def _static_subsample(self, indicator, batch_size, labels):
"""Returns subsampled minibatch.
Args:
indicator: boolean tensor of shape [N] whose True entries can be sampled.
N should be a complie time constant.
batch_size: desired batch size. This scalar cannot be None.
labels: boolean tensor of shape [N] denoting positive(=True) and negative
(=False) examples. N should be a complie time constant.
Returns:
sampled_idx_indicator: boolean tensor of shape [N], True for entries which
are sampled.
Raises:
ValueError: if labels and indicator are not 1D boolean tensors.
"""
# Check if indicator and labels have a static size.
if not indicator.shape.is_fully_defined():
raise ValueError('indicator must be static in shape when is_static is'
'True')
if not labels.shape.is_fully_defined():
raise ValueError('labels must be static in shape when is_static is'
'True')
if not isinstance(batch_size, int):
raise ValueError('batch_size has to be an integer when is_static is'
'True.')
input_length = tf.shape(indicator)[0]
# Shuffle indicator and label. Need to store the permutation to restore the
# order post sampling.
permutation = tf.random_shuffle(tf.range(input_length))
indicator = ops.matmul_gather_on_zeroth_axis(
tf.cast(indicator, tf.float32), permutation)
labels = ops.matmul_gather_on_zeroth_axis(
tf.cast(labels, tf.float32), permutation)
# index (starting from 1) when cls_weight is True, 0 when False
indicator_idx = tf.where(
tf.cast(indicator, tf.bool), tf.range(1, input_length + 1),
tf.zeros(input_length, tf.int32))
# Replace -1 for negative, +1 for positive labels
signed_label = tf.where(
tf.cast(labels, tf.bool), tf.ones(input_length, tf.int32),
tf.scalar_mul(-1, tf.ones(input_length, tf.int32)))
# negative of index for negative label, positive index for positive label,
# 0 when indicator is False.
signed_indicator_idx = tf.multiply(indicator_idx, signed_label)
sorted_signed_indicator_idx = tf.nn.top_k(
signed_indicator_idx, input_length, sorted=True).values
[num_positive_samples,
num_negative_samples] = self._get_num_pos_neg_samples(
sorted_signed_indicator_idx, batch_size)
sampled_idx = self._get_values_from_start_and_end(
sorted_signed_indicator_idx, num_positive_samples,
num_negative_samples, batch_size)
# Shift the indices to start from 0 and remove any samples that are set as
# False.
sampled_idx = tf.abs(sampled_idx) - tf.ones(batch_size, tf.int32)
sampled_idx = tf.multiply(
tf.cast(tf.greater_equal(sampled_idx, tf.constant(0)), tf.int32),
sampled_idx)
sampled_idx_indicator = tf.cast(tf.reduce_sum(
tf.one_hot(sampled_idx, depth=input_length),
axis=0), tf.bool)
# project back the order based on stored permutations
reprojections = tf.one_hot(permutation, depth=input_length, dtype=tf.int32)
return tf.cast(tf.tensordot(
tf.cast(sampled_idx_indicator, tf.int32),
reprojections, axes=[0, 0]), tf.bool)
def _static_subsample(self, indicator, batch_size, labels):
"""Returns subsampled minibatch.
Args:
indicator: boolean tensor of shape [N] whose True entries can be sampled.
N should be a complie time constant.
batch_size: desired batch size. This scalar cannot be None.
labels: boolean tensor of shape [N] denoting positive(=True) and negative
(=False) examples. N should be a complie time constant.
Returns:
sampled_idx_indicator: boolean tensor of shape [N], True for entries which
are sampled. It ensures the length of output of the subsample is always
batch_size, even when number of examples set to True in indicator is
less than batch_size.
Raises:
ValueError: if labels and indicator are not 1D boolean tensors.
"""
# Check if indicator and labels have a static size.
if not indicator.shape.is_fully_defined():
raise ValueError('indicator must be static in shape when is_static is'
'True')
if not labels.shape.is_fully_defined():
raise ValueError('labels must be static in shape when is_static is'
'True')
if not isinstance(batch_size, int):
raise ValueError('batch_size has to be an integer when is_static is'
'True.')
input_length = tf.shape(indicator)[0]
# Set the number of examples set True in indicator to be at least
# batch_size.
num_true_sampled = tf.reduce_sum(tf.cast(indicator, tf.float32))
additional_false_sample = tf.less_equal(
tf.cumsum(tf.cast(tf.logical_not(indicator), tf.float32)),
batch_size - num_true_sampled)
indicator = tf.logical_or(indicator, additional_false_sample)
# Shuffle indicator and label. Need to store the permutation to restore the
# order post sampling.
permutation = tf.random_shuffle(tf.range(input_length))
indicator = ops.matmul_gather_on_zeroth_axis(
tf.cast(indicator, tf.float32), permutation)
labels = ops.matmul_gather_on_zeroth_axis(
tf.cast(labels, tf.float32), permutation)
# index (starting from 1) when indicator is True, 0 when False
indicator_idx = tf.where(
tf.cast(indicator, tf.bool), tf.range(1, input_length + 1),
tf.zeros(input_length, tf.int32))
# Replace -1 for negative, +1 for positive labels
signed_label = tf.where(
tf.cast(labels, tf.bool), tf.ones(input_length, tf.int32),
tf.scalar_mul(-1, tf.ones(input_length, tf.int32)))
# negative of index for negative label, positive index for positive label,
# 0 when indicator is False.
signed_indicator_idx = tf.multiply(indicator_idx, signed_label)
sorted_signed_indicator_idx = tf.nn.top_k(
signed_indicator_idx, input_length, sorted=True).values
[num_positive_samples,
num_negative_samples] = self._get_num_pos_neg_samples(
sorted_signed_indicator_idx, batch_size)
sampled_idx = self._get_values_from_start_and_end(
sorted_signed_indicator_idx, num_positive_samples,
num_negative_samples, batch_size)
# Shift the indices to start from 0 and remove any samples that are set as
# False.
sampled_idx = tf.abs(sampled_idx) - tf.ones(batch_size, tf.int32)
sampled_idx = tf.multiply(
tf.cast(tf.greater_equal(sampled_idx, tf.constant(0)), tf.int32),
sampled_idx)
sampled_idx_indicator = tf.cast(tf.reduce_sum(
tf.one_hot(sampled_idx, depth=input_length),
axis=0), tf.bool)
# project back the order based on stored permutations
reprojections = tf.one_hot(permutation, depth=input_length,
dtype=tf.float32)
return tf.cast(tf.tensordot(
tf.cast(sampled_idx_indicator, tf.float32),
reprojections, axes=[0, 0]), tf.bool)
def graph_fn(params, indices): return ops.matmul_gather_on_zeroth_axis(params, indices)
