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clear_dl_modelClearDlModelClearDlModelclear_dl_modelT_clear_dl_model🔗

Short description🔗

clear_dl_modelClearDlModelClearDlModelclear_dl_modelT_clear_dl_model — Clear a deep learning model.

Signature🔗

clear_dl_model( dl_model DLModelHandle )void ClearDlModel( const HTuple& DLModelHandle )static void HOperatorSet.ClearDlModel( HTuple DLModelHandle )def clear_dl_model( dlmodel_handle: MaybeSequence[HHandle] ) -> None

Herror T_clear_dl_model( const Htuple DLModelHandle )

static void HDlModel::ClearDlModel( const HDlModelArray& DLModelHandle )

void HDlModel::ClearDlModel( ) const

static void HDlModel.ClearDlModel( HDlModel[] DLModelHandle )

void HDlModel.ClearDlModel( )

Description🔗

clear_dl_modelClearDlModel clears the handle of the deep learning model given by DLModelHandleDLModelHandledlmodel_handle and frees all memory required for the model. After calling clear_dl_modelClearDlModel, the model can no longer be used and the handle DLModelHandleDLModelHandledlmodel_handle becomes invalid.

For further explanations to deep learning models in HALCON, see the chapter Deep Learning / Model.

Execution information🔗

Execution information

  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).

  • Multithreading scope: local (may only be called from the same thread in which the window, model, or tool instance was created).

  • Processed without parallelization.

This operator modifies the state of the following input parameter:

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters🔗

DLModelHandleDLModelHandledlmodel_handle (input_control, state is modified) dl_model(-array) → (handle)HTuple (HHandle)HDlModel, HTuple (IntPtr)MaybeSequence[HHandle]Htuple (handle)

Handle of the deep learning model.

Result🔗

If the parameters are valid, the operator clear_dl_modelClearDlModel returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Combinations with other operators🔗

Combinations

Possible predecessors

read_dl_modelReadDlModel, apply_dl_modelApplyDlModel, train_dl_model_batchTrainDlModelBatch, train_dl_model_anomaly_datasetTrainDlModelAnomalyDataset

Module🔗

This operator uses dynamic licensing (see the ‘Installation Guide’). Which of the following modules is required depends on the specific usage of the operator:

3D Metrology, OCR/OCV, Deep Learning Enhanced, Deep Learning Professional, Matching