Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/model_conditioning_examples/quantization_aware_training.py b/model_conditioning_examples/quantization_aware_training.py
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+# 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 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.
+
+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.
+
+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.
+
+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 quantization aware training
+see: https://www.tensorflow.org/model_optimization/guide/quantization/training
+"""
+import pathlib
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_model_optimization as tfmot
+
+from training_utils import get_data, create_model
+
+
+def quantize_and_convert_to_tflite(keras_model):
+ """Quantize and convert Keras model trained with QAT to TensorFlow Lite.
+
+ TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing.
+
+ Args:
+ keras_model: Keras model trained with quantization aware training.
+
+ Returns:
+ Quantized TensorFlow Lite model.
+ """
+
+ converter = tf.lite.TFLiteConverter.from_keras_model(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.
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ tflite_model = converter.convert()
+
+ 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.
+
+ Args:
+ tflite_save_path: Path to TensorFlow Lite model to test.
+ x_test: numpy array of testing data.
+ y_test: numpy array of testing labels (sparse categorical).
+ """
+
+ interpreter = tf.lite.Interpreter(model_path=str(tflite_save_path))
+
+ interpreter.allocate_tensors()
+ input_details = interpreter.get_input_details()
+ output_details = interpreter.get_output_details()
+
+ accuracy_count = 0
+ num_test_images = len(y_test)
+
+ for i in range(num_test_images):
+ interpreter.set_tensor(input_details[0]['index'], x_test[i][np.newaxis, ...])
+ interpreter.invoke()
+ output_data = interpreter.get_tensor(output_details[0]['index'])
+
+ if np.argmax(output_data) == y_test[i]:
+ accuracy_count += 1
+
+ print(f"Test accuracy quantized: {accuracy_count / num_test_images:.3f}")
+
+
+def main():
+ 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.
+ quant_aware_model = tfmot.quantization.keras.quantize_model(model)
+
+ quant_aware_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+ loss=tf.keras.losses.sparse_categorical_crossentropy,
+ metrics=['accuracy'])
+
+ 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)
+ print(f"Test accuracy quant aware: {test_acc:.3f}")
+
+ # Quantize and save the resulting TensorFlow Lite model to file.
+ tflite_model = quantize_and_convert_to_tflite(quant_aware_model)
+
+ tflite_models_dir = pathlib.Path('./conditioned_models/')
+ tflite_models_dir.mkdir(exist_ok=True, parents=True)
+
+ quant_model_save_path = tflite_models_dir / 'qat_quant_model.tflite'
+ with open(quant_model_save_path, 'wb') as f:
+ f.write(tflite_model)
+
+ # 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])
+
+
+if __name__ == "__main__":
+ main()