Model

List of Operators ↓

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

By concept, a deep learning model in HALCON is an internal representation of 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 are implemented in HALCON as model:

• Anomaly Detection, see Deep Learning / Anomaly Detection.

• Classification, see Deep Learning / Classification.

• Object detection, see Deep Learning / Object Detection.

• Semantic segmentation, see Deep Learning / Semantic Segmentation.

Each functionality is identified by its unique model type. 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 in general are given 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

Deep Learning applications have different types of data to be distinguished. Roughly spoken these are: The raw images with possible annotations, data preprocessed in a way suitable for the model, and output data.

Before the different types of data and the entries of the specific dictionaries are explained, we will have a look how the data is connected. Thereby, symbols and colors refer to the schematic overviews given below.

In brief, the data structure for training or evaluation starts with the raw images and their ground truth annotations (gray frames). With the read data the following dictionaries are created: A dictionary DLDataset (red), which serves as database and refers to a specific dictionary (yellow) for every input image. The dictionary DLSample (orange) contains the data for a sample in the way the network can process it. A batch of DLSample is handed to the model in DLSampleBatch. For evaluation, DLResultBatch is returned, a tuple of dictionaries DLResult (dark blue), one for every sample. They are needed to obtain the evaluation results EvaluationResults. For training, the training results (e.g., loss values) are returned in the dictionary DLTrainResult (light blue). The most important steps concerning modifying or creating a dictionary:

• reading the raw data (symbol: paper with arrow)

• preprocessing the data (symbol: cogs)

• training (symbol: transparent brain in an arc)

• evaluation of the model (symbol: graph)

• evaluation of a sample (symbol: magnifying glass)

For inference no annotations are needed. Thus, the data structure starts with the raw images (gray frames). The dictionary DLSample (orange) contains the data for a sample in the way the network can process it. The results for a sample are returned in a dictionary DLResult (dark blue). The most important steps concerning modifying or creating a dictionary:

• reading the raw data (symbol: paper with arrow)

• preprocessing the data (symbol: cogs)

• inference (symbol: brain in a circle)

• evaluation of a sample (symbol: magnifying glass)

In order for the model to process the data, the data needs to follow certain conventions about what is needed and how it is given to the model. As visible from the figures above, in HALCON the data is transferred using dictionaries.

In the following we explain the involved dictionaries, how they can be created, and their entries. Thereby, we group them according to the main step of a deep learning application they are created in and whether they serve as input or output data. The following abbreviations mark for which methods the entry applies:

• 'Any': any method

• 'AD': anomaly detection

• 'CL': classification

• 'OD': object detection

  In case the entry is only applicable for a certain 'instance_type'"instance_type""instance_type""instance_type""instance_type""instance_type", the specification 'r1': 'rectangle1'"rectangle1""rectangle1""rectangle1""rectangle1""rectangle1", 'r2': 'rectangle2'"rectangle2""rectangle2""rectangle2""rectangle2""rectangle2" is added

• 'SE': 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 DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset, which serves as a database. More precisely, it stores the general information about the dataset and the dictionaries of the individual samples collected under the key samples. When the actual image data is needed, a dictionary DLSampleDLSampleDLSampleDLSampleDLSampledlsample is created (or read if it already exists) for each image required. The relation of these dictionaries is illustrated in the figure below.

Schematic illustration of the different dataset dictionaries used for training and evaluation. For visibility purpose only few entries are registered and BatchSizeBatchSizeBatchSizeBatchSizebatchSizebatch_size is set to three. In this example we have samples. Thereof three samples are chosen randomly: i,j, and k. The corresponding dictionaries DLSampleDLSampleDLSampleDLSampleDLSampledlsample are created and joined in the tuple DLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchdlsample_batch.

In the following we look at these dictionaries.

DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset

The dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset serves as a database. It stores general information about the dataset and collects the dictionaries of the individual samples. Thereby iconic data is not included in DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset but the paths to the respective images. The dictionary DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset is used by the training and evaluation procedures. It is not necessary for the model, but we highly recommend to create it. Its necessary entries are described below. This dictionary is directly created when labeling your data using the MVTec Deep Learning Tool. Alternatively it is created when reading in your data using one of the following procedures:

• read_dl_dataset_anomaly (anomaly detection)

• read_dl_dataset_classification (classification)

• read_dl_dataset_from_coco (object detection with 'instance_type'"instance_type""instance_type""instance_type""instance_type""instance_type" = 'rectangle1'"rectangle1""rectangle1""rectangle1""rectangle1""rectangle1")

• read_dl_dataset_segmentation (semantic segmentation).

