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TensorFlow定义Python包装器用于数据集和迭代器
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""数据集和迭代器的Python包装器.""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import numpy as np
from tensorflow.contrib.data.python.framework import function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import parsing_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.platform import gfile
from tensorflow.python.util import nest
class Iterator(object):
"""Represents the state of iterating through a `Dataset`."""
def __init__(self, iterator_resource, initializer, output_types,
output_shapes):
"""Creates a new iterator from the given iterator resource.
NOTE(mrry): Most users will not call this initializer directly, and will
instead use `Iterator.from_dataset()` or `Dataset.make_one_shot_iterator()`.
Args:
iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
iterator.
initializer: A `tf.Operation` that should be run to initialize this
iterator.
output_types: A nested structure of `tf.DType` objects corresponding to
each component of an element of this iterator.
output_shapes: A nested structure of `tf.TensorShape` objects
corresponding to each component of an element of this dataset.
"""
self._iterator_resource = iterator_resource
self._initializer = initializer
self._output_types = output_types
self._output_shapes = output_shapes
@staticmethod
def from_dataset(dataset, shared_name=None):
"""Creates a new, uninitialized `Iterator` from the given `Dataset`.
To initialize this iterator, you must run its `initializer`:
```python
dataset = ...
iterator = Iterator.from_dataset(dataset)
# ...
sess.run(iterator.initializer)
```
Args:
dataset: A `Dataset` object.
shared_name: (Optional.) If non-empty, this iterator will be shared under
the given name across multiple sessions that share the same devices
(e.g. when using a remote server).
Returns:
An `Iterator`.
"""
if shared_name is None:
shared_name = ""
iterator_resource = gen_dataset_ops.iterator(
container="",
shared_name=shared_name,
output_types=nest.flatten(dataset.output_types),
output_shapes=nest.flatten(dataset.output_shapes))
initializer = gen_dataset_ops.make_iterator(dataset.make_dataset_resource(),
iterator_resource)
return Iterator(iterator_resource, initializer, dataset.output_types,
dataset.output_shapes)
@staticmethod
def from_structure(output_types, output_shapes=None, shared_name=None):
"""Creates a new, uninitialized `Iterator` with the given structure.
This iterator-constructing method can be used to create an iterator that
is reusable with many different datasets.
The returned iterator is not bound to a particular dataset, and it has
no `initializer`. To initialize the iterator, run the operation returned by
`Iterator.make_initializer(dataset)`.
The following is an example
```python
iterator = Iterator.from_structure(tf.int64, tf.TensorShape([]))
dataset_range = Dataset.range(10)
range_initializer = iterator.make_initializer(dataset_range)
dataset_evens = dataset_range.filter(lambda x: x % 2 == 0)
evens_initializer = iterator.make_initializer(dataset_evens)
# Define a model based on the iterator; in this example, the model_fn
# is expected to take scalar tf.int64 Tensors as input (see
# the definition of 'iterator' above).
prediction, loss = model_fn(iterator.get_next())
# Train for `num_epochs`, where for each epoch, we first iterate over
# dataset_range, and then iterate over dataset_evens.
for _ in range(num_epochs):
# Initialize the iterator to `dataset_range`
sess.run(range_initializer)
while True:
try:
pred, loss_val = sess.run([prediction, loss])
except tf.errors.OutOfRangeError:
break
# Initialize the iterator to `dataset_evens`
sess.run(evens_initializer)
while True:
try:
pred, loss_val = sess.run([prediction, loss])
except tf.errors.OutOfRangeError:
break
```
Args:
output_types: A nested structure of `tf.DType` objects corresponding to
each component of an element of this iterator.
output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
corresponding to each component of an element of this dataset. If
omitted, each component will have an unconstrainted shape.
shared_name: (Optional.) If non-empty, this iterator will be shared under
the given name across multiple sessions that share the same devices
(e.g. when using a remote server).
Returns:
An `Iterator`.
Raises:
TypeError: If the structures of `output_shapes` and `output_types` are
not the same.
"""
output_types = nest.map_structure(dtypes.as_dtype, output_types)
if output_shapes is None:
output_shapes = nest.map_structure(
lambda _: tensor_shape.TensorShape(None), output_types)
else:
output_shapes = nest.map_structure_up_to(
output_types, tensor_shape.as_shape, output_shapes)
nest.assert_same_structure(output_types, output_shapes)
if shared_name is None:
shared_name = ""
iterator_resource = gen_dataset_ops.iterator(
container="",
shared_name=shared_name,
output_types=nest.flatten(output_types),
output_shapes=nest.flatten(output_shapes))
return Iterator(iterator_resource, None, output_types, output_shapes)
@property
def initializer(self):
"""A `tf.Operation` that should be run to initialize this iterator.
Returns:
A `tf.Operation` that should be run to initialize this iterator
Raises:
ValueError: If this iterator initializes itself automatically.
"""
if self._initializer is not None:
return self._initializer
else:
# TODO(mrry): Consider whether one-shot iterators should have
# initializers that simply reset their state to the beginning.
raise ValueError("Iterator does not have an initializer.")
def make_initializer(self, dataset):
"""Returns a `tf.Operation` that initializes this iterator on `dataset`.
Args:
dataset: A `Dataset` with compatible structure to this iterator.
Returns:
A `tf.Operation` that can be run to initialize this iterator on the given
`dataset`.
Raises:
TypeError: If `dataset` and this iterator do not have a compatible
element structure.
"""
nest.assert_same_structure(self._output_types, dataset.output_types)
nest.assert_same_structure(self._output_shapes, dataset.output_shapes)
for iterator_dtype, dataset_dtype in zip(
nest.flatten(self._output_types), nest.flatten(dataset.output_types)):
if iterator_dtype != dataset_dtype:
raise TypeError(
"Expected output types %r but got dataset with output types %r." %
(self._output_types, dataset.output_types))
for iterator_shape, dataset_shape in zip(
nest.flatten(self._output_shapes), nest.flatten(dataset.output_shapes)):
if not iterator_shape.is_compatible_with(dataset_shape):
raise TypeError("Expected output shapes compatible with %r but got "
"dataset with output shapes %r." %
(self._output_shapes, dataset.output_shapes))
return gen_dataset_ops.make_iterator(dataset.make_dataset_resource(),
self._iterator_resource)
def get_next(self, name=None):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
Args:
name: (Optional.) A name for the created operation.
