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 <Alex.Tawse@arm.com>
Change-Id: I8d5af8279bc08bb8acfe8f6ee7df34965552bbe5
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 @@
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 @@
# 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__":