Merge remote-tracking branch 'upstream/master' into scatter_eu_reference

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md

@@ -7,6 +7,8 @@ Introduction to ONNX
 
 `ONNX <https://github.com/onnx/onnx>`__ is a representation format for deep learning models that allows AI developers to easily transfer models between different frameworks. It is hugely popular among deep learning tools, like PyTorch, Caffe2, Apache MXNet, Microsoft Cognitive Toolkit, and many others.
 
+.. note:: ONNX models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
+
 Converting an ONNX Model
 ########################
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md

@@ -4,6 +4,8 @@
 
 This page provides general instructions on how to convert a model from a PaddlePaddle format to the OpenVINO IR format using Model Optimizer. The instructions are different depending on PaddlePaddle model format.
 
+.. note:: PaddlePaddle models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
+
 Converting PaddlePaddle Model Inference Format
 ##############################################
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -4,6 +4,8 @@
 
 This page provides general instructions on how to run model conversion from a TensorFlow format to the OpenVINO IR format. The instructions are different depending on whether your model was created with TensorFlow v1.X or TensorFlow v2.X.
 
+.. note:: TensorFlow models are supported via :doc:`FrontEnd API <openvino_docs_MO_DG_TensorFlow_Frontend>`. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
+
 To use model conversion API, install OpenVINO Development Tools by following the :doc:`installation instructions <openvino_docs_install_guides_install_dev_tools>`.
 
 Converting TensorFlow 1 Models

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow_Lite.md

@@ -8,7 +8,7 @@ To convert a TensorFlow Lite model, use the ``mo`` script and specify the path t
 
    mo --input_model <INPUT_MODEL>.tflite
 
-.. note:: TensorFlow Lite models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API.
+.. note:: TensorFlow Lite models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
 
 Supported TensorFlow Lite Layers
 ###################################

docs/nbdoc/consts.py

@@ -8,7 +8,7 @@
 
 repo_name = "openvino_notebooks"
 
-artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20230529220816/dist/rst_files/"
+artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20230621220808/dist/rst_files/"
 
 blacklisted_extensions = ['.xml', '.bin']
 

docs/notebooks/001-hello-world-with-output.rst

@@ -5,7 +5,7 @@ This basic introduction to OpenVINO™ shows how to do inference with an
 image classification model.
 
 A pre-trained `MobileNetV3
-model <https://docs.openvino.ai/latest/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
+model <https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
 from `Open Model
 Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used in
 this tutorial. For more information about how OpenVINO IR models are
@@ -18,18 +18,59 @@ Imports
 
 .. code:: ipython3
 
+    from pathlib import Path
+    import sys
+    
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
     from openvino.runtime import Core
+    
+    sys.path.append("../utils")
+    from notebook_utils import download_file
+
+Download the Model and data samples
+-----------------------------------
+
+.. code:: ipython3
+
+    base_artifacts_dir = Path('./artifacts').expanduser()
+    
+    model_name = "v3-small_224_1.0_float"
+    model_xml_name = f'{model_name}.xml'
+    model_bin_name = f'{model_name}.bin'
+    
+    model_xml_path = base_artifacts_dir / model_xml_name
+    
+    base_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/'
+    
+    if not model_xml_path.exists():
+        download_file(base_url + model_xml_name, model_xml_name, base_artifacts_dir)
+        download_file(base_url + model_bin_name, model_bin_name, base_artifacts_dir)
+    else:
+        print(f'{model_name} already downloaded to {base_artifacts_dir}')
+
+
+
+
+.. parsed-literal::
+
+    artifacts/v3-small_224_1.0_float.xml:   0%|          | 0.00/294k [00:00<?, ?B/s]
+
+
+
+.. parsed-literal::
+
+    artifacts/v3-small_224_1.0_float.bin:   0%|          | 0.00/4.84M [00:00<?, ?B/s]
+
 
 Load the Model
 --------------
 
 .. code:: ipython3
 
     ie = Core()
-    model = ie.read_model(model="model/v3-small_224_1.0_float.xml")
+    model = ie.read_model(model=model_xml_path)
     compiled_model = ie.compile_model(model=model, device_name="CPU")
     
     output_layer = compiled_model.output(0)
@@ -47,11 +88,11 @@ Load an Image
     
     # Reshape to model input shape.
     input_image = np.expand_dims(input_image, 0)
-    plt.imshow(image)
+    plt.imshow(image);
 
 
 
-.. image:: 001-hello-world-with-output_files/001-hello-world-with-output_6_0.png
+.. image:: 001-hello-world-with-output_files/001-hello-world-with-output_8_0.png
 
 
 Do Inference

docs/notebooks/001-hello-world-with-output_files/index.html

@@ -1,7 +1,7 @@
 <html>
-<head><title>Index of /projects/ov-notebook/0.1.0-latest/20230529220816/dist/rst_files/001-hello-world-with-output_files/</title></head>
+<head><title>Index of /projects/ov-notebook/0.1.0-latest/20230621220808/dist/rst_files/001-hello-world-with-output_files/</title></head>
 <body bgcolor="white">
-<h1>Index of /projects/ov-notebook/0.1.0-latest/20230529220816/dist/rst_files/001-hello-world-with-output_files/</h1><hr><pre><a href="../">../</a>
-<a href="001-hello-world-with-output_6_0.png">001-hello-world-with-output_6_0.png</a>                30-May-2023 00:09              387941
+<h1>Index of /projects/ov-notebook/0.1.0-latest/20230621220808/dist/rst_files/001-hello-world-with-output_files/</h1><hr><pre><a href="../">../</a>
+<a href="001-hello-world-with-output_8_0.png">001-hello-world-with-output_8_0.png</a>                22-Jun-2023 00:06              387941
 </pre><hr></body>
 </html>