Returns:
A nested structure of `tf.Tensor` objects.
"""
return nest.pack_sequence_as(
self._output_types,
gen_dataset_ops.iterator_get_next(
self._iterator_resource,
output_types=nest.flatten(self._output_types),
output_shapes=nest.flatten(self._output_shapes),
name=name))
def dispose_op(self, name=None):
"""Returns a `tf.Operation` that destroys this iterator.
The returned operation may be used to release any resources consumed by
this iterator without closing the session.
Args:
name: (Optional.) A name for the created operation.
Returns:
A `tf.Operation`.
"""
return gen_dataset_ops.iterator_dispose(self._iterator_resource, name=name)
@property
def output_shapes(self):
"""Returns the shape of each component of an element of this iterator.
Returns:
A nested structure of `tf.TensorShape` objects corresponding to each
component of an element of this iterator.
"""
return self._output_shapes
@property
def output_types(self):
"""Returns the type of each component of an element of this iterator.
Returns:
A nested structure of `tf.DType` objects corresponding to each component
of an element of this iterator.
"""
return self._output_types
def _calculate_acceptance_probs(initial_probs, target_probs):
"""Calculate the per-class acceptance rates.
Args:
initial_probs: The class probabilities of the data.
target_probs: The desired class proportion in minibatches.
Returns:
A list of the per-class acceptance probabilities.
This method is based on solving the following analysis:
Let F be the probability of a rejection (on any example).
Let p_i be the proportion of examples in the data in class i (init_probs)
Let a_i is the rate the rejection sampler should *accept* class i
Let t_i is the target proportion in the minibatches for class i (target_probs)
```
F = sum_i(p_i * (1-a_i))
= 1 - sum_i(p_i * a_i) using sum_i(p_i) = 1
```
An example with class `i` will be accepted if `k` rejections occur, then an
example with class `i` is seen by the rejector, and it is accepted. This can
be written as follows:
```
t_i = sum_k=0^inf(F^k * p_i * a_i)
= p_i * a_j / (1 - F) using geometric series identity, since 0 <= F < 1
= p_i * a_i / sum_j(p_j * a_j) using F from above
```
Note that the following constraints hold:
```
0 <= p_i <= 1, sum_i(p_i) = 1
0 <= a_i <= 1
0 <= t_i <= 1, sum_i(t_i) = 1
```
A solution for a_i in terms of the other variabes is the following:
```a_i = (t_i / p_i) / max_i[t_i / p_i]```
"""
# Add tiny to initial_probs to avoid divide by zero.
denom = (initial_probs + np.finfo(initial_probs.dtype.as_numpy_dtype).tiny)
ratio_l = target_probs / denom
# Calculate list of acceptance probabilities.
max_ratio = math_ops.reduce_max(ratio_l)
return ratio_l / max_ratio
def _estimate_data_distribution(c, num_examples_per_class_seen):
"""Estimate data distribution as labels are seen.
Args:
c: The class labels. Type `int32`, shape `[batch_size]`.
num_examples_per_class_seen: A `ResourceVariable` containing counts.
Type `int64`, shape `[num_classes]`.
Returns:
dist: The updated distribution. Type `float32`, shape `[num_classes]`.
"""
num_classes = num_examples_per_class_seen.get_shape()[0].value
# Update the class-count based on what labels are seen in
# batch. But do this asynchronously to avoid performing a
# cross-device round-trip. Just use the cached value.
num_examples_per_class_seen = num_examples_per_class_seen.assign_add(
math_ops.reduce_sum(
array_ops.one_hot(c, num_classes, dtype=dtypes.int64),
0))
init_prob_estimate = math_ops.truediv(
num_examples_per_class_seen,
math_ops.reduce_sum(num_examples_per_class_seen))
return math_ops.cast(init_prob_estimate, dtypes.float32)
class Dataset(object):
"""Represents a potentially large set of elements.
A `Dataset` can be used to represent an input pipeline as a
collection of elements (nested structures of tensors) and a "logical
plan" of transformations that act on those elements.
"""
__metaclass__ = abc.ABCMeta
def __init__(self):
pass
@abc.abstractmethod
def make_dataset_resource(self):
"""Creates a `tf.Tensor` of `tf.resource` tensor representing this dataset.
Returns:
A scalar `tf.Tensor` of `tf.resource` type, which represents this dataset.
"""
raise NotImplementedError("Dataset.make_dataset_resource")
def make_initializable_iterator(self, shared_name=None):
"""Creates an `Iterator` for enumerating the elements of this dataset.
**N.B.** The returned iterator will be in an uninitialized state,
and you must run the `iterator.initializer` operation before using it.
Args:
shared_name: (Optional.) If non-empty, this iterator will be shared under
the given name across multiple sessions that share the same devices
(e.g. when using a remote server).
Returns:
An `Iterator` over the elements of this dataset.
"""
return Iterator.from_dataset(self, shared_name)
def make_one_shot_iterator(self):
"""Creates an `Iterator` for enumerating the elements of this dataset.
**N.B.** The returned iterator will be initialized automatically.
A "one-shot" iterator does not currently support re-initialization.
Returns:
An `Iterator` over the elements of this dataset.
"""
# NOTE(mrry): We capture by value here to ensure that `_make_dataset()` is
# a 0-argument function.
@function.Defun(capture_by_value=True)
def _make_dataset():
return self.make_dataset_resource()
_make_dataset.add_to_graph(ops.get_default_graph())
return Iterator(
gen_dataset_ops.one_shot_iterator(
dataset_factory=_make_dataset,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes)), None,
self.output_types, self.output_shapes)
@abc.abstractproperty
def output_shapes(self):
"""Returns the shape of each component of an element of this dataset.
Returns:
A nested structure of `tf.TensorShape` objects corresponding to each
component of an element of this dataset.
"""
raise NotImplementedError("Dataset.output_shapes")
@abc.abstractproperty
def output_types(self):
"""Returns the type of each component of an element of this dataset.
Returns:
A nested structure of `tf.DType` objects corresponding to each component
of an element of this dataset.
"""
raise NotImplementedError("Dataset.output_types")
def __repr__(self):
output_shapes = nest.map_structure(str, self.output_shapes)
output_shapes = str(output_shapes).replace("'", "")
output_types = nest.map_structure(repr, self.output_types)
output_types = str(output_types).replace("'", "")
return ("<%s shapes: %s, types: %s>"
% (type(self).__name__, output_shapes, output_types))
@staticmethod
def from_tensors(tensors):
"""Creates a `Dataset` with a single element, comprising the given tensors.
