From daba3cf2e3633cbd0e4f8aabe7578b97e88deee1 Mon Sep 17 00:00:00 2001 From: Alex Tawse Date: Fri, 29 Sep 2023 15:55:38 +0100 Subject: MLECO-3995: Pylint + Shellcheck compatibility * All Python scripts updated to abide by Pylint rules * good-names updated to permit short variable names: i, j, k, f, g, ex * ignore-long-lines regex updated to allow long lines for licence headers * Shell scripts now compliant with Shellcheck Signed-off-by: Alex Tawse Change-Id: I8d5af8279bc08bb8acfe8f6ee7df34965552bbe5 --- .../post_training_quantization.py | 61 ++++++++++----- .../quantization_aware_training.py | 68 +++++++++++------ model_conditioning_examples/setup.sh | 9 ++- model_conditioning_examples/training_utils.py | 5 +- model_conditioning_examples/weight_clustering.py | 87 ++++++++++++++-------- model_conditioning_examples/weight_pruning.py | 75 +++++++++++++------ 6 files changed, 205 insertions(+), 100 deletions(-) (limited to 'model_conditioning_examples') diff --git a/model_conditioning_examples/post_training_quantization.py b/model_conditioning_examples/post_training_quantization.py index a39be0e..42069f5 100644 --- a/model_conditioning_examples/post_training_quantization.py +++ b/model_conditioning_examples/post_training_quantization.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,28 +13,34 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This script will provide you with an example of how to perform post-training quantization in TensorFlow. +This script will provide you with an example of how to perform +post-training quantization in TensorFlow. -The output from this example will be a TensorFlow Lite model file where weights and activations are quantized to 8bit -integer values. +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. +Quantization helps reduce the size of your models and is necessary +for running models on certain hardware such as Arm Ethos NPU. -In addition to quantizing weights, post-training quantization uses a calibration dataset to -capture the minimum and maximum values of all variable tensors in your model. -By capturing these ranges it is possible to fully quantize not just the weights of the model but also the activations. +In addition to quantizing weights, post-training quantization uses +a calibration dataset to capture the minimum and maximum values of +all variable tensors in your model. By capturing these ranges it +is possible to fully quantize not just the weights of the model +but also the activations. -Depending on the model you are quantizing there may be some accuracy loss, but for a lot of models the loss should -be minimal. +Depending on the model you are quantizing there may be some accuracy loss, +but for a lot of models the loss should be minimal. -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 post-training quantization -see: https://www.tensorflow.org/lite/performance/post_training_integer_quant +For more information on using Vela see: + https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/ +For more information on post-training quantization see: + https://www.tensorflow.org/lite/performance/post_training_integer_quant """ + import pathlib import numpy as np @@ -44,7 +50,8 @@ from training_utils import get_data, create_model def post_training_quantize(keras_model, sample_data): - """Quantize Keras model using post-training quantization with some sample data. + """ + Quantize Keras model using post-training quantization with some sample data. TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing. @@ -76,8 +83,14 @@ def post_training_quantize(keras_model, sample_data): 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. +# pylint: disable=duplicate-code +def evaluate_tflite_model( + tflite_save_path: pathlib.Path, + x_test: np.ndarray, + y_test: np.ndarray +): + """ + Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter. Args: tflite_save_path: Path to TensorFlow Lite model to test. @@ -106,6 +119,9 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test): def main(): + """ + Run post-training quantization + """ x_train, y_train, x_test, y_test = get_data() model = create_model() @@ -117,7 +133,7 @@ def main(): model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True) # Test the fp32 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 float: {test_acc:.3f}") # Quantize and export the resulting TensorFlow Lite model to file. @@ -132,7 +148,12 @@ def main(): # Test the 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]) + evaluate_tflite_model( + quant_model_save_path, + x_test[0:num_test_samples], + y_test[0:num_test_samples] + ) +# pylint: enable=duplicate-code if __name__ == "__main__": diff --git a/model_conditioning_examples/quantization_aware_training.py b/model_conditioning_examples/quantization_aware_training.py index 3d492a7..d590763 100644 --- a/model_conditioning_examples/quantization_aware_training.py +++ b/model_conditioning_examples/quantization_aware_training.