Open standard for machine learning interoperability
ONNX v1.15.0 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.
Added new operators: ImageDecoderhttps://github.com/onnx/onnx/pull/5294 RegexFullMatchhttps://github.com/onnx/onnx/pull/5401 StringConcathttps://github.com/onnx/onnx/issues/5350 StringSplithttps://github.com/onnx/onnx/pull/5371 AffineGridhttps://github.com/onnx/onnx/issues/5225 Geluhttps://github.com/onnx/onnx/issues/5277
Updated existing operators: ConstantOfShapehttps://github.com/onnx/onnx/pull/5390 GridSamplehttps://github.com/onnx/onnx/pull/5010 ReduceMaxhttps://github.com/onnx/onnx/pull/5539 ReduceMinhttps://github.com/onnx/onnx/pull/5539 IsNanhttps://github.com/onnx/onnx/pull/5583 IsInfhttps://github.com/onnx/onnx/pull/5583 DFThttps://github.com/onnx/onnx/pull/5514 LabelEncoderhttps://github.com/onnx/onnx/pull/5453
New features, bug fixes, and document updates
New Operators (ai.onnx):
Operator Updates (ai.onnx):
inf/-inf
as float literals. PR#5528
Users are now able to serialize the model proto to a text format by specifying supported file extensions or supplying the format=
argument in save_model
.
For example
# model: onnx.ModelProto
onnx.save_model(model, "model.json")
will save the model as a json file.
You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
python setup.py develop
deprecationDirect invocation of setup.py
is deprecated following https://setuptools.pypa.io/en/latest/deprecated/commands.html. To build ONNX, users should switch to use
# Editable installation
# Before: python setup.py develop
# Now
pip install -e .
# Build wheel
# Before: python setup.py bdist_wheel
# Now
pip install --upgrade build
python -m build .
Thanks to these individuals for their contributions in this release since last 1.15.0 release: @adityagoel4512 @AlexandreEichenberger @andife @AtanasDimitrovQC @BowenBao @cbourjau @ClifHouck @guoyuhong @gramalingam @ilya-lavrenov @jantonguirao @jbachurski @jcwchen @justinchuby @leso-kn @linkerzhang @liqunfu @prasanthpul @slowlyideal @smk2007 @snnn @take-cheeze @xadupre @yuanyao-nv @zhenhuaw-me
ONNX v1.14.1 is a patch release based on v1.14.1.
shape
data propagation function to handle missing optional parameters #5219ONNX v1.14.0 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.
DeformConv added in https://github.com/onnx/onnx/pull/4783
Equal - Support for string data type added in https://github.com/onnx/onnx/pull/4828
AveragePool - New attribute dilations
https://github.com/onnx/onnx/pull/4790
Pad - Added new wrap
to the mode
attribute to support circular padding https://github.com/onnx/onnx/pull/4793
Resize - Added half_pixel_symmetric
to the coordinate_transformation_mode
attribute https://github.com/onnx/onnx/pull/4862
Replaced real models with light models in backend tests. https://github.com/onnx/onnx/pull/4861 https://github.com/onnx/onnx/pull/4960
Now ONNX supports Protobuf v21: https://github.com/onnx/onnx/pull/4956
You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
Thanks to these individuals for their contributions in this release since last 1.13.0 release: @jcwchen, @andife, @gramalingam, @xadupre, @justinchuby, @liqunfu, @yuanyao-nv, @jbachurski, @p-wysocki, @prasanthpul, @jantonguirao, @take-cheeze, @smk2007, @AlexandreEichenberger, @snnn, @daquexian, @linkerzhang.
ONNX v1.13.1 is a patch release based on 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.
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
axes
#4190
max
and min
as supported reduction attributes #4411
num_outputs
attribute #4481
ceil_mode
and dilations
#4534
Reference Python runtime dependent on only Python and numpy has been added. #4483
ONNX 1.13.0 supports Python 3.11. #4490
Support for M1/M2 ARM processors has been added. #4642
ONNX 1.13.0 also comes with numerous:
For full details see Logistics for ONNX Release 1.13.0.
TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE
has been deprecated #4270
You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
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
The list has been acquired with a script written by Aaron Bockover.
ONNX v1.12.0 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.
input
(#4044)You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
Thanks to these individuals for their contributions in this release since last 1.11.0 release. (Contributor list obtained with: https://github.com/onnx/onnx/graphs/contributors?from=2022-02-08&to=2022-05-24&type=c): @jcwchen, @gramalingam, @xuzijian629, @garymm, @diyessi, @liqunfu, @jantonguirao, @daquexian, @fdwr, @andife, @wschin, @xadupre, @xkszltl, @snnn
ONNX v1.11.0 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.
You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
Thanks to these individuals for their contributions in this release since last 1.10.0 release. (Contributor list obtained with: https://github.com/onnx/onnx/graphs/contributors?from=2021-07-30&to=2022-02-08&type=c): @jcwchen, @gramalingam, @garymm, @mhamilton723, @TomWildenhain-Microsoft, @neginraoof, @xuzijian629, @liqunfu, @gwang-msft, @chudegao, @AlexandreEichenberger, @rajeevsrao, @matteosal, @stillmatic, @askhade, @liuyu21, @jantonguirao, @shinh, @kevinch-nv, @shubhambhokare1, @hwangdeyu, @jiafatom, @postrational, @snnn, @jackwish
This release is a patch release based on v1.10.0.
Bug fix:
ONNX v1.10.0 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.
Optional
and SparseTensor
types. https://github.com/onnx/onnx/pull/3407 https://github.com/onnx/onnx/pull/3398
Reshape
, Squeeze
, NonZero
, DynamicQuantizeLinear
.Optional
and SparseTensor
https://github.com/onnx/onnx/pull/3407 https://github.com/onnx/onnx/pull/3398
bfloat16
support for Pow. https://github.com/onnx/onnx/pull/3412
start
,end
. https://github.com/onnx/onnx/pull/3580
NonZero
. https://github.com/onnx/onnx/pull/3364
Dynamic QuantizeLinear
. https://github.com/onnx/onnx/pull/3539
Reshape
shape inference. https://github.com/onnx/onnx/pull/3592
Squeeze
. https://github.com/onnx/onnx/pull/3516
Squeeze
without axes. https://github.com/onnx/onnx/pull/3465
onnx.parser
). https://github.com/onnx/onnx/pull/3540
MatMulInteger
and QLinearMatMul
. https://github.com/onnx/onnx/pull/3585
strict_model
for ONNX checker. https://github.com/onnx/onnx/pull/3348
Shape
to be rank-1. https://github.com/onnx/onnx/pull/3394
BatchNormalization
outputs updated for training mode. https://github.com/onnx/onnx/pull/3379
You can upgrade to the latest release using pip install onnx --upgrade
or build from source following the README instructions.
Thanks to these individuals for their contributions in this release: @jcwchen, @askhade, @gramalingam, @neginraoof, @matteosal, @postrational, @garymm, @yuslepukhin, @fdwr, @jackwish, @manbearian, @etusien, @impactaky, @rajeevsrao, @prasanthpul, @take-cheeze, @chudegao, @mindest, @yufenglee, @annajung, @hwangdeyu, @calvinmccarter-at-lightmatter, @ashbhandare, @xuzijian629, @IceTDrinker, @mrry