opennn::PoolOp Struct Reference

2D pooling operator supporting max and average reductions. More...

#include <operators.h>

Inheritance diagram for opennn::PoolOp:

Public Types
enum	Method { Max , Average }
	Supported pooling reductions. More...

Public Member Functions
void	set (Index input_h, Index input_w, Index input_c, Index pool_h, Index pool_w, Index row_stride, Index column_stride, Index padding_h, Index padding_w, Method method)
	Configures the pooling geometry.
void	destroy_cuda () override
	Releases CUDA resources owned by the operator; called from destructors.
	~PoolOp () override
	PoolOp ()=default
	PoolOp (const PoolOp &)=delete
PoolOp &	operator= (const PoolOp &)=delete
void	forward_propagate (ForwardPropagation &fp, size_t layer, bool is_training) noexcept override
	Runs the operator's forward computation.
void	back_propagate (ForwardPropagation &fp, BackPropagation &bp, size_t layer) const noexcept override
	Runs the operator's backward computation, accumulating into gradient/delta buffers.
Public Member Functions inherited from opennn::Operator
virtual	~Operator ()=default
virtual vector< TensorSpec >	parameter_specs () const
	Returns the tensor specs of trainable parameters owned by this operator.
virtual vector< TensorSpec >	state_specs () const
	Returns the tensor specs of persistent state owned by this operator.
virtual void	link_parameters (span< const TensorView >)
	Binds parameter views provided by the hosting layer.
virtual void	link_gradients (span< const TensorView >)
	Binds gradient views provided by the hosting layer.
virtual void	link_states (span< const TensorView >)
	Binds state views provided by the hosting layer.
virtual void	set_parameters_random ()
	Initializes parameters with random values.
virtual void	set_parameters_glorot ()
	Initializes parameters using Glorot (Xavier) initialization.
virtual void	to_JSON (JsonWriter &) const
	Serializes the operator configuration to a JSON writer.
virtual void	from_JSON (const Json *)
	Restores the operator configuration from a JSON node.
virtual void	load_state_from_JSON (const Json *)
	Restores persistent state (e.g. running statistics) from a JSON node.
TensorView &	get_input (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept
vector< TensorView > &	get_inputs (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept
TensorView &	get_output (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept
TensorView &	get_output_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept
TensorView &	get_input_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept

Public Attributes
Index	input_height = 0
Index	input_width = 0
Index	input_channels = 0
Index	pool_height = 1
Index	pool_width = 1
Index	row_stride = 1
Index	column_stride = 1
Index	padding_height = 0
Index	padding_width = 0
Method	method = Max
Public Attributes inherited from opennn::Operator
vector< size_t >	input_slots = {0}
vector< size_t >	output_slots = {1}
vector< size_t >	input_delta_slots = {1}
vector< size_t >	output_delta_slots = {0}

Detailed Description

2D pooling operator supporting max and average reductions.

Member Enumeration Documentation

Method

enum opennn::PoolOp::Method

Supported pooling reductions.

Enumerator
Max
Average

Constructor & Destructor Documentation

~PoolOp()

opennn::PoolOp::~PoolOp ( )

inlineoverride

PoolOp() [1/2]

opennn::PoolOp::PoolOp ( )

default

PoolOp() [2/2]

opennn::PoolOp::PoolOp ( const PoolOp & )

delete

Member Function Documentation

back_propagate()

void opennn::PoolOp::back_propagate ( ForwardPropagation & fp, BackPropagation & bp, size_t layer ) const

overridevirtualnoexcept

Runs the operator's backward computation, accumulating into gradient/delta buffers.

Parameters

fp	Forward propagation workspace (read-only).
bp	Back propagation workspace receiving gradients and deltas.
layer	Index of the hosting layer in the workspace.

Reimplemented from opennn::Operator.

destroy_cuda()

void opennn::PoolOp::destroy_cuda ( )

overridevirtual

Releases CUDA resources owned by the operator; called from destructors.

Reimplemented from opennn::Operator.

forward_propagate()

void opennn::PoolOp::forward_propagate ( ForwardPropagation & fp, size_t layer, bool is_training )

overridevirtualnoexcept

Runs the operator's forward computation.

Parameters

fp	Forward propagation workspace.
layer	Index of the hosting layer in the workspace.
is_training	If true, enables training-only behavior (e.g. dropout sampling).

Reimplemented from opennn::Operator.

operator=()

PoolOp & opennn::PoolOp::operator= ( const PoolOp & )

delete

set()

void opennn::PoolOp::set	(	Index	input_h,
		Index	input_w,
		Index	input_c,
		Index	pool_h,
		Index	pool_w,
		Index	row_stride,
		Index	column_stride,
		Index	padding_h,
		Index	padding_w,
		Method	method )

Configures the pooling geometry.

Parameters

input_h	Input height in pixels.
input_w	Input width in pixels.
input_c	Number of input channels.
pool_h	Pooling window height.
pool_w	Pooling window width.
row_stride	Vertical stride.
column_stride	Horizontal stride.
padding_h	Vertical padding.
padding_w	Horizontal padding.
method	Reduction method (Max or Average).

Member Data Documentation

column_stride

Index opennn::PoolOp::column_stride = 1

input_channels

Index opennn::PoolOp::input_channels = 0

input_height

Index opennn::PoolOp::input_height = 0

input_width

Index opennn::PoolOp::input_width = 0

method

Method opennn::PoolOp::method = Max

padding_height

Index opennn::PoolOp::padding_height = 0

padding_width

Index opennn::PoolOp::padding_width = 0

pool_height

Index opennn::PoolOp::pool_height = 1

pool_width

Index opennn::PoolOp::pool_width = 1

row_stride

Index opennn::PoolOp::row_stride = 1