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,31 +13,38 @@ # 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 +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. +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. +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. +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. +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. +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 +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 quantization aware training -see: https://www.tensorflow.org/model_optimization/guide/quantization/training +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 @@ -64,13 +71,15 @@ def quantize_and_convert_to_tflite(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. + # 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 +# pylint: disable=duplicate-code def evaluate_tflite_model(tflite_save_path, x_test, y_test): """Calculate the accuracy of a TensorFlow Lite model using TensorFlow Lite interpreter. @@ -101,13 +110,19 @@ def evaluate_tflite_model(tflite_save_path, x_test, y_test): def main(): + """ + Run quantization aware training + """ 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. + # When working with the TensorFlow Keras API and theTF 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), @@ -117,7 +132,7 @@ def main(): 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) + test_loss, test_acc = quant_aware_model.evaluate(x_test, y_test) # pylint: disable=unused-variable print(f"Test accuracy quant aware: {test_acc:.3f}") # Quantize and save the resulting TensorFlow Lite model to file. @@ -132,7 +147,12 @@ def main(): # 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]) + evaluate_tflite_model( + quant_model_save_path, + x_test[0:num_test_samples], + y_test[0:num_test_samples] + ) +# pylint: enable=duplicate-code if __name__ == "__main__": diff --git a/model_conditioning_examples/setup.sh b/model_conditioning_examples/setup.sh index 92de78a..678f9d3 100644 --- a/model_conditioning_examples/setup.sh +++ b/model_conditioning_examples/setup.sh @@ -1,5 +1,7 @@ +#!/bin/bash + #---------------------------------------------------------------------------- -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -14,8 +16,9 @@ # See the License for the specific language governing permissions and # limitations under the License. #---------------------------------------------------------------------------- -#!/bin/bash + python3 -m venv ./env +# shellcheck disable=SC1091 source ./env/bin/activate pip install -U pip -pip install -r requirements.txt \ No newline at end of file +pip install -r requirements.txt diff --git a/model_conditioning_examples/training_utils.py b/model_conditioning_examples/training_utils.py index a022bd1..2ce94b8 100644 --- a/model_conditioning_examples/training_utils.py +++ b/model_conditioning_examples/training_utils.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -49,7 +49,8 @@ def create_model(): """ keras_model = tf.keras.models.Sequential([ - tf.keras.layers.Conv2D(32, 3, padding='same', input_shape=(28, 28, 1), activation=tf.nn.relu), + tf.keras.layers.Conv2D(32, 3, padding='same', + input_shape=(28, 28, 1), activation=tf.nn.relu), tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu), tf.keras.layers.MaxPool2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation=tf.nn.relu), 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 +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # 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__": diff --git a/model_conditioning_examples/weight_pruning.py b/model_conditioning_examples/weight_pruning.py index cbf9cf9..303b6df 100644 --- a/model_conditioning_examples/weight_pruning.py +++ b/model_conditioning_examples/weight_pruning.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2021, 2023 Arm Limited and/or its affiliates # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,23 +13,31 @@ # 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 +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 +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. +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. +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 +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 weight pruning see: https://www.tensorflow.org/model_optimization/guide/pruning +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 @@ -43,13 +51,20 @@ from post_training_quantization import post_training_quantize, evaluate_tflite_m 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. + # 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) + # 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), @@ -60,11 +75,15 @@ def prepare_for_pruning(keras_model): def main(): + """ + Run weight pruning + """ 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. + # 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']) @@ -72,7 +91,7 @@ def main(): 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 pruning: {test_acc:.3f}") # Prepare the model for pruning and add the pruning update callback needed in training. @@ -80,14 +99,23 @@ def main(): 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) + 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. + # 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/') @@ -97,9 +125,14 @@ def main(): 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. + # 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]) + evaluate_tflite_model( + pruned_quant_model_save_path, + x_test[0:num_test_samples], + y_test[0:num_test_samples] + ) if __name__ == "__main__": -- cgit v1.2.1