From b7e8338e29f32528bfd2dc9d2045dd90f2b20e00 Mon Sep 17 00:00:00 2001 From: Sebastian Golebiewski Date: Tue, 18 Jul 2023 14:51:23 +0200 Subject: [PATCH] update (#18615) --- docs/notebooks/108-gpu-device-with-output.rst | 1295 ----------------- docs/notebooks/115-async-api-with-output.rst | 2 +- .../211-speech-to-text-with-output.rst | 2 +- ...-detection-and-recognition-with-output.rst | 2 +- ...nsorflow-training-openvino-with-output.rst | 3 +- .../402-pose-estimation-with-output.rst | 2 +- ...-action-recognition-webcam-with-output.rst | 2 +- .../404-style-transfer-with-output.rst | 2 +- .../406-3D-pose-estimation-with-output.rst | 2 +- docs/tutorials.md | 2 - 10 files changed, 8 insertions(+), 1306 deletions(-) delete mode 100644 docs/notebooks/108-gpu-device-with-output.rst diff --git a/docs/notebooks/108-gpu-device-with-output.rst b/docs/notebooks/108-gpu-device-with-output.rst deleted file mode 100644 index 487cb42531d..00000000000 --- a/docs/notebooks/108-gpu-device-with-output.rst +++ /dev/null @@ -1,1295 +0,0 @@ -Working with GPUs in OpenVINO™ -============================== - -This tutorial provides a high-level overview of working with Intel GPUs -in OpenVINO. It shows how to use Query Device to list system GPUs and -check their properties, and it explains some of the key properties. It -shows how to compile a model on GPU with performance hints and how to -use multiple GPUs using MULTI or CUMULATIVE_THROUGHPUT. - -The tutorial also shows example commands for benchmark_app that can be -run to compare GPU performance in different configurations. It also -provides the code for a basic end-to-end application that compiles a -model on GPU and uses it to run inference. - -Introduction ------------- - -Originally, graphic processing units (GPUs) began as specialized chips, -developed to accelerate the rendering of computer graphics. In contrast -to CPUs, which have few but powerful cores, GPUs have many more -specialized cores, making them ideal for workloads that can be -parallelized into simpler tasks. Nowadays, one such workload is deep -learning, where GPUs can easily accelerate inference of neural networks -by splitting operations across multiple cores. - -OpenVINO supports inference on Intel integrated GPUs (which are included -with most `Intel® Core™ desktop and mobile -processors `__) -or on Intel discrete GPU products like the `Intel® Arc™ A-Series -Graphics -cards `__ -and `Intel® Data Center GPU Flex -Series `__. -To get started, first `install -OpenVINO `__ -on a system equipped with one or more Intel GPUs. Follow the `GPU -configuration -instructions `__ -to configure OpenVINO to work with your GPU. Then, read on to learn how -to accelerate inference with GPUs in OpenVINO! - -Install required packages -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - !pip install -q "openvino-dev>=2023.0.0" - !pip install -q tensorflow - - # Fetch `notebook_utils` module - import urllib.request - urllib.request.urlretrieve( - url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py', - filename='notebook_utils.py' - ) - - - - -.. parsed-literal:: - - ('notebook_utils.py', ) - - - -Checking GPUs with Query Device -------------------------------- - -In this section, we will see how to list the available GPUs and check -their properties. Some of the key properties will also be defined. - -List GPUs with core.available_devices -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -OpenVINO Runtime provides the ``available_devices`` method for checking -which devices are available for inference. The following code will -output a list of compatible OpenVINO devices, in which Intel GPUs should -appear. - -.. code:: ipython3 - - from openvino.runtime import Core - - core = Core() - core.available_devices - - - - -.. parsed-literal:: - - ['CPU', 'GPU'] - - - -Note that GPU devices are numbered starting at 0, where the integrated -GPU always takes the id ``0`` if the system has one. For instance, if -the system has a CPU, an integrated and discrete GPU, we should expect -to see a list like this: ``['CPU', 'GPU.0', 'GPU.1']``. To simplify its -use, the “GPU.0” can also be addressed with just “GPU”. For more -details, see the `Device Naming -Convention `__ -section. - -If the GPUs are installed correctly on the system and still do not -appear in the list, follow the steps described -`here `__ -to configure your GPU drivers to work with OpenVINO. Once we have the -GPUs working with OpenVINO, we can proceed with the next sections. - -Check Properties with core.get_property -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -To get information about the GPUs, we can use device properties. In -OpenVINO, devices have properties that describe their characteristics -and configuration. Each property has a name and associated value that -can be queried with the ``get_property`` method. - -To get the value of a property, such as the device name, we can use the -``get_property`` method as follows: - -.. code:: ipython3 - - device = "GPU" - - core.get_property(device, "FULL_DEVICE_NAME") - - - - -.. parsed-literal:: - - 'Intel(R) Graphics [0x46a6] (iGPU)' - - - -Each device also has a specific property called -``SUPPORTED_PROPERTIES``, that enables viewing all the available -properties in the device. We can check the value for each property by -simply looping through the dictionary returned by -``core.get_property("GPU", "SUPPORTED_PROPERTIES")`` and then querying -for that property. - -.. code:: ipython3 - - print(f"{device} SUPPORTED_PROPERTIES:\n") - supported_properties = core.get_property(device, "SUPPORTED_PROPERTIES") - indent = len(max(supported_properties, key=len)) - - for property_key in supported_properties: - if property_key not in ('SUPPORTED_METRICS', 'SUPPORTED_CONFIG_KEYS', 'SUPPORTED_PROPERTIES'): - try: - property_val = core.get_property(device, property_key) - except TypeError: - property_val = 'UNSUPPORTED TYPE' - print(f"{property_key:<{indent}}: {property_val}") - - -.. parsed-literal:: - - GPU SUPPORTED_PROPERTIES: - - AVAILABLE_DEVICES : ['0'] - RANGE_FOR_ASYNC_INFER_REQUESTS: (1, 2, 1) - RANGE_FOR_STREAMS : (1, 2) - OPTIMAL_BATCH_SIZE : 1 - MAX_BATCH_SIZE : 1 - CACHING_PROPERTIES : {'GPU_UARCH_VERSION': 'RO', 'GPU_EXECUTION_UNITS_COUNT': 'RO', 'GPU_DRIVER_VERSION': 'RO', 'GPU_DEVICE_ID': 'RO'} - DEVICE_ARCHITECTURE : GPU: v12.0.0 - FULL_DEVICE_NAME : Intel(R) Graphics [0x46a6] (iGPU) - DEVICE_UUID : UNSUPPORTED TYPE - DEVICE_TYPE : Type.INTEGRATED - DEVICE_GOPS : UNSUPPORTED TYPE - OPTIMIZATION_CAPABILITIES : ['FP32', 'BIN', 'FP16', 'INT8'] - GPU_DEVICE_TOTAL_MEM_SIZE : UNSUPPORTED TYPE - GPU_UARCH_VERSION : 12.0.0 - GPU_EXECUTION_UNITS_COUNT : 96 - GPU_MEMORY_STATISTICS : UNSUPPORTED TYPE - PERF_COUNT : False - MODEL_PRIORITY : Priority.MEDIUM - GPU_HOST_TASK_PRIORITY : Priority.MEDIUM - GPU_QUEUE_PRIORITY : Priority.MEDIUM - GPU_QUEUE_THROTTLE : Priority.MEDIUM - GPU_ENABLE_LOOP_UNROLLING : True - CACHE_DIR : - PERFORMANCE_HINT : PerformanceMode.UNDEFINED - COMPILATION_NUM_THREADS : 20 - NUM_STREAMS : 1 - PERFORMANCE_HINT_NUM_REQUESTS : 0 - INFERENCE_PRECISION_HINT : - DEVICE_ID : 0 - - -Brief Descriptions of Key Properties -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Each device has several properties as seen in the last command. Some of -the key properties are: - -- ``FULL_DEVICE_NAME`` - The product name of the GPU and whether it is - an integrated or discrete GPU (iGPU or dGPU). -- ``OPTIMIZATION_CAPABILITIES`` - The model data types (INT8, FP16, - FP32, etc) that are supported by this GPU. -- ``GPU_EXECUTION_UNITS_COUNT`` - The execution cores available in the - GPU’s architecture, which is a relative measure of the GPU’s - processing power. -- ``RANGE_FOR_STREAMS`` - The number of processing streams available on - the GPU that can be used to execute parallel inference requests. When - compiling a model in LATENCY or THROUGHPUT mode, OpenVINO will - automatically select the best number of streams for low latency or - high throughput. -- ``PERFORMANCE_HINT`` - A high-level way to tune the device for a - specific performance metric, such as latency or throughput, without - worrying about device-specific settings. -- ``CACHE_DIR`` - The directory where the model cache data is stored to - speed up compilation time. - -To learn more about devices and properties, see the `Query Device -Properties `__ -page. - -Compiling a Model on GPU ------------------------- - -Now, we know how to list the GPUs in the system and check their -properties. We can easily use one for compiling and running models with -OpenVINO `GPU -plugin `__. - -Download and Convert a Model -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -This tutorial uses the ``ssdlite_mobilenet_v2`` model. The -``ssdlite_mobilenet_v2`` model is used for object detection. The model -was trained on `Common Objects in Context -(COCO) `__ dataset version with 91 -categories of object. For details, see the -`paper `__. - -Download and unpack the Model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Use the ``download_file`` function from the ``notebook_utils`` to -download an archive with the model. It automatically creates a directory -structure and downloads the selected model. This step is skipped if the -package is already downloaded. - -.. code:: ipython3 - - import sys - import tarfile - from pathlib import Path - - sys.path.append("../utils") - - import notebook_utils as utils - - # A directory where the model will be downloaded. - base_model_dir = Path("./model").expanduser() - - model_name = "ssdlite_mobilenet_v2" - archive_name = Path(f"{model_name}_coco_2018_05_09.tar.gz") - - # Download the archive - downloaded_model_path = base_model_dir / archive_name - if not downloaded_model_path.exists(): - model_url = f"http://download.tensorflow.org/models/object_detection/{archive_name}" - utils.download_file(model_url, downloaded_model_path.name, downloaded_model_path.parent) - - # Unpack the model - tf_model_path = base_model_dir / archive_name.with_suffix("").stem / "frozen_inference_graph.pb" - if not tf_model_path.exists(): - with tarfile.open(downloaded_model_path) as file: - file.extractall(base_model_dir) - - - -.. parsed-literal:: - - model/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz: 0%| | 0.00/48.7M [00:00`__. - -.. code:: ipython3 - - from openvino.tools import mo - from openvino.runtime import serialize - from openvino.tools.mo.front import tf as ov_tf_front - - precision = 'FP16' - - # The output path for the conversion. - model_path = base_model_dir / 'ir_model' / f'{model_name}_{precision.lower()}.xml' - - trans_config_path = Path(ov_tf_front.