2D convolution + ReLU fused into a single forward op on GPU. More...

#include <convolutional_relu_layer.h>

Inheritance diagram for opennn::ConvolutionalRelu:

Public Member Functions
	ConvolutionalRelu (const Shape &input_shape={3, 3, 1}, const Shape &kernel_shape={3, 3, 1, 1}, const Shape &strides={1, 1}, const string &convolution_type="Valid", const string &label="convolutional_relu_layer")
	Constructs a ConvolutionalRelu layer.

Shape	get_input_shape () const override
	Returns the per-sample input shape (height, width, channels).

Shape	get_output_shape () const override
	Returns the per-sample output shape.

Index	get_output_height () const
	Output spatial height after applying stride and padding.

Index	get_output_width () const
	Output spatial width after applying stride and padding.

Index	get_input_height () const
	Configured input height.

Index	get_input_width () const
	Configured input width.

Index	get_input_channels () const
	Configured input channel count.

Index	get_kernel_height () const
	Kernel height in pixels.

Index	get_kernel_width () const
	Kernel width in pixels.

Index	get_kernel_channels () const
	Kernel channel count (must equal input channels).

Index	get_kernels_number () const
	Number of kernels (output channel count).

Index	get_row_stride () const
	Vertical stride in pixels.

Index	get_column_stride () const
	Horizontal stride in pixels.

pair< Index, Index >	get_padding () const
	Returns padding sizes for the configured convolution type.

Index	get_padding_height () const
	Padding rows added on each side.

Index	get_padding_width () const
	Padding columns added on each side.

bool	get_use_padding () const
	True if padding is applied (i.e. convolution type is not "Valid").

Activation::Function	get_output_activation () const override
	Activation function fused at the end of this layer (always ReLU).

vector< Operator * >	get_operators () override
	Returns the single operator (Convolution with fused ReLU on GPU).

vector< pair< Shape, Type > >	get_forward_specs (Index batch_size) const override
	Specifications of the forward intermediate buffers.

void	set (const Shape &input_shape={0, 0, 0}, const Shape &kernel_shape={3, 3, 1, 1}, const Shape &strides={1, 1}, const string &convolution_type="Valid", const string &label="convolutional_relu_layer")
	Re-initializes the layer; same arguments as the constructor.

void	set_input_shape (const Shape &) override
	Updates the input shape and re-shapes kernel tensors accordingly.

void	on_compute_dtype_changed () override
	Recreates the convolution operator descriptor when the dtype changes.

void	set_row_stride (const Index new_row_stride)
	Sets the vertical (row) stride.

void	set_column_stride (const Index new_column_stride)
	Sets the horizontal (column) stride.

void	set_convolution_type (const string &)
	Sets the padding mode by name.

void	forward_propagate (ForwardPropagation &, size_t, bool) noexcept override
	Forward pass: convolution + ReLU fused on GPU, sequential on CPU.

void	back_propagate (ForwardPropagation &, BackPropagation &, size_t) const noexcept override
	Backward pass through the fused ReLU and Convolution operators.

void	read_JSON_body (const Json *) override
	Reads the layer-specific JSON body (kernel shape, strides, padding) from the given JSON node.

void	write_JSON_body (JsonWriter &) const override
	Writes the layer-specific JSON body (kernel shape, strides, padding) to the given JSON writer.

Public Member Functions inherited from opennn::Layer
virtual	~Layer ()=default
	Virtual destructor; subclasses are owned via unique_ptr<Layer>.

const string &	get_label () const
	Returns the user-assigned label of this layer.

const string &	get_name () const
	Returns the canonical type name of this layer.

LayerType	get_type () const
	Returns the LayerType enumerator for this layer.

virtual void	set_output_shape (const Shape &)
	Sets the per-sample output shape of this layer.

void	set_label (string new_label)
	Sets the human-readable label of this layer.