Please see the respective procedure documentation for the requirements on the data in order to use these procedures. In case you create DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset 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 DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset.

Depending on the model type, this dictionary can have the following entries:

image_dir: Any

Common base path to all images.

format: string

dlsample_dir: Any [1]

Common base path of all sample files (if present).

format: string

class_names: Any

Names of all classes that are to be distinguished.

format: tuple of strings

class_ids: Any

IDs of all classes that are to be distinguished (range: 0-65534).

format: tuple of integers

preprocess_param: Any [1]

All parameter values used during preprocessing.

format: dictionary

samples: Any

Collection of sample descriptions.

format: tuple of dictionaries

class_weights: CL, SE [1]

Weights of the different classes.

format: tuple of reals

anomaly_dir: AD

Common base path of all anomaly regions (regions indicating anomalies in the image).

format: string

segmentation_dir: SE

Common base path of all segmentation images.

format: string

This dictionary is directly created when labeling your data using the MVTec Deep Learning Tool. It is also created by the procedures mentioned above for reading in your data. The entries marked with [1] are added by the preprocessing procedures.

samples

The DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset key samples 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:

image_file_name: Any

File name of the image and its path relative to image_dir.

format: string

image_id: Any

Unique image ID (encoding format: UINT8).

format: integer

split: Any [2]

Specifies the assigned split subset ('train'"train""train""train""train""train",'validation'"validation""validation""validation""validation""validation",'test'"test""test""test""test""test").

format: string

dlsample_file_name: Any [3]

File name of the corresponding dictionary DLSampleDLSampleDLSampleDLSampleDLSampledlsample and its path relative to dlsample_dir.

format: string

anomaly_file_name: AD

Optional. Path to region files with ground truth annotations (relative to anomaly_dir).

format: string

anomaly_label: AD

Ground truth anomaly label on image level (in the form of class_names).

format: string

image_label_id: CL

Ground truth label for the image (in the form of class_ids).

format: tuple of integers

bbox_label_id: OD

Ground truth labels for the bounding boxes (in the form of class_ids).

format: tuple of integers

bbox_row1: OD:r1 [4]

Ground truth bounding boxes: upper left corner, row coordinate.

format: tuple of reals

bbox_col1: OD:r1 [4]

Ground truth bounding boxes: upper left corner, column coordinate.

format: tuple of reals

bbox_row2: OD:r1 [4]

Ground truth bounding boxes: lower right corner, row coordinate.

format: tuple of reals

bbox_col2: OD:r1 [4]

Ground truth bounding boxes: lower right corner, column coordinate.

format: tuple of reals

coco_raw_annotations: OD:r1

Optional. It contains for every bbox_label_id within this image a dictionary with all raw COCO annotation information.

format: tuple of dictionaries

bbox_row: OD:r2 [4]

Ground truth bounding boxes: center point, row coordinate.

format: tuple of reals

bbox_col: OD:r2 [4]

Ground truth bounding boxes: center point, column coordinate.

format: tuple of reals

bbox_phi: OD:r2 [4]

Ground truth bounding boxes: angle phi.

format: tuple of reals

bbox_length1: OD:r2 [4]

Ground truth bounding boxes: half length of edge 1.

format: tuple of reals

bbox_length2: OD:r2 [4]

Ground truth bounding boxes: half length of edge 2.

format: tuple of reals

segmentation_file_name: SE

File name of the ground truth segmentation image and its path relative to segmentation_dir.

format: string

These dictionaries are part of DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset and thus they are created concurrently. An exception are the entries with a mark in the table, [2]: the procedure split_dl_dataset adds split, [3]: the procedure preprocess_dl_samples adds dlsample_file_name. [4]: Used coordinates: Pixel centered, subpixel accurate coordinates.

DLSampleDLSampleDLSampleDLSampleDLSampledlsample

The dictionary DLSampleDLSampleDLSampleDLSampleDLSampledlsample serves as input for the model. For a batch, they are handed over as the entries of the tuple DLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchdlsample_batch. They are created out of DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset for every image sample by the procedure gen_dl_samples. 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 DLSampleDLSampleDLSampleDLSampleDLSampledlsample dictionary.

