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
    
• 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
    
• 1D operator is mapped on the respective 2D operator by adding a singleton dimension on the input: (12,3) -> (12, 1, 3)

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)
    
  • …

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)

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)
    
• 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)

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)

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)
    
• 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)
    
• 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)
    
  • …