Classes#
Summary#
class |
class parent |
truncated documentation |
---|---|---|
Abs === Absolute takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the absolute is, y = … |
||
Acos ==== Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise. Inputs |
||
Acosh ===== Calculates the hyperbolic arccosine of the given input tensor element-wise. Inputs |
||
Add === Performs element-wise binary addition (with Numpy-style broadcasting support). This operator supports **multidirectional … |
||
Structure which contains the necessary information to display a graph using an adjacency matrix. |
||
And === Returns the tensor resulted from performing the and logical operation elementwise on the input tensors A and … |
||
ArgMax ====== Computes the indices of the max elements of the input tensor’s element along the provided axis. The resulting … |
||
ArgMax ====== Computes the indices of the max elements of the input tensor’s element along the provided axis. The resulting … |
||
ArgMin ====== Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting … |
||
ArgMin ====== Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting … |
||
ArrayFeatureExtractor (ai.onnx.ml) ================================== Select elements of the input tensor based on the … |
||
Mocks an array without changing the data it receives. Notebooks Time processing for every ONNX nodes in a graph illustrates the weaknesses … |
||
Asin ==== Calculates the arcsine (inverse of sine) of the given input tensor, element-wise. Inputs |
||
Asinh ===== Calculates the hyperbolic arcsine of the given input tensor element-wise. Inputs |
||
Atan ==== Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise. Inputs |
||
Atanh ===== Calculates the hyperbolic arctangent of the given input tensor element-wise. Inputs |
||
Class wrapping a function to make it simple as a parameter. |
||
Extends the API to automatically look for exporters. |
||
Extends the API to automatically look for exporters. |
||
AveragePool =========== AveragePool consumes an input tensor X and applies average pooling across the tensor according … |
||
Base class to |
||
BatchNormalization ================== Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. … |
||
BatchNormalization ================== Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. … |
||
Bernoulli ========= Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor … |
||
BiGraph representation. |
||
Binarizer (ai.onnx.ml) ====================== Maps the values of the input tensor to either 0 or 1, element-wise, based … |
||
BitShift ======== Bitwise shift operator performs element-wise operation. For each input element, if the attribute “direction” … |
||
BroadcastGradientArgs (mlprodict) ================================= Version Onnx name: BroadcastGradientArgs … |
||
Defines a schema for operators added in this package such as |
||
CDist (mlprodict) ================= Version Onnx name: CDist … |
||
Defines a schema for operators added in this package such as |
||
Stores all the necessary information to cache the preprocessing of a an einsum equation. |
||
Cast ==== The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns … |
||
CastLike ======== The operator casts the elements of a given input tensor (the first input) to the same data type as the … |
||
CategoryMapper (ai.onnx.ml) =========================== Converts strings to integers and vice versa. Two sequences of … |
||
Ceil ==== Ceil takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the ceil is, y = ceil(x), … |
||
Celu ==== Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor … |
||
Clip ==== Clip operator limits the given input within an interval. The interval is specified by the inputs ‘min’ and ‘max’. … |
||
Clip ==== Clip operator limits the given input within an interval. The interval is specified by the inputs ‘min’ and ‘max’. … |
||
Defines a visitor which walks though the syntax tree of the code. |
||
Visits the code, implements verification rules. |
||
Class which converts a Python function into something else. It must implements methods visit and depart. |
||
Runtime for operator Split. |
||
Raised when a compilation error was detected. |
||
ComplexAbs (mlprodict) ====================== Version Onnx name: ComplexAbs … |
||
Defines a schema for operators added in this package such as |
||
Compress ======== Selects slices from an input tensor along a given axis where condition evaluates to True for each axis … |
||
Concat ====== Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for … |
||
ConcatFromSequence ================== Concatenate a sequence of tensors into a single tensor. All input tensors must have … |
||
