# 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. # ============================================================================== """Python wrappers for Datasets.""" import abc import functools import multiprocessing import sys import threading import warnings import numpy as np import six from six.moves import queue as Queue # pylint: disable=redefined-builtin from tensorflow.core.framework import dataset_metadata_pb2 from tensorflow.core.framework import dataset_options_pb2 from tensorflow.core.framework import graph_pb2 from tensorflow.python import tf2 from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import options as options_lib from tensorflow.python.data.ops import structured_function from tensorflow.python.data.util import nest from tensorflow.python.data.util import random_seed from tensorflow.python.data.util import structure from tensorflow.python.data.util import traverse from tensorflow.python.eager import context from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import auto_control_deps_utils as acd_utils from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed as core_random_seed from tensorflow.python.framework import smart_cond from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.framework import type_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import gen_stateless_random_ops from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import script_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.trackable import asset from tensorflow.python.trackable import base as tracking_base from tensorflow.python.trackable import resource as resource_lib from tensorflow.python.types import trace from tensorflow.python.util import deprecation from tensorflow.python.util import lazy_loader from tensorflow.python.util import nest as tf_nest from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.tf_export import tf_export # Symbols forwarded for legacy access through dataset_ops.py. These forwarded # symbols can be removed once all internal uses are updated. StructuredFunctionWrapper = structured_function.StructuredFunctionWrapper # Loaded lazily due to a circular dependency (roughly # tf.function->wrap_function->dataset->autograph->tf.function). # TODO(b/133251390): Use a regular import. wrap_function = lazy_loader.LazyLoader( "wrap_function", globals(), "tensorflow.python.eager.wrap_function") # Loaded lazily due to a circular dependency # dataset_ops->def_function->func_graph->autograph->dataset_ops # TODO(kathywu): Use a regular import. def_function = lazy_loader.LazyLoader( "def_function", globals(), "tensorflow.python.eager.def_function") # Loaded lazily due to a circular dependency # dataset_ops->parsing_ops->dataset_ops # TODO(varshaan): Use a regular import. parsing_ops = lazy_loader.LazyLoader( "parsing_ops", globals(), "tensorflow.python.ops.parsing_ops") ops.NotDifferentiable("ReduceDataset") # A constant that can be used to enable auto-tuning. AUTOTUNE = -1 tf_export("data.AUTOTUNE").export_constant(__name__, "AUTOTUNE") # TODO(b/168128531): Deprecate and remove this symbol. tf_export("data.experimental.AUTOTUNE").export_constant(__name__, "AUTOTUNE") # Constants representing infinite and unknown cardinalities. INFINITE = -1 UNKNOWN = -2 COMPRESSION_GZIP = "GZIP" COMPRESSION_SNAPPY = "NONE" DATASET_SPEC_FILENAME = "dataset_spec.pb" tf_export("data.INFINITE_CARDINALITY").export_constant(__name__, "INFINITE") tf_export("data.UNKNOWN_CARDINALITY").export_constant(__name__, "UNKNOWN") def _validate_and_encode(name): if not name.isidentifier(): raise ValueError("Invalid `name`. The argument `name` needs to be a valid " "identifier. Value is considered a valid identifier if it " "only contains alphanumeric characters (a-z), (A-Z), and " "(0-9), or underscores (_). A valid identifier cannot " "start with a number, or contain any spaces.") return name.encode("utf-8") def _get_type(value): """Returns the type of `value` if it is a TypeSpec.""" if isinstance(value, type_spec.TypeSpec): return value.value_type() else: return type(value) @tf_export("data.Dataset", v1=[]) @six.add_metaclass(abc.ABCMeta) class DatasetV2(collections_abc.Iterable, tracking_base.Trackable, composite_tensor.CompositeTensor): """Represents a potentially large set of elements. The `tf.data.Dataset` API supports writing descriptive and efficient input pipelines. `Dataset` usage follows a common pattern: 1. Create a source dataset from your input data. 2. Apply dataset transformations to preprocess the data. 3. Iterate over the dataset and process the elements. Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory. Source Datasets: The simplest way to create a dataset is to create it from a python `list`: >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> for element in dataset: ... print(element) tf.Tensor(1, shape=(), dtype=int32) tf.Tensor(2, shape=(), dtype=int32) tf.Tensor(3, shape=(), dtype=int32) To process lines from files, use `tf.data.TextLineDataset`: >>> dataset = tf.data.TextLineDataset(["file1.txt", "file2.txt"]) To process records written in the `TFRecord` format, use `TFRecordDataset`: >>> dataset = tf.data.TFRecordDataset(["file1.tfrecords", "file2.tfrecords"]) To create a dataset of all files matching a pattern, use `tf.data.Dataset.list_files`: ```python dataset = tf.data.Dataset.list_files("/path/*.txt") ``` See `tf.data.FixedLengthRecordDataset` and `tf.data.Dataset.from_generator` for more ways to create datasets. Transformations: Once you have a dataset, you can apply transformations to prepare the data for your model: >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> dataset = dataset.map(lambda x: x*2) >>> list(dataset.as_numpy_iterator()) [2, 4, 6] Common Terms: **Element**: A single output from calling `next()` on a dataset iterator. Elements may be nested structures containing multiple components. For example, the element `(1, (3, "apple"))` has one tuple nested in another tuple. The components are `1`, `3`, and `"apple"`. **Component**: The leaf in the nested structure of an element. Supported types: Elements can be nested structures of tuples, named tuples, and dictionaries. Note that Python lists are *not* treated as nested structures of components. Instead, lists are converted to tensors and treated as components. For example, the element `(1, [1, 2, 3])` has only two components; the tensor `1` and the tensor `[1, 2, 3]`. Element components can be of any type representable by `tf.TypeSpec`, including `tf.Tensor`, `tf.data.Dataset`, `tf.sparse.SparseTensor`, `tf.RaggedTensor`, and `tf.TensorArray`. ```python a = 1 # Integer element b = 2.0 # Float element c = (1, 2) # Tuple element with 2 components d = {"a": (2, 2), "b": 3} # Dict element with 3 components Point = collections.namedtuple("Point", ["x", "y"]) e = Point(1, 2) # Named tuple f = tf.data.Dataset.range(10) # Dataset element ``` For more information, read [this guide](https://www.tensorflow.org/guide/data). """ def __init__(self, variant_tensor): """Creates a DatasetV2 object. This is a difference between DatasetV1 and DatasetV2. DatasetV1 does not take anything in its constructor whereas in the DatasetV2, we expect subclasses to create a variant_tensor and pass it in to the super() call. Args: variant_tensor: A DT_VARIANT tensor that represents the dataset. """ self._variant_tensor_attr = variant_tensor self._graph_attr = ops.get_default_graph() # Initialize the options for this dataset and its inputs. self._options_attr = options_lib.Options() for input_dataset in self._inputs(): input_options = None if isinstance(input_dataset, DatasetV1): # If the V1 dataset does not have the `_dataset` attribute, we assume it # is a dataset source and hence does not have options. Otherwise, we # grab the options of `_dataset` object if hasattr(input_dataset, "_dataset"): if not isinstance(input_dataset._dataset, DatasetV2): raise TypeError( f"Each input of dataset {type(self)} should be a subclass of " f"`tf.data.Dataset` but encountered " f"{type(input_dataset._dataset)}.") input_options = input_dataset._dataset._options_attr elif isinstance(input_dataset, DatasetV2): input_options = input_dataset._options_attr else: raise TypeError( f"Each input of dataset {type(self)} should be a subclass of " f"`tf.data.Dataset` but encountered {type(input_dataset)}.") if input_options is not None: self._options_attr = self._options_attr.merge(input_options) self._options_attr._set_mutable(False) # pylint: disable=protected-access @property def _variant_tensor(self): return self._variant_tensor_attr @_variant_tensor.setter def _variant_tensor(self, _): raise ValueError("The `_variant_tensor` property cannot be modified.") @deprecation.deprecated_args(None, "Use external_state_policy instead", "allow_stateful") def _as_serialized_graph( self, allow_stateful=None, strip_device_assignment=None, external_state_policy=options_lib.ExternalStatePolicy.WARN): """Produces serialized graph representation of the dataset. Args: allow_stateful: If true, we allow stateful ops to be present in the graph def. In that case, the state in these ops would be thrown away. strip_device_assignment: If true, non-local (i.e. job and task) device assignment is stripped from ops in the serialized graph. external_state_policy: The ExternalStatePolicy enum that determines how we handle input pipelines that depend on external state. By default, its set to WARN. Returns: A scalar `tf.Tensor` of `tf.string` type, representing this dataset as a serialized graph. """ if external_state_policy: policy = external_state_policy.value return gen_dataset_ops.dataset_to_graph_v2( self._variant_tensor, external_state_policy=policy, strip_device_assignment=strip_device_assignment) if strip_device_assignment: return gen_dataset_ops.dataset_to_graph( self._variant_tensor, allow_stateful=allow_stateful, strip_device_assignment=strip_device_assignment) return gen_dataset_ops.dataset_to_graph( self._variant_tensor, allow_stateful=allow_stateful) def _maybe_track_assets(self, graph_def): """Finds and tracks nodes in `graph_def` that refer to asset files. Args: graph_def: Serialized graph representation of this dataset. Returns: A dictionary mapping the node name of an asset constant to a tracked `asset.Asset` object. """ asset_tracker = {} for node in graph_def.node: if node.name.startswith("FileIdentity"): asset_tracker[node.input[0]] = None if not asset_tracker: return {} for node in graph_def.node: if node.name in asset_tracker: tensor_proto = node.attr["value"].tensor with context.eager_mode(), ops.device("CPU"): node_value = parsing_ops.parse_tensor( tensor_proto.SerializeToString(), dtypes.string).numpy() asset_tracker[node.name] = ([ self._track_trackable(asset.Asset(n), name=node.name + "_" + str(i), overwrite=True) for i, n in enumerate(node_value) ]) return asset_tracker def _trackable_children(self, save_type=tracking_base.SaveType.CHECKPOINT, **kwargs): if save_type != tracking_base.SaveType.SAVEDMODEL: return {} # _trace_variant_creation only works when executing eagerly, so we don't # want to run it in the object initialization. @def_function.function(input_signature=[], autograph=False) def _creator(): resource = self._trace_variant_creation()() # pylint: disable=protected-access return resource _creator.get_concrete_function() # Trigger asset tracking children = super(DatasetV2, self)._trackable_children(save_type, **kwargs) children["_variant_tracker"] = _VariantTracker(self._variant_tensor, _creator) return children def _trace_variant_creation(self): """Traces a function which outputs a variant `tf.Tensor` for this dataset. Note that creating this function involves evaluating an op, and is currently only supported when executing eagerly. Returns: A zero-argument `ConcreteFunction` which outputs a variant `tf.Tensor`. """ variant = self._variant_tensor if not isinstance(variant, ops.EagerTensor): raise NotImplementedError( "Constructing a tf.function that reproduces a given dataset is only " "supported for datasets created eagerly. Please file a feature " "request if this is important to you.") with context.eager_mode(), ops.device("CPU"): # pylint: disable=protected-access graph_def = graph_pb2.GraphDef().FromString( self._as_serialized_graph(external_state_policy=options_lib .ExternalStatePolicy.FAIL).numpy()) output_node_names = [] for node in graph_def.node: if node.op == "_Retval": output_node_names = node.input if len(output_node_names) != 1: raise AssertionError( f"Dataset graph is expected to only have one return value but found " f"{len(output_node_names)} return values: {output_node_names}.") output_node_name = output_node_names[0] file_path_nodes = {} # When building a tf.function, track files as `saved_model.Asset`s. if ops.get_default_graph().building_function: asset_tracker = self._maybe_track_assets(graph_def) for key in asset_tracker: assets_list = [ array_ops.expand_dims(asset.asset_path, axis=0) for asset in asset_tracker[key] ] file_path_nodes[key] = array_ops.concat(assets_list, axis=0) # Add functions used in this Dataset to the function's graph, since they # need to follow it around (and for example be added to a SavedModel which # references the dataset). variant_function = wrap_function.function_from_graph_def( graph_def, inputs=[], outputs=output_node_name + ":0", captures=file_path_nodes) for used_function in self._functions(): used_function.function.add_to_graph(variant_function.graph) return variant_function @abc.abstractmethod def _inputs(self): """Returns a list of the input datasets of the dataset.""" raise NotImplementedError(f"{type(self)}._inputs()") @property def _graph(self): return self._graph_attr @_graph.setter def _graph(self, _): raise ValueError("The `_graph` property cannot be modified.") # TODO(jsimsa): Change this to be the transitive closure of functions used # by this dataset and its inputs. def _functions(self): """Returns a list of functions associated with this dataset. Returns: A list of `StructuredFunctionWrapper` objects. """ return [] def _options(self): """Returns the options tensor for this dataset.""" # pylint: disable=protected-access return gen_dataset_ops.get_options(self._variant_tensor) @classmethod def _options_tensor_to_options(cls, serialized_options): """Converts options tensor to tf.data.Options object.""" options = options_lib.Options() if tensor_util.constant_value(serialized_options) is not None: pb = dataset_options_pb2.Options.FromString(tensor_util.constant_value( serialized_options)) options._from_proto(pb) # pylint: disable=protected-access return options def options(self): """Returns the options for this dataset and its inputs. Returns: A `tf.data.Options` object representing the dataset options. """ if context.executing_eagerly(): options = self._options_tensor_to_options(self._options()) options._set_mutable(False) # pylint: disable=protected-access return options warnings.warn("To make it possible to preserve tf.data options across " "serialization boundaries, their implementation has moved to " "be part of the TensorFlow graph. As a consequence, the " "options value is in general no longer known at graph " "construction time. Invoking this method in graph mode " "retains the legacy behavior of the original implementation, " "but note that the returned value might not reflect the " "actual value of the options.") return self._options_attr def _apply_debug_options(self): if DEBUG_MODE: # Disable autotuning and static optimizations that could introduce # parallelism or asynchrony. options = options_lib.Options() options.autotune.enabled = False options.experimental_optimization.filter_parallelization = False options.experimental_optimization.map_and_batch_fusion = False options.experimental_optimization.map_parallelization = False dataset = _OptionsDataset(self, options) else: dataset = self return dataset def __iter__(self): """Creates an iterator for elements of this dataset. The returned iterator implements the Python Iterator protocol. Returns: An `tf.data.Iterator` for the elements of this dataset. Raises: RuntimeError: If not inside of tf.function and not executing eagerly. """ if context.executing_eagerly() or ops.inside_function(): with ops.colocate_with(self._variant_tensor): return iterator_ops.OwnedIterator(self) else: raise RuntimeError("`tf.data.Dataset` only supports Python-style " "iteration in eager mode or within tf.function.") def __bool__(self): return True # Required as __len__ is defined __nonzero__ = __bool__ # Python 2 backward compatibility def __len__(self): """Returns the length of the dataset if it is known and finite. This method requires that you are running in eager mode, and that the length of the dataset is known and non-infinite. When the length may be unknown or infinite, or if you are running in graph mode, use `tf.data.Dataset.cardinality` instead. Returns: An integer representing the length of the dataset. Raises: RuntimeError: If the dataset length is unknown or infinite, or if eager execution is not enabled. """ if not context.executing_eagerly(): raise TypeError("`tf.data.Dataset` only supports `len` in eager mode. " "Use `tf.data.Dataset.cardinality()` instead.") length = self.cardinality() if length.numpy() == INFINITE: raise TypeError("The dataset is infinite.") if length.numpy() == UNKNOWN: raise TypeError("The dataset length is unknown.") return length @abc.abstractproperty def element_spec(self): """The type specification of an element of this dataset. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> dataset.element_spec TensorSpec(shape=(), dtype=tf.int32, name=None) For more information, read [this guide](https://www.tensorflow.org/guide/data#dataset_structure). Returns: A (nested) structure of `tf.TypeSpec` objects matching the structure of an element of this dataset and specifying the type of individual components. """ raise NotImplementedError(f"{type(self)}.element_spec()") def __repr__(self): type_ = type(self._dataset if isinstance(self, DatasetV1Adapter) else self) return f"<{type_.__name__} element_spec={self.element_spec}>" def __debug_string__(self): """Returns a string showing the type of the dataset and its inputs. This string is intended only for debugging purposes, and may change without warning. """ lines = [] to_process = [(self, 0)] # Stack of (dataset, depth) pairs. while to_process: dataset, depth = to_process.pop() lines.append("-"*2*depth + repr(dataset)) to_process.extend([(ds, depth+1) for ds in dataset._inputs()]) # pylint: disable=protected-access return "\n".join(lines) def as_numpy_iterator(self): """Returns an iterator which converts all elements of the dataset to numpy. Use `as_numpy_iterator` to inspect the content of your dataset. To see element shapes and types, print dataset elements directly instead of using `as_numpy_iterator`. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> for element in dataset: ... print(element) tf.Tensor(1, shape=(), dtype=int32) tf.Tensor(2, shape=(), dtype=int32) tf.Tensor(3, shape=(), dtype=int32) This method requires that you are running in eager mode and the dataset's element_spec contains only `TensorSpec` components. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> for element in dataset.as_numpy_iterator(): ... print(element) 1 2 3 >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> print(list(dataset.as_numpy_iterator())) [1, 2, 3] `as_numpy_iterator()` will preserve the nested structure of dataset elements. >>> dataset = tf.data.Dataset.from_tensor_slices({'a': ([1, 2], [3, 4]), ... 'b': [5, 6]}) >>> list(dataset.as_numpy_iterator()) == [{'a': (1, 3), 'b': 5}, ... {'a': (2, 4), 'b': 6}] True Returns: An iterable over the elements of the dataset, with their tensors converted to numpy arrays. Raises: TypeError: if an element contains a non-`Tensor` value. RuntimeError: if eager execution is not enabled. """ if not context.executing_eagerly(): raise RuntimeError("`tf.data.Dataset.as_numpy_iterator()` is only " "supported in eager mode.") for component_spec in nest.flatten(self.element_spec): if not isinstance(component_spec, (tensor_spec.TensorSpec, ragged_tensor.RaggedTensorSpec, structure.NoneTensorSpec)): raise TypeError( f"`tf.data.Dataset.as_numpy_iterator()` is not supported for " f"datasets that produce values of type {component_spec.value_type}") return _NumpyIterator(self) @property def _flat_shapes(self): """Returns a list `tf.TensorShapes`s for the element tensor representation. Returns: A list `tf.TensorShapes`s for the element tensor representation. """ return structure.get_flat_tensor_shapes(self.element_spec) @property def _flat_types(self): """Returns a list `tf.DType`s for the element tensor representation. Returns: A list `tf.DType`s for the element tensor representation. """ return structure.get_flat_tensor_types(self.element_spec) @property def _flat_structure(self): """Helper for setting `output_shapes` and `output_types` attrs of an op. Most dataset op constructors expect `output_shapes` and `output_types` arguments that represent the flattened structure of an element. This helper function generates these attrs as a keyword argument dictionary, allowing `Dataset._variant_tensor` implementations to pass `**self._flat_structure` to the op constructor. Returns: A dictionary of keyword arguments that can be passed to a dataset op constructor. """ return { "output_shapes": self._flat_shapes, "output_types": self._flat_types, } @property def _metadata(self): """Helper for generating dataset metadata.""" metadata = dataset_metadata_pb2.Metadata() if self._name: metadata.name = _validate_and_encode(self._name) return metadata @property def _common_args(self): """Helper for generating arguments that are common across most dataset ops. Most dataset op constructors expect `output_shapes` and `output_types` arguments that represent the flattened structure of an element, as well as a `metadata` argument for additional metadata such as user-defined dataset name. This helper function generates common attributes as a keyword argument dictionary, allowing `Dataset._variant_tensor` implementations to pass `**self._common_args` to the op constructor. Returns: A dictionary of keyword arguments that can be passed to a dataset op constructor. """ return { "metadata": self._metadata.SerializeToString(), "output_shapes": self._flat_shapes, "output_types": self._flat_types, } @property def _type_spec(self): return DatasetSpec(self.element_spec) @staticmethod def from_tensors(tensors, name=None): """Creates a `Dataset` with a single element, comprising the given tensors. `from_tensors` produces a dataset containing only a single element. To slice the input tensor into multiple elements, use `from_tensor_slices` instead. >>> dataset = tf.data.Dataset.from_tensors([1, 2, 3]) >>> list(dataset.as_numpy_iterator()) [array([1, 2, 3], dtype=int32)] >>> dataset = tf.data.Dataset.from_tensors(([1, 2, 3], 'A')) >>> list(dataset.as_numpy_iterator()) [(array([1, 2, 3], dtype=int32), b'A')] >>> # You can use `from_tensors` to produce a dataset which repeats >>> # the same example many times. >>> example = tf.constant([1,2,3]) >>> dataset = tf.data.Dataset.from_tensors(example).repeat(2) >>> list(dataset.as_numpy_iterator()) [array([1, 2, 3], dtype=int32), array([1, 2, 3], dtype=int32)] Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If `tensors` contains one or more large NumPy arrays, consider the alternative described in [this guide](https://tensorflow.org/guide/data#consuming_numpy_arrays). Args: tensors: A dataset "element". Supported values are documented [here](https://www.tensorflow.org/guide/data#dataset_structure). name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return TensorDataset(tensors, name=name) @staticmethod def from_tensor_slices(tensors, name=None): """Creates a `Dataset` whose elements are slices of the given tensors. The given tensors are sliced along their first dimension. This operation preserves the structure of the input tensors, removing the first dimension of each tensor and using it as the dataset dimension. All input tensors must have the same size in their first dimensions. >>> # Slicing a 1D tensor produces scalar tensor elements. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> list(dataset.as_numpy_iterator()) [1, 2, 3] >>> # Slicing a 2D tensor produces 1D tensor elements. >>> dataset = tf.data.Dataset.from_tensor_slices([[1, 2], [3, 4]]) >>> list(dataset.as_numpy_iterator()) [array([1, 2], dtype=int32), array([3, 4], dtype=int32)] >>> # Slicing a tuple of 1D tensors produces tuple elements containing >>> # scalar tensors. >>> dataset = tf.data.Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6])) >>> list(dataset.as_numpy_iterator()) [(1, 3, 5), (2, 4, 6)] >>> # Dictionary structure is also preserved. >>> dataset = tf.data.Dataset.from_tensor_slices({"a": [1, 2], "b": [3, 4]}) >>> list(dataset.as_numpy_iterator()) == [{'a': 1, 'b': 3}, ... {'a': 2, 'b': 4}] True >>> # Two tensors can be combined into one Dataset object. >>> features = tf.constant([[1, 3], [2, 1], [3, 3]]) # ==> 3x2 tensor >>> labels = tf.constant(['A', 'B', 'A']) # ==> 3x1 tensor >>> dataset = Dataset.from_tensor_slices((features, labels)) >>> # Both the features and the labels tensors can be converted >>> # to a Dataset object separately and combined after. >>> features_dataset = Dataset.from_tensor_slices(features) >>> labels_dataset = Dataset.from_tensor_slices(labels) >>> dataset = Dataset.zip((features_dataset, labels_dataset)) >>> # A batched feature and label set can be converted to a Dataset >>> # in similar fashion. >>> batched_features = tf.constant([[[1, 3], [2, 3]], ... [[2, 1], [1, 2]], ... [[3, 3], [3, 2]]], shape=(3, 2, 2)) >>> batched_labels = tf.constant([['A', 'A'], ... ['B', 'B'], ... ['A', 'B']], shape=(3, 2, 1)) >>> dataset = Dataset.from_tensor_slices((batched_features, batched_labels)) >>> for element in dataset.as_numpy_iterator(): ... print(element) (array([[1, 3], [2, 3]], dtype=int32), array([[b'A'], [b'A']], dtype=object)) (array([[2, 1], [1, 2]], dtype=int32), array([[b'B'], [b'B']], dtype=object)) (array([[3, 3], [3, 2]], dtype=int32), array([[b'A'], [b'B']], dtype=object)) Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If `tensors` contains one or more large NumPy arrays, consider the alternative described in [this guide]( https://tensorflow.org/guide/data#consuming_numpy_arrays). Args: tensors: A dataset element, whose components have the same first dimension. Supported values are documented [here](https://www.tensorflow.org/guide/data#dataset_structure). name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return TensorSliceDataset(tensors, name=name) class _GeneratorState(object): """Stores outstanding iterators created from a Python generator. This class keeps track of potentially multiple iterators that may have been created from a generator, e.g. in the case that the dataset is repeated, or nested within a parallel computation. """ def __init__(self, generator): self._generator = generator self._lock = threading.Lock() self._next_id = 0 # GUARDED_BY(self._lock) self._args = {} self._iterators = {} def _normalize_id(self, iterator_id): # In debug mode, iterator ids may be eagerly-generated np.arrays instead # of Tensors. We convert them to scalars to make them hashable. if isinstance(iterator_id, np.ndarray): return iterator_id.item() return iterator_id def get_next_id(self, *args): with self._lock: ret = self._next_id self._next_id += 1 self._args[ret] = args # NOTE(mrry): Explicitly create an array of `np.int64` because implicit # casting in `py_func()` will create an array of `np.int32` on Windows, # leading to a runtime error. return np.array(ret, dtype=np.int64) def get_iterator(self, iterator_id): iterator_id = self._normalize_id(iterator_id) try: return self._iterators[iterator_id] except KeyError: iterator = iter(self._generator(*self._args.pop(iterator_id))) self._iterators[iterator_id] = iterator return iterator def iterator_completed(self, iterator_id): del self._iterators[self._normalize_id(iterator_id)] @staticmethod @deprecation.deprecated_args(None, "Use output_signature instead", "output_types", "output_shapes") def from_generator(generator, output_types=None, output_shapes=None, args=None, output_signature=None, name=None): """Creates a `Dataset` whose elements are generated by `generator`. Note: The current implementation of `Dataset.from_generator()` uses `tf.numpy_function` and inherits the same constraints. In particular, it requires the dataset and iterator related operations to be placed on a device in the same process as the Python program that called `Dataset.from_generator()`. In particular, using `from_generator` will preclude the use of tf.data service for scaling out dataset processing. The body of `generator` will not be serialized in a `GraphDef`, and you should not use this method if you need to serialize your model and restore it in a different environment. The `generator` argument must be a callable object that returns an object that supports the `iter()` protocol (e.g. a generator function). The elements generated by `generator` must be compatible with either the given `output_signature` argument or with the given `output_types` and (optionally) `output_shapes` arguments, whichever was specified. The recommended way to call `from_generator` is to use the `output_signature` argument. In this case the output will be assumed to consist of objects with the classes, shapes and types defined by `tf.TypeSpec` objects from `output_signature` argument: >>> def gen(): ... ragged_tensor = tf.ragged.constant([[1, 2], [3]]) ... yield 42, ragged_tensor >>> >>> dataset = tf.data.Dataset.from_generator( ... gen, ... output_signature=( ... tf.TensorSpec(shape=(), dtype=tf.int32), ... tf.RaggedTensorSpec(shape=(2, None), dtype=tf.int32))) >>> >>> list(dataset.take(1)) [(, )] There is also a deprecated way to call `from_generator` by either with `output_types` argument alone or together with `output_shapes` argument. In this case the output of the function will be assumed to consist of `tf.Tensor` objects with the types defined by `output_types` and with the shapes which are either unknown or defined by `output_shapes`. Note: If `generator` depends on mutable global variables or other external state, be aware that the runtime may invoke `generator` multiple times (in order to support repeating the `Dataset`) and at any time between the call to `Dataset.from_generator()` and the production of the first element from the generator. Mutating global variables or external state can cause undefined behavior, and we recommend that you explicitly cache any external state in `generator` before calling `Dataset.from_generator()`. Note: While the `output_signature` parameter makes it possible to yield `Dataset` elements, the scope of `Dataset.from_generator()` should be limited to logic that cannot be expressed through tf.data operations. Using tf.data operations within the generator function is an anti-pattern and may result in incremental memory growth. Args: generator: A callable object that returns an object that supports the `iter()` protocol. If `args` is not specified, `generator` must take no arguments; otherwise it must take as many arguments as there are values in `args`. output_types: (Optional.) A (nested) structure of `tf.DType` objects corresponding to each component of an element yielded by `generator`. output_shapes: (Optional.) A (nested) structure of `tf.TensorShape` objects corresponding to each component of an element yielded by `generator`. args: (Optional.) A tuple of `tf.Tensor` objects that will be evaluated and passed to `generator` as NumPy-array arguments. output_signature: (Optional.) A (nested) structure of `tf.TypeSpec` objects corresponding to each component of an element yielded by `generator`. name: (Optional.) A name for the tf.data operations used by `from_generator`. Returns: Dataset: A `Dataset`. """ if not callable(generator): raise TypeError("`generator` must be a Python callable.") if output_signature is not None: if output_types is not None: raise TypeError("The `output_types` argument can not be used together " "with the `output_signature` argument.") if output_shapes is not None: raise TypeError("The `output_shapes` argument can not be used together " "with the `output_signature` argument.") for spec in nest.flatten(output_signature): if not isinstance(spec, type_spec.TypeSpec): raise TypeError(f"`output_signature` must contain objects that are " f"subclass of `tf.TypeSpec` but found {type(spec)} " f"which is not.") else: if output_types is None: raise TypeError("To specify the output signature you need to provide " "either the `output_signature` argument or the " "`output_types` argument.") if output_signature is None: 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) output_signature = nest.map_structure_up_to(output_types, tensor_spec.TensorSpec, output_shapes, output_types) if all( isinstance(x, tensor_spec.TensorSpec) for x in nest.flatten(output_signature)): output_types = nest.pack_sequence_as( output_signature, [x.dtype for x in nest.flatten(output_signature)]) output_shapes = nest.pack_sequence_as( output_signature, [x.shape for x in nest.flatten(output_signature)]) if args is None: args = () else: args = tuple(ops.convert_n_to_tensor(args, name="args")) generator_state = DatasetV2._GeneratorState(generator) def get_iterator_id_fn(unused_dummy): """Creates a unique `iterator_id` for each pass over the dataset. The returned `iterator_id` disambiguates between multiple concurrently existing iterators. Args: unused_dummy: Ignored value. Returns: A `tf.int64` tensor whose value uniquely identifies an iterator in `generator_state`. """ return script_ops.numpy_function(generator_state.get_next_id, args, dtypes.int64) def generator_next_fn(iterator_id_t): """Generates the next element from iterator with ID `iterator_id_t`. We map this function across an infinite repetition of the `iterator_id_t`, and raise `StopIteration` to terminate the iteration. Args: iterator_id_t: A `tf.int64` tensor whose value uniquely identifies the iterator in `generator_state` from which to generate an element. Returns: The next element to generate from the iterator. """ if output_types and output_shapes: flattened_types = [ dtypes.as_dtype(dt) for dt in nest.flatten(output_types) ] flattened_shapes = nest.flatten(output_shapes) def generator_py_func(iterator_id): """A `py_func` that will be called to invoke the iterator.""" # `next()` raises `StopIteration` when there are no more # elements remaining to be generated. values = next(generator_state.get_iterator(iterator_id)) # Use the same _convert function from the py_func() implementation to # convert the returned values to arrays early, so that we can inspect # their values. try: flattened_values = nest.flatten_up_to(output_types, values) except (TypeError, ValueError): six.reraise( TypeError, TypeError( f"`generator` yielded an element that did not match the " f"expected structure. The expected structure was " f"{output_types}, but the yielded element was {values}."), sys.exc_info()[2]) ret_arrays = [] for ret, dtype in zip(flattened_values, flattened_types): try: ret_arrays.append( script_ops.FuncRegistry._convert( # pylint: disable=protected-access ret, dtype=dtype.as_numpy_dtype)) except (TypeError, ValueError): six.reraise( TypeError, TypeError( f"`generator` yielded an element that could not be " f"converted to the expected type. The expected type was " f"{dtype.name}, but the yielded element was {ret}."), sys.exc_info()[2]) # Additional type and shape checking to ensure that the components of # the generated element match the `output_types` and `output_shapes` # arguments. for (ret_array, expected_dtype, expected_shape) in zip(ret_arrays, flattened_types, flattened_shapes): if ret_array.dtype != expected_dtype.as_numpy_dtype: raise TypeError( f"`generator` yielded an element of type {ret_array.dtype} " f"where an element of type {expected_dtype.as_numpy_dtype} " f"was expected.") if not expected_shape.is_compatible_with(ret_array.shape): raise TypeError( f"`generator` yielded an element of shape {ret_array.shape} " f"where an element of shape {expected_shape} was expected.") return ret_arrays flat_values = script_ops.numpy_function(generator_py_func, [iterator_id_t], flattened_types) # In debug mode the numpy_function will return a scalar if # generator_py_func produces only a single value. if not isinstance(flat_values, (list, tuple)): flat_values = [flat_values] # The `py_func()` op drops the inferred shapes, so we add them back in # here. if output_shapes is not None: for ret_t, shape in zip(flat_values, flattened_shapes): ret_t.set_shape(shape) return nest.pack_sequence_as(output_types, flat_values) else: flat_output_types = structure.get_flat_tensor_types(output_signature) def generator_py_func(iterator_id): """A `py_func` that will be called to invoke the iterator.""" # `next()` raises `StopIteration` when there are no more # elements remaining to be generated. values = next(generator_state.get_iterator(iterator_id.numpy())) try: values = structure.normalize_element(values, output_signature) except (TypeError, ValueError): six.reraise( TypeError, TypeError( f"`generator` yielded an element that did not match the " f"expected structure. The expected structure was " f"{output_signature}, but the yielded element was " f"{values}."), sys.exc_info()[2]) values_spec = structure.type_spec_from_value(values) if not structure.are_compatible(values_spec, output_signature): raise TypeError( f"`generator` yielded an element of {values_spec} where an " f"element of {output_signature} was expected.") return structure.to_tensor_list(output_signature, values) return script_ops.eager_py_func( generator_py_func, inp=[iterator_id_t], Tout=flat_output_types) def finalize_fn(iterator_id_t): """Releases host-side state for the iterator with ID `iterator_id_t`.""" def finalize_py_func(iterator_id): generator_state.iterator_completed(iterator_id) # We return a dummy value so that the `finalize_fn` has a valid # signature. # NOTE(mrry): Explicitly create an array of `np.int64` because implicit # casting in `py_func()` will create an array of `np.int32` on Windows, # leading to a runtime error. return np.array(0, dtype=np.int64) return script_ops.numpy_function(finalize_py_func, [iterator_id_t], dtypes.int64) # This function associates each traversal of `generator` with a unique # iterator ID. def flat_map_fn(dummy_arg): # The `get_iterator_id_fn` gets a unique ID for the current instance of # of the generator. # The `generator_next_fn` gets the next element from the iterator with the # given ID, and raises StopIteration when that iterator contains no # more elements. return _GeneratorDataset( dummy_arg, get_iterator_id_fn, generator_next_fn, finalize_fn, output_signature, name=name) # A single-element dataset that, each time it is evaluated, contains a # freshly-generated and unique (for the returned dataset) int64 # ID that will be used to identify the appropriate Python state, which # is encapsulated in `generator_state`, and captured in # `get_iterator_id_map_fn`. dummy = 0 id_dataset = Dataset.from_tensors(dummy, name=name) # A dataset that contains all of the elements generated by a # single iterator created from `generator`, identified by the # iterator ID contained in `id_dataset`. Lifting the iteration # into a flat_map here enables multiple repetitions and/or nested # versions of the returned dataset to be created, because it forces # the generation of a new ID for each version. return id_dataset.flat_map(flat_map_fn, name=name) @staticmethod def range(*args, **kwargs): """Creates a `Dataset` of a step-separated range of values. >>> list(Dataset.range(5).as_numpy_iterator()) [0, 1, 2, 3, 4] >>> list(Dataset.range(2, 5).as_numpy_iterator()) [2, 3, 4] >>> list(Dataset.range(1, 5, 2).as_numpy_iterator()) [1, 3] >>> list(Dataset.range(1, 5, -2).as_numpy_iterator()) [] >>> list(Dataset.range(5, 1).as_numpy_iterator()) [] >>> list(Dataset.range(5, 1, -2).as_numpy_iterator()) [5, 3] >>> list(Dataset.range(2, 5, output_type=tf.int32).as_numpy_iterator()) [2, 3, 4] >>> list(Dataset.range(1, 5, 2, output_type=tf.float32).as_numpy_iterator()) [1.0, 3.0] Args: *args: follows the same semantics as python's range. 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], step = args[2]. **kwargs: - output_type: Its expected dtype. (Optional, default: `tf.int64`). - name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `RangeDataset`. Raises: ValueError: if len(args) == 0. """ return RangeDataset(*args, **kwargs) @staticmethod def zip(datasets, name=None): """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 a (nested) structure of `Dataset` objects. The supported nesting mechanisms are documented [here] (https://www.tensorflow.org/guide/data#dataset_structure). >>> # The nested structure of the `datasets` argument determines the >>> # structure of elements in the resulting dataset. >>> a = tf.data.Dataset.range(1, 4) # ==> [ 1, 2, 3 ] >>> b = tf.data.Dataset.range(4, 7) # ==> [ 4, 5, 6 ] >>> ds = tf.data.Dataset.zip((a, b)) >>> list(ds.as_numpy_iterator()) [(1, 4), (2, 5), (3, 6)] >>> ds = tf.data.Dataset.zip((b, a)) >>> list(ds.as_numpy_iterator()) [(4, 1), (5, 2), (6, 3)] >>> >>> # The `datasets` argument may contain an arbitrary number of datasets. >>> c = tf.data.Dataset.range(7, 13).batch(2) # ==> [ [7, 8], ... # [9, 10], ... # [11, 12] ] >>> ds = tf.data.Dataset.zip((a, b, c)) >>> for element in ds.as_numpy_iterator(): ... print(element) (1, 4, array([7, 8])) (2, 5, array([ 9, 10])) (3, 6, array([11, 12])) >>> >>> # The number of elements in the resulting dataset is the same as >>> # the size of the smallest dataset in `datasets`. >>> d = tf.data.Dataset.range(13, 15) # ==> [ 13, 14 ] >>> ds = tf.data.Dataset.zip((a, d)) >>> list(ds.as_numpy_iterator()) [(1, 13), (2, 14)] Args: datasets: A (nested) structure of datasets. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return ZipDataset(datasets, name=name) def concatenate(self, dataset, name=None): """Creates a `Dataset` by concatenating the given dataset with this dataset. >>> a = tf.data.Dataset.range(1, 4) # ==> [ 1, 2, 3 ] >>> b = tf.data.Dataset.range(4, 8) # ==> [ 4, 5, 6, 7 ] >>> ds = a.concatenate(b) >>> list(ds.as_numpy_iterator()) [1, 2, 3, 4, 5, 6, 7] >>> # The input dataset and dataset to be concatenated should have >>> # compatible element specs. >>> c = tf.data.Dataset.zip((a, b)) >>> a.concatenate(c) Traceback (most recent call last): TypeError: Two datasets to concatenate have different types and (tf.int64, tf.int64) >>> d = tf.data.Dataset.from_tensor_slices(["a", "b", "c"]) >>> a.concatenate(d) Traceback (most recent call last): TypeError: Two datasets to concatenate have different types and Args: dataset: `Dataset` to be concatenated. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return ConcatenateDataset(self, dataset, name=name) def prefetch(self, buffer_size, name=None): """Creates a `Dataset` that prefetches elements from this dataset. Most dataset input pipelines should end with a call to `prefetch`. This allows later elements to be prepared while the current element is being processed. This often improves latency and throughput, at the cost of using additional memory to store prefetched elements. Note: Like other `Dataset` methods, prefetch operates on the elements of the input dataset. It has no concept of examples vs. batches. `examples.prefetch(2)` will prefetch two elements (2 examples), while `examples.batch(20).prefetch(2)` will prefetch 2 elements (2 batches, of 20 examples each). >>> dataset = tf.data.Dataset.range(3) >>> dataset = dataset.prefetch(2) >>> list(dataset.as_numpy_iterator()) [0, 1, 2] Args: buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the maximum number of elements that will be buffered when prefetching. If the value `tf.data.AUTOTUNE` is used, then the buffer size is dynamically tuned. name: Optional. A name for the tf.data transformation. Returns: Dataset: A `Dataset`. """ if DEBUG_MODE: return self return PrefetchDataset(self, buffer_size, name=name) @staticmethod def list_files(file_pattern, shuffle=None, seed=None, name=None): """A dataset of all files matching one or more glob patterns. The `file_pattern` argument should be a small number of glob patterns. If your filenames have already been globbed, use `Dataset.from_tensor_slices(filenames)` instead, as re-globbing every filename with `list_files` may result in poor performance with remote storage systems. Note: The default behavior of this method is to return filenames in a non-deterministic random shuffled order. Pass a `seed` or `shuffle=False` to get results in a deterministic order. Example: If we had the following files on our filesystem: - /path/to/dir/a.txt - /path/to/dir/b.py - /path/to/dir/c.py If we pass "/path/to/dir/*.py" as the directory, the dataset would produce: - /path/to/dir/b.py - /path/to/dir/c.py Args: file_pattern: A string, a list of strings, or a `tf.Tensor` of string type (scalar or vector), representing the filename glob (i.e. shell wildcard) pattern(s) that will be matched. shuffle: (Optional.) If `True`, the file names will be shuffled randomly. Defaults to `True`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See `tf.random.set_seed` for behavior. name: Optional. A name for the tf.data operations used by `list_files`. Returns: Dataset: A `Dataset` of strings corresponding to file names. """ with ops.name_scope("list_files"): if shuffle is None: shuffle = True file_pattern = ops.convert_to_tensor( file_pattern, dtype=dtypes.string, name="file_pattern") matching_files = gen_io_ops.matching_files(file_pattern) # Raise an exception if `file_pattern` does not match any files. condition = math_ops.greater(array_ops.shape(matching_files)[0], 0, name="match_not_empty") message = math_ops.add( "No files matched pattern: ", string_ops.reduce_join(file_pattern, separator=", "), name="message") assert_not_empty = control_flow_ops.Assert( condition, [message], summarize=1, name="assert_not_empty") with ops.control_dependencies([assert_not_empty]): matching_files = array_ops.identity(matching_files) dataset = TensorSliceDataset(matching_files, is_files=True, name=name) if issubclass(Dataset, DatasetV1): dataset = DatasetV1Adapter(dataset) if shuffle: # NOTE(mrry): The shuffle buffer size must be greater than zero, but the # list of files might be empty. buffer_size = math_ops.maximum( array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1) dataset = dataset.shuffle(buffer_size, seed=seed, name=name) return dataset def repeat(self, count=None, name=None): """Repeats this dataset so each original value is seen `count` times. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> dataset = dataset.repeat(3) >>> list(dataset.as_numpy_iterator()) [1, 2, 3, 1, 2, 3, 1, 2, 3] Note: If the input dataset depends on global state (e.g. a random number generator) or its output is non-deterministic (e.g. because of upstream `shuffle`), then different repetitions may produce different elements. Args: count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of times the dataset should be repeated. The default behavior (if `count` is `None` or `-1`) is for the dataset be repeated indefinitely. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return RepeatDataset(self, count, name=name) def enumerate(self, start=0, name=None): """Enumerates the elements of this dataset. It is similar to python's `enumerate`. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> dataset = dataset.enumerate(start=5) >>> for element in dataset.as_numpy_iterator(): ... print(element) (5, 1) (6, 2) (7, 3) >>> # The (nested) structure of the input dataset determines the >>> # structure of elements in the resulting dataset. >>> dataset = tf.data.Dataset.from_tensor_slices([(7, 8), (9, 10)]) >>> dataset = dataset.enumerate() >>> for element in dataset.as_numpy_iterator(): ... print(element) (0, array([7, 8], dtype=int32)) (1, array([ 9, 10], dtype=int32)) Args: start: A `tf.int64` scalar `tf.Tensor`, representing the start value for enumeration. name: Optional. A name for the tf.data operations used by `enumerate`. Returns: Dataset: A `Dataset`. """ max_value = np.iinfo(dtypes.int64.as_numpy_dtype).max range_dataset = Dataset.range(start, max_value, name=name) # Replicate the range component so that each split is enumerated # independently. This avoids the need for prohibitively expensive # cross-split coordination. range_dataset = _apply_rewrite(range_dataset, "replicate_on_split") return Dataset.zip((range_dataset, self), name=name) def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None, name=None): """Randomly shuffles the elements of this dataset. This dataset fills a buffer with `buffer_size` elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. For perfect shuffling, a buffer size greater than or equal to the full size of the dataset is required. For instance, if your dataset contains 10,000 elements but `buffer_size` is set to 1,000, then `shuffle` will initially select a random element from only the first 1,000 elements in the buffer. Once an element is selected, its space in the buffer is replaced by the next (i.e. 1,001-st) element, maintaining the 1,000 element buffer. `reshuffle_each_iteration` controls whether the shuffle order should be different for each epoch. In TF 1.X, the idiomatic way to create epochs was through the `repeat` transformation: ```python dataset = tf.data.Dataset.range(3) dataset = dataset.shuffle(3, reshuffle_each_iteration=True) dataset = dataset.repeat(2) # [1, 0, 2, 1, 2, 0] dataset = tf.data.Dataset.range(3) dataset = dataset.shuffle(3, reshuffle_each_iteration=False) dataset = dataset.repeat(2) # [1, 0, 2, 1, 0, 2] ``` In TF 2.0, `tf.data.Dataset` objects are Python iterables which makes it possible to also create epochs through Python iteration: ```python dataset = tf.data.Dataset.range(3) dataset = dataset.shuffle(3, reshuffle_each_iteration=True) list(dataset.as_numpy_iterator()) # [1, 0, 2] list(dataset.as_numpy_iterator()) # [1, 2, 0] ``` ```python dataset = tf.data.Dataset.range(3) dataset = dataset.shuffle(3, reshuffle_each_iteration=False) list(dataset.as_numpy_iterator()) # [1, 0, 2] list(dataset.as_numpy_iterator()) # [1, 0, 2] ``` 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.random.set_seed` for behavior. reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.) name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return ShuffleDataset( self, buffer_size, seed, reshuffle_each_iteration, name=name) def cache(self, filename="", name=None): """Caches the elements in this dataset. The first time the dataset is iterated over, its elements will be cached either in the specified file or in memory. Subsequent iterations will use the cached data. Note: To guarantee that the cache gets finalized, the input dataset must be iterated through in its entirety, until it raises StopIteration. Otherwise, subsequent iterations may not use cached data. >>> dataset = tf.data.Dataset.range(5) >>> dataset = dataset.map(lambda x: x**2) >>> dataset = dataset.cache() >>> # The first time reading through the data will generate the data using >>> # `range` and `map`. >>> list(dataset.as_numpy_iterator()) [0, 1, 4, 9, 16] >>> # Subsequent iterations read from the cache. >>> list(dataset.as_numpy_iterator()) [0, 1, 4, 9, 16] When caching to a file, the cached data will persist across runs. Even the first iteration through the data will read from the cache file. Changing the input pipeline before the call to `.cache()` will have no effect until the cache file is removed or the filename is changed. ```python dataset = tf.data.Dataset.range(5) dataset = dataset.cache("/path/to/file") list(dataset.as_numpy_iterator()) # [0, 1, 2, 3, 4] dataset = tf.data.Dataset.range(10) dataset = dataset.cache("/path/to/file") # Same file! list(dataset.as_numpy_iterator()) # [0, 1, 2, 3, 4] ``` Note: `cache` will produce exactly the same elements during each iteration through the dataset. If you wish to randomize the iteration order, make sure to call `shuffle` *after* calling `cache`. Args: filename: A `tf.string` scalar `tf.Tensor`, representing the name of a directory on the filesystem to use for caching elements in this Dataset. If a filename is not provided, the dataset will be cached in memory. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return CacheDataset(self, filename, name=name) def take(self, count, name=None): """Creates a `Dataset` with at most `count` elements from this dataset. >>> dataset = tf.data.Dataset.range(10) >>> dataset = dataset.take(3) >>> list(dataset.as_numpy_iterator()) [0, 1, 2] 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. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return TakeDataset(self, count, name=name) def skip(self, count, name=None): """Creates a `Dataset` that skips `count` elements from this dataset. >>> dataset = tf.data.Dataset.range(10) >>> dataset = dataset.skip(7) >>> list(dataset.as_numpy_iterator()) [7, 8, 9] 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. