def image_dataset_from_directory(directory,
labels='inferred',
label_mode='int',
class_names=None,
color_mode='rgb',
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation='bilinear',
follow_links=False,
crop_to_aspect_ratio=False,
**kwargs):
"""Generates a `tf.data.Dataset` from image files in a directory.
If your directory structure is:
```
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
```
Then calling `image_dataset_from_directory(main_directory, labels='inferred')`
will return a `tf.data.Dataset` that yields batches of images from
the subdirectories `class_a` and `class_b`, together with labels
0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
Supported image formats: jpeg, png, bmp, gif.
Animated gifs are truncated to the first frame.
Args:
directory: Directory where the data is located.
If `labels` is "inferred", it should contain
subdirectories, each containing images for a class.
Otherwise, the directory structure is ignored.
labels: Either "inferred"
(labels are generated from the directory structure),
None (no labels),
or a list/tuple of integer labels of the same size as the number of
image files found in the directory. Labels should be sorted according
to the alphanumeric order of the image file paths
(obtained via `os.walk(directory)` in Python).
label_mode: String describing the encoding of `labels`. Options are:
- 'int': means that the labels are encoded as integers
(e.g. for `sparse_categorical_crossentropy` loss).
- 'categorical' means that the labels are
encoded as a categorical vector
(e.g. for `categorical_crossentropy` loss).
- 'binary' means that the labels (there can be only 2)
are encoded as `float32` scalars with values 0 or 1
(e.g. for `binary_crossentropy`).
- None (no labels).
class_names: Only valid if "labels" is "inferred". This is the explicit
list of class names (must match names of subdirectories). Used
to control the order of the classes
(otherwise alphanumerical order is used).
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
Whether the images will be converted to
have 1, 3, or 4 channels.
batch_size: Size of the batches of data. Default: 32.
If `None`, the data will not be batched
(the dataset will yield individual samples).
image_size: Size to resize images to after they are read from disk,
specified as `(height, width)`. Defaults to `(256, 256)`.
Since the pipeline processes batches of images that must all have
the same size, this must be provided.
shuffle: Whether to shuffle the data. Default: True.
If set to False, sorts the data in alphanumeric order.
seed: Optional random seed for shuffling and transformations.
validation_split: Optional float between 0 and 1,
fraction of data to reserve for validation.
subset: Subset of the data to return.
One of "training" or "validation".
Only used if `validation_split` is set.
interpolation: String, the interpolation method used when resizing images.
Defaults to `bilinear`. Supports `bilinear`, `nearest`, `bicubic`,
`area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.
follow_links: Whether to visits subdirectories pointed to by symlinks.
Defaults to False.
crop_to_aspect_ratio: If True, resize the images without aspect
ratio distortion. When the original aspect ratio differs from the target
aspect ratio, the output image will be cropped so as to return the largest
possible window in the image (of size `image_size`) that matches
the target aspect ratio. By default (`crop_to_aspect_ratio=False`),
aspect ratio may not be preserved.
**kwargs: Legacy keyword arguments.
Returns:
A `tf.data.Dataset` object.
- If `label_mode` is None, it yields `float32` tensors of shape
`(batch_size, image_size[0], image_size[1], num_channels)`,
encoding images (see below for rules regarding `num_channels`).
- Otherwise, it yields a tuple `(images, labels)`, where `images`
has shape `(batch_size, image_size[0], image_size[1], num_channels)`,
and `labels` follows the format described below.
Rules regarding labels format:
- if `label_mode` is `int`, the labels are an `int32` tensor of shape
`(batch_size,)`.
- if `label_mode` is `binary`, the labels are a `float32` tensor of
1s and 0s of shape `(batch_size, 1)`.
- if `label_mode` is `categorical`, the labels are a `float32` tensor
of shape `(batch_size, num_classes)`, representing a one-hot
encoding of the class index.
Rules regarding number of channels in the yielded images:
- if `color_mode` is `grayscale`,
there's 1 channel in the image tensors.
- if `color_mode` is `rgb`,
there are 3 channel in the image tensors.
