| # 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 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. |
| |
| 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. |
| |
| 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 |
| 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 |
| """ |
| import pathlib |
| |
| import numpy as np |
| import tensorflow as tf |
| |
| 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. |
| |
| TensorFlow Lite will have fp32 inputs/outputs and the model will handle quantizing/dequantizing. |
| |
| Args: |
| keras_model: Keras model to quantize. |
| sample_data: A numpy array of data to use as a representative dataset. |
| |
| Returns: |
| Quantized TensorFlow Lite model. |
| """ |
| |
| converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) |
| |
| # We set the following converter options to ensure our model is fully quantized. |
| # An error should get thrown if there is any ops that can't be quantized. |
| converter.optimizations = [tf.lite.Optimize.DEFAULT] |
| converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] |
| |
| # To use post training quantization we must provide some sample data that will be used to |
| # calculate activation ranges for quantization. This data should be representative of the data |
| # we expect to feed the model and must be provided by a generator function. |
| def generate_repr_dataset(): |
| for i in range(100): # 100 samples is all we should need in this example. |
| yield [np.expand_dims(sample_data[i], axis=0)] |
| |
| converter.representative_dataset = generate_repr_dataset |
| 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() |
| |
| # Compile and train the model in fp32 as normal. |
| 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 fp32 model accuracy. |
| test_loss, test_acc = model.evaluate(x_test, y_test) |
| print(f"Test accuracy float: {test_acc:.3f}") |
| |
| # Quantize and export the resulting TensorFlow Lite model to file. |
| tflite_model = post_training_quantize(model, x_train) |
| |
| tflite_models_dir = pathlib.Path('./conditioned_models/') |
| tflite_models_dir.mkdir(exist_ok=True, parents=True) |
| |
| quant_model_save_path = tflite_models_dir / 'post_training_quant_model.tflite' |
| with open(quant_model_save_path, 'wb') as f: |
| f.write(tflite_model) |
| |
| # 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]) |
| |
| |
| if __name__ == "__main__": |
| main() |