Args:
tensors: A nested structure of tensors.
Returns:
A `Dataset`.
"""
return TensorDataset(tensors)
@staticmethod
def from_tensor_slices(tensors):
"""Creates a `Dataset` whose elements are slices of the given tensors.
Args:
tensors: A nested structure of tensors, each having the same size in the
0th dimension.
Returns:
A `Dataset`.
"""
return TensorSliceDataset(tensors)
@staticmethod
def from_sparse_tensor_slices(sparse_tensor):
"""Splits each rank-N `tf.SparseTensor` in this dataset row-wise.
Args:
sparse_tensor: A `tf.SparseTensor`.
Returns:
A `Dataset` of rank-(N-1) sparse tensors.
"""
return SparseTensorSliceDataset(sparse_tensor)
@staticmethod
def range(*args):
"""Creates a `Dataset` of a step-separated range of values.
For example:
```python
Dataset.range(5) == [0, 1, 2, 3, 4]
Dataset.range(2, 5) == [2, 3, 4]
Dataset.range(1, 5, 2) == [1, 3]
Dataset.range(1, 5, -2) == []
Dataset.range(5, 1) == []
Dataset.range(5, 1, -2) == [5, 3]
```
Args:
*args: follow same semantics as python's xrange.
len(args) == 1 -> start = 0, stop = args[0], step = 1
len(args) == 2 -> start = args[0], stop = args[1], step = 1
len(args) == 3 -> start = args[0], stop = args[1, stop = args[2]
Returns:
A `RangeDataset`.
Raises:
ValueError: if len(args) == 0.
"""
return RangeDataset(*args)
@staticmethod
def zip(datasets):
"""Creates a `Dataset` by zipping together the given datasets.
This method has similar semantics to the built-in `zip()` function
in Python, with the main difference being that the `datasets`
argument can be an arbitrary nested structure of `Dataset` objects.
For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { 4, 5, 6 }
c = { (7, 8), (9, 10), (11, 12) }
d = { 13, 14 }
# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) }
Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) }
# The `datasets` argument may contain an arbitrary number of
# datasets.
Dataset.zip((a, b, c) == { (1, 4, (7, 8)),
(2, 5, (9, 10)),
(3, 6, (11, 12)) }
# The number of elements in the resulting dataset is the same as
# the size of the smallest dataset in `datasets`.
Dataset.zip((a, d)) == { (1, 13), (2, 14) }
```
Args:
datasets: A nested structure of datasets.
Returns:
A `Dataset`.
"""
return ZipDataset(datasets)
@staticmethod
def read_batch_features(file_pattern,
batch_size,
features,
reader,
reader_args=None,
randomize_input=True,
num_epochs=None,
capacity=10000):
"""Reads batches of Examples.
Args:
file_pattern: A string pattern or a placeholder with list of filenames.
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
features: A `dict` mapping feature keys to `FixedLenFeature` or
`VarLenFeature` values. See `tf.parse_example`.
reader: A function or class that can be called with a `filenames` tensor
and (optional) `reader_args` and returns a `Dataset` of serialized
Examples.
reader_args: Additional arguments to pass to the reader class.
randomize_input: Whether the input should be randomized.
num_epochs: Integer specifying the number of times to read through the
dataset. If None, cycles through the dataset forever.
capacity: Capacity of the ShuffleDataset.
Returns:
A `Dataset`.
"""
if isinstance(file_pattern, str):
filenames = _get_file_names(file_pattern, randomize_input)
else:
filenames = file_pattern
if reader_args:
dataset = reader(filenames, *reader_args)
else:
dataset = reader(filenames)
dataset = dataset.repeat(num_epochs)
if randomize_input:
dataset = dataset.shuffle(capacity)
dataset = dataset.map(
lambda x: _parse_example(nest.flatten(x), features)
)
dataset = dataset.batch(batch_size)
return dataset
def repeat(self, count=None):
"""Repeats this dataset `count` times.
Args:
count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
number of times the elements of this dataset should be repeated. The
default behavior (if `count` is `None` or `-1`) is for the elements to
be repeated indefinitely.
Returns:
A `Dataset`.
"""
return RepeatDataset(self, count)
def enumerate(self, start=0):
"""Enumerate the elements of this dataset. Similar to python's `enumerate`.
For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { (7, 8), (9, 10), (11, 12) }
# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a.enumerate(start=5) == { (5, 1), (6, 2), (7, 3) }
b.enumerate() == { (0, (7, 8)), (1, (9, 10)), (2, (11, 12)) }
Args:
start: A `tf.int64` scalar `tf.Tensor`, representing the start
value for enumeration.
Returns:
A `Dataset`.
"""
max_value = np.iinfo(dtypes.int64.as_numpy_dtype).max
return Dataset.zip((Dataset.range(start, max_value), self))
def shuffle(self, buffer_size, seed=None):
"""Randomly shuffles the elements of this dataset.
Args:
buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
number of elements from this dataset from which the new
dataset will sample.
seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
random seed that will be used to create the distribution. See
@{tf.set_random_seed} for behavior.
Returns:
A `Dataset`.
"""
return ShuffleDataset(self, buffer_size, seed)
def take(self, count):
"""Creates a `Dataset` with at most `count` elements from this dataset.
Args:
count: A `tf.int64` scalar `tf.Tensor`, representing the number of
elements of this dataset that should be taken to form the new dataset.
If `count` is -1, or if `count` is greater than the size of this
dataset, the new dataset will contain all elements of this dataset.
Returns:
A `Dataset`.
"""
return TakeDataset(self, count)
def skip(self, count):
"""Creates a `Dataset` that skips `count` elements from this dataset.
Args:
count: A `tf.int64` scalar `tf.Tensor`, representing the number
of elements of this dataset that should be skipped to form the
new dataset. If `count` is greater than the size of this
dataset, the new dataset will contain no elements. If `count`
is -1, skips the entire dataset.
Returns:
A `Dataset`.
"""
return SkipDataset(self, count)
def batch(self, batch_size):
"""Combines consecutive elements of this dataset into batches.
Args:
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
Returns:
A `Dataset`.
"""
return BatchDataset(self, batch_size)
def padded_batch(self, batch_size, padded_shapes, padding_values=None):
"""Combines consecutive elements of this dataset into padded batches.