- if `color_mode` is `rgba`,
there are 4 channel in the image tensors.
"""
if 'smart_resize' in kwargs:
crop_to_aspect_ratio = kwargs.pop('smart_resize')
if kwargs:
raise TypeError(
f'Unknown keywords argument(s): {tuple(kwargs.keys())}')
if labels not in ('inferred', None):
if not isinstance(labels, (list, tuple)):
raise ValueError(
'`labels` argument should be a list/tuple of integer labels, of '
'the same size as the number of image files in the target '
'directory. If you wish to infer the labels from the subdirectory '
'names in the target directory, pass `labels="inferred"`. '
'If you wish to get a dataset that only contains images '
f'(no labels), pass `labels=None`. Received: labels={labels}')
if class_names:
raise ValueError(
'You can only pass `class_names` if '
f'`labels="inferred"`. Received: labels={labels}, and '
f'class_names={class_names}')
if label_mode not in {'int', 'categorical', 'binary', None}:
raise ValueError(
'`label_mode` argument must be one of "int", "categorical", "binary", '
f'or None. Received: label_mode={label_mode}')
if labels is None or label_mode is None:
labels = None
label_mode = None
if color_mode == 'rgb':
num_channels = 3
elif color_mode == 'rgba':
num_channels = 4
elif color_mode == 'grayscale':
num_channels = 1
else:
raise ValueError(
'`color_mode` must be one of {"rgb", "rgba", "grayscale"}. '
f'Received: color_mode={color_mode}')
interpolation = image_utils.get_interpolation(interpolation)
dataset_utils.check_validation_split_arg(validation_split, subset, shuffle,
seed)
if seed is None:
seed = np.random.randint(1e6)
image_paths, labels, class_names = dataset_utils.index_directory(
directory,
labels,
formats=ALLOWLIST_FORMATS,
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links)
if label_mode == 'binary' and len(class_names) != 2:
raise ValueError(
f'When passing `label_mode="binary"`, there must be exactly 2 '
f'class_names. Received: class_names={class_names}')
image_paths, labels = dataset_utils.get_training_or_validation_split(
image_paths, labels, validation_split, subset)
if not image_paths:
raise ValueError(f'No images found in directory {directory}. '
f'Allowed formats: {ALLOWLIST_FORMATS}')
dataset = paths_and_labels_to_dataset(
image_paths=image_paths,
image_size=image_size,
num_channels=num_channels,
labels=labels,
label_mode=label_mode,
num_classes=len(class_names),
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
if batch_size is not None:
if shuffle:
# Shuffle locally at each iteration
dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
dataset = dataset.batch(batch_size)
else:
if shuffle:
dataset = dataset.shuffle(buffer_size=1024, seed=seed)
# Users may need to reference `class_names`.
dataset.class_names = class_names
# Include file paths for images as attribute.
dataset.file_paths = image_paths
return dataset
def text_dataset_from_directory(
directory,
labels="inferred",
label_mode="int",
class_names=None,
batch_size=32,
max_length=None,
shuffle=True,
seed=None,
validation_split=None,
subset=None,
follow_links=False,
):
"""Generates a `tf.data.Dataset` from text files in a directory.
If your directory structure is:
```
main_directory/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt
```
Then calling `text_dataset_from_directory(main_directory, labels='inferred')`
will return a `tf.data.Dataset` that yields batches of texts from
the subdirectories `class_a` and `class_b`, together with labels
0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
Only `.txt` files are supported at this time.
Args:
directory: Directory where the data is located.
If `labels` is "inferred", it should contain
subdirectories, each containing text files for a class.
Otherwise, the directory structure is ignored.
labels: Either "inferred"
(labels are generated from the directory structure),
None (no labels),
or a list/tuple of integer labels of the same size as the number of
text files found in the directory. Labels should be sorted according
to the alphanumeric order of the text file paths
(obtained via `os.walk(directory)` in Python).
label_mode: String describing the encoding of `labels`. Options are:
- 'int': means that the labels are encoded as integers
(e.g. for `sparse_categorical_crossentropy` loss).
