module onnxrt.ops_cpu.op_cast#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_cast

Short summary#

module mlprodict.onnxrt.ops_cpu.op_cast

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

Cast

Cast ==== The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns …

CastLike

CastLike ======== The operator casts the elements of a given input tensor (the first input) to the same data type as the …

Properties#

property

truncated documentation

args_default

Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …

args_default

Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …

args_default_modified

Returns the list of modified parameters.

args_default_modified

Returns the list of modified parameters.

args_mandatory

Returns the list of optional arguments.

args_mandatory

Returns the list of optional arguments.

args_optional

Returns the list of optional arguments.

args_optional

Returns the list of optional arguments.

atts_value

Returns all parameters in a dictionary.

atts_value

Returns all parameters in a dictionary.

Methods#

method

truncated documentation

__init__

__init__

_run

_run

_run_inplace

_run_inplace

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_cast.Cast(onnx_node, desc=None, **options)#

Bases: OpRun

The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns an output tensor of the same size in the converted type. The ‘to’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.

Casting from string tensor in plain (e.g., “3.14” and “1000”) and scientific numeric representations (e.g., “1e-5” and “1E8”) to float types is supported. For example, converting string “100.5” to an integer may result 100. There are some string literals reserved for special floating-point values; “+INF” (and “INF”), “-INF”, and “NaN” are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match “+INF” in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to “INF” and “NaN”. When casting from numeric tensors to string tensors, plain floating-point representation (such as “314.15926”) would be used. Converting non-numerical-literal string such as “Hello World!” is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as “2.718”, to INT is an undefined behavior.

Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can’t be stored in the targeted type.

Attributes

  • to (required): The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto default value cannot be automatically retrieved (INT)

Inputs

  • input (heterogeneous)T1: Input tensor to be cast.

Outputs

  • output (heterogeneous)T2: Output tensor with the same shape as input with type specified by the ‘to’ argument

Type Constraints

  • T1 tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16): Constrain input types. Casting from complex is not supported.

  • T2 tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16): Constrain output types. Casting to complex is not supported.

Version

Onnx name: Cast

This version of the operator has been available since version 13.

Runtime implementation: Cast

__init__(onnx_node, desc=None, **options)#
_run(x, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.

source on GitHub

_run_inplace(x)#
class mlprodict.onnxrt.ops_cpu.op_cast.CastLike(onnx_node, desc=None, **options)#

Bases: OpRun

The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details.

Inputs

  • input (heterogeneous)T1: Input tensor to be cast.

  • target_type (heterogeneous)T2: The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.

Outputs

  • output (heterogeneous)T2: Output tensor produced by casting the first input tensor to have the same type as the second input tensor.

Type Constraints

  • T1 tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16): Constrain input types. Casting from complex is not supported.

  • T2 tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16): Constrain output types. Casting to complex is not supported.

Version

Onnx name: CastLike

This version of the operator has been available since version 15.

Runtime implementation: CastLike

__init__(onnx_node, desc=None, **options)#
_run(x, y, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.

source on GitHub

_run_inplace(x, y)#