Index	get_parameters_number () const
	Total number of trainable parameters in this layer.

virtual vector< pair< Shape, Type > >	get_parameter_specs () const
	Specifications of the trainable parameter tensors owned by this layer.

virtual vector< pair< Shape, Type > >	get_state_specs () const
	Specifications of the persistent state tensors of this layer.

virtual vector< pair< Shape, Type > >	get_backward_specs (Index batch_size) const
	Specifications of the backward intermediate buffers for one batch.

vector< Shape >	get_parameter_shapes () const
	Shape-only view of get_parameter_specs().

vector< Shape >	get_state_shapes () const
	Shape-only view of get_state_specs().

vector< Shape >	get_forward_shapes (Index b) const
	Shape-only view of get_forward_specs() for batch size `b`.

vector< Shape >	get_backward_shapes (Index b) const
	Shape-only view of get_backward_specs() for batch size `b`.

vector< Type >	get_parameter_dtypes () const
	Dtype-only view of get_parameter_specs().

vector< Type >	get_forward_dtypes (Index b) const
	Dtype-only view of get_forward_specs() for batch size `b`.

vector< Type >	get_backward_dtypes (Index b) const
	Dtype-only view of get_backward_specs() for batch size `b`.

Index	get_inputs_number () const
	Total number of scalar inputs per sample (product of input dims).

Index	get_outputs_number () const
	Total number of scalar outputs per sample (product of output dims).

virtual void	from_JSON (const JsonDocument &document)
	Loads the layer configuration (hyperparameters) from JSON.

virtual void	load_state_from_JSON (const JsonDocument &document)
	Loads parameter and state tensors from a JSON document.

virtual void	to_JSON (JsonWriter &writer) const
	Writes the layer configuration to JSON.

virtual void	print () const
	Prints a human-readable summary of the layer to stdout.

bool	get_is_trainable () const
	Whether this layer has trainable parameters.

Type	get_compute_dtype () const
	Numerical type used for forward/backward computation.

void	set_compute_dtype (Type new_compute_dtype)
	Sets the compute dtype and triggers on_compute_dtype_changed().

virtual float *	link_parameters (float *pointer)
	Wires this layer's parameter TensorViews onto an external buffer.

virtual float *	link_states (float *pointer)
	Wires this layer's state TensorViews onto an external buffer.

vector< TensorView > &	get_parameter_views ()
	Mutable access to this layer's parameter TensorViews.

const vector< TensorView > &	get_parameter_views () const
	Read-only access to this layer's parameter TensorViews.

vector< TensorView > &	get_state_views ()
	Mutable access to this layer's state TensorViews.

const vector< TensorView > &	get_state_views () const
	Read-only access to this layer's state TensorViews.

void	redistribute_parameters_to_operators ()
	Forwards the current parameter views down to each composing Operator.

void	redistribute_parameter_gradients_to_operators (vector< TensorView > &gradient_views)
	Forwards externally provided gradient views down to each Operator.

void	redistribute_states_to_operators ()
	Forwards the current state views down to each composing Operator.

Additional Inherited Members
Protected Member Functions inherited from opennn::Layer
	Layer ()=default
	Default constructor; only invoked by subclasses.

float *	link_views (float pointer, const vector< Shape > &shapes, vector< TensorView > &views, const char tag) const
	Builds `views` over a contiguous float buffer using `shapes`.

void	distribute_to_operators (vector< TensorView > &views, void(Operator::link)(const vector< TensorView > &), vector< pair< Shape, Type > >(Operator::specs)() const)
	Generic helper used by the redistribute_*_to_operators() routines.

Protected Attributes inherited from opennn::Layer
string	label = "my_layer"
	User-visible label for this layer instance (default "my_layer").

string	name = "layer"
	Canonical type name set by the subclass (e.g. "dense").

LayerType	layer_type = LayerType::Dense
	Layer type tag set by the subclass.

bool	is_trainable = true
	True if the layer has parameters that participate in training.

bool	is_first_layer = false
	True if this layer is the network's input layer.

Type	compute_dtype = Type::FP32
	Numerical type used for forward and backward computation.

vector< TensorView >	parameters
	Parameter TensorViews bound to the network's parameter arena.

vector< TensorView >	states
	State TensorViews bound to the network's state arena.

vector< unique_ptr< Layer > >	layers
	Sub-layers, when this layer is itself a composite.

Detailed Description

2D convolution + ReLU fused into a single forward op on GPU.

