load_dl_model_weightsT_load_dl_model_weightsLoadDlModelWeightsLoadDlModelWeightsload_dl_model_weights (Operator)
Name
load_dl_model_weightsT_load_dl_model_weightsLoadDlModelWeightsLoadDlModelWeightsload_dl_model_weights — Load the weights of a source model into a target model.
Signature
Description
The operator load_dl_model_weightsload_dl_model_weightsLoadDlModelWeightsLoadDlModelWeightsLoadDlModelWeightsload_dl_model_weights loads weights of a source model
DLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourcedlmodel_handle_source into a target model DLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetdlmodel_handle_target.
Thereby applies for every layer in the target model: Its weights are only
changed if there is a layer in the source model having the same name and the
same weight-shape.
Note that DLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourcedlmodel_handle_source must be different from
DLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetdlmodel_handle_target, i.e., you cannot use the same model handle as
source and target.
ChangesByLayerChangesByLayerChangesByLayerChangesByLayerchangesByLayerchanges_by_layer is a tuple indicating for every target layer how many
weights changed.
Its entries are sorted by ascending layer IDs. The layer IDs can be queried
via the operator get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParamget_dl_model_param with the parameter
'summary'"summary""summary""summary""summary""summary".
Note, that 'weights' means all weights and biases for all layers which
can have such values (e.g., convolutional layer, batch normalization layer,
etc.).
Attention
The operator load_dl_model_weightsload_dl_model_weightsLoadDlModelWeightsLoadDlModelWeightsLoadDlModelWeightsload_dl_model_weights is only applicable to self-created
networks. For networks delivered by HALCON, the operator is not working.
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
DLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourceDLModelHandleSourcedlmodel_handle_source (input_control) dl_model → HDlModel, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
Handle of the source deep learning model.
DLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetDLModelHandleTargetdlmodel_handle_target (input_control) dl_model → HDlModel, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
Handle of the target deep learning model.
ChangesByLayerChangesByLayerChangesByLayerChangesByLayerchangesByLayerchanges_by_layer (output_control) integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Indicates for every target layer how many weights
changed.
Result
If the parameters are valid, the operator load_dl_model_weightsload_dl_model_weightsLoadDlModelWeightsLoadDlModelWeightsLoadDlModelWeightsload_dl_model_weights
returns the value TRUE. If necessary, an exception is raised.
Module
Deep Learning Training