Model Preparation

Model Preparation#

Note

When getting started with Intel® DL Streamer, the best way to obtain a collection of models ready for use in video analytics pipelines is to run download_omz_models.sh and download_public_models.sh. These scripts will download models from Open Model Zoo and other sources, handle the necessary conversions and put model files in a directory specified by the MODELS_PATH environment variable.

This way, you will be able to easily perform the most popular tasks, such as object detection and classification, instance segmentation, face localization and many others. For examples of how to set up Intel® DL Streamer pipelines that carry out these functions, please refer to the sample directory.

If you’re interested in designing custom pipelines, make sure to review the Supported Models table for guidance on whether Intel® DL Streamer elements require specific configurations (defined by the model-proc or labels files) for your selected model.

1. Model file 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, NPU, FPGA) and require the model to be converted from the 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 called Intermediate Representation (IR) 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:

  1. Choose model(s) from the extensive set of pre-trained models available in Open Model Zoo (already in IR format)

  2. Use OpenVINO™ Toolkit Model Conversion method 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 Model Optimization and Compression 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.

2. Model pre- and post-processing#

Intel® Deep Learning Streamer (Intel® DL Streamer) plugins are capable of optionally performing certain pre- and post-processing operations before/after inference.

Pre- and post-processing can be configured using:

  • The “model-proc” file (mostly applicable to models from OpenModel zoo).Its format and all possible pre- and post-processing configuration parameters are described on the model-proc description page.

  • The “model_info” inside OpenVINO™ model.xml file with network topology, described on the on the model_info description

Both methods yield same results, but the “model_info” is recommended as it simplifies dynamic deployments (like re-training models with Intel® Geti™) as certain model pre- and post-processing parameters are tightly coupled with model training results.

Pre-processing is an input data transformation into an appropriate form which a neural network expects it to be. Most pre-processing actions are designed for 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, va - for GPU memory (VAMemory 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
va

Should be used in pipelines with GPU memory. Performs mapping to the system memory and uses VA pre-processor.
VAMemory
and
DMABuf

Yes
va-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.
VAMemory

Partially

Post-processing is a raw inference results processing using so called converters. Converters just transform the results from raw output tensors to GStreamer representation 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 must be specified by the mandatory ‘model’ property of GStreamer inference elements - gvainference/gvadetect/gvaclassify. For example, this is a pipeline with object detection (gvadetect) and classification (gvaclassify)

gvadetect model=MODEL1_FILE_PATH.xml ! gvaclassify model=MODEL1_FILE_PATH.xml

The .bin file is expected to be located in the same folder as .xml file, and to have the same filename (with different extension).

An example pipeline 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
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