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/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 <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");
@@ -13,28 +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 @@
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 @@
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 main():
+ """
+ Run post-training quantization
+ """
x_train, y_train, x_test, y_test = get_data()
model = create_model()
@@ -117,7 +133,7 @@
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 @@
# 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__":