Model

List of Operators ↓

This chapter explains the general concept of the deep learning model in HALCON and the data handling.

By concept, a deep learning model in HALCON is a deep neural network. Each deep neural network has an architecture defining its function, i.e., the tasks it can be used for. There can be several possible network architectures for one functionality. Currently, networks for the following functionalities, also referred to as methods or types, are implemented in HALCON as model:

• Object detection, see Deep Learning / Object Detection.

• Semantic segmentation, see Deep Learning / Semantic Segmentation.

For the implemented methods you can find further information about the specific workflow, data requirements, and validation measures in the corresponding chapters. Information to deep learning (DL) in general are in the chapter Deep Learning.

In this chapter you find the information, which data a DL model needs and returns as well as how this data is transferred.

Data

Independent of the deep learning method used, the data has to be provided to the model following certain conventions. As basic concept, the model handles data over dictionaries. More precisely, the model receives for every input image a dictionary DLSampleDLSampleDLSampleDLSampleDLSample. Such a dictionary contains the image and, in case of training and evaluation, information like e.g., the ground truth annotations. As output the model returns a dictionary DLResultDLResultDLResultDLResultDLResult with the results. An illustration is given in the figure below.

During training and evaluation, an additional dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset serves as a database and collects all the individual image information dictionaries (stored in the key samplessamplessamplessamplessamples). Out of this database the model input dictionaries, DLSampleDLSampleDLSampleDLSampleDLSample, are created, see the illustration below and the section “Training and evaluation input data”.

Although not necessary for the model itself, the dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset is used from the training and evaluation procedures. Therefore we highly recommend to create a dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset out of your data. Its necessary entries are described below. This dictionary is conveniently created for you by the procedure read_dl_dataset_from_coco in case of object detection and read_dl_dataset_segmentation in case of semantic segmentation. Please see the respective procedure documentation for the requirements on the data in order to use these procedures. In case you create DLDatasetDLDatasetDLDatasetDLDatasetDLDataset in an other way, it has to contain at least the entries not marked with a number in the description below. During the preprocessing of your dataset the respective procedures include the further entries of the dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset.

In the following, we explain the different data and the involved dictionaries. Thereby, we use the following abbreviations to mark the methods (m) using the entry: 'A': any method, 'D': object detection, 'S': semantic segmentation. The entries only applicable for certain methods are described more extensively in the corresponding chapter reference.

Training and evaluation input data

The dataset consists of images and corresponding information. They have to be provided in a way the model can process them. Concerning the image requirements, find more information in the section “Images” below.

The information about the images and the dataset is represented in a dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset, which serves as a database. More precisely, it stores the general information about the dataset and the dictionaries of the individual samples collected under samplessamplessamplessamplessamples. When the actual image data is needed, a dictionary DLSampleDLSampleDLSampleDLSampleDLSample is created (or read if it already exists) for each image required. The relation of these dictionaries is illustrated in the figure above. In the following we will explain these dictionaries with their key/value pairs in more details.

DLDatasetDLDatasetDLDatasetDLDatasetDLDataset

The dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset stores general information about the dataset and collects the dictionaries of the individual samples. Thereby iconic data is not included in DLDatasetDLDatasetDLDatasetDLDatasetDLDataset but the paths to the respective images. Depending on the model type, this dictionary can have the following entries:

key	description	format	m
image_dirimage_dirimage_dirimage_dirimageDir	common base path to all images	string	A
dlsample_dirdlsample_dirdlsample_dirdlsample_dirdlsampleDir [1]	common base path of all sample files (if present)	string	A
class_namesclass_namesclass_namesclass_namesclassNames	names of all classes that are to be distinguished	tuple of strings	A
class_idsclass_idsclass_idsclass_idsclassIds	IDs of all classes that are to be distinguished (range: 0-65535)	tuple of integers	A
preprocess_parampreprocess_parampreprocess_parampreprocess_parampreprocessParam [1]	all parameter values used during preprocessing	dictionary	A
samplessamplessamplessamplessamples	collection of sample descriptions	tuple of dictionaries	A
class_weightsclass_weightsclass_weightsclass_weightsclassWeights [1]	weights of the different classes	tuple of reals	S
segmentation_dirsegmentation_dirsegmentation_dirsegmentation_dirsegmentationDir	common base path of all segmentation images	string	S

This dictionary is conveniently created for you by the procedure read_dl_dataset_from_coco in case of object detection and read_dl_dataset_segmentation in case of semantic segmentation. Please see the respective procedure documentation for the requirements on the data in order to use these procedures. The entries marked with [1] are included from the preprocessing procedures.

