Object Detection SSD Python* Demo, Async API performance showcase

This demo showcases Object Detection with SSD and new Async API. Async API usage can improve overall frame-rate of the application, because rather than wait for inference to complete, the app can continue doing things on the host, while accelerator is busy. Specifically, this demo keeps two parallel infer requests and while the current is processed, the input frame for the next is being captured. This essentially hides the latency of capturing, so that the overall framerate is rather determined by the MAXIMUM(detection time, input capturing time) and not the SUM(detection time, input capturing time).

The technique can be generalized to any available parallel slack, for example, doing inference and simultaneously encoding the resulting (previous) frames or running further inference, like some emotion detection on top of the face detection results. There are important performance caveats though, for example the tasks that run in parallel should try to avoid oversubscribing the shared compute resources. For example, if the inference is performed on the FPGA, and the CPU is essentially idle, than it makes sense to do things on the CPU in parallel. But if the inference is performed say on the GPU, than it can take little gain to do the (resulting video) encoding on the same GPU in parallel, because the device is already busy.

This and other performance implications and tips for the Async API are covered in the Optimization Guide

Other demo objectives are:

How It Works

On the start-up the application reads command line parameters and loads a network to the Inference Engine. Upon getting a frame from the OpenCV VideoCapture it performs inference and displays the results.

New "Async API" operates with new notion of the "Infer Request" that encapsulates the inputs/outputs and separates scheduling and waiting for result, next section. And here what makes the performance look different:

  1. In the default ("Sync") mode the frame is captured and then immediately processed, below in pseudo-code:
    while(true) {
    capture frame
    populate CURRENT InferRequest
    start CURRENT InferRequest //this call is async and returns immediately
    wait for the CURRENT InferRequest
    display CURRENT result
    So, this is rather reference implementation, where the new Async API is used in the serialized/synch fashion.
  2. In the "true" ASync mode the frame is captured and then immediately processed:
    while(true) {
    capture frame
    populate NEXT InferRequest
    start NEXT InferRequest //this call is async and returns immediately
    wait for the CURRENT InferRequest (processed in a dedicated thread)
    display CURRENT result
    swap CURRENT and NEXT InferRequests
    In this case, the NEXT request is populated in the main (app) thread, while the CURRENT request is processed (this is handled in the dedicated thread, internal to the IE runtime).

NOTE: By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Async API

The Inference Engine offers new API based on the notion of Infer Requests. One specific usability upside is that the requests encapsulate the inputs and outputs allocation, so you just need to access the blob with GetBlob method.

More importantly, you can execute a request asynchronously (in the background) and wait until ready, when the result is actually needed. In a mean time your app can continue :

// load plugin for the device as usual
InferencePlugin plugin = PluginDispatcher({"../../../lib/intel64", ""}).getSuitablePlugin(
// load network
CNNNetReader network_reader;
// populate inputs etc
auto input = async_infer_request.GetBlob(input_name);
// start the async infer request (puts the request to the queue and immediately returns)
// here you can continue execution on the host until results of the current request are really needed
auto output = async_infer_request.GetBlob(output_name);

Notice that there is no direct way to measure execution time of the infer request that is running asynchronously, unless you measure the Wait executed immediately after the StartAsync. But this essentially would mean the serialization and synchronous execution. This is what demo does for the default "SYNC" mode and reports as the "Detection time/fps" message on the screen. In the truly asynchronous ("ASYNC") mode the host continues execution in the master thread, in parallel to the infer request. And if the request is completed earlier than the Wait is called in the main thread (i.e. earlier than OpenCV decoded a new frame), that reporting the time between StartAsync and Wait would obviously incorrect. That is why in the "ASYNC" mode the inference speed is not reported.

For more details on the requests-based Inference Engine API, including the Async execution, refer to Integrate the Inference Engine New Request API with Your Application.


Run the application with the -h option to see the usage message:

python3 object_detection_demo_ssd_async.py -h

The command yields the following usage message:

usage: object_detection_demo_ssd_async.py [-h] -m MODEL -i INPUT
[--labels LABELS] [-pt PROB_THRESHOLD]
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-i INPUT, --input INPUT
Required. Path to video file or image. 'cam' for
capturing video stream from camera
Optional. Required for CPU custom layers. Absolute
path to a shared library with the kernels
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
look for a suitable plugin for device specified.
Default value is CPU
--labels LABELS Optional. Path to labels mapping file
Optional. Probability threshold for detections
--no_show Optional. Don't show output
Optional. List of monitors to show initially.

Running the application with the empty list of options yields the usage message given above and an error message. You can use the following command to do inference on GPU with a pre-trained object detection model:

python3 object_detection_demo_ssd_async.py -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/ssd.xml -d GPU

To run the demo, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO Model Downloader or from https://download.01.org/opencv/.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

The only GUI knob is using Tab to switch between the synchronized execution and the true Async mode.

Demo Output

The demo uses OpenCV to display the resulting frame with detections (rendered as bounding boxes and labels, if provided). In the default mode the demo reports

See Also