init_dl_continual_learning🔗
Short description🔗
init_dl_continual_learning — Converts a deep learning model to a model suitable for Continual Learning.
Signature🔗
init_dl_continual_learning( dl_model DLModelHandle, dict DLDataset, dict GenParam )
Description🔗
init_dl_continual_learning initializes a deep learning model
DLModelHandle of 'type' = 'classification'
for Continual Learning. With Continual Learning, it is possible to extend
a model with new classes or changes within known classes without having to
retrain it from scratch, even if the model has been optimized for an
AI²-interface.
See Deep Learning / Continual Learning for further information.
If a DLDataset is provided, it must be preprocessed and split.
init_dl_continual_learning can be called with either one of
the pretrained classification models provided with HALCON or with a
model that has previously been trained with train_dl_model (fine-tuned).
The internal architecture of the model will be slightly changed such that
Continual Learning becomes possible. At this process, the internal ability
of the model to distinguish between previously learned classes will be removed
unless the dataset previously used to fine-tune the model is provided. The
internal knowledge for feature extraction is retained in all cases. If no
DLDataset is provided, the resulting Continual Learning model will not
be able to perform any kind of classification, regardless of being a pretrained
or fine-tuned model. To prevent potentially unwanted changes when passing no
DLDataset or a dataset with different classes than the ones previously
known to the model, it is only possible by a further confirmation via setting the
GenParam 'reset_class_information' to 'true'. If a
DLDataset is provided, the Continual Learning model will learn from the
provided data in a special way. Note that providing an initial DLDataset
within init_dl_continual_learning will achieve a similar model to providing
no DLDataset within init_dl_continual_learning, but within
extend_dl_continual_learning. If a fine-tuned model is provided,
DLDataset must be the same dataset (including split and preprocessing
parameters) that was used during fine-tuning.
In general, it is recommended to start with a pretrained model and
initialize it for Continual Learning with the dataset that should be learned.
The model can be evaluated with evaluate_dl_model.
If the model accuracy is not sufficient, a fine-tuning on the initial dataset
before initializing the model for Continual Learning may help. However, extensive
fine-tuning can reduce the ability of the model to learn new classes compared to
a pretrained model.
Due to changes in the model architecture, the predictions of the initialized model may differ from those of the originally fine-tuned model. While the overall accuracy remains similar if the original training dataset is provided, the confidence values may be lower. This is because the confidence values are calculated differently and does not mean that the model is less reliable. Confidence values should always be interpreted relative to the dataset in order to draw any conclusions from them. If sufficient samples are available, the model performs an internal calibration to ensure that correctly classified samples fall into a defined confidence range.
init_dl_continual_learning also extends the model for
Out-of-Distribution Detection. Thus, when apply_dl_model is called,
the result will include the additional entries related to
Out-of-Distribution Detection.
See fit_dl_out_of_distribution for further information. Note that it
is crucial that the provided DLDataset contains diverse
and sufficient samples for each class to ensure reliable Out-of-Distribution
Detection.
The usage of validation images differs within init_dl_continual_learning
compared to fine-tuning a model with train_dl_model. To be more specific,
validation images are only used for model confidence calibration and the calculation
of 'ood_threshold' within Out-of-Distribution Detection.
If neither is required for your particular use-case,
you can also only split the dataset into training and test images. Note that if
the provided DLDataset contains less than or equal to 15 training samples
per class and no validation samples, the Out-of-Distribution Detection threshold
calculation will fail and the threshold will be set to a very high value. In result,
the model will never predict a sample as Out-of-Distribution, unless the threshold
is changed by the user.
init_dl_continual_learning can be applied to any classification model
supported by HALCON. For models created using
Deep Learning / Framework operators or read from an
ONNX model file, Continual Learning compatibility may
vary depending on the architecture.
GenParam is a dictionary for setting generic parameters.
The following GenParam are currently supported:
-
'‘reset_class_information’': Determines whether the parameter
DLDatasetcan be set to an empty tuple or to a dataset with different classes than the ones previously known to the model. This serves as a protection, as without a dataset, the existing class knowledge is reset wheninit_dl_continual_learningis called. In order to allow a model initialization with no dataset or a dataset with classes that differ from the ones known by the model, '‘reset_class_information’' must be set to '‘true’'. This can be useful, for example, if a pretrained model should be initialized for Continual Learning at a point in time at which samples of the initial classes are not yet available.List of values: 'false', 'true'
Default: 'false'
Attention🔗
If fit_dl_out_of_distribution is called for a model before calling
init_dl_continual_learning, the previous internal
calculations for Out-of-Distribution Detection are discarded and the model is
adapted anew.
Certain modifications to the model, such as continuing standard fine-tuning
with train_dl_model, cannot be performed once the model has been
initialized for Continual Learning. Also note that calculating a classification
heatmap is currently not supported.
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
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🔗
DLModelHandle (input_control, state is modified) dl_model → (handle)
Handle of a deep learning classification model.
DLDataset (input_control) dict → (handle)
Dataset for initial Continual Learning training step.
GenParam (input_control) dict → (handle)
Dictionary for generic parameters.
Default: []
Combinations with other operators🔗
Combinations
Possible predecessors
Possible successors
Module🔗
Deep Learning Professional