Source code for quantizeml.models.transforms.sanitize

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2023 Brainchip Holdings Ltd.
#
# 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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"""
Helper that prepares a model for quantization.
"""

__all__ = ['sanitize']

from . import (align_rescaling, invert_batchnorm_pooling, fold_batchnorms, remove_zeropadding2d,
               invert_relu_maxpool, replace_lambda)


def sanitize(model):
    """ Sanitize a model preparing it for quantization.

    This is a wrapping successive calls to several model transformations which aims at making the
    model quantization ready.

    Args:
        model (keras.Model): the input model

    Returns:
        keras.Model: the sanitized model
    """

    # Replace lambda layers
    model = replace_lambda(model)

    # Align Rescaling (if needed)
    model = align_rescaling(model)

    # Invert ReLU <-> MaxPool layers so that MaxPool comes first
    model = invert_relu_maxpool(model)

    # Invert BN <-> Pooling layers and fold BN into their preceding layers
    model = invert_batchnorm_pooling(model)
    model = fold_batchnorms(model)

    # Remove unsupported ZeroPadding2D layers and replace them with 'same' padding convolution when
    # possible
    model = remove_zeropadding2d(model)
    return model