ConstantOfShape =============== Generate a tensor with given value and shape. Attributes |
||
Constant ======== This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, … |
||
Constant ======== This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, … |
||
Conv ==== The convolution operator consumes an input tensor and a filter, and computes the output. Attributes |
||
Implements float runtime for operator Conv. The code is inspired from conv.cc … |
||
Implements float runtime for operator Conv. The code is inspired from conv.cc … |
||
ConvTranspose ============= The convolution transpose operator consumes an input tensor and a filter, and computes the … |
||
Implements float runtime for operator Conv. The code is inspired from conv_transpose.cc … |
||
Implements float runtime for operator Conv. The code is inspired from conv_transpose.cc … |
||
Cos === Calculates the cosine of the given input tensor, element-wise. Inputs |
||
Cosh ==== Calculates the hyperbolic cosine of the given input tensor element-wise. Inputs |
||
CumSum ====== Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively … |
||
Wraps a scoring function into a transformer. Function @see fn register_scorers must be called to register the converter … |
||
DEBUG (mlprodict) ================= Version Onnx name: DEBUG … |
||
Defines a schema for operators added in this package such as |
||
Default value for parameters when the parameter is not set but the operator has a default behaviour for it. |
||
DequantizeLinear ================ The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero … |
||
Det === Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape … |
||
Wrapper around a |
||
DictVectorizer (ai.onnx.ml) =========================== Uses an index mapping to convert a dictionary to an array. Given … |
||
One dimension of a shape. |
||
Div === Performs element-wise binary division (with Numpy-style broadcasting support). This operator supports **multidirectional … |
||
Dropout ======= Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional … |
||
Dropout ======= Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional … |
||
Einsum ====== An einsum of the form |
||
Defines a sub operation used in Einsum decomposition. |
||
Elu === Elu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, … |
||
Equal ===== Returns the tensor resulted from performing the equal logical operation elementwise on the input tensors … |
||
Erf === Computes the error function of the given input tensor element-wise. Inputs |
||
Exp === Calculates the exponential of the given input tensor, element-wise. Inputs |
||
Expand ====== Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar … |
||
Expand ====== Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar … |
||
Expected failure. |
||
EyeLike ======= Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are … |
||
FFT (mlprodict) =============== Version Onnx name: FFT … |
||
FFT2D (mlprodict) ================= Version Onnx name: FFT2D … |
||
Defines a schema for operators added in this package such as |
||
Defines a schema for operators added in this package such as |
||
Identifies a version of a function based on its arguments and its parameters. |
||
Very similar to |
||
Flatten ======= Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, . |
||
Raised when a float is out of range and cannot be converted into a float32. |
||
Floor ===== Floor takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the floor is, y = floor(x), … |
||
FusedMatMul (mlprodict) ======================= Version Onnx name: FusedMatMul … |
||
Defines a schema for operators added in this package such as |
||
Wraps GPT2Tokenizer … |
||
Gather ====== Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis dimension … |
||
Implements runtime for operator Gather. The code is inspired from tfidfvectorizer.cc … |
||
GatherElements ============== GatherElements takes two inputs data and indices of the same rank r >= 1 and an optional … |
||
Implements runtime for operator Gather. The code is inspired from tfidfvectorizer.cc … |
||
Implements runtime for operator Gather. The code is inspired from tfidfvectorizer.cc … |
||
Gemm ==== General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A’ = transpose(A) … |
||
GlobalAveragePool ================= GlobalAveragePool consumes an input tensor X and applies average pooling across the … |
||
Helpers to build graph. |
||
Class gathering all nodes produced to explicit einsum operators. |
||
Greater ======= Returns the tensor resulted from performing the greater logical operation elementwise on the input tensors … |
||
GreaterOrEqual ============== Returns the tensor resulted from performing the greater_equal logical operation elementwise … |
||
