#include <pooling.hpp>

Inheritance diagram for lbann::pooling_layer< TensorDataType, T_layout, Dev >:

Collaboration diagram for lbann::pooling_layer< TensorDataType, T_layout, Dev >:

Public Member Functions
	pooling_layer (lbann_comm *comm, int num_data_dims, int pool_dim, int pad, int stride, pooling_mode mode)

	pooling_layer (lbann_comm *comm, int num_data_dims, std::vector< int > pool_dims, std::vector< int > pads, std::vector< int > strides, pooling_mode mode)

	pooling_layer (const pooling_layer &other)

pooling_layer &	operator= (const pooling_layer &other)

	~pooling_layer () override=default

pooling_layer *	copy () const override
	Copy function. This function dynamically allocates memory for a layer instance and instantiates a copy. The caller is responsible for deallocating the instance. More...

std::string	get_type () const override
	Get the layer type's name. More...

data_layout	get_data_layout () const override
	Get data layout of the data tensors. We assume that the data layouts of the previous activations, activations, previous error signals, and error signals are the same. Each concrete layer that is templated on its data layout should override this function to return its template parameter. More...

El::Device	get_device_allocation () const override
	Get the device allocation for the data tensors. We assume that the decice allocation of the previous activations, activations, previous error signals, and error signals are the same. Each concrete layer that is templated on its device allocation should override this function to return its template parameter. More...

bool	can_run_inplace () const override
	If True, the computation can run in-place (feeding each input activations tensor as the corresponding output activations) More...

int	get_backprop_requirements () const override
	Returns the necessary tensors for computing backpropagation. More...

description	get_description () const override
	Human-readable description. More...

Serialization
template<typename ArchiveT >
void	serialize (ArchiveT &ar)

Public Member Functions inherited from lbann::data_type_layer< TensorDataType >
	data_type_layer (lbann_comm *, bool persistent_error_signals=false)

virtual	~data_type_layer ()=default

std::string	get_datatype_name () const override

void	forward_prop () final

void	summarize_matrices (lbann_summary &summarizer, int step) override

void	check_setup () override

const OutputAbsDistMatrixType &	get_activations (const Layer &child) const override

OutputAbsDistMatrixType &	get_activations (int child_index=0)

const OutputAbsDistMatrixType &	get_activations (int child_index=0) const

const InputAbsDistMatrixType &	get_error_signals (const Layer &parent) const override

InputAbsDistMatrixType &	get_error_signals (int parent_index=0)

const InputAbsDistMatrixType &	get_error_signals (int parent_index=0) const

El::Int	current_output_mini_batch_size () const override

El::Int	infer_mini_batch_size_from_parents_or_default_to_current () const override

OutputAbsDistMatrixType &	get_temp_grad ()

InputAbsDistMatrixType &	get_branch_tag_input (int tag)

std::vector< std::unique_ptr< InputAbsDistMatrixType > > &	get_branch_tag_input_vector ()

std::vector< std::unique_ptr< OutputAbsDistMatrixType > > &	get_all_activations ()

std::vector< std::unique_ptr< InputAbsDistMatrixType > > &	get_all_prev_activations ()

std::vector< std::unique_ptr< OutputAbsDistMatrixType > > &	get_all_prev_error_signals ()

std::vector< std::unique_ptr< InputAbsDistMatrixType > > &	get_all_error_signals ()

OutputAbsMatrixType &	get_local_activations (int child_index=0)

const OutputAbsMatrixType &	get_local_activations (int child_index=0) const

InputAbsMatrixType &	get_local_error_signals (int parent_index=0)

const InputAbsMatrixType &	get_local_error_signals (int parent_index=0) const

void	set_keep_error_signals (bool) override
	Set whether to keep or dynamically reallocate error signals. More...

El::mpi::Comm &	get_subgrid_comm ()

void	serialize (ArchiveT &ar)

Public Member Functions inherited from lbann::Layer
void	write_proto (lbann_data::Layer &proto) const
	Write layer to proto file. More...

lbann_comm *	get_comm () const

int	get_grid_tag () const noexcept
	Identifying tag for process grid. More...

void	set_grid_tag (int tag)
	Set process grid. More...

bool	runs_inplace () const
	If true, the layer will run in-place (the input and output activations point to the same tensor). Value is set during graph setup (in setup_pointers) based on layer traits and neighboring layers. More...

bool	distconv_enabled () const
	Indicate whether distconv is enabled. More...