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return SkipDataset(self, count, name=name) def shard(self, num_shards, index, name=None): """Creates a `Dataset` that includes only 1/`num_shards` of this dataset. `shard` is deterministic. The Dataset produced by `A.shard(n, i)` will contain all elements of A whose index mod n = i. >>> A = tf.data.Dataset.range(10) >>> B = A.shard(num_shards=3, index=0) >>> list(B.as_numpy_iterator()) [0, 3, 6, 9] >>> C = A.shard(num_shards=3, index=1) >>> list(C.as_numpy_iterator()) [1, 4, 7] >>> D = A.shard(num_shards=3, index=2) >>> list(D.as_numpy_iterator()) [2, 5, 8] This dataset operator is very useful when running distributed training, as it allows each worker to read a unique subset. When reading a single input file, you can shard elements as follows: ```python d = tf.data.TFRecordDataset(input_file) d = d.shard(num_workers, worker_index) d = d.repeat(num_epochs) d = d.shuffle(shuffle_buffer_size) d = d.map(parser_fn, num_parallel_calls=num_map_threads) ``` Important caveats: - Be sure to shard before you use any randomizing operator (such as shuffle). - Generally it is best if the shard operator is used early in the dataset pipeline. For example, when reading from a set of TFRecord files, shard before converting the dataset to input samples. This avoids reading every file on every worker. The following is an example of an efficient sharding strategy within a complete pipeline: ```python d = Dataset.list_files(pattern) d = d.shard(num_workers, worker_index) d = d.repeat(num_epochs) d = d.shuffle(shuffle_buffer_size) d = d.interleave(tf.data.TFRecordDataset, cycle_length=num_readers, block_length=1) d = d.map(parser_fn, num_parallel_calls=num_map_threads) ``` Args: num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of shards operating in parallel. index: A `tf.int64` scalar `tf.Tensor`, representing the worker index. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. Raises: InvalidArgumentError: if `num_shards` or `index` are illegal values. Note: error checking is done on a best-effort basis, and errors aren't guaranteed to be caught upon dataset creation. (e.g. providing in a placeholder tensor bypasses the early checking, and will instead result in an error during a session.run call.) """ return ShardDataset(self, num_shards, index, name=name) def save(self, path, compression=None, shard_func=None, checkpoint_args=None): """Saves the content of the given dataset. Example usage: >>> import tempfile >>> path = os.path.join(tempfile.gettempdir(), "saved_data") >>> # Save a dataset >>> dataset = tf.data.Dataset.range(2) >>> dataset.save(path) >>> new_dataset = tf.data.Dataset.load(path) >>> for elem in new_dataset: ... print(elem) tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) The saved dataset is saved in multiple file "shards". By default, the dataset output is divided to shards in a round-robin fashion but custom sharding can be specified via the `shard_func` function. For example, you can save the dataset to using a single shard as follows: ```python dataset = make_dataset() def custom_shard_func(element): return np.int64(0) dataset.save( path="/path/to/data", ..., shard_func=custom_shard_func) ``` To enable checkpointing, pass in `checkpoint_args` to the `save` method as follows: ```python dataset = tf.data.Dataset.range(100) save_dir = "..." checkpoint_prefix = "..." step_counter = tf.Variable(0, trainable=False) checkpoint_args = { "checkpoint_interval": 50, "step_counter": step_counter, "directory": checkpoint_prefix, "max_to_keep": 20, } dataset.save(dataset, save_dir, checkpoint_args=checkpoint_args) ``` NOTE: The directory layout and file format used for saving the dataset is considered an implementation detail and may change. For this reason, datasets saved through `tf.data.Dataset.save` should only be consumed through `tf.data.Dataset.load`, which is guaranteed to be backwards compatible. Args: path: Required. A directory to use for saving the dataset. compression: Optional. The algorithm to use to compress data when writing it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`. shard_func: Optional. A function to control the mapping of dataset elements to file shards. The function is expected to map elements of the input dataset to int64 shard IDs. If present, the function will be traced and executed as graph computation. checkpoint_args: Optional args for checkpointing which will be passed into the `tf.train.CheckpointManager`. If `checkpoint_args` are not specified, then checkpointing will not be performed. The `save()` implementation creates a `tf.train.Checkpoint` object internally, so users should not set the `checkpoint` argument in `checkpoint_args`. Raises: ValueError if `checkpoint` is passed into `checkpoint_args`. """ # Loaded lazily due to a circular dependency # dataset_ops->save_ops->dataset_ops from tensorflow.python.data.ops import save_op # pylint: disable=g-import-not-at-top save_op.save(self, path, compression, shard_func, checkpoint_args) @staticmethod def load(path, element_spec=None, compression=None, reader_func=None): """Loads a previously saved dataset. Example usage: >>> import tempfile >>> path = os.path.join(tempfile.gettempdir(), "saved_data") >>> # Save a dataset >>> dataset = tf.data.Dataset.range(2) >>> tf.data.Dataset.save(dataset, path) >>> new_dataset = tf.data.Dataset.load(path) >>> for elem in new_dataset: ... print(elem) tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) If the default option of sharding the saved dataset was used, the element order of the saved dataset will be preserved when loading it. The `reader_func` argument can be used to specify a custom order in which elements should be loaded from the individual shards. The `reader_func` is expected to take a single argument -- a dataset of datasets, each containing elements of one of the shards -- and return a dataset of elements. For example, the order of shards can be shuffled when loading them as follows: ```python def custom_reader_func(datasets): datasets = datasets.shuffle(NUM_SHARDS) return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE) dataset = tf.data.Dataset.load( path="/path/to/data", ..., reader_func=custom_reader_func) ``` Args: path: Required. A path pointing to a previously saved dataset. element_spec: Optional. A nested structure of `tf.TypeSpec` objects matching the structure of an element of the saved dataset and specifying the type of individual element components. If not provided, the nested structure of `tf.TypeSpec` saved with the saved dataset is used. Note that this argument is required in graph mode. compression: Optional. The algorithm to use to decompress the data when reading it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`. reader_func: Optional. A function to control how to read data from shards. If present, the function will be traced and executed as graph computation. Returns: A `tf.data.Dataset` instance. Raises: FileNotFoundError: If `element_spec` is not specified and the saved nested structure of `tf.TypeSpec` can not be located with the saved dataset. ValueError: If `element_spec` is not specified and the method is executed in graph mode. """ # Loaded lazily due to a circular dependency # dataset_ops->load_ops->dataset_ops from tensorflow.python.data.ops import load_op # pylint: disable=g-import-not-at-top return load_op.load( path=path, element_spec=element_spec, compression=compression, reader_func=reader_func) def batch(self, batch_size, drop_remainder=False, num_parallel_calls=None, deterministic=None, name=None): """Combines consecutive elements of this dataset into batches. >>> dataset = tf.data.Dataset.range(8) >>> dataset = dataset.batch(3) >>> list(dataset.as_numpy_iterator()) [array([0, 1, 2]), array([3, 4, 5]), array([6, 7])] >>> dataset = tf.data.Dataset.range(8) >>> dataset = dataset.batch(3, drop_remainder=True) >>> list(dataset.as_numpy_iterator()) [array([0, 1, 2]), array([3, 4, 5])] The components of the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly and `drop_remainder` is `False`). If your program depends on the batches having the same outer dimension, you should set the `drop_remainder` argument to `True` to prevent the smaller batch from being produced. Note: If your program requires data to have a statically known shape (e.g., when using XLA), you should use `drop_remainder=True`. Without `drop_remainder=True` the shape of the output dataset will have an unknown leading dimension due to the possibility of a smaller final batch. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in the case it has fewer than `batch_size` elements; the default behavior is not to drop the smaller batch. num_parallel_calls: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of batches to compute asynchronously in parallel. If not specified, batches will be computed sequentially. If the value `tf.data.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available resources. deterministic: (Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.deterministic` option (`True` by default) controls the behavior. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ if num_parallel_calls is None or DEBUG_MODE: if deterministic is not None and not DEBUG_MODE: warnings.warn("The `deterministic` argument has no effect unless the " "`num_parallel_calls` argument is specified.") return BatchDataset(self, batch_size, drop_remainder, name=name) else: return ParallelBatchDataset( self, batch_size, drop_remainder, num_parallel_calls, deterministic, name=name) def padded_batch(self, batch_size, padded_shapes=None, padding_values=None, drop_remainder=False, name=None): """Combines consecutive elements of this dataset into padded batches. This transformation combines multiple consecutive elements of the input dataset into a single element. Like `tf.data.Dataset.batch`, the components of the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly and `drop_remainder` is `False`). If your program depends on the batches having the same outer dimension, you should set the `drop_remainder` argument to `True` to prevent the smaller batch from being produced. Unlike `tf.data.Dataset.batch`, the input elements to be batched may have different shapes, and this transformation will pad each component to the respective shape in `padded_shapes`. The `padded_shapes` argument determines the resulting shape for each dimension of each component in an output element: * If the dimension is a constant, the component will be padded out to that length in that dimension. * If the dimension is unknown, the component will be padded out to the maximum length of all elements in that dimension. >>> A = (tf.data.Dataset ... .range(1, 5, output_type=tf.int32) ... .map(lambda x: tf.fill([x], x))) >>> # Pad to the smallest per-batch size that fits all elements. >>> B = A.padded_batch(2) >>> for element in B.as_numpy_iterator(): ... print(element) [[1 0] [2 2]] [[3 3 3 0] [4 4 4 4]] >>> # Pad to a fixed size. >>> C = A.padded_batch(2, padded_shapes=5) >>> for element in C.as_numpy_iterator(): ... print(element) [[1 0 0 0 0] [2 2 0 0 0]] [[3 3 3 0 0] [4 4 4 4 0]] >>> # Pad with a custom value. >>> D = A.padded_batch(2, padded_shapes=5, padding_values=-1) >>> for element in D.as_numpy_iterator(): ... print(element) [[ 1 -1 -1 -1 -1] [ 2 2 -1 -1 -1]] [[ 3 3 3 -1 -1] [ 4 4 4 4 -1]] >>> # Components of nested elements can be padded independently. >>> elements = [([1, 2, 3], [10]), ... ([4, 5], [11, 12])] >>> dataset = tf.data.Dataset.from_generator( ... lambda: iter(elements), (tf.int32, tf.int32)) >>> # Pad the first component of the tuple to length 4, and the second >>> # component to the smallest size that fits. >>> dataset = dataset.padded_batch(2, ... padded_shapes=([4], [None]), ... padding_values=(-1, 100)) >>> list(dataset.as_numpy_iterator()) [(array([[ 1, 2, 3, -1], [ 4, 5, -1, -1]], dtype=int32), array([[ 10, 100], [ 11, 12]], dtype=int32))] >>> # Pad with a single value and multiple components. >>> E = tf.data.Dataset.zip((A, A)).padded_batch(2, padding_values=-1) >>> for element in E.as_numpy_iterator(): ... print(element) (array([[ 1, -1], [ 2, 2]], dtype=int32), array([[ 1, -1], [ 2, 2]], dtype=int32)) (array([[ 3, 3, 3, -1], [ 4, 4, 4, 4]], dtype=int32), array([[ 3, 3, 3, -1], [ 4, 4, 4, 4]], dtype=int32)) See also `tf.data.experimental.dense_to_sparse_batch`, which combines elements that may have different shapes into a `tf.sparse.SparseTensor`. 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: (Optional.) 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 will be padded to the maximum size of that dimension in each batch. If unset, all dimensions of all components are padded to the maximum size in the batch. `padded_shapes` must be set if any component has an unknown rank. padding_values: (Optional.) A (nested) structure of scalar-shaped `tf.Tensor`, representing the padding values to use for the respective components. None represents that the (nested) structure should be padded with default values. Defaults are `0` for numeric types and the empty string for string types. The `padding_values` should have the same (nested) structure as the input dataset. If `padding_values` is a single element and the input dataset has multiple components, then the same `padding_values` will be used to pad every component of the dataset. If `padding_values` is a scalar, then its value will be broadcasted to match the shape of each component. drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in the case it has fewer than `batch_size` elements; the default behavior is not to drop the smaller batch. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. Raises: ValueError: If a component has an unknown rank, and the `padded_shapes` argument is not set. TypeError: If a component is of an unsupported type. The list of supported types is documented in https://www.tensorflow.org/guide/data#dataset_structure. """ if padded_shapes is None: padded_shapes = get_legacy_output_shapes(self) for i, shape in enumerate(nest.flatten(padded_shapes)): # A `tf.TensorShape` is only false if its *rank* is unknown. if not shape: raise ValueError(f"You must provide `padded_shapes` argument because " f"component {i} has unknown rank.") return PaddedBatchDataset( self, batch_size, padded_shapes, padding_values, drop_remainder, name=name) def map(self, map_func, num_parallel_calls=None, deterministic=None, name=None): """Maps `map_func` across the elements of this dataset. This transformation applies `map_func` to each element of this dataset, and returns a new dataset containing the transformed elements, in the same order as they appeared in the input. `map_func` can be used to change both the values and the structure of a dataset's elements. Supported structure constructs are documented [here](https://www.tensorflow.org/guide/data#dataset_structure). For example, `map` can be used for adding 1 to each element, or projecting a subset of element components. >>> dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ] >>> dataset = dataset.map(lambda x: x + 1) >>> list(dataset.as_numpy_iterator()) [2, 3, 4, 5, 6] The input signature of `map_func` is determined by the structure of each element in this dataset. >>> dataset = Dataset.range(5) >>> # `map_func` takes a single argument of type `tf.Tensor` with the same >>> # shape and dtype. >>> result = dataset.map(lambda x: x + 1) >>> # Each element is a tuple containing two `tf.Tensor` objects. >>> elements = [(1, "foo"), (2, "bar"), (3, "baz")] >>> dataset = tf.data.Dataset.from_generator( ... lambda: elements, (tf.int32, tf.string)) >>> # `map_func` takes two arguments of type `tf.Tensor`. This function >>> # projects out just the first component. >>> result = dataset.map(lambda x_int, y_str: x_int) >>> list(result.as_numpy_iterator()) [1, 2, 3] >>> # Each element is a dictionary mapping strings to `tf.Tensor` objects. >>> elements = ([{"a": 1, "b": "foo"}, ... {"a": 2, "b": "bar"}, ... {"a": 3, "b": "baz"}]) >>> dataset = tf.data.Dataset.from_generator( ... lambda: elements, {"a": tf.int32, "b": tf.string}) >>> # `map_func` takes a single argument of type `dict` with the same keys >>> # as the elements. >>> result = dataset.map(lambda d: str(d["a"]) + d["b"]) The value or values returned by `map_func` determine the structure of each element in the returned dataset. >>> dataset = tf.data.Dataset.range(3) >>> # `map_func` returns two `tf.Tensor` objects. >>> def g(x): ... return tf.constant(37.0), tf.constant(["Foo", "Bar", "Baz"]) >>> result = dataset.map(g) >>> result.element_spec (TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(3,), \ dtype=tf.string, name=None)) >>> # Python primitives, lists, and NumPy arrays are implicitly converted to >>> # `tf.Tensor`. >>> def h(x): ... return 37.0, ["Foo", "Bar"], np.array([1.0, 2.0], dtype=np.float64) >>> result = dataset.map(h) >>> result.element_spec (TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(2,), \ dtype=tf.string, name=None), TensorSpec(shape=(2,), dtype=tf.float64, \ name=None)) >>> # `map_func` can return nested structures. >>> def i(x): ... return (37.0, [42, 16]), "foo" >>> result = dataset.map(i) >>> result.element_spec ((TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.int32, name=None)), TensorSpec(shape=(), dtype=tf.string, name=None)) `map_func` can accept as arguments and return any type of dataset element. Note that irrespective of the context in which `map_func` is defined (eager vs. graph), tf.data traces the function and executes it as a graph. To use Python code inside of the function you have a few options: 1) Rely on AutoGraph to convert Python code into an equivalent graph computation. The downside of this approach is that AutoGraph can convert some but not all Python code. 2) Use `tf.py_function`, which allows you to write arbitrary Python code but will generally result in worse performance than 1). For example: >>> d = tf.data.Dataset.from_tensor_slices(['hello', 'world']) >>> # transform a string tensor to upper case string using a Python function >>> def upper_case_fn(t: tf.Tensor): ... return t.numpy().decode('utf-8').upper() >>> d = d.map(lambda x: tf.py_function(func=upper_case_fn, ... inp=[x], Tout=tf.string)) >>> list(d.as_numpy_iterator()) [b'HELLO', b'WORLD'] 3) Use `tf.numpy_function`, which also allows you to write arbitrary Python code. Note that `tf.py_function` accepts `tf.Tensor` whereas `tf.numpy_function` accepts numpy arrays and returns only numpy arrays. For example: >>> d = tf.data.Dataset.from_tensor_slices(['hello', 'world']) >>> def upper_case_fn(t: np.ndarray): ... return t.decode('utf-8').upper() >>> d = d.map(lambda x: tf.numpy_function(func=upper_case_fn, ... inp=[x], Tout=tf.string)) >>> list(d.as_numpy_iterator()) [b'HELLO', b'WORLD'] Note that the use of `tf.numpy_function` and `tf.py_function` in general precludes the possibility of executing user-defined transformations in parallel (because of Python GIL). Performance can often be improved by setting `num_parallel_calls` so that `map` will use multiple threads to process elements. If deterministic order isn't required, it can also improve performance to set `deterministic=False`. >>> dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ] >>> dataset = dataset.map(lambda x: x + 1, ... num_parallel_calls=tf.data.AUTOTUNE, ... deterministic=False) The order of elements yielded by this transformation is deterministic if `deterministic=True`. If `map_func` contains stateful operations and `num_parallel_calls > 1`, the order in which that state is accessed is undefined, so the values of output elements may not be deterministic regardless of the `deterministic` flag value. Args: map_func: A function mapping a dataset element to another dataset element. num_parallel_calls: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number elements to process asynchronously in parallel. If not specified, elements will be processed sequentially. If the value `tf.data.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU. deterministic: (Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.deterministic` option (`True` by default) controls the behavior. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ if num_parallel_calls is None or DEBUG_MODE: if deterministic is not None and not DEBUG_MODE: warnings.warn("The `deterministic` argument has no effect unless the " "`num_parallel_calls` argument is specified.") return MapDataset(self, map_func, preserve_cardinality=True, name=name) else: return ParallelMapDataset( self, map_func, num_parallel_calls, deterministic, preserve_cardinality=True, name=name) def flat_map(self, map_func, name=None): """Maps `map_func` across this dataset and flattens the result. The type signature is: ``` def flat_map( self: Dataset[T], map_func: Callable[[T], Dataset[S]] ) -> Dataset[S] ``` Use `flat_map` if you want to make sure that the order of your dataset stays the same. For example, to flatten a dataset of batches into a dataset of their elements: >>> dataset = tf.data.Dataset.from_tensor_slices( ... [[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> dataset = dataset.flat_map(tf.data.Dataset.from_tensor_slices) >>> list(dataset.as_numpy_iterator()) [1, 2, 3, 4, 5, 6, 7, 8, 9] `tf.data.Dataset.interleave()` is a generalization of `flat_map`, since `flat_map` produces the same output as `tf.data.Dataset.interleave(cycle_length=1)` Args: map_func: A function mapping a dataset element to a dataset. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return FlatMapDataset(self, map_func, name=name) def interleave(self, map_func, cycle_length=None, block_length=None, num_parallel_calls=None, deterministic=None, name=None): """Maps `map_func` across this dataset, and interleaves the results. The type signature is: ``` def interleave( self: Dataset[T], map_func: Callable[[T], Dataset[S]] ) -> Dataset[S] ``` For example, you can use `Dataset.interleave()` to process many input files concurrently: >>> # Preprocess 4 files concurrently, and interleave blocks of 16 records >>> # from each file. >>> filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ... "/var/data/file3.txt", "/var/data/file4.txt"] >>> dataset = tf.data.Dataset.from_tensor_slices(filenames) >>> def parse_fn(filename): ... return tf.data.Dataset.range(10) >>> dataset = dataset.interleave(lambda x: ... tf.data.TextLineDataset(x).map(parse_fn, num_parallel_calls=1), ... cycle_length=4, block_length=16) The `cycle_length` and `block_length` arguments control the order in which elements are produced. `cycle_length` controls the number of input elements that are processed concurrently. If you set `cycle_length` to 1, this transformation will handle one input element at a time, and will produce identical results to `tf.data.Dataset.flat_map`. In general, this transformation will apply `map_func` to `cycle_length` input elements, open iterators on the returned `Dataset` objects, and cycle through them producing `block_length` consecutive elements from each iterator, and consuming the next input element each time it reaches the end of an iterator. For example: >>> dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ] >>> # NOTE: New lines indicate "block" boundaries. >>> dataset = dataset.interleave( ... lambda x: Dataset.from_tensors(x).repeat(6), ... cycle_length=2, block_length=4) >>> list(dataset.as_numpy_iterator()) [1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5] Note: The order of elements yielded by this transformation is deterministic, as long as `map_func` is a pure function and `deterministic=True`. If `map_func` contains any stateful operations, the order in which that state is accessed is undefined. Performance can often be improved by setting `num_parallel_calls` so that `interleave` will use multiple threads to fetch elements. If determinism isn't required, it can also improve performance to set `deterministic=False`. >>> filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ... "/var/data/file3.txt", "/var/data/file4.txt"] >>> dataset = tf.data.Dataset.from_tensor_slices(filenames) >>> dataset = dataset.interleave(lambda x: tf.data.TFRecordDataset(x), ... cycle_length=4, num_parallel_calls=tf.data.AUTOTUNE, ... deterministic=False) Args: map_func: A function that takes a dataset element and returns a `tf.data.Dataset`. cycle_length: (Optional.) The number of input elements that will be processed concurrently. If not set, the tf.data runtime decides what it should be based on available CPU. If `num_parallel_calls` is set to `tf.data.AUTOTUNE`, the `cycle_length` argument identifies the maximum degree of parallelism. block_length: (Optional.) The number of consecutive elements to produce from each input element before cycling to another input element. If not set, defaults to 1. num_parallel_calls: (Optional.) If specified, the implementation creates a threadpool, which is used to fetch inputs from cycle elements asynchronously and in parallel. The default behavior is to fetch inputs from cycle elements synchronously with no parallelism. If the value `tf.data.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU. deterministic: (Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`True` or `False`), it controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.deterministic` option (`True` by default) controls the behavior. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ if block_length is None: block_length = 1 if cycle_length is None: cycle_length = AUTOTUNE if num_parallel_calls is None or DEBUG_MODE: if deterministic is not None and not DEBUG_MODE: warnings.warn("The `deterministic` argument has no effect unless the " "`num_parallel_calls` argument is specified.") return InterleaveDataset( self, map_func, cycle_length, block_length, name=name) else: return ParallelInterleaveDataset( self, map_func, cycle_length, block_length, num_parallel_calls, deterministic=deterministic, name=name) def filter(self, predicate, name=None): """Filters this dataset according to `predicate`. >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) >>> dataset = dataset.filter(lambda x: x < 3) >>> list(dataset.as_numpy_iterator()) [1, 2] >>> # `tf.math.equal(x, y)` is required for equality comparison >>> def filter_fn(x): ... return tf.math.equal(x, 1) >>> dataset = dataset.filter(filter_fn) >>> list(dataset.as_numpy_iterator()) [1] Args: predicate: A function mapping a dataset element to a boolean. name: (Optional.) A name for the tf.data operation. Returns: Dataset: The `Dataset` containing the elements of this dataset for which `predicate` is `True`. """ return FilterDataset(self, predicate, name=name) def apply(self, transformation_func): """Applies a transformation function to this dataset. `apply` enables chaining of custom `Dataset` transformations, which are represented as functions that take one `Dataset` argument and return a transformed `Dataset`. >>> dataset = tf.data.Dataset.range(100) >>> def dataset_fn(ds): ... return ds.filter(lambda x: x < 5) >>> dataset = dataset.apply(dataset_fn) >>> list(dataset.as_numpy_iterator()) [0, 1, 2, 3, 4] Args: transformation_func: A function that takes one `Dataset` argument and returns a `Dataset`. Returns: Dataset: The `Dataset` returned by applying `transformation_func` to this dataset. """ dataset = transformation_func(self) if not isinstance(dataset, DatasetV2): raise TypeError( f"`transformation_func` must return a `tf.data.Dataset` object. " f"Got {type(dataset)}.") dataset._input_datasets = [self] # pylint: disable=protected-access return dataset def window(self, size, shift=None, stride=1, drop_remainder=False, name=None): """Returns a dataset of "windows". Each "window" is a dataset that contains a subset of elements of the input dataset. These are finite datasets of size `size` (or possibly fewer if there are not enough input elements to fill the window and `drop_remainder` evaluates to `False`). For example: >>> dataset = tf.data.Dataset.range(7).window(3) >>> for window in dataset: ... print(window) <...Dataset element_spec=TensorSpec(shape=(), dtype=tf.int64, name=None)> <...Dataset element_spec=TensorSpec(shape=(), dtype=tf.int64, name=None)> <...Dataset element_spec=TensorSpec(shape=(), dtype=tf.int64, name=None)> Since windows are datasets, they can be iterated over: >>> for window in dataset: ... print([item.numpy() for item in window]) [0, 1, 2] [3, 4, 5] [6] #### Shift The `shift` argument determines the number of input elements to shift between the start of each window. If windows and elements are both numbered starting at 0, the first element in window `k` will be element `k * shift` of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset. >>> dataset = tf.data.Dataset.range(7).window(3, shift=1, ... drop_remainder=True) >>> for window in dataset: ... print(list(window.as_numpy_iterator())) [0, 1, 2] [1, 2, 3] [2, 3, 4] [3, 4, 5] [4, 5, 6] #### Stride The `stride` argument determines the stride between input elements within a window. >>> dataset = tf.data.Dataset.range(7).window(3, shift=1, stride=2, ... drop_remainder=True) >>> for window in dataset: ... print(list(window.as_numpy_iterator())) [0, 2, 4] [1, 3, 5] [2, 4, 6] #### Nested elements When the `window` transformation is applied to a dataset whos elements are nested structures, it produces a dataset where the elements have the same nested structure but each leaf is replaced by a window. In other words, the nesting is applied outside of the windows as opposed inside of them. The type signature is: ``` def window( self: Dataset[Nest[T]], ... ) -> Dataset[Nest[Dataset[T]]] ``` Applying `window` to a `Dataset` of tuples gives a tuple of windows: >>> dataset = tf.data.Dataset.from_tensor_slices(([1, 2, 3, 4, 5], ... [6, 7, 8, 9, 10])) >>> dataset = dataset.window(2) >>> windows = next(iter(dataset)) >>> windows (<...Dataset element_spec=TensorSpec(shape=(), dtype=tf.int32, name=None)>, <...Dataset element_spec=TensorSpec(shape=(), dtype=tf.int32, name=None)>) >>> def to_numpy(ds): ... return list(ds.as_numpy_iterator()) >>> >>> for windows in dataset: ... print(to_numpy(windows[0]), to_numpy(windows[1])) [1, 2] [6, 7] [3, 4] [8, 9] [5] [10] Applying `window` to a `Dataset` of dictionaries gives a dictionary of `Datasets`: >>> dataset = tf.data.Dataset.from_tensor_slices({'a': [1, 2, 3], ... 'b': [4, 5, 6], ... 'c': [7, 8, 9]}) >>> dataset = dataset.window(2) >>> def to_numpy(ds): ... return list(ds.as_numpy_iterator()) >>> >>> for windows in dataset: ... print(tf.nest.map_structure(to_numpy, windows)) {'a': [1, 2], 'b': [4, 5], 'c': [7, 8]} {'a': [3], 'b': [6], 'c': [9]} #### Flatten a dataset of windows The `Dataset.flat_map` and `Dataset.interleave` methods can be used to flatten a dataset of windows into a single dataset. The argument to `flat_map` is a function that takes an element from the dataset and returns a `Dataset`. `flat_map` chains together the resulting datasets sequentially. For example, to turn each window into a dense tensor: >>> size = 3 >>> dataset = tf.data.Dataset.range(7).window(size, shift=1, ... drop_remainder=True) >>> batched = dataset.flat_map(lambda x:x.batch(3)) >>> for batch in batched: ... print(batch.numpy()) [0 1 2] [1 2 3] [2 3 4] [3 4 5] [4 5 6] Args: size: A `tf.int64` scalar `tf.Tensor`, representing the number of elements of the input dataset to combine into a window. Must be positive. shift: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number of input elements by which the window moves in each iteration. Defaults to `size`. Must be positive. stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the stride of the input elements in the sliding window. Must be positive. The default value of 1 means "retain every input element". drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last windows should be dropped if their size is smaller than `size`. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset` of (nests of) windows. Each window is a finite datasets of flat elements. """ if shift is None: shift = size return WindowDataset(self, size, shift, stride, drop_remainder, name=name) def reduce(self, initial_state, reduce_func, name=None): """Reduces the input dataset to a single element. The transformation calls `reduce_func` successively on every element of the input dataset until the dataset is exhausted, aggregating information in its internal state. The `initial_state` argument is used for the initial state and the final state is returned as the result. >>> tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, _: x + 1).numpy() 5 >>> tf.data.Dataset.range(5).reduce(np.int64(0), lambda x, y: x + y).numpy() 10 Args: initial_state: An element representing the initial state of the transformation. reduce_func: A function that maps `(old_state, input_element)` to `new_state`. It must take two arguments and return a new element The structure of `new_state` must match the structure of `initial_state`. name: (Optional.) A name for the tf.data operation. Returns: A dataset element corresponding to the final state of the transformation. """ with ops.name_scope("initial_state"): initial_state = structure.normalize_element(initial_state) state_structure = structure.type_spec_from_value(initial_state) # Iteratively rerun the reduce function until reaching a fixed point on # `state_structure`. need_to_rerun = True while need_to_rerun: wrapped_func = structured_function.StructuredFunctionWrapper( reduce_func, "reduce()", input_structure=(state_structure, self.element_spec), add_to_graph=False) # Extract and validate class information from the returned values. output_classes = wrapped_func.output_classes state_classes = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access state_structure) for new_state_class, state_class in zip( nest.flatten(output_classes), nest.flatten(state_classes)): if not issubclass(new_state_class, state_class): raise TypeError( f"The element classes for the new state must match the initial " f"state. Expected {state_classes} but got " f"{wrapped_func.output_classes}.") # Extract and validate type information from the returned values. output_types = wrapped_func.output_types state_types = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access state_structure) for new_state_type, state_type in zip( nest.flatten(output_types), nest.flatten(state_types)): if new_state_type != state_type: raise TypeError( f"The element types for the new state must match the initial " f"state. Expected {state_types} but got " f"{wrapped_func.output_types}.") # Extract shape information from the returned values. output_shapes = wrapped_func.output_shapes state_shapes = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access state_structure) flat_state_shapes = nest.flatten(state_shapes) flat_new_state_shapes = nest.flatten(output_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) for original, new in zip(flat_state_shapes, flat_new_state_shapes) ] need_to_rerun = False for original_shape, weakened_shape in zip(flat_state_shapes, weakened_state_shapes): if original_shape.ndims is not None and ( weakened_shape.ndims is None or original_shape.as_list() != weakened_shape.as_list()): need_to_rerun = True break if need_to_rerun: # TODO(b/110122868): Support a "most specific compatible structure" # method for combining structures, to avoid using legacy structures # here. state_structure = structure.convert_legacy_structure( state_types, nest.pack_sequence_as(state_shapes, weakened_state_shapes), state_classes) reduce_func = wrapped_func.function reduce_func.add_to_graph(ops.get_default_graph()) dataset = self._apply_debug_options() # pylint: disable=protected-access metadata = dataset_metadata_pb2.Metadata() if name: metadata.name = _validate_and_encode(name) return structure.from_compatible_tensor_list( state_structure, gen_dataset_ops.reduce_dataset( dataset._variant_tensor, structure.to_tensor_list(state_structure, initial_state), reduce_func.captured_inputs, f=reduce_func, output_shapes=structure.get_flat_tensor_shapes(state_structure), output_types=structure.get_flat_tensor_types(state_structure), metadata=metadata.SerializeToString())) def get_single_element(self, name=None): """Returns the single element of the `dataset`. The function enables you to use a `tf.data.Dataset` in a stateless "tensor-in tensor-out" expression, without creating an iterator. This facilitates the ease of data transformation on tensors using the optimized `tf.data.Dataset` abstraction on top of them. For example, lets consider a `preprocessing_fn` which would take as an input the raw features and returns the processed feature along with it's label. ```python def preprocessing_fn(raw_feature): # ... the raw_feature is preprocessed as per the use-case return feature raw_features = ... # input batch of BATCH_SIZE elements. dataset = (tf.data.Dataset.from_tensor_slices(raw_features) .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) .batch(BATCH_SIZE)) processed_features = dataset.get_single_element() ``` In the above example, the `raw_features` tensor of length=BATCH_SIZE was converted to a `tf.data.Dataset`. Next, each of the `raw_feature` was mapped using the `preprocessing_fn` and the processed features were grouped into a single batch. The final `dataset` contains only one element which is a batch of all the processed features. NOTE: The `dataset` should contain only one element. Now, instead of creating an iterator for the `dataset` and retrieving the batch of features, the `tf.data.get_single_element()` function is used to skip the iterator creation process and directly output the batch of features. This can be particularly useful when your tensor transformations are expressed as `tf.data.Dataset` operations, and you want to use those transformations while serving your model. #### Keras ```python model = ... # A pre-built or custom model class PreprocessingModel(tf.keras.Model): def __init__(self, model): super().__init__(self) self.model = model @tf.function(input_signature=[...]) def serving_fn(self, data): ds = tf.data.Dataset.from_tensor_slices(data) ds = ds.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) ds = ds.batch(batch_size=BATCH_SIZE) return tf.argmax(self.model(ds.get_single_element()), axis=-1) preprocessing_model = PreprocessingModel(model) your_exported_model_dir = ... # save the model to this path. tf.saved_model.save(preprocessing_model, your_exported_model_dir, signatures={'serving_default': preprocessing_model.serving_fn} ) ``` #### Estimator In the case of estimators, you need to generally define a `serving_input_fn` which would require the features to be processed by the model while inferencing. ```python def serving_input_fn(): raw_feature_spec = ... # Spec for the raw_features input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( raw_feature_spec, default_batch_size=None) ) serving_input_receiver = input_fn() raw_features = serving_input_receiver.features def preprocessing_fn(raw_feature): # ... the raw_feature is preprocessed as per the use-case return feature dataset = (tf.data.Dataset.from_tensor_slices(raw_features) .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE) .batch(BATCH_SIZE)) processed_features = dataset.get_single_element() # Please note that the value of `BATCH_SIZE` should be equal to # the size of the leading dimension of `raw_features`. This ensures # that `dataset` has only element, which is a pre-requisite for # using `dataset.get_single_element()`. return tf.estimator.export.ServingInputReceiver( processed_features, serving_input_receiver.receiver_tensors) estimator = ... # A pre-built or custom estimator estimator.export_saved_model(your_exported_model_dir, serving_input_fn) ``` Args: name: (Optional.) A name for the tf.data operation. Returns: A nested structure of `tf.Tensor` objects, corresponding to the single element of `dataset`. Raises: InvalidArgumentError: (at runtime) if `dataset` does not contain exactly one element. """ metadata = dataset_metadata_pb2.Metadata() if name: metadata.name = _validate_and_encode(name) return structure.from_compatible_tensor_list( self.element_spec, gen_dataset_ops.dataset_to_single_element( self._variant_tensor, metadata=metadata.SerializeToString(), **self._flat_structure)) # pylint: disable=protected-access def unbatch(self, name=None): """Splits elements of a dataset into multiple elements. For example, if elements of the dataset are shaped `[B, a0, a1, ...]`, where `B` may vary for each input element, then for each element in the dataset, the unbatched dataset will contain `B` consecutive elements of shape `[a0, a1, ...]`. >>> elements = [ [1, 2, 3], [1, 2], [1, 2, 3, 4] ] >>> dataset = tf.data.Dataset.from_generator(lambda: elements, tf.int64) >>> dataset = dataset.unbatch() >>> list(dataset.as_numpy_iterator()) [1, 2, 3, 1, 2, 1, 2, 3, 4] Note: `unbatch` requires a data copy to slice up the batched tensor into smaller, unbatched tensors. When optimizing performance, try to avoid unnecessary usage of `unbatch`. Args: name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. """ normalized_dataset = normalize_to_dense(self) return _UnbatchDataset(normalized_dataset, name=name) def with_options(self, options, name=None): """Returns a new `tf.data.Dataset` with the given options set. The options are "global" in the sense they apply to the entire dataset. If options are set multiple times, they are merged as long as different options do not use different non-default values. >>> ds = tf.data.Dataset.range(5) >>> ds = ds.interleave(lambda x: tf.data.Dataset.range(5), ... cycle_length=3, ... num_parallel_calls=3) >>> options = tf.data.Options() >>> # This will make the interleave order non-deterministic. >>> options.deterministic = False >>> ds = ds.with_options(options) Args: options: A `tf.data.Options` that identifies the options the use. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset` with the given options. Raises: ValueError: when an option is set more than once to a non-default value """ return _OptionsDataset(self, options, name=name) def cardinality(self): """Returns the cardinality of the dataset, if known. `cardinality` may return `tf.data.INFINITE_CARDINALITY` if the dataset contains an infinite number of elements or `tf.data.UNKNOWN_CARDINALITY` if the analysis fails to determine the number of elements in the dataset (e.g. when the dataset source is a file). >>> dataset = tf.data.Dataset.range(42) >>> print(dataset.cardinality().numpy()) 42 >>> dataset = dataset.repeat() >>> cardinality = dataset.cardinality() >>> print((cardinality == tf.data.INFINITE_CARDINALITY).numpy()) True >>> dataset = dataset.filter(lambda x: True) >>> cardinality = dataset.cardinality() >>> print((cardinality == tf.data.UNKNOWN_CARDINALITY).numpy()) True Returns: A scalar `tf.int64` `Tensor` representing the cardinality of the dataset. If the cardinality is infinite or unknown, `cardinality` returns the named constants `tf.data.INFINITE_CARDINALITY` and `tf.data.UNKNOWN_CARDINALITY` respectively. """ return gen_dataset_ops.dataset_cardinality(self._variant_tensor) def group_by_window(self, key_func, reduce_func, window_size=None, window_size_func=None, name=None): """Groups windows of elements by key and reduces them. This transformation maps each consecutive element in a dataset to a key using `key_func` and groups the elements by key. It then applies `reduce_func` to at most `window_size_func(key)` elements matching the same key. All except the final window for each key will contain `window_size_func(key)` elements; the final window may be smaller. You may provide either a constant `window_size` or a window size determined by the key through `window_size_func`. >>> dataset = tf.data.Dataset.range(10) >>> window_size = 5 >>> key_func = lambda x: x%2 >>> reduce_func = lambda key, dataset: dataset.batch(window_size) >>> dataset = dataset.group_by_window( ... key_func=key_func, ... reduce_func=reduce_func, ... window_size=window_size) >>> for elem in dataset.as_numpy_iterator(): ... print(elem) [0 2 4 6 8] [1 3 5 7 9] 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 `window_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`. Mutually exclusive with `window_size_func`. window_size_func: A function mapping a key to 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`. Mutually exclusive with `window_size`. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. Raises: ValueError: if neither or both of {`window_size`, `window_size_func`} are passed. """ if (window_size is not None and window_size_func or not (window_size is not None or window_size_func)): raise ValueError("Either the `window_size` argument or the " "`window_size_func` argument must be specified.") if window_size is not None: def constant_window_func(unused_key): return ops.convert_to_tensor(window_size, dtype=dtypes.int64) window_size_func = constant_window_func assert window_size_func is not None return _GroupByWindowDataset( self, key_func, reduce_func, window_size_func, name=name) def bucket_by_sequence_length(self, element_length_func, bucket_boundaries, bucket_batch_sizes, padded_shapes=None, padding_values=None, pad_to_bucket_boundary=False, no_padding=False, drop_remainder=False, name=None): """A transformation that buckets elements in a `Dataset` by length. Elements of the `Dataset` are grouped together by length and then are padded and batched. This is useful for sequence tasks in which the elements have variable length. Grouping together elements that have similar lengths reduces the total fraction of padding in a batch which increases training step efficiency. Below is an example to bucketize the input data to the 3 buckets "[0, 3), [3, 5), [5, inf)" based on sequence length, with batch size 2. >>> elements = [ ... [0], [1, 2, 3, 4], [5, 6, 7], ... [7, 8, 9, 10, 11], [13, 14, 15, 16, 19, 20], [21, 22]] >>> dataset = tf.data.Dataset.from_generator( ... lambda: elements, tf.int64, output_shapes=[None]) >>> dataset = dataset.bucket_by_sequence_length( ... element_length_func=lambda elem: tf.shape(elem)[0], ... bucket_boundaries=[3, 5], ... bucket_batch_sizes=[2, 2, 2]) >>> for elem in dataset.as_numpy_iterator(): ... print(elem) [[1 2 3 4] [5 6 7 0]] [[ 7 8 9 10 11 0] [13 14 15 16 19 20]] [[ 0 0] [21 22]] Args: element_length_func: function from element in `Dataset` to `tf.int32`, determines the length of the element, which will determine the bucket it goes into. bucket_boundaries: `list`, upper length boundaries of the buckets. bucket_batch_sizes: `list`, batch size per bucket. Length should be `len(bucket_boundaries) + 1`. padded_shapes: Nested structure of `tf.TensorShape` to pass to `tf.data.Dataset.padded_batch`. If not provided, will use `dataset.output_shapes`, which will result in variable length dimensions being padded out to the maximum length in each batch. padding_values: Values to pad with, passed to `tf.data.Dataset.padded_batch`. Defaults to padding with 0. pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown size to maximum length in batch. If `True`, will pad dimensions with unknown size to bucket boundary minus 1 (i.e., the maximum length in each bucket), and caller must ensure that the source `Dataset` does not contain any elements with length longer than `max(bucket_boundaries)`. no_padding: `bool`, indicates whether to pad the batch features (features need to be either of type `tf.sparse.SparseTensor` or of same shape). drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing whether the last batch should be dropped in the case it has fewer than `batch_size` elements; the default behavior is not to drop the smaller batch. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. Raises: ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`. """ if len(bucket_batch_sizes) != (len(bucket_boundaries) + 1): raise ValueError( f"`len(bucket_batch_sizes)` must equal `len(bucket_boundaries) + 1` " f"but `len(bucket_batch_sizes)={len(bucket_batch_sizes)}` and " f"`len(bucket_boundaries)={len(bucket_boundaries)}`.") batch_sizes = constant_op.constant(bucket_batch_sizes, dtype=dtypes.int64) def element_to_bucket_id(*args): """Return int64 id of the length bucket for this element.""" seq_length = element_length_func(*args) boundaries = list(bucket_boundaries) buckets_min = [np.iinfo(np.int32).min] + boundaries buckets_max = boundaries + [np.iinfo(np.int32).max] conditions_c = math_ops.logical_and( math_ops.less_equal(buckets_min, seq_length), math_ops.less(seq_length, buckets_max)) bucket_id = math_ops.reduce_min(array_ops.where(conditions_c)) return bucket_id def window_size_fn(bucket_id): # The window size is set to the batch size for this bucket window_size = batch_sizes[bucket_id] return window_size def make_padded_shapes(shapes, none_filler=None): padded = [] for shape in nest.flatten(shapes): shape = tensor_shape.TensorShape(shape) shape = [ none_filler if tensor_shape.dimension_value(d) is None else d for d in shape ] padded.append(shape) return nest.pack_sequence_as(shapes, padded) def batching_fn(bucket_id, grouped_dataset): """Batch elements in dataset.""" batch_size = window_size_fn(bucket_id) if no_padding: return grouped_dataset.batch( batch_size, drop_remainder=drop_remainder, name=name) none_filler = None if pad_to_bucket_boundary: err_msg = ("When pad_to_bucket_boundary=True, elements must have " "length < max(bucket_boundaries).") check = check_ops.assert_less( bucket_id, constant_op.constant( len(bucket_batch_sizes) - 1, dtype=dtypes.int64), message=err_msg) with ops.control_dependencies([check]): boundaries = constant_op.constant( bucket_boundaries, dtype=dtypes.int64) bucket_boundary = boundaries[bucket_id] none_filler = bucket_boundary - 1 input_shapes = get_legacy_output_shapes(grouped_dataset) shapes = make_padded_shapes( padded_shapes or input_shapes, none_filler=none_filler) return grouped_dataset.padded_batch( batch_size, shapes, padding_values, drop_remainder=drop_remainder, name=name) return self.group_by_window( key_func=element_to_bucket_id, reduce_func=batching_fn, window_size_func=window_size_fn, name=name) @staticmethod def random(seed=None, name=None): """Creates a `Dataset` of pseudorandom values. The dataset generates a sequence of uniformly distributed integer values. >>> ds1 = tf.data.Dataset.random(seed=4).take(10) >>> ds2 = tf.data.Dataset.random(seed=4).take(10) >>> print(list(ds2.as_numpy_iterator())==list(ds2.as_numpy_iterator())) True Args: seed: (Optional) If specified, the dataset produces a deterministic sequence of values. name: (Optional.) A name for the tf.data operation. Returns: Dataset: A `Dataset`. """ return RandomDataset(seed=seed, name=name) def snapshot(self, path, compression="AUTO", reader_func=None, shard_func=None, name=None): """API to persist the output of the input dataset. The snapshot API allows users to transparently persist the output of their preprocessing pipeline to disk, and materialize the pre-processed data on a different training run. This API enables repeated preprocessing steps to be consolidated, and allows re-use of already processed data, trading off disk storage and network bandwidth for freeing up more valuable CPU resources and accelerator compute time. https://github.com/tensorflow/community/blob/master/rfcs/20200107-tf-data-snapshot.md has detailed design documentation of this feature. Users can specify various options to control the behavior of snapshot, including how snapshots are read from and written to by passing in user-defined functions to the `reader_func` and `shard_func` parameters. `shard_func` is a user specified function that maps input elements to snapshot shards. Users may want to specify this function to control how snapshot files should be written to disk. Below is an example of how a potential `shard_func` could be written. ```python dataset = ... dataset = dataset.enumerate() dataset = dataset.snapshot("/path/to/snapshot/dir", shard_func=lambda x, y: x % NUM_SHARDS, ...) dataset = dataset.map(lambda x, y: y) ``` `reader_func` is a user specified function that accepts a single argument: (1) a Dataset of Datasets, each representing a "split" of elements of the original dataset. The cardinality of the input dataset matches the number of the shards specified in the `shard_func` (see above). The function should return a Dataset of elements of the original dataset. Users may want specify this function to control how snapshot files should be read from disk, including the amount of shuffling and parallelism. Here is an example of a standard reader function a user can define. This function enables both dataset shuffling and parallel reading of datasets: ```python def user_reader_func(datasets): # shuffle the datasets splits datasets = datasets.shuffle(NUM_CORES) # read datasets in parallel and interleave their elements return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE) dataset = dataset.snapshot("/path/to/snapshot/dir", reader_func=user_reader_func) ``` By default, snapshot parallelizes reads by the number of cores available on the system, but will not attempt to shuffle the data. Args: path: Required. A directory to use for storing / loading the snapshot to / from. compression: Optional. The type of compression to apply to the snapshot written to disk. Supported options are `GZIP`, `SNAPPY`, `AUTO` or None. Defaults to `AUTO`, which attempts to pick an appropriate compression algorithm for the dataset. reader_func: Optional. A function to control how to read data from snapshot shards. shard_func: Optional. A function to control how to shard data when writing a snapshot. