Working with Open Model Zoo Models

This Jupyter notebook can be launched on-line, opening an interactive environment in a browser window. You can also make a local installation. Choose one of the following options:

This tutorial shows how to download a model from Open Model Zoo, convert it to OpenVINO™ IR format, show information about the model, and benchmark the model.

Table of contents:

• OpenVINO and Open Model Zoo Tools

• Preparation

  • Model Name

  • Imports

  • Settings and Configuration

• Download a Model from Open Model Zoo

• Convert a Model to OpenVINO IR format

• Get Model Information

• Run Benchmark Tool

  • Benchmark with Different Settings

OpenVINO and Open Model Zoo Tools

OpenVINO and Open Model Zoo tools are listed in the table below.

Tool	Command	Description
Model Downloader	omz_downlo ader	Download models from Open Model Zoo.
Model Converter	omz_conver ter	Convert Open Model Zoo models to OpenVINO’s IR format.
Info Dumper	omz_info_d umper	Print information about Open Model Zoo models.
Benchmark Tool	benchmark_ app	Benchmark model performance by computing inference time.

# Install openvino package
%pip install -q "openvino-dev>=2024.0.0" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu

Note: you may need to restart the kernel to use updated packages.

Preparation

Model Name

Set model_name to the name of the Open Model Zoo model to use in this notebook. Refer to the list of public and Intel pre-trained models for a full list of models that can be used. Set model_name to the model you want to use.

# model_name = "resnet-50-pytorch"
model_name = "mobilenet-v2-pytorch"

Imports

import json
from pathlib import Path

import openvino as ov
from IPython.display import Markdown, display

# Fetch `notebook_utils` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)
from notebook_utils import DeviceNotFoundAlert, NotebookAlert

Settings and Configuration

Set the file and directory paths. By default, this notebook downloads models from Open Model Zoo to the open_model_zoo_models directory in your $HOME directory. On Windows, the $HOME directory is usually c:\users\username, on Linux /home/username. To change the folder, change base_model_dir in the cell below.

The following settings can be changed:

• base_model_dir: Models will be downloaded into the intel and public folders in this directory.

• omz_cache_dir: Cache folder for Open Model Zoo. Specifying a cache directory is not required for Model Downloader and Model Converter, but it speeds up subsequent downloads.

• precision: If specified, only models with this precision will be downloaded and converted.

base_model_dir = Path("model")
omz_cache_dir = Path("cache")
precision = "FP16"

# Check if an GPU is available on this system to use with Benchmark App.
core = ov.Core()
gpu_available = "GPU" in core.available_devices

print(f"base_model_dir: {base_model_dir}, omz_cache_dir: {omz_cache_dir}, gpu_availble: {gpu_available}")

base_model_dir: model, omz_cache_dir: cache, gpu_availble: False

Download a Model from Open Model Zoo

Specify, display and run the Model Downloader command to download the model.

## Uncomment the next line to show help in omz_downloader which explains the command-line options.

# !omz_downloader --help

download_command = f"omz_downloader --name {model_name} --output_dir {base_model_dir} --cache_dir {omz_cache_dir}"
display(Markdown(f"Download command: `{download_command}`"))
display(Markdown(f"Downloading {model_name}..."))
! $download_command

Download command: omz_downloader --name mobilenet-v2-pytorch --output_dir model --cache_dir cache

Downloading mobilenet-v2-pytorch…

################|| Downloading mobilenet-v2-pytorch ||################

========== Downloading model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth

Convert a Model to OpenVINO IR format

Specify, display and run the Model Converter command to convert the model to OpenVINO IR format. Model conversion may take a while. The output of the Model Converter command will be displayed. When the conversion is successful, the last lines of the output will include: [ SUCCESS ] Generated IR version 11 model. For downloaded models that are already in OpenVINO IR format, conversion will be skipped.

## Uncomment the next line to show Help in omz_converter which explains the command-line options.

# !omz_converter --help

convert_command = f"omz_converter --name {model_name} --precisions {precision} --download_dir {base_model_dir} --output_dir {base_model_dir}"
display(Markdown(f"Convert command: `{convert_command}`"))
display(Markdown(f"Converting {model_name}..."))

! $convert_command

Convert command: omz_converter --name mobilenet-v2-pytorch --precisions FP16 --download_dir model --output_dir model

Converting mobilenet-v2-pytorch…

========== Converting mobilenet-v2-pytorch to ONNX
Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-name=mobilenet_v2 --weights=model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth --import-module=torchvision.models --input-shape=1,3,224,224 --output-file=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx --input-names=data --output-names=prob

ONNX check passed successfully.

========== Converting mobilenet-v2-pytorch to IR (FP16)
Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/mobilenet-v2-pytorch/FP16 --model_name=mobilenet-v2-pytorch --input=data '--mean_values=data[123.675,116.28,103.53]' '--scale_values=data[58.624,57.12,57.375]' --reverse_input_channels --output=prob --input_model=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 224, 224]' --compress_to_fp16=True

[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin

Get Model Information

The Info Dumper prints the following information for Open Model Zoo models:

• Model name

• Description

• Framework that was used to train the model

• License URL

• Precisions supported by the model

• Subdirectory: the location of the downloaded model

• Task type

This information can be shown by running omz_info_dumper --name model_name in a terminal. The information can also be parsed and used in scripts.