	Layer ()

virtual	~Layer ()=default

void	set_name (const std::string name)
	Set the layer instance's name. Each layer in a model should have a unique, preferably human-readable, name. More...

void	set_model (model *m)
	Set the model that manages this layer. More...

std::string	get_name () const
	Get the layer instance's name. More...

model *	get_model () const noexcept
	Get a reference to the model that manages this layer. More...

int	get_expected_num_parent_layers () const noexcept
	Get expected number of parent layers. A negative value indicates no limit. More...

int	get_expected_num_child_layers () const noexcept
	Get expected number of child layers. A negative value indicates no limit. More...

ParallelStrategy &	get_parallel_strategy () noexcept
	Get the parallel strategy for the layer. More...

ParallelStrategy const &	get_parallel_strategy () const noexcept
	Get the parallel strategy for the layer. More...

bool	using_gpus () const noexcept
	Whether the layer is using a GPU implementation. More...

void	back_prop ()
	Backward propagation step. Given the objective function gradients w.r.t. the output tensors, compute the gradients w.r.t. the input tensors and w.r.t. the weights. This is essentially an application of the chain rule. More...

bool	update ()
	Update step. Update the layer's internal members. Note that the optimization step for the weights happens elsewhere. More...

virtual void	setup (size_t max_mini_batch_size, const std::vector< El::Grid *> &grids)
	Setup layer members. More...

void	summarize_stats (lbann_summary &summarizer, int step)

void	reset_counters ()
	Reset layer stat counters. More...

void	set_communication_flag (SubGraphCommunication type)

SubGraphCommunication	get_communication_flag ()

void	set_num_spliting_groups (El::Int spliting_groups)

El::Int	get_num_spliting_groups () const

std::shared_ptr< El::Grid >	get_mygrid () const

void	reset_inter_subgrid_vc_comm (std::shared_ptr< El::mpi::Comm > mpi_comm)

void	set_subgraph_parallelism_execution ()

bool	subgraph_parallelism_execution () const noexcept

void	set_run_layer_in_subgraph ()

bool	get_run_layer_in_subgraph () const noexcept

const Layer &	get_parent_layer (size_t index=0) const

const Layer &	get_child_layer (size_t index=0) const

std::vector< const Layer * >	get_parent_layers () const

std::vector< const Layer * >	get_child_layers () const

size_t	find_parent_layer_index (const Layer &l) const

size_t	find_child_layer_index (const Layer &l) const

int	get_num_parents () const noexcept
	Get number of parent layers. More...

int	get_num_children () const noexcept
	Get number of child layers. More...

void	add_parent_layer (ViewingLayerPtr parent)
	Add a parent layer. More...

void	add_child_layer (ViewingLayerPtr child)
	Add a child layer. More...

void	replace_parent_layer (ViewingLayerPtr l, size_t index)

void	replace_child_layer (ViewingLayerPtr l, size_t index)

void	clear_parent_layers ()
	Remove pointers to parent layers. More...

void	clear_child_layers ()
	Remove pointers to child layers. More...

ViewingLayerPtr	get_parent_layer_pointer (size_t index) const

ViewingLayerPtr	get_child_layer_pointer (size_t index) const

virtual std::vector< ViewingLayerPtr >	get_layer_pointers ()
	List of pointers to other layers. More...

virtual void	set_layer_pointers (std::vector< ViewingLayerPtr > layers)
	Set list of pointers to other layers. More...

std::vector< ViewingWeightsPtr >	get_weights_pointers () const
	List of pointers to weights. More...

void	set_weights_pointers (std::vector< ViewingWeightsPtr > ptrs)
	Set list of pointers to weights. More...

void	replace_weights (Layer const &other_layer)
	Replace weights with another Layer's weights. More...

std::vector< int >	get_input_dims (size_t input_index=0) const
	Get input tensor dimensions. More...

int	get_input_size (size_t input_index=0) const
	Get input tensor size. More...

std::vector< int >	get_output_dims (size_t output_index=0) const
	Get output tensor dimensions. More...

int	get_output_size (size_t output_index=0) const
	Get output tensor size. More...

void	set_output_dims (std::vector< int > dims, size_t output_index=0)
	Set output tensor dimensions. More...

