Code cleanup and revised benchmarking API
This commit is contained in:
parent
8f32fda6d8
commit
46c6c9403c
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@ -14,6 +14,17 @@
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"""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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33
mediapipe/tasks/python/benchmark/vision/BUILD
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33
mediapipe/tasks/python/benchmark/vision/BUILD
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@ -0,0 +1,33 @@
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# Copyright 2022 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Placeholder for internal Python strict library and test compatibility macro.
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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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main = "benchmark.py",
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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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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/tasks/python/core:base_options",
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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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],
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)
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104
mediapipe/tasks/python/benchmark/vision/benchmark.py
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104
mediapipe/tasks/python/benchmark/vision/benchmark.py
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@ -0,0 +1,104 @@
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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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe vision benchmarker."""
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import argparse
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from mediapipe.tasks.python.core import base_options
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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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def benchmarker(benchmark_function, default_model_name, default_image_name):
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"""Executes a benchmarking process using a specified function and
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a default model (or a specified model) and reports the benchmarking
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statistics.
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Args:
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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
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should be capable of utilizing a model specified by its name.
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default_model_name: The name or path of the default model to be used in
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the benchmarking process. This is useful when the benchmarking function
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requires a model and no other model is explicitly specified.
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"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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'--mode',
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help='Benchmarking mode (e.g., "nth_percentile").',
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required=False,
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default='nth_percentile'
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)
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parser.add_argument(
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'--model',
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help='Path to the model.',
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default=None
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)
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parser.add_argument(
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'--iterations',
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help='Number of iterations for benchmarking.',
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type=int,
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default=100
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)
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parser.add_argument(
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'--percentile',
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help='Percentile for benchmarking statistics.',
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type=float,
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default=95.0
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)
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args = parser.parse_args()
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# Get the model path
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default_model_path = bu.get_test_data_path(
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base_vision_benchmark_api.VISION_TEST_DATA_DIR,
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default_model_name
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)
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model_path = bu.get_model_path(args.model, default_model_path)
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# Define a mapping of modes to their respective function argument lists
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mode_args_mapping = {
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'nth_percentile': {'percentile': args.percentile},
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'average': {}
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# Add other modes and their arguments here
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}
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# Check if the mode is supported and get the argument dictionary
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if args.mode not in mode_args_mapping:
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raise ValueError(f"Unsupported benchmarking mode: {args.mode}")
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mode_args = mode_args_mapping[args.mode]
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# Run the benchmark for both CPU and GPU and calculate results based on mode
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results = {}
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for delegate_type in [
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base_options.BaseOptions.Delegate.CPU,
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base_options.BaseOptions.Delegate.GPU
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]:
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inference_times = benchmark_function(model_path, args.iterations,
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delegate_type)
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# Calculate the benchmark result based on the mode
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if args.mode == 'nth_percentile':
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results[delegate_type] = bu.nth_percentile(inference_times, **mode_args)
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elif args.mode == 'average':
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results[delegate_type] = bu.average(inference_times)
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# Report benchmarking results
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for delegate_type, result in results.items():
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print(f'Inference time {delegate_type} {mode_args_mapping[args.mode]}: '
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f'{result:.6f} milliseconds')
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe vision benchmark base api."""
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import os
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import time
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import numpy as np
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VISION_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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def nth_percentile(func, image, n_iterations, percentile):
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"""Run a nth percentile benchmark for a given task using the function.
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def benchmark_task(func, image, n_iterations):
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"""Collect inference times for a given task after benchmarking
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Args:
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func: The method associated with a given task used for benchmarking.
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func: The task function used for benchmarking.
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image: The input MediaPipe Image.
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n_iterations: Number of iterations to run the benchmark.
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percentile: Percentage for the percentiles to compute. Values must be
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between 0 and 100 inclusive.
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Returns:
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The n-th percentile of the inference times in milliseconds.
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List of inference times in milliseconds.
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"""
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inference_times = []
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end_time_ns = time.time_ns()
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inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
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return np.percentile(inference_times, percentile)
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return inference_times
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Placeholder for internal Python strict library and test compatibility macro.
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# Placeholder for internal Python strict binary compatibility macro.
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package(default_visibility = ["//visibility:public"])
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"//mediapipe/tasks/python/vision:face_aligner",
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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/benchmark/vision:benchmark",
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],
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)
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# limitations under the License.
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"""MediaPipe face aligner benchmark."""
