read_dl_model🔗
Short description🔗
read_dl_model — Read a deep learning model from a file.
Signature🔗
read_dl_model( filename.read FileName, out dl_model DLModelHandle )
Description🔗
The operator read_dl_model reads a deep learning model.
Such models have to be in the HALCON format or in the ONNX format
(see the reference below). Restrictions apply to the latter.
As a result, the handle DLModelHandle is returned.
The model is loaded from the file FileName.
This file is thereby searched in the directory $HALCONROOT/dl/
as well as in the currently used directory.
The default HALCON file extension for deep learning networks is
'.hdl'.
Please note that the values of runtime specific parameters are not written
to file, see write_dl_model.
As a consequence, when reading a model, these parameters are initialized
with their default value, see get_dl_model_param.
Models that require a higher HALCON version ('min_version') than
the one currently in use cannot be read. Before writing a model the
'min_version' can be checked with the operator get_dl_model_param.
For further explanations on deep learning models in HALCON, see the chapter Deep Learning / Model.
Reading a Model provided by HALCON🔗
HALCON provides pretrained neural networks for classification and semantic segmentation. These neural networks are good starting points when training a custom network. They have been pretrained on a large image dataset. For anomaly detection, HALCON provides initial models.
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Models for 3D Gripping Point Detection The following network is provided for 3D Gripping Point Detection:
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'pretrained_dl_3d_gripping_point.hdl' The network expects up to 5 images of type
real:-
'image': intensity (gray value) image
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'x': X-image (values need to increase from left to right)
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'y': Y-image (values need to increase from top to bottom)
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'z': Z-image (values need to increase from points close to the sensor to far points; this is for example the case if the data is given in the camera coordinate system)
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'normals': 2D mappings
Additionally, the network requires certain image properties (for all input images mentioned above). The corresponding values can be retrieved with
get_dl_model_param. Here we list the default values:-
'image_width': 640
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'image_height': 480
The network architecture allows changes concerning the image dimensions.
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Models for Anomaly Detection The following networks are provided for anomaly detection:
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'initial_dl_anomaly_medium.hdl' This neural network is designed to be memory and runtime efficient.
The network expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values:-
'image_width': 480
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'image_height': 480
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'image_num_channels': 3
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'image_range_min': -2
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'image_range_max': 2
The network architecture allows changes concerning the image dimensions, but the sizes 'image_width' and 'image_height' have to be multiples of 32 pixels, resulting in a minimum of 32 pixels.
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'initial_dl_anomaly_large.hdl' This neural network is assumed to be better suited for more complex anomaly detection tasks. This comes at the cost of being more time and memory demanding.
The network expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values:-
'image_width': 480
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'image_height': 480
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'image_num_channels': 3
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'image_range_min': -2
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'image_range_max': 2
The network architecture allows changes concerning the image dimensions, but the sizes 'image_width' and 'image_height' have to be multiples of 32 pixels, resulting in a minimum of 32 pixels.
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Models for Global Context Anomaly Detection The following networks are provided for Global Context Anomaly Detection:
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'pretrained_dl_anomaly_global_context.hdl' The network expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values:-
'image_width': 256
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'image_height': 256
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
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Models for Classification The following pretrained neural networks are provided for classification and usable as backbones for detection:
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'pretrained_dl_classifier_alexnet.hdl': This neural network is designed for simple classification tasks. It is characterized by its convolution kernels in the first convolution layers, which are larger than those in other networks with comparable classification performance (e.g., 'pretrained_dl_classifier_compact.hdl'). This may be beneficial for feature extraction.
This classifier expects the images to be of the type
real. Additionally, the network is designed for certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 29 pixels. There is no maximum image size, but large image sizes will increase the memory demand and the runtime significantly. Changing the image size will reinitialize the weights of the fully connected layers and therefore makes a retraining necessary.
Note that one can improve the runtime for this network by fusing the convolution and ReLU layers, see
set_dl_model_paramand the parameter 'fuse_conv_relu'. -
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'pretrained_dl_classifier_compact.hdl': This neural network is designed to be more memory and runtime efficient.
