278 lines
10 KiB
ReStructuredText
278 lines
10 KiB
ReStructuredText
Classification with ConvNeXt and OpenVINO
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=========================================
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The
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`torchvision.models <https://pytorch.org/vision/stable/models.html>`__
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subpackage contains definitions of models for addressing different
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tasks, including: image classification, pixelwise semantic segmentation,
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object detection, instance segmentation, person keypoint detection,
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video classification, and optical flow. Throughout this notebook we will
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show how to use one of them.
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The ConvNeXt model is based on the `A ConvNet for the
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2020s <https://arxiv.org/abs/2201.03545>`__ paper. The outcome of this
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exploration is a family of pure ConvNet models dubbed ConvNeXt.
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Constructed entirely from standard ConvNet modules, ConvNeXts compete
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favorably with Transformers in terms of accuracy and scalability,
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achieving 87.8% ImageNet top-1 accuracy and outperforming Swin
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Transformers on COCO detection and ADE20K segmentation, while
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maintaining the simplicity and efficiency of standard ConvNets. The
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``torchvision.models`` subpackage
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`contains <https://pytorch.org/vision/main/models/convnext.html>`__
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several pretrained ConvNeXt model. In this tutorial we will use ConvNeXt
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Tiny model.
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Table of contents:
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^^^^^^^^^^^^^^^^^^
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- `Prerequisites <#prerequisites>`__
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- `Get a test image <#get-a-test-image>`__
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- `Get a pretrained model <#get-a-pretrained-model>`__
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- `Define a preprocessing and prepare an input
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data <#define-a-preprocessing-and-prepare-an-input-data>`__
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- `Use the original model to run an
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inference <#use-the-original-model-to-run-an-inference>`__
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- `Convert the model to OpenVINO Intermediate representation
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format <#convert-the-model-to-openvino-intermediate-representation-format>`__
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- `Use the OpenVINO IR model to run an
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inference <#use-the-openvino-ir-model-to-run-an-inference>`__
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Prerequisites\
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-------------------------------------------------------
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.. code:: ipython3
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%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision
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%pip install -q "openvino>=2023.1.0"
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.. parsed-literal::
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Note: you may need to restart the kernel to use updated packages.
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.. parsed-literal::
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Note: you may need to restart the kernel to use updated packages.
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Get a test image
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----------------
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First of all lets get a test
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image from an open dataset.
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.. code:: ipython3
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import urllib.request
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from torchvision.io import read_image
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import torchvision.transforms as transforms
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img_path = 'cats_image.jpeg'
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urllib.request.urlretrieve(
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url='https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg',
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filename=img_path
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)
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image = read_image(img_path)
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display(transforms.ToPILImage()(image))
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.. image:: 125-convnext-classification-with-output_files/125-convnext-classification-with-output_4_0.png
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Get a pretrained model
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----------------------
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Torchvision provides a
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mechanism of `listing and retrieving available
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models <https://pytorch.org/vision/stable/models.html#listing-and-retrieving-available-models>`__.
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.. code:: ipython3
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import torchvision.models as models
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# List available models
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all_models = models.list_models()
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# List of models by type. Classification models are in the parent module.
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classification_models = models.list_models(module=models)
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print(classification_models)
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.. parsed-literal::
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['alexnet', 'convnext_base', 'convnext_large', 'convnext_small', 'convnext_tiny', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'efficientnet_v2_l', 'efficientnet_v2_m', 'efficientnet_v2_s', 'googlenet', 'inception_v3', 'maxvit_t', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3', 'mobilenet_v2', 'mobilenet_v3_large', 'mobilenet_v3_small', 'regnet_x_16gf', 'regnet_x_1_6gf', 'regnet_x_32gf', 'regnet_x_3_2gf', 'regnet_x_400mf', 'regnet_x_800mf', 'regnet_x_8gf', 'regnet_y_128gf', 'regnet_y_16gf', 'regnet_y_1_6gf', 'regnet_y_32gf', 'regnet_y_3_2gf', 'regnet_y_400mf', 'regnet_y_800mf', 'regnet_y_8gf', 'resnet101', 'resnet152', 'resnet18', 'resnet34', 'resnet50', 'resnext101_32x8d', 'resnext101_64x4d', 'resnext50_32x4d', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0', 'squeezenet1_0', 'squeezenet1_1', 'swin_b', 'swin_s', 'swin_t', 'swin_v2_b', 'swin_v2_s', 'swin_v2_t', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', 'vit_b_16', 'vit_b_32', 'vit_h_14', 'vit_l_16', 'vit_l_32', 'wide_resnet101_2', 'wide_resnet50_2']
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We will use ``convnext_tiny``. To get a pretrained model just use
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``models.get_model("convnext_tiny", weights='DEFAULT')`` or a specific
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method of ``torchvision.models`` for this model using `default
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weights <https://pytorch.org/vision/stable/models/generated/torchvision.models.convnext_tiny.html#torchvision.models.ConvNeXt_Tiny_Weights>`__
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that is equivalent to ``ConvNeXt_Tiny_Weights.IMAGENET1K_V1``. If you
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don’t specify ``weight`` or specify ``weights=None`` it will be a random
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initialization. To get all available weights for the model you can call
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``weights_enum = models.get_model_weights("convnext_tiny")``, but there
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is only one for this model. You can find more information how to
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initialize pre-trained models
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`here <https://pytorch.org/vision/stable/models.html#initializing-pre-trained-models>`__.