Like `Dataset.dense_to_sparse_batch()`, this method combines
multiple consecutive elements of this dataset, which might have
different shapes, into a single element. The tensors in the
resulting element have an additional outer dimension, and are
padded to the respective shape in `padded_shapes`.
Args:
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
padded_shapes: A nested structure of `tf.TensorShape` or
`tf.int64` vector tensor-like objects representing the shape
to which the respective component of each input element should
be padded prior to batching. Any unknown dimensions
(e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
tensor-like object) will be padded to the maximum size of that
dimension in each batch.
padding_values: (Optional.) A nested structure of scalar-shaped
`tf.Tensor`, representing the padding values to use for the
respective components. Defaults are `0` for numeric types and
the empty string for string types.
Returns:
A `Dataset`.
"""
return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values)
def dense_to_sparse_batch(self, batch_size, row_shape):
"""Batches ragged elements of this dataset into `tf.SparseTensor`s.
Like `Dataset.padded_batch()`, this method combines multiple
consecutive elements of this dataset, which might have different
shapes, into a single element. The resulting element has three
components (`indices`, `values`, and `dense_shape`), which
comprise a `tf.SparseTensor` that represents the same data. The
`row_shape` represents the dense shape of each row in the
resulting `tf.SparseTensor`, to which the effective batch size is
prepended. For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { ['a', 'b', 'c'], ['a', 'b'], ['a', 'b', 'c', 'd'] }
a.dense_to_sparse_batch(batch_size=2, row_shape=[6]) == {
([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]], # indices
['a', 'b', 'c', 'a', 'b'], # values
[2, 6]), # dense_shape
([[2, 0], [2, 1], [2, 2], [2, 3]],
['a', 'b', 'c', 'd'],
[1, 6])
}
```
Args:
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the
number of consecutive elements of this dataset to combine in a
single batch.
row_shape: A `tf.TensorShape` or `tf.int64` vector tensor-like
object representing the equivalent dense shape of a row in the
resulting `tf.SparseTensor`. Each element of this dataset must
have the same rank as `row_shape`, and must have size less
than or equal to `row_shape` in each dimension.
Returns:
A `Dataset`.
"""
return DenseToSparseBatchDataset(self, batch_size, row_shape)
def group_by_window(self, key_func, reduce_func, window_size):
"""Performs a windowed "group-by" operation on this dataset.
This method maps each consecutive element in this dataset to a key
using `key_func` and groups the elements by key. It then applies
`reduce_func` to at most `window_size` elements matching the same
key. All execpt the final window for each key will contain
`window_size` elements; the final window may be smaller.
Args:
key_func: A function mapping a nested structure of tensors
(having shapes and types defined by `self.output_shapes` and
`self.output_types`) to a scalar `tf.int64` tensor.
reduce_func: A function mapping a key and a dataset of up to `batch_size`
consecutive elements matching that key to another dataset.
window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements matching the same key to combine in a single
batch, which will be passed to `reduce_func`.
Returns:
A `Dataset`.
"""
return GroupByWindowDataset(self, key_func, reduce_func, window_size)
def map(self, map_func, num_threads=None, output_buffer_size=None):
"""Maps `map_func` across this datset.
Args:
map_func: A function mapping a nested structure of tensors (having
shapes and types defined by `self.output_shapes` and
`self.output_types`) to another nested structure of tensors.
num_threads: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing
the number of threads to use for processing elements in parallel. If
not specified, elements will be processed sequentially without
buffering.
output_buffer_size: (Optional.) A `tf.int64` scalar `tf.Tensor`,
representing the maximum number of processed elements that will be
buffered when processing in parallel.
Returns:
A `Dataset`.
"""
return MapDataset(self, map_func, num_threads, output_buffer_size)
def flat_map(self, map_func):
"""Maps `map_func` across this dataset and flattens the result.
Args:
map_func: A function mapping a nested structure of tensors (having shapes
and types defined by `self.output_shapes` and `self.output_types`) to a
`Dataset`.
Returns:
A `Dataset`.
"""
return FlatMapDataset(self, map_func)
def unbatch(self):
"""Splits elements of this dataset into sequences of consecutive elements.
For example, if elements of this dataset are shaped `[B, a0, a1, ...]`,
where `B` may vary from element to element, then for each element in
this dataset, the unbatched dataset will contain `B` consecutive elements
of shape `[a0, a1, ...]`.
Returns:
A `Dataset`.
"""
return self.flat_map(map_func=Dataset.from_tensor_slices)
def filter(self, predicate):
"""Filters this dataset according to `predicate`.
Args:
predicate: A function mapping a nested structure of tensors (having shapes
and types defined by `self.output_shapes` and `self.output_types`) to a
scalar `tf.bool` tensor.
Returns:
A `Dataset`.
"""
return FilterDataset(self, predicate)
class TensorDataset(Dataset):
"""A `Dataset` with a single element, viz. a nested structure of tensors."""
def __init__(self, tensors):
"""See `Dataset.from_tensors()` for details."""
super(TensorDataset, self).__init__()
with ops.name_scope("tensors"):
self._tensors = nest.pack_sequence_as(tensors, [
ops.convert_to_tensor(t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(tensors))
])
def make_dataset_resource(self):
return gen_dataset_ops.tensor_dataset(
nest.flatten(self._tensors),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return nest.pack_sequence_as(self._tensors,
[t.shape for t in nest.flatten(self._tensors)])
@property
def output_types(self):
return nest.pack_sequence_as(self._tensors,
[t.dtype for t in nest.flatten(self._tensors)])
class TensorSliceDataset(Dataset):
"""A `Dataset` of slices from a nested structure of tensors."""
def __init__(self, tensors):
"""See `Dataset.from_tensor_slices()` for details."""
super(TensorSliceDataset, self).__init__()
with ops.name_scope("tensors"):
flat_tensors = [
ops.convert_to_tensor(t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(tensors))
]
self._tensors = nest.pack_sequence_as(tensors, flat_tensors)
batch_dim = flat_tensors[0].get_shape()[0]
for t in flat_tensors[1:]:
batch_dim.assert_is_compatible_with(t.get_shape()[0])
def make_dataset_resource(self):
return gen_dataset_ops.tensor_slice_dataset(
nest.flatten(self._tensors),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return nest.pack_sequence_as(self._tensors, [
tensor_shape.TensorShape(t.shape[1:])
for t in nest.flatten(self._tensors)
])
@property
def output_types(self):
return nest.pack_sequence_as(self._tensors,
[t.dtype for t in nest.flatten(self._tensors)])
class SparseTensorSliceDataset(Dataset):
"""A `Dataset` that splits a rank-N `tf.SparseTensor` into its rows."""