El::Int	infer_mini_batch_size_from_parents () const

void	set_hint_layer (ViewingLayerPtr l)
	Set hint layer. More...

const Layer *	get_hint_layer () const
	Get hint layer. More...

void	freeze ()

void	unfreeze ()

bool	is_frozen () const

template<typename ArchiveT >
void	serialize (ArchiveT &ar)

Protected Member Functions
void	write_specific_proto (lbann_data::Layer &proto) const final

	pooling_layer ()

void	setup_dims () override
	Setup tensor dimensions Called by the 'setup' function. If there are any input tensors, the base method sets all uninitialized output tensor dimensions equal to the first input tensor dimensions. More...

void	setup_gpu () override
	Initialize GPU objects. More...

void	fp_compute () override
	Apply layer operation. Called by the 'forward_prop' function. Given the input tensors, the output tensors are populated with computed values. More...

void	bp_compute () override
	Compute objective funciton gradients. Called by the 'back_prop' function. Given the input, output, and gradient w.r.t. output tensors, the gradient w.r.t. input tensors are populated with the computed values and the gradients w.r.t. the weights are sent to the appropriate optimizers. More...

Protected Member Functions inherited from lbann::data_type_layer< TensorDataType >
InputAbsDistMatrixType &	get_prev_activations (int parent_index=0)

const InputAbsDistMatrixType &	get_prev_activations (int parent_index=0) const

OutputAbsDistMatrixType &	get_prev_error_signals (int child_index=0)

const OutputAbsDistMatrixType &	get_prev_error_signals (int child_index=0) const

const InputAbsMatrixType &	get_local_prev_activations (int parent_index=0) const

const OutputAbsMatrixType &	get_local_prev_error_signals (int child_index=0) const

void	setup_matrices (const std::vector< El::Grid * > &grids) override

void	setup_data (size_t max_mini_batch_size) override

void	fp_setup_inputs () override

void	fp_setup_outputs () override

void	bp_setup_gradient_wrt_inputs () override

void	bp_compute () override

InputAbsDistMatrixType const &	weights_values (size_t idx) const
	Get the values matrix for a specific weights object. More...

weights &	master_weights (size_t idx)
	Get a specific master weights object. More...

weights const &	master_weights (size_t idx) const

	data_type_layer (data_type_layer &&other)=default
	Protected lifecycle functions. More...

	data_type_layer (data_type_layer const &other)

data_type_layer &	operator= (data_type_layer &&other)=default

data_type_layer &	operator= (data_type_layer const &other)

Protected Member Functions inherited from lbann::Layer
void	setup_grid ()
	Setup process grid. More...

virtual void	setup_pointers ()
	Setup layer pointers. Called by the 'setup' function. Pointers to parent/child layers are assumed to be already initialized. More...

virtual bool	update_compute ()
	Perform the computation for the update step. Returns false if the layer must reset for a new training epoch. More...

	Layer (Layer &&other)=default

	Layer (Layer const &other)

Layer &	operator= (Layer &&other)=default

Layer &	operator= (Layer const &other)

void	add_weights (ViewingWeightsPtr w)

size_t	num_weights () const noexcept

bool	has_weights () const noexcept

bool	has_weights (size_t idx) const noexcept

void	set_num_weights (size_t n)

void	set_weights (size_t idx, ViewingWeightsPtr w)

weights const &	get_weights (size_t idx) const

weights &	get_weights (size_t idx)

void	add_as_gradient_source ()

void	remove_as_gradient_source ()

Private Member Functions
void	fp_compute_dnn ()
	Pooling forward propagation with DNN library. More...

void	bp_compute_dnn ()
	Pooling backward propagation with DNN library. More...

void	fp_compute_im2col ()
	Pooling forward propagation with im2col. More...

void	bp_compute_im2col ()
	Pooling forward propagation with im2col. More...

