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

1 files changed, 54 insertions, 21 deletions

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 <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");
@@ -13,23 +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__":