def __init__(self, sparse_tensor):
"""See `Dataset.from_sparse_tensor_slices()` for details."""
super(SparseTensorSliceDataset, self).__init__()
if not isinstance(sparse_tensor, sparse_tensor_lib.SparseTensor):
raise TypeError("`sparse_tensor` must be a `tf.SparseTensor` object.")
self._sparse_tensor = sparse_tensor
def make_dataset_resource(self):
return gen_dataset_ops.sparse_tensor_slice_dataset(
self._sparse_tensor.indices, self._sparse_tensor.values,
self._sparse_tensor.dense_shape)
@property
def output_shapes(self):
indices_shape = self._sparse_tensor.indices.get_shape()
shape_shape = self._sparse_tensor.dense_shape.get_shape()
rank = (indices_shape[1] - 1).merge_with(shape_shape[0] - 1)
num_values = tensor_shape.Dimension(None)
return (tensor_shape.TensorShape([num_values, rank]),
tensor_shape.TensorShape([num_values]), tensor_shape.TensorShape(
[rank]))
@property
def output_types(self):
return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64)
class ZipDataset(Dataset):
"""A `Dataset` that zips its inputs together."""
def __init__(self, datasets):
"""See `Dataset.zip()` for details."""
super(ZipDataset, self).__init__()
self._datasets = datasets
def make_dataset_resource(self):
return gen_dataset_ops.zip_dataset(
[ds.make_dataset_resource() for ds in nest.flatten(self._datasets)],
output_shapes=[
s
for ds in nest.flatten(self._datasets)
for s in nest.flatten(ds.output_shapes)
],
output_types=[
t
for ds in nest.flatten(self._datasets)
for t in nest.flatten(ds.output_types)
])
@property
def output_shapes(self):
return nest.pack_sequence_as(self._datasets, [
ds.output_shapes for ds in nest.flatten(self._datasets)])
@property
def output_types(self):
return nest.pack_sequence_as(self._datasets, [
ds.output_types for ds in nest.flatten(self._datasets)])
class RepeatDataset(Dataset):
"""A `Dataset` that repeats its input several times."""
def __init__(self, input_dataset, count):
"""See `Dataset.repeat()` for details."""
super(RepeatDataset, self).__init__()
self._input_dataset = input_dataset
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(count, dtype=dtypes.int64,
name="count")
def make_dataset_resource(self):
return gen_dataset_ops.repeat_dataset(
self._input_dataset.make_dataset_resource(),
count=self._count,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class RangeDataset(Dataset):
"""A `Dataset` of a step separated range of values."""
def __init__(self, *args):
"""See `Dataset.range()` for details."""
super(RangeDataset, self).__init__()
self._parse_args(*args)
def _parse_args(self, *args):
if len(args) == 1:
self._start = self._build_tensor(0, "start")
self._stop = args[0]
self._step = self._build_tensor(1, "step")
elif len(args) == 2:
self._start = args[0]
self._stop = args[1]
self._step = self._build_tensor(1, "step")
elif len(args) == 3:
self._start = args[0]
self._stop = args[1]
self._step = args[2]
else:
raise ValueError("Invalid arguments to RangeDataset: %s" % str(args))
def _build_tensor(self, int64_value, name):
return constant_op.constant(int64_value, dtype=dtypes.int64, name=name)
def make_dataset_resource(self):
return gen_dataset_ops.range_dataset(
start=self._start,
stop=self._stop,
step=self._step,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.int64
class ShuffleDataset(Dataset):
"""A `Dataset` that randomly shuffles the elements of its input."""
def __init__(self, input_dataset, buffer_size, seed=None):
"""See `Dataset.shuffle()` for details."""
super(ShuffleDataset, self).__init__()
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
seed, seed2 = random_seed.get_seed(seed)
if seed is None:
self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
else:
self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
if seed2 is None:
self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
else:
self._seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64,
name="seed2")
def make_dataset_resource(self):
return gen_dataset_ops.shuffle_dataset(
self._input_dataset.make_dataset_resource(),
buffer_size=self._buffer_size,
seed=self._seed,
seed2=self._seed2,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class TakeDataset(Dataset):
"""A `Dataset` containing the first `count` elements from its input."""
def __init__(self, input_dataset, count):
"""See `Dataset.take()` for details."""
super(TakeDataset, self).__init__()
self._input_dataset = input_dataset
self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count")
def make_dataset_resource(self):
return gen_dataset_ops.take_dataset(
self._input_dataset.make_dataset_resource(),
count=self._count,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class SkipDataset(Dataset):
"""A `Dataset` skipping the first `count` elements from its input."""
def __init__(self, input_dataset, count):
"""See `Dataset.skip()` for details."""
super(SkipDataset, self).__init__()
self._input_dataset = input_dataset
self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count")
def make_dataset_resource(self):
return gen_dataset_ops.skip_dataset(
self._input_dataset.make_dataset_resource(),
count=self._count,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class BatchDataset(Dataset):
"""A `Dataset` that batches contiguous elements from its input."""
def __init__(self, input_dataset, batch_size):
"""See `Dataset.batch()` for details."""
super(BatchDataset, self).__init__()
self._input_dataset = input_dataset
self._batch_size = batch_size
def make_dataset_resource(self):
return gen_dataset_ops.batch_dataset(
self._input_dataset.make_dataset_resource(),
batch_size=self._batch_size,
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
input_shapes = self._input_dataset.output_shapes
return nest.pack_sequence_as(input_shapes, [
tensor_shape.vector(None).concatenate(s)
for s in nest.flatten(self._input_dataset.output_shapes)
])
@property
def output_types(self):
return self._input_dataset.output_types
def _partial_shape_to_tensor(shape_like):
try:
# First attempt to convert the input to a shape, and return the
# "canonical" tensor representation, which uses `-1` in place of
# `None`.
shape_like = tensor_shape.as_shape(shape_like)
return ops.convert_to_tensor(
[dim if dim is not None else -1 for dim in shape_like.as_list()],
dtype=dtypes.int64)
except (TypeError, ValueError):
# The argument was not trivially convertible to a
# `tf.TensorShape`, so fall back on the conversion to tensor
# machinery.
return ops.convert_to_tensor(shape_like, dtype=dtypes.int64)
def _padding_value_to_tensor(value, output_type):
"""Converts the padding value to a tensor.
