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* update hello_classification readme

* update classification_async readme

* update hello_query_device readme

* Fix hello_classification launch line

* Update hello_reshape_ssd readme

* update speech sample docs

* update ngraph sample docs

* fix launch command

* refactor py ngraph imports

* Replace `network` with `model`

* update example section with openvino-dev

* Update samples/python/classification_sample_async/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/classification_sample_async/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/hello_classification/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/hello_classification/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/hello_reshape_ssd/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/ngraph_function_creation_sample/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/ngraph_function_creation_sample/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/ngraph_function_creation_sample/README.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update samples/python/ngraph_function_creation_sample/README.md

Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>

* Replace `Inference Engine` with `OpenVINO`

* fix ngraph ref

* Replace `Inference Engine` by `OpenVINO™ Runtime`

* Fix IR mentions

Co-authored-by: Vladimir Dudnik <vladimir.dudnik@intel.com>
Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>
Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
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Dmitry Pigasin
2022-02-14 19:03:45 +03:00
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# Hello Classification Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_classification_README}
This sample demonstrates how to do inference of image classification networks using Synchronous Inference Request API.
This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API.
Models with only 1 input and output are supported.
The following Inference Engine Python API is used in the application:
The following Python API is used in the application:
| Feature | API | Description |
| :----------------- | :-------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------- |
| Basic Infer Flow | [IECore], [IECore.read_network], [IECore.load_network] | Common API to do inference |
| Synchronous Infer | [ExecutableNetwork.infer] | Do synchronous inference |
| Network Operations | [IENetwork.input_info], [IENetwork.outputs], [InputInfoPtr.precision], [DataPtr.precision], [InputInfoPtr.input_data.shape] | Managing of network: configure input and output blobs |
| Feature | API | Description |
| :---------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Basic Infer Flow | [openvino.runtime.Core], [openvino.runtime.Core.read_model], [openvino.runtime.Core.compile_model] | Common API to do inference |
| Synchronous Infer | [openvino.runtime.CompiledModel.infer_new_request] | Do synchronous inference |
| Model Operations | [openvino.runtime.Model.inputs], [openvino.runtime.Model.outputs] | Managing of model |
| Preprocessing | [openvino.preprocess.PrePostProcessor], [openvino.preprocess.InputTensorInfo.set_element_type],[openvino.preprocess.InputTensorInfo.set_layout],[openvino.preprocess.InputTensorInfo.set_spatial_static_shape],[openvino.preprocess.PreProcessSteps.resize],[openvino.preprocess.InputModelInfo.set_layout],[openvino.preprocess.OutputTensorInfo.set_element_type],[openvino.preprocess.PrePostProcessor.build] | Set image of the original size as input for a model with other input size. Resize and layout conversions will be performed automatically by the corresponding plugin just before inference |
| Options | Values |
| :------------------------- | :-------------------------------------------------------------------------------------------------------- |
| 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/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Options | Values |
| :------------------------- | :------------------------------------------------------------------------------------------------------ |
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1) |
| Model Format | OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| 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
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.
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the OpenVINO™ Runtime 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/OV_Runtime_UG/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 OpenVINO™ Runtime with Your Application" guide.
## Running
Run the application with the `-h` option to see the usage message:
```
python <path_to_sample>/hello_classification.py -h
```
Usage message:
```
usage: hello_classification.py [-h] -m MODEL -i INPUT [-d DEVICE]
[--labels LABELS] [-nt NUMBER_TOP]
Options:
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml or .onnx file with a trained
model.
-i INPUT, --input INPUT