- 'categorical' means that the labels are
encoded as a categorical vector
(e.g. for `categorical_crossentropy` loss).
- 'binary' means that the labels (there can be only 2)
are encoded as `float32` scalars with values 0 or 1
(e.g. for `binary_crossentropy`).
- None (no labels).
class_names: Only valid if "labels" is "inferred". This is the explicit
list of class names (must match names of subdirectories). Used
to control the order of the classes
(otherwise alphanumerical order is used).
batch_size: Size of the batches of data. Default: 32.
If `None`, the data will not be batched
(the dataset will yield individual samples).
max_length: Maximum size of a text string. Texts longer than this will
be truncated to `max_length`.
shuffle: Whether to shuffle the data. Default: True.
If set to False, sorts the data in alphanumeric order.
seed: Optional random seed for shuffling and transformations.
validation_split: Optional float between 0 and 1,
fraction of data to reserve for validation.
subset: Subset of the data to return.
One of "training", "validation" or "both".
Only used if `validation_split` is set.
When `subset="both"`, the utility returns a tuple of two datasets
(the training and validation datasets respectively).
follow_links: Whether to visits subdirectories pointed to by symlinks.
Defaults to False.
Returns:
A `tf.data.Dataset` object.
- If `label_mode` is None, it yields `string` tensors of shape
`(batch_size,)`, containing the contents of a batch of text files.
- Otherwise, it yields a tuple `(texts, labels)`, where `texts`
has shape `(batch_size,)` and `labels` follows the format described
below.
Rules regarding labels format:
- if `label_mode` is `int`, the labels are an `int32` tensor of shape
`(batch_size,)`.
- if `label_mode` is `binary`, the labels are a `float32` tensor of
1s and 0s of shape `(batch_size, 1)`.
- if `label_mode` is `categorical`, the labels are a `float32` tensor
of shape `(batch_size, num_classes)`, representing a one-hot
encoding of the class index.
"""
if labels not in ("inferred", None):
if not isinstance(labels, (list, tuple)):
raise ValueError(
"`labels` argument should be a list/tuple of integer labels, of "
"the same size as the number of text files in the target "
"directory. If you wish to infer the labels from the subdirectory "
'names in the target directory, pass `labels="inferred"`. '
"If you wish to get a dataset that only contains text samples "
f"(no labels), pass `labels=None`. Received: labels={labels}")
if class_names:
raise ValueError(
"You can only pass `class_names` if "
f'`labels="inferred"`. Received: labels={labels}, and '
f"class_names={class_names}")
if label_mode not in {"int", "categorical", "binary", None}:
raise ValueError(
'`label_mode` argument must be one of "int", "categorical", "binary", '
f"or None. Received: label_mode={label_mode}")
if labels is None or label_mode is None:
labels = None
label_mode = None
dataset_utils.check_validation_split_arg(validation_split, subset, shuffle,
seed)
if seed is None:
seed = np.random.randint(1e6)
file_paths, labels, class_names = dataset_utils.index_directory(
directory,
labels,
formats=(".txt", ),
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links,
)
if label_mode == "binary" and len(class_names) != 2:
raise ValueError(
f'When passing `label_mode="binary"`, there must be exactly 2 '
f"class_names. Received: class_names={class_names}")
if subset == "both":
(
file_paths_train,
labels_train,
) = dataset_utils.get_training_or_validation_split(
file_paths, labels, validation_split, "training")
(
file_paths_val,
labels_val,
) = dataset_utils.get_training_or_validation_split(
file_paths, labels, validation_split, "validation")
if not file_paths_train:
raise ValueError(
f"No training text files found in directory {directory}. "
f"Allowed format: .txt")
if not file_paths_val:
raise ValueError(
f"No validation text files found in directory {directory}. "
f"Allowed format: .txt")
train_dataset = paths_and_labels_to_dataset(
file_paths=file_paths_train,
labels=labels_train,
label_mode=label_mode,
num_classes=len(class_names),
max_length=max_length,
)
val_dataset = paths_and_labels_to_dataset(
file_paths=file_paths_val,
labels=labels_val,
label_mode=label_mode,
num_classes=len(class_names),
max_length=max_length,
)
train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
if batch_size is not None:
if shuffle:
# Shuffle locally at each iteration
train_dataset = train_dataset.shuffle(buffer_size=batch_size *
8,
seed=seed)
train_dataset = train_dataset.batch(batch_size)
val_dataset = val_dataset.batch(batch_size)
else:
if shuffle:
train_dataset = train_dataset.shuffle(buffer_size=1024,
seed=seed)
