athena__base_layer

Module containing the abstract base layer type

This module contains the abstract base layer type, from which all other layers are derived. The module also contains the abstract derived types for the following layer types: - padding - pooling - dropout - learnable - convolutional - batch normalisation

The following procedures are based on code from the neural-fortran library https://github.com/modern-fortran/neural-fortran/blob/main/src/nf/nf_layer.f90

Uses

- diffstruc
- coreutils
- graphstruc
- athena__clipper
- athena__diffstruc_extd
- athena__misc_types

Used by

- Descendants:
- athena__base_layer_submodule
- athena__base_layer_submodule_init
- athena__base_layer_submodule_io

Interfaces

interface

private module function add_learnable(a, b) result(output)

Add two layers

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(in)			::	a	Instances of the layers
class(learnable_layer_type),	intent(in)			::	b	Instances of the layers

Return Value class(learnable_layer_type), allocatable

Output layer

interface

private module subroutine build_from_onnx_base(this, node, initialisers, value_info, verbose)

Build layer from ONNX node

Arguments

Type	Intent	Attributes		Name
class(base_layer_type),	intent(inout)		::	this	Instance of the layer
type(onnx_node_type),	intent(in)		::	node	ONNX node
type(onnx_initialiser_type),	intent(in),	dimension(:)	::	initialisers	ONNX initialisers
type(onnx_tensor_type),	intent(in),	dimension(:)	::	value_info	ONNX value info
integer,	intent(in)		::	verbose	Verbosity level

interface

private module function calc_input_shape(this, input_shapes) result(input_shape)

Calculate input shape based on shapes of input layers

Arguments

Type	Intent	Optional	Attributes		Name
class(merge_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in),		dimension(:,:)	::	input_shapes	Input shapes

Return Value integer, allocatable, dimension(:)

Calculated input shape

interface

private module subroutine combine_merge(this, input_list)

Combine two layers (forward)

Arguments

Type	Intent	Optional	Attributes		Name
class(merge_layer_type),	intent(inout)			::	this	Instance of the layer
type(array_ptr_type),	intent(in),		dimension(:)	::	input_list	Input values

interface

private module subroutine emit_onnx_graph_inputs_base(this, prefix, graph_inputs, num_inputs)

Emit graph input tensor declarations for this layer Default implementation does nothing; override for GNN layers

Arguments

Type	Intent	Attributes		Name
class(base_layer_type),	intent(in)		::	this	Instance of the layer
character(len=*),	intent(in)		::	prefix	Input name prefix (e.g. "input_1")
type(onnx_tensor_type),	intent(inout),	dimension(:)	::	graph_inputs	Accumulator for graph inputs
integer,	intent(inout)		::	num_inputs	Current number of inputs

interface

private module subroutine emit_onnx_nodes_base(this, prefix, nodes, num_nodes, max_nodes, inits, num_inits, max_inits, input_name, is_last_layer, format)

Emit ONNX JSON nodes for this layer Default implementation does nothing; override for GNN/NOP layers

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this	Instance of the layer
character(len=*),	intent(in)			::	prefix	Node name prefix (e.g. "node_2")
type(onnx_node_type),	intent(inout),		dimension(:)	::	nodes	Accumulator for ONNX nodes
integer,	intent(inout)			::	num_nodes	Current number of nodes
integer,	intent(in)			::	max_nodes	Maximum capacity
type(onnx_initialiser_type),	intent(inout),		dimension(:)	::	inits	Accumulator for ONNX initialisers
integer,	intent(inout)			::	num_inits	Current number of initialisers
integer,	intent(in)			::	max_inits	Maximum capacity
character(len=*),	intent(in),	optional		::	input_name	Upstream tensor name used by sequential expanded ONNX format
logical,	intent(in),	optional		::	is_last_layer	Whether this is the last non-input layer in the network
integer,	intent(in),	optional		::	format	Export format selector 1 = ONNX athena abstract format (default) 2 = ONNX expanded format

interface

private module subroutine extract_output_base(this, output)

Extract the output of the layer as a standard real array

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this	Instance of the layer
real(kind=real32),	intent(out),		dimension(..), allocatable	::	output	Output values

interface

private module subroutine forward_base(this, input)

Forward pass of layer

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout)			::	this	Instance of the layer
class(array_type),	intent(in),		dimension(:,:)	::	input	Input data

interface

private module function forward_eval_base(this, input) result(output)

