Merge pull request #4966 from kinaryml:python-vision-benchmark-scripts
PiperOrigin-RevId: 586349225
This commit is contained in:
commit
bb4906bcd3
24
mediapipe/tasks/python/benchmark/BUILD
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24
mediapipe/tasks/python/benchmark/BUILD
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# Copyright 2023 The MediaPipe Authors.
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#
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||||||
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||||
|
# you may not use this file except in compliance with the License.
|
||||||
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# You may obtain a copy of the License at
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||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
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||||||
|
#
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||||||
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# Unless required by applicable law or agreed to in writing, software
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||||||
|
# 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.
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||||||
|
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||||||
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# Placeholder: load py_library
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package(default_visibility = ["//visibility:public"])
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licenses(["notice"])
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py_library(
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name = "benchmark_utils",
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srcs = ["benchmark_utils.py"],
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)
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70
mediapipe/tasks/python/benchmark/benchmark_utils.py
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70
mediapipe/tasks/python/benchmark/benchmark_utils.py
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# Copyright 2023 The MediaPipe Authors.
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||||||
|
#
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||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
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||||||
|
# you may not use this file except in compliance with the License.
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||||||
|
# You may obtain a copy of the License at
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||||||
|
#
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||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
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||||||
|
#
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||||||
|
# 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."""
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import os
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import numpy as np
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def nth_percentile(inference_times, percentile):
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"""Calculate the nth percentile of the inference times."""
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return np.percentile(inference_times, percentile)
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def average(inference_times):
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"""Calculate the average of the inference times."""
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return np.mean(inference_times)
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def get_test_data_path(test_srcdir, file_or_dirname_path: str) -> str:
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|
"""Determine the test data path.
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|
Args:
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|
test_srcdir: The path to the test source directory.
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|
file_or_dirname_path: The path to the file or directory.
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|
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|
Returns:
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|
The full test data path.
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|
"""
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for directory, subdirs, files in os.walk(test_srcdir):
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|
for f in subdirs + files:
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path = os.path.join(directory, f)
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|
if path.endswith(file_or_dirname_path):
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|
return path
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|
raise ValueError(
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||||||
|
"No %s in test directory: %s." % (file_or_dirname_path, test_srcdir)
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|
)
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|
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||||||
|
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||||||
|
def get_model_path(custom_model, default_model_path):
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|
"""Determine the model path based on the existence of the custom model.
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|
|
||||||
|
Args:
|
||||||
|
custom_model: The path to the custom model provided by the user.
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|
default_model_path: The path to the default model.
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|
|
||||||
|
Returns:
|
||||||
|
The path to the model to be used.
|
||||||
|
"""
|
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|
if custom_model is not None and os.path.exists(custom_model):
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|
print(f"Using provided model: {custom_model}")
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|
return custom_model
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|
else:
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|
if custom_model is not None:
|
||||||
|
print(
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|
f"Warning: Provided model '{custom_model}' not found. "
|
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|
"Using default model instead."
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|
)
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|
print(f"Using default model: {default_model_path}")
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|
return default_model_path
|
33
mediapipe/tasks/python/benchmark/vision/BUILD
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33
mediapipe/tasks/python/benchmark/vision/BUILD
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|
# 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: load py_binary
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||||||
|
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||||||
|
package(default_visibility = ["//visibility:public"])
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|
py_binary(
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|
name = "benchmark",
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|
srcs = ["benchmark.py"],
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|
data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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|
],
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main = "benchmark.py",
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|
deps = [
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"//mediapipe/python:_framework_bindings",
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|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
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"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
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"//mediapipe/tasks/python/core:base_options",
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|
],
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|
)
|
99
mediapipe/tasks/python/benchmark/vision/benchmark.py
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99
mediapipe/tasks/python/benchmark/vision/benchmark.py
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|
# 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 benchmarker."""
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|
import argparse
|
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|
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|
from mediapipe.tasks.python.benchmark import benchmark_utils as bu
|
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|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
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||||||
|
from mediapipe.tasks.python.core import base_options
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||||||
|
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||||||
|
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||||||
|
def benchmarker(benchmark_function, default_model_name):
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||||||
|
"""Executes a benchmarking process using a specified function ann model.
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||||||
|
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||||||
|
Args:
|
||||||
|
benchmark_function: A callable function to be executed for benchmarking.