__file__).parent / "ssd_v2_support.json" - pipeline_config = base_model_dir / archive_name.with_suffix("").stem / "pipeline.config" - - model = None - if not model_path.exists(): - model = mo.convert_model(input_model=tf_model_path, - input_shape=[1, 300, 300, 3], - layout='NHWC', - compress_to_fp16=True if precision == 'FP16' else False, - transformations_config=trans_config_path, - tensorflow_object_detection_api_pipeline_config=pipeline_config, - reverse_input_channels=True) - serialize(model, str(model_path)) - print("IR model saved to {}".format(model_path)) - else: - print("Read IR model from {}".format(model_path)) - model = core.read_model(model_path) - - -.. parsed-literal:: - - [ WARNING ] The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept. - - -.. parsed-literal:: - - IR model saved to model/ir_model/ssdlite_mobilenet_v2_fp16.xml - - -Compile with Default Configuration -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -When the model is ready, first we need to read it, using the -``read_model`` method. Then, we can use the ``compile_model`` method and -specify the name of the device we want to compile the model on, in this -case, “GPU”. - -.. code:: ipython3 - - compiled_model = core.compile_model(model, device) - -If you have multiple GPUs in the system, you can specify which one to -use by using “GPU.0”, “GPU.1”, etc. Any of the device names returned by -the ``available_devices`` method are valid device specifiers. You may -also use “AUTO”, which will automatically select the best device for -inference (which is often the GPU). To learn more about AUTO plugin, -visit the `Automatic Device -Selection `__ -page as well as the `AUTO device -tutorial `__. - -Reduce Compile Time through Model Caching -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Depending on the model used, device-specific optimizations and network -compilations can cause the compile step to be time-consuming, especially -with larger models, which may lead to bad user experience in the -application, in which they are used. To solve this, OpenVINO can cache -the model once it is compiled on supported devices and reuse it in later -``compile_model`` calls by simply setting a cache folder beforehand. For -instance, to cache the same model we compiled above, we can do the -following: - -.. code:: ipython3 - - import time - from pathlib import Path - - # Create cache folder - cache_folder = Path("cache") - cache_folder.mkdir(exist_ok=True) - - start = time.time() - core = Core() - - # Set cache folder - core.set_property({'CACHE_DIR': cache_folder}) - - # Compile the model as before - model = core.read_model(model=model_path) - compiled_model = core.compile_model(model, device) - print(f"Cache enabled (first time) - compile time: {time.time() - start}s") - - -.. parsed-literal:: - - Cache enabled (first time) - compile time: 1.692436695098877s - - -To get an idea of the effect that caching can have, we can measure the -compile times with caching enabled and disabled as follows: - -.. code:: ipython3 - - start = time.time() - core = Core() - core.set_property({'CACHE_DIR': 'cache'}) - model = core.read_model(model=model_path) - compiled_model = core.compile_model(model, device) - print(f"Cache enabled - compile time: {time.time() - start}s") - - start = time.time() - core = Core() - model = core.read_model(model=model_path) - compiled_model = core.compile_model(model, device) - print(f"Cache disabled - compile time: {time.time() - start}s") - - -.. parsed-literal:: - - Cache enabled - compile time: 0.26888394355773926s - Cache disabled - compile time: 1.982884168624878s - - -The actual time improvements will depend on the environment as well as -the model being used but it is definitely something to consider when -optimizing an application. To read more about this, see the `Model -Caching `__ -docs. - -Throughput and Latency Performance Hints -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -To simplify device and pipeline configuration, OpenVINO provides -high-level performance hints that automatically set the batch size and -number of parallel threads to use for inference. The “LATENCY” -performance hint optimizes for fast inference times while the -“THROUGHPUT” performance hint optimizes for high overall bandwidth or -FPS. - -To use the “LATENCY” performance hint, add -``{"PERFORMANCE_HINT": "LATENCY"}`` when compiling the model as shown -below. For GPUs, this automatically minimizes the batch size and number -of parallel streams such that all of the compute resources can focus on -completing a single inference as fast as possible. - -.. code:: ipython3 - - compiled_model = core.compile_model(model, device, {"PERFORMANCE_HINT": "LATENCY"}) - -To use the “THROUGHPUT” performance hint, add -``{"PERFORMANCE_HINT": "THROUGHPUT"}`` when compiling the model. For -GPUs, this creates multiple processing streams to efficiently utilize -all the execution cores and optimizes the batch size to fill the -available memory. - -.. code:: ipython3 - - compiled_model = core.compile_model(model, device, {"PERFORMANCE_HINT": "THROUGHPUT"}) - -Using Multiple GPUs with Multi-Device and Cumulative Throughput -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The latency and throughput hints mentioned above are great and can make -a difference when used adequately but they usually use just one device, -either due to the `AUTO -plugin `__ -or by manual specification of the device name as above. When we have -multiple devices, such as an integrated and discrete GPU, we may use -both at the same time to improve the utilization of the resources. In -order to do this, OpenVINO provides a virtual device called -`MULTI `__, -which is just a combination of the existent devices that knows how to -split inference work between them, leveraging the capabilities of each -device. - -As an example, if we want to use both integrated and discrete GPUs and -the CPU at the same time, we can compile the model as follows: - -``compiled_model = core.compile_model(model=model, device_name="MULTI:GPU.1,GPU.0,CPU")`` - -Note that we always need to explicitly specify the device list for MULTI -to work, otherwise MULTI does not know which devices are available for -inference. However, this is not the only way to use multiple devices in -OpenVINO. There is another performance hint called -“CUMULATIVE_THROUGHPUT” that works similar to MULTI, except it uses the -devices automatically selected by AUTO. This way, we do not need to -manually specify devices to use. Below is an example showing how to use -“CUMULATIVE_THROUGHPUT”, equivalent to the MULTI one: - -``compiled_model = core.