Forward pass of layer and return output for evaluation

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout),		target	::	this	Instance of the layer
class(array_type),	intent(in),		dimension(:,:)	::	input	Input data

Return Value type(array_type), pointer, (:,:)

Output data

interface

private module function get_attributes_base(this) result(attributes)

Get the attributes of the layer (for ONNX export)

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this	Instance of the layer

Return Value type(onnx_attribute_type), allocatable, dimension(:)

Attributes of the layer

interface

private module function get_attributes_batch(this) result(attributes)

Arguments

Type	Intent	Optional	Attributes		Name
class(batch_layer_type),	intent(in)			::	this	Instance of the layer

Return Value type(onnx_attribute_type), allocatable, dimension(:)

Attributes of the layer

interface

private module function get_attributes_conv(this) result(attributes)

Arguments

Type	Intent	Optional	Attributes		Name
class(conv_layer_type),	intent(in)			::	this

Return Value type(onnx_attribute_type), allocatable, dimension(:)

interface

private module function get_attributes_pool(this) result(attributes)

Get the attributes of the layer (for ONNX export)

Arguments

Type	Intent	Optional	Attributes		Name
class(pool_layer_type),	intent(in)			::	this	Instance of the layer

Return Value type(onnx_attribute_type), allocatable, dimension(:)

Attributes of the layer

interface

private pure module function get_gradients(this, clip_method) result(gradients)

Get parameter gradients of layer

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(in)			::	this	Instance of the layer
type(clip_type),	intent(in),	optional		::	clip_method	Clip method

Return Value real(kind=real32), dimension(this%num_params)

Parameter gradients

interface

private pure module function get_num_params(this) result(num_params)

Get number of parameters in layer

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this	Instance of the layer

Return Value integer

Number of parameters

interface

private pure module function get_num_params_base(this) result(num_params)

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this

Return Value integer

interface

private pure module function get_num_params_batch(this) result(num_params)

Arguments

Type	Intent	Optional	Attributes		Name
class(batch_layer_type),	intent(in)			::	this

Return Value integer

interface

private pure module function get_num_params_conv(this) result(num_params)

Arguments

Type	Intent	Optional	Attributes		Name
class(conv_layer_type),	intent(in)			::	this

Return Value integer

interface

private pure module function get_params(this) result(params)

Get learnable parameters of layer

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(in)			::	this	Instance of the layer

Return Value real(kind=real32), dimension(this%num_params)

Learnable parameters

interface

private module subroutine init_batch(this, input_shape, verbose)

Arguments

Type	Intent	Optional	Attributes		Name
class(batch_layer_type),	intent(inout)			::	this
integer,	intent(in),		dimension(:)	::	input_shape
integer,	intent(in),	optional		::	verbose

interface

private module subroutine init_conv(this, input_shape, verbose)

Arguments

Type	Intent	Optional	Attributes		Name
class(conv_layer_type),	intent(inout)			::	this
integer,	intent(in),		dimension(:)	::	input_shape
integer,	intent(in),	optional		::	verbose

interface

private module subroutine init_pad(this, input_shape, verbose)

Arguments

Type	Intent	Optional	Attributes		Name
class(pad_layer_type),	intent(inout)			::	this
integer,	intent(in),		dimension(:)	::	input_shape
integer,	intent(in),	optional		::	verbose

interface

private module subroutine init_pool(this, input_shape, verbose)

Arguments

Type	Intent	Optional	Attributes		Name
class(pool_layer_type),	intent(inout)			::	this
integer,	intent(in),		dimension(:)	::	input_shape
integer,	intent(in),	optional		::	verbose

interface

private module subroutine initialise(this, input_shape, verbose)

Initialise the layer

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout)			::	this	Instance of the layer
integer,	intent(in),		dimension(:)	::	input_shape	Input shape
integer,	intent(in),	optional		::	verbose	Verbosity level

interface

private module subroutine nullify_graph_base(this)

Nullify the forward pass data of the layer to free memory

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout)			::	this	Instance of the layer

interface

private module subroutine print_base(this, file, unit, print_header_footer)

Print the layer to a file with additional information

Arguments

Type	Intent	Optional		Name
class(base_layer_type),	intent(in)		::	this	Instance of the layer
character(len=*),	intent(in),	optional	::	file	File name
integer,	intent(in),	optional	::	unit	Unit number
logical,	intent(in),	optional	::	print_header_footer	Boolean whether to print header and footer

interface

private module subroutine print_to_unit_base(this, unit)

