diff options
Diffstat (limited to 'model_conditioning_examples/post_training_quantization.py')
| -rw-r--r-- | model_conditioning_examples/post_training_quantization.py | 61 |
1 files changed, 41 insertions, 20 deletions
diff --git a/model_conditioning_examples/post_training_quantization.py b/model_conditioning_examples/post_training_quantization.py index a39be0e..42069f5 100644 --- a/model_conditioning_examples/post_training_quantization.py +++ b/model_conditioning_examples/post_training_quantization.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com> +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com> # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,28 +13,34 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This script will provide you with an example of how to perform post-training quantization in TensorFlow. +This script will provide you with an example of how to perform +post-training quantization in TensorFlow. -The output from this example will be a TensorFlow Lite model file where weights and activations are quantized to 8bit -integer values. +The output from this example will be a TensorFlow Lite model file +where weights and activations are quantized to 8bit integer values. -Quantization helps reduce the size of your models and is necessary for running models on certain hardware such as Arm -Ethos NPU. +Quantization helps reduce the size of your models and is necessary +for running models on certain hardware such as Arm Ethos NPU. -In addition to quantizing weights, post-training quantization uses a calibration dataset to -capture the minimum and maximum values of all variable tensors in your model. -By capturing these ranges it is possible to fully quantize not just the weights of the model but also the activations. +In addition to quantizing weights, post-training quantization uses +a calibration dataset to capture the minimum and maximum values of +all variable tensors in your model. By capturing these ranges it +is possible to fully quantize not just the weights of the model +but also the activations. -Depending on the model you are quantizing there may be some accuracy loss, but for a lot of models the loss should -be minimal. +Depending on the model you are quantizing there may be some accuracy loss, +but for a lot of models the loss should be minimal. -If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela +If you are targeting an Arm Ethos-U55 NPU then the output +TensorFlow Lite file will also need to be passed through the Vela compiler for further optimizations before it can be used. -For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/ -For more information on post-training quantization -see: https://www.tensorflow.org/lite/performance/post_training_integer_quant +For more information on using Vela see: + https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/ +For more information on post-training quantization see: + https://www.tensorflow.org/lite/performance/post_training_integer_quant """ + import pathlib import numpy as np @@ -44,7 +50,8 @@ from training_utils import get_data, create_model def post_training_quantize(keras_model, sample_data): - """Quantize Keras model using post-training quantization with some sample data. + """ + Quantize Keras model using post-training quantization with some sample data. TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing. @@ -76,8 +83,14 @@ def post_training_quantize(keras_model, sample_data): return tflite_model -def evaluate_tflite_model(tflite_save_path, x_test, y_test): - """Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter. +# pylint: disable=duplicate-code +def evaluate_tflite_model( + tflite_save_path: pathlib.Path, + x_test: np.ndarray, + y_test: np.ndarray +): + """ + Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter. Args: tflite_save_path: Path to TensorFlow Lite model to test. @@ -106,6 +119,9 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test): def main(): + """ + Run post-training quantization + """ x_train, y_train, x_test, y_test = get_data() model = create_model() @@ -117,7 +133,7 @@ def main(): model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True) # Test the fp32 model accuracy. - test_loss, test_acc = model.evaluate(x_test, y_test) + test_loss, test_acc = model.evaluate(x_test, y_test) # pylint: disable=unused-variable print(f"Test accuracy float: {test_acc:.3f}") # Quantize and export the resulting TensorFlow Lite model to file. @@ -132,7 +148,12 @@ def main(): # Test the quantized model accuracy. Save time by only testing a subset of the whole data. num_test_samples = 1000 - evaluate_tflite_model(quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples]) + evaluate_tflite_model( + quant_model_save_path, + x_test[0:num_test_samples], + y_test[0:num_test_samples] + ) +# pylint: enable=duplicate-code if __name__ == "__main__": |