# Users may need to reference `class_names`.
train_dataset.class_names = class_names
val_dataset.class_names = class_names
dataset = [train_dataset, val_dataset]
else:
file_paths, labels = dataset_utils.get_training_or_validation_split(
file_paths, labels, validation_split, subset)
if not file_paths:
raise ValueError(f"No text files found in directory {directory}. "
f"Allowed format: .txt")
dataset = paths_and_labels_to_dataset(
file_paths=file_paths,
labels=labels,
label_mode=label_mode,
num_classes=len(class_names),
max_length=max_length,
)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
if batch_size is not None:
if shuffle:
# Shuffle locally at each iteration
dataset = dataset.shuffle(buffer_size=batch_size * 8,
seed=seed)
dataset = dataset.batch(batch_size)
else:
if shuffle:
dataset = dataset.shuffle(buffer_size=1024, seed=seed)
# Users may need to reference `class_names`.
dataset.class_names = class_names
return dataset
def audio_dataset_from_directory(
directory,
labels="inferred",
label_mode="int",
class_names=None,
batch_size=32,
sampling_rate=None,
output_sequence_length=None,
ragged=False,
shuffle=True,
seed=None,
validation_split=None,
subset=None,
follow_links=False,
):
"""Generates a `tf.data.Dataset` from audio files in a directory.
If your directory structure is:
```
main_directory/
...class_a/
......a_audio_1.wav
......a_audio_2.wav
...class_b/
......b_audio_1.wav
......b_audio_2.wav
```
Then calling `audio_dataset_from_directory(main_directory,
labels='inferred')`
will return a `tf.data.Dataset` that yields batches of audio files from
the subdirectories `class_a` and `class_b`, together with labels
0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
Only `.wav` files are supported at this time.
Args:
directory: Directory where the data is located. If `labels` is "inferred",
it should contain subdirectories, each containing audio files for a
class. Otherwise, the directory structure is ignored.
labels: Either "inferred" (labels are generated from the directory
structure), None (no labels), or a list/tuple of integer labels of the
same size as the number of audio files found in the directory. Labels
should be sorted according to the alphanumeric order of the audio file
paths (obtained via `os.walk(directory)` in Python).
label_mode: String describing the encoding of `labels`. Options are:
- 'int': means that the labels are encoded as integers (e.g. for
`sparse_categorical_crossentropy` loss). - 'categorical' means that
the labels are encoded as a categorical vector (e.g. for
`categorical_crossentropy` loss). - 'binary' means that the labels
(there can be only 2) are encoded as `float32` scalars with values 0
or 1 (e.g. for `binary_crossentropy`). - None (no labels).
class_names: Only valid if "labels" is "inferred". This is the explicit
list of class names (must match names of subdirectories). Used to
control the order of the classes (otherwise alphanumerical order is
used).
batch_size: Size of the batches of data. Default: 32. If `None`, the data
will not be batched (the dataset will yield individual samples).
sampling_rate: Audio sampling rate (in samples per second).
output_sequence_length: Maximum length of an audio sequence. Audio files
longer than this will be truncated to `output_sequence_length`. If set
to `None`, then all sequences in the same batch will be padded to the
length of the longest sequence in the batch.
ragged: Whether to return a Ragged dataset (where each sequence has its
own length). Default: False.
shuffle: Whether to shuffle the data. Default: True. If set to False,
sorts the data in alphanumeric order.
seed: Optional random seed for shuffling and transformations.
validation_split: Optional float between 0 and 1, fraction of data to
reserve for validation.
subset: Subset of the data to return. One of "training", "validation" or
"both". Only used if `validation_split` is set.
follow_links: Whether to visits subdirectories pointed to by symlinks.
