Model Creation C++ Sample¶

This sample demonstrates how to execute an synchronous inference using model 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.

Options	Values
Validated Models	LeNet
Model Format	model weights file (*.bin)
Validated images	single-channel `MNIST ubyte` images
Supported devices	All
Other language realization	Python

How It Works¶

At startup, the sample application does the following:

Reads command line parameters
Build a Model 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 section of “Integrate OpenVINO™ Runtime with Your Application” guide.

Building¶

To build the sample, please use instructions available at Build the Sample Applications 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 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 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¶

Deprecation Begins	June 1, 2020
Removal Date	December 1, 2020