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT"})`` - - **Important**: **The “THROUGHPUT”, “MULTI”, and - “CUMULATIVE_THROUGHPUT” modes are only applicable to asynchronous - inferencing pipelines. The example at the end of this article shows - how to set up an asynchronous pipeline that takes advantage of - parallelism to increase throughput.** To learn more, see - `Asynchronous - Inferencing `__ - in OpenVINO as well as the `Asynchronous Inference - notebook `__. - -Performance Comparison with benchmark_app ------------------------------------------ - -Given all the different options available when compiling a model, it may -be difficult to know which settings work best for a certain application. -Thankfully, OpenVINO provides ``benchmark_app`` - a performance -benchmarking tool. - -The basic syntax of ``benchmark_app`` is as follows: - -``benchmark_app -m PATH_TO_MODEL -d TARGET_DEVICE -hint {throughput,cumulative_throughput,latency,none}`` - -where ``TARGET_DEVICE`` is any device shown by the ``available_devices`` -method as well as the MULTI and AUTO devices we saw previously, and the -value of hint should be one of the values between brackets. - -Note that benchmark_app only requires the model path to run but both the -device and hint arguments will be useful to us. For more advanced -usages, the tool itself has other options that can be checked by running -``benchmark_app -h`` or reading the -`docs `__. -The following example shows how to benchmark a simple model, using a GPU -with a latency focus: - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d GPU -hint latency - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 14.02 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ INFO ] Compile model took 1932.50 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: frozen_inference_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1 - [ INFO ] PERF_COUNT: False - [ INFO ] MODEL_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_HOST_TASK_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_THROTTLE: Priority.MEDIUM - [ INFO ] GPU_ENABLE_LOOP_UNROLLING: True - [ INFO ] CACHE_DIR: - [ INFO ] PERFORMANCE_HINT: PerformanceMode.LATENCY - [ INFO ] COMPILATION_NUM_THREADS: 20 - [ INFO ] NUM_STREAMS: 1 - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] DEVICE_ID: 0 - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'image_tensor'!. This input will be filled with random values! - [ INFO ] Fill input 'image_tensor' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 1 inference requests, limits: 60000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 6.17 ms - [Step 11/11] Dumping statistics report - [ INFO ] Count: 12710 iterations - [ INFO ] Duration: 60006.58 ms - [ INFO ] Latency: - [ INFO ] Median: 4.52 ms - [ INFO ] Average: 4.57 ms - [ INFO ] Min: 3.13 ms - [ INFO ] Max: 17.62 ms - [ INFO ] Throughput: 211.81 FPS - - -For completeness, let us list here some of the comparisons we may want -to do by varying the device and hint used. Note that the actual -performance may depend on the hardware used. Generally, we should expect -GPU to be better than CPU, whereas multiple GPUs should be better than a -single GPU as long as there is enough work for each of them. - -CPU vs GPU with Latency Hint -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d CPU -hint latency - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] CPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 30.38 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ INFO ] Compile model took 127.72 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: frozen_inference_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1 - [ INFO ] NUM_STREAMS: 1 - [ INFO ] AFFINITY: Affinity.CORE - [ INFO ] INFERENCE_NUM_THREADS: 14 - [ INFO ] PERF_COUNT: False - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] PERFORMANCE_HINT: PerformanceMode.LATENCY - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'image_tensor'!. This input will be filled with random values! - [ INFO ] Fill input 'image_tensor' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 1 inference requests, limits: 60000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 4.42 ms - [Step 11/11] Dumping statistics report - [ INFO ] Count: 15304 iterations - [ INFO ] Duration: 60005.72 ms - [ INFO ] Latency: - [ INFO ] Median: 3.87 ms - [ INFO ] Average: 3.88 ms - [ INFO ] Min: 3.49 ms - [ INFO ] Max: 5.95 ms - [ INFO ] Throughput: 255.04 FPS - - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d GPU -hint latency - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 14.65 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ INFO ] Compile model took 2254.81 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: frozen_inference_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 1 - [ INFO ] PERF_COUNT: False - [ INFO ] MODEL_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_HOST_TASK_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_THROTTLE: