Benchmark Python* Tool

This topic demonstrates how to run the Benchmark Python* Tool, which performs inference using convolutional networks. Performance can be measured for two inference modes: synchronous (latency-oriented) and asynchronous (throughput-oriented).

NOTE: This topic describes usage of Python implementation of the Benchmark Tool. For the C++ implementation, refer to Benchmark C++ Tool.

How It Works

Upon start-up, the application reads command-line parameters and loads a network and images/binary files to the Inference Engine plugin, which is chosen depending on a specified device. The number of infer requests and execution approach depend on the mode defined with the -api command-line parameter.

NOTE: By default, Inference Engine samples, tools and 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 sample or 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.

Synchronous API

For synchronous mode, the primary metric is latency. The application creates one infer request and executes the Infer method. A number of executions is defined by one of the two values:

  • Number of iterations defined with the -niter command-line argument
  • Time duration specified with the -t command-line argument
  • Both of them (execution will continue until both conditions are met)
  • Predefined duration if -niter and -t are not specified. Predefined duration value depends on device.

During the execution, the application collects two types of metrics:

  • Latency for each infer request executed with Infer method
  • Duration of all executions

Reported latency value is calculated as mean value of all collected latencies. Reported throughput value is a derivative from reported latency and additionally depends on batch size.

Asynchronous API

For asynchronous mode, the primary metric is throughput in frames per second (FPS). The application creates a certain number of infer requests and executes the StartAsync method. A number of executions is defined by one of the two values:

  • Number of iterations defined with the -niter command-line argument
  • Time duration specified with the -t command-line argument
  • Both of them (execution will continue until both conditions are met)
  • Predefined duration if -niter and -t are not specified. Predefined duration value depends on device.

The infer requests are executed asynchronously. Callback is used to wait for previous execution to complete. The application measures all infer requests executions and reports the throughput metric based on batch size and total execution duration.

Run the Tool

Before running the Benchmark tool, install the requirements:

pip install -r requirements.txt

Notice that the benchmark_app usually produces optimal performance for any device out of the box.

So in most cases you don't need to play the app options explicitly and the plain device name is enough, for example, for CPU:

python3 benchmark_app.py -m <model> -i <input> -d CPU

But it is still may be non-optimal for some cases, especially for very small networks. More details can read in Introduction to Performance Topics.

Running the application with the -h or --help' option yields the following usage message:

usage: benchmark_app.py [-h] [-i PATH_TO_INPUT] -m PATH_TO_MODEL
[-d TARGET_DEVICE]
[-l PATH_TO_EXTENSION] [-c PATH_TO_CLDNN_CONFIG]
[-api {sync,async}] [-niter NUMBER_ITERATIONS]
[-b BATCH_SIZE]
[-stream_output [STREAM_OUTPUT]] [-t TIME]
[-progress [PROGRESS]] [-nstreams NUMBER_STREAMS]
[-nthreads NUMBER_THREADS] [-pin {YES,NO}]
[--exec_graph_path EXEC_GRAPH_PATH]
[-pc [PERF_COUNTS]]
Options:
-h, --help Show this help message and exit.
-i PATH_TO_INPUT, --path_to_input PATH_TO_INPUT
Optional. Path to a folder with images and/or binaries
or to specific image or binary file.
-m PATH_TO_MODEL, --path_to_model PATH_TO_MODEL
Required. Path to an .xml/.onnx/.prototxt file with a
trained model or to a .blob file with a trained
compiled model.
-d TARGET_DEVICE, --target_device TARGET_DEVICE
Optional. Specify a target device to infer on: CPU,
GPU, FPGA, HDDL or MYRIAD.
Use "-d HETERO:<comma separated devices list>" format to specify HETERO plugin.
Use "-d MULTI:<comma separated devices list>" format to specify MULTI plugin.
The application looks for a suitable plugin for the specified device.
-l PATH_TO_EXTENSION, --path_to_extension PATH_TO_EXTENSION
Optional. Required for CPU custom layers. Absolute
path to a shared library with the kernels
implementations.
-c PATH_TO_CLDNN_CONFIG, --path_to_cldnn_config PATH_TO_CLDNN_CONFIG
Optional. Required for GPU custom kernels. Absolute
path to an .xml file with the kernels description.
-api {sync,async}, --api_type {sync,async}
Optional. Enable using sync/async API. Default value
is async.
-niter NUMBER_ITERATIONS, --number_iterations NUMBER_ITERATIONS
Optional. Number of iterations. If not specified, the
number of iterations is calculated depending on a
device.
-b BATCH_SIZE, --batch_size BATCH_SIZE
Optional. Batch size value. If not specified, the
batch size value is determined from IR
-stream_output [STREAM_OUTPUT]
Optional. Print progress as a plain text. When
specified, an interactive progress bar is replaced
with a multiline output.
-t TIME, --time TIME Optional. Time in seconds to execute topology.
-progress [PROGRESS] Optional. Show progress bar (can affect performance
measurement). Default values is "False".
-nstreams NUMBER_STREAMS, --number_streams NUMBER_STREAMS
Optional. Number of streams to use for inference on the CPU/GPU in throughput mode
(for HETERO and MULTI device cases use format <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>).
Default value is determined automatically for a device.
Please note that although the automatic selection usually provides a reasonable performance,
it still may be non-optimal for some cases, especially for very small networks.
-nthreads NUMBER_THREADS, --number_threads NUMBER_THREADS
Number of threads to use for inference on the CPU
(including HETERO and MULTI cases).
-pin {YES,NUMA,NO}, --infer_threads_pinning {YES,NUMA,NO}
Optional. Enable threads->cores ("YES", default), threads->(NUMA)nodes ("NUMA") or completely disable
("NO") CPU threads pinning for CPU-involved inference.
--exec_graph_path EXEC_GRAPH_PATH
Optional. Path to a file where to store executable
graph information serialized.
-pc [PERF_COUNTS], --perf_counts [PERF_COUNTS]
Optional. Report performance counters.