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||||||
|
This function should contain the logic of the task to be benchmarked and
|
||||||
|
should be capable of utilizing a model specified by its name.
|
||||||
|
default_model_name: The name or path of the default model to be used in
|
||||||
|
the benchmarking process. This is useful when the benchmarking function
|
||||||
|
requires a model and no other model is explicitly specified.
|
||||||
|
"""
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter
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||||||
|
)
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||||||
|
|
||||||
|
parser.add_argument(
|
||||||
|
'--mode',
|
||||||
|
help='Benchmarking mode (e.g., "nth_percentile").',
|
||||||
|
required=False,
|
||||||
|
default='nth_percentile',
|
||||||
|
)
|
||||||
|
parser.add_argument('--model', help='Path to the model.', default=None)
|
||||||
|
parser.add_argument(
|
||||||
|
'--iterations',
|
||||||
|
help='Number of iterations for benchmarking.',
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||||||
|
type=int,
|
||||||
|
default=100,
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
'--percentile',
|
||||||
|
help='Percentile for benchmarking statistics.',
|
||||||
|
type=float,
|
||||||
|
default=95.0,
|
||||||
|
)
|
||||||
|
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||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Get the model path
|
||||||
|
default_model_path = bu.get_test_data_path(
|
||||||
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, default_model_name
|
||||||
|
)
|
||||||
|
model_path = bu.get_model_path(args.model, default_model_path)
|
||||||
|
|
||||||
|
# Define a mapping of modes to their respective function argument lists
|
||||||
|
mode_args_mapping = {
|
||||||
|
'nth_percentile': {'percentile': args.percentile},
|
||||||
|
'average': {},
|
||||||
|
}
|
||||||
|
|
||||||
|
# Check if the mode is supported and get the argument dictionary
|
||||||
|
if args.mode not in mode_args_mapping:
|
||||||
|
raise ValueError(f'Unsupported benchmarking mode: {args.mode}')
|
||||||
|
|
||||||
|
mode_args = mode_args_mapping[args.mode]
|
||||||
|
|
||||||
|
# Run the benchmark for both CPU and GPU and calculate results based on mode
|
||||||
|
results = {}
|
||||||
|
for delegate_type in [
|
||||||
|
base_options.BaseOptions.Delegate.CPU,
|
||||||
|
base_options.BaseOptions.Delegate.GPU,
|
||||||
|
]:
|
||||||
|
inference_times = benchmark_function(
|
||||||
|
model_path, args.iterations, delegate_type
|
||||||
|
)
|
||||||
|
|
||||||
|
# Calculate the benchmark result based on the mode
|
||||||
|
if args.mode == 'nth_percentile':
|
||||||
|
results[delegate_type] = bu.nth_percentile(inference_times, **mode_args)
|
||||||
|
elif args.mode == 'average':
|
||||||
|
results[delegate_type] = bu.average(inference_times)
|
||||||
|
|
||||||
|
# Report benchmarking results
|
||||||
|
for delegate_type, result in results.items():
|
||||||
|
print(
|
||||||
|
f'Inference time {delegate_type} {mode_args_mapping[args.mode]}: '
|
||||||
|
f'{result:.6f} milliseconds'
|
||||||
|
)
|
22
mediapipe/tasks/python/benchmark/vision/core/BUILD
Normal file
22
mediapipe/tasks/python/benchmark/vision/core/BUILD
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|
||||||
|
# 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: load py_library
|
||||||
|
|
||||||
|
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
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|
||||||
|
"""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 +1,40 @@
|
||||||
|
# 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 time
|
||||||
|
|
||||||
|
VISION_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
|
||||||
|
|
||||||
|
|
||||||
|
def benchmark_task(func, image, n_iterations):
|
||||||
|
"""Collect inference times for a given task after benchmarking.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
func: The task function used for benchmarking.
|
||||||
|
image: The input MediaPipe Image.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of 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 inference_times
|
35
mediapipe/tasks/python/benchmark/vision/face_aligner/BUILD
Normal file
35
mediapipe/tasks/python/benchmark/vision/face_aligner/BUILD
Normal file
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_aligner_benchmark",
|
||||||
|
srcs = ["face_aligner_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "face_aligner_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_aligner",
|
||||||
|
],
|
||||||
|
)
|
|
@ -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.
