f9c0e9690a
* Fix samples debug * Fix linter * Fix speech sample --------- Co-authored-by: p-wysocki <przemyslaw.wysocki@intel.com>
80 lines
3.0 KiB
Python
Executable File
80 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import statistics
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import sys
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from time import perf_counter
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import numpy as np
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import openvino as ov
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from openvino.runtime import get_version
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from openvino.runtime.utils.types import get_dtype
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def fill_tensor_random(tensor):
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dtype = get_dtype(tensor.element_type)
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rand_min, rand_max = (0, 1) if dtype == bool else (np.iinfo(np.uint8).min, np.iinfo(np.uint8).max)
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# np.random.uniform excludes high: add 1 to have it generated
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if np.dtype(dtype).kind in ['i', 'u', 'b']:
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rand_max += 1
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rs = np.random.RandomState(np.random.MT19937(np.random.SeedSequence(0)))
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if 0 == tensor.get_size():
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raise RuntimeError("Models with dynamic shapes aren't supported. Input tensors must have specific shapes before inference")
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tensor.data[:] = rs.uniform(rand_min, rand_max, list(tensor.shape)).astype(dtype)
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def main():
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log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
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log.info('OpenVINO:')
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log.info(f"{'Build ':.<39} {get_version()}")
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if len(sys.argv) != 2:
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log.info(f'Usage: {sys.argv[0]} <path_to_model>')
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return 1
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# Optimize for latency. Most of the devices are configured for latency by default,
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# but there are exceptions like GNA
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latency = {'PERFORMANCE_HINT': 'LATENCY'}
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# Create Core and use it to compile a model.
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# Pick a device by replacing CPU, for example AUTO:GPU,CPU.
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# Using MULTI device is pointless in sync scenario
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# because only one instance of openvino.runtime.InferRequest is used
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core = ov.Core()
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compiled_model = core.compile_model(sys.argv[1], 'CPU', latency)
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ireq = compiled_model.create_infer_request()
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# Fill input data for the ireq
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for model_input in compiled_model.inputs:
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fill_tensor_random(ireq.get_tensor(model_input))
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# Warm up
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ireq.infer()
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# Benchmark for seconds_to_run seconds and at least niter iterations
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seconds_to_run = 10
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niter = 10
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latencies = []
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start = perf_counter()
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time_point = start
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time_point_to_finish = start + seconds_to_run
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while time_point < time_point_to_finish or len(latencies) < niter:
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ireq.infer()
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iter_end = perf_counter()
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latencies.append((iter_end - time_point) * 1e3)
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time_point = iter_end
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end = time_point
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duration = end - start
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# Report results
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fps = len(latencies) / duration
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log.info(f'Count: {len(latencies)} iterations')
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log.info(f'Duration: {duration * 1e3:.2f} ms')
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log.info('Latency:')
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log.info(f' Median: {statistics.median(latencies):.2f} ms')
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log.info(f' Average: {sum(latencies) / len(latencies):.2f} ms')
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log.info(f' Min: {min(latencies):.2f} ms')
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log.info(f' Max: {max(latencies):.2f} ms')
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log.info(f'Throughput: {fps:.2f} FPS')
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if __name__ == '__main__':
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main()
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