athena__flatten_layer Module

Module containing implementation of a flattening layer

This module implements a reshape layer that flattens multi-dimensional inputs into 1D vectors. Commonly used between convolutional and dense layers.

Mathematical operation: Reshape: (d1, d2, ..., dn) -> (d1 * d2 * ... * dn)

Example: (28, 28, 32) -> (25088)

No parameters, pure reshape operation Preserves all information, just changes tensor structure


Uses


Interfaces

public interface flatten_layer_type

Interface for setting up the flattening layer

  • private module function layer_setup(input_shape, input_rank, verbose) result(layer)

    Set up the flattening layer

    Arguments

    Type IntentOptional Attributes Name
    integer, intent(in), optional, dimension(:) :: input_shape

    Input shape
    integer, intent(in), optional :: input_rank

    Input rank
    integer, intent(in), optional :: verbose

    Verbosity level

    Return Value type(flatten_layer_type)

    Instance of the flattening layer


Derived Types

type, public, extends(base_layer_type) ::  flatten_layer_type

Type for 1D flattening layer with overloaded procedures

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_outputs

Number of outputs
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

Constructor

Interface for setting up the flattening layer

private module function layer_setup (input_shape, input_rank, verbose)

Set up the flattening layer

Type-Bound Procedures

procedure, public, pass(this) :: build_from_onnx => build_from_onnx_flatten

Build flattening layer from ONNX node and initialisers
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_flatten

Forward propagation derived type handler
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_flatten

Initialise flattening 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_flatten

Print flatten layer to unit
procedure, public, pass(this) :: read => read_flatten

Read flattening 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_hyperparams => set_hyperparams_flatten

Set hyperparameters for flattening layer
procedure, public, pass(this) :: set_rank => set_rank_flatten

Set input and output rank for flattening layer
procedure, public, pass(this) :: set_shape => set_shape_base

Set the input shape of the layer

Functions

public function create_from_onnx_flatten_layer(node, initialisers, value_info, verbose) result(layer)

Build flattening layer from attributes and return layer

Arguments

Type IntentOptional Attributes Name
type(onnx_node_type), intent(in) :: node

ONNX node information
type(onnx_initialiser_type), intent(in), dimension(:) :: initialisers

ONNX initialiser information
type(onnx_tensor_type), intent(in), dimension(:) :: value_info

ONNX value info
integer, intent(in), optional :: verbose

Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the flattening layer

public function read_flatten_layer(unit, verbose) result(layer)

Read flattening layer from file and return layer

Arguments

Type IntentOptional Attributes Name
integer, intent(in) :: unit

File unit
integer, intent(in), optional :: verbose

Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the flattening layer

private module function layer_setup(input_shape, input_rank, verbose) result(layer)

Set up the flattening layer

Arguments

Type IntentOptional Attributes Name
integer, intent(in), optional, dimension(:) :: input_shape

Input shape
integer, intent(in), optional :: input_rank

Input rank
integer, intent(in), optional :: verbose

Verbosity level

Return Value type(flatten_layer_type)

Instance of the flattening layer


Subroutines

private subroutine build_from_onnx_flatten(this, node, initialisers, value_info, verbose)

Read ONNX attributes for flattening layer

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the flattening layer
type(onnx_node_type), intent(in) :: node

ONNX node information
type(onnx_initialiser_type), intent(in), dimension(:) :: initialisers

ONNX initialiser information
type(onnx_tensor_type), intent(in), dimension(:) :: value_info

ONNX value info
integer, intent(in) :: verbose

Verbosity level

private subroutine forward_flatten(this, input)

Forward propagation

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the fully connected layer
class(array_type), intent(in), dimension(:,:) :: input

Input values

private subroutine init_flatten(this, input_shape, verbose)

Initialise flattening layer

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the flattening layer
integer, intent(in), dimension(:) :: input_shape

Input shape
integer, intent(in), optional :: verbose

Verbosity level

private subroutine print_to_unit_flatten(this, unit)

Print flatten layer to unit

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(in) :: this

Instance of the flatten layer
integer, intent(in) :: unit

File unit

private subroutine read_flatten(this, unit, verbose)

Read flattening layer from file

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the flattening layer
integer, intent(in) :: unit

File unit
integer, intent(in), optional :: verbose

Verbosity level

private subroutine set_hyperparams_flatten(this, input_rank, verbose)

Set hyperparameters for flattening layer

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the flattening layer
integer, intent(in) :: input_rank

Input rank
integer, intent(in), optional :: verbose

Verbosity level

private subroutine set_rank_flatten(this, input_rank, output_rank)

Set the input and output ranks of the layer

Arguments

Type IntentOptional Attributes Name
class(flatten_layer_type), intent(inout) :: this

Instance of the layer
integer, intent(in) :: input_rank

Input rank
integer, intent(in) :: output_rank

Output rank