Model Preparation
Contents
Model Preparation#
1. Model files format used by OpenVINO™ Toolkit#
Video Analytics GStreamer plugins utilize Intel® Distribution of OpenVINO™ Toolkit as a back-end for high efficiency-inference on Intel® CPU and accelerators (GPU, VPU, FPGA) and require CNN model to be converted from training framework format (e.g., TensorFlow or Caffe) into a format optimized for inference on the target device. The model format used by OpenVINO™ Toolkit is termed called Intermediate Representation (IR) format and consists of two files:
xml: XML file with a description of the network topology
bin: Binary file (potentially big) with model weights
You can either:
Choose model(s) from the extensive set of pre-trained models available in Open Model Zoo (already in IR format)
Use Model Optimizer from OpenVINO™ Toolkit tool for converting your model from training framework format (e.g., TensorFlow) into IR format
When using a pre-trained model from Open Model Zoo, consider using the Model Downloader tool to facilitate the model downloading process.
In the case of converting a custom model, you can optionally utilize the Post-Training Optimization Toolkit for converting the model into a performance efficient, and more hardware-friendly representation, for example quantize from 32-bit floating point-precision into 8-bit integer precision. This gives a significant performance boost (up to 4x) on some hardware platforms including the CPU, with only a minor accuracy drop.
If you prefer graphical UI over command-line tools, consider the web-based Deep Learning Workbench tool which provides all the functionality mentioned below and more in a web-browser graphical interface
download pre-trained model
convert a model from training framework format
quantize into INT8 precision with accuracy checking
profile per-layer performance
tune hyper parameters for throughput vs latency tradeoff
2. Model pre- and post-processing specification file#
Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework plugins are capable of optionally performing certain pre- and post-processing operations before/after inference.
Pre- and post-processing are configured with the “model-proc” file. Its format and all possible pre- and post-processing configuration parameters are described on the model-proc description page.
Pre-processing is an input data transformation into an appropriate form which a neural network expects it to be. Since Pipeline Framework mostly supports Convolutional Neural Networks, most pre-processing actions are performed under images. Pipeline Framework provides several pre-processing back-ends depending on your use case.
To use one of them, set the pre-process-backend property of the inference element to one from the table below.
Default behavior: If the property is not set, Pipeline Framework will pick
ie if system memory is used in pipeline, vaapi - for GPU memory
(VASurface and DMABuf).
pre-process-backend |
Description |
Memory type in pipeline |
Configurable with model-proc? |
|---|---|---|---|
ie |
Short for “Inference Engine”. It resizes an image with a bilinear algorithm and sets color format which is deduced from current media. All that’s done with capabilities provided by Inference Engine from OpenVINO™ Toolkit. |
System |
No |
opencv |
All power of OpenCV is leveraged for input image pre-processing. Provides a wide variety of operations that can be performed on image. |
System |
Yes |
vaapi |
Should be used in pipelines with GPU memory. Performs mapping to the system memory and uses opencv pre-processor. |
VASurface
and
DMABuf
|
Yes |
vaapi-surface-sharing |
Should be used in pipelines with GPU memory and GPU inference device. Doesn’t perform mapping to the system memory. As a pre-processor, it performs image resize, crop, and sets color format to NV12. |
VASurface
and
DMABuf
|
Partially |
Post-processing is a raw inference results processing using so
called converters. Converters just transform the results from raw output
InferenceEngine::Blob to required representation. Once output blob
is processed and the data is transformed, it is attached to a frame as
meta data.
If there’s no suitable pre- and/or post-processing implementation in DL Streamer, Custom Processing can be used.
3. Specify model files in GStreamer elements#
The .xml model file path specified in mandatory property ‘model’ of GStreamer inference elements gvainference/gvadetect/gvaclassify. For example pipeline with object detection (gvadetect) and classification (gvaclassify)
gvadetect model=MODEL1_FILE_PATH.xml ! gvaclassify model=MODEL1_FILE_PATH.xml
The .bin file expected to be located under same folder as .xml file, same filename but different extension .bin.
Pipeline example including gvadetect and gvaclassify with pre-processing/post-processing rules may look like
gvadetect model=MODEL1_FILE_PATH.xml model-proc=MODEL1_FILE_PATH.json ! gvaclassify model=MODEL2_FILE_PATH.xml model-proc=MODEL2_FILE_PATH.json
4. Workflow diagram#
The following diagram illustrates overall workflow