|
||||||
|
"""MediaPipe face aligner benchmark."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_aligner
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_landmarker_v2.task'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run a face aligner benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the face aligner
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
aligner.align, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
35
mediapipe/tasks/python/benchmark/vision/face_detector/BUILD
Normal file
35
mediapipe/tasks/python/benchmark/vision/face_detector/BUILD
Normal file
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_detector_benchmark",
|
||||||
|
srcs = ["face_detector_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "face_detector_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_detector",
|
||||||
|
],
|
||||||
|
)
|
|
@ -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.
|
||||||
|
"""MediaPipe image embedder benchmark."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_detector
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_detection_short_range.tflite'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run a face detector benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the face detector
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
detector.detect, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "face_landmarker_benchmark",
|
||||||
|
srcs = ["face_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "face_landmarker_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:face_landmarker",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,60 @@
|
||||||
|
# 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."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import face_landmarker
|
||||||
|
|
||||||
|
_MODEL_FILE = 'face_landmarker_v2.task'
|
||||||
|
_IMAGE_FILE = 'portrait.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run a face landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the face landmarker
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
landmarker.detect, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "hand_landmarker_benchmark",
|
||||||
|
srcs = ["hand_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "hand_landmarker_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:hand_landmarker",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,60 @@
|
||||||
|
# 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."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import hand_landmarker
|
||||||
|
|
||||||
|
_MODEL_FILE = 'hand_landmarker.task'
|
||||||
|
_IMAGE_FILE = 'thumb_up.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run a hand landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the hand landmarker
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
landmarker.detect, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -12,15 +12,23 @@
|
||||||
# See the License for the specific language governing permissions and
|
# See the License for the specific language governing permissions and
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
|
|
||||||
# Placeholder for internal Python strict library and test compatibility macro.
|
# Placeholder: load py_binary
|
||||||
|
|
||||||
package(default_visibility = ["//visibility:public"])
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
py_library(
|
py_binary(
|
||||||
name = "image_classifier_benchmark",
|
name = "image_classifier_benchmark",
|
||||||
srcs = ["image_classifier_benchmark.py"],
|
srcs = ["image_classifier_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "image_classifier_benchmark.py",
|
||||||
deps = [
|
deps = [
|
||||||
"//mediapipe/python:_framework_bindings",
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
"//mediapipe/tasks/python/core:base_options",
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
"//mediapipe/tasks/python/vision:image_classifier",
|
"//mediapipe/tasks/python/vision:image_classifier",
|
||||||
],
|
],
|
||||||
|
|
|
@ -9,7 +9,6 @@ Run this commands to download the TFLite models and image files:
|
||||||
```
|
```
|
||||||
cd mediapipe/mediapipe/tasks/python/benchmark/vision/image_classifier
|
cd mediapipe/mediapipe/tasks/python/benchmark/vision/image_classifier
|
||||||
wget -O classifier.tflite -q https://storage.googleapis.com/mediapipe-models/image_classifier/efficientnet_lite0/float32/1/efficientnet_lite0.tflite
|
wget -O classifier.tflite -q https://storage.googleapis.com/mediapipe-models/image_classifier/efficientnet_lite0/float32/1/efficientnet_lite0.tflite
|
||||||
wget -O burger.jpg https://storage.googleapis.com/mediapipe-assets/burger.jpg
|
|
||||||
```
|
```
|
||||||
|
|
||||||
## Run the benchmark
|
## Run the benchmark
|
||||||
|
@ -18,7 +17,7 @@ bazel run -c opt //mediapipe/tasks/python/benchmark/vision/image_classifier:imag
|
||||||
```
|
```
|
||||||
* You can optionally specify the `model` parameter to set the TensorFlow Lite
|
* You can optionally specify the `model` parameter to set the TensorFlow Lite
|
||||||
model to be used:
|
model to be used:
|
||||||
* The default value is `classifier.tflite`
|
* The default value is `mobilenet_v2_1.0_224.tflite`
|
||||||
* TensorFlow Lite image classification models **with metadata**
|
* TensorFlow Lite image classification models **with metadata**
|
||||||
* Models from [TensorFlow Hub](https://tfhub.dev/tensorflow/collections/lite/task-library/image-classifier/1)
|
* Models from [TensorFlow Hub](https://tfhub.dev/tensorflow/collections/lite/task-library/image-classifier/1)
|
||||||
* Models from [MediaPipe Models](https://developers.google.com/mediapipe/solutions/vision/image_classifier/index#models)
|
* Models from [MediaPipe Models](https://developers.google.com/mediapipe/solutions/vision/image_classifier/index#models)
|
||||||
|
@ -29,7 +28,7 @@ bazel run -c opt //mediapipe/tasks/python/benchmark/vision/image_classifier:imag
|
||||||
* Default value: `100`
|
* Default value: `100`
|
||||||
* Example usage:
|
* Example usage:
|
||||||
```
|
```
|
||||||
bazel run -c opt :image_classifier_benchmark \
|
bazel run -c opt :image_classifier_benchmark -- \
|
||||||
--model classifier.tflite \
|
--model classifier.tflite \
|
||||||
--iterations 200
|
--iterations 200
|
||||||
```
|
```
|
||||||
|
|
|
@ -11,107 +11,51 @@
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
# See the License for the specific language governing permissions and
|
# See the License for the specific language governing permissions and
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
"""MediaPipe image classsifier benchmark."""