Print the layer to a file

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in)			::	unit	File unit

interface

private module subroutine print_to_unit_batch(this, unit)

Print layer to unit

Arguments

Type	Intent	Optional	Attributes		Name
class(batch_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in)			::	unit	File unit

interface

private module subroutine print_to_unit_conv(this, unit)

Print layer to unit

Arguments

Type	Intent	Optional	Attributes		Name
class(conv_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in)			::	unit	File unit

interface

private module subroutine print_to_unit_pad(this, unit)

Print layer to unit

Arguments

Type	Intent	Optional	Attributes		Name
class(pad_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in)			::	unit	File unit

interface

private module subroutine print_to_unit_pool(this, unit)

Print layer to unit

Arguments

Type	Intent	Optional	Attributes		Name
class(pool_layer_type),	intent(in)			::	this	Instance of the layer
integer,	intent(in)			::	unit	File unit

interface

private module subroutine read_layer(this, unit, verbose)

Read layer from file

Arguments

Type	Intent	Optional		Name
class(base_layer_type),	intent(inout)		::	this	Instance of the layer
integer,	intent(in)		::	unit	File unit
integer,	intent(in),	optional	::	verbose	Verbosity level

interface

private module subroutine reduce_learnable(this, input)

Merge another learnable layer into this one

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(inout)			::	this	Instance of the layer
class(learnable_layer_type),	intent(in)			::	input	Other layer to merge

interface

private module subroutine set_gradients(this, gradients)

Set learnable parameters of layer

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(inout)			::	this	Instance of the layer
real(kind=real32),	intent(in),		dimension(..)	::	gradients	Learnable parameters

interface

private module subroutine set_graph_base(this, graph)

Set the graph structure of the input data

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout)			::	this	Instance of the layer
type(graph_type),	intent(in),		dimension(:)	::	graph	Graph structure of input data

interface

private module subroutine set_params(this, params)

Set learnable parameters of layer

Arguments

Type	Intent	Optional	Attributes		Name
class(learnable_layer_type),	intent(inout)			::	this	Instance of the layer
real(kind=real32),	intent(in),		dimension(this%num_params)	::	params	Learnable parameters

interface

private module subroutine set_rank_base(this, input_rank, output_rank)

Set the input and output ranks of the layer

Arguments

Type	Intent		Name
class(base_layer_type),	intent(inout)	::	this	Instance of the layer
integer,	intent(in)	::	input_rank	Input rank
integer,	intent(in)	::	output_rank	Output rank

interface

private module subroutine set_shape_base(this, input_shape)

Set the input shape of the layer

Arguments

Type	Intent	Optional	Attributes		Name
class(base_layer_type),	intent(inout)			::	this	Instance of the layer
integer,	intent(in),		dimension(:)	::	input_shape	Input shape

Abstract Interfaces

abstract interface

private subroutine generate_mask(this)

Generate dropout mask

Arguments

Type	Intent	Optional	Attributes		Name
class(drop_layer_type),	intent(inout)			::	this	Instance of the layer

Derived Types

type, public, abstract :: base_layer_type

Type for base layer, from which all other layers are derived

Components

Type	Visibility	Attributes		Name		Initial
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
character(len=:),	public,	allocatable	::	name			Layer name
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure(initialise), public, deferred, pass(this) :: init	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_base	Print the layer to a unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(learnable_layer_type) :: batch_layer_type

