Added more benchmark scripts for the Tasks Python API
This commit is contained in:
parent
7287056674
commit
8f32fda6d8
24
mediapipe/tasks/python/benchmark/BUILD
Normal file
24
mediapipe/tasks/python/benchmark/BUILD
Normal file
|
@ -0,0 +1,24 @@
|
||||||
|
# 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
licenses(["notice"])
|
||||||
|
|
||||||
|
py_library(
|
||||||
|
name = "benchmark_utils",
|
||||||
|
srcs = ["benchmark_utils.py"]
|
||||||
|
)
|
58
mediapipe/tasks/python/benchmark/benchmark_utils.py
Normal file
58
mediapipe/tasks/python/benchmark/benchmark_utils.py
Normal file
|
@ -0,0 +1,58 @@
|
||||||
|
# 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 utils for MediaPipe Tasks."""
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
def get_test_data_path(test_srcdir, file_or_dirname_path: str) -> str:
|
||||||
|
"""Determine the test data path.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
test_srcdir: The path to the test source directory.
|
||||||
|
file_or_dirname_path: The path to the file or directory.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The full test data path.
|
||||||
|
"""
|
||||||
|
"""Returns full test data path."""
|
||||||
|
for directory, subdirs, files in os.walk(test_srcdir):
|
||||||
|
for f in subdirs + files:
|
||||||
|
path = os.path.join(directory, f)
|
||||||
|
if path.endswith(file_or_dirname_path):
|
||||||
|
return path
|
||||||
|
raise ValueError(
|
||||||
|
"No %s in test directory: %s." % (file_or_dirname_path, test_srcdir)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def get_model_path(custom_model, default_model_path):
|
||||||
|
"""Determine the model path based on the existence of the custom model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
custom_model: The path to the custom model provided by the user.
|
||||||
|
default_model_path: The path to the default model.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The path to the model to be used.
|
||||||
|
"""
|
||||||
|
if custom_model is not None and os.path.exists(custom_model):
|
||||||
|
print(f"Using provided model: {custom_model}")
|
||||||
|
return custom_model
|
||||||
|
else:
|
||||||
|
if custom_model is not None:
|
||||||
|
print(f"Warning: Provided model '{custom_model}' not found. "
|
||||||
|
f"Using default model instead.")
|
||||||
|
print(f"Using default model: {default_model_path}")
|
||||||
|
return default_model_path
|
22
mediapipe/tasks/python/benchmark/vision/core/BUILD
Normal file
22
mediapipe/tasks/python/benchmark/vision/core/BUILD
Normal file
|
@ -0,0 +1,22 @@
|
||||||
|
# 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_library(
|
||||||
|
name = "base_vision_benchmark_api",
|
||||||
|
srcs = ["base_vision_benchmark_api.py"]
|
||||||
|
)
|
14
mediapipe/tasks/python/benchmark/vision/core/__init__.py
Normal file
14
mediapipe/tasks/python/benchmark/vision/core/__init__.py
Normal file
|
@ -0,0 +1,14 @@
|
||||||
|
"""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.
|
||||||
|
"""
|
|
@ -0,0 +1,44 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe vision benchmark base api."""
|
||||||
|
import os
|
||||||
|
import time
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
VISION_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
|
||||||
|
|
||||||
|
|
||||||
|
def nth_percentile(func, image, n_iterations, percentile):
|
||||||
|
"""Run a nth percentile benchmark for a given task using the function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
func: The method associated with a given task used for benchmarking.
|
||||||
|
image: The input MediaPipe Image.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
percentile: Percentage for the percentiles to compute. Values must be
|
||||||
|
between 0 and 100 inclusive.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The n-th percentile of the inference times in milliseconds.