|
"""MediaPipe image classifier benchmark."""
|
||||||
|
|
||||||
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.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
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
|
||||||
|
|
||||||
|
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
|
||||||
_IMAGE_FILE = 'burger.jpg'
|
_IMAGE_FILE = 'burger.jpg'
|
||||||
|
|
||||||
|
|
||||||
def run(
|
def run(model_path, n_iterations, delegate):
|
||||||
model: str,
|
"""Run an image classifier benchmark.
|
||||||
n_iterations: int,
|
|
||||||
delegate: base_options.BaseOptions.Delegate,
|
|
||||||
percentile: float,
|
|
||||||
):
|
|
||||||
"""Run an image classification benchmark.
|
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
model: Path to the TFLite model.
|
model_path: 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:
|
Returns:
|
||||||
The n-th percentile of the inference times.
|
List of inference times.
|
||||||
"""
|
"""
|
||||||
inference_times = []
|
|
||||||
|
|
||||||
# Initialize the image classifier
|
# Initialize the image classifier
|
||||||
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(
|
||||||
start_time_ns = time.time_ns()
|
options
|
||||||
classifier.classify(mp_image)
|
) as classifier:
|
||||||
end_time_ns = time.time_ns()
|
mp_image = image.Image.create_from_file(
|
||||||
# Convert to milliseconds
|
benchmark_utils.get_test_data_path(
|
||||||
inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
|
base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
|
||||||
|
)
|
||||||
classifier.close()
|
)
|
||||||
return np.percentile(inference_times, percentile)
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
classifier.classify, mp_image, n_iterations
|
||||||
|
)
|
||||||
def main():
|
return inference_times
|
||||||
parser = argparse.ArgumentParser(
|
|
||||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--model',
|
|
||||||
help='Path to image classification model.',
|
|
||||||
required=False,
|
|
||||||
default='classifier.tflite',
|
|
||||||
)
|
|
||||||
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__':
|
if __name__ == '__main__':
|
||||||
main()
|
benchmark.benchmarker(run, _MODEL_FILE)
|
||||||
|
|
35
mediapipe/tasks/python/benchmark/vision/image_embedder/BUILD
Normal file
35
mediapipe/tasks/python/benchmark/vision/image_embedder/BUILD
Normal file
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "image_embedder_benchmark",
|
||||||
|
srcs = ["image_embedder_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "image_embedder_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:image_embedder",
|
||||||
|
],
|
||||||
|
)
|
|
@ -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.
|
||||||
|
"""MediaPipe image embedder benchmark."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import image_embedder
|
||||||
|
|
||||||
|
_MODEL_FILE = 'mobilenet_v3_small_100_224_embedder.tflite'
|
||||||
|
_IMAGE_FILE = 'burger.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run an image embedding benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the image embedder
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
embedder.embed, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "image_segmenter_benchmark",
|
||||||
|
srcs = ["image_segmenter_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "image_segmenter_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:image_segmenter",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,60 @@
|
||||||
|
# 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."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import image_segmenter
|
||||||
|
|
||||||
|
_MODEL_FILE = 'deeplabv3.tflite'
|
||||||
|
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run an image segmenter benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the image segmenter
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
segmenter.segment, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "interactive_segmenter_benchmark",
|
||||||
|
srcs = ["interactive_segmenter_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "interactive_segmenter_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:interactive_segmenter",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,68 @@
|
||||||
|
# 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."""
|
||||||
|
import functools
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.components.containers import keypoint
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import interactive_segmenter
|
||||||
|
|
||||||
|
_MODEL_FILE = 'ptm_512_hdt_ptm_woid.tflite'
|
||||||
|
_IMAGE_FILE = 'cats_and_dogs.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run an interactive segmenter benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the image segmenter
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
functools.partial(segmenter.segment, roi=roi), mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "object_detector_benchmark",
|
||||||
|
srcs = ["object_detector_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "object_detector_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:object_detector",
|
||||||
|
],
|
||||||
|
)
|
|
@ -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.
