ONNX toolbox support
ST Edge AI Core Technology 2.2.0
Overview
This document lists the layers (or operators) which can be imported and converted. Supported operators allow to address a large range of classical topologies targeting a Mobile or IoT resource-constrained runtime environment: SqueezeNet, MobileNet V1 or V2, Inception, SSD MobileNet v1,..
Purpose of this document is to list the operators and their associated constraints or limitations, please refer to the original documentation for details on a given layer.
ONNX is an open format built to represent machine learning models. A part of a subset of operators from Opset 7, 8, 9, 10 up to 20 of ONNX 1.15 is supported.
This file was automatically generated.
- ST Edge AI Core version : 2.2
- 132 operators found
Summary table
Following table contains the list of the operators that can be imported, if the constraints or limitations are met.
- supported optional fused activation (or non-linearity): gelu, linear, relu, quantized_relu, relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid, hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus, softsign, abs, acos, acosh, asin, asinh, atan, atanh, ceil, clip, cos, cosh, erf, flexerf, exp, floor, identity, log, logistic, neg, logical_not, prelu, probit, reciprocal, relu_generic, relu_thresholded, round, sign, sin, sinh, softmax_zero, sqrt, swish, tan
Common constraints
- input and output tensors must be not dynamic.
- variable-length batch dimension (i.e.
(None,)
) is considered as equal to 1
- must not be greater than 6D
- dimension must be in the range
[0, 65536[
- batch dimension is partially supported in the axis/axes
parameters
- variable-length batch dimension (i.e.
- data type for the weights/activations tensors must be:
- float32, int8, uint8
- only int32 for the bias tensor is considered
- for some operators, bool and binary types are also
supported
- float32, int8, uint8
- 1D operator is mapped on the respective 2D operator by adding a singleton dimension on the input: (12,3) -> (12, 1, 3)
Operators
Abs
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Acos
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Acosh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Add
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
And
Performs boolean element-wise operation
- category: eltwise operator
- input data types: bool
- output data types: bool
ArgMax
Computes the indices of the max elements of the input tensor’s element along the provided axis.
- category: generic layer
- input data types: float32
- output data types: int32
ArgMin
Computes the indices of the min elements of the input tensor’s element along the provided axis.
- category: generic layer
- input data types: float32
- output data types: int32
ArrayFeatureExtractor
Select elements of the input tensor besed of the indices passed
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- Nu-Support Vector Classification (sklearn.svm.NuSVC)
- C-Support Vector Classification. (sklearn.svm.SVC)
- A random forest classifier
(sklearn.ensemble.RandomForestClassifier)
- …
- Nu-Support Vector Classification (sklearn.svm.NuSVC)
Asin
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Asinh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Atan
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Atanh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
AveragePool
Downsamples the input
- category: pooling layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary pool sizes, provided that they are smaller than the
input size
- The attribute dilations different from the default value is not supported
BatchNormalization
Performs the normalization of the input
- category: normalization layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Only one output (Y) is supported
BitShift
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Cast
Cast elements of the input tensor to the specified output tensor data
- category: conversion layer
- input data types: bool, int8, uint8, float32
- output data types: bool, int8, uint8, float32
Specific constraints/recommendations:
- The attribute saturate different from the default value is not supported
Ceil
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Clip
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Concat
Performs concatenation of a list of inputs
- category: merge operator
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- concatenating on the batch dimension is not supported
Constant
- input data types: float32
- output data types: float32
ConstantOfShape
Generates a tensor with given value and shape
- category: constant layer
- input data types: float32
- output data types: float32
Conv
Performs convolution operation
- category: convolutional layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary filter kernel sizes, provided that they are smaller than the input size
ConvTranspose
Transposed convolutional layer
- category: convolutional layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary filter kernel sizes, provided that they are smaller than the input size
Cos
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Cosh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
DepthToSpace
Permutes the dimensions of the input according to a given pattern
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- transposing the batch dimension is not supported
DequantizeLinear
Computes element-wise data conversion low precision to full precision, based on the scale/zeropoint parameters
- category: conversion layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- The attribute block_size different from the default value is not supported
Div
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Dropout
Applies Dropout to the input
- category: regularization layers
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- operator is dropped during the conversion
Einsum
Einsum (Einstein Summation) operator
- category: einsum operator
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- related ONNX operator: Einsum
- requires 3 operators: 2 inputs and 1 output
- only 4-D operators are supported
Elu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Equal
Performs logical element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: bool
Erf
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Exp
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Expand
Constructs a tensor by tiling the input tensor
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- tiling on batch-dimension is not supported
Flatten
Flattens the non-batch input dimensions to a vector
- category: Reshaping operation
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Flatten on the batch dimension is not supported
