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Diffstat (limited to 'model_conditioning_examples/quantization_aware_training.py')
| -rw-r--r-- | model_conditioning_examples/quantization_aware_training.py | 68 |
1 files changed, 44 insertions, 24 deletions
diff --git a/model_conditioning_examples/quantization_aware_training.py b/model_conditioning_examples/quantization_aware_training.py index 3d492a7..d590763 100644 --- a/model_conditioning_examples/quantization_aware_training.py +++ b/model_conditioning_examples/quantization_aware_training.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"); @@ -13,31 +13,38 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This script will provide you with a short example of how to perform quantization aware training in TensorFlow using the +This script will provide you with a short example of how to perform +quantization aware training in TensorFlow using the TensorFlow Model Optimization Toolkit. -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 quantization aware training (QAT), the error introduced with quantizing from fp32 to int8 is simulated using -fake quantization nodes. By simulating this quantization error when training, the model can learn better adapted -weights and minimize accuracy losses caused by the reduced precision. +In quantization aware training (QAT), the error introduced with +quantizing from fp32 to int8 is simulated using fake quantization nodes. +By simulating this quantization error when training, +the model can learn better adapted weights and minimize accuracy losses +caused by the reduced precision. -Minimum and maximum values for activations are also captured during training so activations for every layer can be -quantized along with the weights later. +Minimum and maximum values for activations are also captured +during training so activations for every layer can be quantized +along with the weights later. -Quantization is only simulated during training and the training backward passes are still performed in full float -precision. Actual quantization happens when generating a TensorFlow Lite model. +Quantization is only simulated during training and the +training backward passes are still performed in full float precision. +Actual quantization happens when generating a TensorFlow Lite model. -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 quantization aware training -see: https://www.tensorflow.org/model_optimization/guide/quantization/training +For more information on using vela see: + https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/ +For more information on quantization aware training see: + https://www.tensorflow.org/model_optimization/guide/quantization/training """ import pathlib @@ -64,13 +71,15 @@ def quantize_and_convert_to_tflite(keras_model): # After doing quantization aware training all the information for creating a fully quantized # TensorFlow Lite model is already within the quantization aware Keras model. - # This means we only need to call convert with default optimizations to generate the quantized TensorFlow Lite model. + # This means we only need to call convert with default optimizations to + # generate the quantized TensorFlow Lite model. converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_model = converter.convert() return tflite_model +# pylint: disable=duplicate-code def evaluate_tflite_model(tflite_save_path, x_test, y_test): """Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter. @@ -101,13 +110,19 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test): def main(): + """ + Run quantization aware training + """ x_train, y_train, x_test, y_test = get_data() model = create_model() - # When working with the TensorFlow Keras API and the TF Model Optimization Toolkit we can make our - # model quantization aware in one line. Once this is done we compile the model and train as normal. - # It is important to note that the model is only quantization aware and is not quantized yet. The weights are - # still floating point and will only be converted to int8 when we generate the TensorFlow Lite model later on. + # When working with the TensorFlow Keras API and theTF Model Optimization Toolkit + # we can make our model quantization aware in one line. + # Once this is done we compile the model and train as normal. + # It is important to note that the model is only quantization aware + # and is not quantized yet. + # The weights are still floating point and will only be converted + # to int8 when we generate the TensorFlow Lite model later on. quant_aware_model = tfmot.quantization.keras.quantize_model(model) quant_aware_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), @@ -117,7 +132,7 @@ def main(): quant_aware_model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True) # Test the quantization aware model accuracy. - test_loss, test_acc = quant_aware_model.evaluate(x_test, y_test) + test_loss, test_acc = quant_aware_model.evaluate(x_test, y_test) # pylint: disable=unused-variable print(f"Test accuracy quant aware: {test_acc:.3f}") # Quantize and save the resulting TensorFlow Lite model to file. @@ -132,7 +147,12 @@ def main(): # Test 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__": |