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author | alexander <alexander.efremov@arm.com> | 2021-03-26 21:42:19 +0000 |
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committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-03-29 16:29:55 +0100 |
commit | 3c79893217bc632c9b0efa815091bef3c779490c (patch) | |
tree | ad06b444557eb8124652b45621d736fa1b92f65d /model_conditioning_examples/quantization_aware_training.py | |
parent | 6ad6d55715928de72979b04194da1bdf04a4c51b (diff) | |
download | ml-embedded-evaluation-kit-3c79893217bc632c9b0efa815091bef3c779490c.tar.gz |
Opensource ML embedded evaluation kit21.03
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
Diffstat (limited to 'model_conditioning_examples/quantization_aware_training.py')
-rw-r--r-- | model_conditioning_examples/quantization_aware_training.py | 139 |
1 files changed, 139 insertions, 0 deletions
diff --git a/model_conditioning_examples/quantization_aware_training.py b/model_conditioning_examples/quantization_aware_training.py new file mode 100644 index 0000000..acb768c --- /dev/null +++ b/model_conditioning_examples/quantization_aware_training.py @@ -0,0 +1,139 @@ +# 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() |