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import argparse
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from mediapipe.python._framework_bindings import image
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from mediapipe.tasks.python.core import base_options
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from mediapipe.tasks.python.vision import face_aligner
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from mediapipe.tasks.python.benchmark import benchmark_utils
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from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
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from mediapipe.tasks.python.benchmark.vision import benchmark
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_MODEL_FILE = 'face_landmarker_v2.task'
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_IMAGE_FILE = 'portrait.jpg'
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def run(
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model: str,
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n_iterations: int,
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delegate: base_options.BaseOptions.Delegate,
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percentile: float,
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):
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"""Run an face aligner benchmark.
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def run(model_path, n_iterations, delegate):
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"""Run a face aligner benchmark.
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Args:
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model: Path to the TFLite model.
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model_path: Path to the TFLite model.
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n_iterations: Number of iterations to run the benchmark.
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delegate: CPU or GPU delegate for inference.
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percentile: Percentage for the percentiles to compute. Values must be
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between 0 and 100 inclusive.
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Returns:
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The n-th percentile of the inference times.
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List of inference times.
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"""
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# Initialize the face aligner
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default_model_path = benchmark_utils.get_test_data_path(
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base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
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)
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model_path = benchmark_utils.get_model_path(model, default_model_path)
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options = face_aligner.FaceAlignerOptions(
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base_options=base_options.BaseOptions(
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model_asset_path=model_path, delegate=delegate
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)
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base_options=base_options.BaseOptions(
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model_asset_path=model_path, delegate=delegate
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)
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)
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with face_aligner.FaceAligner.create_from_options(options) as aligner:
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base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
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)
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)
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# Run the benchmark and return the nth percentile of the inference times
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nth_percentile = base_vision_benchmark_api.nth_percentile(
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aligner.align, mp_image, n_iterations, percentile
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inference_times = base_vision_benchmark_api.benchmark_task(
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aligner.align, mp_image, n_iterations
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)
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return nth_percentile
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def main():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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'--model',
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help='Path to face aligner task.',
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required=False,
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default=None,
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)
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parser.add_argument(
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'--iterations',
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help='Number of iterations for benchmarking.',
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type=int,
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default=100,
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)
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parser.add_argument(
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'--percentile',
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help='Percentile for benchmarking statistics.',
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type=float,
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default=95.0,
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)
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args = parser.parse_args()
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# Run benchmark on CPU
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cpu_time = run(
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args.model,
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args.iterations,
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base_options.BaseOptions.Delegate.CPU,
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args.percentile,
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)
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print(
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f'{args.percentile}th Percentile Inference Time on CPU: '
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f'{cpu_time:.6f} milliseconds'
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)
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# Run benchmark on GPU
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gpu_time = run(
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args.model,
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args.iterations,
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base_options.BaseOptions.Delegate.GPU,
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args.percentile,
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)
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print(
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f'{args.percentile}th Percentile Inference Time on GPU: '
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f'{gpu_time:.6f} milliseconds'
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)
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return inference_times
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if __name__ == '__main__':
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main()
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benchmark.benchmarker(run, _MODEL_FILE)
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|
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@ -12,7 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Placeholder for internal Python strict library and test compatibility macro.
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# Placeholder for internal Python strict binary compatibility macro.
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package(default_visibility = ["//visibility:public"])
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"//mediapipe/tasks/python/vision:face_detector",
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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/benchmark/vision:benchmark",
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],
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)
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@ -11,47 +11,35 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe face detector benchmark."""
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import argparse
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"""MediaPipe image embedder benchmark."""
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from mediapipe.python._framework_bindings import image
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from mediapipe.tasks.python.core import base_options
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from mediapipe.tasks.python.vision import face_detector
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from mediapipe.tasks.python.benchmark import benchmark_utils
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from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
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from mediapipe.tasks.python.benchmark.vision import benchmark
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_MODEL_FILE = 'face_detection_short_range.tflite'
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_IMAGE_FILE = 'portrait.jpg'
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def run(
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model: str,
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n_iterations: int,
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delegate: base_options.BaseOptions.Delegate,
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percentile: float,
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):
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"""Run an face detector benchmark.
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def run(model_path, n_iterations, delegate):
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"""Run a face detector benchmark.
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Args:
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model: Path to the TFLite model.
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model_path: Path to the TFLite model.
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n_iterations: Number of iterations to run the benchmark.
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delegate: CPU or GPU delegate for inference.
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percentile: Percentage for the percentiles to compute. Values must be
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between 0 and 100 inclusive.
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Returns:
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The n-th percentile of the inference times.
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List of inference times.
