269 lines
10 KiB
Python
269 lines
10 KiB
Python
import numpy as np
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from keras_core.api_export import keras_core_export
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from keras_core.utils import dataset_utils
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from keras_core.utils.module_utils import tensorflow as tf
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@keras_core_export(
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[
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"keras_core.utils.text_dataset_from_directory",
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"keras_core.preprocessing.text_dataset_from_directory",
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]
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)
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def text_dataset_from_directory(
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directory,
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labels="inferred",
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label_mode="int",
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class_names=None,
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batch_size=32,
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max_length=None,
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shuffle=True,
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seed=None,
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validation_split=None,
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subset=None,
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follow_links=False,
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):
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"""Generates a `tf.data.Dataset` from text files in a directory.
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If your directory structure is:
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```
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main_directory/
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...class_a/
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......a_text_1.txt
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......a_text_2.txt
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...class_b/
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......b_text_1.txt
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......b_text_2.txt
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```
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Then calling `text_dataset_from_directory(main_directory,
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labels='inferred')` will return a `tf.data.Dataset` that yields batches of
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texts from the subdirectories `class_a` and `class_b`, together with labels
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0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
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Only `.txt` files are supported at this time.
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Args:
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directory: Directory where the data is located.
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If `labels` is `"inferred"`, it should contain
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subdirectories, each containing text files for a class.
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Otherwise, the directory structure is ignored.
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labels: Either `"inferred"`
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(labels are generated from the directory structure),
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`None` (no labels),
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or a list/tuple of integer labels of the same size as the number of
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text files found in the directory. Labels should be sorted according
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to the alphanumeric order of the text file paths
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(obtained via `os.walk(directory)` in Python).
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label_mode: String describing the encoding of `labels`. Options are:
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- `"int"`: means that the labels are encoded as integers
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(e.g. for `sparse_categorical_crossentropy` loss).
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- `"categorical"` means that the labels are
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encoded as a categorical vector
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(e.g. for `categorical_crossentropy` loss).
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- `"binary"` means that the labels (there can be only 2)
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are encoded as `float32` scalars with values 0 or 1
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(e.g. for `binary_crossentropy`).
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- `None` (no labels).
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class_names: Only valid if `"labels"` is `"inferred"`.
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This is the explicit list of class names
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(must match names of subdirectories). Used to control the order
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of the classes (otherwise alphanumerical order is used).
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batch_size: Size of the batches of data. Defaults to 32.
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If `None`, the data will not be batched
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(the dataset will yield individual samples).
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max_length: Maximum size of a text string. Texts longer than this will
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be truncated to `max_length`.
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shuffle: Whether to shuffle the data. Defaults to `True`.
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If set to `False`, sorts the data in alphanumeric order.
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seed: Optional random seed for shuffling and transformations.
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validation_split: Optional float between 0 and 1,
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fraction of data to reserve for validation.
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subset: Subset of the data to return.
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One of `"training"`, `"validation"` or `"both"`.
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Only used if `validation_split` is set.
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When `subset="both"`, the utility returns a tuple of two datasets
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(the training and validation datasets respectively).
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follow_links: Whether to visits subdirectories pointed to by symlinks.
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Defaults to `False`.
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Returns:
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A `tf.data.Dataset` object.
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- If `label_mode` is `None`, it yields `string` tensors of shape
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`(batch_size,)`, containing the contents of a batch of text files.
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- Otherwise, it yields a tuple `(texts, labels)`, where `texts`
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has shape `(batch_size,)` and `labels` follows the format described
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below.
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Rules regarding labels format:
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- if `label_mode` is `int`, the labels are an `int32` tensor of shape
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`(batch_size,)`.
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- if `label_mode` is `binary`, the labels are a `float32` tensor of
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1s and 0s of shape `(batch_size, 1)`.
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- if `label_mode` is `categorical`, the labels are a `float32` tensor
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of shape `(batch_size, num_classes)`, representing a one-hot
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encoding of the class index.
