Added image classifier benchmark

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Kinar 2023-11-11 03:25:36 -08:00
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commit 35f2f36733
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# 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.

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# 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.

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# MediaPipe Image Classifier Benchmark
## Download the repository
First, clone this Git repo.
Run this script to install the required dependencies and download the TFLite models:
```
cd mediapipe/tasks/python/benchmark/vision/image_classifier
sh setup.sh
```
## Run the benchmark
```
python3 image_classifier_benchmark.py
```
* You can optionally specify the `model` parameter to set the TensorFlow Lite
model to be used:
* The default value is `classifier.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)
* Models trained with [MediaPipe Model Maker](https://developers.google.com/mediapipe/solutions/customization/image_classifier) are supported.
* You can optionally specify the `iterations` parameter to limit the number of
iterations for benchmarking:
* Supported value: A positive integer.
* Default value: `100`
* Example usage:
```
python3 image_classifier_benchmark.py \
--model classifier.tflite \
--iterations 200
```

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# 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.
"""Benchmark for the image classifier task."""
import argparse
import time
import numpy as np
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
_IMAGE_FILE = 'burger.jpg'
def run(model: str, n_iterations: int, delegate: python.BaseOptions.Delegate):
"""Run asynchronous inference on images and benchmark.
Args:
model: Path to the TFLite model.
n_iterations: Number of iterations to run the benchmark.
delegate: CPU or GPU delegate for inference.
"""
inference_times = []
# Initialize the image classifier
base_options = python.BaseOptions(model_asset_path=model, delegate=delegate)
options = vision.ImageClassifierOptions(
base_options=base_options, running_mode=vision.RunningMode.IMAGE,
max_results=1)
classifier = vision.ImageClassifier.create_from_options(options)
mp_image = mp.Image.create_from_file(_IMAGE_FILE)
for _ in range(n_iterations):
start_time_ns = time.time_ns()
classifier.classify(mp_image)
end_time_ns = time.time_ns()
# Convert to milliseconds
inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
classifier.close()
return np.percentile(inference_times, 95)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--model', help='Path to image classification model.', required=True)
parser.add_argument(
'--iterations', help='Number of iterations for benchmarking.', type=int,
default=100)
args = parser.parse_args()
# Run benchmark on CPU
cpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.CPU)
print(f"95th Percentile Inference Time on CPU: {cpu_time:.6f} milliseconds")
# Run benchmark on GPU
gpu_time = run(args.model, args.iterations, python.BaseOptions.Delegate.GPU)
print(f"95th Percentile Inference Time on GPU: {gpu_time:.6f} milliseconds")
if __name__ == '__main__':
main()

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# Install Python dependencies.
python3 -m pip install pip --upgrade
python3 -m pip install mediapipe
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