Private Attributes
pooling_mode	m_pool_mode

std::vector< int >	m_pool_dims

int	m_pool_size

std::vector< int >	m_pads

std::vector< int >	m_strides

std::vector< int >	m_max_pool_indices

Friends
class	unpooling_layer< TensorDataType, T_layout, Dev >

class	cereal::access

Additional Inherited Members
Public Types inherited from lbann::data_type_layer< TensorDataType >
using	InputAbsDistMatrixType = El::AbstractDistMatrix< TensorDataType >
	The tensor type expected in this object. More...

using	OutputAbsDistMatrixType = El::AbstractDistMatrix< TensorDataType >

using	InputAbsDistMatReadProxyType = El::AbstractDistMatrixReadDeviceProxy< TensorDataType, D >
	The proxy tensor type expected in this object. More...

using	OutputAbsDistMatReadProxyType = El::AbstractDistMatrixReadDeviceProxy< TensorDataType, D >

using	InputAbsMatrixType = El::AbstractMatrix< TensorDataType >
	The local tensor type expected in this object. More...

using	OutputAbsMatrixType = El::AbstractMatrix< TensorDataType >

using	WeightsProxyType = weights_proxy< TensorDataType >
	The proxy type for weights used by this object. More...

Protected Attributes inherited from lbann::Layer
int	m_expected_num_parent_layers = 1

int	m_expected_num_child_layers = 1
	Expected number of child layers. A negative value indicates no limit. More...

model *	m_model = nullptr
	Reference to model managing this layer. More...

bool	m_frozen
	Avoid back prop if frozen. More...

EvalType	m_fp_time
	Time spent in forward propagation. More...

EvalType	m_fp_compute_time
	Time spent in the forward propagation computation. More...

EvalType	m_bp_time
	Time spent in backward propagation. More...

EvalType	m_bp_compute_time
	Time spent in the backward propagation computation. More...

EvalType	m_update_time
	Time spent in updates. More...

std::string	m_name
	Layer instance's name. Each layer in a model should have a unique, preferably human-readable, name. More...

bool	m_runs_inplace = false
	If true, the layer will run in-place (the input and output activations point to the same tensor). Value is set during graph setup (in setup_pointers) based on layer traits and neighboring layers. More...

int	m_grid_tag = -1
	Identifying tag for process grid. More...

SubGraphCommunication	subgraph_communication_method = PT2PT

bool	m_subgraph_parallelism_execution = false

bool	run_layer_in_subgraph = false

std::unique_ptr< std::set< int > >	m_subgrid_ranks

El::Int	m_num_spliting_groups = 1

std::shared_ptr< El::mpi::Comm >	m_interSubGridVCComm

Detailed Description

template<typename TensorDataType, data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>
class lbann::pooling_layer< TensorDataType, T_layout, Dev >

Definition at line 108 of file layers/transform/pooling.hpp.

Constructor & Destructor Documentation

◆ pooling_layer() [1/4]

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

lbann::pooling_layer< TensorDataType, T_layout, Dev >::pooling_layer	(	lbann_comm *	comm,
		int	num_data_dims,
		int	pool_dim,
		int	pad,
		int	stride,
		pooling_mode	mode
	)

inline

Definition at line 144 of file layers/transform/pooling.hpp.

◆ pooling_layer() [2/4]

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

lbann::pooling_layer< TensorDataType, T_layout, Dev >::pooling_layer	(	lbann_comm *	comm,
		int	num_data_dims,
		std::vector< int >	pool_dims,
		std::vector< int >	pads,
		std::vector< int >	strides,
		pooling_mode	mode
	)

inline

Definition at line 158 of file layers/transform/pooling.hpp.

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◆ pooling_layer() [3/4]

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

lbann::pooling_layer< TensorDataType, T_layout, Dev >::pooling_layer ( const pooling_layer< TensorDataType, T_layout, Dev > & other )

inline

Definition at line 178 of file layers/transform/pooling.hpp.