The classifier expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
This network does not contain any fully connected layer. The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 15 pixels.
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'pretrained_dl_classifier_enhanced.hdl': This neural network has more hidden layers than 'pretrained_dl_classifier_compact.hdl' and is therefore assumed to be better suited for more complex classification tasks. This comes at the cost of being more time and memory demanding.
The classifier expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 47 pixels. There is no maximum image size, but large image sizes will increase the memory demand and the runtime significantly. Changing the image size will reinitialize the weights of the fully connected layers and therefore makes a retraining necessary.
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'pretrained_dl_classifier_mobilenet_v2.hdl': This classifier is a small and low-power model, for what reason it is more suitable for mobile and embedded vision applications.
The classifier expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 32 pixels. There is no maximum image size, but large image sizes will increase the memory demand and the runtime significantly.
On the GPU, the network architecture can benefit greatly from special optimizations, without which the network can be significantly slower.
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'pretrained_dl_classifier_resnet18.hdl': As the neural network 'pretrained_dl_classifier_enhanced.hdl', this classifier is suited for more complex tasks. However, due to its special structure, it provides the advantage of making the training more stable and internally more robust. Compared to the neural network 'pretrained_dl_classifier_resnet50.hdl' it is less complex and has faster inference times.
The classifier expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 32 pixels. There is no maximum image size, but large image sizes will increase the memory demand and the runtime significantly. Despite the fully connected layer a change of the image size does not lead to a reinitialization of the weights.
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'pretrained_dl_classifier_resnet50.hdl': As the neural network 'pretrained_dl_classifier_enhanced.hdl', this classifier is suited for more complex tasks. However, due to its special structure, it provides the advantage of making the training more stable and internally more robust.
The classifier expects the images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the classifier has been trained:-
'image_width': 224
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'image_height': 224
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions. 'image_width' and 'image_height' should not be less than 32 pixels. There is no maximum image size, but large image sizes will increase the memory demand and the runtime significantly. Despite the fully connected layer a change of the image size does not lead to a reinitialization of the weights.
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Models for Advanced Object Detection The following pretrained neural networks are provided for advanced object detection:
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'pretrained_dl_detection_rectangle1_small.hdl':
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'pretrained_dl_detection_rectangle1_medium.hdl':
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'pretrained_dl_detection_rectangle1_large.hdl':
All provided models support multi-scale detection. The approach is characterized by feature extraction, multi-scale accumulation and anchor-free prediction. The models differ in the number of trainable parameters. Larger models better capture fine details in large objects. The small model prioritizes speed.
The model expects the input images to be of the type
real. Additionally, the network requires certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values:-
'image_width': 640
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'image_height': 640
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions, but the sizes 'image_width' and 'image_height' have to be multiples of 32 pixels, resulting in a minimum of 32 pixels.
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Models for Semantic Segmentation The following pretrained neural networks are provided for semantic segmentation:
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'pretrained_dl_edge_extractor.hdl': This neural network is designed and pretrained for edge extraction. As a consequence this model is meant for two class problems with one class for edges and one for background.
This network expects the images to be of the type
real. Additionally, the network is designed for certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the model has been trained:-
'image_width': 512
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'image_height': 512
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'image_num_channels': 1
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'image_range_min': -127.0
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'image_range_max': 128.0
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'num_classes': 2
The network architecture allows changes concerning the image dimensions, but the sizes 'image_width' and 'image_height' have to be multiples of 16 pixels, resulting in a minimum of 16 pixels.
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'pretrained_dl_segmentation_compact.hdl': This neural network is designed to handle segmentation tasks with detailed structures and uses only few memory and is runtime efficient.
The network architecture allows changes concerning the image dimensions, but requires a minimum 'image_width' and 'image_height' of 21 pixels.
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'pretrained_dl_segmentation_enhanced.hdl': This neural network has more hidden layers than 'pretrained_dl_segmentation_compact.hdl' and is therefore better suited for segmentation tasks including more complex scenes.
The network architecture allows changes concerning the image dimensions, but requires a minimum 'image_width' and 'image_height' of 47 pixels.