samplessamplessamplessamplessamples

The DLDatasetDLDatasetDLDatasetDLDatasetDLDataset key samplessamplessamplessamplessamples gets a tuple of dictionaries as value, one for each sample in the dataset. These dictionaries contain the information concerning an individual sample of the dataset. Depending on the model type, this dictionary can have the following entries:

key	description	format	m
image_file_nameimage_file_nameimage_file_nameimage_file_nameimageFileName	file name of the image and its path relative to image_dirimage_dirimage_dirimage_dirimageDir	string	A
image_idimage_idimage_idimage_idimageId	unique image ID (encoding format: UINT8)	integer	A
splitsplitsplitsplitsplit [2]	specifies the assigned split subset ('train'"train""train""train""train",'validation'"validation""validation""validation""validation",'test'"test""test""test""test")	string	A
dlsample_file_namedlsample_file_namedlsample_file_namedlsample_file_namedlsampleFileName [3]	file name of the corresponding dictionary DLSampleDLSampleDLSampleDLSampleDLSample and its path relative to dlsample_dirdlsample_dirdlsample_dirdlsample_dirdlsampleDir	string	A
segmentation_file_namesegmentation_file_namesegmentation_file_namesegmentation_file_namesegmentationFileName	file name of the ground truth segmentation image and its path relative to segmentation_dirsegmentation_dirsegmentation_dirsegmentation_dirsegmentationDir	string	S
bbox_label_idbbox_label_idbbox_label_idbbox_label_idbboxLabelId	ground truth labels for the bounding boxes (in the form of class_idsclass_idsclass_idsclass_idsclassIds)	tuple of integers	D
bbox_row1bbox_row1bbox_row1bbox_row1bboxRow1 [4]	ground truth bounding box coordinates: row 1	tuple of reals	D
bbox_col1bbox_col1bbox_col1bbox_col1bboxCol1 [4]	ground truth bounding box coordinates: column 1	tuple of reals	D
bbox_row2bbox_row2bbox_row2bbox_row2bboxRow2 [4]	ground truth bounding box coordinates: row 2	tuple of reals	D
bbox_col2bbox_col2bbox_col2bbox_col2bboxCol2 [4]	ground truth bounding box coordinates: column 2	tuple of reals	D
coco_raw_annotationscoco_raw_annotationscoco_raw_annotationscoco_raw_annotationscocoRawAnnotations	Optional. It contains for every bbox_label_idbbox_label_idbbox_label_idbbox_label_idbboxLabelId within this image a dictionary with all raw COCO annotation information	tuple of dictionaries	D

These dictionaries are part of DLDatasetDLDatasetDLDatasetDLDatasetDLDataset and thus they are created concurrently by the procedure read_dl_dataset_from_coco in case of object detection and read_dl_dataset_segmentation in case of semantic segmentation. An exception are the entries with a mark in the table, [2]: the procedure split_dl_dataset adds splitsplitsplitsplitsplit, [3]: the procedure preprocess_dl_samples adds dlsample_file_namedlsample_file_namedlsample_file_namedlsample_file_namedlsampleFileName. [4]: For the bounding boxes, pixel centered, subpixel accurate coordinates are used.

DLSampleDLSampleDLSampleDLSampleDLSample

The dictionary DLSampleDLSampleDLSampleDLSampleDLSample serves as input for the model. For a batch, they are handed over as the entries of the tuple DLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatch.

A dictionary DLSampleDLSampleDLSampleDLSampleDLSample is created out of DLDatasetDLDatasetDLDatasetDLDatasetDLDataset for every image sample by the procedure gen_dl_samples. It contains all ground truth annotations of an image. If preprocessing is done using the standard procedure preprocess_dl_samples, they are created automatically therein. Note, preprocessing steps may lead to an update of the corresponding DLSampleDLSampleDLSampleDLSampleDLSample dictionary.

Depending on the model type, DLSampleDLSampleDLSampleDLSampleDLSample can have the following entries:

key	description	format	m
imageimageimageimageimage	input image	image	A
image_idimage_idimage_idimage_idimageId	unique image ID (as in DLDatasetDLDatasetDLDatasetDLDatasetDLDataset)	integer	A
segmentation_imagesegmentation_imagesegmentation_imagesegmentation_imagesegmentationImage	image with the ground truth segmentations, read from segmentation_file_namesegmentation_file_namesegmentation_file_namesegmentation_file_namesegmentationFileName	image	S
weight_imageweight_imageweight_imageweight_imageweightImage [5]	image with the pixel weights	image	S
bbox_label_idbbox_label_idbbox_label_idbbox_label_idbboxLabelId	ground truth labels for the image part within the bounding box (in the form of class_idsclass_idsclass_idsclass_idsclassIds)	tuple of integers	D
bbox_row1bbox_row1bbox_row1bbox_row1bboxRow1 [4]	ground truth bounding box coordinates: row 1	tuple of reals	D
bbox_col1bbox_col1bbox_col1bbox_col1bboxCol1 [4]	ground truth bounding box coordinates: column 1	tuple of reals	D
bbox_row2bbox_row2bbox_row2bbox_row2bboxRow2 [4]	ground truth bounding box coordinates: row 2	tuple of reals	D
bbox_col2bbox_col2bbox_col2bbox_col2bboxCol2 [4]	ground truth bounding box coordinates: column 2	tuple of reals	D

These dictionaries are created by the procedure gen_dl_samples. An exception is the entry marked in the table above, [5]: created by the procedure gen_dl_segmentation_weights. [4]: For the bounding boxes, pixel centered, subpixel accurate coordinates are used.