◆ ~pooling_layer()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

lbann::pooling_layer< TensorDataType, T_layout, Dev >::~pooling_layer ( )

overridedefault

◆ pooling_layer() [4/4]

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

lbann::pooling_layer< TensorDataType, T_layout, Dev >::pooling_layer ( )

inlineprotected

Definition at line 298 of file layers/transform/pooling.hpp.

Member Function Documentation

◆ bp_compute()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::bp_compute ( )

overrideprotectedvirtual

Compute objective funciton gradients. Called by the 'back_prop' function. Given the input, output, and gradient w.r.t. output tensors, the gradient w.r.t. input tensors are populated with the computed values and the gradients w.r.t. the weights are sent to the appropriate optimizers.

Reimplemented from lbann::Layer.

◆ bp_compute_dnn()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::bp_compute_dnn ( )

private

Pooling backward propagation with DNN library.

◆ bp_compute_im2col()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::bp_compute_im2col ( )

private

Pooling forward propagation with im2col.

◆ can_run_inplace()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

bool lbann::pooling_layer< TensorDataType, T_layout, Dev >::can_run_inplace ( ) const

inlineoverridevirtual

If True, the computation can run in-place (feeding each input activations tensor as the corresponding output activations)

Reimplemented from lbann::Layer.

Definition at line 229 of file layers/transform/pooling.hpp.

◆ copy()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

pooling_layer* lbann::pooling_layer< TensorDataType, T_layout, Dev >::copy ( ) const

inlineoverridevirtual

Copy function. This function dynamically allocates memory for a layer instance and instantiates a copy. The caller is responsible for deallocating the instance.

Implements lbann::Layer.

Definition at line 216 of file layers/transform/pooling.hpp.

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◆ fp_compute()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::fp_compute ( )

overrideprotectedvirtual

Apply layer operation. Called by the 'forward_prop' function. Given the input tensors, the output tensors are populated with computed values.

Implements lbann::Layer.

◆ fp_compute_dnn()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::fp_compute_dnn ( )

private

Pooling forward propagation with DNN library.

◆ fp_compute_im2col()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::fp_compute_im2col ( )

private

Pooling forward propagation with im2col.

◆ get_backprop_requirements()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

int lbann::pooling_layer< TensorDataType, T_layout, Dev >::get_backprop_requirements ( ) const

inlineoverridevirtual

Returns the necessary tensors for computing backpropagation.

Reimplemented from lbann::Layer.

Definition at line 230 of file layers/transform/pooling.hpp.

◆ get_data_layout()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

data_layout lbann::pooling_layer< TensorDataType, T_layout, Dev >::get_data_layout ( ) const

inlineoverridevirtual

Get data layout of the data tensors. We assume that the data layouts of the previous activations, activations, previous error signals, and error signals are the same. Each concrete layer that is templated on its data layout should override this function to return its template parameter.

Implements lbann::Layer.

Definition at line 227 of file layers/transform/pooling.hpp.

◆ get_description()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

description lbann::pooling_layer< TensorDataType, T_layout, Dev >::get_description ( ) const

inlineoverridevirtual

Human-readable description.

Reimplemented from lbann::Layer.

Definition at line 239 of file layers/transform/pooling.hpp.

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◆ get_device_allocation()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

El::Device lbann::pooling_layer< TensorDataType, T_layout, Dev >::get_device_allocation ( ) const

inlineoverridevirtual

Get the device allocation for the data tensors. We assume that the decice allocation of the previous activations, activations, previous error signals, and error signals are the same. Each concrete layer that is templated on its device allocation should override this function to return its template parameter.

Implements lbann::Layer.

Definition at line 228 of file layers/transform/pooling.hpp.

◆ get_type()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

std::string lbann::pooling_layer< TensorDataType, T_layout, Dev >::get_type ( ) const

inlineoverridevirtual

Get the layer type's name.

A layer type name should be brief, unique, and human-readable description of the layer's mathematical operation that is recognizable to ML practitioners (e.g., "Convolution", "ReLU")

Implements lbann::Layer.

Definition at line 226 of file layers/transform/pooling.hpp.