Calls cudnnConvolutionBiasActivationForward with CUDNN_ACTIVATION_RELU. On CPU the activation runs as a separate step. No batch normalization, activation hard-wired to ReLU — keeps forward_propagate() branch-free for CUDA Graph capture.

Use this layer instead of Convolutional when ReLU is the desired activation and the runtime cost of branching matters.

Constructor & Destructor Documentation

◆ ConvolutionalRelu()

opennn::ConvolutionalRelu::ConvolutionalRelu	(	const Shape &	input_shape = {3, 3, 1},
		const Shape &	kernel_shape = {3, 3, 1, 1},
		const Shape &	strides = {1, 1},
		const string &	convolution_type = "Valid",
		const string &	label = "convolutional_relu_layer" )

Constructs a ConvolutionalRelu layer.

Parameters

input_shape	Per-sample input shape (height, width, channels).
kernel_shape	Kernel shape (height, width, channels, count).
strides	Row and column strides (one entry each).
convolution_type	Padding mode ("Valid" or "Same").
label	Human-readable label assigned to this layer.

Member Function Documentation

◆ back_propagate()

void opennn::ConvolutionalRelu::back_propagate	(	ForwardPropagation &	,
		BackPropagation &	,
		size_t	) const

overridevirtualnoexcept

Backward pass through the fused ReLU and Convolution operators.

Receives the forward intermediates, the BackPropagation buffer in which to accumulate gradients, and this layer's index inside the network.

Reimplemented from opennn::Layer.

◆ forward_propagate()

void opennn::ConvolutionalRelu::forward_propagate	(	ForwardPropagation &	,
		size_t	,
		bool	)

overridevirtualnoexcept

Forward pass: convolution + ReLU fused on GPU, sequential on CPU.

Receives the ForwardPropagation buffer slice, this layer's index and the training flag.

Reimplemented from opennn::Layer.

◆ get_column_stride()

Index opennn::ConvolutionalRelu::get_column_stride ( ) const

inline

Horizontal stride in pixels.

◆ get_forward_specs()

vector< pair< Shape, Type > > opennn::ConvolutionalRelu::get_forward_specs ( Index batch_size ) const

overridevirtual

Specifications of the forward intermediate buffers.

Parameters

batch_size Batch size used for sizing.

Returns: Specs for the Input and Output slots in the Forward enum.

Reimplemented from opennn::Layer.

◆ get_input_channels()

Index opennn::ConvolutionalRelu::get_input_channels ( ) const

inline

Configured input channel count.

◆ get_input_height()

Index opennn::ConvolutionalRelu::get_input_height ( ) const

inline

Configured input height.

◆ get_input_shape()

Shape opennn::ConvolutionalRelu::get_input_shape ( ) const

inlineoverridevirtual

Returns the per-sample input shape (height, width, channels).

Implements opennn::Layer.

◆ get_input_width()

Index opennn::ConvolutionalRelu::get_input_width ( ) const

inline

Configured input width.

◆ get_kernel_channels()

Index opennn::ConvolutionalRelu::get_kernel_channels ( ) const

inline

Kernel channel count (must equal input channels).

◆ get_kernel_height()

Index opennn::ConvolutionalRelu::get_kernel_height ( ) const

inline

Kernel height in pixels.

◆ get_kernel_width()

Index opennn::ConvolutionalRelu::get_kernel_width ( ) const

inline

Kernel width in pixels.

◆ get_kernels_number()

Index opennn::ConvolutionalRelu::get_kernels_number ( ) const

inline

Number of kernels (output channel count).

◆ get_operators()

vector< Operator * > opennn::ConvolutionalRelu::get_operators ( )

inlineoverridevirtual

Returns the single operator (Convolution with fused ReLU on GPU).

Reimplemented from opennn::Layer.

◆ get_output_activation()

Activation::Function opennn::ConvolutionalRelu::get_output_activation ( ) const

inlineoverridevirtual

Activation function fused at the end of this layer (always ReLU).

Reimplemented from opennn::Layer.

◆ get_output_height()

Index opennn::ConvolutionalRelu::get_output_height ( ) const

Output spatial height after applying stride and padding.

◆ get_output_shape()

Shape opennn::ConvolutionalRelu::get_output_shape ( ) const

overridevirtual

Returns the per-sample output shape.

