# Copyright 2017 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.
# ==============================================================================
"""Base minibatch sampler module.
The job of the minibatch_sampler is to subsample a minibatch based on some
criterion.
The main function call is:
subsample(indicator, batch_size, **params).
Indicator is a 1d boolean tensor where True denotes which examples can be
sampled. It returns a boolean indicator where True denotes an example has been
sampled..
Subclasses should implement the Subsample function and can make use of the
@staticmethod SubsampleIndicator.
"""
from abc import ABCMeta
from abc import abstractmethod
import tensorflow as tf
from avod.core import ops
class MinibatchSampler(object):
"""Abstract base class for subsampling minibatches."""
__metaclass__ = ABCMeta
def __init__(self):
"""Constructs a minibatch sampler."""
pass
@abstractmethod
def subsample(self, indicator, batch_size, **params):
"""Returns subsample of entries in indicator.
Args:
indicator: boolean tensor of shape [N] whose
True entries can be sampled.
batch_size: desired batch size.
**params: additional keyword arguments for
specific implementations of the MinibatchSampler.
Returns:
sample_indicator: boolean tensor of shape [N] whose
True entries have been sampled.
If sum(indicator) >= batch_size, sum(is_sampled) = batch_size
"""
pass
@staticmethod
def subsample_indicator(indicator, num_samples):
"""Subsample indicator vector.
Given a boolean indicator vector with M elements set to `True`, the function
assigns all but `num_samples` of these previously `True` elements to
`False`. If `num_samples` is greater than M, the original indicator vector
is returned.
Args:
indicator: a 1-dimensional boolean tensor indicating which elements
are allowed to be sampled and which are not.
num_samples: int32 scalar tensor
Returns:
a boolean tensor with the same shape as input (indicator) tensor
"""
indices = tf.where(indicator)
indices = tf.random_shuffle(indices)
indices = tf.reshape(indices, [-1])
num_samples = tf.minimum(tf.size(indices), num_samples)
selected_indices = tf.slice(indices, [0], tf.reshape(num_samples, [1]))
selected_indicator = ops.indices_to_dense_vector(selected_indices,
tf.shape(indicator)[
0])
return tf.equal(selected_indicator, 1)