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Models for Deep OCR The following pretrained neural networks are provided for Deep OCR:
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'pretrained_deep_ocr_detection.hdl': This neural network is the default pretrained detection component of a Deep OCR model, but can be retrained, too. It is designed to detect words in images.
This network expects the images to be of the type
real. Additionally, the network is designed for certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the model has been trained:-
'image_width': 1024
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'image_height': 1024
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'image_num_channels': 3
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'image_range_min': -127.0
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'image_range_max': 128.0
The network architecture allows changes concerning the image dimensions 'image_width' and 'image_height'.
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'pretrained_deep_ocr_detection_compact.hdl': This pretrained network is a compact alternative to the default text detection model 'pretrained_deep_ocr_detection.hdl'. It is designed to be more memory and runtime efficient, but it can be less accurate than the default model.
Regarding the input images and image dimensions, this network has the same requirements as the default model 'pretrained_deep_ocr_detection.hdl'.
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'pretrained_deep_ocr_recognition.hdl': This neural network is the default pretrained recognition component of a Deep OCR model, but can be retrained, too. It is designed to recognize words in images that are cropped to a single word.
This network expects the images to be of the type
real. Additionally, the network is designed for certain image properties. The corresponding values can be retrieved withget_dl_model_param. Here we list the default values with which the model has been trained:-
'image_width': 120
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'image_height': 32
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'image_num_channels': 1
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'image_range_min': -1.0
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'image_range_max': 1.0
The network architecture allows changes concerning the image width 'image_width'. The image height 'image_height' cannot be changed. The parameter 'image_width' is very important: its value can be decreased or increased to adapt to the expected lengths of words, e.g., due to the average width per character. A bigger 'image_width' will consume more time and memory resources. The image width 'image_width' may be changed after training.
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'pretrained_deep_ocr_recognition_compact.hdl': This pretrained network is a compact alternative to the default text recognition model 'pretrained_deep_ocr_recognition.hdl'. It is designed to be more memory and runtime efficient, but it can be less accurate than the default model.
Regarding the input images and image dimensions, this network has the same requirements as the default model 'pretrained_deep_ocr_recognition.hdl'.
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Reading a Model in the ONNX Format🔗
You can read in an ONNX model, but there are some points to consider.
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Restrictions Reading in ONNX models with
read_dl_model, some restrictions apply:-
Only models up to ONNX operator set version (OpSetVersion) 13 are supported. For operators using a higher OpSetVersion, support is not guaranteed. For further limitations, please refer to the supported ONNX operations listing.
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Only 32 bit floating point tensors are supported.
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Only models ending with a SoftMax layer are automatically recognized as classifiers. All other models are considered as generic model, thus models of 'type' = 'generic'.
set_dl_model_paramcan be used to change the model type. -
The input graph nodes (images) must be of shape dimension 4: Number of images (='batch_size'), 'num_channels', 'image_height', and 'image_width'.
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Automatic transformations After reading an ONNX model with
read_dl_model, some network transformations are executed automatically:-
Every non-global pooling layer with a resulting feature map of size 1x1 is converted to a global pooling layer. Doing so enables resizable input images. For more information about pooling layer and possible modes of operation, see the
“Solution Guide on Classification”. -
Layer pairs consisting of a convolution layer without activation and a directly connected activation layer with ReLU activation are fused. In order to do so, the output of the convolution layer is only used as input for the activation layer. As a result a convolution layer with activation mode ReLU is obtained. For more information about layers and possible modes of operation, see the
“Solution Guide on Classification”. -
When reading split layers, each split is converted into slice layers. The conversion process creates corresponding slice layers that extract the exact subsections of data. This ensures that the original split functionality is preserved, even though it appears as separate slice operations instead of a single split layer.
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Permutation layers are added before and after elementwise layers in case the ONNX elementwise node`s inputs have different dimensions, to enable broadcasting.
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Permutation layers are added before and after softmax layers in case the ONNX softmax node`s axis attribute is not equal 1.
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Slice layers that follow a shape layer are fused with it, if possible. This requires that the slice layer is the only consumer of the shape layer`s output and that its step size is 1. This improves performance and reduces memory consumption.