Priority.MEDIUM - [ INFO ] GPU_ENABLE_LOOP_UNROLLING: True - [ INFO ] CACHE_DIR: - [ INFO ] PERFORMANCE_HINT: PerformanceMode.LATENCY - [ INFO ] COMPILATION_NUM_THREADS: 20 - [ INFO ] NUM_STREAMS: 1 - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] DEVICE_ID: 0 - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'image_tensor'!. This input will be filled with random values! - [ INFO ] Fill input 'image_tensor' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 1 inference requests, limits: 60000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 8.79 ms - [Step 11/11] Dumping statistics report - [ INFO ] Count: 11354 iterations - [ INFO ] Duration: 60007.21 ms - [ INFO ] Latency: - [ INFO ] Median: 4.57 ms - [ INFO ] Average: 5.16 ms - [ INFO ] Min: 3.18 ms - [ INFO ] Max: 34.87 ms - [ INFO ] Throughput: 189.21 FPS - - -CPU vs GPU with Throughput Hint -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d CPU -hint throughput - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] CPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 29.56 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor:0 , image_tensor (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor:0 , image_tensor (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ INFO ] Compile model took 158.91 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: frozen_inference_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 5 - [ INFO ] NUM_STREAMS: 5 - [ INFO ] AFFINITY: Affinity.CORE - [ INFO ] INFERENCE_NUM_THREADS: 20 - [ INFO ] PERF_COUNT: False - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] PERFORMANCE_HINT: PerformanceMode.THROUGHPUT - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'image_tensor'!. This input will be filled with random values! - [ INFO ] Fill input 'image_tensor' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 5 inference requests, limits: 60000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 8.15 ms - [Step 11/11] Dumping statistics report - [ INFO ] Count: 25240 iterations - [ INFO ] Duration: 60010.99 ms - [ INFO ] Latency: - [ INFO ] Median: 10.16 ms - [ INFO ] Average: 11.84 ms - [ INFO ] Min: 7.96 ms - [ INFO ] Max: 37.53 ms - [ INFO ] Throughput: 420.59 FPS - - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d GPU -hint throughput - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 15.45 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ INFO ] Compile model took 2249.04 ms - [Step 8/11] Querying optimal runtime parameters - [ INFO ] Model: - [ INFO ] NETWORK_NAME: frozen_inference_graph - [ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 4 - [ INFO ] PERF_COUNT: False - [ INFO ] MODEL_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_HOST_TASK_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_PRIORITY: Priority.MEDIUM - [ INFO ] GPU_QUEUE_THROTTLE: Priority.MEDIUM - [ INFO ] GPU_ENABLE_LOOP_UNROLLING: True - [ INFO ] CACHE_DIR: - [ INFO ] PERFORMANCE_HINT: PerformanceMode.THROUGHPUT - [ INFO ] COMPILATION_NUM_THREADS: 20 - [ INFO ] NUM_STREAMS: 2 - [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 - [ INFO ] INFERENCE_PRECISION_HINT: - [ INFO ] DEVICE_ID: 0 - [Step 9/11] Creating infer requests and preparing input tensors - [ WARNING ] No input files were given for input 'image_tensor'!. This input will be filled with random values! - [ INFO ] Fill input 'image_tensor' with random values - [Step 10/11] Measuring performance (Start inference asynchronously, 4 inference requests, limits: 60000 ms duration) - [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop). - [ INFO ] First inference took 9.17 ms - [Step 11/11] Dumping statistics report - [ INFO ] Count: 19588 iterations - [ INFO ] Duration: 60023.47 ms - [ INFO ] Latency: - [ INFO ] Median: 11.31 ms - [ INFO ] Average: 12.15 ms - [ INFO ] Min: 9.26 ms - [ INFO ] Max: 36.04 ms - [ INFO ] Throughput: 326.34 FPS - - -Single GPU vs Multiple GPUs -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d GPU.1 -hint throughput - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [ WARNING ] Device GPU.1 does not support performance hint property(-hint). - [ ERROR ] Config for device with 1 ID is not registered in GPU plugin - Traceback (most recent call last): - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/tools/benchmark/main.py", line 329, in main - benchmark.set_config(config) - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/tools/benchmark/benchmark.py", line 57, in set_config - self.core.set_property(device, config[device]) - RuntimeError: Config for device with 1 ID is not registered in GPU plugin - - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d AUTO:GPU.1,GPU.0 -hint cumulative_throughput - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] AUTO - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [ WARNING ] Device GPU.1 does not support performance hint property(-hint). - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 26.66 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor , image_tensor:0 (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ ERROR ] Config for device with 1 ID is not registered in GPU plugin - Traceback (most recent call last): - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/tools/benchmark/main.py", line 414, in main - compiled_model = benchmark.core.compile_model(model, benchmark.device) - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/runtime/ie_api.py", line 399, in compile_model - super().compile_model(model, device_name, {} if