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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