HardSigmoid =========== HardSigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the … |
||
Hardmax ======= The operator computes the hardmax values for the given input: Hardmax(element in input, axis) = 1 if … |
||
Identity ======== Identity operator Inputs |
||
If == If conditional Attributes |
||
Raised if the code shows errors. |
||
Imputer (ai.onnx.ml) ==================== Replaces inputs that equal one value with another, leaving all other elements … |
||
Wrappers around InferenceSession from onnxruntime. |
||
Overwrites class InferenceSession to capture the standard output and error. |
||
Instance of |
||
IsInf ===== Map infinity to true and other values to false. Attributes |
||
IsNaN ===== Returns which elements of the input are NaN. Inputs |
||
LabelEncoder (ai.onnx.ml) ========================= Maps each element in the input tensor to another value. The mapping … |
||
LeakyRelu ========= LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one output data (Tensor<T>) … |
||
Less ==== Returns the tensor resulted from performing the less logical operation elementwise on the input tensors A … |
||
LessOrEqual =========== Returns the tensor resulted from performing the less_equal logical operation elementwise on … |
||
LinearClassifier (ai.onnx.ml) ============================= Linear classifier Attributes |
||
LinearRegressor (ai.onnx.ml) ============================ Generalized linear regression evaluation. If targets is set … |
||
Log === Calculates the natural log of the given input tensor, element-wise. Inputs |
||
LogSoftmax ========== The operator computes the log of softmax values for the given input: LogSoftmax(input, axis) = … |
||
Loop ==== Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified … |
||
LpNormalization =============== Given a matrix, apply Lp-normalization along the provided axis. Attributes |
||
Base class for every action. |
||
Addition |
||
Any binary operation. |
||
Cast into another type. |
||
Concatenate number of arrays into an array. |
||
Constant |
||
A function. |
||
Any function call. |
||
Addition |
||
Returns a results. |
||
Sign of an expression: 1=positive, 0=negative. |
||
Tensor addition. |
||
Tensor division. |
||
Scalar product. |
||
Tensor multiplication. |
||
Tensor soustraction. |
||
Extracts an element of the tensor. |
||
Tensor operation. |
||
Operator |
||
Operator |
||
Any binary operation. |
||
Variable. The constant is only needed to guess the variable type. |
||
Base class for every machine learned model |
||
Base class for numerical types. |
||
A numpy.bool. |
||
A numpy.float32. |
||
A numpy.float64. |
||
A numpy.int32. |
||
A numpy.int64. |
||
int32 or float32 |
||
Defines a tensor with a dimension and a single type for what it contains. |
||
Base class for every type. |
||
MatMul ====== Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html … |
||
Max === Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs … |
||
MaxPool ======= MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, … |
||
Implements float runtime for operator Conv. The code is inspired from pool.cc … |
||
Implements float runtime for operator Conv. The code is inspired from pool.cc … |
||
Mean ==== Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs … |
||
Min === Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs … |
||
Missing operator. |
||
Raised when a variable is missing. |
||
A string. |
||
A string and a shape. |
||
A string and a shape and a type. |
||
Mocked lightgbm. |
||
Mod === Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the … |
||
Mul === Performs element-wise binary multiplication (with Numpy-style broadcasting support). This operator supports **multidirectional … |
||
Class used to return multiple |
||
Used to annotation ONNX numpy functions. |
||
Shortcut to simplify signature description. |
||
Shortcut to simplify signature description. |
||
Shortcut to simplify signature description. |
||
Shortcut to simplify signature description. |
||
Neg === Neg takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where each element flipped sign, … |
||
Python runtime for function NegativeLogLikelihoodLoss. |
||
Defines a schema for operators added in this package such as |
||
Defines a result name for a node. |
||
Normalizer (ai.onnx.ml) ======================= Normalize the input. There are three normalization modes, which have … |
||
Not === Returns the negation of the input tensor element-wise. Inputs |
||
Converts an ONNX operators into numpy code. |
||
ONNX specifications does not mention the possibility to change the output type, sparse, dense, float, double. … |
||
Expected failure. |
||
Raised when onnxruntime or mlprodict does not implement a new operator defined in the latest onnx. … |