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. """ project_func = None input_dataset = self if shard_func is None: input_dataset = input_dataset.enumerate(name=name) # This sets the amount of parallelism based on the number of CPU cores on # the machine where this Python code is executed, which may differ from # the number of CPU cores where the input pipeline graph is actually # executed (e.g. remote Cloud TPU workers). local_shard_func = lambda index, _: index % multiprocessing.cpu_count() project_func = lambda _, elem: elem else: local_shard_func = shard_func dataset = _SnapshotDataset( input_dataset=input_dataset, path=path, compression=compression, reader_func=reader_func, # This will not do the right thing where the graph is built on a # different machine than the executor (e.g. Cloud TPUs). shard_func=local_shard_func, name=name) if project_func is not None: dataset = dataset.map(project_func, name=name) return dataset def scan(self, initial_state, scan_func, name=None): """A transformation that scans a function across an input dataset. This transformation is a stateful relative of `tf.data.Dataset.map`. In addition to mapping `scan_func` across the elements of the input dataset, `scan()` accumulates one or more state tensors, whose initial values are `initial_state`. >>> dataset = tf.data.Dataset.range(10) >>> initial_state = tf.constant(0, dtype=tf.int64) >>> scan_func = lambda state, i: (state + i, state + i) >>> dataset = dataset.scan(initial_state=initial_state, scan_func=scan_func) >>> list(dataset.as_numpy_iterator()) [0, 1, 3, 6, 10, 15, 21, 28, 36, 45] Args: initial_state: A nested structure of tensors, representing the initial state of the accumulator. scan_func: A function that maps `(old_state, input_element)` to `(new_state, output_element)`. It must take two arguments and return a pair of nested structures of tensors. The `new_state` must match the structure of `initial_state`. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. """ return _ScanDataset( self, initial_state=initial_state, scan_func=scan_func, name=name) def take_while(self, predicate, name=None): """A transformation that stops dataset iteration based on a `predicate`. >>> dataset = tf.data.Dataset.range(10) >>> dataset = dataset.take_while(lambda x: x < 5) >>> list(dataset.as_numpy_iterator()) [0, 1, 2, 3, 4] Args: predicate: A function that maps a nested structure of tensors (having shapes and types defined by `self.output_shapes` and `self.output_types`) to a scalar `tf.bool` tensor. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. """ return _TakeWhileDataset(self, predicate, name=name) def unique(self, name=None): """A transformation that discards duplicate elements of a `Dataset`. Use this transformation to produce a dataset that contains one instance of each unique element in the input. For example: >>> dataset = tf.data.Dataset.from_tensor_slices([1, 37, 2, 37, 2, 1]) >>> dataset = dataset.unique() >>> sorted(list(dataset.as_numpy_iterator())) [1, 2, 37] Note: This transformation only supports datasets which fit into memory and have elements of either `tf.int32`, `tf.int64` or `tf.string` type. Args: name: (Optional.) A name for the tf.data operation. Returns: A `Dataset`. """ return _UniqueDataset(self, name=name) def rejection_resample(self, class_func, target_dist, initial_dist=None, seed=None, name=None): """A transformation that resamples a dataset to a target distribution. Lets consider the following example where a dataset with an initial data distribution of `init_dist` needs to be resampled into a dataset with `target_dist` distribution. >>> initial_dist = [0.6, 0.4] >>> num_classes = len(initial_dist) >>> num_samples = 1000 >>> data_np = np.random.choice(num_classes, num_samples, p=initial_dist) >>> dataset = tf.data.Dataset.from_tensor_slices(data_np) The value of `x` will be close to `{0: 50000, 1: 50000}` as per the `initial_dist` distribution. >>> target_dist = [0.5, 0.5] >>> resampled_dataset = dataset.rejection_resample( ... class_func=lambda x: x, ... target_dist=target_dist, ... initial_dist=initial_dist) >>> resampled_dataset = resampled_dataset.map( ... lambda class_func_result, data: data) The value distribution of classes in the resampled_distribution will be now be close to the target distribution. Args: class_func: A function mapping an element of the input dataset 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. name: (Optional.) A name for the tf.data operation. Returns: A `Dataset` """ target_dist_t = ops.convert_to_tensor(target_dist, name="target_dist") target_dist_t = math_ops.cast(target_dist_t, dtypes.float32) # Get initial distribution. if initial_dist is not None: initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist") initial_dist_t = math_ops.cast(initial_dist_t, dtypes.float32) acceptance_dist, prob_of_original = ( _calculate_acceptance_probs_with_mixing(initial_dist_t, target_dist_t)) initial_dist_ds = DatasetV2.from_tensors( initial_dist_t, name=name).repeat(name=name) acceptance_dist_ds = DatasetV2.from_tensors( acceptance_dist, name=name).repeat(name=name) prob_of_original_ds = DatasetV2.from_tensors( prob_of_original, name=name).repeat(name=name) else: initial_dist_ds = _estimate_initial_dist_ds( target_dist_t, self.map(class_func, name=name), name=name) acceptance_and_original_prob_ds = initial_dist_ds.map( lambda initial: _calculate_acceptance_probs_with_mixing( # pylint: disable=g-long-lambda initial, target_dist_t), name=name) acceptance_dist_ds = acceptance_and_original_prob_ds.map( lambda accept_prob, _: accept_prob, name=name) prob_of_original_ds = acceptance_and_original_prob_ds.map( lambda _, prob_original: prob_original, name=name) filtered_ds = _filter_ds(self, acceptance_dist_ds, initial_dist_ds, class_func, seed) # Prefetch filtered dataset for speed. filtered_ds = filtered_ds.prefetch(3, name=name) prob_original_static = _get_prob_original_static( initial_dist_t, target_dist_t) if initial_dist is not None else None def add_class_value(*x): if len(x) == 1: return class_func(*x), x[0] else: return class_func(*x), x if prob_original_static == 1: return self.map(add_class_value, name=name) elif prob_original_static == 0: return filtered_ds else: return Dataset.sample_from_datasets( [self.map(add_class_value), filtered_ds], weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]), seed=seed, stop_on_empty_dataset=True) @staticmethod def sample_from_datasets(datasets, weights=None, seed=None, stop_on_empty_dataset=False): """Samples elements at random from the datasets in `datasets`. Creates a dataset by interleaving elements of `datasets` with `weight[i]` probability of picking an element from dataset `i`. Sampling is done without replacement. For example, suppose we have 2 datasets: ```python dataset1 = tf.data.Dataset.range(0, 3) dataset2 = tf.data.Dataset.range(100, 103) ``` Suppose that we sample from these 2 datasets with the following weights: ```python sample_dataset = tf.data.Dataset.sample_from_datasets( [dataset1, dataset2], weights=[0.5, 0.5]) ``` One possible outcome of elements in sample_dataset is: ``` print(list(sample_dataset.as_numpy_iterator())) # [100, 0, 1, 101, 2, 102] ``` Args: datasets: A non-empty list of `tf.data.Dataset` objects with compatible structure. weights: (Optional.) A list or Tensor of `len(datasets)` floating-point values where `weights[i]` represents the probability to sample from `datasets[i]`, or a `tf.data.Dataset` object where each element is such a list. Defaults to a uniform distribution across `datasets`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See `tf.random.set_seed` for behavior. stop_on_empty_dataset: If `True`, sampling stops if it encounters an empty dataset. If `False`, it continues sampling and skips any empty datasets. It is recommended to set it to `True`. Otherwise, the distribution of samples starts off as the user intends, but may change as input datasets become empty. This can be difficult to detect since the dataset starts off looking correct. Default to `False` for backward compatibility. Returns: A dataset that interleaves elements from `datasets` at random, according to `weights` if provided, otherwise with uniform probability. Raises: TypeError: If the `datasets` or `weights` arguments have the wrong type. ValueError: - If `datasets` is empty, or - If `weights` is specified and does not match the length of `datasets`. """ def _skip_datasets_with_zero_weight(datasets, weights): datasets_and_weights = [(dataset, weight) for (dataset, weight) in zip(datasets, weights) if weight > 0] return (zip(*datasets_and_weights) if datasets_and_weights else ([datasets[0].take(0)], [1.])) if not datasets: raise ValueError("Invalid `datasets`. `datasets` should not be empty.") if not isinstance(weights, DatasetV2): if weights is None: # Select inputs with uniform probability. logits = [[1.0] * len(datasets)] else: if isinstance(weights, ops.Tensor): if not weights.shape.is_compatible_with([len(datasets)]): raise ValueError(f"Invalid `weights`. The shape of `weights` " f"should be compatible with `[len(datasets)]` " f"but is {weights.shape}.") else: if len(datasets) != len(weights): raise ValueError(f"Invalid `weights`. `weights` should have the " f"same length as `datasets` but got " f"`len(weights)={len(weights)}` vs. " f"`len(datasets)={len(datasets)}`.") # Use the given `weights` as the probability of choosing the respective # input. if not isinstance(weights, ops.Tensor): datasets, weights = _skip_datasets_with_zero_weight(datasets, weights) weights = ops.convert_to_tensor(weights, name="weights") if weights.dtype not in (dtypes.float32, dtypes.float64): raise TypeError(f"Invalid `weights`. `weights` type must be either " f"`tf.float32` or `tf.float64` but is " f"{weights.dtype}.") # The `stateless_multinomial()` op expects log-probabilities, as opposed # to weights. logits = array_ops.expand_dims(math_ops.log(weights, name="logits"), 0) # NOTE(mrry): We only specialize when `weights` is not a `Dataset`. When # it is a `Dataset`, it is possible that evaluating it has a side effect # the user depends on. if len(datasets) == 1: return datasets[0] def select_dataset_constant_logits(seed): return array_ops.squeeze( gen_stateless_random_ops.stateless_multinomial( logits, 1, seed=seed), axis=[0, 1]) selector_input = MapDataset( RandomDataset(seed).batch(2), select_dataset_constant_logits, use_inter_op_parallelism=False) else: # Use each element of the given `weights` dataset as the probability of # choosing the respective input. # # The `stateless_multinomial()` op expects log-probabilities, as opposed # to weights. logits_ds = weights.map(lambda *p: math_ops.log(p, name="logits")) def select_dataset_varying_logits(logits, seed): return array_ops.squeeze( gen_stateless_random_ops.stateless_multinomial( logits, 1, seed=seed), axis=[0, 1]) logits_and_seeds = Dataset.zip((logits_ds, RandomDataset(seed).batch(2))) selector_input = MapDataset( logits_and_seeds, select_dataset_varying_logits, use_inter_op_parallelism=False) return _DirectedInterleaveDataset(selector_input, datasets, stop_on_empty_dataset) @staticmethod def choose_from_datasets(datasets, choice_dataset, stop_on_empty_dataset=True): """Creates a dataset that deterministically chooses elements from `datasets`. For example, given the following datasets: ```python datasets = [tf.data.Dataset.from_tensors("foo").repeat(), tf.data.Dataset.from_tensors("bar").repeat(), tf.data.Dataset.from_tensors("baz").repeat()] # Define a dataset containing `[0, 1, 2, 0, 1, 2, 0, 1, 2]`. choice_dataset = tf.data.Dataset.range(3).repeat(3) result = tf.data.Dataset.choose_from_datasets(datasets, choice_dataset) ``` The elements of `result` will be: ``` "foo", "bar", "baz", "foo", "bar", "baz", "foo", "bar", "baz" ``` Args: datasets: A non-empty list of `tf.data.Dataset` objects with compatible structure. choice_dataset: A `tf.data.Dataset` of scalar `tf.int64` tensors between `0` and `len(datasets) - 1`. stop_on_empty_dataset: If `True`, selection stops if it encounters an empty dataset. If `False`, it skips empty datasets. It is recommended to set it to `True`. Otherwise, the selected elements start off as the user intends, but may change as input datasets become empty. This can be difficult to detect since the dataset starts off looking correct. Defaults to `True`. Returns: A dataset that interleaves elements from `datasets` according to the values of `choice_dataset`. Raises: TypeError: If `datasets` or `choice_dataset` has the wrong type. ValueError: If `datasets` is empty. """ if not datasets: raise ValueError("Invalid `datasets`. `datasets` should not be empty.") if not isinstance(choice_dataset, DatasetV2): raise TypeError(f"Invalid `choice_dataset`. `choice_dataset` should be a " f"`tf.data.Dataset` but is {type(choice_dataset)}.") if not structure.are_compatible(choice_dataset.element_spec, tensor_spec.TensorSpec([], dtypes.int64)): raise TypeError(f"Invalid `choice_dataset`. Elements of `choice_dataset` " f"must be scalar `tf.int64` tensors but are " f"{choice_dataset.element_spec}.") # Replicate the `choice_dataset` component so that each split makes choices # independently. This avoids the need for prohibitively expensive # cross-split coordination. choice_dataset = _apply_rewrite(choice_dataset, "replicate_on_split") # pylint: disable=protected-access return _DirectedInterleaveDataset(choice_dataset, datasets, stop_on_empty_dataset) @tf_export(v1=["data.Dataset"]) class DatasetV1(DatasetV2): """Represents a potentially large set of elements. A `Dataset` can be used to represent an input pipeline as a collection of elements and a "logical plan" of transformations that act on those elements. """ def __init__(self): try: variant_tensor = self._as_variant_tensor() except AttributeError as e: if "_as_variant_tensor" in str(e): raise AttributeError("Please use `_variant_tensor` instead of " "`_as_variant_tensor()` to obtain the variant " "associated with a dataset.") raise AttributeError("{}: A likely cause of this error is that the super " "call for this dataset is not the last line of the " "`__init__` method. The base class invokes the " "`_as_variant_tensor()` method in its constructor " "and if that method uses attributes defined in the " "`__init__` method, those attributes need to be " "defined before the super call.".format(e)) super(DatasetV1, self).__init__(variant_tensor) @abc.abstractmethod def _as_variant_tensor(self): """Creates a scalar `tf.Tensor` of `tf.variant` representing this dataset. Returns: A scalar `tf.Tensor` of `tf.variant` type, which represents this dataset. """ raise NotImplementedError(f"{type(self)}.as_variant_tensor()") @deprecation.deprecated( None, "This is a deprecated API that should only be used in TF 1 graph " "mode and legacy TF 2 graph mode available through `tf.compat.v1`. In " "all other situations -- namely, eager mode and inside `tf.function` -- " "you can consume dataset elements using `for elem in dataset: ...` or " "by explicitly creating iterator via `iterator = iter(dataset)` and " "fetching its elements via `values = next(iterator)`. Furthermore, " "this API is not available in TF 2. During the transition from TF 1 " "to TF 2 you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)` " "to create a TF 1 graph mode style iterator for a dataset created " "through TF 2 APIs. Note that this should be a transient state of your " "code base as there are in general no guarantees about the " "interoperability of TF 1 and TF 2 code.") def make_one_shot_iterator(self): """Creates an iterator for elements of this dataset. Note: The returned iterator will be initialized automatically. A "one-shot" iterator does not currently support re-initialization. For that see `make_initializable_iterator`. Example: ```python # Building graph ... dataset = ... next_value = dataset.make_one_shot_iterator().get_next() # ... from within a session ... try: while True: value = sess.run(next_value) ... except tf.errors.OutOfRangeError: pass ``` Returns: An `tf.data.Iterator` for elements of this dataset. """ return self._make_one_shot_iterator() def _make_one_shot_iterator(self): # pylint: disable=missing-docstring if context.executing_eagerly(): with ops.colocate_with(self._variant_tensor): return iterator_ops.OwnedIterator(self) _ensure_same_dataset_graph(self) # Some ops (e.g. dataset ops) are marked as stateful but are stil safe to # to capture by value. We must allowlist these ops so that the capturing # logic captures the ops instead of raising an exception. allowlisted_stateful_ops = traverse.obtain_capture_by_value_ops(self) graph_level_seed, op_level_seed = core_random_seed.get_seed(None) # NOTE(mrry): We capture by value here to ensure that `_make_dataset()` is # a 0-argument function. @function.Defun( capture_by_value=True, allowlisted_stateful_ops=allowlisted_stateful_ops) def _make_dataset(): """Factory function for a dataset.""" # NOTE(mrry): `Defun` does not capture the graph-level seed from the # enclosing graph, so if a graph-level seed is present we set the local # graph seed based on a combination of the graph- and op-level seeds. if graph_level_seed is not None: assert op_level_seed is not None core_random_seed.set_random_seed( (graph_level_seed + 87654321 * op_level_seed) % (2 ** 63 - 1)) dataset = self._apply_debug_options() return dataset._variant_tensor # pylint: disable=protected-access try: _make_dataset.add_to_graph(ops.get_default_graph()) except ValueError as err: if "Cannot capture a stateful node" in str(err): raise ValueError( "{}: A likely cause of this error is that the dataset for which " "you are calling `make_one_shot_iterator()` captures a stateful " "object, such as a `tf.Variable` or `tf.lookup.StaticHashTable`, " "which is not supported. Use `make_initializable_iterator()` " "instead.".format(err)) else: six.reraise(ValueError, err) with ops.colocate_with(self._variant_tensor): # pylint: disable=protected-access return iterator_ops.Iterator( gen_dataset_ops.one_shot_iterator( dataset_factory=_make_dataset, **self._flat_structure), None, get_legacy_output_types(self), get_legacy_output_shapes(self), get_legacy_output_classes(self)) @deprecation.deprecated( None, "This is a deprecated API that should only be used in TF 1 graph " "mode and legacy TF 2 graph mode available through `tf.compat.v1`. " "In all other situations -- namely, eager mode and inside `tf.function` " "-- you can consume dataset elements using `for elem in dataset: ...` " "or by explicitly creating iterator via `iterator = iter(dataset)` " "and fetching its elements via `values = next(iterator)`. " "Furthermore, this API is not available in TF 2. During the transition " "from TF 1 to TF 2 you can use " "`tf.compat.v1.data.make_initializable_iterator(dataset)` to create a TF " "1 graph mode style iterator for a dataset created through TF 2 APIs. " "Note that this should be a transient state of your code base as there " "are in general no guarantees about the interoperability of TF 1 and TF " "2 code.") def make_initializable_iterator(self, shared_name=None): """Creates an iterator for elements of this dataset. Note: The returned iterator will be in an uninitialized state, and you must run the `iterator.initializer` operation before using it: ```python # Building graph ... dataset = ... iterator = dataset.make_initializable_iterator() next_value = iterator.get_next() # This is a Tensor. # ... from within a session ... sess.run(iterator.initializer) try: while True: value = sess.run(next_value) ... except tf.errors.OutOfRangeError: pass ``` Args: shared_name: (Optional.) If non-empty, the returned iterator will be shared under the given name across multiple sessions that share the same devices (e.g. when using a remote server). Returns: A `tf.data.Iterator` for elements of this dataset. Raises: RuntimeError: If eager execution is enabled. """ return self._make_initializable_iterator(shared_name) def _make_initializable_iterator(self, shared_name=None): # pylint: disable=missing-docstring if context.executing_eagerly(): raise RuntimeError("`make_initializable_iterator()` is not supported in " "eager mode. Use Python-style iteration instead.") _ensure_same_dataset_graph(self) dataset = self._apply_debug_options() if shared_name is None: shared_name = "" with ops.colocate_with(self._variant_tensor): iterator_resource = gen_dataset_ops.iterator_v2( container="", shared_name=shared_name, **self._flat_structure) initializer = gen_dataset_ops.make_iterator( dataset._variant_tensor, # pylint: disable=protected-access iterator_resource) # pylint: disable=protected-access return iterator_ops.Iterator(iterator_resource, initializer, get_legacy_output_types(dataset), get_legacy_output_shapes(dataset), get_legacy_output_classes(dataset)) @property @deprecation.deprecated( None, "Use `tf.compat.v1.data.get_output_classes(dataset)`.") def output_classes(self): """Returns the class of each component of an element of this dataset. Returns: A (nested) structure of Python `type` objects corresponding to each component of an element of this dataset. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access self.element_spec) @property @deprecation.deprecated( None, "Use `tf.compat.v1.data.get_output_shapes(dataset)`.") 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. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access self.element_spec) @property @deprecation.deprecated( None, "Use `tf.compat.v1.data.get_output_types(dataset)`.") 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. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access self.element_spec) @property def element_spec(self): # TODO(b/110122868): Remove this override once all `Dataset` instances # implement `element_structure`. return structure.convert_legacy_structure( self.output_types, self.output_shapes, self.output_classes) @staticmethod @functools.wraps(DatasetV2.from_tensors) def from_tensors(tensors, name=None): return DatasetV1Adapter(DatasetV2.from_tensors(tensors, name=name)) @staticmethod @functools.wraps(DatasetV2.from_tensor_slices) def from_tensor_slices(tensors, name=None): return DatasetV1Adapter(DatasetV2.from_tensor_slices(tensors, name=name)) @staticmethod @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.") def from_sparse_tensor_slices(sparse_tensor): """Splits each rank-N `tf.sparse.SparseTensor` in this dataset row-wise. Args: sparse_tensor: A `tf.sparse.SparseTensor`. Returns: Dataset: A `Dataset` of rank-(N-1) sparse tensors. """ return DatasetV1Adapter(SparseTensorSliceDataset(sparse_tensor)) @staticmethod @functools.wraps(DatasetV2.from_generator) @deprecation.deprecated_args(None, "Use output_signature instead", "output_types", "output_shapes") def from_generator(generator, output_types=None, output_shapes=None, args=None, output_signature=None, name=None): # Calling DatasetV2.from_generator with output_shapes or output_types is # deprecated, but this is already checked by the decorator on this function. with deprecation.silence(): return DatasetV1Adapter( DatasetV2.from_generator( generator, output_types, output_shapes, args, output_signature, name=name)) @staticmethod @functools.wraps(DatasetV2.range) def range(*args, **kwargs): return DatasetV1Adapter(DatasetV2.range(*args, **kwargs)) @staticmethod @functools.wraps(DatasetV2.zip) def zip(datasets, name=None): return DatasetV1Adapter(DatasetV2.zip(datasets, name=name)) @functools.wraps(DatasetV2.concatenate) def concatenate(self, dataset, name=None): return DatasetV1Adapter( super(DatasetV1, self).concatenate(dataset, name=name)) @functools.wraps(DatasetV2.prefetch) def prefetch(self, buffer_size, name=None): return DatasetV1Adapter( super(DatasetV1, self).prefetch(buffer_size, name=name)) @staticmethod @functools.wraps(DatasetV2.list_files) def list_files(file_pattern, shuffle=None, seed=None, name=None): return DatasetV1Adapter( DatasetV2.list_files(file_pattern, shuffle, seed, name=name)) @functools.wraps(DatasetV2.repeat) def repeat(self, count=None, name=None): return DatasetV1Adapter(super(DatasetV1, self).repeat(count, name=name)) @functools.wraps(DatasetV2.shuffle) def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None, name=None): return DatasetV1Adapter( super(DatasetV1, self).shuffle( buffer_size, seed, reshuffle_each_iteration, name=name)) @functools.wraps(DatasetV2.cache) def cache(self, filename="", name=None): return DatasetV1Adapter(super(DatasetV1, self).cache(filename, name=name)) @functools.wraps(DatasetV2.take) def take(self, count, name=None): return DatasetV1Adapter(super(DatasetV1, self).take(count, name=name)) @functools.wraps(DatasetV2.skip) def skip(self, count, name=None): return DatasetV1Adapter(super(DatasetV1, self).skip(count, name=name)) @functools.wraps(DatasetV2.shard) def shard(self, num_shards, index, name=None): return DatasetV1Adapter( super(DatasetV1, self).shard(num_shards, index, name=name)) @functools.wraps(DatasetV2.batch) def batch(self, batch_size, drop_remainder=False, num_parallel_calls=None, deterministic=None, name=None): return DatasetV1Adapter( super(DatasetV1, self).batch( batch_size, drop_remainder, num_parallel_calls, deterministic, name=name)) @functools.wraps(DatasetV2.padded_batch) def padded_batch(self, batch_size, padded_shapes=None, padding_values=None, drop_remainder=False, name=None): return DatasetV1Adapter( super(DatasetV1, self).padded_batch( batch_size, padded_shapes, padding_values, drop_remainder, name=name)) @functools.wraps(DatasetV2.map) def map(self, map_func, num_parallel_calls=None, deterministic=None, name=None): if num_parallel_calls is None or DEBUG_MODE: return DatasetV1Adapter( MapDataset(self, map_func, preserve_cardinality=False)) else: return DatasetV1Adapter( ParallelMapDataset( self, map_func, num_parallel_calls, deterministic, preserve_cardinality=False)) @deprecation.deprecated(None, "Use `tf.data.Dataset.map()") def map_with_legacy_function(self, map_func, num_parallel_calls=None, deterministic=None): """Maps `map_func` across the elements of this dataset. Note: This is an escape hatch for existing uses of `map` that do not work with V2 functions. New uses are strongly discouraged and existing uses should migrate to `map` as this method will be removed in V2. 