[BENCHMARK_APP] Help message cleanup (#14786)
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Tomasz Dołbniak
GitHub
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99c7ee1c18
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13d01d969b
@ -113,43 +113,43 @@ Running the application with the `-h` or `--help` option yields the following us
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benchmark_app [OPTION]
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Options:
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-h, --help Print a usage message
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-m "<path>" Required. Path to an .xml/.onnx file with a trained model or to a .blob files with a trained compiled model.
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-i "<path>" Optional. Path to a folder with images and/or binaries or to specific image or binary file.
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-h, --help Print the usage message
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-m <path> Required. Path to an .xml/.onnx file with a trained model or to a .blob files with a trained compiled model.
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-i <path> Optional. Path to a folder with images and/or binaries or to specific image or binary file.
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In case of dynamic shapes models with several inputs provide the same number of files for each input (except cases with single file for any input):"input1:1.jpg input2:1.bin", "input1:1.bin,2.bin input2:3.bin input3:4.bin,5.bin ". Also you can pass specific keys for inputs: "random" - for fillling input with random data, "image_info" - for filling input with image size.
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You should specify either one files set to be used for all inputs (without providing input names) or separate files sets for every input of model (providing inputs names).
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-d "<device>" Optional. Specify a target device to infer on (the list of available devices is shown below). Default value is CPU. 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.
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-extensions "<absolute_path>" Required for custom layers (extensions). Absolute path to a shared library with the kernels implementations.
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-c "<absolute_path>" Required for GPU custom kernels. Absolute path to an .xml file with the kernels description.
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-hint "performance hint (latency or throughput or cumulative_throughput or none)" Optional. Performance hint allows the OpenVINO device to select the right model-specific settings.
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-d <device> Optional. Specify a target device to infer on (the list of available devices is shown below). Default value is CPU. 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.
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-extensions <absolute_path> Required for custom layers (extensions). Absolute path to a shared library with the kernels implementations.
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-c <absolute_path> Required for GPU custom kernels. Absolute path to an .xml file with the kernels description.
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-hint <performance hint> (latency or throughput or cumulative_throughput or none) Optional. Performance hint allows the OpenVINO device to select the right model-specific settings.
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'throughput' or 'tput': device performance mode will be set to THROUGHPUT.
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'cumulative_throughput' or 'ctput': device performance mode will be set to CUMULATIVE_THROUGHPUT.
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'latency': device performance mode will be set to LATENCY.
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'none': no device performance mode will be set.
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Using explicit 'nstreams' or other device-specific options, please set hint to 'none'
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-api "<sync/async>" Optional (deprecated). Enable Sync/Async API. Default value is "async".
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-niter "<integer>" Optional. Number of iterations. If not specified, the number of iterations is calculated depending on a device.
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-nireq "<integer>" Optional. Number of infer requests. Default value is determined automatically for device.
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-b "<integer>" Optional. Batch size value. If not specified, the batch size value is determined from Intermediate Representation.
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-api <sync/async> Optional (deprecated). Enable Sync/Async API. Default value is "async".
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-niter <integer> Optional. Number of iterations. If not specified, the number of iterations is calculated depending on a device.
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-nireq <integer> Optional. Number of infer requests. Default value is determined automatically for device.
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-b <integer> Optional. Batch size value. If not specified, the batch size value is determined from Intermediate Representation.
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-t Optional. Time in seconds to execute topology.
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-shape Optional. Set shape for model input. For example, "input1[1,3,224,224],input2[1,4]" or "[1,3,224,224]" in case of one input size. This parameter affect model input shape and can be dynamic. For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?]. For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].
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-data_shape Required for models with dynamic shapes. Set shape for input blobs. In case of one input size: "[1,3,224,224]" or "input1[1,3,224,224],input2[1,4]". In case of several input sizes provide the same number for each input (except cases with single shape for any input): "[1,3,128,128][3,3,128,128][1,3,320,320]", "input1[1,1,128,128][1,1,256,256],input2[80,1]" or "input1[1,192][1,384],input2[1,192][1,384],input3[1,192][1,384],input4[1,192][1,384]". If model shapes are all static specifying the option will cause an exception.
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-layout Optional. Prompts how model layouts should be treated by application. For example, "input1[NCHW],input2[NC]" or "[NCHW]" in case of one input size.
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-cache_dir "<path>" Optional. Enables caching of loaded models to specified directory. List of devices which support caching is shown at the end of this message.
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-cache_dir <path> Optional. Enables caching of loaded models to specified directory. List of devices which support caching is shown at the end of this message.
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-load_from_file Optional. Loads model from file directly without read_model. All CNNNetwork options (like re-shape) will be ignored
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-latency_percentile Optional. Defines the percentile to be reported in latency metric. The valid range is [1, 100]. The default value is 50 (median).
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device-specific performance options:
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-nstreams "<integer>" Optional. Number of streams to use for inference on the CPU, GPU or MYRIAD devices (for HETERO and MULTI device cases use format <dev1>:<nstreams1>,<dev2>:<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 models. See sample's README for more details. Also, using nstreams>1 is inherently throughput-oriented option, while for the best-latency estimations the number of streams should be set to 1.
