Sourced from onnx's releases.

v1.13.0

ONNX v1.13.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

New operators

• Col2Im added in #3948
• BitwiseNot added in #4497
• BitwiseAnd, BitwiseOr and BitwiseXor added in #4496

Operator extensions

• Resize - New attributes: antialias, axes and keep_aspect_ratio_policy, allow for both scales and sizes to be provided when one of them is an empty constant #4126, #4388
• Pad - New attribute axes #4190
• OptionalHasElement - New input types handling #4326
• OptionalHasElement and OptionalGetElement - Accept tensor and sequence types #4421
• ScatterElement and ScatterND - Add max and min as supported reduction attributes #4411
• Split - Add support for uneven tensor splitting and a new num_outputs attribute #4481
• LpPool - New attributes: ceil_mode and dilations #4534

Function updates

• CenterCropPad added in #4190
• mish added in #4350
• GroupNormalization added in #4621

Reference Python runtime

Reference Python runtime dependent on only Python and numpy has been added. #4483

Python 3.11 support

ONNX 1.13.0 supports Python 3.11. #4490

Apple Silicon support

Support for M1/M2 ARM processors has been added. #4642

More

ONNX 1.13.0 also comes with numerous:

• bugfixes
• infrastructure improvements
• CI improvements
• documentation updates
• security updates

For full details see Logistics for ONNX Release 1.13.0.

Deprecation notice

• TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE has been deprecated #4270
• ONNXIFI: ONNX Interface for Framework Integration has been deprecated #4431

Installation

You can upgrade to the latest release using pip install onnx --upgrade or build from source following the README instructions.

Contributors

Thanks to these individuals for their contributions in this release since last 1.12.0 release: @​AnandKri, @​cbourjau, @​jcwchen, @​gramalingam, @​garymm, @​GaetanLepage, @​ilya-lavrenov, @​jnovikov, @​JackBoosY, @​jbachurski, @​tjich, @​jantonguirao, @​justinchuby, @​natke, @​philass, @​prasanthpul, @​p-wysocki, @​SpaceIm, @​stephenneuendorffer,@​take-cheeze, @​sechkova, @​thiagocrepaldi, @​xadupre, @​mszhanyi, @​yuanyao-nv, @​andife, @​daquexian, @​kylesayrs, @​liqunfu, @​longlee0622, @​HSQ79815, @​williamberman, @​YanBC

... (truncated)

Sourced from onnx's changelog.

Operator Changelog

This file is automatically generated from the def files via this script. Do not modify directly and instead edit operator definitions.

For an operator input/output's differentiability, it can be differentiable, non-differentiable, or undefined. If a variable's differentiability is not specified, that variable has undefined differentiability.

ai.onnx (default)

Version 1 of the default ONNX operator set

Abs-1

Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise.

Version

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes

Inputs

Outputs

Type Constraints

... (truncated)

• 1ba7856 Mark final RC (#4696)
• 7adb421 misc fixes for issues found in ort integration (#4681) (#4695)
• a913015 Mark release as rc1 (#4674)
• 3fd41d2 Bump version (#4666)
• bad0697 Add LpPool-18 - add ceil_mode and dilations attributes (#4534)
• 7a1fae4 make primary ops function step 2 (#4512)
• 8fb26ed Fixed some typos in python.rst (#4668)
• 9955f35 Fix typo in python.rst (#4667)
• cd6e5db Consider GraphInferenceContext in inference functions: InferenceContext (#4632)
• 466edb7 Add Python 3.11 support (#4490)
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