ONNX 1.2.1 release.
The following changes have been made since the 1.1.2 release:
IR Changes
- Adds function and attribute reference (PR #802).
- Adds dimension denotation (PR #443) and type denotation (PR #879).
Operator Changes
The operator set version of onnx 1.2 is 7 for ONNX domain and 1 for ONNX_ML domain.
- Type and shape inference function added for all operators.
- Adds new operators.
o Upsample (PR #861) – promoted from experimental, attributes and behavior updated to support arbitrary # of dimensions.
o Identity (PR #892) – promoted from experimental.
o Acos, Asin, Atan, Cos, Sin, Tan (PR #869).
o Multinomial (PR #897) - Removes FC (experimental) op (PR #977).
- Moves to numpy broadcasting semantics (PR #907).
- Clarifies “optional” semantics for input/output and adjust RNN/GRU/LSTM/BatchNormalization/Dropout accordingly (PR #1006, PR #1014).
- AveragePool – formulas for output shape updated (PR #751), extended to support average count including padding (PR #884)
- BatchNormalization – clarify outputs can be n-dim (PR #733)
- Cast – change to attr from string to int (PR #727)
- ConstantFill (exp) – change value attr from optional to default value of 0 (PR #808)
- InstanceNormalization – clarify outputs can be n-dim (PR #733)
- MaxPool – formulas for output shape updated (PR #751)
- AveragePool, MaxPool, Conv – update to support dimension denotation (PR #443)
- Reshape – add output shape as an input (PR #608)
- Size – change output from int to scalar tensor (PR #759)
- Tile – replace tiles and axis inputs with repeats to match numpy (PR #757)
- ZipMap – update type constrains from map to seq (PR #818)
- Affine – add default values for alpha and beta attributes (PR #820)
- FeatureVectorizer – update behavior (PR #843)
- LinearClassifier – coefficient attribute is now required (PR #836)
- RandomNormalLike, RandomUniformLike – change input type constraints and change behavior to copy shape instead of compute it (PR #846)
- Selu – change default value of attributes to match other frameworks (PR #839)
- ArgMax, ArgMin – specify default values for axis attribute (PR #847)
- DepthToSpace, SpaceToDepth – blocksize attribute is now required (PR #847)
- GRU, LSTM, RNN – specify default value for activation_* attributes (PR #847)
- Reduce* – specify default behavior for axes attribute (PR #847)
- Concat, Gather, Squeeze, Unsqueeze – accept any tensor type (PR #957)
- Add, Div, Mul, Pow, Sub – enhance 1-element broadcast case (PR #902)
- Pad – clarify pads attribute (PR #962)
- LRN – specify default values and clarify behavior (PR #965)
- ConvTranspose – clarify padding behavior and remove restriction on output_padding attribute (PR #1012)
- All ops – updated type constraints (PR #666)