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-nthreads "<integer>" Optional. Number of threads to use for inference on the CPU (including HETERO and MULTI cases).
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-pin ("YES"|"CORE")/"HYBRID_AWARE"/("NO"|"NONE")/"NUMA" Optional. Explicit inference threads binding options (leave empty to let the OpenVINO to make a choice):
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-nstreams <integer> Optional. Number of streams to use for inference on the CPU, GPU or MYRIAD devices (for HETERO and MULTI device cases use format <dev1>:<nstreams1>,<dev2>:<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 models. See sample's README for more details. Also, using nstreams>1 is inherently throughput-oriented option, while for the best-latency estimations the number of streams should be set to 1.
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-nthreads <integer> Optional. Number of threads to use for inference on the CPU (including HETERO and MULTI cases).
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-pin <string> ("YES"|"CORE") / "HYBRID_AWARE" / ("NO"|"NONE") / "NUMA" Optional. Explicit inference threads binding options (leave empty to let the OpenVINO make a choice):
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enabling threads->cores pinning("YES", which is already default for any conventional CPU),
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letting the runtime to decide on the threads->different core types("HYBRID_AWARE", which is default on the hybrid CPUs)
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threads->(NUMA)nodes("NUMA") or
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completely disable("NO") CPU inference threads pinning
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Statistics dumping options:
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-report_type "<type>" Optional. Enable collecting statistics report. "no_counters" report contains configuration options specified, resulting FPS and latency. "average_counters" report extends "no_counters" report and additionally includes average PM counters values for each layer from the model. "detailed_counters" report extends "average_counters" report and additionally includes per-layer PM counters and latency for each executed infer request.
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-report_type <type> Optional. Enable collecting statistics report. "no_counters" report contains configuration options specified, resulting FPS and latency. "average_counters" report extends "no_counters" report and additionally includes average PM counters values for each layer from the model. "detailed_counters" report extends "average_counters" report and additionally includes per-layer PM counters and latency for each executed infer request.
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-report_folder Optional. Path to a folder where statistics report is stored.
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-json_stats Optional. Enables JSON-based statistics output (by default reporting system will use CSV format). Should be used together with -report_folder option.
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-exec_graph_path Optional. Path to a file where to store executable graph information serialized.
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@ -180,10 +180,10 @@ Options:
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}
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}
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-infer_precision "<element type>"Optional. Inference precision
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-infer_precision Optional. Specifies the inference precision. Example #1: '-infer_precision bf16'. Example #2: '-infer_precision CPU:bf16,GPU:f32'
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-ip <value> Optional. Specifies precision for all input layers of the model.
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-op <value> Optional. Specifies precision for all output layers of the model.
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-iop "<value>" Optional. Specifies precision for input and output layers by name.
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-iop <value> Optional. Specifies precision for input and output layers by name.
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Example: -iop "input:FP16, output:FP16".
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Notice that quotes are required.
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Overwrites precision from ip and op options for specified layers.
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@ -24,7 +24,7 @@
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#endif
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/// @brief message for help argument
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static const char help_message[] = "Print a usage message";
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static const char help_message[] = "Print the usage message";
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/// @brief message for images argument
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static const char input_message[] =
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@ -121,7 +121,7 @@ static const char batch_size_message[] =
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// @brief message for CPU threads pinning option
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static const char infer_threads_pinning_message[] =
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"Optional. Explicit inference threads binding options (leave empty to let the OpenVINO to make a choice):\n"
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"Optional. Explicit inference threads binding options (leave empty to let the OpenVINO make a choice):\n"
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"\t\t\t\tenabling threads->cores pinning(\"YES\", which is already default for any conventional CPU), \n"
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"\t\t\t\tletting the runtime to decide on the threads->different core types(\"HYBRID_AWARE\", which is default on "
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"the hybrid CPUs) \n"
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@ -226,7 +226,9 @@ static const char load_from_file_message[] = "Optional. Loads model from file di
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" All CNNNetwork options (like re-shape) will be ignored";
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// @brief message for inference_precision
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static const char inference_precision_message[] = "Optional. Inference precision";
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static const char inference_precision_message[] =
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"Optional. Specifies the inference precision. Example #1: '-infer_precision bf16'. Example #2: '-infer_precision "
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"CPU:bf16,GPU:f32'";
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static constexpr char inputs_precision_message[] = "Optional. Specifies precision for all input layers of the model.";
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@ -403,34 +405,34 @@ static void show_usage() {
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std::cout << "Options:" << std::endl;
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std::cout << std::endl;
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std::cout << " -h, --help " << help_message << std::endl;
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std::cout << " -m \"<path>\" " << model_message << std::endl;
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std::cout << " -i \"<path>\" " << input_message << std::endl;
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std::cout << " -d \"<device>\" " << target_device_message << std::endl;