|
||||||
|
"""
|
||||||
|
inference_times = []
|
||||||
|
|
||||||
|
for _ in range(n_iterations):
|
||||||
|
start_time_ns = time.time_ns()
|
||||||
|
# Run the method for the task (e.g., classify)
|
||||||
|
func(image)
|
||||||
|
end_time_ns = time.time_ns()
|
||||||
|
inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
|
||||||
|
|
||||||
|
return np.percentile(inference_times, percentile)
|
34
mediapipe/tasks/python/benchmark/vision/face_aligner/BUILD
Normal file
34
mediapipe/tasks/python/benchmark/vision/face_aligner/BUILD
Normal file
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_aligner_benchmark",
|
||||||
|
main = "face_aligner_benchmark.py",
|
||||||
|
srcs = ["face_aligner_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_aligner",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe face aligner benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_aligner
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_landmarker_v2.task'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an face aligner benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the face aligner
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = face_aligner.FaceAlignerOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with face_aligner.FaceAligner.create_from_options(options) as aligner:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
aligner.align, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to face aligner task.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
34
mediapipe/tasks/python/benchmark/vision/face_detector/BUILD
Normal file
34
mediapipe/tasks/python/benchmark/vision/face_detector/BUILD
Normal file
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_detector_benchmark",
|
||||||
|
main = "face_detector_benchmark.py",
|
||||||
|
srcs = ["face_detector_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_detector",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe face detector benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_detector
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_detection_short_range.tflite'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an face detector benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the face detector
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = face_detector.FaceDetectorOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with face_detector.FaceDetector.create_from_options(options) as detector:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
detector.detect, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to face detector task.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_landmarker_benchmark",
|
||||||
|
main = "face_landmarker_benchmark.py",
|
||||||
|
srcs = ["face_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_landmarker",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe face landmarker benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_landmarker
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_landmarker_v2.task'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an face landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the face landmarker
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = face_landmarker.FaceLandmarkerOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with face_landmarker.FaceLandmarker.create_from_options(options) as landmarker:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
landmarker.detect, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to face landmarker task.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "hand_landmarker_benchmark",
|
||||||
|
main = "hand_landmarker_benchmark.py",
|
||||||
|
srcs = ["hand_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:hand_landmarker",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe hand landmarker benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import hand_landmarker
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'hand_landmarker.task'
|
||||||
|
_IMAGE_FILE = 'thumb_up.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an hand landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the hand landmarker
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = hand_landmarker.HandLandmarkerOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with hand_landmarker.HandLandmarker.create_from_options(options) as landmarker:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
landmarker.detect, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to hand landmarker task.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -16,12 +16,19 @@
|
||||||
|
|
||||||
package(default_visibility = ["//visibility:public"])
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
py_library(
|
py_binary(
|
||||||
name = "image_classifier_benchmark",
|
name = "image_classifier_benchmark",
|
||||||
|
main = "image_classifier_benchmark.py",
|
||||||
srcs = ["image_classifier_benchmark.py"],
|
srcs = ["image_classifier_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
deps = [
|
deps = [
|
||||||
"//mediapipe/python:_framework_bindings",
|
"//mediapipe/python:_framework_bindings",
|
||||||
"//mediapipe/tasks/python/core:base_options",
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
"//mediapipe/tasks/python/vision:image_classifier",
|
"//mediapipe/tasks/python/vision:image_classifier",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
],
|
],
|
||||||
)
|
)
|
||||||
|
|
|
@ -14,12 +14,14 @@
|
||||||
"""MediaPipe image classsifier benchmark."""
|
"""MediaPipe image classsifier benchmark."""
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
import time
|
|
||||||
import numpy as np
|
|
||||||
from mediapipe.python._framework_bindings import image
|
from mediapipe.python._framework_bindings import image
|
||||||
from mediapipe.tasks.python.core import base_options
|
from mediapipe.tasks.python.core import base_options
|
||||||
from mediapipe.tasks.python.vision import image_classifier
|
from mediapipe.tasks.python.vision import image_classifier
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
|
||||||
_IMAGE_FILE = 'burger.jpg'
|
_IMAGE_FILE = 'burger.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
@ -41,27 +43,29 @@ def run(
|
||||||
Returns:
|
Returns:
|
||||||
The n-th percentile of the inference times.
|
The n-th percentile of the inference times.
|
||||||
"""
|
"""
|
||||||
inference_times = []
|
|
||||||
|
|
||||||
# Initialize the image classifier
|
# Initialize the image classifier
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
options = image_classifier.ImageClassifierOptions(
|
options = image_classifier.ImageClassifierOptions(
|
||||||
base_options=base_options.BaseOptions(
|
base_options=base_options.BaseOptions(
|
||||||
model_asset_path=model, delegate=delegate
|
model_asset_path=model_path, delegate=delegate
|
||||||
),
|
),
|
||||||
max_results=1,
|
max_results=1,
|
||||||
)
|
)
|
||||||
classifier = image_classifier.ImageClassifier.create_from_options(options)
|
|
||||||
mp_image = image.Image.create_from_file(_IMAGE_FILE)
|
|
||||||
|
|
||||||
for _ in range(n_iterations):
|
with image_classifier.ImageClassifier.create_from_options(options) as classifier:
|
||||||
start_time_ns = time.time_ns()
|
mp_image = image.Image.create_from_file(
|
||||||
classifier.classify(mp_image)
|
benchmark_utils.get_test_data_path(
|
||||||
end_time_ns = time.time_ns()
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
# Convert to milliseconds
|
)
|
||||||
inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
classifier.close()
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
return np.percentile(inference_times, percentile)
|
classifier.classify, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
|
@ -72,7 +76,7 @@ def main():
|
||||||
'--model',
|
'--model',
|
||||||
help='Path to image classification model.',
|
help='Path to image classification model.',
|
||||||
required=False,
|
required=False,
|
||||||
default='classifier.tflite',
|
default=None,
|
||||||
)
|
)
|
||||||
parser.add_argument(
|
parser.add_argument(
|
||||||
'--iterations',
|
'--iterations',
|
||||||
|
|
34
mediapipe/tasks/python/benchmark/vision/image_embedder/BUILD
Normal file
34
mediapipe/tasks/python/benchmark/vision/image_embedder/BUILD
Normal file
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "image_embedder_benchmark",
|
||||||
|
main = "image_embedder_benchmark.py",
|
||||||
|
srcs = ["image_embedder_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:image_embedder",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe image embedder benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import image_embedder
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'mobilenet_v3_small_100_224_embedder.tflite'
|
||||||
|
_IMAGE_FILE = 'burger.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an image embedding extraction benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the image embedder
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = image_embedder.ImageEmbedderOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with image_embedder.ImageEmbedder.create_from_options(options) as embedder:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
embedder.embed, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to image embedding extraction model.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "image_segmenter_benchmark",
|
||||||
|
main = "image_segmenter_benchmark.py",
|
||||||
|
srcs = ["image_segmenter_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:image_segmenter",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,121 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe image segmenter benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import image_segmenter
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'deeplabv3.tflite'
|
||||||
|
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an image segmentation benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the image segmenter
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = image_segmenter.ImageSegmenterOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
),
|
||||||
|
output_confidence_masks=True, output_category_mask=True
|
||||||
|
)
|
||||||
|
|
||||||
|
with image_segmenter.ImageSegmenter.create_from_options(options) as segmenter:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
segmenter.segment, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to image segmentation model.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "interactive_segmenter_benchmark",
|
||||||
|
main = "interactive_segmenter_benchmark.py",
|
||||||
|
srcs = ["interactive_segmenter_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:interactive_segmenter",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,128 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe interactive segmenter benchmark."""
