Sourced fromonnx'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 visitonnx.ai to learn more about ONNX and associated projects.

New operators

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

Operator extensions

• Resize - New attributes:antialias,axes andkeep_aspect_ratio_policy, allow for bothscales andsizes to be provided when one of them is an empty constant#4126,#4388
• Pad - New attributeaxes#4190
• OptionalHasElement - New input types handling#4326
• OptionalHasElement andOptionalGetElement - Accept tensor and sequence types#4421
• ScatterElement andScatterND - Addmax andmin as supported reduction attributes#4411
• Split - Add support for uneven tensor splitting and a newnum_outputs attribute#4481
• LpPool - New attributes:ceil_mode anddilations#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 seeLogistics 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 usingpip install onnx --upgrade or build from source following the READMEinstructions.

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 fromonnx's changelog.

Operator Changelog

This file is automatically generated from thedef files viathis 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 differentiabilityis 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 tothe 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 - addceil_mode anddilations 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)
• Additional commits viewable incompare view

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