◆ operator=()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

pooling_layer& lbann::pooling_layer< TensorDataType, T_layout, Dev >::operator= ( const pooling_layer< TensorDataType, T_layout, Dev > & other )

inline

Definition at line 197 of file layers/transform/pooling.hpp.

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◆ serialize()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

template<typename ArchiveT >

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::serialize ( ArchiveT & ar )

◆ setup_dims()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::setup_dims ( )

inlineoverrideprotectedvirtual

Setup tensor dimensions Called by the 'setup' function. If there are any input tensors, the base method sets all uninitialized output tensor dimensions equal to the first input tensor dimensions.

Reimplemented from lbann::Layer.

Definition at line 300 of file layers/transform/pooling.hpp.

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◆ setup_gpu()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::setup_gpu ( )

inlineoverrideprotectedvirtual

Initialize GPU objects.

Reimplemented from lbann::Layer.

Definition at line 314 of file layers/transform/pooling.hpp.

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◆ write_specific_proto()

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

void lbann::pooling_layer< TensorDataType, T_layout, Dev >::write_specific_proto ( lbann_data::Layer & proto ) const

finalprotectedvirtual

Add layer specific data to prototext

Implements lbann::Layer.

Friends And Related Function Documentation

◆ cereal::access

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

friend class cereal::access

friend

Definition at line 297 of file layers/transform/pooling.hpp.

◆ unpooling_layer< TensorDataType, T_layout, Dev >

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

friend class unpooling_layer< TensorDataType, T_layout, Dev >

friend

Definition at line 141 of file layers/transform/pooling.hpp.

Member Data Documentation

◆ m_max_pool_indices

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

std::vector<int> lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_max_pool_indices

private

Input indices for max pooling. Each entry corresponds to a local entry in the activations matrix. The entry gives the index of the maximum entry within the pooling window.

Definition at line 131 of file layers/transform/pooling.hpp.

◆ m_pads

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

std::vector<int> lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_pads

private

Pooling padding.

Definition at line 122 of file layers/transform/pooling.hpp.

◆ m_pool_dims

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

std::vector<int> lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_pool_dims

private

Pooling window dimensions.

Definition at line 118 of file layers/transform/pooling.hpp.

◆ m_pool_mode

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

pooling_mode lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_pool_mode

private

Pooling mode.

Definition at line 111 of file layers/transform/pooling.hpp.

◆ m_pool_size

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

int lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_pool_size

private

Size of pooling window.

Definition at line 120 of file layers/transform/pooling.hpp.

◆ m_strides

template<typename TensorDataType , data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>

std::vector<int> lbann::pooling_layer< TensorDataType, T_layout, Dev >::m_strides

private

Pooling strides.

Definition at line 124 of file layers/transform/pooling.hpp.

The documentation for this class was generated from the following file:

layers/transform/pooling.hpp

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Friends

Additional Inherited Members

Detailed Description

template<typename TensorDataType, data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU> class lbann::pooling_layer< TensorDataType, T_layout, Dev >

Constructor & Destructor Documentation

◆ pooling_layer() [1/4]

◆ pooling_layer() [2/4]

◆ pooling_layer() [3/4]

◆ ~pooling_layer()

◆ pooling_layer() [4/4]

Member Function Documentation

◆ bp_compute()

◆ bp_compute_dnn()

◆ bp_compute_im2col()

◆ can_run_inplace()

◆ copy()

◆ fp_compute()

◆ fp_compute_dnn()

◆ fp_compute_im2col()

◆ get_backprop_requirements()

◆ get_data_layout()

◆ get_description()

◆ get_device_allocation()

◆ get_type()

◆ operator=()

◆ serialize()

◆ setup_dims()

◆ setup_gpu()

◆ write_specific_proto()

Friends And Related Function Documentation

◆ cereal::access

◆ unpooling_layer< TensorDataType, T_layout, Dev >

Member Data Documentation

◆ m_max_pool_indices

◆ m_pads

◆ m_pool_dims

◆ m_pool_mode

◆ m_pool_size

◆ m_strides

template<typename TensorDataType, data_layout T_layout = data_layout::DATA_PARALLEL, El::Device Dev = El::Device::CPU>
class lbann::pooling_layer< TensorDataType, T_layout, Dev >