0000 benchmark openvino.md

benchmark_app

benchmark.py

import itertools
import subprocess
from argparse import Namespace
from dataclasses import dataclass
from typing import List
import pandas as pd
import re


@dataclass
class BenchmarkResult:
    stdout: str = ''
    stderr: str = ''
    avg_latency: float = 0.
    throughput: float = 0.


def run_benchmark(cmd):
    with subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as p:
        p.wait()
        stdout = p.stdout.read().decode()
        stderr = p.stderr.read().decode()
        # print(stdout)
        # print(stderr)
        stdout = stdout.strip()
        avg_line = filter(None, stdout.split('\n')[-4].split())
        throughput_line = filter(None, stdout.split('\n')[-1].split())
        avg_line = list(avg_line)
        throughput_line = list(throughput_line)
        assert 'Average:' in avg_line and 'Throughput:' in throughput_line

        return BenchmarkResult(
            stdout=stdout,
            stderr=stderr,
            avg_latency=float(list(avg_line)[-2]),
            throughput=float(list(throughput_line)[-2]),
        )


results = []
for model in ['model.onnx']:
    print('***' * 20, model)
    result = run_benchmark(cmd=f'benchmark_app -m {model} -niter 3 -hint latency')
    # result = run_benchmark(cmd=f'benchmark_app -m {model} -hint throughput -t 60')
    results.append(
        dict(
            model=model,
            latency=result.avg_latency,
            throughput=result.throughput,
        )
    )
    df = pd.DataFrame(results)
    # df.to_csv('./dgx1.csv')