|
||||||
|
"""MediaPipe object detector benchmark."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import object_detector
|
||||||
|
|
||||||
|
_MODEL_FILE = 'coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite'
|
||||||
|
_IMAGE_FILE = 'cats_and_dogs.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run an object detector benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the object detector
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
detector.detect, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
|
@ -0,0 +1,35 @@
|
||||||
|
# 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: load py_binary
|
||||||
|
|
||||||
|
package(default_visibility = ["//visibility:public"])
|
||||||
|
|
||||||
|
py_binary(
|
||||||
|
name = "pose_landmarker_benchmark",
|
||||||
|
srcs = ["pose_landmarker_benchmark.py"],
|
||||||
|
data = [
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_images",
|
||||||
|
"//mediapipe/tasks/testdata/vision:test_models",
|
||||||
|
],
|
||||||
|
main = "pose_landmarker_benchmark.py",
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||||
|
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||||
|
"//mediapipe/tasks/python/core:base_options",
|
||||||
|
"//mediapipe/tasks/python/vision:pose_landmarker",
|
||||||
|
],
|
||||||
|
)
|
|
@ -0,0 +1,60 @@
|
||||||
|
# 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."""
|
||||||
|
|
||||||
|
from mediapipe.python._framework_bindings import image
|
||||||
|
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||||
|
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||||
|
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||||
|
from mediapipe.tasks.python.core import base_options
|
||||||
|
from mediapipe.tasks.python.vision import pose_landmarker
|
||||||
|
|
||||||
|
_MODEL_FILE = 'pose_landmarker.task'
|
||||||
|
_IMAGE_FILE = 'pose.jpg'
|
||||||
|
|
||||||
|
|
||||||
|
def run(model_path, n_iterations, delegate):
|
||||||
|
"""Run an pose landmarker benchmark.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_path: Path to the TFLite model.
|
||||||
|
n_iterations: Number of iterations to run the benchmark.
|
||||||
|
delegate: CPU or GPU delegate for inference.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of inference times.
|
||||||
|
"""
|
||||||
|
# Initialize the pose landmarker
|
||||||
|
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
|
||||||
|
)
|
||||||
|
)
|
||||||
|
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||||
|
landmarker.detect, mp_image, n_iterations
|
||||||
|
)
|
||||||
|
return inference_times
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
benchmark.benchmarker(run, _MODEL_FILE)
|
2
mediapipe/tasks/testdata/vision/BUILD
vendored
2
mediapipe/tasks/testdata/vision/BUILD
vendored
|
@ -90,6 +90,7 @@ mediapipe_files(srcs = [
|
||||||
"pose_landmark_lite.tflite",
|
"pose_landmark_lite.tflite",
|
||||||
"pose_landmarker.task",
|
"pose_landmarker.task",
|
||||||
"pose_segmentation_mask_golden.png",
|
"pose_segmentation_mask_golden.png",
|
||||||
|
"ptm_512_hdt_ptm_woid.tflite",
|
||||||
"right_hands.jpg",
|
"right_hands.jpg",
|
||||||
"right_hands_rotated.jpg",
|
"right_hands_rotated.jpg",
|
||||||
"segmentation_golden_rotation0.png",
|
"segmentation_golden_rotation0.png",
|
||||||
|
@ -202,6 +203,7 @@ filegroup(
|
||||||
"pose_detection.tflite",
|
"pose_detection.tflite",
|
||||||
"pose_landmark_lite.tflite",
|
"pose_landmark_lite.tflite",
|
||||||
"pose_landmarker.task",
|
"pose_landmarker.task",
|
||||||
|
"ptm_512_hdt_ptm_woid.tflite",
|
||||||
"selfie_segm_128_128_3.tflite",
|
"selfie_segm_128_128_3.tflite",
|
||||||
"selfie_segm_144_256_3.tflite",
|
"selfie_segm_144_256_3.tflite",
|
||||||
"selfie_segmentation.tflite",
|
"selfie_segmentation.tflite",
|
||||||
|
|
Loading…
Reference in New Issue
Block a user