| # 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 clustering of weights (weight sharing) in |
| TensorFlow using the TensorFlow Model Optimization Toolkit. |
| |
| The output from this example will be a TensorFlow Lite model file where weights in each layer have been 'clustered' into |
| 16 clusters during training - quantization has then been applied on top of this. |
| |
| By clustering the model we can improve compression of the model file. This can be essential for deploying certain |
| models on systems with limited resources - such as embedded systems using an Arm Ethos NPU. |
| |
| After performing clustering we do post-training quantization to quantize the model and then generate a TensorFlow Lite file. |
| |
| 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 clustering see: https://www.tensorflow.org/model_optimization/guide/clustering |
| """ |
| import pathlib |
| |
| import tensorflow as tf |
| import tensorflow_model_optimization as tfmot |
| |
| from training_utils import get_data, create_model |
| from post_training_quantization import post_training_quantize, evaluate_tflite_model |
| |
| |
| def prepare_for_clustering(keras_model): |
| """Prepares a Keras model for clustering.""" |
| |
| # Choose the number of clusters to use and how to initialize them. Using more clusters will generally |
| # reduce accuracy so you will need to find the optimal number for your use-case. |
| number_of_clusters = 16 |
| cluster_centroids_init = tfmot.clustering.keras.CentroidInitialization.LINEAR |
| |
| # Apply the clustering wrapper to the whole model so weights in every layer will get clustered. You may find that |
| # to avoid too much accuracy loss only certain non-critical layers in your model should be clustered. |
| clustering_ready_model = tfmot.clustering.keras.cluster_weights(keras_model, |
| number_of_clusters=number_of_clusters, |
| cluster_centroids_init=cluster_centroids_init) |
| |
| # We must recompile the model after making it ready for clustering. |
| clustering_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), |
| loss=tf.keras.losses.sparse_categorical_crossentropy, |
| metrics=['accuracy']) |
| |
| return clustering_ready_model |
| |
| |
| def main(): |
| x_train, y_train, x_test, y_test = get_data() |
| model = create_model() |
| |
| # Compile and train the model first. |
| # In general it is easier to do clustering as a fine-tuning step after the model is fully trained. |
| model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), |
| loss=tf.keras.losses.sparse_categorical_crossentropy, |
| metrics=['accuracy']) |
| |
| model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True) |
| |
| # Test the trained model accuracy. |
| test_loss, test_acc = model.evaluate(x_test, y_test) |
| print(f"Test accuracy before clustering: {test_acc:.3f}") |
| |
| # Prepare the model for clustering. |
| clustered_model = prepare_for_clustering(model) |
| |
| # Continue training the model but now with clustering applied. |
| clustered_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True) |
| test_loss, test_acc = clustered_model.evaluate(x_test, y_test) |
| print(f"Test accuracy after clustering: {test_acc:.3f}") |
| |
| # Remove all variables that clustering only needed in the training phase. |
| model_for_export = tfmot.clustering.keras.strip_clustering(clustered_model) |
| |
| # Apply post-training quantization on top of the clustering and save the resulting TensorFlow Lite model to file. |
| tflite_model = post_training_quantize(model_for_export, x_train) |
| |
| tflite_models_dir = pathlib.Path('./conditioned_models/') |
| tflite_models_dir.mkdir(exist_ok=True, parents=True) |
| |
| clustered_quant_model_save_path = tflite_models_dir / 'clustered_post_training_quant_model.tflite' |
| with open(clustered_quant_model_save_path, 'wb') as f: |
| f.write(tflite_model) |
| |
| # Test the clustered quantized model accuracy. Save time by only testing a subset of the whole data. |
| num_test_samples = 1000 |
| evaluate_tflite_model(clustered_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples]) |
| |
| |
| if __name__ == "__main__": |
| main() |