Named model inputs & outputs

Both our library models and any user generated model can be used to trigger contextual pre- and post-processing and easy visualizations depending on named outputs of the initial ONNX model graph.
This mechanism enables automatic configuration to the rest of the AI Manager functionalities by just upload an ONNX model having certain properties, namely the input and output names. 
In other words, by naming an output in a certain way, it'll be, automatically, subject to one or more post-processing algorithm(s). 
For example, if a model has an output named linecrossing_bboxes example, then the line-crossing post-processor will count unique entities, "stored" in that output, passing by a line in both direction.
Below is a summary of the supported output names, and post-processing algorithm each one of them triggers.
licenseplate_bboxes: will be post-processed to return the license plate characters.
linecrossing_bboxes: will be post-processed to return number of unique which crossed the user-specified line in both directions.
barcode_bboxes: will be post-processed to return all recognized bar-codes
alarm: will be used to raise an alarm and upload the image online (depending on how the user configures the AI Manager).
bboxes: will be used in the visualization part in AI Manager (ie. draw the bounding-boxes detected by the model on input image).
scores: will be used to return the most probable class id and its corresponding probability value.
Below we list the named inputs and outputs that provide contextual options and we provide some examples of their use.
The syntax used to name an input or output is:
[input/output name]-[key1:value1];[key2:value2];...;[keyN:valueN]
The key-value pairs are integrated to provide more information about the named IO.
Inputs
If a model contains an input representing an image, then it's named: image-
If a model contains a binary mask, as input, to ignore certain areas of the image,  then that input is named: mask-
To enable a model to raise an alarm when number of occurrences of an object is above a specific threshold, then the model should contain an input named as follows: thresholds-0:class_name1;1:class_name2; ...;K-1:class_nameK
Example: thresholds-0:car;1:person
If a model is fed data from a sensor, then that input is named: sensor-
Outputs
If a model generates bounding-boxes after execution, then the corresponding output is named as follows: bboxes-format:{xyxyc, xyxysc, xyxy};class_id1:class_name1;...;class_idK:class_nameK
Example: bboxes-format:xyxysc;0:person;2:car
xyxysc: means (x1, y1, x2, y2, score, class)
xyxyc: means (x1, y1, x2, y2, class)
xyxy: means (x1, y1, x2, y2)
If a model generates bounding-boxes delimiting barcodes and QR codes, then the corresponding output is named: barcode_bboxes-format:xyxy
The output should have a shaped of [N, 4] to be parsable.
If a model generates bounding-boxes delimiting license plates, then the corresponding ouptut is named: licenseplate_bboxes-format:{xyxy,xyxyxsxyxyxyxy}
Example: licenseplate_bboxes-format:xyxy
xyxy: means (x1, y1, x2, y2)
xyxyxsxyxyxyxy: means (x1, y1, x2, y2, score, ... landmark coordinates)
If a model generates probabilities per class after execution, then the corresponding output is named as follows: scores-0:class_name1; ...;K-1:class_nameK
Example: scores-0:not face;1:face
The scores output is expected to have a shape of [N, K] or [K] where each column i is the probability of that object belonging to the i-th category.
If a model generates number of objects per class after execution, then the corresponding output is named as follows: counts-0:class_name1;1:class_name2; ...;K-1:class_nameK
If a model generates an alarm based on number of objects per class, then the corresponding output is named as follows: alarm-0:class_name1;1:class_name2; ...;K-1:class_nameK
To configure a model's output to be post-processed using the line-crossing counter, then that output is named as follows: linecrossing_bboxes-format:xyxysc
xyxysc: means (x1, y1, x2, y2, score, class)
Examples (as visualized on the ONNX graph using Netron)
A model that locates and counts people and cars has the following IO names:
Input: image-
Output 1: bboxes-format:xyxysc;0:person;2:car
Output 2: counts-0:person;1:car

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A Model that locates, counts, and raises alarms for the people and cars categories, has the following IO names:
Input 1: image-
Input 2: mask-
Input 3: thresholds-0:person;1:car
Output 1: bboxes-format:xyxysc;0:person;2:car
Output 2: counts-0:person;1:car
Output 3: alarm-0:person;1:car
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To allow any user to add our post-processing algorithms to the uploaded model, we provide functions to rename the inputs and outputs of the model to match the predefined naming of the desired functionality.
Below we list the I/O renaming functions that you can use by using our sclblonnx package.
Inputs renaming
rename_input_image(graph, image_input_name): this function take an ".onnx" model and the current name of the image type input then rename it as image-.
rename_input_mask(graph, mask_input_name): this function take an ".onnx" model and the current name of the binary mask input then rename it as mask-.
rename_input_sensor(graph, sensor_input_name): this function take an ".onnx" model and the current name of a sensor input then rename it as sensor-.
rename_input_threshold(graph, threshold_input_name, class_list): this function take an ".onnx" model, the current name of the threshold input and a list of the classes name then rename the input as thresholds-0:class_name1;1:class_name2; ...;K-1:class_nameK.
Outputs renamning
rename_bbox_output(graph, bboxes_output_name, format, class_list): this function take an ".onnx" model, the current name of the bounding boxes output, its format and a list of the classes name then rename the output as bboxes-format:{xyxyc, xyxysc, xyxy};class_id1:class_name1;...;class_idK:class_nameK.
rename_barcode_output(graph, barcode_output_name): this function take an ".onnx" model and the current name of the barcode output then rename the output as barcode_bboxes-format:xyxy.
rename_licenseplate_output(graph, licenseplate_output_name, format): this function take an ".onnx" model, the current name of the licenseplate output and its format then rename the output as licenseplate_bboxes-format:{xyxy,xyxyxsxyxyxyxy}.
rename_class_probabilities_output(graph, output_name, class_list): this function take an ".onnx" model that generates probabilities per class after execution, the current name of its output and a list of the classes name then rename the output as scores-0:class_name1; ...;K-1:class_nameK.
rename_object_count_output(graph, output_name, class_list): this function take an ".onnx" model that generates number of objects per class, the current name of its output and a list of the classes name then rename the output as counts-0:class_name1;1:class_name2; ...;K-1:class_nameK.
rename_alarm_output(graph, output_name, class_list): this function take an ".onnx" model that generates an alarm based on number of objects per class, the current name of its output and a list of the classes name then rename the output as alarm-0:class_name1;1:class_name2; ...;K-1:class_nameK.
rename_linecrossing_bboxes_output(graph, output_name): this function take an ".onnx" model that will be post-processed using the line-crossing counter and the current name of its output then rename it as linecrossing_bboxes-format:xyxysc.
Example
"model_test.onnx" is an onnx model whose input is an image and the output is bounding boxes and we want to rename the I/O, we use thus:
rename_input_image("model_test.onnx", "images:norm")
rename_bbox_output("model_test.onnx", "nms_output", "xy", ['person', 'car', 'bycicle'])
This page is subject to active development until the end of March 2023. In case of any undesired behavior, please reach out to us.
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Last modified 9d ago