Args:
value: The padding value.
output_type: Its expected dtype.
Returns:
A scalar `Tensor`.
Raises:
ValueError: if the padding value is not a scalar.
TypeError: if the padding value's type does not match `output_type`.
"""
value = ops.convert_to_tensor(value, name="padding_value")
if not value.shape.is_compatible_with(tensor_shape.scalar()):
raise ValueError(
"Padding value should be a scalar, but is not: %s" % value)
if value.dtype != output_type:
raise TypeError(
"Padding value tensor (%s) does not match output type: %s"
% (value, output_type))
return value
class PaddedBatchDataset(Dataset):
"""A `Dataset` that batches and pads contiguous elements from its input."""
def __init__(self, input_dataset, batch_size, padded_shapes, padding_values):
"""See `Dataset.batch()` for details."""
super(PaddedBatchDataset, self).__init__()
self._input_dataset = input_dataset
self._batch_size = batch_size
padding_values = (padding_values if padding_values is not None else
self._default_padding(input_dataset))
self._padded_shapes = nest.map_structure_up_to(input_dataset.output_shapes,
_partial_shape_to_tensor,
padded_shapes)
self._padding_values = nest.map_structure_up_to(input_dataset.output_shapes,
_padding_value_to_tensor,
padding_values,
input_dataset.output_types)
def _default_padding(self, input_dataset):
def make_zero(t):
if t.base_dtype == dtypes.string:
return ""
else:
return np.zeros_like(t.as_numpy_dtype())
return nest.map_structure(make_zero, input_dataset.output_types)
def make_dataset_resource(self):
return gen_dataset_ops.padded_batch_dataset(
self._input_dataset.make_dataset_resource(),
batch_size=self._batch_size,
padded_shapes=[
ops.convert_to_tensor(s, dtype=dtypes.int64)
for s in nest.flatten(self._padded_shapes)
],
padding_values=nest.flatten(self._padding_values),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
def _padded_shape_to_batch_shape(s):
return tensor_shape.vector(None).concatenate(
tensor_util.constant_value_as_shape(s))
return nest.map_structure(_padded_shape_to_batch_shape, self._padded_shapes)
@property
def output_types(self):
return self._input_dataset.output_types
class DenseToSparseBatchDataset(Dataset):
"""A `Dataset` that batches ragged dense elements into `tf.SparseTensor`s."""
def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""
super(DenseToSparseBatchDataset, self).__init__()
if not isinstance(input_dataset.output_types, dtypes.DType):
raise TypeError("DenseToSparseDataset requires an input whose elements "
"have a single component, whereas the input has %r."
% input_dataset.output_types)
self._input_dataset = input_dataset
self._batch_size = batch_size
self._row_shape = _partial_shape_to_tensor(row_shape)
def make_dataset_resource(self):
return gen_dataset_ops.dense_to_sparse_batch_dataset(
self._input_dataset.make_dataset_resource(),
self._batch_size,
self._row_shape,
output_shapes=self.output_shapes,
output_types=self.output_types)
@property
def output_shapes(self):
num_elements = tensor_shape.Dimension(None)
return (tensor_shape.matrix(num_elements, self._row_shape.shape[0] + 1),
tensor_shape.vector(num_elements),
tensor_shape.vector(self._row_shape.shape[0] + 1))
@property
def output_types(self):
return (dtypes.int64, self._input_dataset.output_types, dtypes.int64)
class _ResourceDataset(Dataset):
"""A Dataset wrapper for a tf.resource-typed function argument."""
def __init__(self, dataset_resource, output_types, output_shapes):
super(_ResourceDataset, self).__init__()
self._dataset_resource = dataset_resource,
self._output_types = output_types
self._output_shapes = output_shapes
def make_dataset_resource(self):
return self._dataset_resource
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
class GroupByWindowDataset(Dataset):
"""A `Dataset` that groups its input and performs a windowed reduction."""
def __init__(self, input_dataset, key_func, reduce_func, window_size):
"""See `Dataset.group_by_window()` for details."""
super(GroupByWindowDataset, self).__init__()
self._input_dataset = input_dataset
self._window_size = window_size
@function.Defun(*nest.flatten(input_dataset.output_types))
def tf_key_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
ret = key_func(*nested_args)
else:
ret = key_func(nested_args)
ret = ops.convert_to_tensor(ret, dtype=dtypes.int64)
if ret.dtype != dtypes.int64:
raise ValueError("`key_func` must return a single tf.int64 tensor.")
return ret
self._key_func = tf_key_func
self._key_func.add_to_graph(ops.get_default_graph())
@function.Defun(dtypes.int64, dtypes.resource)
def tf_reduce_func(key, window_dataset_resource):
"""A wrapper for Defun that facilitates shape inference."""
key.set_shape([])
window_dataset = _ResourceDataset(window_dataset_resource,
input_dataset.output_types,
input_dataset.output_shapes)
output_dataset = reduce_func(key, window_dataset)
if not isinstance(output_dataset, Dataset):
raise TypeError("`reduce_func` must return a `Dataset` object.")
self._output_types = output_dataset.output_types
self._output_shapes = output_dataset.output_shapes
return output_dataset.make_dataset_resource()
self._reduce_func = tf_reduce_func
self._reduce_func.add_to_graph(ops.get_default_graph())
def make_dataset_resource(self):
return gen_dataset_ops.group_by_window_dataset(
self._input_dataset.make_dataset_resource(),
self._key_func.captured_inputs,
self._reduce_func.captured_inputs,
self._window_size,
key_func=self._key_func,
reduce_func=self._reduce_func,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
def _most_specific_compatible_shape(s1, s2):
"""Returns the most specific shape compatible with `s1` and `s2`."""
if s1.dims is None:
return s1
if s2.dims is None:
return s2
s1.assert_same_rank(s2)
dims = []
for dim1, dim2 in zip(s1, s2):
if dim1.value is None or dim2.value is None or dim1.value != dim2.value:
dims.append(tensor_shape.Dimension(None))
else:
dims.append(dim1.value)
return tensor_shape.TensorShape(dims)
class MapDataset(Dataset):
"""A `Dataset` that maps a function over elements in its input."""