||
Definition of a backend test. It starts with a folder, in this folder, one onnx file must be there, then a subfolder … |
||
Defines a custom operator for BroadcastGradientArgs. Returns the reduction axes for computing gradients of s0 op s1 … |
||
Defines a custom operator for BroadcastGradientArgs. Returns the reduction axes for computing gradients of s0 op s1 … |
||
Defines a custom operator for ComplexAbs. |
||
Defines a custom operator for ComplexAbs. |
||
Defines a custom operator for FFT2D. |
||
Defines a custom operator for FFT2D. |
||
Defines a custom operator for FFT. |
||
Defines a custom operator for FFT. |
||
MatMul and Gemm without a C. |
||
MatMul and Gemm without a C. |
||
Loads an ONNX file or object or stream. Computes the output of the ONNX graph. Several runtimes … |
||
onnxruntime API |
||
ONNX backend following the pattern from onnx/backend/base.py. … |
||
Same backend as @see cl OnnxInferenceBackend but runtime is @see cl OnnxMicroRuntime. |
||
Same backend as @see cl OnnxInferenceBackend but runtime is onnxruntime1. |
||
Same backend as @see cl OnnxInferenceBackend but runtime is python_compiled. |
||
Same backend as @see cl OnnxInferenceBackend but runtime is @see cl OnnxShapeInference. |
||
Computes the prediction for an ONNX graph loaded with @see cl OnnxInference. |
||
Same backend as @see cl OnnxInferenceBackend but runtime is @see cl OnnxShapeInference. |
||
Implements methods to export a instance of |
||
A node to execute. |
||
Describes a result type. |
||
Automatically creating all operators from onnx packages takes time. That’s why function |
||
Implements a micro runtime for ONNX graphs. It does not implements all the operator types. |
||
Defines magic commands to help with notebooks |
||
Implements a class which runs onnx graph. |
||
Class wrapping a function build with |
||
Overwrites |
||
Overwrites |
||
Ancestor to every ONNX operator exposed in |
||
Base class for |
||
This operator is used to insert existing ONNX function into the ONNX graph being built. |
||
Accessor to one of the output returned by a |
||
Class used to return multiple |
||
The pipeline overwrites method fit, it trains and converts every steps into ONNX before training the next step … |
||
Defines a custom operator for FFT. |
||
Defines a custom operator for FFT. |
||
Raised when a new operator was added but cannot be found. |
||
Implements a micro runtime for ONNX graphs. It does not implements all the operator types. |
||
Gradient of Softmax. SoftmaxGrad computes . ONNX does not have a dot product, … |
||
Gradient of Softmax. SoftmaxGrad computes . ONNX does not have a dot product, … |
||
Trains with scikit-learn, transform with ONNX. |
||
Trains with scikit-learn, transform with ONNX. |
||
Trains with scikit-learn, transform with ONNX. |
||
Trains with scikit-learn, transform with ONNX. |
||
This operator is used to call the converter of a model to insert the node coming from the conversion into a bigger … |
||
This operator is used to insert existing ONNX into the ONNX graph being built. |
||
Defines a custom operator not defined by ONNX specifications but in onnxruntime. |
||
Defines a custom operator not defined by ONNX specifications but in onnxruntime. |
||
Calls onnxruntime or the runtime implemented in this package to transform input based on a ONNX graph. It … |
||
Class which converts a Python function into an ONNX function. It must implements methods visit and depart. … |
||
Variables used into onnx computation. |
||
Overloads |
||
Runs the prediction for a single ONNX, it lets the runtime handle the graph logic as well. |
||
Defines a custom operator for YieldOp. |
||
Defines a custom operator for YieldOp. |
||
Ancestor to all operators in this subfolder. The runtime for every node can checked into ONNX unit tests. … |
||
Ancestor to all unary operators in this subfolder and which produces position of extremas (ArgMax, …). Checks … |
||
Ancestor to all binary operators in this subfolder. Checks that inputs type are the same. |
||
Ancestor to all binary operators in this subfolder comparing tensors. |
||
Ancestor to all binary operators in this subfolder. Checks that inputs type are the same. |
||
Implements the inplaces logic. numpy_fct is a binary numpy function which takes two matrices and has a argument … |
||
Ancestor to all binary operators in this subfolder. Checks that inputs type are the same. |
||
Automates some methods for custom operators defined outside mlprodict. |
||
Unique operator for an empty runtime. |
||
Unique operator which calls onnxruntime to compute predictions for one operator. |
||
Implements the reduce logic. It must have a parameter axes. |