- operator is dropped during the conversion
Floor
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Gather
Gathers values along a specified axis
- category: activation function
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Gather is not supported with indices dimensions > 2 (Batch is not considered), axis > 3and axis = 0, batch_dims attribute is not handled
GatherElements
Gathers Elements along a specified axis
- category: activation function
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- GatherElements is not supported with indices dimensions > 2 (Batch is not considered), axis > 3and axis = 0, batch_dims attribute is not handled
GatherND
Gathers slices from input tensor into an output tensor with shape specified by indices
- category: activation function
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Batch_dims attribute is not handled. 4D or more indices (BATCH is not included) are not implementedLast dimension of indices > 4 with 2D inputs tensor case is not handled
Gelu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Gemm
General Matrix multiplication: Compute Y = alphaAB + beta*C
- category: core layer
- input data types: float32
- output data types: float32
GlobalAveragePool
Downsamples the input
- category: pooling layer
- input data types: float32
- output data types: float32
GlobalMaxPool
Downsamples the input
- category: pooling layer
- input data types: float32
- output data types: float32
Greater
Performs logical element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: bool
GreaterOrEqual
Performs logical element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: bool
GRU
Gated Recurrent Unit
- category: recurrent layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- stateless and stateful (batch=1 only) mode support
- fused activation: gelu, linear, relu, quantized_relu,
relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid,
hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus,
softsign
- fused recurrent activation: gelu, linear, relu, quantized_relu,
relu_n1_to_1, relu_0_to_1, leaky_relu, relu6, elu, selu, sigmoid,
hard_sigmoid, hard_swish, exponential, tanh, softmax, softplus,
softsign
return_state
not supported
Hardmax
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- The value 1 is supported as the default value of the axis attribute, not the value -1
HardSigmoid
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- It is supported only for 1D tensor and only on the channel dimension
HardSwish
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Identity
This layer should be used as a placeholder when no operation is to be performed.The layer just returns its inputs argument as output.
- category: Identity layer
- input data types: float32
- output data types: float32
InstanceNormalization
Apply instance normalization
- category: normalization function
- input data types: float32
- output data types: float32
LabelEncoder
Maps each element in the input tensor to another value.
- category: LabelEncoder
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Warning: This operator is not supported for C code generation,
it is only used during iForest generation. Support is not
complete.
- Attributes keys_floats, keys_strings, values_int64s, values_strings are not supported
LeakyRelu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Less
Performs logical element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: bool
LessOrEqual
Performs logical element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: bool
LinearClassifier
LinearClassifier layer
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- Linear Support Vector Classification.
(sklearn.svm.LinearSVC)
- Logistic Regression (aka logit, MaxEnt) classifier.
(sklearn.linear_model.LogisticRegression)
- Classifier using Ridge regression.
(sklearn.linear_model.RidgeClassifier)
- Linear Discriminant Analysis. (sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
- Linear Support Vector Classification.
(sklearn.svm.LinearSVC)
Log
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
LogSoftmax
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- It is supported only for 1D tensor and only on the channel
dimension
- The value 1 is supported as the default value of the axis attribute, not the value -1
LpNormalization
Apply Lp-normalization along the provided axis
- category: normalization function
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- order of the normalization (1 and 2 supported)
LRN
Apply Local Response Normalization over local input regions
- category: normalization function
- input data types: float32
- output data types: float32
LSTM
Computes a multi-layer long short-term memory (LSTM) RNN to an input sequence (batch=1, timesteps, features)
- category: recurrent layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- stateless mode support only
- fused activation: sigmoid
- fused recurrent activation: sigmoid
return_state
not supported
- Only 1 input is allowed for LSTM; the others should be constant
and placed into the initializers
- layout=1 attribute is not supported
MatMul
General Matrix multiplication: Compute Y = alphaAB + beta*C
- category: core layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Only up to 3D matrix multiplication is supported
Max
Computes the maximum (element-wise) a list of inputs
- category: eltwise operator
- input data types: float32
- output data types: float32
MaxPool
Downsamples the input
- category: pooling layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary pool sizes, provided that they are smaller than the
input size
- The attribute dilations different from the default value is not
supported
- The attribute storage_order different from the default value is not supported
Mean
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Min
Computes the minimum (element-wise) a list of inputs
- category: eltwise operator
- input data types: float32
- output data types: float32
Mod
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Mul
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Neg
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Normalizer
Normalize the input.
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- Logistic Regression (aka logit, MaxEnt) classifier.
(sklearn.linear_model.LogisticRegression)
- Normalize samples individually to unit norm.
(sklearn.preprocessing.Normalizer)
- Logistic Regression (aka logit, MaxEnt) classifier.
(sklearn.linear_model.LogisticRegression)
- Note that the implementation of the max norm is aligned with sklearn implementation, not the onnx implementation. The difference is that sklearn is using absolute values to get a positive divisor when computing the norm. Onnx implementation does not use absolute values.