Type for batch normalisation layers

Components

Type	Visibility	Attributes		Name		Initial
class(base_actv_type),	public,	allocatable	::	activation			Activation function
real(kind=real32),	public		::	beta_init_mean	=	0._real32	Initialisation parameters for beta
real(kind=real32),	public		::	beta_init_std	=	0.01_real32	Initialisation parameters for beta
class(base_init_type),	public,	allocatable	::	bias_init			Initialisers for kernel and bias
character(len=14),	public		::	bias_initialiser	=	''	Initialisers for kernel and bias
integer,	public,	allocatable, dimension(:)	::	bias_shape			Shape of biases
real(kind=real32),	public		::	epsilon	=	0.001_real32	Epsilon factor
real(kind=real32),	public		::	gamma_init_mean	=	1._real32	Initialisation parameters for gamma
real(kind=real32),	public		::	gamma_init_std	=	0.01_real32	Initialisation parameters for gamma
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
class(base_init_type),	public,	allocatable	::	kernel_init			Initialisers for kernel and bias
character(len=14),	public		::	kernel_initialiser	=	''	Initialisers for kernel and bias
real(kind=real32),	public,	allocatable, dimension(:)	::	mean			Mean and variance (not learnable)
real(kind=real32),	public		::	momentum	=	0.99_real32	Momentum factor NOTE: if momentum = 0, mean and variance batch-dependent values NOTE: if momentum > 0, mean and variance are running averages
class(base_init_type),	public,	allocatable	::	moving_mean_init			Initialisers for moving mean and variance
class(base_init_type),	public,	allocatable	::	moving_variance_init			Initialisers for moving mean and variance
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_channels			Number of channels
integer,	public		::	num_params	=	0	Number of learnable parameters
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
type(array_type),	public,	allocatable, dimension(:)	::	params			Learnable parameters
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_bias	=	.false.	Layer has bias
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output
real(kind=real32),	public,	allocatable, dimension(:)	::	variance			Mean and variance (not learnable)
integer,	public,	allocatable, dimension(:,:)	::	weight_shape			Shape of weights

Type-Bound Procedures

procedure, public :: add_t_t => add_learnable	Add two layers
procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_batch	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_gradients	Get parameter gradients of layer
procedure, public, pass(this) :: get_num_params => get_num_params_batch	Get the number of parameters in the layer
procedure, public, pass(this) :: get_params	Get learnable parameters of layer
procedure, public, pass(this) :: init => init_batch	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
generic, public :: operator(+) => add_t_t	Operator overloading for addition
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_batch	Print layer to unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: reduce => reduce_learnable	Merge another learnable layer into this one
procedure, public, pass(this) :: set_gradients	Set learnable parameters of layer
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_params	Set learnable parameters of layer
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(learnable_layer_type) :: conv_layer_type

Components

Type	Visibility	Attributes		Name		Initial
class(base_actv_type),	public,	allocatable	::	activation			Activation function
real(kind=real32),	public,	pointer	::	bias(:)	=>	null()	Bias pointer
class(base_init_type),	public,	allocatable	::	bias_init			Initialisers for kernel and bias
character(len=14),	public		::	bias_initialiser	=	''	Initialisers for kernel and bias
integer,	public,	allocatable, dimension(:)	::	bias_shape			Shape of biases
class(array_type),	public,	allocatable	::	di_padded			Padded input gradients
integer,	public,	allocatable, dimension(:)	::	dil			Kernel, stride, and dilation sizes
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
class(base_init_type),	public,	allocatable	::	kernel_init			Initialisers for kernel and bias
character(len=14),	public		::	kernel_initialiser	=	''	Initialisers for kernel and bias
integer,	public,	allocatable, dimension(:)	::	knl			Kernel, stride, and dilation sizes
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_channels			Number of channels
integer,	public		::	num_filters			Number of filters
integer,	public		::	num_params	=	0	Number of learnable parameters
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
class(pad_layer_type),	public,	allocatable	::	pad_layer			Optional preprocess padding layer
type(array_type),	public,	allocatable, dimension(:)	::	params			Learnable parameters
integer,	public,	allocatable, dimension(:)	::	stp			Kernel, stride, and dilation sizes
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_bias	=	.false.	Layer has bias
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output
integer,	public,	allocatable, dimension(:,:)	::	weight_shape			Shape of weights
type(array_type),	public,	dimension(2)	::	z			Temporary arrays for forward propagation

Type-Bound Procedures

procedure, public :: add_t_t => add_learnable	Add two layers
procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_conv	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_gradients	Get parameter gradients of layer
procedure, public, pass(this) :: get_num_params => get_num_params_conv	Get the number of parameters in the layer
procedure, public, pass(this) :: get_params	Get learnable parameters of layer
procedure, public, pass(this) :: init => init_conv	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
generic, public :: operator(+) => add_t_t	Operator overloading for addition
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_conv	Print layer to unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: reduce => reduce_learnable	Merge another learnable layer into this one
procedure, public, pass(this) :: set_gradients	Set learnable parameters of layer
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_params	Set learnable parameters of layer
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(base_layer_type) :: drop_layer_type