||
Ancestor to all unary operators in this subfolder. Checks that inputs type are the same. |
||
Ancestor to all unary and numerical operators in this subfolder. Checks that inputs type are the same. |
||
Defines a schema for operators added in this package such as |
||
Or == Returns the tensor resulted from performing the or logical operation elementwise on the input tensors A and … |
||
Instance of |
||
PRelu ===== PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where … |
||
Pad === Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad … |
||
Pow === Pow takes input data (Tensor<T>) and exponent Tensor, and produces one output data (Tensor<T>) where the function … |
||
QLinearConv =========== The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized … |
||
Implements int8 runtime for operator QLinearConv. The code is inspired from qlinearconv.cc … |
||
Implements uint8 runtime for operator QLinearConvUInt8. The code is inspired from qlinearconv.cc … |
||
QuantizeLinear ============== The linear quantization operator. It consumes a high precision tensor, a scale, and a zero … |
||
Instantiates a quantized tensor (uint8) with bias from a float tensor. |
||
Instantiates a quantized tensor (uint8) from a float tensor. |
||
RFFT (mlprodict) ================ Version Onnx name: RFFT … |
||
Defines a schema for operators added in this package such as |
||
RNN === Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. … |
||
RNN === Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. … |
||
RandomNormal ============ Generate a tensor with random values drawn from a normal distribution. The shape of the tensor … |
||
RandomNormalLike ================ Generate a tensor with random values drawn from a normal distribution. The shape of … |
||
RandomUniform ============= Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor … |
||
RandomUniformLike ================= Generate a tensor with random values drawn from a uniform distribution. The shape … |
||
Range ===== Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta … |
||
Reciprocal ========== Reciprocal takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the reciprocal … |
||
ReduceL1 ======== Computes the L1 norm of the input tensor’s element along the provided axes. The resulted tensor has … |
||
ReduceL2 ======== Computes the L2 norm of the input tensor’s element along the provided axes. The resulted tensor has … |
||
ReduceLogSum ============ Computes the log sum of the input tensor’s element along the provided axes. The resulted tensor … |
||
ReduceLogSumExp =============== Computes the log sum exponent of the input tensor’s element along the provided axes. The … |
||
ReduceMax ========= Computes the max of the input tensor’s element along the provided axes. The resulted tensor has the … |
||
ReduceMean ========== Computes the mean of the input tensor’s element along the provided axes. The resulted tensor has … |
||
ReduceMin ========= Computes the min of the input tensor’s element along the provided axes. The resulted tensor has the … |
||
ReduceProd ========== Computes the product of the input tensor’s element along the provided axes. The resulted tensor … |
||
ReduceSumSquare =============== Computes the sum square of the input tensor’s element along the provided axes. The resulted … |
||
ReduceSum ========= Computes the sum of the input tensor’s element along the provided axes. The resulted tensor has the … |
||
ReduceSum ========= Computes the sum of the input tensor’s element along the provided axes. The resulted tensor has the … |
||
Relu ==== Relu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the rectified linear function, … |
||
Reshape ======= Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape … |
||
Reshape ======= Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape … |
||
Round ===== Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for … |
||
Raised when the results are too different from scikit-learn. |
||
Implements runtime for operator SVMClassifierDouble. The code is inspired from svm_classifier.cc … |
||
Implements runtime for operator SVMClassifier. The code is inspired from svm_classifier.cc … |
||
Implements Double runtime for operator SVMRegressor. The code is inspired from svm_regressor.cc … |
||
Implements float runtime for operator SVMRegressor. The code is inspired from svm_regressor.cc … |
||
Implements runtime for operator TfIdfVectorizer. The code is inspired from tfidfvectorizer.cc … |
||
Implements runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_classifier.cc … |
||
Implements runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_classifier.cc … |
||
Implements double runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc … |
||
Implements float runtime for operator TreeEnsembleClassifier. The code is inspired from tree_ensemble_Classifier.cc … |
||
Implements double runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc … |
||
Implements float runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc … |
||
Implements double runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc … |
||
Implements float runtime for operator TreeEnsembleRegressor. The code is inspired from tree_ensemble_regressor.cc … |
||
Raised when a type of a variable is unexpected. |
||
SVMClassifier (ai.onnx.ml) ========================== Support Vector Machine classifier Attributes |
||
SVMClassifierDouble (mlprodict) =============================== Version Onnx name: SVMClassifierDouble … |
||
Defines a schema for operators added in this package such as |
||
SVMRegressor (ai.onnx.ml) ========================= Support Vector Machine regression prediction and one-class SVM anomaly … |
||
SVMRegressorDouble (mlprodict) ============================== Version Onnx name: SVMRegressorDouble … |
||
Defines a schema for operators added in this package such as |
||
Scaler (ai.onnx.ml) =================== Rescale input data, for example to standardize features by removing the mean and … |
||
Scan ==== Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. … |
||
ScatterElements =============== ScatterElements takes three inputs data, updates, and indices of the same rank r … |
||
Selu ==== Selu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the scaled exponential … |
||
Wraps SentencePieceTokenizer … |
||
SequenceAt ========== Outputs a tensor copy from the tensor at ‘position’ in ‘input_sequence’. Accepted range for ‘position’ … |
||
SequenceConstruct ================= Construct a tensor sequence containing ‘inputs’ tensors. All tensors in ‘inputs’ must … |
||
SequenceInsert ============== Outputs a tensor sequence that inserts ‘tensor’ into ‘input_sequence’ at ‘position’. ‘tensor’ … |
||
Represents a sequence type. Used in @see methd infer_types. |
||
Shape ===== Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional … |
||
Base class for shape binary operator defined by a function. |
||
Base class for shape binary operator. |
||
One constraint. |
||
A list of ShapeConstraint. |
||
Stores all infered shapes as |
||
Raised when shape inference fails. |
||
Handles mathematical operations around shapes. It stores a type (numpy type), and a name to somehow have … |
||
Computes a shape depending on a user defined function. See |
||
Base class for all shapes operator. |
||
Shape addition. |
||
Shape comparison. |
||
Best on each dimension. |
||
Shape multiplication. |
||
Contains information about shape and type of a result in an onnx graph. |
||
Sigmoid ======= Sigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the sigmoid function, … |
||
Sign ==== Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. … |
||
Simple wrapper around InferenceSession which imitates |
||
Sin === Calculates the sine of the given input tensor, element-wise. Inputs |
||
Sinh ==== Calculates the hyperbolic sine of the given input tensor element-wise. Inputs |
||
Size ==== Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input … |
||
Slice ===== Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html … |
||
Slice ===== Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html … |
||
Softmax ======= The operator computes the normalized exponential values for the given input: Softmax(input, axis) = … |
||
Python runtime for function SoftmaxCrossEntropyLoss. |
||
Defines a schema for operators added in this package such as |
||
SoftmaxGrad computes . ONNX does not have a dot product, which can be … |
||
SoftmaxGrad computes . ONNX does not have a dot product, which can be … |
||
Solve (mlprodict) ================= Version Onnx name: Solve … |
||
Defines a schema for operators added in this package such as |
||
Runtime for operator Split. |
||
Runtime for operator Split. |
||
Runtime for operator Split. |
||
Runtime for operator Split. |
||
Sqrt ==== Square root takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the square root … |
||
Squeeze ======= Remove single-dimensional entries from the shape of a tensor. Takes an input axes with a list of axes … |
||
Squeeze ======= Remove single-dimensional entries from the shape of a tensor. Takes an input axes with a list of axes … |
||
The operator is not really threadsafe as python cannot play with two locales at the same time. stop words should … |
||
Sub === Performs element-wise binary subtraction (with Numpy-style broadcasting support). This operator supports **multidirectional … |
||