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.. code:: ipython3
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model = models.convnext_tiny(weights=models.ConvNeXt_Tiny_Weights.DEFAULT)
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Define a preprocessing and prepare an input data
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------------------------------------------------
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You can use
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``torchvision.transforms`` to make a preprocessing or
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use\ `preprocessing transforms from the model
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wight <https://pytorch.org/vision/stable/models.html#using-the-pre-trained-models>`__.
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.. code:: ipython3
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import torch
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preprocess = models.ConvNeXt_Tiny_Weights.DEFAULT.transforms()
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input_data = preprocess(image)
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input_data = torch.stack([input_data], dim=0)
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.. parsed-literal::
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/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).
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warnings.warn(
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Use the original model to run an inference\
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------------------------------------------------------------------------------------
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.. code:: ipython3
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outputs = model(input_data)
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And print results
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.. code:: ipython3
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import urllib.request
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# download class number to class label mapping
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imagenet_classes_file_path = "imagenet_2012.txt"
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urllib.request.urlretrieve(
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url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
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filename=imagenet_classes_file_path
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)
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imagenet_classes = open(imagenet_classes_file_path).read().splitlines()
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def print_results(outputs: torch.Tensor):
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_, predicted_class = outputs.max(1)
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predicted_probability = torch.softmax(outputs, dim=1)[0, predicted_class].item()
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print(f"Predicted Class: {predicted_class.item()}")
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print(f"Predicted Label: {imagenet_classes[predicted_class.item()]}")
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print(f"Predicted Probability: {predicted_probability}")
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.. code:: ipython3
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print_results(outputs)
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.. parsed-literal::
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Predicted Class: 281
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Predicted Label: n02123045 tabby, tabby cat
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Predicted Probability: 0.6184040307998657
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Convert the model to OpenVINO Intermediate representation format
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----------------------------------------------------------------
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OpenVINO supports PyTorch through conversion to OpenVINO Intermediate
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Representation (IR) format. To take the advantage of OpenVINO
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optimization tools and features, the model should be converted using the
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OpenVINO Converter tool (OVC). The ``openvino.convert_model`` function
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provides Python API for OVC usage. The function returns the instance of
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the OpenVINO Model class, which is ready for use in the Python
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interface. However, it can also be saved on disk using
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``openvino.save_model`` for future execution.
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.. code:: ipython3
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from pathlib import Path
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import openvino as ov
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ov_model_xml_path = Path('models/ov_convnext_model.xml')
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if not ov_model_xml_path.exists():
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ov_model_xml_path.parent.mkdir(parents=True, exist_ok=True)
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converted_model = ov.convert_model(model, example_input=torch.randn(1, 3, 224, 224))
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# add transform to OpenVINO preprocessing converting
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ov.save_model(converted_model, ov_model_xml_path)
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else:
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print(f"IR model {ov_model_xml_path} already exists.")
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When the ``openvino.save_model`` function is used, an OpenVINO model is
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serialized in the file system as two files with ``.xml`` and ``.bin``
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extensions. This pair of files is called OpenVINO Intermediate
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Representation format (OpenVINO IR, or just IR) and useful for efficient
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model deployment. OpenVINO IR can be loaded into another application for
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inference using the ``openvino.Core.read_model`` function.
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Select device from dropdown list for running inference using OpenVINO
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.. code:: ipython3
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import ipywidgets as widgets
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core = ov.Core()
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device = widgets.Dropdown(
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options=core.available_devices + ["AUTO"],
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value='AUTO',
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description='Device:',
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disabled=False,
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)
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device
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.. parsed-literal::
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Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
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.. code:: ipython3
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core = ov.Core()
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compiled_model = core.compile_model(ov_model_xml_path, device_name=device.value)
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Use the OpenVINO IR model to run an inference\
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---------------------------------------------------------------------------------------
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.. code:: ipython3
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outputs = compiled_model(input_data)[0]
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print_results(torch.from_numpy(outputs))
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.. parsed-literal::
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Predicted Class: 281
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Predicted Label: n02123045 tabby, tabby cat
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Predicted Probability: 0.6132654547691345
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