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_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing the number elements to process asynchronously in parallel. If not specified, elements will be processed sequentially. If the value `tf.data.AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available CPU. deterministic: (Optional.) When `num_parallel_calls` is specified, this boolean controls the order in which the transformation produces elements. If set to `False`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.deterministic` option (`True` by default) controls the behavior. Returns: Dataset: A `Dataset`. """ if num_parallel_calls is None: if deterministic is not None: warnings.warn("The `deterministic` argument has no effect unless the " "`num_parallel_calls` argument is specified.") return DatasetV1Adapter( MapDataset( self, map_func, preserve_cardinality=False, use_legacy_function=True)) else: return DatasetV1Adapter( ParallelMapDataset( self, map_func, num_parallel_calls, deterministic, preserve_cardinality=False, use_legacy_function=True)) @functools.wraps(DatasetV2.flat_map) def flat_map(self, map_func, name=None): return DatasetV1Adapter( super(DatasetV1, self).flat_map(map_func, name=name)) @functools.wraps(DatasetV2.interleave) def interleave(self, map_func, cycle_length=None, block_length=None, num_parallel_calls=None, deterministic=None, name=None): return DatasetV1Adapter( super(DatasetV1, self).interleave( map_func, cycle_length, block_length, num_parallel_calls, deterministic, name=name)) @functools.wraps(DatasetV2.filter) def filter(self, predicate, name=None): return DatasetV1Adapter(super(DatasetV1, self).filter(predicate, name=name)) @deprecation.deprecated(None, "Use `tf.data.Dataset.filter()") def filter_with_legacy_function(self, predicate): """Filters this dataset according to `predicate`. Note: This is an escape hatch for existing uses of `filter` that do not work with V2 functions. New uses are strongly discouraged and existing uses should migrate to `filter` as this method will be removed in V2. 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: Dataset: The `Dataset` containing the elements of this dataset for which `predicate` is `True`. """ return FilterDataset(self, predicate, use_legacy_function=True) @functools.wraps(DatasetV2.apply) def apply(self, transformation_func): return DatasetV1Adapter(super(DatasetV1, self).apply(transformation_func)) @functools.wraps(DatasetV2.window) def window(self, size, shift=None, stride=1, drop_remainder=False, name=None): return DatasetV1Adapter( super(DatasetV1, self).window(size, shift, stride, drop_remainder, name=name)) @functools.wraps(DatasetV2.unbatch) def unbatch(self, name=None): return DatasetV1Adapter(super(DatasetV1, self).unbatch(name=name)) @functools.wraps(DatasetV2.with_options) def with_options(self, options, name=None): return DatasetV1Adapter( super(DatasetV1, self).with_options(options, name=name)) if tf2.enabled(): Dataset = DatasetV2 else: Dataset = DatasetV1 class DatasetV1Adapter(DatasetV1): """Wraps a V2 `Dataset` object in the `tf.compat.v1.data.Dataset` API.""" def __init__(self, dataset): self._dataset = dataset super(DatasetV1Adapter, self).__init__() def _as_variant_tensor(self): return self._dataset._variant_tensor # pylint: disable=protected-access def _inputs(self): return self._dataset._inputs() # pylint: disable=protected-access def _functions(self): return self._dataset._functions() # pylint: disable=protected-access def options(self): return self._dataset.options() @property def element_spec(self): return self._dataset.element_spec # pylint: disable=protected-access def __iter__(self): return iter(self._dataset) def _ensure_same_dataset_graph(dataset): """Walks the dataset graph to ensure all datasets come from the same graph.""" # pylint: disable=protected-access current_graph = ops.get_default_graph() bfs_q = Queue.Queue() bfs_q.put(dataset) visited = [] while not bfs_q.empty(): ds = bfs_q.get() visited.append(ds) ds_graph = ds._graph if current_graph != ds_graph: raise ValueError( f"The graph {current_graph} of the iterator is different from the " f"graph {ds_graph} the dataset: {ds._variant_tensor} was created in. " f"If you are using the Estimator API, make sure that no part of the " f"dataset returned by the `input_fn` function is defined outside the " f"`input_fn` function. Otherwise, make sure that the dataset is " f"created in the same graph as the iterator.") for input_ds in ds._inputs(): if input_ds not in visited: bfs_q.put(input_ds) @tf_export(v1=["data.make_one_shot_iterator"]) def make_one_shot_iterator(dataset): """Creates an iterator for elements of `dataset`. Note: The returned iterator will be initialized automatically. A "one-shot" iterator does not support re-initialization. Args: dataset: A `tf.data.Dataset`. Returns: A `tf.data.Iterator` for elements of `dataset`. @compatibility(TF2) This is a legacy API for consuming dataset elements and should only be used during transition from TF 1 to TF 2. Note that using this API should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code. In TF 2 datasets are Python iterables which means you can consume their elements using `for elem in dataset: ...` or by explicitly creating iterator via `iterator = iter(dataset)` and fetching its elements via `values = next(iterator)`. @end_compatibility """ try: # Call the defined `_make_one_shot_iterator()` if there is one, because some # datasets (e.g. for prefetching) override its behavior. return dataset._make_one_shot_iterator() # pylint: disable=protected-access except AttributeError: return DatasetV1Adapter(dataset)._make_one_shot_iterator() # pylint: disable=protected-access @tf_export(v1=["data.make_initializable_iterator"]) def make_initializable_iterator(dataset, shared_name=None): """Creates an iterator for elements of `dataset`. Note: The returned iterator will be in an uninitialized state, and you must run the `iterator.initializer` operation before using it: ```python dataset = ... iterator = tf.compat.v1.data.make_initializable_iterator(dataset) # ... sess.run(iterator.initializer) ``` Args: dataset: A `tf.data.Dataset`. shared_name: (Optional.) If non-empty, the returned iterator will be shared under the given name across multiple sessions that share the same devices (e.g. when using a remote server). Returns: A `tf.data.Iterator` for elements of `dataset`. Raises: RuntimeError: If eager execution is enabled. @compatibility(TF2) This is a legacy API for consuming dataset elements and should only be used during transition from TF 1 to TF 2. Note that using this API should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code. In TF 2 datasets are Python iterables which means you can consume their elements using `for elem in dataset: ...` or by explicitly creating iterator via `iterator = iter(dataset)` and fetching its elements via `values = next(iterator)`. @end_compatibility """ try: # Call the defined `_make_initializable_iterator()` if there is one, because # some datasets (e.g. for prefetching) override its behavior. return dataset._make_initializable_iterator(shared_name) # pylint: disable=protected-access except AttributeError: return DatasetV1Adapter(dataset)._make_initializable_iterator(shared_name) # pylint: disable=protected-access @tf_export("data.experimental.get_structure") def get_structure(dataset_or_iterator): """Returns the type signature for elements of the input dataset / iterator. Args: dataset_or_iterator: A `tf.data.Dataset` or an `tf.data.Iterator`. Returns: A (nested) structure of `tf.TypeSpec` objects matching the structure of an element of `dataset_or_iterator` and specifying the type of individual components. Raises: TypeError: If input is not a `tf.data.Dataset` or an `tf.data.Iterator` object. """ try: return dataset_or_iterator.element_spec # pylint: disable=protected-access except AttributeError: raise TypeError(f"Invalid `dataset_or_iterator`. `dataset_or_iterator` " f"must be a `tf.data.Dataset` or tf.data.Iterator object, " f"but got {type(dataset_or_iterator)}.") @tf_export(v1=["data.get_output_classes"]) def get_legacy_output_classes(dataset_or_iterator): """Returns the output classes for elements of the input dataset / iterator. Args: dataset_or_iterator: A `tf.data.Dataset` or `tf.data.Iterator`. Returns: A (nested) structure of Python `type` objects matching the structure of the dataset / iterator elements and specifying the class of the individual components. @compatibility(TF2) This is a legacy API for inspecting the type signature of dataset elements. In TF 2, you should use the `tf.data.Dataset.element_spec` attribute instead. @end_compatibility """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access get_structure(dataset_or_iterator)) @tf_export(v1=["data.get_output_shapes"]) def get_legacy_output_shapes(dataset_or_iterator): """Returns the output shapes for elements of the input dataset / iterator. Args: dataset_or_iterator: A `tf.data.Dataset` or `tf.data.Iterator`. Returns: A (nested) structure of `tf.TensorShape` objects matching the structure of the dataset / iterator elements and specifying the shape of the individual components. @compatibility(TF2) This is a legacy API for inspecting the type signature of dataset elements. In TF 2, you should use the `tf.data.Dataset.element_spec` attribute instead. @end_compatibility """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access get_structure(dataset_or_iterator)) @tf_export(v1=["data.get_output_types"]) def get_legacy_output_types(dataset_or_iterator): """Returns the output shapes for elements of the input dataset / iterator. Args: dataset_or_iterator: A `tf.data.Dataset` or `tf.data.Iterator`. Returns: A (nested) structure of `tf.DType` objects matching the structure of dataset / iterator elements and specifying the shape of the individual components. @compatibility(TF2) This is a legacy API for inspecting the type signature of dataset elements. In TF 2, you should use the `tf.data.Dataset.element_spec` attribute instead. @end_compatibility """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access get_structure(dataset_or_iterator)) class DatasetSource(DatasetV2): """Abstract class representing a dataset with no inputs.""" def _inputs(self): return [] class UnaryDataset(DatasetV2): """Abstract class representing a dataset with one input.""" def __init__(self, input_dataset, variant_tensor): self._input_dataset = input_dataset super(UnaryDataset, self).__init__(variant_tensor) def _inputs(self): return [self._input_dataset] class UnaryUnchangedStructureDataset(UnaryDataset): """Represents a unary dataset with the same input and output structure.""" def __init__(self, input_dataset, variant_tensor): self._input_dataset = input_dataset super(UnaryUnchangedStructureDataset, self).__init__( input_dataset, variant_tensor) @property def element_spec(self): return self._input_dataset.element_spec class _VariantDataset(DatasetV2): """A Dataset wrapper around a `tf.variant`-typed function argument.""" def __init__(self, dataset_variant, element_spec): self._element_spec = element_spec super(_VariantDataset, self).__init__(dataset_variant) def _inputs(self): return [] @property def element_spec(self): return self._element_spec class _NestedVariant(composite_tensor.CompositeTensor): def __init__(self, variant_tensor, element_spec, dataset_shape): self._variant_tensor = variant_tensor self._element_spec = element_spec self._dataset_shape = dataset_shape @property def _type_spec(self): return DatasetSpec(self._element_spec, self._dataset_shape) @tf_export("data.experimental.from_variant") def from_variant(variant, structure): """Constructs a dataset from the given variant and (nested) structure. Args: variant: A scalar `tf.variant` tensor representing a dataset. structure: A (nested) structure of `tf.TypeSpec` objects representing the structure of each element in the dataset. Returns: A `tf.data.Dataset` instance. """ return _VariantDataset(variant, structure) # pylint: disable=protected-access @tf_export("data.experimental.to_variant") def to_variant(dataset): """Returns a variant representing the given dataset. Args: dataset: A `tf.data.Dataset`. Returns: A scalar `tf.variant` tensor representing the given dataset. """ return dataset._variant_tensor # pylint: disable=protected-access @tf_export( "data.DatasetSpec", v1=["data.DatasetSpec", "data.experimental.DatasetStructure"]) class DatasetSpec(type_spec.BatchableTypeSpec): """Type specification for `tf.data.Dataset`. See `tf.TypeSpec` for more information about TensorFlow type specifications. >>> dataset = tf.data.Dataset.range(3) >>> tf.data.DatasetSpec.from_value(dataset) DatasetSpec(TensorSpec(shape=(), dtype=tf.int64, name=None), TensorShape([])) """ __slots__ = ["_element_spec", "_dataset_shape"] def __init__(self, element_spec, dataset_shape=()): self._element_spec = element_spec self._dataset_shape = tensor_shape.as_shape(dataset_shape) @property def value_type(self): return Dataset @property def element_spec(self): """The inner element spec.""" return self._element_spec def is_subtype_of(self, other): """See base class.""" if type(self) is not type(other): return False # TODO(b/220385675): _element_spec should always be a TypeSpec. try: tf_nest.assert_same_structure(self.element_spec, other.element_spec) except (TypeError, ValueError): return False self_elements = tf_nest.flatten(self.element_spec) other_elements = tf_nest.flatten(other.element_spec) def is_subtype_or_equal(a, b): if isinstance(a, trace.TraceType): return a.is_subtype_of(b) else: return a == b for self_element, other_element in zip(self_elements, other_elements): if not is_subtype_or_equal(self_element, other_element): return False return self._dataset_shape.is_subtype_of(other._dataset_shape) # pylint: disable=protected-access def most_specific_common_supertype(self, others): """See base class.""" if not all(type(self) is type(other) for other in others): return None try: for other in others: tf_nest.assert_same_structure(self.element_spec, other.element_spec) except (TypeError, ValueError): return None self_components = tf_nest.flatten(self.element_spec) others_components = [ tf_nest.flatten(other.element_spec) for other in others ] common_components = [None] * len(self_components) def common_supertype_or_equal(a, bs): if isinstance(a, trace.TraceType): return a.most_specific_common_supertype(bs) else: return a if all(a == b for b in bs) else None for i, self_component in enumerate(self_components): common_components[i] = common_supertype_or_equal( self_component, [other_components[i] for other_components in others_components]) if self_component is not None and common_components[i] is None: return None common_element_spec = tf_nest.pack_sequence_as(self._element_spec, common_components) common_dataset_shape = self._dataset_shape.most_specific_common_supertype( [other._dataset_shape for other in others]) # pylint: disable=protected-access if common_dataset_shape is None: return None return DatasetSpec(common_element_spec, common_dataset_shape) # TODO(b/220385675): Once _element_spec is guaranteed to be TypeSpec, the # following functions do not need to be overloaded: is_subtype_of, # most_specific_common_supertype, __hash__ and __eq__ def _serialize(self): return (self._element_spec, self._dataset_shape) @property def _component_specs(self): return tensor_spec.TensorSpec(self._dataset_shape, dtypes.variant) def _to_components(self, value): return value._variant_tensor # pylint: disable=protected-access def _from_components(self, components): # pylint: disable=protected-access if self._dataset_shape.ndims == 0: return _VariantDataset(components, self._element_spec) else: return _NestedVariant(components, self._element_spec, self._dataset_shape) def _to_tensor_list(self, value): return [ ops.convert_to_tensor( tf_nest.map_structure(lambda x: x._variant_tensor, value)) # pylint: disable=protected-access ] @staticmethod def from_value(value): """Creates a `DatasetSpec` for the given `tf.data.Dataset` value.""" return DatasetSpec(value.element_spec) # pylint: disable=protected-access def _batch(self, batch_size): return DatasetSpec( self._element_spec, tensor_shape.TensorShape([batch_size]).concatenate(self._dataset_shape)) def _unbatch(self): if self._dataset_shape.ndims == 0: raise ValueError("Slicing dataset elements is not supported for rank 0.") return DatasetSpec(self._element_spec, self._dataset_shape[1:]) def _to_batched_tensor_list(self, value): if self._dataset_shape.ndims == 0: raise ValueError("Slicing dataset elements is not supported for rank 0.") return self._to_tensor_list(value) def _to_legacy_output_types(self): return self def _to_legacy_output_shapes(self): return self def _to_legacy_output_classes(self): return self def __hash__(self): # TODO(b/220385675): attributes can be dicts and hence unhashable. return hash(DatasetSpec) def __eq__(self, other): return (isinstance(other, DatasetSpec) and self._element_spec == other._element_spec and self._dataset_shape == other._dataset_shape) class _NumpyIterator(object): """Iterator over a dataset with elements converted to numpy.""" __slots__ = ["_iterator"] def __init__(self, dataset): self._iterator = iter(dataset) def __iter__(self): return self def __next__(self): def to_numpy(x): numpy = x._numpy() # pylint: disable=protected-access if isinstance(numpy, np.ndarray): # `numpy` shares the same underlying buffer as the `x` Tensor. # Tensors are expected to be immutable, so we disable writes. numpy.setflags(write=False) return numpy return nest.map_structure(to_numpy, next(self._iterator)) def next(self): return self.__next__() class _VariantTracker(resource_lib.CapturableResource): """Allows export of functions capturing a Dataset in SavedModels. When saving a SavedModel, `tf.saved_model.save` traverses the object graph. Since Datasets reference _VariantTracker objects, that traversal will find a _VariantTracker for each Dataset and so know how to save and restore functions which reference the Dataset's variant Tensor. """ def __init__(self, variant_tensor, resource_creator): """Record that `variant_tensor` is associated with `resource_creator`. Args: variant_tensor: The variant-dtype Tensor associated with the Dataset. This Tensor will be a captured input to functions which use the Dataset, and is used by saving code to identify the corresponding _VariantTracker. resource_creator: A zero-argument function which creates a new variant-dtype Tensor. This function will be included in SavedModels and run to re-create the Dataset's variant Tensor on restore. """ super(_VariantTracker, self).__init__(device="CPU") self._resource_handle = variant_tensor if not isinstance(resource_creator, def_function.Function): # Internal validation -- _VariantTracker assumes that resource creator is # already a tf.function. raise TypeError("Resource creator should already be a tf.function.") self._create_resource = resource_creator def _trackable_children(self, save_type=tracking_base.SaveType.CHECKPOINT, **kwargs): if save_type != tracking_base.SaveType.SAVEDMODEL: return {} children = super(_VariantTracker, self)._trackable_children(save_type, **kwargs) # Overwrite the _create_resource function, since `self._create_resource` # is already a tf.function. children["_create_resource"] = self._create_resource return children class TensorDataset(DatasetSource): """A `Dataset` with a single element.""" def __init__(self, element, name=None): """See `Dataset.from_tensors()` for details.""" element = structure.normalize_element(element) self._structure = structure.type_spec_from_value(element) self._tensors = structure.to_tensor_list(self._structure, element) self._name = name variant_tensor = gen_dataset_ops.tensor_dataset( self._tensors, output_shapes=structure.get_flat_tensor_shapes(self._structure), metadata=self._metadata.SerializeToString()) super(TensorDataset, self).__init__(variant_tensor) @property def element_spec(self): return self._structure class TensorSliceDataset(DatasetSource): """A `Dataset` of slices from a dataset element.""" def __init__(self, element, is_files=False, name=None): """See `Dataset.from_tensor_slices()` for details.""" element = structure.normalize_element(element) batched_spec = structure.type_spec_from_value(element) self._tensors = structure.to_batched_tensor_list(batched_spec, element) if not self._tensors: raise ValueError("Invalid `element`. `element` should not be empty.") self._structure = nest.map_structure( lambda component_spec: component_spec._unbatch(), batched_spec) # pylint: disable=protected-access self._name = name batch_dim = tensor_shape.Dimension( tensor_shape.dimension_value(self._tensors[0].get_shape()[0])) for t in self._tensors[1:]: batch_dim.assert_is_compatible_with( tensor_shape.Dimension( tensor_shape.dimension_value(t.get_shape()[0]))) variant_tensor = gen_dataset_ops.tensor_slice_dataset( self._tensors, output_shapes=structure.get_flat_tensor_shapes(self._structure), is_files=is_files, metadata=self._metadata.SerializeToString()) super(TensorSliceDataset, self).__init__(variant_tensor) @property def element_spec(self): return self._structure class SparseTensorSliceDataset(DatasetSource): """A `Dataset` that splits a rank-N `tf.sparse.SparseTensor` into its rows.""" def __init__(self, sparse_tensor): """See `Dataset.from_sparse_tensor_slices()` for details.""" if not isinstance(sparse_tensor, sparse_tensor_lib.SparseTensor): raise TypeError(f"Invalid `sparse_tensor`. `sparse_tensor` must be a " f"`tf.sparse.SparseTensor`. Got {type(sparse_tensor)}.") self._sparse_tensor = sparse_tensor indices_shape = self._sparse_tensor.indices.get_shape() shape_shape = self._sparse_tensor.dense_shape.get_shape() rank = (indices_shape.dims[1] - 1).merge_with(shape_shape.dims[0] - 1) self._structure = (tensor_spec.TensorSpec([None, rank], dtypes.int64), tensor_spec.TensorSpec([None], self._sparse_tensor.dtype), tensor_spec.TensorSpec([rank], dtypes.int64)) variant_tensor = gen_dataset_ops.sparse_tensor_slice_dataset( self._sparse_tensor.indices, self._sparse_tensor.values, self._sparse_tensor.dense_shape) super(SparseTensorSliceDataset, self).__init__(variant_tensor) @property def element_spec(self): return self._structure class _GeneratorDataset(DatasetSource): """A `Dataset` that generates elements by invoking a function.""" def __init__(self, init_args, init_func, next_func, finalize_func, output_signature, name=None): """Constructs a `_GeneratorDataset`. Args: init_args: A (nested) structure representing the arguments to `init_func`. init_func: A TensorFlow function that will be called on `init_args` each time a C++ iterator over this dataset is constructed. Returns a (nested) structure representing the "state" of the dataset. next_func: A TensorFlow function that will be called on the result of `init_func` to produce each element, and that raises `OutOfRangeError` to terminate iteration. finalize_func: A TensorFlow function that will be called on the result of `init_func` immediately before a C++ iterator over this dataset is destroyed. The return value is ignored. output_signature: A (nested) structure of `tf.TypeSpec` objects describing the output of `next_func`. name: Optional. A name for the tf.data transformation. """ self._init_args = init_args self._init_structure = structure.type_spec_from_value(init_args) self._init_func = structured_function.StructuredFunctionWrapper( init_func, self._transformation_name(), input_structure=self._init_structure) self._next_func = structured_function.StructuredFunctionWrapper( next_func, self._transformation_name(), input_structure=self._init_func.output_structure) self._finalize_func = structured_function.StructuredFunctionWrapper( finalize_func, self._transformation_name(), input_structure=self._init_func.output_structure) self._output_signature = output_signature self._name = name variant_tensor = gen_dataset_ops.generator_dataset( structure.to_tensor_list(self._init_structure, self._init_args) + self._init_func.function.captured_inputs, self._next_func.function.captured_inputs, self._finalize_func.function.captured_inputs, init_func=self._init_func.function, next_func=self._next_func.function, finalize_func=self._finalize_func.function, **self._common_args) super(_GeneratorDataset, self).__init__(variant_tensor) @property def element_spec(self): return self._output_signature def _transformation_name(self): return "Dataset.from_generator()" class ZipDataset(DatasetV2): """A `Dataset` that zips its inputs together.""" def __init__(self, datasets, name=None): """See `Dataset.zip()` for details.""" for ds in nest.flatten(datasets): if not isinstance(ds, DatasetV2): if isinstance(ds, list): raise TypeError("Invalid `datasets`. `datasets` is expected to be a " "(nested) structure of `tf.data.Dataset` objects. " "Python `list` is not supported and you should use " "`tuple` instead.") else: raise TypeError(f"Invalid `datasets`. `datasets` is expected to be a " f"(nested) structure of `tf.data.Dataset` objects " f"but encountered object of type {type(ds)}.") self._datasets = datasets self._structure = nest.pack_sequence_as( self._datasets, [ds.element_spec for ds in nest.flatten(self._datasets)]) self._name = name variant_tensor = gen_dataset_ops.zip_dataset( [ds._variant_tensor for ds in nest.flatten(self._datasets)], **self._common_args) super(ZipDataset, self).__init__(variant_tensor) def _inputs(self): return nest.flatten(self._datasets) @property def element_spec(self): return self._structure class ConcatenateDataset(DatasetV2): """A `Dataset` that concatenates its input with given dataset.""" def __init__(self, input_dataset, dataset_to_concatenate, name=None): """See `Dataset.concatenate()` for details.""" self._input_dataset = input_dataset self._dataset_to_concatenate = dataset_to_concatenate def common_supertype(a, b): result = a.most_specific_common_supertype([b]) if result is None: raise TypeError(f"No common supertype of {a} and {b}.") return result try: self._structure = tf_nest.map_structure( common_supertype, input_dataset.element_spec, dataset_to_concatenate.element_spec) except (TypeError, ValueError) as e: raise TypeError( f"Incompatible dataset elements:\n" f" {input_dataset.element_spec} vs. " f" {dataset_to_concatenate.element_spec}") from e self._input_datasets = [input_dataset, dataset_to_concatenate] self._name = name # pylint: disable=protected-access variant_tensor = gen_dataset_ops.concatenate_dataset( input_dataset._variant_tensor, dataset_to_concatenate._variant_tensor, **self._common_args) # pylint: enable=protected-access super(ConcatenateDataset, self).