Running the application with the empty list of options yields the usage message given above and an error message.

Application supports topologies with one or more inputs. If a topology is not data sensitive, you can skip the input parameter. In this case, inputs are filled with random values. If a model has only image input(s), please a provide folder with images or a path to an image as input. If a model has some specific input(s) (not images), please prepare a binary file(s), which is filled with data of appropriate precision and provide a path to them as input. If a model has mixed input types, input folder should contain all required files. Image inputs are filled with image files one by one. Binary inputs are filled with binary inputs one by one.

To run the tool, you can use public or Intel's pre-trained models. To download the models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.

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

Examples of Running the Tool

This section provides step-by-step instructions on how to run the Benchmark Tool with the googlenet-v1 public model on CPU or FPGA devices. As an input, the car.png file from the <INSTALL_DIR>/deployment_tools/demo/ directory is used.

NOTE: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.

  1. Download the model. Go to the the Model Downloader directory and run the downloader.py script with specifying the model name and directory to download the model to:
    cd <INSTAL_DIR>/deployment_tools/open_model_zoo/tools/downloader
python3 downloader.py --name googlenet-v1 -o <models_dir>
  1. Convert the model to the Inference Engine IR format. Go to the Model Optimizer directory and run the mo.py script with specifying the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
    cd <INSTALL_DIR>/deployment_tools/model_optimizer
python3 mo.py --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
  1. Run the tool with specifying the <INSTALL_DIR>/deployment_tools/demo/car.png file as an input image, the IR of the googlenet-v1 model and a device to perform inference on. The following commands demonstrate running the Benchmark Tool in the asynchronous mode on CPU and FPGA devices:
    • On CPU:
      python3 benchmark_app.py -m <ir_dir>/googlenet-v1.xml -d CPU -api async -i <INSTALL_DIR>/deployment_tools/demo/car.png --progress true -b 1
    • On FPGA:
      python3 benchmark_app.py -m <ir_dir>/googlenet-v1.xml -d HETERO:FPGA,CPU -api async -i <INSTALL_DIR>/deployment_tools/demo/car.png --progress true -b 1

The application outputs number of executed iterations, total duration of execution, latency and throughput. Additionally, if you set the -pc parameter, the application outputs performance counters. If you set -exec_graph_path, the application reports executable graph information serialized.

Below are fragments of sample output for CPU and FPGA devices:

  • For CPU:
    [Step 8/9] Measuring performance (Start inference asyncronously, 60000 ms duration, 4 inference requests in parallel using 4 streams)
    Progress: |................................| 100.00%
    [Step 9/9] Dumping statistics report
    Progress: |................................| 100.00%
    Count: 4408 iterations
    Duration: 60153.52 ms
    Latency: 51.8244 ms
    Throughput: 73.28 FPS
  • For FPGA:
    [Step 10/11] Measuring performance (Start inference asyncronously, 5 inference requests using 1 streams for CPU, limits: 120000 ms duration)
    Progress: |................................| 100%
    [Step 11/11] Dumping statistics report
    Count: 98075 iterations
    Duration: 120011.03 ms
    Latency: 5.65 ms
    Throughput: 817.22 FPS

See Also