def __init__(self,
input_dataset,
map_func,
num_threads=None,
output_buffer_size=None):
"""See `Dataset.map()` for details."""
super(MapDataset, self).__init__()
self._input_dataset = input_dataset
self._output_shapes = None
self._output_types = None
@function.Defun(*nest.flatten(input_dataset.output_types))
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
ret = map_func(*nested_args)
else:
ret = map_func(nested_args)
# Extract shape information from the returned values.
flattened_ret = [ops.convert_to_tensor(t) for t in nest.flatten(ret)]
self._output_shapes = nest.pack_sequence_as(
ret, [t.get_shape() for t in flattened_ret])
self._output_types = nest.pack_sequence_as(
ret, [t.dtype for t in flattened_ret])
return flattened_ret
self._map_func = tf_map_func
self._map_func.add_to_graph(ops.get_default_graph())
if num_threads is not None:
self._num_threads = ops.convert_to_tensor(
num_threads, dtype=dtypes.int32, name="num_threads")
if output_buffer_size is not None:
self._output_buffer_size = ops.convert_to_tensor(
output_buffer_size, dtype=dtypes.int64, name="output_buffer_size")
else:
self._output_buffer_size = self._num_threads
else:
self._num_threads = None
self._output_buffer_size = None
def make_dataset_resource(self):
input_resource = self._input_dataset.make_dataset_resource()
if self._num_threads is None:
return gen_dataset_ops.map_dataset(
input_resource,
self._map_func.captured_inputs,
f=self._map_func,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
else:
return gen_dataset_ops.parallel_map_dataset(
input_resource,
self._map_func.captured_inputs,
f=self._map_func,
num_threads=self._num_threads,
output_buffer_size=self._output_buffer_size,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
class FlatMapDataset(Dataset):
"""A `Dataset` that maps a function over its input and flattens the result."""
def __init__(self,
input_dataset,
map_func):
"""See `Dataset.flat_map()` for details."""
super(FlatMapDataset, self).__init__()
self._input_dataset = input_dataset
@function.Defun(*nest.flatten(input_dataset.output_types))
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
dataset = map_func(*nested_args)
else:
dataset = map_func(nested_args)
if not isinstance(dataset, Dataset):
raise TypeError("`map_func` must return a `Dataset` object.")
self._output_types = dataset.output_types
self._output_shapes = dataset.output_shapes
return dataset.make_dataset_resource()
self._map_func = tf_map_func
self._map_func.add_to_graph(ops.get_default_graph())
def make_dataset_resource(self):
return gen_dataset_ops.flat_map_dataset(
self._input_dataset.make_dataset_resource(),
self._map_func.captured_inputs,
f=self._map_func,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
class FilterDataset(Dataset):
"""A `Dataset` that filters its input according to a predicate function."""
def __init__(self, input_dataset, predicate):
"""See `Dataset.filter()` for details."""
super(FilterDataset, self).__init__()
self._input_dataset = input_dataset
@function.Defun(*nest.flatten(input_dataset.output_types))
def tf_predicate(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
ret = predicate(*nested_args)
else:
ret = predicate(nested_args)
ret = ops.convert_to_tensor(ret, dtype=dtypes.bool)
if not (ret.dtype == dtypes.bool and
ret.shape.is_compatible_with(tensor_shape.scalar())):
raise ValueError("`predicate` must return a scalar boolean tensor.")
return ret
self._predicate = tf_predicate
self._predicate.add_to_graph(ops.get_default_graph())
def make_dataset_resource(self):
return gen_dataset_ops.filter_dataset(
self._input_dataset.make_dataset_resource(),
other_arguments=self._predicate.captured_inputs,
predicate=self._predicate,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class TextLineDataset(Dataset):
"""A `Dataset` comprising lines from one or more text files."""
def __init__(self, filenames):
"""Creates a `TextLineDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
"""
super(TextLineDataset, self).__init__()
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
def make_dataset_resource(self):
return gen_dataset_ops.text_line_dataset(self._filenames)
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.string
class TFRecordDataset(Dataset):
"""A `Dataset` comprising records from one or more TFRecord files."""
def __init__(self, filenames, compression_type=None):
"""Creates a `TFRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: A `tf.string` scalar evaluating to one of `""` (no
compression), `"ZLIB"`, or `"GZIP"`.
"""
super(TFRecordDataset, self).__init__()
self._filenames = ops.convert_to_tensor(filenames, name="filenames")
if compression_type is not None:
self._compression_type = ops.convert_to_tensor(
compression_type, dtype=dtypes.string, name="compression_type")
else:
self._compression_type = constant_op.constant("", name="compression_type")
def make_dataset_resource(self):
return gen_dataset_ops.tf_record_dataset(self._filenames,
self._compression_type)
@property
def output_shapes(self):
return tensor_shape.TensorShape([])
@property
def output_types(self):
return dtypes.string
class FixedLengthRecordDataset(Dataset):
"""A `Dataset` of fixed-length records from one or more binary files."""
def __init__(self,
filenames,
record_bytes,
header_bytes=None,
footer_bytes=None):
"""Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in
each record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to ignore at the end of a file.
"""
super(FixedLengthRecordDataset, self).__init__()
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
self._record_bytes = ops.convert_to_tensor(
record_bytes, dtype=dtypes.int64, name="record_bytes")
if header_bytes is not None:
self._header_bytes = ops.convert_to_tensor(
header_bytes, dtype=dtypes.int64, name="header_bytes")
else:
self._header_bytes = constant_op.constant(
0, dtype=dtypes.int64, name="header_bytes")
if footer_bytes is not None:
self._footer_bytes = ops.convert_to_tensor(
footer_bytes, dtype=dtypes.int64, name="footer_bytes")
else:
self._footer_bytes = constant_op.constant(
0, dtype=dtypes.int64, name="footer_bytes")
def make_dataset_resource(self):
return gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes)
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.string
def rejection_resample(dataset, class_func, target_dist,
initial_dist=None, seed=None):
"""Resamples this dataset to achieve a target class distribution.
**NOTE** Resampling is performed via rejection sampling; some fraction
of the input values will be dropped.
Args:
dataset: A `Dataset` object.
class_func: A function mapping a nested structure of tensors (having
shapes and types defined by `dataset.output_shapes` and
`dataset.output_types`) to a scalar `tf.int32` tensor. Values should
be in `[0, num_classes)`.
target_dist: A floating point type tensor, shaped `[num_classes].
initial_dist: (Optional.) A floating point type tensor, shaped
`[num_classes]`. If not provided, the true class distribution is
estimated live in a streaming fashion.
seed: (Optional.) Python integer seed for the resampler.