__init__(variant_tensor) def _inputs(self): return self._input_datasets @property def element_spec(self): return self._structure class RepeatDataset(UnaryUnchangedStructureDataset): """A `Dataset` that repeats its input several times.""" def __init__(self, input_dataset, count, name=None): """See `Dataset.repeat()` for details.""" 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") self._name = name variant_tensor = gen_dataset_ops.repeat_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access count=self._count, **self._common_args) super(RepeatDataset, self).__init__(input_dataset, variant_tensor) class RangeDataset(DatasetSource): """A `Dataset` of a step separated range of values.""" def __init__(self, *args, **kwargs): """See `Dataset.range()` for details.""" self._parse_args(*args, **kwargs) self._structure = tensor_spec.TensorSpec([], self._output_type) variant_tensor = gen_dataset_ops.range_dataset( start=self._start, stop=self._stop, step=self._step, **self._common_args) super(RangeDataset, self).__init__(variant_tensor) def _parse_args(self, *args, **kwargs): """Parse arguments according to the same rules as the `range()` builtin.""" if len(args) == 1: self._start = self._build_tensor(0, "start") self._stop = self._build_tensor(args[0], "stop") self._step = self._build_tensor(1, "step") elif len(args) == 2: self._start = self._build_tensor(args[0], "start") self._stop = self._build_tensor(args[1], "stop") self._step = self._build_tensor(1, "step") elif len(args) == 3: self._start = self._build_tensor(args[0], "start") self._stop = self._build_tensor(args[1], "stop") self._step = self._build_tensor(args[2], "step") else: raise ValueError(f"Invalid `args`. The lenght of `args` should be " f"between 1 and 3 but was {len(args)}.") if "output_type" in kwargs: self._output_type = kwargs["output_type"] else: self._output_type = dtypes.int64 self._name = kwargs["name"] if "name" in kwargs else None def _build_tensor(self, int64_value, name): return ops.convert_to_tensor(int64_value, dtype=dtypes.int64, name=name) @property def element_spec(self): return self._structure class CacheDataset(UnaryUnchangedStructureDataset): """A `Dataset` that caches elements of its input.""" def __init__(self, input_dataset, filename, name=None): """See `Dataset.cache()` for details.""" self._input_dataset = input_dataset self._filename = ops.convert_to_tensor( filename, dtype=dtypes.string, name="filename") self._name = name if tf2.enabled() and (context.executing_eagerly() or ops.inside_function()): variant_tensor = gen_dataset_ops.cache_dataset_v2( input_dataset._variant_tensor, # pylint: disable=protected-access filename=self._filename, cache=gen_dataset_ops.dummy_memory_cache(), **self._common_args) else: variant_tensor = gen_dataset_ops.cache_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access filename=self._filename, **self._common_args) super(CacheDataset, self).__init__(input_dataset, variant_tensor) class ShuffleDataset(UnaryUnchangedStructureDataset): """A `Dataset` that randomly shuffles the elements of its input.""" def __init__(self, input_dataset, buffer_size, seed=None, reshuffle_each_iteration=None, name=None): """See `Dataset.shuffle()` for details.""" self._input_dataset = input_dataset self._buffer_size = ops.convert_to_tensor( buffer_size, dtype=dtypes.int64, name="buffer_size") self._seed, self._seed2 = random_seed.get_seed(seed) if reshuffle_each_iteration is None: reshuffle_each_iteration = True self._reshuffle_each_iteration = reshuffle_each_iteration self._name = name if (tf2.enabled() and (context.executing_eagerly() or ops.inside_function())): variant_tensor = gen_dataset_ops.shuffle_dataset_v3( input_dataset._variant_tensor, # pylint: disable=protected-access buffer_size=self._buffer_size, seed=self._seed, seed2=self._seed2, seed_generator=gen_dataset_ops.dummy_seed_generator(), reshuffle_each_iteration=self._reshuffle_each_iteration, **self._common_args) else: variant_tensor = gen_dataset_ops.shuffle_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access buffer_size=self._buffer_size, seed=self._seed, seed2=self._seed2, reshuffle_each_iteration=self._reshuffle_each_iteration, **self._common_args) super(ShuffleDataset, self).__init__(input_dataset, variant_tensor) class TakeDataset(UnaryUnchangedStructureDataset): """A `Dataset` containing the first `count` elements from its input.""" def __init__(self, input_dataset, count, name=None): """See `Dataset.take()` for details.""" self._input_dataset = input_dataset self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count") self._name = name variant_tensor = gen_dataset_ops.take_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access count=self._count, **self._common_args) super(TakeDataset, self).__init__(input_dataset, variant_tensor) class SkipDataset(UnaryUnchangedStructureDataset): """A `Dataset` skipping the first `count` elements from its input.""" def __init__(self, input_dataset, count, name=None): """See `Dataset.skip()` for details.""" self._input_dataset = input_dataset self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count") self._name = name variant_tensor = gen_dataset_ops.skip_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access count=self._count, **self._common_args) super(SkipDataset, self).__init__(input_dataset, variant_tensor) class ShardDataset(UnaryUnchangedStructureDataset): """A `Dataset` for sharding its input.""" def __init__(self, input_dataset, num_shards, index, name=None): """See `Dataset.shard()` for details.""" self._input_dataset = input_dataset self._num_shards = ops.convert_to_tensor( num_shards, dtype=dtypes.int64, name="num_shards") self._index = ops.convert_to_tensor(index, dtype=dtypes.int64, name="index") self._name = name variant_tensor = gen_dataset_ops.shard_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access num_shards=self._num_shards, index=self._index, **self._common_args) super(ShardDataset, self).__init__(input_dataset, variant_tensor) class BatchDataset(UnaryDataset): """A `Dataset` that batches contiguous elements from its input.""" def __init__(self, input_dataset, batch_size, drop_remainder, name=None): """See `Dataset.batch()` for details.""" self._input_dataset = input_dataset self._batch_size = ops.convert_to_tensor( batch_size, dtype=dtypes.int64, name="batch_size") self._drop_remainder = ops.convert_to_tensor( drop_remainder, dtype=dtypes.bool, name="drop_remainder") constant_drop_remainder = tensor_util.constant_value(self._drop_remainder) # pylint: disable=protected-access if constant_drop_remainder: # NOTE(mrry): `constant_drop_remainder` may be `None` (unknown statically) # or `False` (explicitly retaining the remainder). # pylint: disable=g-long-lambda constant_batch_size = tensor_util.constant_value(self._batch_size) self._structure = nest.map_structure( lambda component_spec: component_spec._batch(constant_batch_size), input_dataset.element_spec) else: self._structure = nest.map_structure( lambda component_spec: component_spec._batch(None), input_dataset.element_spec) self._name = name variant_tensor = gen_dataset_ops.batch_dataset_v2( input_dataset._variant_tensor, batch_size=self._batch_size, drop_remainder=self._drop_remainder, **self._common_args) super(BatchDataset, self).__init__(input_dataset, variant_tensor) @property def element_spec(self): return self._structure class ParallelBatchDataset(UnaryDataset): """A `Dataset` that batches contiguous elements from its input in parallel.""" def __init__(self, input_dataset, batch_size, drop_remainder, num_parallel_calls, deterministic, name=None): """See `Dataset.batch()` for details.""" self._input_dataset = input_dataset self._batch_size = ops.convert_to_tensor( batch_size, dtype=dtypes.int64, name="batch_size") self._drop_remainder = ops.convert_to_tensor( drop_remainder, dtype=dtypes.bool, name="drop_remainder") self._num_parallel_calls = ops.convert_to_tensor( num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls") if deterministic is None: self._deterministic = "default" elif deterministic: self._deterministic = "true" else: self._deterministic = "false" constant_drop_remainder = tensor_util.constant_value(self._drop_remainder) # pylint: disable=protected-access if constant_drop_remainder: # NOTE(mrry): `constant_drop_remainder` may be `None` (unknown statically) # or `False` (explicitly retaining the remainder). # pylint: disable=g-long-lambda constant_batch_size = tensor_util.constant_value(self._batch_size) self._structure = nest.map_structure( lambda component_spec: component_spec._batch(constant_batch_size), input_dataset.element_spec) else: self._structure = nest.map_structure( lambda component_spec: component_spec._batch(None), input_dataset.element_spec) self._name = name variant_tensor = gen_dataset_ops.parallel_batch_dataset( input_dataset._variant_tensor, batch_size=self._batch_size, num_parallel_calls=self._num_parallel_calls, drop_remainder=self._drop_remainder, deterministic=self._deterministic, **self._common_args) super(ParallelBatchDataset, self).__init__(input_dataset, variant_tensor) @property def element_spec(self): return self._structure def _is_padded_shape_compatible_with(padded_shape, input_component_shape): """Returns `True` if `input_component_shape` can be padded to `padded_shape`. Args: padded_shape: A `tf.TensorShape`. input_component_shape: A `tf.TensorShape`. Returns: `True` if `input_component_shape` can be padded to `padded_shape`, otherwise `False`. """ if padded_shape.dims is None or input_component_shape.dims is None: return True if len(padded_shape.dims) != len(input_component_shape.dims): return False for padded_dim, input_dim in zip( padded_shape.dims, input_component_shape.dims): if (padded_dim.value is not None and input_dim.value is not None and padded_dim.value < input_dim.value): return False return True def _padded_shape_to_tensor(padded_shape, input_component_shape): """Converts `padded_shape` to a `tf.Tensor` representing that shape. Args: padded_shape: A shape-like object, which may be a `tf.TensorShape`, a Python sequence, or a 1-D `tf.Tensor` of `tf.int64` elements. input_component_shape: A `tf.TensorShape`, with which `padded_shape` must be compatible. Returns: A 1-D `tf.Tensor` of `tf.int64` elements, representing `padded_shape`. Raises: ValueError: If `padded_shape` is not a shape or not compatible with `input_component_shape`. TypeError: If `padded_shape` is not convertible to a `tf.int64` tensor. """ try: # Try to convert the `padded_shape` to a `tf.TensorShape` padded_shape_as_shape = tensor_shape.as_shape(padded_shape) # We will return the "canonical" tensor representation, which uses # `-1` in place of `None`. ret = ops.convert_to_tensor( [dim if dim is not None else -1 for dim in padded_shape_as_shape.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. ret = ops.convert_to_tensor(padded_shape, preferred_dtype=dtypes.int64) if ret.shape.dims is not None and len(ret.shape.dims) != 1: six.reraise(ValueError, ValueError( f"Padded shape {padded_shape} must be a `tf.int64` vector tensor, " f"but its shape was {ret.shape}."), sys.exc_info()[2]) if ret.dtype != dtypes.int64: six.reraise( TypeError, TypeError(f"Padded shape {padded_shape} must be a `tf.int64` vector " f"tensor, but its element type was {ret.dtype.name}."), sys.exc_info()[2]) padded_shape_as_shape = tensor_util.constant_value_as_shape(ret) if not _is_padded_shape_compatible_with(padded_shape_as_shape, input_component_shape): raise ValueError(f"The padded shape {padded_shape_as_shape} is not " f"compatible with the shape {input_component_shape} of " f"the corresponding input component.") return ret 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.TensorShape([])): raise ValueError(f"Invalid `padding_values`. `padding_values` values " f"should be scalars, but got {value.shape}.") if value.dtype != output_type: raise TypeError(f"Invalid `padding_values`. `padding_values` values " f"type {value.dtype} does not match type {output_type} " f"of the corresponding input component.") return value def _padding_values_or_default(padding_values, input_dataset): """Returns padding values with None elements replaced with default values.""" def make_zero(t): if t.base_dtype == dtypes.string: return "" elif t.base_dtype == dtypes.variant: raise TypeError("Unable to create default padding value for a component " "of type 'variant'.") elif t.base_dtype == dtypes.bfloat16: # Special case `bfloat16` because it is not supported by NumPy. return constant_op.constant(0, dtype=dtypes.bfloat16) else: return np.zeros_like(t.as_numpy_dtype()) def value_or_default(value, default): return default if value is None else value default_padding = nest.map_structure( make_zero, get_legacy_output_types(input_dataset)) return nest.map_structure_up_to(padding_values, value_or_default, padding_values, default_padding) class PaddedBatchDataset(UnaryDataset): """A `Dataset` that batches and pads contiguous elements from its input.""" def __init__(self, input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, name=None): """See `Dataset.batch()` for details.""" self._input_dataset = input_dataset def check_types(component_spec): if not isinstance(component_spec, tensor_spec.TensorSpec): raise TypeError(f"`padded_batch` is only supported for datasets that " f"produce tensor elements but the input dataset " f"produces elements of unsupported type " f"{component_spec.value_type()}.") nest.map_structure(check_types, input_dataset.element_spec) self._input_dataset = input_dataset self._batch_size = ops.convert_to_tensor( batch_size, dtype=dtypes.int64, name="batch_size") padding_values = _padding_values_or_default(padding_values, input_dataset) input_shapes = get_legacy_output_shapes(input_dataset) flat_padded_shapes = nest.flatten_up_to(input_shapes, padded_shapes) flat_padded_shapes_as_tensors = [] for input_component_shape, padded_shape in zip( nest.flatten(input_shapes), flat_padded_shapes): flat_padded_shapes_as_tensors.append( _padded_shape_to_tensor(padded_shape, input_component_shape)) self._padded_shapes = nest.pack_sequence_as(input_shapes, flat_padded_shapes_as_tensors) # If padding_values is a single element and input_shapes is a structure, # "broadcast" padding_values to the same structure as input_shapes. if nest.is_nested(input_shapes) and not nest.is_nested(padding_values): padding_values = nest.map_structure(lambda _: padding_values, input_shapes) self._padding_values = nest.map_structure_up_to( input_shapes, _padding_value_to_tensor, padding_values, get_legacy_output_types(input_dataset)) self._drop_remainder = ops.convert_to_tensor( drop_remainder, dtype=dtypes.bool, name="drop_remainder") def _padded_shape_to_batch_shape(s): return tensor_shape.TensorShape([ tensor_util.constant_value(self._batch_size) if smart_cond.smart_constant_value(self._drop_remainder) else None ]).concatenate(tensor_util.constant_value_as_shape(s)) output_shapes = nest.map_structure( _padded_shape_to_batch_shape, self._padded_shapes) self._structure = structure.convert_legacy_structure( get_legacy_output_types(self._input_dataset), output_shapes, get_legacy_output_classes(self._input_dataset)) self._name = name # pylint: disable=protected-access variant_tensor = gen_dataset_ops.padded_batch_dataset_v2( input_dataset._variant_tensor, # pylint: disable=protected-access 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), drop_remainder=self._drop_remainder, output_shapes=structure.get_flat_tensor_shapes(self._structure), metadata=self._metadata.SerializeToString()) super(PaddedBatchDataset, self).__init__(input_dataset, variant_tensor) @property def element_spec(self): return self._structure class MapDataset(UnaryDataset): """A `Dataset` that maps a function over elements in its input.""" def __init__(self, input_dataset, map_func, use_inter_op_parallelism=True, preserve_cardinality=False, use_legacy_function=False, name=None): """See `Dataset.map()` for details.""" self._input_dataset = input_dataset self._use_inter_op_parallelism = use_inter_op_parallelism self._preserve_cardinality = preserve_cardinality self._map_func = structured_function.StructuredFunctionWrapper( map_func, self._transformation_name(), dataset=input_dataset, use_legacy_function=use_legacy_function) self._name = name variant_tensor = gen_dataset_ops.map_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access self._map_func.function.captured_inputs, f=self._map_func.function, use_inter_op_parallelism=self._use_inter_op_parallelism, preserve_cardinality=self._preserve_cardinality, **self._common_args) super(MapDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._map_func] @property def element_spec(self): return self._map_func.output_structure def _transformation_name(self): return "Dataset.map()" class ParallelMapDataset(UnaryDataset): """A `Dataset` that maps a function over elements in its input in parallel.""" def __init__(self, input_dataset, map_func, num_parallel_calls, deterministic, use_inter_op_parallelism=True, preserve_cardinality=False, use_legacy_function=False, name=None): """See `Dataset.map()` for details.""" self._input_dataset = input_dataset self._use_inter_op_parallelism = use_inter_op_parallelism self._map_func = structured_function.StructuredFunctionWrapper( map_func, self._transformation_name(), dataset=input_dataset, use_legacy_function=use_legacy_function) if deterministic is None: self._deterministic = "default" elif deterministic: self._deterministic = "true" else: self._deterministic = "false" self._preserve_cardinality = preserve_cardinality self._num_parallel_calls = ops.convert_to_tensor( num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls") self._name = name variant_tensor = gen_dataset_ops.parallel_map_dataset_v2( input_dataset._variant_tensor, # pylint: disable=protected-access self._map_func.function.captured_inputs, f=self._map_func.function, num_parallel_calls=self._num_parallel_calls, deterministic=self._deterministic, use_inter_op_parallelism=self._use_inter_op_parallelism, preserve_cardinality=self._preserve_cardinality, **self._common_args) super(ParallelMapDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._map_func] @property def element_spec(self): return self._map_func.output_structure def _transformation_name(self): return "Dataset.map()" class FlatMapDataset(UnaryDataset): """A `Dataset` that maps a function over its input and flattens the result.""" def __init__(self, input_dataset, map_func, name=None): """See `Dataset.flat_map()` for details.""" self._input_dataset = input_dataset self._map_func = structured_function.StructuredFunctionWrapper( map_func, self._transformation_name(), dataset=input_dataset) if not isinstance(self._map_func.output_structure, DatasetSpec): raise TypeError( "The `map_func` argument must return a `Dataset` object. Got " f"{_get_type(self._map_func.output_structure)!r}.") self._structure = self._map_func.output_structure._element_spec # pylint: disable=protected-access self._name = name variant_tensor = gen_dataset_ops.flat_map_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access self._map_func.function.captured_inputs, f=self._map_func.function, **self._common_args) super(FlatMapDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._map_func] @property def element_spec(self): return self._structure def _transformation_name(self): return "Dataset.flat_map()" class InterleaveDataset(UnaryDataset): """A `Dataset` that interleaves the result of transformed inputs.""" def __init__(self, input_dataset, map_func, cycle_length, block_length, name=None): """See `Dataset.interleave()` for details.""" self._input_dataset = input_dataset self._map_func = structured_function.StructuredFunctionWrapper( map_func, self._transformation_name(), dataset=input_dataset) if not isinstance(self._map_func.output_structure, DatasetSpec): raise TypeError( "The `map_func` argument must return a `Dataset` object. Got " f"{_get_type(self._map_func.output_structure)!r}.") self._structure = self._map_func.output_structure._element_spec # pylint: disable=protected-access self._cycle_length = ops.convert_to_tensor( cycle_length, dtype=dtypes.int64, name="cycle_length") self._block_length = ops.convert_to_tensor( block_length, dtype=dtypes.int64, name="block_length") self._name = name variant_tensor = gen_dataset_ops.interleave_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access self._map_func.function.captured_inputs, # pylint: disable=protected-access self._cycle_length, self._block_length, f=self._map_func.function, **self._common_args) super(InterleaveDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._map_func] @property def element_spec(self): return self._structure def _transformation_name(self): return "Dataset.interleave()" class ParallelInterleaveDataset(UnaryDataset): """A `Dataset` that maps a function over its input and interleaves the result.""" def __init__(self, input_dataset, map_func, cycle_length, block_length, num_parallel_calls, buffer_output_elements=AUTOTUNE, prefetch_input_elements=AUTOTUNE, deterministic=None, name=None): """See `Dataset.interleave()` for details.""" self._input_dataset = input_dataset self._map_func = structured_function.StructuredFunctionWrapper( map_func, self._transformation_name(), dataset=input_dataset) if not isinstance(self._map_func.output_structure, DatasetSpec): raise TypeError( "The `map_func` argument must return a `Dataset` object. Got " f"{_get_type(self._map_func.output_structure)!r}.") self._structure = self._map_func.output_structure._element_spec # pylint: disable=protected-access self._cycle_length = ops.convert_to_tensor( cycle_length, dtype=dtypes.int64, name="cycle_length") self._block_length = ops.convert_to_tensor( block_length, dtype=dtypes.int64, name="block_length") self._buffer_output_elements = ops.convert_to_tensor( buffer_output_elements, dtype=dtypes.int64, name="buffer_output_elements") self._prefetch_input_elements = ops.convert_to_tensor( prefetch_input_elements, dtype=dtypes.int64, name="prefetch_input_elements") self._num_parallel_calls = ops.convert_to_tensor( num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls") if deterministic is None: deterministic_string = "default" elif deterministic: deterministic_string = "true" else: deterministic_string = "false" self._name = name variant_tensor = gen_dataset_ops.parallel_interleave_dataset_v4( input_dataset._variant_tensor, # pylint: disable=protected-access self._map_func.function.captured_inputs, # pylint: disable=protected-access self._cycle_length, self._block_length, self._buffer_output_elements, self._prefetch_input_elements, self._num_parallel_calls, f=self._map_func.function, deterministic=deterministic_string, **self._common_args) super(ParallelInterleaveDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._map_func] @property def element_spec(self): return self._structure def _transformation_name(self): return "Dataset.interleave()" class FilterDataset(UnaryUnchangedStructureDataset): """A `Dataset` that filters its input according to a predicate function.""" def __init__(self, input_dataset, predicate, use_legacy_function=False, name=None): """See `Dataset.filter()` for details.""" self._input_dataset = input_dataset wrapped_func = structured_function.StructuredFunctionWrapper( predicate, self._transformation_name(), dataset=input_dataset, use_legacy_function=use_legacy_function) if not wrapped_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.bool)): raise ValueError(f"Invalid `predicate`. `predicate` must return a " f"`tf.bool` scalar tensor, but its return type is " f"{wrapped_func.output_structure}.") self._predicate = wrapped_func self._name = name variant_tensor = gen_dataset_ops.filter_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access other_arguments=self._predicate.function.captured_inputs, predicate=self._predicate.function, **self._common_args) super(FilterDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._predicate] def _transformation_name(self): return "Dataset.filter()" class PrefetchDataset(UnaryUnchangedStructureDataset): """A `Dataset` that asynchronously prefetches its input.""" def __init__(self, input_dataset, buffer_size, slack_period=None, name=None): """See `Dataset.prefetch()` for details.""" self._input_dataset = input_dataset if buffer_size is None: buffer_size = AUTOTUNE self._buffer_size = ops.convert_to_tensor( buffer_size, dtype=dtypes.int64, name="buffer_size") self._name = name # pylint: disable=protected-access # We colocate the prefetch dataset with its input as this collocation only # happens automatically in graph mode. with ops.colocate_with(input_dataset._variant_tensor): variant_tensor = gen_dataset_ops.prefetch_dataset( input_dataset._variant_tensor, buffer_size=self._buffer_size, slack_period=slack_period, **self._common_args) super(PrefetchDataset, self).__init__(input_dataset, variant_tensor) class WindowDataset(UnaryDataset): """A dataset that creates window datasets from the input elements.""" def __init__(self, input_dataset, size, shift, stride, drop_remainder, name=None): """See `window()` for more details.""" self._input_dataset = input_dataset self._size = ops.convert_to_tensor(size, dtype=dtypes.int64, name="size") self._shift = ops.convert_to_tensor(shift, dtype=dtypes.int64, name="shift") self._stride = ops.convert_to_tensor( stride, dtype=dtypes.int64, name="stride") self._drop_remainder = ops.convert_to_tensor( drop_remainder, dtype=dtypes.bool, name="drop_remainder") self._structure = nest.pack_sequence_as( get_legacy_output_classes(input_dataset), [ DatasetSpec( # pylint: disable=g-complex-comprehension structure.convert_legacy_structure( output_type, output_shape, output_class)) for output_class, output_shape, output_type in zip( nest.flatten(get_legacy_output_classes(input_dataset)), nest.flatten(get_legacy_output_shapes(input_dataset)), nest.flatten(get_legacy_output_types(input_dataset))) ]) self._name = name variant_tensor = gen_dataset_ops.window_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access size=self._size, shift=self._shift, stride=self._stride, drop_remainder=self._drop_remainder, **self._common_args) super(WindowDataset, self).