write_dl_modelT_write_dl_modelWriteDlModelWriteDlModelwrite_dl_model (Operator)

Name

write_dl_modelT_write_dl_modelWriteDlModelWriteDlModelwrite_dl_model — Write a deep learning model in a file.

Signature

write_dl_model( : : DLModelHandle, FileName : )

Description

write_dl_modelwrite_dl_modelWriteDlModelWriteDlModelwrite_dl_model writes the deep learning model DLModelHandleDLModelHandleDLModelHandleDLModelHandledlmodel_handle to the file given by FileNameFileNameFileNamefileNamefile_name. Please note that the values of the runtime specific parameters 'gpu'"gpu""gpu""gpu""gpu", 'runtime'"runtime""runtime""runtime""runtime", and 'runtime_init'"runtime_init""runtime_init""runtime_init""runtime_init" are not written.

The default HALCON file extension for deep learning models is '.hdl'.

The model can be read with read_dl_modelread_dl_modelReadDlModelReadDlModelread_dl_model.

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

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

DLModelHandleDLModelHandleDLModelHandleDLModelHandledlmodel_handle (input_control) dl_model → (handle)

Handle of the deep learning model.

FileNameFileNameFileNamefileNamefile_name (input_control) filename.write → (string)

Filename

File extension: .hdl

Result

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

Possible Predecessors

read_dl_modelread_dl_modelReadDlModelReadDlModelread_dl_model, train_dl_model_batchtrain_dl_model_batchTrainDlModelBatchTrainDlModelBatchtrain_dl_model_batch, train_dl_model_anomaly_datasettrain_dl_model_anomaly_datasetTrainDlModelAnomalyDatasetTrainDlModelAnomalyDatasettrain_dl_model_anomaly_dataset, set_dl_model_paramset_dl_model_paramSetDlModelParamSetDlModelParamset_dl_model_param

Module

Foundation. 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, Matching, Deep Learning Inference

Operators