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review.mlplatform.org / ml / ethos-u / ml-embedded-evaluation-kit / f32a86a6969508d7a156decbed0bfc9466ad92fa / . / model_conditioning_examples / post_training_quantization.py

# SPDX-FileCopyrightText: Copyright 2021 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"); | |

# 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, | |

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# 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() |