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"""
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# Initialize the face detector
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default_model_path = benchmark_utils.get_test_data_path(
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base_vision_benchmark_api.VISION_TEST_DATA_DIR, _MODEL_FILE
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)
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model_path = benchmark_utils.get_model_path(model, default_model_path)
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options = face_detector.FaceDetectorOptions(
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base_options=base_options.BaseOptions(
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model_asset_path=model_path, delegate=delegate
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)
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base_options=base_options.BaseOptions(
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model_asset_path=model_path, delegate=delegate
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)
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)
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with face_detector.FaceDetector.create_from_options(options) as detector:
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|
@ -60,61 +48,11 @@ def run(
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base_vision_benchmark_api.VISION_TEST_DATA_DIR, _IMAGE_FILE
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)
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)
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# Run the benchmark and return the nth percentile of the inference times
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nth_percentile = base_vision_benchmark_api.nth_percentile(
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detector.detect, mp_image, n_iterations, percentile
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inference_times = base_vision_benchmark_api.benchmark_task(
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detector.detect, mp_image, n_iterations
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)
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return nth_percentile
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def main():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
|
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'--model',
|
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help='Path to face detector task.',
|
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required=False,
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default=None,
|
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)
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parser.add_argument(
|
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'--iterations',
|
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help='Number of iterations for benchmarking.',
|
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type=int,
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default=100,
|
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)
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parser.add_argument(
|
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'--percentile',
|
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help='Percentile for benchmarking statistics.',
|
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type=float,
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default=95.0,
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)
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args = parser.parse_args()
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# Run benchmark on CPU
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cpu_time = run(
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args.model,
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args.iterations,
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base_options.BaseOptions.Delegate.CPU,
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args.percentile,
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)
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print(
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f'{args.percentile}th Percentile Inference Time on CPU: '
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f'{cpu_time:.6f} milliseconds'
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)
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# Run benchmark on GPU
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gpu_time = run(
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args.model,
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args.iterations,
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base_options.BaseOptions.Delegate.GPU,
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args.percentile,
|
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)
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print(
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f'{args.percentile}th Percentile Inference Time on GPU: '
|
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f'{gpu_time:.6f} milliseconds'
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)
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return inference_times
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if __name__ == '__main__':
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main()
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benchmark.benchmarker(run, _MODEL_FILE)
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|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:face_landmarker",
|
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"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,41 +13,29 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_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.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run a face landmarker benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
|
@ -60,61 +48,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
landmarker.detect, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:hand_landmarker",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,45 +13,33 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_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.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run a hand landmarker benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
)
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
)
|
||||
)
|
||||
|
||||
with hand_landmarker.HandLandmarker.create_from_options(options) as landmarker:
|
||||
|
@ -60,61 +48,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
landmarker.detect, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:image_classifier",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -9,7 +9,6 @@ Run this commands to download the TFLite models and image files:
|
|||
```
|
||||
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 burger.jpg https://storage.googleapis.com/mediapipe-assets/burger.jpg
|
||||
```
|
||||
|
||||
## 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
|
||||
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**
|
||||
* 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)
|
||||
|
@ -29,7 +28,7 @@ bazel run -c opt //mediapipe/tasks/python/benchmark/vision/image_classifier:imag
|
|||
* Default value: `100`
|
||||
* Example usage:
|
||||
```
|
||||
bazel run -c opt :image_classifier_benchmark \
|
||||
bazel run -c opt :image_classifier_benchmark -- \
|
||||
--model classifier.tflite \
|
||||
--iterations 200
|
||||
```
|
||||
|
|
|
@ -11,48 +11,36 @@
|
|||
# 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 classsifier benchmark."""
|
||||
|
||||
import argparse
|
||||
"""MediaPipe image classifier benchmark."""
|
||||
|
||||
from mediapipe.python._framework_bindings import image
|
||||
from mediapipe.tasks.python.core import base_options
|
||||
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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
|
||||
_IMAGE_FILE = 'burger.jpg'
|
||||
|
||||
|
||||
def run(
|
||||
model: str,
|
||||
n_iterations: int,
|
||||
delegate: base_options.BaseOptions.Delegate,
|
||||
percentile: float,
|
||||
):
|
||||
"""Run an image classification benchmark.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an image classifier benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of inference times.