Type for dropout layers

Components

Type	Visibility	Attributes		Name		Initial
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
character(len=:),	public,	allocatable	::	name			Layer name
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
real(kind=real32),	public		::	rate	=	0.1_real32	Dropout rate, rate = 1 - keep_prob -- typical = 0.05-0.25
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure(generate_mask), public, deferred, pass(this) :: generate_mask	Generate dropout mask
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure(initialise), public, deferred, pass(this) :: init	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_base	Print the layer to a unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(base_layer_type) :: learnable_layer_type

Type for layers with learnable parameters

Components

Type	Visibility	Attributes		Name		Initial
class(base_actv_type),	public,	allocatable	::	activation			Activation function
class(base_init_type),	public,	allocatable	::	bias_init			Initialisers for kernel and bias
character(len=14),	public		::	bias_initialiser	=	''	Initialisers for kernel and bias
integer,	public,	allocatable, dimension(:)	::	bias_shape			Shape of biases
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
class(base_init_type),	public,	allocatable	::	kernel_init			Initialisers for kernel and bias
character(len=14),	public		::	kernel_initialiser	=	''	Initialisers for kernel and bias
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_params	=	0	Number of learnable parameters
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
type(array_type),	public,	allocatable, dimension(:)	::	params			Learnable parameters
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_bias	=	.false.	Layer has bias
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output
integer,	public,	allocatable, dimension(:,:)	::	weight_shape			Shape of weights

Type-Bound Procedures

procedure, public :: add_t_t => add_learnable	Add two layers
procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_gradients	Get parameter gradients of layer
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure, public, pass(this) :: get_params	Get learnable parameters of layer
procedure(initialise), public, deferred, pass(this) :: init	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
generic, public :: operator(+) => add_t_t	Operator overloading for addition
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_base	Print the layer to a unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: reduce => reduce_learnable	Merge another learnable layer into this one
procedure, public, pass(this) :: set_gradients	Set learnable parameters of layer
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_params	Set learnable parameters of layer
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(base_layer_type) :: merge_layer_type

Type for merge layers (i.e. add, multiply, concatenate)

Components

Type	Visibility	Attributes		Name		Initial
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public,	allocatable, dimension(:)	::	input_layer_ids			IDs of input layers
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
integer,	public		::	merge_mode	=	1	Integer code for fundamental merge method 1 = pointwise 2 = concatenate 3 = reduction 4 = parametric (NOT IMPLEMENTED)
character(len=20),	public		::	method			Merge method
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_input_layers	=	0	Number of input layers
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure(calc_input_shape), public, deferred, pass(this) :: calc_input_shape	Calculate input shape based on shapes of input layers
procedure(combine_merge), public, deferred, pass(this) :: combine	Merge two layers (forward)
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure(initialise), public, deferred, pass(this) :: init	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_base	Print the layer to a unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(base_layer_type) :: pad_layer_type

Type for padding layers

Components

Type	Visibility	Attributes		Name		Initial
integer,	public,	allocatable, dimension(:,:)	::	dest_bound			Original and destination bounds
type(facets_type),	public,	dimension(:), allocatable	::	facets			Facets of the layer
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
integer,	public		::	imethod	=	0	Method for padding
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
character(len=20),	public		::	method	=	'valid'	Padding method
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_channels			Number of channels
integer,	public,	allocatable, dimension(:,:)	::	orig_bound			Original and destination bounds
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
integer,	public,	allocatable, dimension(:)	::	pad			Padding size
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure, public, pass(this) :: init => init_pad	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_pad	Print layer to unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

type, public, abstract, extends(base_layer_type) :: pool_layer_type

Type for pooling layers

Components

Type	Visibility	Attributes		Name		Initial
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_channels			Number of channels
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
class(pad_layer_type),	public,	allocatable	::	pad_layer			Padding layer
integer,	public,	allocatable, dimension(:)	::	pool			Pooling and stride sizes
integer,	public,	allocatable, dimension(:)	::	strd			Pooling and stride sizes
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_base	Forward pass of layer
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_pool	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base	Get the number of parameters in the layer
procedure, public, pass(this) :: init => init_pool	Initialise the layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_pool	Print layer to unit
procedure(read_layer), public, deferred, pass(this) :: read	Read layer from file
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

athena__base_layer Module

Contents

Interfaces

Abstract Interfaces

Derived Types

Uses

Used by

Descendants:

