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Diffstat (limited to 'model_conditioning_examples/post_training_quantization.py')
-rw-r--r-- | model_conditioning_examples/post_training_quantization.py | 139 |
1 files changed, 139 insertions, 0 deletions
diff --git a/model_conditioning_examples/post_training_quantization.py b/model_conditioning_examples/post_training_quantization.py new file mode 100644 index 0000000..ab535ac --- /dev/null +++ b/model_conditioning_examples/post_training_quantization.py @@ -0,0 +1,139 @@ +# 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() |