get_dl_model_param — Return the parameters of a deep learning model.
get_dl_model_param( : : DLModelHandle, GenParamName : GenParamValue)
get_dl_model_param returns the parameter values
GenParamValue of GenParamName for the deep learning
model DLModelHandle.
For a deep learning model, parameters GenParamName can be set using
set_dl_model_param or create_dl_model_detection, depending
on the parameter and the model type.
With this operator, get_dl_model_param, you can retrieve
the parameter values GenParamValue.
Below we give an overview of the different parameters and an explanation,
except of those you can only set. For latter ones, please see the
documentation of corresponding operator.
The parameters are listed for each model type. Thereby, the symbols denote
the model type for which the parameter can be get or set and has a possible
influence, corresponding to the deep learning model methods:
'type'='anomaly_detection' (Anomaly Detection)
'type'='classification' (Classification)
'type'='ocr_recognition' (Deep OCR recognition component)
'type'='gc_anomaly_detection' (Global Context Anomaly Detection)
'type'='detection' (Object Detection, Instance Segmentation)
'type'='segmentation' (Semantic Segmentation, Edge Extraction)
GenParamName |
'AD' | 'CL' | 'DO' | 'GC-AD' | 'OD' | 'SE' |
|---|---|---|---|---|---|---|
| 'adam_beta1' | ||||||
| 'adam_beta2' | ||||||
| 'adam_epsilon' | ||||||
| 'batch_size' | ||||||
| 'batch_size_multiplier' | ||||||
| 'batchnorm_momentum' | ||||||
| 'class_ids' | ||||||
| 'class_names' | ||||||
| 'class_weights' | ||||||
| 'device' | ||||||
| 'enable_resizing' | ||||||
| 'fuse_bn_relu' | ||||||
| 'fuse_conv_relu' | ||||||
| 'gpu' | ||||||
| 'image_dimensions' | ||||||
| 'image_height' | ||||||
| 'image_width' | ||||||
| 'image_num_channels' | ||||||
| 'image_range_max' | ||||||
| 'image_range_min' | ||||||
| 'input_dimensions' | ||||||
| 'layer_names' | ||||||
| 'learning_rate' | ||||||
| 'meta_data' | ||||||
| 'momentum' | ||||||
'num_classes' (NumClasses) |
||||||
| 'num_trainable_params' | ||||||
| 'optimize_for_inference' | ||||||
| 'precision' | ||||||
| 'precision_is_converted' | ||||||
| 'runtime' | ||||||
| 'runtime_init' | ||||||
| 'solver_type' | ||||||
| 'summary' | ||||||
| 'type' | ||||||
| 'weight_prior' |
GenParamName |
'AD' | 'CL' | 'DO' | 'GC-AD' | 'OD' | 'SE' |
|---|---|---|---|---|---|---|
| 'complexity' | ||||||
| 'standard_deviation_factor' | ||||||
| 'extract_feature_maps' | ||||||
| 'alphabet' | ||||||
| 'alphabet_internal' | ||||||
| 'alphabet_mapping' | ||||||
| 'anomaly_score_tolerance' | ||||||
| 'gc_anomaly_networks' | ||||||
| 'patch_size' | ||||||
| 'anchor_angles' | ||||||
| 'anchor_aspect_ratios' | ||||||
| 'anchor_num_subscales' | ||||||
'backbone' (Backbone) |
||||||
| 'backbone_docking_layers' | ||||||
| 'bbox_heads_weight' | ||||||
| 'capacity' | ||||||
| 'class_heads_weight' | ||||||
| 'class_ids_no_orientation' | ||||||
| 'freeze_backbone_level' | ||||||
| 'ignore_direction' | ||||||
| 'instance_segmentation' | ||||||
| 'instance_type' | ||||||
| 'max_level' | ||||||
| 'min_level' | ||||||
| 'max_num_detections' | ||||||
| 'max_overlap' | ||||||
| 'max_overlap_class_agnostic' | ||||||
| 'min_confidence' | ||||||
| 'mask_head_weight' | ||||||
| 'ignore_class_ids' |
Thereby, the symbols denote the following :
's': The parameter can be set using set_dl_model_param.
