Operator Reference

HALCON Operator Reference / Deep Learning / Framework
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create_dl_layer_matmulT_create_dl_layer_matmulCreateDlLayerMatmulCreateDlLayerMatmulcreate_dl_layer_matmul (Operator)

create_dl_layer_matmulT_create_dl_layer_matmulCreateDlLayerMatmulCreateDlLayerMatmulcreate_dl_layer_matmul — Create a MatMul layer.

Signature

create_dl_layer_matmul( : : DLLayerA, DLLayerB, LayerName, GenParamName, GenParamValue : DLLayerMatMul)

Herror T_create_dl_layer_matmul(const Htuple DLLayerA, const Htuple DLLayerB, const Htuple LayerName, const Htuple GenParamName, const Htuple GenParamValue, Htuple* DLLayerMatMul)

void CreateDlLayerMatmul(const HTuple& DLLayerA, const HTuple& DLLayerB, const HTuple& LayerName, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* DLLayerMatMul)

HDlLayer HDlLayer::CreateDlLayerMatmul(const HDlLayer& DLLayerB, const HString& LayerName, const HTuple& GenParamName, const HTuple& GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerMatmul(const HDlLayer& DLLayerB, const HString& LayerName, const HString& GenParamName, const HString& GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerMatmul(const HDlLayer& DLLayerB, const char* LayerName, const char* GenParamName, const char* GenParamValue) const

HDlLayer HDlLayer::CreateDlLayerMatmul(const HDlLayer& DLLayerB, const wchar_t* LayerName, const wchar_t* GenParamName, const wchar_t* GenParamValue) const   ( Windows only)

def create_dl_layer_matmul(dllayer_a: HHandle, dllayer_b: HHandle, layer_name: str, gen_param_name: MaybeSequence[str], gen_param_value: MaybeSequence[Union[int, float, str]]) -> HHandle

Description

The operator create_dl_layer_matmulcreate_dl_layer_matmulCreateDlLayerMatmulCreateDlLayerMatmulcreate_dl_layer_matmul creates a MatMul layer whose handle is returned in DLLayerMatMulDLLayerMatMulDLLayerMatMulDLLayerMatMuldllayer_mat_mul.

A MatMul layer multiplies the 2D matrices, given in the latter two dimensions (H, W) of input DLLayerADLLayerADLLayerADLLayerAdllayer_a, with the corresponding 2D matrices of input DLLayerBDLLayerBDLLayerBDLLayerBdllayer_b, also given in the latter two dimensions (H, W). The output in DLLayerMatMulDLLayerMatMulDLLayerMatMulDLLayerMatMuldllayer_mat_mul is hence given by .

The MatMul layer supports broadcasting for the first input DLLayerADLLayerADLLayerADLLayerAdllayer_a. That means, if the batch size or the number of channels in DLLayerADLLayerADLLayerADLLayerAdllayer_a equals one then the first batch item or channel of DLLayerADLLayerADLLayerADLLayerAdllayer_a is multiplied with all batch items or channels of DLLayerBDLLayerBDLLayerBDLLayerBdllayer_b, respectively.

To make the multiplication work, the width of DLLayerADLLayerADLLayerADLLayerAdllayer_a must be equal to the height of DLLayerBDLLayerBDLLayerBDLLayerBdllayer_b.

The following generic parameters GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output":

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true") or not ('false'"false""false""false""false").

Default: 'false'"false""false""false""false"

'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params":

Number of trainable parameters (weights and biases) of the layer.

'transpose_a'"transpose_a""transpose_a""transpose_a""transpose_a":

Matrices of input DLLayerADLLayerADLLayerADLLayerAdllayer_a are transposed: .

Default: 'false'"false""false""false""false"

'transpose_b'"transpose_b""transpose_b""transpose_b""transpose_b":

Matrices of input DLLayerBDLLayerBDLLayerBDLLayerBdllayer_b are transposed: .

Default: 'false'"false""false""false""false"

Certain parameters of layers created using this operator create_dl_layer_matmulcreate_dl_layer_matmulCreateDlLayerMatmulCreateDlLayerMatmulcreate_dl_layer_matmul can be set and retrieved using further operators. The following tables give an overview, which parameters can be set using set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model.

Layer Parameters set get
'input_layer'"input_layer""input_layer""input_layer""input_layer" x
'name'"name""name""name""name" (LayerNameLayerNameLayerNamelayerNamelayer_name) x x
'output_layer'"output_layer""output_layer""output_layer""output_layer" (DLLayerMatMulDLLayerMatMulDLLayerMatMulDLLayerMatMuldllayer_mat_mul) x
'shape'"shape""shape""shape""shape" x
'transpose_a'"transpose_a""transpose_a""transpose_a""transpose_a" x
'transpose_b'"transpose_b""transpose_b""transpose_b""transpose_b" x
'type'"type""type""type""type" x
Generic Layer Parameters set get
'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output" x x
'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params" x

Execution Information

  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).
  • Multithreading scope: global (may be called from any thread).
  • Processed without parallelization.

Parameters

DLLayerADLLayerADLLayerADLLayerAdllayer_a (input_control)  dl_layer → HDlLayer, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Input layer A.

DLLayerBDLLayerBDLLayerBDLLayerBdllayer_b (input_control)  dl_layer → HDlLayer, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Input layer B.

LayerNameLayerNameLayerNamelayerNamelayer_name (input_control)  string → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Name of the output layer.

GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control)  attribute.name(-array) → HTupleMaybeSequence[str]HTupleHtuple (string) (string) (HString) (char*)

Generic input parameter names.

Default: []

List of values: 'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output", 'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params", 'transpose_a'"transpose_a""transpose_a""transpose_a""transpose_a", 'transpose_b'"transpose_b""transpose_b""transpose_b""transpose_b"

GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control)  attribute.value(-array) → HTupleMaybeSequence[Union[int, float, str]]HTupleHtuple (string / integer / real) (string / int / long / double) (HString / Hlong / double) (char* / Hlong / double)

Generic input parameter values.

Default: []

Suggested values: 'true'"true""true""true""true", 'false'"false""false""false""false"

DLLayerMatMulDLLayerMatMulDLLayerMatMulDLLayerMatMuldllayer_mat_mul (output_control)  dl_layer → HDlLayer, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

MatMul layer.

Module

Deep Learning Professional

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