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"""
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if labels not in ("inferred", None):
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if not isinstance(labels, (list, tuple)):
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raise ValueError(
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"`labels` argument should be a list/tuple of integer labels, "
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"of the same size as the number of text files in the target "
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"directory. If you wish to infer the labels from the "
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"subdirectory names in the target directory, "
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'pass `labels="inferred"`. '
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"If you wish to get a dataset that only contains text samples "
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f"(no labels), pass `labels=None`. Received: labels={labels}"
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)
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if class_names:
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raise ValueError(
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"You can only pass `class_names` if "
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f'`labels="inferred"`. Received: labels={labels}, and '
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f"class_names={class_names}"
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)
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if label_mode not in {"int", "categorical", "binary", None}:
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raise ValueError(
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'`label_mode` argument must be one of "int", '
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'"categorical", "binary", '
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f"or None. Received: label_mode={label_mode}"
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)
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if labels is None or label_mode is None:
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labels = None
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label_mode = None
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dataset_utils.check_validation_split_arg(
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validation_split, subset, shuffle, seed
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)
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if seed is None:
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seed = np.random.randint(1e6)
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file_paths, labels, class_names = dataset_utils.index_directory(
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directory,
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labels,
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formats=(".txt",),
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class_names=class_names,
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shuffle=shuffle,
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seed=seed,
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follow_links=follow_links,
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)
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if label_mode == "binary" and len(class_names) != 2:
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raise ValueError(
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'When passing `label_mode="binary"`, there must be exactly 2 '
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f"class_names. Received: class_names={class_names}"
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)
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if subset == "both":
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(
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file_paths_train,
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labels_train,
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) = dataset_utils.get_training_or_validation_split(
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file_paths, labels, validation_split, "training"
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)
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(
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file_paths_val,
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labels_val,
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) = dataset_utils.get_training_or_validation_split(
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file_paths, labels, validation_split, "validation"
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)
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if not file_paths_train:
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raise ValueError(
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f"No training text files found in directory {directory}. "
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"Allowed format: .txt"
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)
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if not file_paths_val:
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raise ValueError(
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f"No validation text files found in directory {directory}. "
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"Allowed format: .txt"
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)
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train_dataset = paths_and_labels_to_dataset(
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file_paths=file_paths_train,
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labels=labels_train,
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label_mode=label_mode,
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num_classes=len(class_names),
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max_length=max_length,
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)
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val_dataset = paths_and_labels_to_dataset(
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file_paths=file_paths_val,
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labels=labels_val,
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label_mode=label_mode,
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num_classes=len(class_names),
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max_length=max_length,
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)
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train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
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val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
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if batch_size is not None:
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if shuffle:
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# Shuffle locally at each iteration
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train_dataset = train_dataset.shuffle(
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buffer_size=batch_size * 8, seed=seed
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)
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train_dataset = train_dataset.batch(batch_size)
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val_dataset = val_dataset.batch(batch_size)
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else:
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if shuffle:
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train_dataset = train_dataset.shuffle(
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buffer_size=1024, seed=seed
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)
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# Users may need to reference `class_names`.
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train_dataset.class_names = class_names
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val_dataset.class_names = class_names
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dataset = [train_dataset, val_dataset]
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else:
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file_paths, labels = dataset_utils.get_training_or_validation_split(
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file_paths, labels, validation_split, subset
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)
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if not file_paths:
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raise ValueError(
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f"No text files found in directory {directory}. "
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"Allowed format: .txt"
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)
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dataset = paths_and_labels_to_dataset(
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file_paths=file_paths,
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labels=labels,
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label_mode=label_mode,
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num_classes=len(class_names),
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max_length=max_length,
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)
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dataset = dataset.prefetch(tf.data.AUTOTUNE)
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if batch_size is not None:
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if shuffle:
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# Shuffle locally at each iteration
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dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
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dataset = dataset.batch(batch_size)
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else:
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if shuffle:
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dataset = dataset.shuffle(buffer_size=1024, seed=seed)
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# Users may need to reference `class_names`.
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dataset.class_names = class_names
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return dataset
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def paths_and_labels_to_dataset(
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file_paths, labels, label_mode, num_classes, max_length
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):
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"""Constructs a dataset of text strings and labels."""
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path_ds = tf.data.Dataset.from_tensor_slices(file_paths)
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string_ds = path_ds.map(
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lambda x: path_to_string_content(x, max_length),
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num_parallel_calls=tf.data.AUTOTUNE,
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)
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if label_mode:
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label_ds = dataset_utils.labels_to_dataset(
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labels, label_mode, num_classes
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)
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string_ds = tf.data.Dataset.zip((string_ds, label_ds))
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return string_ds
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def path_to_string_content(path, max_length):
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txt = tf.io.read_file(path)
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if max_length is not None:
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txt = tf.strings.substr(txt, 0, max_length)
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return txt
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