Source code for aimet_tensorflow.keras.bn_reestimation

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"""BatchNorm Reestimation"""
from typing import List, Dict
import numpy as np
import tensorflow as tf
from aimet_common.utils import Handle, AimetLogger

logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)

def _get_bn_submodules(model: tf.keras.Model) -> List[tf.keras.layers.Layer]:
    bn_layers = []
    for layer in model.submodules:
        if isinstance(layer, tf.keras.layers.BatchNormalization):
            bn_layers.append(layer)
    return bn_layers


def _reset_bn_stats(bn_layers: List[tf.keras.layers.Layer], bn_mean_checkpoints: Dict, bn_var_checkpoints: Dict, bn_momentum_checkpoints: Dict) -> Handle:
    """
    reset bn stats
    :param bn_layers: keras bn_layers
    :param bn_mean_checkpoints: Dict for original bn mean
    :param bn_var_checkpoints: Dict for original bn var
    :param bn_momentum_checkpoints: Dict for original bn momentum
    :return:
    """

    def cleanup():
        """
        Restore Bn stats
        """
        for layer in bn_layers:
            move_mean = bn_mean_checkpoints[layer.name]
            move_var = bn_var_checkpoints[layer.name]
            gamma, beta, _, _ = layer.get_weights()
            layer.set_weights([gamma, beta, move_mean, move_var])
            layer.momentum = bn_momentum_checkpoints[layer.name]

    try:
        for layer in bn_layers:
            layer.momentum = 0.0
        return Handle(cleanup)
    except:
        cleanup()
        raise ValueError('exception for reset_bn_stats')  # pylint: disable=raise-missing-from

# pylint: disable=too-many-locals
[docs]def reestimate_bn_stats(model: tf.keras.Model, bn_re_estimation_dataset: tf.data.Dataset, bn_num_batches: int = 100) -> Handle: """ top level api for end user directly call :param model: tf.keras.Model :param bn_re_estimation_dataset: Training dataset :param bn_num_batches: The number of batches to be used for reestimation :returns: Handle that undos the effect of BN reestimation upon handle.remove() """ bn_layers = _get_bn_submodules(model) # save checkpoints bn_mean_ori = {layer.name: layer.moving_mean.numpy() for layer in bn_layers} bn_var_ori = {layer.name: layer.moving_variance.numpy() for layer in bn_layers} bn_momentum_ori = {layer.name: layer.momentum for layer in bn_layers} # 1. switch to re-estimation mode and setup remove handle = _reset_bn_stats(bn_layers, bn_mean_ori, bn_var_ori, bn_momentum_ori) # 2. mean &var initialization mean_sum_dict = {layer.name: np.zeros(layer.moving_mean.shape, dtype=layer.moving_mean.dtype.as_numpy_dtype) for layer in bn_layers} var_sum_dict = {layer.name: np.zeros(layer.moving_variance.shape, dtype=layer.moving_variance.dtype.as_numpy_dtype) for layer in bn_layers} # 3 per batch forward for BN re-estimation, accumulate into mean&var buffers bn_dataset_iterator = iter(bn_re_estimation_dataset) for batch_index in range(bn_num_batches): try: batch_data = next(bn_dataset_iterator) model(batch_data, training=True) for layer in bn_layers: mean_sum_dict[layer.name] += layer.moving_mean.numpy() var_sum_dict[layer.name] += layer.moving_variance.numpy() if batch_index == bn_num_batches - 1: break except tf.errors.OutOfRangeError: logger.info("tf.errors.OutOfRangeError:: End of dataset.") break # 4 average mean&var buffers, Override BN stats with the reestimated stats for layer in bn_layers: move_mean = mean_sum_dict[layer.name]/bn_num_batches move_var = var_sum_dict[layer.name]/bn_num_batches gamma, beta, _, _ = layer.get_weights() layer.set_weights([gamma, beta, move_mean, move_var]) return handle