Model Creation Sample — OpenVINO™ documentation (original) (raw)
This sample demonstrates how to run inference using a modelbuilt on the fly that uses weights from the LeNet classification model, which is known to work well on digit classification tasks. You do not need an XML file, the model is created from the source code on the fly. Before using the sample, refer to the following requirements:
- The sample accepts a model weights file (*.bin).
- The sample has been validated with a LeNet model.
- To build the sample, use instructions available at Build the Sample Applicationssection in “Get Started with Samples” guide.
How It Works#
At startup, the sample application reads command-line parameters, builds a modeland passes the weights file. Then, it loads the model and input data to the OpenVINO™ Runtime plugin. Finally, it 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.
Running#
To run the sample, you need to specify model weights and a device.
Python
python model_creation_sample.py
C++
model_creation_sample
Note
- This sample supports models with FP32 weights only.
- The
lenet.binweights file is generated bymodel conversion APIfrom the public LeNet model, with theinput_shape [64,1,28,28]parameter specified. - The original model is available in theCaffe repository on GitHub.
Example#
Python
python model_creation_sample.py lenet.bin GPU
C++
model_creation_sample lenet.bin GPU
Sample Output#
Python
The sample application logs each step in a standard output stream and outputs 10 inference results.
[ INFO ] Creating OpenVINO Runtime Core [ INFO ] Loading the model using openvino with weights from lenet.bin [ INFO ] Loading the model to the plugin [ INFO ] Starting inference in synchronous mode [ INFO ] Top 1 results: [ INFO ] Image 0 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 0 1.0000000 0 [ INFO ] [ INFO ] Image 1 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 1 1.0000000 1 [ INFO ] [ INFO ] Image 2 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 2 1.0000000 2 [ INFO ] [ INFO ] Image 3 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 3 1.0000000 3 [ INFO ] [ INFO ] Image 4 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 4 1.0000000 4 [ INFO ] [ INFO ] Image 5 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 5 1.0000000 5 [ INFO ] [ INFO ] Image 6 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 6 1.0000000 6 [ INFO ] [ INFO ] Image 7 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 7 1.0000000 7 [ INFO ] [ INFO ] Image 8 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 8 1.0000000 8 [ INFO ] [ INFO ] Image 9 [ INFO ] [ INFO ] classid probability label [ INFO ] ------------------------- [ INFO ] 9 1.0000000 9 [ INFO ] [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
C++
The sample application logs each step in a standard output stream and outputs top-10 inference results.
[ INFO ] OpenVINO Runtime version ......... [ INFO ] Build ........... [ INFO ] [ INFO ] Device info: [ INFO ] GPU [ INFO ] Intel GPU plugin version ......... [ INFO ] 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