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Supported operations ONNX models with the following operations can be read by
read_dl_model:-
'Abs': No restrictions. -
'Acos': No restrictions. -
'Add': No restrictions. -
'ArgMax': The following restrictions apply:-
attribute 'axis': The value must be 1.
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attribute 'keepdims': The value must be 1.
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attribute 'select_last_index': The value must be 0.
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'Asin': No restrictions. -
'Atan': No restrictions. -
'AveragePool': The following restrictions apply:- attribute 'count_include_pad': The value must be 0.
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'BatchNormalization': No restrictions. -
'Ceil': No restrictions. -
'Celu': No restrictions. -
'Clip': The following restrictions apply:-
attribute 'min': The value must be less or equal 'max'.
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attribute 'max': The value must be greater or equal 'min' and less than maximum float number.
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'Concat': No restrictions. -
'Constant': The following restrictions apply:-
attribute 'sparse_value': The attribute is not supported.
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attribute 'value_floats': The attribute is not supported.
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attribute 'value_ints': The attribute is not supported.
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attribute 'value_string': The attribute is not supported.
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attribute 'value_strings': The attribute is not supported.
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'Conv': The following restrictions apply:- attribute 'pads': Padding values greater than or equal to kernel size are not supported.
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'ConvTranspose': The following restrictions apply:-
attribute 'dilations': Only the value '(1, 1)' (no dilations) is supported.
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attribute 'group': Only the value 1 is supported (no grouped transposed convolution).
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attribute 'kernel_shape': Only symmetric kernel shapes are supported.
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attribute 'output_padding': See restrictions mentioned in
create_dl_layer_transposed_convolution. -
attribute 'output_shape': The attribute is not supported.
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attribute 'pads': Padding values greater than or equal to kernel size are not supported.
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attribute 'strides': Only symmetric strides are supported.
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'Cos': No restrictions. -
'Cosh': No restrictions. -
'DepthToSpace': The following restrictions apply:- attribute 'mode': The value must be 'CRD'.
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'Div': No restrictions. -
'Dropout': No restrictions. -
'Elu': No restrictions. -
'Erf': No restrictions. -
'Exp': No restrictions. -
'Floor': No restrictions. -
'Gather': The following restrictions apply:- input 'indices': Indices with rank greater than one are not supported.
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'Gelu': No restrictions. -
'Gemm': The following restrictions apply:-
attribute 'alpha': The value must be 1.
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attribute 'beta': The value must be 1.
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attribute 'transA': The value must be 0.
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'GlobalAveragePool': No restrictions. -
'GlobalMaxPool': The following restrictions apply:- attribute 'dilations': The value must be 1.
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'HardSigmoid': No restrictions. -
'HardSwish': No restrictions. -
'LeakyRelu': No restrictions. -
'Log': No restrictions. -
'LogSoftmax': The following restrictions apply:-
attribute 'axis':
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OpSetVersion < 13: The value must be 1 and the width and height of the input shape must be 1, or the value must be 3.
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OpSetVersion >= 13: No restrictions.
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'LRN': No restrictions. Hint: Attribute 'size' has no effect. -
'MatMul': No restrictions. -
'MaxPool': No restrictions. -
'Mean': No restrictions. -
'Mish': No restrictions. -
'Mul': No restrictions. -
'Neg': No restrictions. -
'Pow': The exponent must be a scalar constant input or initializer. -
'Reciprocal': No restrictions. -
'ReduceL2':-
attribute 'noop_with_empty_axes': The attribute is optional. The value must be 0.
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attribute 'keepdims': The attribute is optional. The value must be 1.
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attribute 'axes': The attribute is optional. If empty reduce all dimensions. In the new opset versions the attribute 'axes' was moved to the inputs.
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'ReduceMax': The following restrictions apply:-
attribute 'axes': The value must be 1.
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attribute 'keepdims': The value must be 1.
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'ReduceSum':-
attribute 'noop_with_empty_axes': The attribute is optional. The value must be 0.
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attribute 'keepdims': The attribute is optional. The value must be 1.
