| # Copyright (c) 2021 Arm Limited. All rights reserved. |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # 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. |
| """ |
| 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. |
| |
| 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. |
| |
| 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. |
| |
| If you are targetting 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 |
| """ |
| import pathlib |
| |
| import numpy as np |
| import tensorflow as tf |
| import tensorflow_model_optimization as tfmot |
| |
| from training_utils import get_data, create_model |
| |
| |
| def quantize_and_convert_to_tflite(keras_model): |
| """Quantize and convert Keras model trained with QAT to TensorFlow Lite. |
| |
| TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing. |
| |
| Args: |
| keras_model: Keras model trained with quantization aware training. |
| |
| Returns: |
| Quantized TensorFlow Lite model. |
| """ |
| |
| converter = tf.lite.TFLiteConverter.from_keras_model(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. |
| converter.optimizations = [tf.lite.Optimize.DEFAULT] |
| tflite_model = converter.convert() |
| |
| 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. |
| |
| Args: |
| tflite_save_path: Path to TensorFlow Lite model to test. |
| x_test: numpy array of testing data. |
| y_test: numpy array of testing labels (sparse categorical). |
| """ |
| |
| interpreter = tf.lite.Interpreter(model_path=str(tflite_save_path)) |
| |
| interpreter.allocate_tensors() |
| input_details = interpreter.get_input_details() |
| output_details = interpreter.get_output_details() |
| |
| accuracy_count = 0 |
| num_test_images = len(y_test) |
| |
| for i in range(num_test_images): |
| interpreter.set_tensor(input_details[0]['index'], x_test[i][np.newaxis, ...]) |
| interpreter.invoke() |
| output_data = interpreter.get_tensor(output_details[0]['index']) |
| |
| if np.argmax(output_data) == y_test[i]: |
| accuracy_count += 1 |
| |
| print(f"Test accuracy quantized: {accuracy_count / num_test_images:.3f}") |
| |
| |
| def main(): |
| 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. |
| quant_aware_model = tfmot.quantization.keras.quantize_model(model) |
| |
| quant_aware_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), |
| loss=tf.keras.losses.sparse_categorical_crossentropy, |
| metrics=['accuracy']) |
| |
| 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) |
| print(f"Test accuracy quant aware: {test_acc:.3f}") |
| |
| # Quantize and save the resulting TensorFlow Lite model to file. |
| tflite_model = quantize_and_convert_to_tflite(quant_aware_model) |
| |
| tflite_models_dir = pathlib.Path('./conditioned_models/') |
| tflite_models_dir.mkdir(exist_ok=True, parents=True) |
| |
| quant_model_save_path = tflite_models_dir / 'qat_quant_model.tflite' |
| with open(quant_model_save_path, 'wb') as f: |
| f.write(tflite_model) |
| |
| # 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]) |
| |
| |
| if __name__ == "__main__": |
| main() |