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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Common modeling utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from typing import Optional, Text
import numpy as np
import tensorflow as tf, tf_keras
import tensorflow.compat.v1 as tf1
from tensorflow.python.tpu import tpu_function
@tf_keras.utils.register_keras_serializable(package='Vision')
class TpuBatchNormalization(tf_keras.layers.BatchNormalization):
"""Cross replica batch normalization."""
def __init__(self, fused: Optional[bool] = False, **kwargs):
if fused in (True, None):
raise ValueError('TpuBatchNormalization does not support fused=True.')
super(TpuBatchNormalization, self).__init__(fused=fused, **kwargs)
def _cross_replica_average(self, t: tf.Tensor, num_shards_per_group: int):
"""Calculates the average value of input tensor across TPU replicas."""
num_shards = tpu_function.get_tpu_context().number_of_shards
group_assignment = None
if num_shards_per_group > 1:
if num_shards % num_shards_per_group != 0:
raise ValueError(
'num_shards: %d mod shards_per_group: %d, should be 0' %
(num_shards, num_shards_per_group))
num_groups = num_shards // num_shards_per_group
group_assignment = [[
x for x in range(num_shards) if x // num_shards_per_group == y
] for y in range(num_groups)]
return tf1.tpu.cross_replica_sum(t, group_assignment) / tf.cast(
num_shards_per_group, t.dtype)
def _moments(self, inputs: tf.Tensor, reduction_axes: int, keep_dims: int):
"""Compute the mean and variance: it overrides the original _moments."""
shard_mean, shard_variance = super(TpuBatchNormalization, self)._moments(
inputs, reduction_axes, keep_dims=keep_dims)
num_shards = tpu_function.get_tpu_context().number_of_shards or 1
if num_shards <= 8: # Skip cross_replica for 2x2 or smaller slices.
num_shards_per_group = 1
else:
num_shards_per_group = max(8, num_shards // 8)
if num_shards_per_group > 1:
# Compute variance using: Var[X]= E[X^2] - E[X]^2.
shard_square_of_mean = tf.math.square(shard_mean)
shard_mean_of_square = shard_variance + shard_square_of_mean
group_mean = self._cross_replica_average(shard_mean, num_shards_per_group)
group_mean_of_square = self._cross_replica_average(
shard_mean_of_square, num_shards_per_group)
group_variance = group_mean_of_square - tf.math.square(group_mean)
return (group_mean, group_variance)
else:
return (shard_mean, shard_variance)
def get_batch_norm(batch_norm_type: Text) -> tf_keras.layers.BatchNormalization:
"""A helper to create a batch normalization getter.
Args:
batch_norm_type: The type of batch normalization layer implementation. `tpu`
will use `TpuBatchNormalization`.
Returns:
An instance of `tf_keras.layers.BatchNormalization`.
"""
if batch_norm_type == 'tpu':
return TpuBatchNormalization
return tf_keras.layers.BatchNormalization # pytype: disable=bad-return-type # typed-keras
def count_params(model, trainable_only=True):
"""Returns the count of all model parameters, or just trainable ones."""
if not trainable_only:
return model.count_params()
else:
return int(
np.sum([
tf_keras.backend.count_params(p) for p in model.trainable_weights
]))
def load_weights(model: tf_keras.Model,
model_weights_path: Text,
weights_format: Text = 'saved_model'):
"""Load model weights from the given file path.
Args:
model: the model to load weights into
model_weights_path: the path of the model weights
weights_format: the model weights format. One of 'saved_model', 'h5', or
'checkpoint'.
"""
if weights_format == 'saved_model':
loaded_model = tf_keras.models.load_model(model_weights_path)
model.set_weights(loaded_model.get_weights())
else:
model.load_weights(model_weights_path)
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