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Documented the return value and added percentile to argparser

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Kinar 2023-11-13 21:17:28 -08:00
parent 38737849e6
commit f8add5ad42
1 changed files with 19 additions and 21 deletions
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38
mediapipe/tasks/python/benchmark/vision/image_classifier/image_classifier_benchmark.py
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@ -1,17 +1,3 @@
# Copyright 2023 The MediaPipe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Benchmark for the image classifier task."""
import argparse import argparse
import time import time
import numpy as np import numpy as np
@ -22,13 +8,18 @@ from mediapipe.tasks.python import vision
_IMAGE_FILE = 'burger.jpg' _IMAGE_FILE = 'burger.jpg'
def run(model: str, n_iterations: int, delegate: python.BaseOptions.Delegate): def run(model: str, n_iterations: int, delegate: python.BaseOptions.Delegate,
percentile: float):
"""Run asynchronous inference on images and benchmark. """Run asynchronous inference on images and benchmark.
Args: Args:
model: Path to the TFLite model. model: Path to the TFLite model.
n_iterations: Number of iterations to run the benchmark. n_iterations: Number of iterations to run the benchmark.
delegate: CPU or GPU delegate for inference. delegate: CPU or GPU delegate for inference.
percentile: Percentage for the percentiles to compute. Values must be
between 0 and 100 inclusive.
Returns:
The n-th percentile of the inference times.
""" """
inference_times = [] inference_times = []
@ -48,7 +39,7 @@ def run(model: str, n_iterations: int, delegate: python.BaseOptions.Delegate):
inference_times.append((end_time_ns - start_time_ns) / 1_000_000) inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
classifier.close() classifier.close()
return np.percentile(inference_times, 95) return np.percentile(inference_times, percentile)
def main(): def main():
@ -60,15 +51,22 @@ def main():
parser.add_argument( parser.add_argument(
'--iterations', help='Number of iterations for benchmarking.', type=int, '--iterations', help='Number of iterations for benchmarking.', type=int,
default=100) default=100)
parser.add_argument(
'--percentile', help='Percentile for benchmarking statistics.',
type=float, default=95.0)
args = parser.parse_args() args = parser.parse_args()
# Run benchmark on CPU # Run benchmark on CPU
cpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.CPU) cpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.CPU,
print(f"95th Percentile Inference Time on CPU: {cpu_time:.6f} milliseconds") args.percentile)
print(f"{args.percentile}th Percentile Inference Time on CPU: "
f"{cpu_time:.6f} milliseconds")
# Run benchmark on GPU # Run benchmark on GPU
gpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.GPU) gpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.GPU,
print(f"95th Percentile Inference Time on GPU: {gpu_time:.6f} milliseconds") args.percentile)
print(f"{args.percentile}th Percentile Inference Time on GPU: "
f"{gpu_time:.6f} milliseconds")
if __name__ == '__main__': if __name__ == '__main__':