Not
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Or
Performs boolean element-wise operation
- category: eltwise operator
- input data types: bool
- output data types: bool
Pad
Pads an input tensor
- category: Reshaping layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Axes are not supported for Pad operator
- wrap value for mode attribute is not supported
Pow
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
PRelu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Only 1 input is allowed for PRelu; the slope should be constant and placed into the initializers
QLinearAdd
Performs element-wise binary addition on 8 bit data types(with Numpy-style broadcasting support).
- category: eltwise operator
- input data types: uint8, int8
- output data types: uint8, int8
QLinearAveragePool
Downsamples an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths
- category: pooling layer
- input data types: uint8, int8
- output data types: uint8, int8
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary pool sizes, provided that they are smaller than the input size
QLinearConcat
Concatenate a list of tensors into a single tensor
- category: merge operator
- input data types: uint8, int8
- output data types: uint8, int8
Specific constraints/recommendations:
- concatenating on the batch dimension is not supported
QLinearConv
Consumes a quantized input tensor
- category: convolutional layer
- input data types: int8, uint8
- output data types: int8, uint8
Specific constraints/recommendations:
- arbitrary strides, provided that they are smaller than the input
size
- arbitrary filter kernel sizes, provided that they are smaller than the input size
QLinearGlobalAveragePool
Downsamples an input tensor X and applies Average pooling acrossthe values in the same channel
- category: pooling layer
- input data types: uint8, int8
- output data types: uint8, int8
QLinearMatMul
Matrix product consuming two quantized input tensors
- category: core layer
- input data types: int8, uint8
- output data types: int8, uint8
QLinearMul
Performs element-wise binary multiplication on 8 bit data types(with Numpy-style broadcasting support).
- category: eltwise operator
- input data types: uint8, int8
- output data types: uint8, int8
QuantizeLinear
Computes element-wise data conversion full precision to low precision, based on the scale/zeropoint parameters
- category: conversion layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- The attribute block_size different from the default value is not
supported
- The attribute saturate different from the default value is not
supported
- The attribute output_dtype different from the default value is not supported
Range
Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta up to limit (exclusive).
- category: generic layer
- input data types: float32
- output data types: float32
Reciprocal
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
ReduceL1
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceL2
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceLogSumExp
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceMax
Computes the Max of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceMean
Computes the Mean of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceMin
Computes the Min of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceProd
Computes the Product of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceSum
Computes the Sum of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
ReduceSumSquare
Computes the Sum Square of the input tensor’s element along the provided axes
- category: reduction operation
- input data types: float32
- output data types: float32
Relu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Reshape
Reshapes a tensor
- category: Reshaping operation
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- The attribute allowzero different from the default value is not supported
Resize
Resize the input tensor: it calculates every value in the output tensor as a weighted average of neighborhood
- category: resizing operation
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Cubic resize is not supported
- half_pixel_symmetric value for coordinate_tansformation_mode is
not supported
- The attribute antialias different from the default value is not
supported
- axes attribute is not supported
- The attribute keep_aspect_ratio_policy different from the default value is not supported
Round
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Scaler
Compute the Scale and Offset of input tensor
- category: onnx.ml
- input data types: float32
- output data types: float
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- Gradient Boosting for classification.
(sklearn.ensemble.GradientBoostingClassifier)
- Transform features by scaling each feature to a given range.
(sklearn.preprocessing.MinMaxScaler)
- Standardize features by removing the mean and scaling to unit
variance. (sklearn.preprocessing.StandardScaler)
- Scale features using statistics that are robust to outliers.
(sklearn.preprocessing.RobustScaler)
- Scale each feature by its maximum absolute value. (sklearn.preprocessing.MaxAbsScaler)
- Gradient Boosting for classification.
(sklearn.ensemble.GradientBoostingClassifier)
ScatterND
Scatters updates into a tensor of shape equal to shape attribute according indices (TFLite),The output of the ScatterND layer is produced by creating a copy of the input data, and then updating its values to values specified by updates at specific index positions specified by indices (ONNX)
- category: activation function
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- 5D or more indices (BATCH is not included) are not implemented, 5D or more data (BATCH is not included) are not implemented
Selu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Shape
Returns a tensor containing the shape of the input tensor
- category: Reshaping operation
- input data types: float32, int8, uint8
- output data types: int32
Specific constraints/recommendations:
- Shape layer is not supported as the only layer in a
network;Shape layer only supported with know shapes in input; when
batch size is undetermined, it is set to 1
- end attribute is not supported
- The attribute start different from the default value is not supported
Sigmoid
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Sign
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Sin
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Sinh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Slice
Crops the input
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Softmax
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- It is supported only for 1D tensor and only on the channel
dimension
- The value 1 is supported as the default value of the axis attribute, not the value -1
Softplus
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Softsign
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
SpaceToDepth
Rearranges blocks of spatial data into depth
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Split
Splits a tensor into a list of sub tensors
- category: split operator
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- Only supported if the number of splits is equal to the size of the splitting dimension
Sqrt
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Squeeze
Reshapes a tensor
- category: Reshaping operation
- input data types: float32
- output data types: float32
Sub
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
Sum
Performs element-wise operation
- category: eltwise operator
- input data types: float32
- output data types: float32
SVMClassifier
SVMClassifier layer
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- C-Support Vector Classification. (sklearn.svm.SVC)
- Nu-Support Vector Classification (sklearn.svm.NuSVC)
- C-Support Vector Classification. (sklearn.svm.SVC)
SVMRegressor
Support Vector Machine regression prediction and one-class SVM anomaly detection
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- kernel_type argument supports one of the following
‘LINEAR’,‘POLY’,‘RBF’,‘SIGMOID’.
