| # 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 magnitude-based weight pruning in TensorFlow |
| using the TensorFlow Model Optimization Toolkit. |
| |
| The output from this example will be a TensorFlow Lite model file where ~75% percent of the weights have been 'pruned' to the |
| value 0 during training - quantization has then been applied on top of this. |
| |
| By pruning 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 Arm Ethos NPU. Also, if the pruned model is run |
| on an Arm Ethos NPU then this pruning can improve the execution time of the model. |
| |
| After pruning is complete 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 weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning |
| """ |
| 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_pruning(keras_model): |
| """Prepares a Keras model for pruning.""" |
| |
| # We use a constant sparsity schedule so the amount of sparsity in the model is kept at the same percent throughout |
| # training. An alternative is PolynomialDecay where sparsity can be gradually increased during training. |
| pruning_schedule = tfmot.sparsity.keras.ConstantSparsity(target_sparsity=0.75, begin_step=0) |
| |
| # Apply the pruning wrapper to the whole model so weights in every layer will get pruned. You may find that to avoid |
| # too much accuracy loss only certain non-critical layers in your model should be pruned. |
| pruning_ready_model = tfmot.sparsity.keras.prune_low_magnitude(keras_model, pruning_schedule=pruning_schedule) |
| |
| # We must recompile the model after making it ready for pruning. |
| pruning_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), |
| loss=tf.keras.losses.sparse_categorical_crossentropy, |
| metrics=['accuracy']) |
| |
| return pruning_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 pruning 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 pruning: {test_acc:.3f}") |
| |
| # Prepare the model for pruning and add the pruning update callback needed in training. |
| pruned_model = prepare_for_pruning(model) |
| callbacks = [tfmot.sparsity.keras.UpdatePruningStep()] |
| |
| # Continue training the model but now with pruning applied - remember to pass in the callbacks! |
| pruned_model.fit(x=x_train, y=y_train, batch_size=128, epochs=1, verbose=1, shuffle=True, callbacks=callbacks) |
| test_loss, test_acc = pruned_model.evaluate(x_test, y_test) |
| print(f"Test accuracy after pruning: {test_acc:.3f}") |
| |
| # Remove all variables that pruning only needed in the training phase. |
| model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model) |
| |
| # Apply post-training quantization on top of the pruning 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) |
| |
| pruned_quant_model_save_path = tflite_models_dir / 'pruned_post_training_quant_model.tflite' |
| with open(pruned_quant_model_save_path, 'wb') as f: |
| f.write(tflite_model) |
| |
| # Test the pruned quantized model accuracy. Save time by only testing a subset of the whole data. |
| num_test_samples = 1000 |
| evaluate_tflite_model(pruned_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples]) |
| |
| |
| if __name__ == "__main__": |
| main() |