Interfaces

interface

private module function add_learnable(a, b) result(output) Implementation →

Arguments

Return Value class(learnable_layer_type), allocatable

interface

private module subroutine build_from_onnx_base(this, node, initialisers, value_info, verbose) Implementation →

Arguments

interface

private module function calc_input_shape(this, input_shapes) result(input_shape)

Arguments

Return Value integer, allocatable, dimension(:)

interface

private module subroutine combine_merge(this, input_list)

Arguments

interface

private module subroutine emit_onnx_graph_inputs_base(this, prefix, graph_inputs, num_inputs) Implementation →

Arguments

interface

private module subroutine emit_onnx_nodes_base(this, prefix, nodes, num_nodes, max_nodes, inits, num_inits, max_inits, input_name, is_last_layer, format) Implementation →

Arguments

interface

private module subroutine extract_output_base(this, output) Implementation →

Arguments

interface

private module subroutine forward_base(this, input) Implementation →

Arguments

interface

private module function forward_eval_base(this, input) result(output) Implementation →

Arguments

Return Value type(array_type), pointer, (:,:)

interface

private module function get_attributes_base(this) result(attributes) Implementation →

Arguments

Return Value type(onnx_attribute_type), allocatable, dimension(:)

interface

private module function get_attributes_batch(this) result(attributes) Implementation →

Arguments

Return Value type(onnx_attribute_type), allocatable, dimension(:)

interface

private module function get_attributes_conv(this) result(attributes) Implementation →

Arguments

Return Value type(onnx_attribute_type), allocatable, dimension(:)

interface

private module function get_attributes_pool(this) result(attributes) Implementation →

Arguments

Return Value type(onnx_attribute_type), allocatable, dimension(:)

interface

private pure module function get_gradients(this, clip_method) result(gradients) Implementation →

Arguments

Return Value real(kind=real32), dimension(this%num_params)

interface

private pure module function get_num_params(this) result(num_params)

Arguments

Return Value integer

interface

private pure module function get_num_params_base(this) result(num_params) Implementation →

Arguments

Return Value integer

interface

private pure module function get_num_params_batch(this) result(num_params) Implementation →

Arguments

Return Value integer

interface

private pure module function get_num_params_conv(this) result(num_params) Implementation →

Arguments

Return Value integer

interface

private pure module function get_params(this) result(params) Implementation →

Arguments

Return Value real(kind=real32), dimension(this%num_params)

interface

private module function add_learnable(a, b) result(output)

private module subroutine build_from_onnx_base(this, node, initialisers, value_info, verbose)

private module subroutine emit_onnx_graph_inputs_base(this, prefix, graph_inputs, num_inputs)

private module subroutine emit_onnx_nodes_base(this, prefix, nodes, num_nodes, max_nodes, inits, num_inits, max_inits, input_name, is_last_layer, format)

private module subroutine extract_output_base(this, output)

private module subroutine forward_base(this, input)

private module function forward_eval_base(this, input) result(output)

private module function get_attributes_base(this) result(attributes)

private module function get_attributes_batch(this) result(attributes)

private module function get_attributes_conv(this) result(attributes)

private module function get_attributes_pool(this) result(attributes)

private pure module function get_gradients(this, clip_method) result(gradients)

private pure module function get_num_params_base(this) result(num_params)

private pure module function get_num_params_batch(this) result(num_params)

private pure module function get_num_params_conv(this) result(num_params)

private pure module function get_params(this) result(params)

private module subroutine init_batch(this, input_shape, verbose)

private module subroutine init_conv(this, input_shape, verbose)

private module subroutine init_pad(this, input_shape, verbose)

private module subroutine init_pool(this, input_shape, verbose)

private module subroutine nullify_graph_base(this)

private module subroutine print_base(this, file, unit, print_header_footer)

private module subroutine print_to_unit_base(this, unit)

private module subroutine print_to_unit_batch(this, unit)

private module subroutine print_to_unit_conv(this, unit)

private module subroutine print_to_unit_pad(this, unit)

private module subroutine print_to_unit_pool(this, unit)

private module subroutine reduce_learnable(this, input)

private module subroutine set_gradients(this, gradients)

private module subroutine set_graph_base(this, graph)

private module subroutine set_params(this, params)

private module subroutine set_rank_base(this, input_rank, output_rank)

private module subroutine set_shape_base(this, input_shape)