openvino/samples/cpp/model_creation_sample/README.md

# Model Creation C++ Sample {#openvino_inference_engine_samples_model_creation_sample_README}

This sample demonstrates how to execute an synchronous inference using [model](../../../docs/OV_Runtime_UG/model_representation.md) built on the fly which uses weights from LeNet classification model, which is known to work well on digit classification tasks.

You do not need an XML file to create a model. The API of ov::Model allows creating a model on the fly from the source code.

The following C++ API is used in the application:

| Feature | API | Description |
| :--- | :--- | :--- |
| OpenVINO Runtime Info | `ov::Core::get_versions` | Get device plugins versions |
| Shape Operations | `ov::Output::get_shape`, `ov::Shape::size`, `ov::shape_size`| Operate with shape |
| Tensor Operations | `ov::Tensor::get_byte_size`, `ov::Tensor:data` | Get tensor byte size and its data |
| Model Operations | `ov::set_batch` | Operate with model batch size |
| Infer Request Operations | `ov::InferRequest::get_input_tensor` | Get a input tensor |
| Model creation objects | `ov::opset8::Parameter`, `ov::Node::output`, `ov::opset8::Constant`, `ov::opset8::Convolution`, `ov::opset8::Add`, `ov::opset1::MaxPool`, `ov::opset8::Reshape`, `ov::opset8::MatMul`, `ov::opset8::Relu`, `ov::opset8::Softmax`, `ov::descriptor::Tensor::set_names`, `ov::opset8::Result`, `ov::Model`, `ov::ParameterVector::vector` | Used to construct an OpenVINO model |

Basic OpenVINO™ Runtime API is covered by [Hello Classification C++ sample](../hello_classification/README.md).

| Options | Values |
| :--- | :--- |
| Validated Models | LeNet |
| Model Format | model weights file (\*.bin) |
| Validated images | single-channel `MNIST ubyte` images |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/model_creation_sample/README.md) |

## How It Works

At startup, the sample application does the following:
- Reads command line parameters
- [Build a Model](../../../docs/OV_Runtime_UG/model_representation.md) and passed weights file
- Loads the model and input data 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 OpenVINO™ Runtime with Your Application" guide.

## Building

To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in OpenVINO™ Toolkit Samples guide.

## Running

```
model_creation_sample <path_to_lenet_weights> <device>
```

> **NOTES**:
>
> - you can use LeNet model weights in the sample folder: `lenet.bin` with FP32 weights file
> - The `lenet.bin` with FP32 weights file was generated by the [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) tool from the public LeNet model with the `--input_shape [64,1,28,28]` parameter specified.
>
> The original model is available in the [Caffe* repository](https://github.com/BVLC/caffe/tree/master/examples/mnist) on GitHub\*.


You can do inference of an image using a pre-trained model on a GPU using the following command:

```
model_creation_sample lenet.bin GPU
```

## Sample Output

The sample application logs each step in a standard output stream and outputs top-10 inference results.

```
[ INFO ] OpenVINO Runtime version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ] Device info:
[ INFO ] GPU
[ INFO ] Intel GPU plugin version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ]
[ INFO ] Create model from weights: lenet.bin
[ INFO ] model name: lenet
[ INFO ]     inputs
[ INFO ]         input name: NONE
[ INFO ]         input type: f32
[ INFO ]         input shape: {64, 1, 28, 28}
[ INFO ]     outputs
[ INFO ]         output name: output_tensor
[ INFO ]         output type: f32
[ INFO ]         output shape: {64, 10}
[ INFO ] Batch size is 10
[ INFO ] model name: lenet
[ INFO ]     inputs
[ INFO ]         input name: NONE
[ INFO ]         input type: u8
[ INFO ]         input shape: {10, 28, 28, 1}
[ INFO ]     outputs
[ INFO ]         output name: output_tensor
[ INFO ]         output type: f32
[ INFO ]         output shape: {10, 10}
[ INFO ] Compiling a model for the GPU device
[ INFO ] Create infer request
[ INFO ] Combine images in batch and set to input tensor
[ INFO ] Start sync inference
[ INFO ] Processing output tensor

Top 1 results:

Image 0

classid probability label
------- ----------- -----
0       1.0000000   0

Image 1

classid probability label
------- ----------- -----
1       1.0000000   1

Image 2

classid probability label
------- ----------- -----
2       1.0000000   2

Image 3

classid probability label
------- ----------- -----
3       1.0000000   3

Image 4

classid probability label
------- ----------- -----
4       1.0000000   4

Image 5

classid probability label
------- ----------- -----
5       1.0000000   5

Image 6

classid probability label
------- ----------- -----
6       1.0000000   6

Image 7

classid probability label
------- ----------- -----
7       1.0000000   7

Image 8

classid probability label
------- ----------- -----
8       1.0000000   8

Image 9

classid probability label
------- ----------- -----
9       1.0000000   9

```

## Deprecation Notice

<table>
  <tr>
    <td><strong>Deprecation Begins</strong></td>
    <td>June 1, 2020</td>
  </tr>
  <tr>
    <td><strong>Removal Date</strong></td>
    <td>December 1, 2020</td>
  </tr>
</table>

## See Also

- [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 Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)