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Merge IE & nGraph DG (#10055)

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This commit is contained in:
Ilya Churaev
2022-02-07 06:57:35 +03:00
committed by GitHub
co-authored by Andrey Zaytsev
parent 788fb5c010
commit a2ca1d4499
170 changed files with 395 additions and 671 deletions
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samples/python/classification_sample_async/README.md
+4 -4
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@@ -16,7 +16,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :-------------------------------------------------------------------------------------------------------- |
| Validated Models | [alexnet](@ref omz_models_model_alexnet) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/classification_sample_async/README.md) |
## How It Works
@@ -24,7 +24,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -146,8 +146,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
samples/python/hello_classification/README.md
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@@ -15,7 +15,7 @@ The following Inference Engine Python API is used in the application:
| :------------------------- | :-------------------------------------------------------------------------------------------------------- |
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_classification/README.md), [C](../../c/hello_classification/README.md) |
## How It Works
@@ -23,7 +23,7 @@ The following Inference Engine Python API is used in the application:
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -117,8 +117,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
samples/python/hello_query_device/README.md
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@@ -1,6 +1,6 @@
# Hello Query Device Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README}
This sample demonstrates how to show Inference Engine devices and prints their metrics and default configuration values using [Query Device API feature](../../../docs/IE_DG/InferenceEngine_QueryAPI.md).
This sample demonstrates how to show Inference Engine devices and prints their metrics and default configuration values using [Query Device API feature](../../../docs/OV_Runtime_UG/InferenceEngine_QueryAPI.md).
The following Inference Engine Python API is used in the application:
@@ -11,7 +11,7 @@ The following Inference Engine Python API is used in the application:
| Options | Values |
| :------------------------- | :---------------------------------------------------------------------- |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_query_device/README.md) |
## How It Works
@@ -103,7 +103,7 @@ The application prints all available devices with their supported metrics and de
```
## See Also
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
[IECore]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html
[IECore.get_metric]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html#af1cdf2ecbea6399c556957c2c2fdf8eb
samples/python/hello_reshape_ssd/README.md
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@@ -1,6 +1,6 @@
# Hello Reshape SSD Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README}
This sample demonstrates how to do synchronous inference of object detection networks using [Shape Inference feature](../../../docs/IE_DG/ShapeInference.md).
This sample demonstrates how to do synchronous inference of object detection networks using [Shape Inference feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
Models with only 1 input and output are supported.
The following Inference Engine Python API is used in the application:
@@ -16,7 +16,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :-------------------------------------------------------------------------------------------------------------------------- |
| Validated Models | [mobilenet-ssd](@ref omz_models_model_mobilenet_ssd) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_reshape_ssd/README.md) |
## How It Works
@@ -25,7 +25,7 @@ At startup, the sample application reads command-line parameters, prepares input
As a result, the program creates an output image, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -114,8 +114,8 @@ The sample application logs each step in a standard output stream and creates an
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
samples/python/ngraph_function_creation_sample/README.md
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@@ -1,6 +1,6 @@
# nGraph Function Creation Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_ngraph_function_creation_sample_README}
This sample demonstrates how to execute an inference using [nGraph function feature](../../../docs/nGraph_DG/build_function.md) to create a network that uses weights from LeNet classification network, which is known to work well on digit classification tasks. So you don't need an XML file, the model will be created from the source code on the fly.
This sample demonstrates how to execute an inference using [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) to create a network that uses weights from LeNet classification network, which is known to work well on digit classification tasks. So you don't need an XML file, the model will be created from the source code on the fly.
In addition to regular grayscale images with a digit, the sample also supports single-channel `ubyte` images as an input.
@@ -18,15 +18,15 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| Validated Models | LeNet |
| Model Format | Network weights file (\*.bin) |
| Validated images | The sample uses OpenCV\* to [read input grayscale image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png) or single-channel `ubyte` image |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/ngraph_function_creation_sample/README.md) |
## How It Works
At startup, the sample application reads command-line parameters, prepares input data, creates a network using [nGraph function feature](../../../docs/nGraph_DG/build_function.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
At startup, the sample application reads command-line parameters, prepares input data, creates a network using [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -109,8 +109,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
samples/python/speech_sample/README.md
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@@ -19,7 +19,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :---------------------------------------------------------------------------------------------------- |
| Validated Models | Acoustic model based on Kaldi* neural networks (see [Model Preparation](#model-preparation) section) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin) |
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/speech_sample/README.md) |
## How It Works
@@ -27,7 +27,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
At startup, the sample application reads command-line parameters, loads a specified model and input data to the Inference Engine plugin, performs synchronous inference on all speech utterances stored in the input file, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## GNA-specific details
@@ -238,8 +238,8 @@ The sample application logs each step in a standard output stream.
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)