In the next cell, run Info Dumper and use json to load the information in a dictionary.

model_info_output = %sx omz_info_dumper --name $model_name
model_info = json.loads(model_info_output.get_nlstr())

if len(model_info) > 1:
    NotebookAlert(
        f"There are multiple IR files for the {model_name} model. The first model in the "
        "omz_info_dumper output will be used for benchmarking. Change "
        "`selected_model_info` in the cell below to select a different model from the list.",
        "warning",
    )

model_info

[{'name': 'mobilenet-v2-pytorch',
  'composite_model_name': None,
  'description': 'MobileNet V2 is image classification model pre-trained on ImageNet dataset. This is a PyTorch* implementation of MobileNetV2 architecture as described in the paper "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" <https://arxiv.org/abs/1801.04381>.nThe model input is a blob that consists of a single image of "1, 3, 224, 224" in "RGB" order.nThe model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.',
  'framework': 'pytorch',
  'license_url': 'https://raw.githubusercontent.com/pytorch/vision/master/LICENSE',
  'accuracy_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/accuracy-check.yml',
  'model_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-674/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/model.yml',
  'precisions': ['FP16', 'FP32'],
  'subdirectory': 'public/mobilenet-v2-pytorch',
  'task_type': 'classification',
  'input_info': [{'name': 'data',
    'shape': [1, 3, 224, 224],
    'layout': 'NCHW'}],
  'model_stages': []}]

Having information of the model in a JSON file enables extraction of the path to the model directory, and building the path to the OpenVINO IR file.

selected_model_info = model_info[0]
model_path = base_model_dir / Path(selected_model_info["subdirectory"]) / Path(f"{precision}/{selected_model_info['name']}.xml")
print(model_path, "exists:", model_path.exists())

model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml exists: True

Run Benchmark Tool

By default, Benchmark Tool runs inference for 60 seconds in asynchronous mode on CPU. It returns inference speed as latency (milliseconds per image) and throughput values (frames per second).

## Uncomment the next line to show Help in benchmark_app which explains the command-line options.
# !benchmark_app --help

benchmark_command = f"benchmark_app -m {model_path} -t 15"
display(Markdown(f"Benchmark command: `{benchmark_command}`"))
display(Markdown(f"Benchmarking {model_name} on CPU with async inference for 15 seconds..."))

! $benchmark_command

Benchmark command: benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -t 15

Benchmarking mobilenet-v2-pytorch on CPU with async inference for 15 seconds…

[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 27.49 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     data (node: data) : f32 / [N,C,H,W] / [1,3,224,224]
[ INFO ] Model outputs:
[ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     data (node: data) : u8 / [N,C,H,W] / [1,3,224,224]
[ INFO ] Model outputs:
[ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 145.46 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: main_graph
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
[ INFO ]   NUM_STREAMS: 6
[ INFO ]   AFFINITY: Affinity.CORE
[ INFO ]   INFERENCE_NUM_THREADS: 24
[ INFO ]   PERF_COUNT: NO
[ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]   PERFORMANCE_HINT: THROUGHPUT
[ INFO ]   EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]   ENABLE_CPU_PINNING: True
[ INFO ]   SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]   ENABLE_HYPER_THREADING: True
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]   LOG_LEVEL: Level.NO
[ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
[ INFO ]   KV_CACHE_PRECISION: <Type: 'float16'>
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'data'!. This input will be filled with random values!
[ INFO ] Fill input 'data' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 5.83 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            20130 iterations
[ INFO ] Duration:         15004.99 ms
[ INFO ] Latency:
[ INFO ]    Median:        4.34 ms
[ INFO ]    Average:       4.35 ms
[ INFO ]    Min:           3.10 ms
[ INFO ]    Max:           12.27 ms
[ INFO ] Throughput:   1341.55 FPS

Benchmark with Different Settings

The benchmark_app tool displays logging information that is not always necessary. A more compact result is achieved when the output is parsed with json.

The following cells show some examples of benchmark_app with different parameters. Below are some useful parameters:

• -d A device to use for inference. For example: CPU, GPU, MULTI. Default: CPU.

• -t Time expressed in number of seconds to run inference. Default: 60.

• -api Use asynchronous (async) or synchronous (sync) inference. Default: async.

• -b Batch size. Default: 1.

Run ! benchmark_app --help to get an overview of all possible command-line parameters.

In the next cell, define the benchmark_model() function that calls benchmark_app. This makes it easy to try different combinations. In the cell below that, you display available devices on the system.

Note: In this notebook, benchmark_app runs for 15 seconds to give a quick indication of performance. For more accurate performance, it is recommended to run inference for at least one minute by setting the t parameter to 60 or higher, and run benchmark_app in a terminal/command prompt after closing other applications. Copy the benchmark command and paste it in a command prompt where you have activated the openvino_env environment.

def benchmark_model(model_xml, device="CPU", seconds=60, api="async", batch=1):
    core = ov.Core()
    model_path = Path(model_xml)
    if ("GPU" in device) and ("GPU" not in core.available_devices):
        DeviceNotFoundAlert("GPU")
    else:
        benchmark_command = f"benchmark_app -m {model_path} -d {device} -t {seconds} -api {api} -b {batch}"
        display(Markdown(f"**Benchmark {model_path.name} with {device} for {seconds} seconds with {api} inference**"))
        display(Markdown(f"Benchmark command: `{benchmark_command}`"))

        benchmark_output = %sx $benchmark_command
        print("command ended")
        benchmark_result = [line for line in benchmark_output if not (line.startswith(r"[") or line.startswith("      ") or line == "")]
        print("\n".join(benchmark_result))

core = ov.Core()

# Show devices available for OpenVINO Runtime
for device in core.available_devices:
    device_name = core.get_property(device, "FULL_DEVICE_NAME")
    print(f"{device}: {device_name}")

CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz

You can select inference device using device widget

import ipywidgets as widgets

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

device

Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')

benchmark_model(model_path, device=device.value, seconds=15, api="async")

Benchmark mobilenet-v2-pytorch.xml with CPU for 15 seconds with async inference

Benchmark command: benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -d CPU -t 15 -api async -b 1

command ended