Note, in case these DLSampleDLSampleDLSampleDLSampleDLSample should be stored, use the procedure write_dl_samples. You can read them with the procedure read_dl_samples.

Inference input data

The inference input data consists of bare images. They have to be provided in a way the model can process them. Concerning the image requirements, find more information in the subsection “Images” below.

The model is set up to hand over all data through a dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDataset. For the inference, such a dictionary containing only the image can be created using the procedure gen_dl_samples_from_images. These dictionaries can be passed one at a time or within a tuple DLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatch.

Training output data

As output from the operator train_dl_classifier_batchtrain_dl_classifier_batchTrainDlClassifierBatchTrainDlClassifierBatchTrainDlClassifierBatch, the model will return a dictionary DLTrainResultDLTrainResultDLTrainResultDLTrainResultDLTrainResult. In this dictionary you will find the current value of the total loss as the value for the key total_losstotal_losstotal_losstotal_losstotalLoss as well as the values for all other losses included in your model.

Inference and evaluation output data

As output from the operator apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModel, the model will return a dictionary DLResultDLResultDLResultDLResultDLResult for each sample. Depending on the model type, this dictionary can have the following entries:

key	description	format	m
segmentation_imagesegmentation_imagesegmentation_imagesegmentation_imagesegmentationImage	image with the segmentation result	image	S
segmentation_confidencesegmentation_confidencesegmentation_confidencesegmentation_confidencesegmentationConfidence	image with the confidences of the segmentation result	image	S
bbox_class_idbbox_class_idbbox_class_idbbox_class_idbboxClassId	predicted class for the bounding box (in the form of class_idsclass_idsclass_idsclass_idsclassIds)	tuple of integers	D
bbox_confidencebbox_confidencebbox_confidencebbox_confidencebboxConfidence	confidence value of the prediction for the bounding box	tuple of reals	D
bbox_row1bbox_row1bbox_row1bbox_row1bboxRow1	predicted bounding box coordinates: row 1	tuple of reals	D
bbox_col1bbox_col1bbox_col1bbox_col1bboxCol1	predicted bounding box coordinates: column 1	tuple of reals	D
bbox_row2bbox_row2bbox_row2bbox_row2bboxRow2	predicted bounding box coordinates: row 2	tuple of reals	D
bbox_col2bbox_col2bbox_col2bbox_col2bboxCol2	predicted bounding box coordinates: column 2	tuple of reals	D

For a further explanation to the output values we refer to the chapter Deep Learning / Semantic Segmentation and Deep Learning / Object Detection, respectively.

Images

Regardless of the application, the network poses requirements on the images. The specific values depend on the network itself and can be queried using get_dl_model_paramget_dl_model_paramGetDlModelParamGetDlModelParamGetDlModelParam. In order to fulfill these requirements, you may have to preprocess your images. Standard preprocessing of the entire dataset and therewith also the images is implemented in preprocess_dl_samples. In case of custom preprocessing this procedure offers guidance on the implementation.

List of Operators

apply_dl_modelApplyDlModelApplyDlModelapply_dl_model

Apply a deep-learning-based network on a set of images for inference.

clear_dl_modelClearDlModelClearDlModelclear_dl_model

Clear a deep learning model.

deserialize_dl_modelDeserializeDlModelDeserializeDlModeldeserialize_dl_model

Deserialize a deep learning model.

get_dl_model_paramGetDlModelParamGetDlModelParamget_dl_model_param

Return the parameters of a deep learning model.

read_dl_modelReadDlModelReadDlModelread_dl_model

Read a deep learning model from a file.

serialize_dl_modelSerializeDlModelSerializeDlModelserialize_dl_model

Serialize a deep learning model.

set_dl_model_paramSetDlModelParamSetDlModelParamset_dl_model_param

Set the parameters of a deep learning model.

train_dl_model_batchTrainDlModelBatchTrainDlModelBatchtrain_dl_model_batch

Train a deep learning model.

write_dl_modelWriteDlModelWriteDlModelwrite_dl_model

Write a deep learning model in a file.