Required. Path to an image file.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, MYRIAD, HDDL or HETERO: is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU.
--labels LABELS Optional. Path to a labels mapping file.
-nt NUMBER_TOP, --number_top NUMBER_TOP
Optional. Number of top results.
python hello_classification.py <path_to_model> <path_to_image> <device_name>
```
To run the sample, you need specify a model and image:
@@ -62,28 +38,35 @@ To run the sample, you need specify a model and image:
> **NOTES**:
>
> - By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
> - By default, OpenVINO™ Toolkit Samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Converting a Model](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
>
> - Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
> - Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
### Example
1. Download a pre-trained model using [Model Downloader](@ref omz_tools_downloader):
```
python <path_to_omz_tools>/downloader.py --name alexnet
```
2. If a model is not in the Inference Engine IR or ONNX format, it must be converted. You can do this using the model converter script:
1. Install the `openvino-dev` Python package to use Open Model Zoo Tools:
```
python <path_to_omz_tools>/converter.py --name alexnet
python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]
```
3. Perform inference of `car.bmp` using `alexnet` model on a `GPU`, for example:
2. Download a pre-trained model:
```
omz_downloader --name alexnet
```
3. If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:
```
python <path_to_sample>/hello_classification.py -m <path_to_model>/alexnet.xml -i <path_to_image>/car.bmp -d GPU
omz_converter --name alexnet
```
4. Perform inference of `banana.jpg` using the `alexnet` model on a `GPU`, for example:
```
python hello_classification.py alexnet.xml banana.jpg GPU
```
## Sample Output
@@ -91,43 +74,46 @@ python <path_to_sample>/hello_classification.py -m <path_to_model>/alexnet.xml -
The sample application logs each step in a standard output stream and outputs top-10 inference results.
```
[ INFO ] Creating Inference Engine
[ INFO ] Reading the network: c:\openvino\deployment_tools\open_model_zoo\tools\downloader\public\alexnet\FP32\alexnet.xml
[ INFO ] Configuring input and output blobs
[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: /models/alexnet/alexnet.xml
[ INFO ] Loading the model to the plugin
[ WARNING ] Image c:\images\car.bmp is resized from (637, 749) to (227, 227)
[ INFO ] Starting inference in synchronous mode
[ INFO ] Image path: c:\images\car.bmp
[ INFO ] Top 10 results:
[ INFO ] classid probability
[ INFO ] -------------------
[ INFO ] 656 0.6645315
[ INFO ] 654 0.1121185
[ INFO ] 581 0.0698451
[ INFO ] 874 0.0334973
[ INFO ] 436 0.0259718
[ INFO ] 817 0.0173190
[ INFO ] 675 0.0109321
[ INFO ] 511 0.0109075
[ INFO ] 569 0.0083093
[ INFO ] 717 0.0063173
[ INFO ] Image path: /images/banana.jpg
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 954 0.9703885
[ INFO ] 666 0.0219518
[ INFO ] 659 0.0033120
[ INFO ] 435 0.0008246
[ INFO ] 809 0.0004433
[ INFO ] 502 0.0003852
[ INFO ] 618 0.0002906
[ INFO ] 910 0.0002848
[ INFO ] 951 0.0002427
[ INFO ] 961 0.0002213
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
```
## See Also
- [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)
- [Integrate the OpenVINO™ Runtime with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using OpenVINO™ Toolkit 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)
[IECore]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html
[IECore.read_network]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html#a0d69c298618fab3a08b855442dca430f
[IENetwork.input_info]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IENetwork.html#data_fields
[IENetwork.outputs]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IENetwork.html#data_fields
[InputInfoPtr.precision]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1InputInfoPtr.html#data_fields
[DataPtr.precision]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1DataPtr.html#data_fields
[IECore.load_network]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html#ac9a2e043d14ccfa9c6bbf626cfd69fcc
[InputInfoPtr.input_data.shape]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1InputInfoPtr.html#data_fields
[ExecutableNetwork.infer]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1ExecutableNetwork.html#aea96e8e534c8e23d8b257bad11063519
<!-- [openvino.runtime.Core]:
[openvino.runtime.Core.read_model]:
[openvino.runtime.Core.compile_model]:
[openvino.runtime.CompiledModel.infer_new_request]:
[openvino.runtime.Model.inputs]:
[openvino.runtime.Model.outputs]:
[openvino.preprocess.PrePostProcessor]:
[openvino.preprocess.InputTensorInfo.set_element_type]:
[openvino.preprocess.InputTensorInfo.set_layout]:
[openvino.preprocess.InputTensorInfo.set_spatial_static_shape]:
[openvino.preprocess.PreProcessSteps.resize]:
[openvino.preprocess.InputModelInfo.set_layout]:
[openvino.preprocess.OutputTensorInfo.set_element_type]:
[openvino.preprocess.PrePostProcessor.build]: -->