DLSampleDLSampleDLSampleDLSampleDLSampledlsample contains the preprocessed image and, in case of training and evaluation, all ground truth annotations. Depending on the model type, it can have the following entries:

image: Any

Input image.

format: image

image_id: Any

Unique image ID (as in DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset).

format: integer

anomaly_ground_truth: AD

Anomaly image or region, read from anomaly_file_name.

format: image or region

anomaly_label: AD

Ground truth anomaly label on image level (in the form of class_names).

format: string

anomaly_label_id: AD

Ground truth anomaly label ID on image level (in the form of class_ids).

format: integer

image_label_id: CL

Ground truth label for the image (in the form of class_ids).

format: tuple of integers

bbox_label_id: OD

Ground truth labels for the image part within the bounding box (in the form of class_ids).

format: tuple of integers

bbox_row1: OD:r1 [4]

Ground truth bounding boxes: upper left corner, row coordinate.

format: tuple of reals

bbox_col1: OD:r1 [4]

Ground truth bounding boxes: upper left corner, column coordinate.

format: tuple of reals

bbox_row2: OD:r1 [4]

Ground truth bounding boxes: lower right corner, row coordinate.

format: tuple of reals

bbox_col2: OD:r1 [4]

Ground truth bounding boxes: lower right corner, column coordinate.

format: tuple of reals

bbox_row: OD:r2 [4]

Ground truth bounding boxes: center point, row coordinate.

format: tuple of reals

bbox_col: OD:r2 [4]

Ground truth bounding boxes: center point, column coordinate.

format: tuple of reals

bbox_phi: OD:r2 [4]

Ground truth bounding boxes: angle phi.

format: tuple of reals

bbox_length1: OD:r2 [4]

Ground truth bounding boxes: half length of edge 1.

format: tuple of reals

bbox_length2: OD:r2 [4]

Ground truth bounding boxes: half length of edge 2.

format: tuple of reals

segmentation_image: SE

Image with the ground truth segmentations, read from segmentation_file_name.

format: image

weight_image: SE [5]

Image with the pixel weights.

format: image

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]: Used coordinates: Pixel centered, subpixel accurate coordinates.

Note, in case these DLSampleDLSampleDLSampleDLSampleDLSampledlsample 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 DLDatasetDLDatasetDLDatasetDLDatasetDLDatasetdldataset. 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 DLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchDLSampleBatchdlsample_batch.

Training output data

The training output data is given in the dictionary DLTrainResultDLTrainResultDLTrainResultDLTrainResultDLTrainResultdltrain_result. Its entries depend on the model and thus on the operator used (for further information see the documentation of the corresponding operator):

CL, OD, SE:

The operator train_dl_model_batchtrain_dl_model_batchTrainDlModelBatchTrainDlModelBatchTrainDlModelBatchtrain_dl_model_batch returns

• total_loss

• possible further losses included in your model

AD:

The operator train_dl_model_anomaly_datasettrain_dl_model_anomaly_datasetTrainDlModelAnomalyDatasetTrainDlModelAnomalyDatasetTrainDlModelAnomalyDatasettrain_dl_model_anomaly_dataset returns

• final_error

• final_epoch

Inference and evaluation output data

As output from the operator apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelApplyDlModelapply_dl_model, the model will return a dictionary DLResultDLResultDLResultDLResultDLResultdlresult for each sample. An illustration is given in the figure below. The evaluation is based on these results and the annotations. Evaluation results are stored in the dictionary EvaluationResultsEvaluationResultsEvaluationResultsEvaluationResultsevaluationResultsevaluation_results.

(1)	(2)

Schematic illustration of the dictionaries serving as model input: (1) Evaluation: DLSampleDLSampleDLSampleDLSampleDLSampledlsample includes the image as well as information about the image and its content. This data serves as basis for the evaluation. For visibility purpose BatchSizeBatchSizeBatchSizeBatchSizebatchSizebatch_size is set to three (containing the randomly chosen samples i,j,and k, see above) and only few entries are registered. (2) Inference: DLSampleDLSampleDLSampleDLSampleDLSampledlsample contains only the image. These dictionaries can be passed one at a time or within a tuple.