__init__(input_dataset, variant_tensor) @property def element_spec(self): return self._structure class _OptionsDataset(UnaryUnchangedStructureDataset): """An identity `Dataset` that stores options.""" def __init__(self, input_dataset, options, name=None): # pylint: disable=protected-access self._input_dataset = input_dataset options_pb = dataset_options_pb2.Options() options_pb.CopyFrom(options._to_proto()) self._name = name with ops.colocate_with(input_dataset._variant_tensor): variant_tensor = gen_dataset_ops.options_dataset( input_dataset._variant_tensor, options_pb.SerializeToString(), **self._common_args) super(_OptionsDataset, self).__init__(input_dataset, variant_tensor) if self._options_attr: self._options_attr._set_mutable(True) self._options_attr = self._options_attr.merge(options) else: self._options_attr = options self._options_attr._set_mutable(False) def normalize_to_dense(dataset): """Normalizes non-tensor components in a dataset to dense representations. This is necessary for dataset transformations that slice along the batch dimension and are oblivious to non-tensors, e.g. `unbatch`, `rebatch`. Args: dataset: Dataset to normalize. Returns: A dataset whose sparse and ragged tensors have been normalized to their dense representations. """ # NOTE(mrry): This leads to a somewhat inefficient re-encoding step for all # non-tensor components. # # TODO(mrry): Consider optimizing this if it turns out to be a bottleneck. if structured_function._should_unpack(dataset.element_spec): # pylint: disable=protected-access def normalize(*args): return structure.to_batched_tensor_list(dataset.element_spec, tuple(args)) else: def normalize(arg): return structure.to_batched_tensor_list(dataset.element_spec, arg) normalized_dataset = dataset.map(normalize) # NOTE(mrry): Our `map()` has lost information about the structure of # non-tensor components, so re-apply the structure of the original dataset. return _RestructuredDataset(normalized_dataset, dataset.element_spec) class _RestructuredDataset(UnaryDataset): """An internal helper for changing the element spec of a dataset.""" def __init__(self, dataset, element_spec): self._input_dataset = dataset self._element_spec = element_spec variant_tensor = self._input_dataset._variant_tensor # pylint: disable=protected-access super(_RestructuredDataset, self).__init__(dataset, variant_tensor) @property def element_spec(self): return self._element_spec class _UnbatchDataset(UnaryDataset): """A dataset that splits the elements of its input into multiple elements.""" def __init__(self, input_dataset, name=None): """See `unbatch()` for more details.""" flat_shapes = input_dataset._flat_shapes # pylint: disable=protected-access if any(s.ndims == 0 for s in flat_shapes): raise ValueError("Cannot unbatch an input with scalar components.") known_batch_dim = tensor_shape.Dimension(None) for s in flat_shapes: try: known_batch_dim = known_batch_dim.merge_with(s[0]) except ValueError: raise ValueError(f"`unbatch()` is only supported for datasets of " f"elements whose components have a matching leading " f"dimension. Encountered both {known_batch_dim} and " f"{s[0]}.") self._input_dataset = input_dataset self._structure = nest.map_structure( lambda component_spec: component_spec._unbatch(), # pylint: disable=protected-access get_structure(input_dataset)) self._name = name variant_tensor = ged_ops.unbatch_dataset( self._input_dataset._variant_tensor, # pylint: disable=protected-access **self._common_args) super(_UnbatchDataset, self).__init__(input_dataset, variant_tensor) @property def element_spec(self): return self._structure class _GroupByWindowDataset(UnaryDataset): """A `Dataset` that groups its input and performs a windowed reduction.""" def __init__(self, input_dataset, key_func, reduce_func, window_size_func, name=None): """See `group_by_window()` for details.""" self._input_dataset = input_dataset self._make_key_func(key_func, input_dataset) self._make_reduce_func(reduce_func, input_dataset) self._make_window_size_func(window_size_func) self._name = name variant_tensor = ged_ops.group_by_window_dataset( self._input_dataset._variant_tensor, # pylint: disable=protected-access self._key_func.function.captured_inputs, self._reduce_func.function.captured_inputs, self._window_size_func.function.captured_inputs, key_func=self._key_func.function, reduce_func=self._reduce_func.function, window_size_func=self._window_size_func.function, **self._common_args) super(_GroupByWindowDataset, self).__init__(input_dataset, variant_tensor) def _make_window_size_func(self, window_size_func): """Make wrapping defun for window_size_func.""" def window_size_func_wrapper(key): return ops.convert_to_tensor(window_size_func(key), dtype=dtypes.int64) self._window_size_func = structured_function.StructuredFunctionWrapper( window_size_func_wrapper, self._transformation_name(), input_structure=tensor_spec.TensorSpec([], dtypes.int64)) if not self._window_size_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.int64)): raise ValueError(f"Invalid `window_size_func`. `window_size_func` must " f"return a single `tf.int64` scalar tensor but its " f"return type is " f"{self._window_size_func.output_structure}.") def _make_key_func(self, key_func, input_dataset): """Make wrapping defun for key_func.""" def key_func_wrapper(*args): return ops.convert_to_tensor(key_func(*args), dtype=dtypes.int64) self._key_func = structured_function.StructuredFunctionWrapper( key_func_wrapper, self._transformation_name(), dataset=input_dataset) if not self._key_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.int64)): raise ValueError(f"Invalid `key_func`. `key_func` must return a single " f"`tf.int64` scalar tensor but its return type is " f"{self._key_func.output_structure}.") def _make_reduce_func(self, reduce_func, input_dataset): """Make wrapping defun for reduce_func.""" nested_dataset = DatasetSpec(input_dataset.element_spec) input_structure = (tensor_spec.TensorSpec([], dtypes.int64), nested_dataset) self._reduce_func = structured_function.StructuredFunctionWrapper( reduce_func, self._transformation_name(), input_structure=input_structure) if not isinstance(self._reduce_func.output_structure, DatasetSpec): raise TypeError(f"Invalid `reduce_func`. `reduce_func` must return a " f"single `tf.data.Dataset` object but its return type " f"is {self._reduce_func.output_structure}.") # pylint: disable=protected-access self._element_spec = (self._reduce_func.output_structure._element_spec) @property def element_spec(self): return self._element_spec def _functions(self): return [self._key_func, self._reduce_func, self._window_size_func] def _transformation_name(self): return "Dataset.group_by_window()" class RandomDataset(DatasetSource): """A `Dataset` of pseudorandom values.""" def __init__(self, seed=None, name=None): """A `Dataset` of pseudorandom values.""" self._seed, self._seed2 = random_seed.get_seed(seed) self._name = name variant_tensor = ged_ops.random_dataset( seed=self._seed, seed2=self._seed2, **self._common_args) super(RandomDataset, self).__init__(variant_tensor) @property def element_spec(self): return tensor_spec.TensorSpec([], dtypes.int64) def _get_prob_original_static(initial_dist_t, target_dist_t): """Returns the static probability of sampling from the original. `tensor_util.constant_value(prob_of_original)` returns `None` if it encounters an Op that it isn't defined for. We have some custom logic to avoid this. Args: initial_dist_t: A tensor of the initial distribution. target_dist_t: A tensor of the target distribution. Returns: The probability of sampling from the original distribution as a constant, if it is a constant, or `None`. """ init_static = tensor_util.constant_value(initial_dist_t) target_static = tensor_util.constant_value(target_dist_t) if init_static is None or target_static is None: return None else: return np.min(target_static / init_static) def _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds, class_func, seed, name=None): """Filters a dataset based on per-class acceptance probabilities. Args: dataset: The dataset to be filtered. acceptance_dist_ds: A dataset of acceptance probabilities. initial_dist_ds: A dataset of the initial probability distribution, given or estimated. class_func: A function mapping an element of the input dataset to a scalar `tf.int32` tensor. Values should be in `[0, num_classes)`. seed: (Optional.) Python integer seed for the resampler. name: (Optional.) A name for the tf.data operation. Returns: A dataset of (class value, data) after filtering. """ 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 = ( DatasetV2.zip((acceptance_dist_ds, initial_dist_ds), name=name).map(maybe_warn_on_large_rejection, name=name)) def _gather_and_copy(acceptance_prob, data): if isinstance(data, tuple): class_val = class_func(*data) else: class_val = class_func(data) return class_val, array_ops.gather(acceptance_prob, class_val), data current_probabilities_and_class_and_data_ds = DatasetV2.zip( (acceptance_dist_ds, dataset), name=name).map( _gather_and_copy, name=name) def _reject(unused_class_val, p, unused_data): return random_ops.random_uniform([], seed=seed, dtype=p.dtype) < p filtered_ds = current_probabilities_and_class_and_data_ds.filter( _reject, name=name) return filtered_ds.map( lambda class_value, _, data: (class_value, data), name=name) # pylint: disable=missing-function-docstring def _estimate_initial_dist_ds(target_dist_t, class_values_ds, dist_estimation_batch_size=32, smoothing_constant=10, name=None): num_classes = (target_dist_t.shape[0] or array_ops.shape(target_dist_t)[0]) initial_examples_per_class_seen = array_ops.fill([num_classes], np.int64(smoothing_constant)) def update_estimate_and_tile(num_examples_per_class_seen, c): updated_examples_per_class_seen, dist = _estimate_data_distribution( c, num_examples_per_class_seen) tiled_dist = array_ops.tile( array_ops.expand_dims(dist, 0), [dist_estimation_batch_size, 1]) return updated_examples_per_class_seen, tiled_dist initial_dist_ds = ( class_values_ds.batch(dist_estimation_batch_size, name=name).scan( initial_examples_per_class_seen, update_estimate_and_tile, name=name).unbatch(name=name)) return initial_dist_ds def _get_target_to_initial_ratio(initial_probs, target_probs): # Add tiny to initial_probs to avoid divide by zero. denom = (initial_probs + np.finfo(initial_probs.dtype.as_numpy_dtype).tiny) return target_probs / denom 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: Type `int64`, shape `[num_classes]`, containing counts. Returns: num_examples_per_lass_seen: Updated counts. Type `int64`, shape `[num_classes]`. dist: The updated distribution. Type `float32`, shape `[num_classes]`. """ num_classes = num_examples_per_class_seen.get_shape()[0] # Update the class-count based on what labels are seen in batch. num_examples_per_class_seen = math_ops.add( num_examples_per_class_seen, 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)) dist = math_ops.cast(init_prob_estimate, dtypes.float32) return num_examples_per_class_seen, dist def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): """Calculates the acceptance probabilities and mixing ratio. In this case, we assume that we can *either* sample from the original data distribution with probability `m`, or sample from a reshaped distribution that comes from rejection sampling on the original distribution. This rejection sampling is done on a per-class basis, with `a_i` representing the probability of accepting data from class `i`. This method is based on solving the following analysis for the reshaped distribution: 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 variables is the following: ```a_i = (t_i / p_i) / max_i[t_i / p_i]``` If we try to minimize the amount of data rejected, we get the following: M_max = max_i [ t_i / p_i ] M_min = min_i [ t_i / p_i ] The desired probability of accepting data if it comes from class `i`: a_i = (t_i/p_i - m) / (M_max - m) The desired probability of pulling a data element from the original dataset, rather than the filtered one: m = M_min Args: initial_probs: A Tensor of the initial probability distribution, given or estimated. target_probs: A Tensor of the corresponding classes. Returns: (A 1D Tensor with the per-class acceptance probabilities, the desired probability of pull from the original distribution.) """ ratio_l = _get_target_to_initial_ratio(initial_probs, target_probs) max_ratio = math_ops.reduce_max(ratio_l) min_ratio = math_ops.reduce_min(ratio_l) # Target prob to sample from original distribution. m = min_ratio # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) return a_i, m class _TakeWhileDataset(UnaryUnchangedStructureDataset): """A dataset that stops iteration when `predicate` returns false.""" def __init__(self, input_dataset, predicate, name=None): """See `take_while()` for details.""" self._input_dataset = input_dataset wrapped_func = structured_function.StructuredFunctionWrapper( predicate, self._transformation_name(), dataset=self._input_dataset) if not wrapped_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.bool)): raise ValueError(f"Invalid `predicate`. `predicate` must return a " f"`tf.bool` scalar tensor but its return type is" f"{wrapped_func.output_structure}.") self._predicate = wrapped_func self._name = name variant_tensor = ged_ops.take_while_dataset( self._input_dataset._variant_tensor, # pylint: disable=protected-access other_arguments=self._predicate.function.captured_inputs, predicate=self._predicate.function, **self._common_args) super(_TakeWhileDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._predicate] def _transformation_name(self): return "Dataset.take_while()" class _UniqueDataset(UnaryUnchangedStructureDataset): """A `Dataset` contains the unique elements from its input.""" def __init__(self, input_dataset, name=None): """See `unique()` for details.""" self._input_dataset = input_dataset for ty in nest.flatten(get_legacy_output_types(input_dataset)): if ty not in (dtypes.int32, dtypes.int64, dtypes.string): raise TypeError( f"`unique()` does not support type {ty}, only `tf.int32`, " f"`tf.int64`, and `tf.string` are supported.") self._name = name variant_tensor = ged_ops.unique_dataset( self._input_dataset._variant_tensor, # pylint: disable=protected-access **self._common_args) super(_UniqueDataset, self).__init__(input_dataset, variant_tensor) class _SnapshotDataset(UnaryUnchangedStructureDataset): """A dataset that allows saving and re-use of already processed data.""" def __init__(self, input_dataset, path, shard_func, compression=None, reader_func=None, pending_snapshot_expiry_seconds=None, use_legacy_function=False, name=None): if reader_func is None: reader_func = lambda datasets: datasets.interleave( # pylint:disable=g-long-lambda lambda x: x, cycle_length=multiprocessing.cpu_count(), num_parallel_calls=AUTOTUNE) self._input_dataset = input_dataset self._path = path self._compression = compression self._reader_func = structured_function.StructuredFunctionWrapper( reader_func, self._transformation_name() + ".reader_func", # Dataset of datasets of input elements input_structure=DatasetSpec(DatasetSpec(input_dataset.element_spec)), use_legacy_function=use_legacy_function) self._shard_func = structured_function.StructuredFunctionWrapper( shard_func, self._transformation_name() + ".shard_func", dataset=input_dataset, use_legacy_function=use_legacy_function) if ((not self._shard_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.int32))) and (not self._shard_func.output_structure.is_compatible_with( tensor_spec.TensorSpec([], dtypes.int64)))): raise TypeError(f"Invalid `shard_func`. `shard_func` must return " f"`tf.int64` scalar tensor but its return type is " f"{self._shard_func.output_structure}.") self._name = name variant_tensor = ged_ops.snapshot_dataset_v2( input_dataset._variant_tensor, # pylint: disable=protected-access path, self._reader_func.function.captured_inputs, self._shard_func.function.captured_inputs, compression=compression, reader_func=self._reader_func.function, shard_func=self._shard_func.function, **self._common_args) super(_SnapshotDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._reader_func, self._shard_func] def _transformation_name(self): return "Dataset.snapshot()" class _ScanDataset(UnaryDataset): """A dataset that scans a function across its input.""" def __init__(self, input_dataset, initial_state, scan_func, use_default_device=None, name=None): """See `scan()` for details.""" self._input_dataset = input_dataset self._initial_state = structure.normalize_element(initial_state) # Compute initial values for the state classes, shapes and types based on # the initial state. The shapes may be refined by running `tf_scan_func` one # or more times below. self._state_structure = structure.type_spec_from_value(self._initial_state) # Iteratively rerun the scan function until reaching a fixed point on # `self._state_shapes`. need_to_rerun = True while need_to_rerun: wrapped_func = structured_function.StructuredFunctionWrapper( scan_func, self._transformation_name(), input_structure=(self._state_structure, input_dataset.element_spec), add_to_graph=False) if not (isinstance(wrapped_func.output_types, collections_abc.Sequence) and len(wrapped_func.output_types) == 2): raise TypeError(f"Invalid `scan_func`. `scan_func` should return a " f"pair consisting of new state and the output value " f"but its return type is " f"{wrapped_func.output_structure}.") new_state_classes, self._output_classes = wrapped_func.output_classes # Extract and validate class information from the returned values. new_state_classes, output_classes = wrapped_func.output_classes old_state_classes = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access self._state_structure) for new_state_class, old_state_class in zip( nest.flatten(new_state_classes), nest.flatten(old_state_classes)): if not issubclass(new_state_class, old_state_class): raise TypeError(f"Invalid `scan_func`. The element classes for the " f"new state must match the initial state. Expected " f"{old_state_classes}, got {new_state_classes}.") # Extract and validate type information from the returned values. new_state_types, output_types = wrapped_func.output_types old_state_types = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access self._state_structure) for new_state_type, old_state_type in zip( nest.flatten(new_state_types), nest.flatten(old_state_types)): if new_state_type != old_state_type: raise TypeError(f"Invalid `scan_func`. The element types for the " f"new state must match the initial state. Expected " f"{old_state_types}, got {new_state_types}.") # Extract shape information from the returned values. new_state_shapes, output_shapes = wrapped_func.output_shapes old_state_shapes = nest.map_structure( lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access self._state_structure) self._element_spec = structure.convert_legacy_structure( output_types, output_shapes, output_classes) flat_state_shapes = nest.flatten(old_state_shapes) flat_new_state_shapes = nest.flatten(new_state_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) for original, new in zip(flat_state_shapes, flat_new_state_shapes) ] need_to_rerun = False for original_shape, weakened_shape in zip(flat_state_shapes, weakened_state_shapes): if original_shape.ndims is not None and ( weakened_shape.ndims is None or original_shape.as_list() != weakened_shape.as_list()): need_to_rerun = True break if need_to_rerun: # TODO(b/110122868): Support a "most specific compatible structure" # method for combining structures, to avoid using legacy structures # in this method. self._state_structure = structure.convert_legacy_structure( old_state_types, nest.pack_sequence_as(old_state_shapes, weakened_state_shapes), old_state_classes) self._scan_func = wrapped_func self._scan_func.function.add_to_graph(ops.get_default_graph()) self._name = name # pylint: disable=protected-access if use_default_device is not None: variant_tensor = ged_ops.scan_dataset( self._input_dataset._variant_tensor, structure.to_tensor_list(self._state_structure, self._initial_state), self._scan_func.function.captured_inputs, f=self._scan_func.function, preserve_cardinality=True, use_default_device=use_default_device, **self._common_args) else: variant_tensor = ged_ops.scan_dataset( self._input_dataset._variant_tensor, structure.to_tensor_list(self._state_structure, self._initial_state), self._scan_func.function.captured_inputs, f=self._scan_func.function, preserve_cardinality=True, **self._common_args) super(_ScanDataset, self).__init__(input_dataset, variant_tensor) def _functions(self): return [self._scan_func] @property def element_spec(self): return self._element_spec def _transformation_name(self): return "Dataset.scan()" class _DirectedInterleaveDataset(DatasetV2): """A substitute for `Dataset.interleave()` on a fixed list of datasets.""" def __init__(self, selector_input, data_inputs, stop_on_empty_dataset=False): self._selector_input = selector_input self._data_inputs = list(data_inputs) self._stop_on_empty_dataset = stop_on_empty_dataset spec = self._data_inputs[0].element_spec for i, data_input in enumerate(self._data_inputs[1:]): def common_supertype(a, b): result = a.most_specific_common_supertype([b]) if result is None: raise TypeError(f"No common supertype of {a} and {b}.") return result try: spec = nest.map_structure(common_supertype, spec, data_input.element_spec) except (TypeError, ValueError) as e: raise TypeError(f"Invalid `datasets`. `datasets` must have compatible " f"element specs.\n Dataset 0 " f"element_spec={data_inputs[0].element_spec}.\n" f"Dataset {i+1} " f"element_spec={data_input.element_spec}.") from e self._element_spec = spec # pylint: disable=protected-access variant_tensor = ( ged_ops.directed_interleave_dataset( self._selector_input._variant_tensor, [data_input._variant_tensor for data_input in self._data_inputs], stop_on_empty_dataset=self._stop_on_empty_dataset, **self._flat_structure)) super(_DirectedInterleaveDataset, self).__init__(variant_tensor) def _inputs(self): return [self._selector_input] + self._data_inputs @property def element_spec(self): return self._element_spec def _apply_rewrite(dataset, rewrite): # pylint: disable=protected-access return _VariantDataset( gen_dataset_ops.rewrite_dataset(dataset._variant_tensor, rewrite, **dataset._flat_structure), dataset.element_spec) def _collect_resource_inputs(op): """Collects resource inputs for the given ops (and its variant inputs).""" def _process(op_queue, seen_ops): """Processes the next element of the op queue. Args: op_queue: Queue of Dataset operations to process. seen_ops: Already processed set of Operations. Returns: A 2-tuple containing sets of resource handles. The first tuple entry contains read-only handles and the second entry contains read-write handles. """ reads = [] writes = [] op = op_queue.pop() if op in seen_ops: return reads, writes seen_ops.add(op) # TODO(b/150139257): All resource inputs are in writes right now since we # have not updated the functional ops to set the special attribute that ACD # uses to figure out which of the op's inputs are read-only. reads, writes = acd_utils.get_read_write_resource_inputs(op) # Conservatively assume that any variant inputs are datasets. op_queue.extend(t.op for t in op.inputs if t.dtype == dtypes.variant) return reads, writes op_queue = [op] seen_ops = set() all_reads = [] all_writes = [] while op_queue: reads, writes = _process(op_queue, seen_ops) all_reads.extend(reads) all_writes.extend(writes) return all_reads, all_writes @auto_control_deps.register_acd_resource_resolver def _resource_resolver(op, resource_reads, resource_writes): """Updates resource inputs for tf.data ops with indirect dependencies.""" updated = False if op.type in [ "DatasetToSingleElement", "DatasetToTFRecord", "ReduceDataset" ]: reads, writes = _collect_resource_inputs(op) for inp in reads: if inp not in resource_reads: updated = True resource_reads.add(inp) for inp in writes: if inp not in resource_writes: updated = True resource_writes.add(inp) if op.type in [ "IteratorGetNext", "IteratorGetNextSync", "IteratorGetNextAsOptional" ]: iterator_resource = op.inputs[0] make_iterator_ops = [ op for op in iterator_resource.consumers() if op.type == "MakeIterator" ] if len(make_iterator_ops) == 1: reads, writes = _collect_resource_inputs(make_iterator_ops[0]) for inp in reads: if inp not in resource_reads: updated = True resource_reads.add(inp) for inp in writes: if inp not in resource_writes: updated = True resource_writes.add(inp) return updated DEBUG_MODE = False @tf_export("data.experimental.enable_debug_mode") def enable_debug_mode(): """Enables debug mode for tf.data. Example usage with pdb module: ``` import tensorflow as tf import pdb tf.data.experimental.enable_debug_mode() def func(x): # Python 3.7 and older requires `pdb.Pdb(nosigint=True).set_trace()` pdb.set_trace() x = x + 1 return x dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) dataset = dataset.map(func) for item in dataset: print(item) ``` The effect of debug mode is two-fold: 1) Any transformations that would introduce asynchrony, parallelism, or non-determinism to the input pipeline execution will be forced to execute synchronously, sequentially, and deterministically. 2) Any user-defined functions passed into tf.data transformations such as `map` will be wrapped in `tf.py_function` so that their body is executed "eagerly" as a Python function as opposed to a traced TensorFlow graph, which is the default behavior. Note that even when debug mode is enabled, the user-defined function is still traced to infer the shape and type of its outputs; as a consequence, any `print` statements or breakpoints will be triggered once during the tracing before the actual execution of the input pipeline. NOTE: As the debug mode setting affects the construction of the tf.data input pipeline, it should be enabled before any tf.data definitions. Raises: ValueError: When invoked from graph mode. """ if context.executing_eagerly(): toggle_debug_mode(True) else: raise ValueError("`enable_debug_mode() is only supported in eager mode.") def toggle_debug_mode(debug_mode): global DEBUG_MODE DEBUG_MODE = debug_mode