module onnxrt.ops_cpu.op_global_average_pool

Short summary

module mlprodict.onnxrt.ops_cpu.op_global_average_pool

Runtime operator.

source on GitHub

Classes

class	truncated documentation
GlobalAveragePool	GlobalAveragePool ================= GlobalAveragePool consumes an input tensor X and applies average pooling across the …
GlobalMaxPool	GlobalMaxPool ============= GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the …

Functions

function	truncated documentation
_global_average_pool
_global_max_pool

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

Documentation

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_global_average_pool.GlobalAveragePool(onnx_node, desc=None, **options)

Bases: OpRun

---

GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor.

Inputs

• X (heterogeneous)T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

• Y (heterogeneous)T: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.

Type Constraints

• T tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Version

Onnx name: GlobalAveragePool

This version of the operator has been available since version 1.

Runtime implementation: GlobalAveragePool

__init__(onnx_node, desc=None, **options)

_run(x, attributes=None, verbose=0, fLOG=None)

Should be overwritten.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_global_average_pool.GlobalMaxPool(onnx_node, desc=None, **options)

Bases: OpRun

---

GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor.

Inputs

• X (heterogeneous)T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

• Y (heterogeneous)T: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.

Type Constraints

• T tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Version

Onnx name: GlobalMaxPool

This version of the operator has been available since version 1.

Runtime implementation: GlobalMaxPool

__init__(onnx_node, desc=None, **options)

_run(x, attributes=None, verbose=0, fLOG=None)

Should be overwritten.

source on GitHub

mlprodict.onnxrt.ops_cpu.op_global_average_pool._global_average_pool(x)

mlprodict.onnxrt.ops_cpu.op_global_average_pool._global_max_pool(x)