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Diffstat (limited to 'model_conditioning_examples/weight_clustering.py')
| -rw-r--r-- | model_conditioning_examples/weight_clustering.py | 87 |
1 files changed, 57 insertions, 30 deletions
diff --git a/model_conditioning_examples/weight_clustering.py b/model_conditioning_examples/weight_clustering.py index 6672d53..e966336 100644 --- a/model_conditioning_examples/weight_clustering.py +++ b/model_conditioning_examples/weight_clustering.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"); @@ -13,22 +13,29 @@ # 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. +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. +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. +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. +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 +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 clustering see: https://www.tensorflow.org/model_optimization/guide/clustering +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 @@ -42,39 +49,52 @@ from post_training_quantization import post_training_quantize, evaluate_tflite_m 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. + # 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) + # 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']) + 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(): + """ + Run weight clustering + """ 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']) + # 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) + test_loss, test_acc = model.evaluate(x_test, y_test) # pylint: disable=unused-variable print(f"Test accuracy before clustering: {test_acc:.3f}") # Prepare the model for clustering. @@ -88,19 +108,26 @@ def main(): # 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. + # 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' + 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. + # 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]) + evaluate_tflite_model( + clustered_quant_model_save_path, + x_test[0:num_test_samples], + y_test[0:num_test_samples] + ) if __name__ == "__main__": |