- used to support for example the skit-learn algo:
- Unsupervised Outlier Detection. (sklearn.svm.OneClassSVM)
Tan
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Tanh
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
ThresholdedRelu
Applies an activation function to the input tensor
- category: activation layer
- input data types: float32
- output data types: float32
Tile
Constructs a tensor by tiling the input tensor
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- tiling on batch-dimension is not supported
TopK
Retrieve the top-K largest or smallest elements along a specified axis
- category: topK operator
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Transpose
Permutes the dimensions of the input according to a given pattern
- category: reshaping layer
- input data types: float32, int8, uint8
- output data types: float32, int8, uint8
Specific constraints/recommendations:
- transposing the batch dimension is not supported
- perm attribute must have a length equal to the rank of the input
TreeEnsembleClassifier
Tree ensemble classifier. Returns the top class for each of N inputs.
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- This layer supports compression levels.
- none: disable compresion and optimization
- lossless (default): enable optimization only
- low, medium, high: enable optimization and weight compression
- used to support for example the skit-learn algo:
- A random forest classifier
(sklearn.ensemble.RandomForestClassifier)
- An extra-trees classifier
(sklearn.ensemble.ExtraTreesClassifier)
- Gradient Boosting for classification.
(sklearn.ensemble.GradientBoostingClassifier)
- Histogram-based Gradient Boosting Classification Tree.
(sklearn.ensemble.HistGradientBoostingClassifier)
- A decision tree classifier.
(sklearn.tree.DecisionTreeClassifier)
- A random forest classifier
(sklearn.ensemble.RandomForestClassifier)
- For detailed information, see Machine Learning support (ONNX-ML operators) [ONNX-ML] article
TreeEnsembleRegressor
Tree Ensemble regressor. Returns the regressed values for each input in N.
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Warning: This operator is not supported for C code generation,
it is only used during iForest generation. Not all arguments are
supported.
- used to support for example the skit-learn algo:
- A Bagging classifier (sklearn.ensemble.BaggingClassifier)
- An AdaBoost classifier
(sklearn.ensemble.AdaBoostClassifier)
- A Bagging regressor (sklearn.ensemble.BaggingRegressor)
- A random forest regressor
(sklearn.ensemble.RandomForestRegressor)
- An extra-trees regressor
(sklearn.ensemble.ExtraTreesRegressor)
- Gradient Boosting for regression
(sklearn.ensemble.GradientBoostingRegressor)
- Histogram-based Gradient Boosting Regression Tree
(sklearn.ensemble.HistGradientBoostingRegressor)
- Isolation Forest Algorithm
(sklearn.ensemble.IsolationForest)
- A Bagging classifier (sklearn.ensemble.BaggingClassifier)
- For detailed information, see Machine Learning support (ONNX-ML operators) [ONNX-ML] article
Unsqueeze
Reshapes a tensor
- category: Reshaping operation
- input data types: float32
- output data types: float32
Upsample
Resize the input tensor: it calculates every value in the output tensor as a weighted average of neighborhood
- category: resizing operation
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- Cubic resize is not supported
- half_pixel_symmetric value for coordinate_tansformation_mode is
not supported
- The attribute antialias different from the default value is not
supported
- axes attribute is not supported
- The attribute keep_aspect_ratio_policy different from the default value is not supported
Where
Where layer
- category: generic layer
- input data types: float32, int8, uint8, int16, uint16, int32,
uint32, bool
- output data types: float32, int8, uint8, int16, uint16, int32, uint32, bool
Xor
Performs boolean element-wise operation
- category: eltwise operator
- input data types: bool
- output data types: bool
ZipMap
Creates a map from the input and the attributes.
- category: onnx.ml
- input data types: float32
- output data types: float32
Specific constraints/recommendations:
- used to support for example the skit-learn algo:
- A decision tree classifier.
(sklearn.tree.DecisionTreeClassifier)
- …
- A decision tree classifier.
(sklearn.tree.DecisionTreeClassifier)