'g': The parameter can be retrieved using get_dl_model_param.
'c': The parameter can be set using
create_dl_model_detection.
'x': The parameter has no effect for this type of model.
'-': The parameter is not applicable for this type of model.
Certain parameters are set as non-optional parameters, the corresponding notation is given in brackets.
In the following we list and explain the parameters GenParamName
for which you can retrieve their value using this operator,
get_dl_model_param.
They are sorted according to the model type.
Note, for models of 'type'='segmentation' the default
values depend on the specific network and therefore have to be retrieved.
This value defines the moment for the linear term in Adam solver.
For more information we refer to the documentation of
train_dl_model_batch.
Only applicable for 'solver_type' = 'adam'.
Default: 'adam_beta1' = 0.9
This value defines the moment for the quadratic term in Adam solver.
For more information we refer to the documentation of
train_dl_model_batch.
Only applicable for 'solver_type' = 'adam'.
Default: 'adam_beta2' = 0.999
This value defines the epsilon in the Adam solver formula and is
purposed to guarantee the numeric stability.
For more information we refer to the documentation of
train_dl_model_batch.
Only applicable for 'solver_type' = 'adam'.
Default: 'adam_epsilon' = 1e-08
Number of input images (and corresponding labels) in a batch that is transferred to device memory.
For a training using train_dl_model_batch, the batch of images
which are processed simultaneously in a single training iteration
contains a number of images which is equal to 'batch_size'
times 'batch_size_multiplier'. Please refer to
train_dl_model_batch for further details.
For inference, the 'batch_size' can be generally set
independently from the number of input images.
See apply_dl_model for details on how to set this parameter
for greater efficiency.
The parameter 'batch_size' is stored in the pretrained
classifier. Per default, the 'batch_size' is set such
that a training of the pretrained classifier with up to
100 classes can be easily performed on a device with
8 gigabyte of memory.
| pretrained classifier | default value of 'batch_size' |
|---|---|
| 'pretrained_dl_classifier_alexnet.hdl' | 230 |
| 'pretrained_dl_classifier_compact.hdl' | 160 |
| 'pretrained_dl_classifier_enhanced.hdl' | 96 |
| 'pretrained_dl_classifier_mobilenet_v2.hdl' | 40 |
| 'pretrained_dl_classifier_resnet50.hdl' | 23 |
Multiplier for 'batch_size' to enable training with larger
numbers of images in one step which would otherwise not be possible
due to GPU memory limitations. This model parameter does only affect
train_dl_model_batch and thus has no
impact during evaluation and inference.
For detailed information see train_dl_model_batch.
The parameter 'batch_size_multiplier' has no effect.
The parameter 'batch_size_multiplier' has no effect.
Default: 'batch_size_multiplier' = 1
Unique IDs of the classes the model shall distinguish. The tuple is of length 'num_classes'.
We stress out the slightly different meanings and restrictions depending on the model type:
'class_ids' is not supported.
The IDs are unique identifiers, which are automatically assigned to each class. The ID of a class corresponds to the index within the tuple 'class_names'.
Only the classes of the objects to be detected are included and therewith no background class. Thereby, you can set any integer within the interval as class ID value.
Note that the values of 'class_ids_no_orientation' depend on 'class_ids'. Thus if 'class_ids' is changed after the creation of the model, 'class_ids_no_orientation' is reset to an empty tuple.
Default: 'class_ids' = '[0,...,num_classes-1]'
'class_ids' is not supported.
'class_ids' is not supported.
Every class used for training has to be included and therewith also the class ID of the 'background' class. Therefore, for such a model the tuple has a minimal length of 2. Thereby, you can set any integer within the interval