Displaying information about available classes¶

The Kenning project provides several scripts for assessing information about classes (such as Dataset, ModelWrapper, Optimizer).

Below, we provide an overview of means to display this information.

First, make sure that Kenning is installed:

pip install "kenning @ git+https://github.com/antmicro/kenning.git"

Kenning list¶

kenning list lists all available classes, grouping them by the base class (group of modules).

The script can be executed as follows:

kenning list

This will return a list similar to the one below:

Optimizers (in kenning.optimizers):

    kenning.optimizers.onnx.ONNXCompiler
    kenning.optimizers.tensorflow_optimizers.TensorFlowOptimizer
    kenning.optimizers.tvm.TVMCompiler
    kenning.optimizers.iree.IREECompiler
    kenning.optimizers.tensorflow_pruning.TensorFlowPruningOptimizer
    kenning.optimizers.tensorflow_clustering.TensorFlowClusteringOptimizer
    kenning.optimizers.nni_pruning.NNIPruningOptimizer
    kenning.optimizers.tflite.TFLiteCompiler
    kenning.optimizers.model_inserter.ModelInserter

Datasets (in kenning.datasets):

    kenning.datasets.random_dataset.RandomizedClassificationDataset
    kenning.datasets.coco_dataset.COCODataset2017
    kenning.datasets.open_images_dataset.OpenImagesDatasetV6
    kenning.datasets.helpers.detection_and_segmentation.ObjectDetectionSegmentationDataset
    kenning.datasets.magic_wand_dataset.MagicWandDataset
    kenning.datasets.common_voice_dataset.CommonVoiceDataset
    kenning.datasets.pet_dataset.PetDataset
    kenning.datasets.random_dataset.RandomizedDetectionSegmentationDataset
    kenning.datasets.imagenet_dataset.ImageNetDataset
    kenning.datasets.visual_wake_words_dataset.VisualWakeWordsDataset

Modelwrappers (in kenning.modelwrappers):

    kenning.modelwrappers.instance_segmentation.pytorch_coco.PyTorchCOCOMaskRCNN
    kenning.modelwrappers.object_detection.darknet_coco.TVMDarknetCOCOYOLOV3
    kenning.modelwrappers.instance_segmentation.yolact.YOLACTWithPostprocessing
    kenning.modelwrappers.classification.tensorflow_imagenet.TensorFlowImageNet
    kenning.modelwrappers.instance_segmentation.yolact.YOLACTWrapper
    kenning.modelwrappers.object_detection.yolo_wrapper.YOLOWrapper
    kenning.modelwrappers.frameworks.tensorflow.TensorFlowWrapper
    kenning.modelwrappers.classification.tflite_magic_wand.MagicWandModelWrapper
    kenning.modelwrappers.classification.tflite_person_detection.PersonDetectionModelWrapper
    kenning.modelwrappers.instance_segmentation.yolact.YOLACT
    kenning.modelwrappers.frameworks.pytorch.PyTorchWrapper
    kenning.modelwrappers.classification.tensorflow_pet_dataset.TensorFlowPetDatasetMobileNetV2
    kenning.modelwrappers.object_detection.yolov4.ONNXYOLOV4
    kenning.modelwrappers.classification.pytorch_pet_dataset.PyTorchPetDatasetMobileNetV2

...

The output of the command can be limited by providing one or more positional arguments representing module groups:

optimizers,
runners,
dataproviders,
datasets,
modelwrappers,
onnxconversions,
outputcollectors,
runtimes.

The command can also be used to list available runtimes:

kenning list runtimes

Which will return a list similar to the one below::

Runtimes (in kenning.runtimes):

    kenning.runtimes.iree.IREERuntime
    kenning.runtimes.tflite.TFLiteRuntime
    kenning.runtimes.pytorch.PyTorchRuntime
    kenning.runtimes.tvm.TVMRuntime
    kenning.runtimes.onnx.ONNXRuntime
    kenning.runtimes.renode.RenodeRuntime

More verbose information is available with -v and -vv flags. They will display dependencies, descriptions and other information for each class.

Kenning info¶

kenning info displays more detailed information about a particular class. This information is especially useful when creating JSON scenario configurations. The command displays the following:

docstrings
dependencies, along with information on availability in the current Python environment
supported input and output formats
argument structure used in JSON

Let’s consider a scenario where we want to compose a Kenning flow utilizing a YOLOv4 ModelWrapper. Execute the following command:

kenning info kenning.modelwrappers.object_detection.yolov4.ONNXYOLOV4

This will display all the necessary information about the class:

Class: ONNXYOLOV4

Input/output specification:
* input
  * shape: (1, 3, keyparams['width'], keyparams['height'])
  * dtype: float32
* output
  * shape: (1, 255, (keyparams['width'] // (8 * (2 ** 0))), (keyparams['height'] // (8 * (2 ** 0))))
  * dtype: float32
* output.3
  * shape: (1, 255, (keyparams['width'] // (8 * (2 ** 1))), (keyparams['height'] // (8 * (2 ** 1))))
  * dtype: float32
* output.7
  * shape: (1, 255, (keyparams['width'] // (8 * (2 ** 2))), (keyparams['height'] // (8 * (2 ** 2))))
  * dtype: float32
* detection_output
  * type: List[DetectObject]

Dependencies:
* torch
* numpy
* onnx
* torch.nn.functional

Arguments specification:
* classes
  * argparse_name: --classes
  * convert-type: builtins.str
  * type
    * string
  * description: File containing Open Images class IDs and class names in CSV format to use (can be generated using kenning.scenarios.open_images_classes_extractor) or class type
  * default: coco
* model_path
  * argparse_name: --model-path
  * convert-type: kenning.utils.resource_manager.ResourceURI
  * type
    * string
  * description: Path to the model
  * required: True

Note

By default, the command only performs static code analysis. For example, some values in the input/output specification are expressions because the command did not import or evaluate any values. This is done to allow for missing dependencies.

Loading a class with arguments¶

To gain access to more detailed information, the --load-class-with-args argument can be used. Provided that all dependencies are satisfied, the script will load the verified module to collect more detailed information about available settings.

In the example above, the ONNXYOLOV4 configuration specifies that the model_path argument is required. All dependencies are available as there is no warning message.

To load a class with arguments, run this command:

kenning info kenning.modelwrappers.object_detection.yolov4.ONNXYOLOV4 \
  --load-class-with-args \
  --model-path kenning:///models/detection/yolov4.onnx

Class: ONNXYOLOV4

Input/output specification:
* input
  * shape: (1, 3, 608, 608)
  * dtype: float32
* output
  * shape: (1, 255, 76, 76)
  * dtype: float32
* output.3
  * shape: (1, 255, 38, 38)
  * dtype: float32
* output.7
  * shape: (1, 255, 19, 19)
  * dtype: float32
* detection_output
  * type: List[DetectObject]

Dependencies:
* onnx
* numpy
* torch.nn.functional
* torch

Arguments specification:
* classes
  * argparse_name: --classes
  * convert-type: builtins.str
  * type
    * string
  * description: File containing Open Images class IDs and class names in CSV format to use (can be generated using kenning.scenarios.open_images_classes_extractor) or class type
  * default: coco
* model_path
  * argparse_name: --model-path
  * convert-type: kenning.utils.resource_manager.ResourceURI
  * type
    * string
  * description: Path to the model
  * required: True

Last update: 2024-04-08