|
||||||
|
|
||||||
|
from functools import partial
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.components.containers import keypoint
|
||||||
|
from mediapipe.tasks.python.vision import interactive_segmenter
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'deeplabv3.tflite'
|
||||||
|
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an interactive segmentation benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the interactive segmenter
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
|
||||||
|
options = interactive_segmenter.InteractiveSegmenterOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
),
|
||||||
|
output_category_mask=True, output_confidence_masks=False
|
||||||
|
)
|
||||||
|
roi = interactive_segmenter.RegionOfInterest(
|
||||||
|
format=interactive_segmenter.RegionOfInterest.Format.KEYPOINT,
|
||||||
|
keypoint=keypoint.NormalizedKeypoint(0.44, 0.7)
|
||||||
|
)
|
||||||
|
|
||||||
|
with interactive_segmenter.InteractiveSegmenter.create_from_options(options) as segmenter:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
partial(segmenter.segment, roi=roi), mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to interactive segmentation model.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "object_detector_benchmark",
|
||||||
|
main = "object_detector_benchmark.py",
|
||||||
|
srcs = ["object_detector_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:object_detector",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe object detector benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import object_detector
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite'
|
||||||
|
_IMAGE_FILE = 'cats_and_dogs.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an object detector benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the object detector
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = object_detector.ObjectDetectorOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with object_detector.ObjectDetector.create_from_options(options) as detector:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
detector.detect, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to object detector model.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
|
@ -0,0 +1,34 @@
|
||||||
|
# Copyright 2022 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.
|
||||||
|
|
||||||
|
# Placeholder for internal Python strict library and test compatibility macro.
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "pose_landmarker_benchmark",
|
||||||
|
main = "pose_landmarker_benchmark.py",
|
||||||
|
srcs = ["pose_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:pose_landmarker",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,120 @@
|
||||||
|
# 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.
|
||||||
|
"""MediaPipe pose landmarker benchmark."""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import pose_landmarker
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
|
||||||
|
_MODEL_FILE = 'pose_landmarker.task'
|
||||||
|
_IMAGE_FILE = 'pose.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(
|
||||||
|
model: str,
|
||||||
|
n_iterations: int,
|
||||||
|
delegate: base_options.BaseOptions.Delegate,
|
||||||
|
percentile: float,
|
||||||
|
):
|
||||||
|
"""Run an pose landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
# Initialize the pose landmarker
|
||||||
|
default_model_path = benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
|
||||||
|
)
|
||||||
|
model_path = benchmark_utils.get_model_path(model, default_model_path)
|
||||||
|
options = pose_landmarker.PoseLandmarkerOptions(
|
||||||
|
base_options=base_options.BaseOptions(
|
||||||
|
model_asset_path=model_path, delegate=delegate
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
with pose_landmarker.PoseLandmarker.create_from_options(options) as landmarker:
|
||||||
|
mp_image = image.Image.create_from_file(
|
||||||
|
benchmark_utils.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# Run the benchmark and return the nth percentile of the inference times
|
||||||
|
nth_percentile = base_vision_benchmark_api.nth_percentile(
|
||||||
|
landmarker.detect, mp_image, n_iterations, percentile
|
||||||
|
)
|
||||||
|
return nth_percentile
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--model',
|
||||||
|
help='Path to pose landmarker task.',
|
||||||
|
required=False,
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
|
||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Run benchmark on CPU
|
||||||
|
cpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on CPU: '
|
||||||
|
f'{cpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run benchmark on GPU
|
||||||
|
gpu_time = run(
|
||||||
|
args.model,
|
||||||
|
args.iterations,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
args.percentile,
|
||||||
|
)
|
||||||
|
print(
|
||||||
|
f'{args.percentile}th Percentile Inference Time on GPU: '
|
||||||
|
f'{gpu_time:.6f} milliseconds'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
Loading…
Reference in New Issue
Block a user