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attribute 'axes': The attribute is optional. If empty reduce all dimensions. In the new opset versions the attribute 'axes' was moved to the inputs.
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'Relu': No restrictions. -
'Resize': The following restrictions apply:-
attribute 'mode': Only the values 'linear', 'bilinear' and 'nearest' are supported.
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attribute 'coordinate_transformation_mode': Only the values 'half_pixel', 'pytorch_half_pixel', 'align_corners' and 'asymmetric' are supported.
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input tensor 'roi': If values are set they have no effect on the inference.
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The attributes 'cubic_coeff_a', 'exclude_outside', 'extrapolation_value', or 'nearest_mode' have no effect.
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'Reshape': The following restrictions apply:- attribute 'allowzero': If the attribute is used its value must be 0.
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'Round': No restrictions. -
'Sigmoid': No restrictions. -
'Sin': No restrictions. -
'Sinh': No restrictions. -
'Slice': No restrictions. -
'Softmax': The following restrictions apply:-
attribute 'axis':
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OpSetVersion < 13: The value must be 1 and the width and height of the input shape must be 1, or the value must be 3.
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OpSetVersion >= 13: No restrictions.
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'Softplus': No restrictions. -
'Softsign': No restrictions. -
'Split': No restrictions. -
'Sqrt': No restrictions. -
'Sub': No restrictions. -
'Sum': No restrictions. -
'Swish': No restrictions. -
'Tan': No restrictions. -
'Tanh': No restrictions. -
'ThresholdedRelu': No restrictions. -
'Transpose': No restrictions.
Moreover the ONNX
'metadata_props'field is supported. It is written to the model parameter 'meta_data'. -
Execution information🔗
Execution information
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Multithreading type: reentrant (runs in parallel with non-exclusive operators).
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Multithreading scope: global (may be called from any thread).
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Processed without parallelization.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Parameters🔗
FileName (input_control) filename.read → (string)
Filename
Default: 'pretrained_dl_classifier_compact.hdl'
List of values: 'initial_dl_anomaly_large.hdl', 'initial_dl_anomaly_medium.hdl', 'pretrained_deep_ocr_detection.hdl', 'pretrained_deep_ocr_detection_compact.hdl', 'pretrained_deep_ocr_recognition.hdl', 'pretrained_deep_ocr_recognition_compact.hdl', 'pretrained_dl_3d_gripping_point.hdl', 'pretrained_dl_anomaly_global_context.hdl', 'pretrained_dl_classifier_alexnet.hdl', 'pretrained_dl_classifier_compact.hdl', 'pretrained_dl_classifier_enhanced.hdl', 'pretrained_dl_classifier_mobilenet_v2.hdl', 'pretrained_dl_classifier_mobilenet_v4_large.hdl', 'pretrained_dl_classifier_mobilenet_v4_medium.hdl', 'pretrained_dl_classifier_mobilenet_v4_small.hdl', 'pretrained_dl_classifier_resnet18.hdl', 'pretrained_dl_classifier_resnet50.hdl', 'pretrained_dl_detection_rectangle1_large.hdl', 'pretrained_dl_detection_rectangle1_medium.hdl', 'pretrained_dl_detection_rectangle1_small.hdl', 'pretrained_dl_edge_extractor.hdl', 'pretrained_dl_segmentation_compact.hdl', 'pretrained_dl_segmentation_enhanced.hdl'
File extension: .hdl, .onnx
DLModelHandle (output_control) dl_model → (handle)
Handle of the deep learning model.
Result🔗
If the parameters are valid, the operator read_dl_model
returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
Combinations with other operators🔗
Combinations
Possible successors
set_dl_model_param, get_dl_model_param, apply_dl_model, train_dl_model_batch, train_dl_model_anomaly_dataset
Alternatives
References🔗
Open Neural Network Exchange (ONNX), https://onnx.ai/
Module🔗
This operator uses dynamic licensing (see the ‘Installation Guide’). Which of the following modules is required depends on the specific usage of the operator:
3D Metrology, OCR/OCV, Deep Learning Enhanced, Deep Learning Professional, Matching