config is None else config), - RuntimeError: Config for device with 1 ID is not registered in GPU plugin - - -.. code:: ipython3 - - !benchmark_app -m {model_path} -d MULTI:GPU.1,GPU.0 -hint throughput - - -.. parsed-literal:: - - [Step 1/11] Parsing and validating input arguments - [ INFO ] Parsing input parameters - [Step 2/11] Loading OpenVINO Runtime - [ INFO ] OpenVINO: - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] Device info: - [ INFO ] GPU - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] MULTI - [ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3 - [ INFO ] - [ INFO ] - [Step 3/11] Setting device configuration - [ WARNING ] Device GPU.1 does not support performance hint property(-hint). - [Step 4/11] Reading model files - [ INFO ] Loading model files - [ INFO ] Read model took 14.84 ms - [ INFO ] Original model I/O parameters: - [ INFO ] Model inputs: - [ INFO ] image_tensor:0 , image_tensor (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [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 ] image_tensor:0 , image_tensor (node: image_tensor) : u8 / [N,H,W,C] / [1,300,300,3] - [ INFO ] Model outputs: - [ INFO ] detection_boxes:0 (node: DetectionOutput) : f32 / [...] / [1,1,100,7] - [Step 7/11] Loading the model to the device - [ ERROR ] Config for device with 1 ID is not registered in GPU plugin - Traceback (most recent call last): - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/tools/benchmark/main.py", line 414, in main - compiled_model = benchmark.core.compile_model(model, benchmark.device) - File "/home/adrian/repos/openvino_notebooks/venv/lib/python3.9/site-packages/openvino/runtime/ie_api.py", line 399, in compile_model - super().compile_model(model, device_name, {} if config is None else config), - RuntimeError: Config for device with 1 ID is not registered in GPU plugin - - -Basic Application Using GPUs ----------------------------- - -We will now show an end-to-end object detection example using GPUs in -OpenVINO. The application compiles a model on GPU with the “THROUGHPUT” -hint, then loads a video and preprocesses every frame to convert them to -the shape expected by the model. Once the frames are loaded, it sets up -an asynchronous pipeline, performs inference and saves the detections -found in each frame. The detections are then drawn on their -corresponding frame and saved as a video, which is displayed at the end -of the application. - -Import Necessary Packages -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - import time - from pathlib import Path - - import cv2 - import numpy as np - from IPython.display import Video - from openvino.runtime import AsyncInferQueue, Core, InferRequest - - # Instantiate OpenVINO Runtime - core = Core() - core.available_devices - - - - -.. parsed-literal:: - - ['CPU', 'GPU'] - - - -Compile the Model -~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # Read model and compile it on GPU in THROUGHPUT mode - model = core.read_model(model=model_path) - device_name = "GPU" - compiled_model = core.compile_model(model=model, device_name=device_name, config={"PERFORMANCE_HINT": "THROUGHPUT"}) - - # Get the input and output nodes - input_layer = compiled_model.input(0) - output_layer = compiled_model.output(0) - - # Get the input size - num, height, width, channels = input_layer.shape - print('Model input shape:', num, height, width, channels) - - -.. parsed-literal:: - - Model input shape: 1 300 300 3 - - -Load and Preprocess Video Frames -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # Load video - video_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4" - video = cv2.VideoCapture(video_file) - framebuf = [] - - # Go through every frame of video and resize it - print('Loading video...') - while video.isOpened(): - ret, frame = video.read() - if not ret: - print('Video loaded!') - video.release() - break - - # Preprocess frames - convert them to shape expected by model - input_frame = cv2.resize(src=frame, dsize=(width, height), interpolation=cv2.INTER_AREA) - input_frame = np.expand_dims(input_frame, axis=0) - - # Append frame to framebuffer - framebuf.append(input_frame) - - - print('Frame shape: ', framebuf[0].shape) - print('Number of frames: ', len(framebuf)) - - # Show original video file - # If the video does not display correctly inside the notebook, please open it with your favorite media player - Video(video_file) - - -.. parsed-literal:: - - Loading video... - Video loaded! - Frame shape: (1, 300, 300, 3) - Number of frames: 288 - - - - -.. raw:: html - - - - - -Define Model Output Classes -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # Define the model's labelmap (this model uses COCO classes) - classes = [ - "background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", - "truck", "boat", "traffic light", "fire hydrant", "street sign", "stop sign", - "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", - "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "eye glasses", - "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", - "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", - "plate", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", - "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", - "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "toilet", - "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", - "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors", - "teddy bear", "hair drier", "toothbrush", "hair brush" - ] - -Set up Asynchronous Pipeline -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Callback Definition -^^^^^^^^^^^^^^^^^^^ - -.. code:: ipython3 - - # Define a callback function that runs every time the asynchronous pipeline completes inference on a frame - def completion_callback(infer_request: InferRequest, frame_id: int) -> None: - global frame_number - stop_time = time.time() - frame_number += 1 - - predictions = next(iter(infer_request.results.values())) - results[frame_id] = predictions[:10] # Grab first 10 predictions for this frame - - total_time = stop_time - start_time - frame_fps[frame_id] = frame_number / total_time - -Create Async Pipeline -^^^^^^^^^^^^^^^^^^^^^ - -.. code:: ipython3 - - # Create asynchronous inference queue with optimal number of infer requests - infer_queue = AsyncInferQueue(compiled_model) - infer_queue.set_callback(completion_callback) - -Perform Inference -~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # Perform inference on every frame in the framebuffer - results = {} - frame_fps = {} - frame_number = 0 - start_time = time.time() - for i, input_frame in enumerate(framebuf): - infer_queue.start_async({0: input_frame}, i) - - infer_queue.wait_all() # Wait until all inference requests in the AsyncInferQueue are completed - stop_time = time.time() - - # Calculate total inference time and FPS - total_time = stop_time - start_time - fps = len(framebuf) / total_time - time_per_frame = 1 / fps - print(f'Total time to infer all frames: {total_time:.3f}s') - print(f'Time per frame: {time_per_frame:.6f}s ({fps:.3f} FPS)') - - -.. parsed-literal:: - - Total time to infer all frames: 1.366s - Time per frame: 0.004744s (210.774 FPS) - - -Process Results -~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - # Set minimum detection threshold - min_thresh = .6 - - # Load video - video = cv2.VideoCapture(video_file) - - # Get video parameters - frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) - frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - fps = int(video.get(cv2.CAP_PROP_FPS)) - fourcc = int(video.get(cv2.CAP_PROP_FOURCC)) - - # Create folder and VideoWriter to save output video - Path('./output').mkdir(exist_ok=True) - output = cv2.VideoWriter('output/output.mp4', fourcc, fps, (frame_width, frame_height)) - - # Draw detection results on every frame of video and save as a new video file - while video.isOpened(): - current_frame = int(video.get(cv2.CAP_PROP_POS_FRAMES)) - ret, frame = video.read() - if not ret: - print('Video loaded!') - output.release() - video.release() - break - - # Draw info at the top left such as current fps, the devices and the performance hint being used - cv2.putText(frame, f"fps {str(round(frame_fps[current_frame], 2))}", (5, 20), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA) - cv2.putText(frame, f"device {device_name}", (5, 40), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA) - cv2.putText(frame, f"hint {compiled_model.get_property('PERFORMANCE_HINT').name}", (5, 60), cv2.FONT_ITALIC, 0.6, (0, 0, 0), 1, cv2.LINE_AA) - - # prediction contains [image_id, label, conf, x_min, y_min, x_max, y_max] according to model - for prediction in np.squeeze(results[current_frame]): - if prediction[2] > min_thresh: - x_min = int(prediction[3] * frame_width) - y_min = int(prediction[4] * frame_height) - x_max = int(prediction[5] * frame_width) - y_max = int(prediction[6] * frame_height) - label = classes[int(prediction[1])] - - # Draw a bounding box with its label above it - cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1, cv2.LINE_AA) - cv2.putText(frame, label, (x_min, y_min - 10), cv2.FONT_ITALIC, 1, (255, 0, 0), 1, cv2.LINE_AA) - - output.write(frame) - - # Show output video file - # If the video does not display correctly inside the notebook, please open it with your favorite media player - Video("output/output.mp4", width=800, embed=True) - - -.. parsed-literal:: - - Video loaded! - - - - -.. raw:: html - - - - - -Conclusion ----------- - -This tutorial demonstrates how easy it is to use one or more GPUs in -OpenVINO, check their properties, and even tailor the model performance -through the different performance hints. It also provides a walk-through -of a basic object detection application that uses a GPU and displays the -detected bounding boxes. - -To read more about any of these topics, feel free to visit their -corresponding documentation: - -* `GPU Plugin `__ -* `AUTO Plugin `__ -* `Model Caching `__ -* `MULTI Device Mode `__ -* `Query Device Properties `__ -* `Configurations for GPUs with OpenVINO `__ -* `Benchmark Python Tool `__ -* `Asynchronous Inferencing `__ diff --git a/docs/notebooks/115-async-api-with-output.rst b/docs/notebooks/115-async-api-with-output.rst index 3aeade9d54b..c7bee23e5b9 100644 --- a/docs/notebooks/115-async-api-with-output.rst +++ b/docs/notebooks/115-async-api-with-output.rst @@ -91,7 +91,7 @@ Load the model # read the network and corresponding weights from file model = ie.read_model(model=model_path) - # compile the model for the CPU (you can choose manually CPU, GPU, MYRIAD etc.) + # compile the model for the CPU (you can choose manually CPU, GPU, etc.) # or let the engine choose the best available device (AUTO) compiled_model = ie.compile_model(model=model, device_name="CPU") diff --git a/docs/notebooks/211-speech-to-text-with-output.rst b/docs/notebooks/211-speech-to-text-with-output.rst index f2b3c94e49e..fc8fbffd21d 100644 --- a/docs/notebooks/211-speech-to-text-with-output.rst +++ b/docs/notebooks/211-speech-to-text-with-output.rst @@ -393,7 +393,7 @@ Now, you can read and load the network. ie = Core() You may run the network on multiple devices. By default, it will load -the model on CPU (you can choose manually CPU, GPU, MYRIAD, etc.) or let +the model on CPU (you can choose manually CPU, GPU, etc.) or let the engine choose the best available device (AUTO). To list all available devices that can be used, run diff --git a/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst b/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst index 723d30d3bf2..136252314ac 100644 --- a/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst +++ b/docs/notebooks/218-vehicle-detection-and-recognition-with-output.rst @@ -144,7 +144,7 @@ specified device. # Read the network and corresponding weights from a file. model = ie_core.read_model(model=model_path) - # Compile the model for CPU (you can use GPU or MYRIAD as well). + # Compile the model for CPU (you can also use GPU). compiled_model = ie_core.compile_model(model=model, device_name="CPU") # Get input and output names of nodes. input_keys = compiled_model.input(0) diff --git a/docs/notebooks/301-tensorflow-training-openvino-with-output.rst b/docs/notebooks/301-tensorflow-training-openvino-with-output.rst index d6f3df9a67d..ad4e1cb2e06 100644 --- a/docs/notebooks/301-tensorflow-training-openvino-with-output.rst +++ b/docs/notebooks/301-tensorflow-training-openvino-with-output.rst @@ -900,9 +900,8 @@ OpenVINO Inference Engine Setup ie = Core() # Neural Compute Stick - # compile the model for the CPU (you can choose manually CPU, GPU, MYRIAD etc.) + # compile the model for the CPU (you can choose manually CPU, GPU, etc.) # or let the engine choose the best available device (AUTO) - # compiled_model = ie.compile_model(model=model, device_name="MYRIAD") compiled_model = ie.compile_model(model=ir_model, device_name="CPU") del ir_model diff --git a/docs/notebooks/402-pose-estimation-with-output.rst b/docs/notebooks/402-pose-estimation-with-output.rst index 18ef77aea36..e3383de9cb6 100644 --- a/docs/notebooks/402-pose-estimation-with-output.rst +++ b/docs/notebooks/402-pose-estimation-with-output.rst @@ -97,7 +97,7 @@ desired device. ie_core = Core() # Read the network from a file. model = ie_core.read_model(model_path) - # Let the AUTO device decide where to load the model (you can use CPU, GPU or MYRIAD as well). + # Let the AUTO device decide where to load the model (you can use CPU or GPU). compiled_model = ie_core.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT": "LATENCY"}) # Get the input and output names of nodes. diff --git a/docs/notebooks/403-action-recognition-webcam-with-output.rst b/docs/notebooks/403-action-recognition-webcam-with-output.rst index b5463579f13..e8b8841c5c1 100644 --- a/docs/notebooks/403-action-recognition-webcam-with-output.rst +++ b/docs/notebooks/403-action-recognition-webcam-with-output.rst @@ -183,7 +183,7 @@ Model Initialization function # Read the network and corresponding weights from a file. model = ie_core.read_model(model=model_path) - # Compile the model for CPU (you can use GPU or MYRIAD as well). + # Compile the model for CPU (you can also use GPU). compiled_model = ie_core.compile_model(model=model, device_name="CPU") # Get input and output names of nodes. input_keys = compiled_model.input(0) diff --git a/docs/notebooks/404-style-transfer-with-output.rst b/docs/notebooks/404-style-transfer-with-output.rst index 299eb12934f..f9f24064362 100644 --- a/docs/notebooks/404-style-transfer-with-output.rst +++ b/docs/notebooks/404-style-transfer-with-output.rst @@ -164,7 +164,7 @@ results. # Read the network and corresponding weights from IR Model. model = ie_core.read_model(model=ir_path) - # Compile the model for CPU (or change to GPU, MYRIAD etc. for other devices) + # Compile the model for CPU (or change to GPU, etc. for other devices) # or let OpenVINO select the best available device with AUTO. compiled_model = ie_core.compile_model(model=model, device_name="AUTO") diff --git a/docs/notebooks/406-3D-pose-estimation-with-output.rst b/docs/notebooks/406-3D-pose-estimation-with-output.rst index 6d3a27048d2..7160bf1a6cc 100644 --- a/docs/notebooks/406-3D-pose-estimation-with-output.rst +++ b/docs/notebooks/406-3D-pose-estimation-with-output.rst @@ -209,7 +209,7 @@ created to infer the compiled model. ie_core = Core() # read the network and corresponding weights from file model = ie_core.read_model(model=ir_model_path, weights=model_weights_path) - # load the model on the CPU (you can use GPU or MYRIAD as well) + # load the model on the CPU (you can also use GPU) compiled_model = ie_core.compile_model(model=model, device_name="CPU") infer_request = compiled_model.create_infer_request() input_tensor_name = model.inputs[0].get_any_name() diff --git a/docs/tutorials.md b/docs/tutorials.md index 228e21dccd8..edd9c91063b 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -117,8 +117,6 @@ Tutorials that explain how to optimize and quantize models with OpenVINO tools. +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+ | `107-speech-recognition-quantization `__ |br| |c107| | Optimize and quantize a pre-trained Data2Vec speech model. | +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+ - | `108-gpu-device `__ | Working with GPUs in OpenVINO™. | - +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+ | `109-performance-tricks `__ | Performance tricks in OpenVINO™. | +----------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+ | `110-ct-segmentation-quantize `__ |br| |n110| | Live inference of a kidney segmentation model and benchmark CT-scan data with OpenVINO. |