltools 7.0b1

• New submodule coremltools.optimize for model quantization and compression
  • coremltools.optimize.coreml for compressing coreml models, in a data free manner. coremltools.compresstion_utils.* APIs have been moved here
  • coremltools.optimize.torch for compressing torch model with training data and fine-tuning. The fine tuned torch model can then be converted using coremltools.convert
• Updated MIL ops for iOS17/macOS14/watchOS10/tvOS17
• pass_pipeline parameter has been added to coremltools.convert to allow controls over which optimizations are performed.
• Python 3.11 support.
• MLModel batch prediction support.
• Support for converting statically quantized PyTorch models
• New Torch layer support: randn, randn_like, scaled_dot_product_attention, stft, tile
• Faster weight palletization for large tensors.
• coremltools.models.ml_program.compression_utils is deprecated.
• Various other bug fixes, enhancements, clean ups and optimizations.

Core ML tools 7.0 guide: https://coremltools.readme.io/v7.0/

Special thanks to our external contributors for this release: @fukatani, @pcuenca, @mlaves, @cclauss, @smpanaro, @nikalra, @jszaday

coremltools 6.3

Core ML Tools 6.3 Release Note

• Torch 2.0 Support
• TensorFlow 2.12.0 Support
• Remove Python 3.6 support
• Functionality for controling graph passes/optimizations, see the pass_pipeline parameter to coremltools.convert.
• A utility function for easily creating pipeline, see: utils.make_pipeline.
• A debug utility function for extracting submodels, see: converters.mil.debugging_utils.extract_submodel
• Various other bug fixes, enhancements, clean ups and optimizations.

Special thanks to our external contributors for this release: @fukatani, @nikalra and @kevin-keraudren.

coremltools 6.2

Core ML Tools 6.2 Release Note

• Support new PyTorch version: torch==1.13.1 and torchvision==0.14.1.
• New ops support:
  • New PyTorch ops support: 1-D and N-D FFT / RFFT / IFFT / IRFFT in torch.fft, torchvision.ops.nms, torch.atan2, torch.bitwise_and, torch.numel,
  • New TensorFlow ops support: FFT / RFFT / IFFT / IRFFT in tf.signal, tf.tensor_scatter_nd_add.
• Existing ops improvements:
  • Supports int input for clamp op.
  • Supports dynamic topk (k not determined during compile time).
  • Supports padding='valid' in PyTorch convolution.
  • Supports PyTorch Adaptive Pooling.
• Supports numpy v1.24.0 (#1718)
• Add int8 affine quantization for the compression_utils.
• Various other bug fixes, optimizations and improvements.

Special thanks to our external contributors for this release: @fukatani, @ChinChangYang, @danvargg, @bhushan23 and @cjblocker.

coremltools 6.1

• Support for TensorFlow 2.10.
• New PyTorch ops supported: baddbmm, glu, hstack, remainder, weight_norm, hann_window, randint, cross, trace, and reshape_as.
• Avoid root logger and use the coremltools logger instead.
• Support dynamic input shapes for PyTorch repeat and expand op.
• Enhance translation of torch where op with only one input.
• Add support for PyTorch einsum equation: 'bhcq,bhck→bhqk’.
• Optimization graph pass improvement
  • 3D convolution batchnorm fusion
  • Consecutive relu fusion
  • Noop elimination
• Actively catch the tensor which has rank >= 6 and error out
• Various other bug fixes, optimizations and improvements.

Special thanks to our external contributors for this release: @fukatani, @piraka9011, @giorgiop, @hollance, @SangamSwadiK, @RobertRiachi, @waylybaye, @GaganNarula, and @sunnypurewal.

coremltools 6.0

• MLProgram compression: affine quantization, palettize, sparsify. See coremltools.compression_utils
• Python 3.10 support.
• Support for latest scikit-learn version (1.1.2).
• Support for latest PyTorch version (1.12.1).
• Support for TensorFlow 2.8.
• Support for options to specify input and output data types, for both images and multiarrays
  • Update coremltools python bindings to work with GRAYSCALE_FLOAT16 image datatype of CoreML
  • New options to set input and output types to multi array of type float16, grayscale image of type float16 and set output type as images, similar to the coremltools.ImageType used with inputs.
• New compute unit enum type: CPU_AND_NE to select the model runtime to the Neural engine and CPU.
• Support for several new TensorFlow and PyTorch ops.
• Changes to opset (available from iOS16, macOS13)
  • New MIL ops: full_like, resample, reshape_like, pixel_unshuffle, topk
  • Existing MIL ops with new functionality: crop_resize, gather, gather_nd, topk, upsample_bilinear.
• API Breaking Changes:
  • Do not assume source prediction column is “predictions”, fixes #58.
  • Remove useCPUOnly parameter from coremltools.convert and coremltools.models.MLModel. Use coremltools.ComputeUnit instead.
  • Remove ONNX support.
  • Remove multi-backend Keras support.
• Various other bug fixes, optimizations and improvements.

coremltools 6.0b2

• Support for new MIL ops added in iOS16/macOS13: pixel_unshuffle, resample, topk
• Update coremltools python bindings to work with GRAYSCALE_FLOAT16 image datatype of CoreML
• New compute unit enum type: CPU_AND_NE
• New PyTorch ops: AdaptiveAvgPool2d, cosine_similarity, eq, linalg.norm, linalg.matrix_norm, linalg.vector_norm, ne, PixelUnshuffle
• Support for identity_n TensorFlow op
• Various other bug fixes, optimizations and improvements.

coremltools 6.0b1

• MLProgram compression: affine quantization, palettize, sparsify. See coremltools.compression_utils.
• New options to set input and output types to multi array of type float16, grayscale image of type float16 and set output type as images, similar to the coremltools.ImageType used with inputs.
• Support for PyTorch 1.11.0.
• Support for TensorFlow 2.8.
• [API Breaking Change] Remove useCPUOnly parameter from coremltools.convert and coremltools.models.MLModel. Use coremltools.ComputeUnit instead.
• Support for many new PyTorch and TensorFlow layers
• Many bug fixes and enhancements.

Known issues

• While conversion and CoreML models with Grayscale Float16 images should work with ios16/macos13 beta, the coremltools-CoreML python binding has an issue which would cause the predict API in coremltools to crash when the either the input or output is of type grayscale float16
• The new Compute unit configuration MLComputeUnitsCPUAndNeuralEngine is not available in coremltools yet

coremltools 5.2

• Support latest version (1.10.2) of PyTorch
• Support TensorFlow 2.6.2
• Support New PyTorch ops:
  • bitwise_not
  • dim
  • dot
  • eye
  • fill
  • hardswish
  • linspace
  • mv
  • new_full
  • new_zeros
  • rrelu
  • selu
• Support TensorFlow ops
  • DivNoNan
  • Log1p
  • SparseSoftmaxCrossEntropyWithLogits
• Various bug fixes, clean ups and optimizations.
• This is the final coremltools version to support Python 3.5

coremltools 5.1

• New supported PyTorch operations: broadcast_tensors, frobenius_norm, full, norm and scatter_add.
• Automatic support for inplace PyTorch operations if non-inplace operation is supported.
• Support PyTorch 1.9.1
• Various other bug fixes, optimizations and improvements.