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std::cout << " -extensions \"<absolute_path>\" " << custom_extensions_library_message << std::endl;
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std::cout << " -c \"<absolute_path>\" " << custom_cldnn_message << std::endl;
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std::cout << " -hint \"performance hint (latency or throughput or cumulative_throughput or none)\" "
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std::cout << " -m <path> " << model_message << std::endl;
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std::cout << " -i <path> " << input_message << std::endl;
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std::cout << " -d <device> " << target_device_message << std::endl;
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std::cout << " -extensions <absolute_path> " << custom_extensions_library_message << std::endl;
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std::cout << " -c <absolute_path> " << custom_cldnn_message << std::endl;
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std::cout << " -hint <performance hint> (latency or throughput or cumulative_throughput or none) "
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<< hint_message << std::endl;
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std::cout << " -api \"<sync/async>\" " << api_message << std::endl;
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std::cout << " -niter \"<integer>\" " << iterations_count_message << std::endl;
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std::cout << " -nireq \"<integer>\" " << infer_requests_count_message << std::endl;
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std::cout << " -b \"<integer>\" " << batch_size_message << std::endl;
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std::cout << " -api <sync/async> " << api_message << std::endl;
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std::cout << " -niter <integer> " << iterations_count_message << std::endl;
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std::cout << " -nireq <integer> " << infer_requests_count_message << std::endl;
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std::cout << " -b <integer> " << batch_size_message << std::endl;
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std::cout << " -t " << execution_time_message << std::endl;
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std::cout << " -shape " << shape_message << std::endl;
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std::cout << " -data_shape " << data_shape_message << std::endl;
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std::cout << " -layout " << layout_message << std::endl;
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std::cout << " -cache_dir \"<path>\" " << cache_dir_message << std::endl;
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std::cout << " -cache_dir <path> " << cache_dir_message << std::endl;
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std::cout << " -load_from_file " << load_from_file_message << std::endl;
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std::cout << " -latency_percentile " << infer_latency_percentile_message << std::endl;
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std::cout << std::endl << " device-specific performance options:" << std::endl;
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std::cout << " -nstreams \"<integer>\" " << infer_num_streams_message << std::endl;
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std::cout << " -nthreads \"<integer>\" " << infer_num_threads_message << std::endl;
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std::cout << " -pin (\"YES\"|\"CORE\")/\"HYBRID_AWARE\"/(\"NO\"|\"NONE\")/\"NUMA\" "
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std::cout << " -nstreams <integer> " << infer_num_streams_message << std::endl;
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std::cout << " -nthreads <integer> " << infer_num_threads_message << std::endl;
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std::cout << " -pin <string> (\"YES\"|\"CORE\") / \"HYBRID_AWARE\" / (\"NO\"|\"NONE\") / \"NUMA\" "
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<< infer_threads_pinning_message << std::endl;
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#ifdef HAVE_DEVICE_MEM_SUPPORT
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std::cout << " -use_device_mem " << use_device_mem_message << std::endl;
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#endif
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std::cout << std::endl << " Statistics dumping options:" << std::endl;
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std::cout << " -report_type \"<type>\" " << report_type_message << std::endl;
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std::cout << " -report_type <type> " << report_type_message << std::endl;
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std::cout << " -report_folder " << report_folder_message << std::endl;
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std::cout << " -json_stats " << json_stats_message << std::endl;
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std::cout << " -exec_graph_path " << exec_graph_path_message << std::endl;
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@ -439,10 +441,10 @@ static void show_usage() {
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std::cout << " -pcseq " << pcseq_message << std::endl;
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std::cout << " -dump_config " << dump_config_message << std::endl;
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std::cout << " -load_config " << load_config_message << std::endl;
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std::cout << " -infer_precision \"<element type>\"" << inference_precision_message << std::endl;
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std::cout << " -infer_precision " << inference_precision_message << std::endl;
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std::cout << " -ip <value> " << inputs_precision_message << std::endl;
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std::cout << " -op <value> " << outputs_precision_message << std::endl;
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std::cout << " -iop \"<value>\" " << iop_message << std::endl;
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std::cout << " -iop <value> " << iop_message << std::endl;
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std::cout << " -mean_values [R,G,B] " << mean_values_message << std::endl;
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std::cout << " -scale_values [R,G,B] " << scale_values_message << std::endl;
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std::cout << " -inference_only " << inference_only_message << std::endl;
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@ -161,7 +161,7 @@ def parse_args():
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" }\n"
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" }\n")
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args.add_argument('-infer_precision', type=str, required=False,
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help='Optional. Hint to specifies inference precision. Example: -infer_precision CPU:bf16,GPU:f32')
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help='Optional. Specifies the inference precision. Example #1: \'-infer_precision bf16\'. Example #2: \'-infer_precision CPU:bf16,GPU:f32\'')
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args.add_argument('-ip', '--input_precision', type=str, required=False, choices=['u8', 'U8', 'f16','FP16', 'f32','FP32'],
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help='Optional. Specifies precision for all input layers of the model.')
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args.add_argument('-op', '--output_precision', type=str, required=False, choices=['u8', 'U8', 'f16','FP16', 'f32','FP32'],
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