Returns:
A `Dataset`.
"""
dist_estimation_batch_size = 32
target_dist = ops.convert_to_tensor(target_dist, name="initial_dist")
class_values_ds = dataset.map(class_func)
if initial_dist is not None:
initial_dist = ops.convert_to_tensor(
initial_dist, name="initial_dist")
acceptance_dist = _calculate_acceptance_probs(initial_dist, target_dist)
initial_dist_ds = Dataset.from_tensors(initial_dist).repeat()
acceptance_dist_ds = Dataset.from_tensors(acceptance_dist).repeat()
else:
num_classes = (target_dist.shape[0].value
or array_ops.shape(target_dist)[0])
smoothing_constant = 10
num_examples_per_class_seen = resource_variable_ops.ResourceVariable(
initial_value=array_ops.fill(
[num_classes], np.int64(smoothing_constant)),
trainable=False,
name="class_count",
dtype=dtypes.int64)
def update_estimate_and_tile(c):
return array_ops.tile(
array_ops.expand_dims(
_estimate_data_distribution(c, num_examples_per_class_seen), 0),
[dist_estimation_batch_size, 1])
initial_dist_ds = (class_values_ds
.batch(dist_estimation_batch_size)
.map(update_estimate_and_tile)
.unbatch())
acceptance_dist_ds = initial_dist_ds.map(
lambda initial: _calculate_acceptance_probs(initial, target_dist))
def maybe_warn_on_large_rejection(accept_dist, initial_dist):
proportion_rejected = math_ops.reduce_sum(
(1 - accept_dist) * initial_dist)
return control_flow_ops.cond(
math_ops.less(proportion_rejected, .5),
lambda: accept_dist,
lambda: logging_ops.Print( # pylint: disable=g-long-lambda
accept_dist, [proportion_rejected, initial_dist, accept_dist],
message="Proportion of examples rejected by sampler is high: ",
summarize=100,
first_n=10))
acceptance_dist_ds = (
Dataset.zip((acceptance_dist_ds, initial_dist_ds))
.map(maybe_warn_on_large_rejection))
current_probabilities_ds = (Dataset
.zip((acceptance_dist_ds, class_values_ds))
.map(array_ops.gather))
filtered_ds = (
Dataset.zip((class_values_ds, current_probabilities_ds, dataset))
.filter(lambda _1, p, _2: random_ops.random_uniform([], seed=seed) < p))
return filtered_ds.map(lambda class_value, _, data: (class_value, data))
def read_batch_features(file_pattern,
batch_size,
features,
reader,
reader_args=None,
randomize_input=True,
num_epochs=None,
capacity=10000):
"""Reads batches of Examples.
Example:
```
serialized_examples = [
features {
feature { key: "age" value { int64_list { value: [ 0 ] } } }
feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } }
},
features {
feature { key: "age" value { int64_list { value: [] } } }
feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
feature { key: "kws" value { bytes_list { value: [ "sports" ] } } }
}
]
```
We can use arguments:
```
features: {
"age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
"gender": FixedLenFeature([], dtype=tf.string),
"kws": VarLenFeature(dtype=tf.string),
}
```
And the expected output is:
```python
{
"age": [[0], [-1]],
"gender": [["f"], ["f"]],
"kws": SparseTensor(
indices=[[0, 0], [0, 1], [1, 0]],
values=["code", "art", "sports"]
dense_shape=[2, 2]),
}
```
Args:
file_pattern: List of files or patterns of file paths containing
`Example` records. See `tf.gfile.Glob` for pattern rules.
batch_size: An int representing the number of consecutive elements of this
dataset to combine in a single batch.
features: A `dict` mapping feature keys to `FixedLenFeature` or
`VarLenFeature` values. See `tf.parse_example`.
reader: A function or class that can be called with a `filenames` tensor
and (optional) `reader_args` and returns a `Dataset` of serialized
Examples.
reader_args: Additional arguments to pass to the reader class.
randomize_input: Whether the input should be randomized.
num_epochs: Integer specifying the number of times to read through the
dataset. If None, cycles through the dataset forever.
capacity: Capacity of the ShuffleDataset. A large capacity ensures better
shuffling but would increase memory usage and startup time.
Returns:
A dict from keys in features to Tensor or SparseTensor objects.
"""
filenames = _get_file_names(file_pattern, randomize_input)
if reader_args:
dataset = reader(filenames, *reader_args)
else:
dataset = reader(filenames)
dataset = dataset.repeat(num_epochs)
if randomize_input:
dataset = dataset.shuffle(capacity)
dataset = dataset.batch(batch_size)
dataset = dataset.map(lambda x: _parse_example(x, features))
iterator = dataset.make_one_shot_iterator()
outputs = iterator.get_next()
index = 0
result = {}
for key in sorted(features.keys()):
feature = features[key]
if isinstance(feature, parsing_ops.FixedLenFeature):
result[key] = outputs[index]
index += 1
else:
result[key] = sparse_tensor_lib.SparseTensor(
indices=outputs[index],
values=outputs[index + 1],
dense_shape=outputs[index + 2])
index += 3
return result
def _parse_example(serialized, features):
parsed = parsing_ops.parse_example(serialized, features)
result = []
for key in sorted(features.keys()):
val = parsed[key]
if isinstance(val, sparse_tensor_lib.SparseTensor):
result.extend([val.indices, val.values, val.dense_shape])
else:
result.append(val)
return result
def _get_file_names(file_pattern, randomize_input):
"""Parse list of file names from pattern, optionally shuffled.
Args:
file_pattern: File glob pattern, or list of glob patterns.
randomize_input: Whether to shuffle the order of file names.
Returns:
List of file names matching `file_pattern`.
Raises:
ValueError: If `file_pattern` is empty, or pattern matches no files.
"""
if isinstance(file_pattern, list):
if not file_pattern:
raise ValueError("File pattern is empty.")
file_names = []
for entry in file_pattern:
file_names.extend(gfile.Glob(entry))
else:
file_names = list(gfile.Glob(file_pattern))
if not file_names:
raise ValueError("No files match %s." % file_pattern)
# Sort files so it will be deterministic for unit tests.
if not randomize_input:
file_names = sorted(file_names)
return file_names
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