Returns: (output_height, output_width, kernels_number).

Implements opennn::Layer.

◆ get_output_width()

Index opennn::ConvolutionalRelu::get_output_width ( ) const

Output spatial width after applying stride and padding.

◆ get_padding()

pair< Index, Index > opennn::ConvolutionalRelu::get_padding ( ) const

inline

Returns padding sizes for the configured convolution type.

Returns: Pair (padding_height, padding_width) in pixels.

◆ get_padding_height()

Index opennn::ConvolutionalRelu::get_padding_height ( ) const

Padding rows added on each side.

◆ get_padding_width()

Index opennn::ConvolutionalRelu::get_padding_width ( ) const

Padding columns added on each side.

◆ get_row_stride()

Index opennn::ConvolutionalRelu::get_row_stride ( ) const

inline

Vertical stride in pixels.

◆ get_use_padding()

bool opennn::ConvolutionalRelu::get_use_padding ( ) const

inline

True if padding is applied (i.e. convolution type is not "Valid").

◆ on_compute_dtype_changed()

void opennn::ConvolutionalRelu::on_compute_dtype_changed ( )

inlineoverridevirtual

Recreates the convolution operator descriptor when the dtype changes.

Reimplemented from opennn::Layer.

◆ read_JSON_body()

void opennn::ConvolutionalRelu::read_JSON_body ( const Json * )

overridevirtual

Reads the layer-specific JSON body (kernel shape, strides, padding) from the given JSON node.

Reimplemented from opennn::Layer.

◆ set()

void opennn::ConvolutionalRelu::set	(	const Shape &	input_shape = {0, 0, 0},
		const Shape &	kernel_shape = {3, 3, 1, 1},
		const Shape &	strides = {1, 1},
		const string &	convolution_type = "Valid",
		const string &	label = "convolutional_relu_layer" )

Re-initializes the layer; same arguments as the constructor.

Parameters

input_shape	Per-sample input shape.
kernel_shape	Kernel shape (height, width, channels, count).
strides	Row and column strides.
convolution_type	Padding mode ("Valid" or "Same").
label	Human-readable label.

◆ set_column_stride()

void opennn::ConvolutionalRelu::set_column_stride ( const Index new_column_stride )

Sets the horizontal (column) stride.

Parameters

new_column_stride New column stride in pixels.

◆ set_convolution_type()

void opennn::ConvolutionalRelu::set_convolution_type ( const string & )

Sets the padding mode by name.

Receives the convolution type, "Valid" (no padding) or "Same" (output spatial dimensions equal to input spatial dimensions).

◆ set_input_shape()

void opennn::ConvolutionalRelu::set_input_shape ( const Shape & )

overridevirtual

Updates the input shape and re-shapes kernel tensors accordingly.

Reimplemented from opennn::Layer.

◆ set_row_stride()

void opennn::ConvolutionalRelu::set_row_stride ( const Index new_row_stride )

Sets the vertical (row) stride.

Parameters

new_row_stride New row stride in pixels.

◆ write_JSON_body()

void opennn::ConvolutionalRelu::write_JSON_body ( JsonWriter & ) const

overridevirtual

Writes the layer-specific JSON body (kernel shape, strides, padding) to the given JSON writer.

Reimplemented from opennn::Layer.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ ConvolutionalRelu()

Member Function Documentation

◆ back_propagate()

◆ forward_propagate()

◆ get_column_stride()

◆ get_forward_specs()

◆ get_input_channels()

◆ get_input_height()

◆ get_input_shape()

◆ get_input_width()

◆ get_kernel_channels()

◆ get_kernel_height()

◆ get_kernel_width()

◆ get_kernels_number()

◆ get_operators()

◆ get_output_activation()

◆ get_output_height()

◆ get_output_shape()

◆ get_output_width()

◆ get_padding()

◆ get_padding_height()

◆ get_padding_width()

◆ get_row_stride()

◆ get_use_padding()

◆ on_compute_dtype_changed()

◆ read_JSON_body()

◆ set()

◆ set_column_stride()

◆ set_convolution_type()

◆ set_input_shape()

◆ set_row_stride()

◆ write_JSON_body()