|
||||
"""
|
||||
# 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(
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
),
|
||||
max_results=1,
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
),
|
||||
max_results=1,
|
||||
)
|
||||
|
||||
with image_classifier.ImageClassifier.create_from_options(options) as classifier:
|
||||
|
@ -61,61 +49,11 @@ def run(
|
|||
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(
|
||||
classifier.classify, mp_image, n_iterations, percentile
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
classifier.classify, mp_image, n_iterations
|
||||
)
|
||||
return nth_percentile
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument(
|
||||
'--model',
|
||||
help='Path to image classification 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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:image_embedder",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,41 +13,29 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_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.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an image embedding benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
|
@ -60,61 +48,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
embedder.embed, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:image_segmenter",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,41 +13,29 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_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.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an image segmenter benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
|
@ -61,61 +49,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
segmenter.segment, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:interactive_segmenter",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -12,45 +12,32 @@
|
|||
# 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.components.containers import keypoint
|
||||
from mediapipe.tasks.python.benchmark import benchmark_utils
|
||||
from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_MODEL_FILE = 'deeplabv3.tflite'
|
||||
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
|
||||
_MODEL_FILE = 'ptm_512_hdt_ptm_woid.tflite'
|
||||
_IMAGE_FILE = 'cats_and_dogs.jpg'
|
||||
|
||||
|
||||
def run(
|
||||
model: str,
|
||||
n_iterations: int,
|
||||
delegate: base_options.BaseOptions.Delegate,
|
||||
percentile: float,
|
||||
):
|
||||
"""Run an interactive segmentation benchmark.
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an interactive segmenter benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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)
|
||||
|
||||
# Initialize the image segmenter
|
||||
options = interactive_segmenter.InteractiveSegmenterOptions(
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
|
@ -68,61 +55,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
partial(segmenter.segment, roi=roi), mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:object_detector",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,45 +13,33 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_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,
|
||||
):
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an object detector benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
)
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
)
|
||||
)
|
||||
|
||||
with object_detector.ObjectDetector.create_from_options(options) as detector:
|
||||
|
@ -60,61 +48,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
detector.detect, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
benchmark.benchmarker(run, _MODEL_FILE)
|
||||
|
|
|
@ -12,7 +12,7 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
# Placeholder for internal Python strict binary compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
||||
|
@ -30,5 +30,6 @@ py_binary(
|
|||
"//mediapipe/tasks/python/vision:pose_landmarker",
|
||||
"//mediapipe/tasks/python/benchmark:benchmark_utils",
|
||||
"//mediapipe/tasks/python/benchmark/vision/core:base_vision_benchmark_api",
|
||||
"//mediapipe/tasks/python/benchmark/vision:benchmark",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,45 +13,33 @@
|
|||
# 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
|
||||
from mediapipe.tasks.python.benchmark.vision import benchmark
|
||||
|
||||
_MODEL_FILE = 'pose_landmarker.task'
|
||||
_IMAGE_FILE = 'pose.jpg'
|
||||
|
||||
|
||||
def run(
|
||||
model: str,
|
||||
n_iterations: int,
|
||||
delegate: base_options.BaseOptions.Delegate,
|
||||
percentile: float,
|
||||
):
|
||||
def run(model_path, n_iterations, delegate):
|
||||
"""Run an pose landmarker benchmark.
|
||||
|
||||
Args:
|
||||
model: Path to the TFLite model.
|
||||
model_path: 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.
|
||||
List of 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
|
||||
)
|
||||
base_options=base_options.BaseOptions(
|
||||
model_asset_path=model_path, delegate=delegate
|
||||
)
|
||||
)
|
||||
|
||||
with pose_landmarker.PoseLandmarker.create_from_options(options) as landmarker:
|
||||
|
@ -60,61 +48,11 @@ def run(
|
|||
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
|
||||
inference_times = base_vision_benchmark_api.benchmark_task(
|
||||
landmarker.detect, mp_image, n_iterations
|
||||
)
|
||||
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'
|
||||
)
|
||||
return inference_times
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
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_landmarker.task",
|
||||
"pose_segmentation_mask_golden.png",
|
||||
"ptm_512_hdt_ptm_woid.tflite",
|
||||
"right_hands.jpg",
|
||||
"right_hands_rotated.jpg",
|
||||
"segmentation_golden_rotation0.png",
|
||||
|
@ -202,6 +203,7 @@ filegroup(
|
|||
"pose_detection.tflite",
|
||||
"pose_landmark_lite.tflite",
|
||||
"pose_landmarker.task",
|
||||
"ptm_512_hdt_ptm_woid.tflite",
|
||||
"selfie_segm_128_128_3.tflite",
|
||||
"selfie_segm_144_256_3.tflite",
|
||||
"selfie_segmentation.tflite",
|
||||
|
|
Loading…
Reference in New Issue
Block a user