Defaults to False.
Returns:
A `tf.data.Dataset` object.
- If `label_mode` is None, it yields `string` tensors of shape
`(batch_size,)`, containing the contents of a batch of audio files.
- Otherwise, it yields a tuple `(audio, labels)`, where `audio`
has shape `(batch_size, sequence_length, num_channels)` and `labels`
follows the format described
below.
Rules regarding labels format:
- if `label_mode` is `int`, the labels are an `int32` tensor of shape
`(batch_size,)`.
- if `label_mode` is `binary`, the labels are a `float32` tensor of
1s and 0s of shape `(batch_size, 1)`.
- if `label_mode` is `categorical`, the labels are a `float32` tensor
of shape `(batch_size, num_classes)`, representing a one-hot
encoding of the class index.
"""
if labels not in ("inferred", None):
if not isinstance(labels, (list, tuple)):
raise ValueError(
"The `labels` argument should be a list/tuple of integer labels, of "
"the same size as the number of audio files in the target "
"directory. If you wish to infer the labels from the subdirectory "
'names in the target directory, pass `labels="inferred"`. '
"If you wish to get a dataset that only contains audio samples "
f"(no labels), pass `labels=None`. Received: labels={labels}")
if class_names:
raise ValueError(
"You can only pass `class_names` if "
f'`labels="inferred"`. Received: labels={labels}, and '
f"class_names={class_names}")
if label_mode not in {"int", "categorical", "binary", None}:
raise ValueError(
'`label_mode` argument must be one of "int", "categorical", "binary", '
f'or None. Received: label_mode={label_mode}')
if ragged and output_sequence_length is not None:
raise ValueError(
"Cannot set both `ragged` and `output_sequence_length`")
if sampling_rate is not None:
if not isinstance(sampling_rate, int):
raise ValueError('`sampling_rate` should have an integer value. '
f'Received: sampling_rate={sampling_rate}')
if sampling_rate <= 0:
raise ValueError(f'`sampling_rate` should be higher than 0. '
f'Received: sampling_rate={sampling_rate}')
if tfio is None:
raise ImportError(
'To use the argument `sampling_rate`, you should install '
'tensorflow_io. You can install it via `pip install tensorflow-io`.'
)
if labels is None or label_mode is None:
labels = None
label_mode = None
dataset_utils.check_validation_split_arg(validation_split, subset, shuffle,
seed)
if seed is None:
seed = np.random.randint(1e6)
file_paths, labels, class_names = dataset_utils.index_directory(
directory,
labels,
formats=ALLOWED_FORMATS,
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links,
)
if label_mode == "binary" and len(class_names) != 2:
raise ValueError(
f'When passing `label_mode="binary"`, there must be exactly 2 '
f"class_names. Received: class_names={class_names}")
if subset == "both":
train_dataset, val_dataset = get_training_and_validation_dataset(
file_paths=file_paths,
labels=labels,
validation_split=validation_split,
directory=directory,
label_mode=label_mode,
class_names=class_names,
sampling_rate=sampling_rate,
output_sequence_length=output_sequence_length,
ragged=ragged,
)
train_dataset = prepare_dataset(
dataset=train_dataset,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
class_names=class_names,
output_sequence_length=output_sequence_length,
ragged=ragged,
)
val_dataset = prepare_dataset(
dataset=val_dataset,
batch_size=batch_size,
shuffle=False,
seed=seed,
class_names=class_names,
output_sequence_length=output_sequence_length,
ragged=ragged,
)
return train_dataset, val_dataset
else:
dataset = get_dataset(
file_paths=file_paths,
labels=labels,
directory=directory,
validation_split=validation_split,
subset=subset,
label_mode=label_mode,
class_names=class_names,
sampling_rate=sampling_rate,
output_sequence_length=output_sequence_length,
ragged=ragged,
)
dataset = prepare_dataset(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
class_names=class_names,
output_sequence_length=output_sequence_length,
ragged=ragged,
)
return dataset