Sum === Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs … |
||
Tan === Calculates the tangent of the given input tensor, element-wise. Inputs |
||
Tanh ==== Calculates the hyperbolic tangent of the given input tensor element-wise. Inputs |
||
asv test for a classifier, Full template can be found in common_asv_skl.py. … |
||
asv test for a classifier, Full template can be found in common_asv_skl.py. … |
||
asv example for a clustering algorithm, Full template can be found in common_asv_skl.py. … |
||
asv example for a classifier, Full template can be found in common_asv_skl.py. … |
||
asv example for an outlier detector, Full template can be found in common_asv_skl.py. … |
||
asv example for a regressor, Full template can be found in common_asv_skl.py. … |
||
asv example for a trainable transform, Full template can be found in common_asv_skl.py. … |
||
asv example for a transform, Full template can be found in common_asv_skl.py. … |
||
asv example for a transform, Full template can be found in common_asv_skl.py. … |
||
Applies the converter on an ONNX graph. |
||
TfIdfVectorizer =============== This transform extracts n-grams from the input sequence and save them as a vector. Input … |
||
See Tokenizer. |
||
Defines a schema for operators added in this package such as |
||
Base class for |
||
TopK ==== Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_1, … |
||
TopK ==== Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_1, … |
||
Transpose ========= Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an … |
||
TreeEnsembleClassifierDouble (mlprodict) ======================================== Version Onnx name: TreeEnsembleClassifierDouble … |
||
Defines a schema for operators added in this package such as |
||
TreeEnsembleClassifier (ai.onnx.ml) =================================== Tree Ensemble classifier. Returns the top class … |
||
TreeEnsembleClassifier (ai.onnx.ml) =================================== Tree Ensemble classifier. Returns the top class … |
||
Runtime for the custom operator TreeEnsembleRegressorDouble. |
||
Defines a schema for operators added in this package such as |
||
TreeEnsembleRegressor (ai.onnx.ml) ================================== Tree Ensemble regressor. Returns the regressed … |
||
TreeEnsembleRegressor (ai.onnx.ml) ================================== Tree Ensemble regressor. Returns the regressed … |
||
Trilu ===== Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). … |
||
Unsqueeze ========= Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input … |
||
Unsqueeze ========= Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input … |
||
An input or output to an ONNX graph. |
||
Where ===== Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) … |
||
A booster can be a classifier, a regressor. Trick to wrap it in a minimal function. |
||
Trick to wrap a LGBMClassifier into a class. |
||
converter for XGBClassifier |
||
common methods for converters |
||
converter class |
||
Xor === Returns the tensor resulted from performing the xor logical operation elementwise on the input tensors A and … |
||
YieldOp (mlprodict) =================== Version Onnx name: YieldOp … |
||
Defines a schema for operators added in this package such as |
||
The class does not output a dictionary as specified in ONNX specifications but a |
||
Custom dictionary class much faster for this runtime, it implements a subset of the same methods. |
||
Base class for runtime for operator ArgMax. … |
||
Base class for runtime for operator ArgMin. … |
||
Labels strings are not natively implemented in C++ runtime. The class stores the strings labels, replaces them by … |
||
Common tests to all benchmarks testing converted scikit-learn models. See benchmark attributes. … |
||
Common class for a classifier. |
||
Common class for a classifier. |
||
Common class for a clustering algorithm. |
||
Common class for a multi-classifier. |
||
Common class for outlier detection. |
||
Common class for a regressor. |
||
Common class for a trainable transformer. |
||
Common class for a transformer. |
||
Common class for a transformer for positive features. |
||
Common methods to all random operators. |
||
Ths class hides a parameter used as a threshold above which the parallelisation is started: |
||
Graph builder. It takes a graph structure made with instances of |
||
Ancestor to custom signature. |
||
Speeds up inference by replacing methods transform or predict by a runtime for ONNX. |
||
Class to store all dynamic classes created by wrappers. |
||
Overloads class |
||
Decorator to register any new converter. |
||
Intermediate wrapper to store a pointer on the compiler (type: |
||
Intermediate wrapper to store a pointer on the compiler (type: |