Depending on the model type, the dictionary DLResultDLResultDLResultDLResultDLResultdlresult can have the following entries:

anomaly_image: AD

Single channel image whose gray values are scores, indicating how likely the corresponding pixel in the input image belongs to an anomaly.

format: image

anomaly_score: AD

Anomaly score on image level calculated from anomaly_image.

format: real

classification_class_ids: CL

Inferred class ids for the image sorted by confidence values.

format: tuple of integers

classification_class_names: CL

Inferred class names for the image sorted by confidence values.

format: tuple of strings

classification_confidences: CL

Confidence values of the image inference for each class.

format: tuple of reals

bbox_class_id: OD

Inferred class for the bounding box (in the form of class_ids).

format: tuple of integers

bbox_confidence: OD

Confidence value of the inference for the bounding box.

format: tuple of reals

bbox_row1: OD:r1 [6]

Inferred bounding boxes: upper left corner, row coordinate.

format: tuple of reals

bbox_col1: OD:r1 [6]

Inferred bounding boxes: upper left corner, column coordinate.

format: tuple of reals

bbox_row2: OD:r1 [6]

Inferred bounding boxes: lower right corner, row coordinate.

format: tuple of reals

bbox_col2: OD:r1 [6]

Inferred bounding boxes: lower right corner, row coordinate.

format: tuple of reals

bbox_row: OD:r2 [6]

Inferred bounding boxes: center point, row coordinate.

format: tuple of reals

bbox_col: OD:r2 [6]

Inferred bounding boxes: center point, column coordinate.

format: tuple of reals

bbox_phi: OD:r2 [6]

Inferred bounding boxes: angle phi.

format: tuple of reals

bbox_length1: OD:r2 [6]

Inferred bounding boxes: half length of edge 1.

format: tuple of reals

bbox_length2: OD:r2 [6]

Inferred bounding boxes: half length of edge 2.

format: tuple of reals

segmentation_image: SE

Image with the segmentation result.

format: image

segmentation_confidence: SE

Image with the confidence values of the segmentation result.

format: image

[6]: Used coordinates: Pixel centered, subpixel accurate coordinates.

For a further explanation to the output values we refer to the chapters of the respective method, e.g., Deep Learning / Semantic Segmentation.

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_paramGetDlModelParamGetDlModelParamGetDlModelParamget_dl_model_param. 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

add_dl_pruning_batchAddDlPruningBatchadd_dl_pruning_batchAddDlPruningBatchadd_dl_pruning_batch

Calculate scores to prune a deep learning model.

apply_dl_modelApplyDlModelapply_dl_modelApplyDlModelapply_dl_model

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

clear_dl_modelClearDlModelclear_dl_modelClearDlModelclear_dl_model

Clear a deep learning model.

create_dl_pruningCreateDlPruningcreate_dl_pruningCreateDlPruningcreate_dl_pruning

Create a pruning data handle.

deserialize_dl_modelDeserializeDlModeldeserialize_dl_modelDeserializeDlModeldeserialize_dl_model

Deserialize a deep learning model.

gen_dl_model_heatmapGenDlModelHeatmapgen_dl_model_heatmapGenDlModelHeatmapgen_dl_model_heatmap

Infer the sample and generate a heatmap.

gen_dl_pruned_modelGenDlPrunedModelgen_dl_pruned_modelGenDlPrunedModelgen_dl_pruned_model

Prune a deep learning model.

get_dl_model_paramGetDlModelParamget_dl_model_paramGetDlModelParamget_dl_model_param

Return the parameters of a deep learning model.

get_dl_pruning_paramGetDlPruningParamget_dl_pruning_paramGetDlPruningParamget_dl_pruning_param

Get information from a pruning data handle.

read_dl_modelReadDlModelread_dl_modelReadDlModelread_dl_model

Read a deep learning model from a file.

serialize_dl_modelSerializeDlModelserialize_dl_modelSerializeDlModelserialize_dl_model

Serialize a deep learning model.

set_dl_model_paramSetDlModelParamset_dl_model_paramSetDlModelParamset_dl_model_param

Set the parameters of a deep learning model.

set_dl_pruning_paramSetDlPruningParamset_dl_pruning_paramSetDlPruningParamset_dl_pruning_param

Set parameter in a pruning data handle.

train_dl_model_batchTrainDlModelBatchtrain_dl_model_batchTrainDlModelBatchtrain_dl_model_batch

Train a deep learning model.

write_dl_modelWriteDlModelwrite_dl_modelWriteDlModelwrite_dl_model

Write a deep learning model in a file.