fiftyone.utils.tf

module documentation

(source)

TensorFlow utilities.

Class	`TFExampleGenerator`	Base class for sample writers that emit `tf.train.Example` protos.
Class	`TFImageClassificationDatasetExporter`	Exporter that writes an image classification dataset to disk as TFRecords.
Class	`TFImageClassificationDatasetImporter`	Importer for TF image classification datasets stored on disk.
Class	`TFImageClassificationExampleGenerator`	Class for generating `tf.train.Example` protos for samples in TF image classification format.
Class	`TFImageClassificationSampleParser`	Parser for image classification samples stored as TFRecords.
Class	`TFObjectDetectionDatasetExporter`	Exporter that writes an object detection dataset to disk as TFRecords in the TF Object Detection API format.
Class	`TFObjectDetectionDatasetImporter`	Importer for TF detection datasets stored on disk.
Class	`TFObjectDetectionExampleGenerator`	Class for generating `tf.train.Example` protos for samples in TF Object Detection API format.
Class	`TFObjectDetectionSampleParser`	Parser for samples in TF Object Detection API format.
Class	`TFRecordSampleParser`	Base class for sample parsers that ingest `tf.train.Example` protos containing labeled images.
Class	`TFRecordsDatasetExporter`	Base class for `fiftyone.utils.data.exporters.LabeledImageDatasetExporter` instances that export labeled images as TFRecords datasets on disk.
Class	`TFRecordsLabeledImageDatasetImporter`	Base class for `fiftyone.utils.data.importers.LabeledImageDatasetImporter` instances that import `tf.train.Example` protos containing labeled images.
Class	`TFRecordsWriter`	Class for writing TFRecords to disk.
Function	`from_image_classification_dir_tree`	Creates a `tf.data.Dataset` for the given image classification dataset directory tree.
Function	`from_image_paths_and_labels`	Creates a `tf.data.Dataset` for an image classification dataset stored as a list of image paths and labels.
Function	`from_images`	Creates a `tf.data.Dataset` for the given list of images.
Function	`from_images_dir`	Creates a `tf.data.Dataset` for the given directory of images.
Function	`from_images_patt`	Creates a `tf.data.Dataset` for the given glob pattern of images.
Function	`from_tf_records`	Creates a `tf.data.Dataset` for the TFRecords at the given path(s).
Function	`write_tf_records`	Writes the given `tf.train.Example` protos to disk as TFRecords.
Variable	`logger`	Undocumented
Function	`_bytes_feature`	Undocumented
Function	`_bytes_list_feature`	Undocumented
Function	`_float_feature`	Undocumented
Function	`_float_list_feature`	Undocumented
Function	`_get_classes_for_detections`	Undocumented
Function	`_int64_feature`	Undocumented
Function	`_int64_list_feature`	Undocumented
Function	`_parse_image_tf`	Undocumented
Function	`_to_labels_map_rev`	Undocumented

def from_image_classification_dir_tree(dataset_dir, force_rgb=False, num_parallel_calls=None): (source) ¶

Creates a tf.data.Dataset for the given image classification dataset directory tree.

The directory should have the following format:

<dataset_dir>/
    <classA>/
        <image1>.<ext>
        <image2>.<ext>
        ...
    <classB>/
        <image1>.<ext>
        <image2>.<ext>
        ...

Parameters
dataset_dir	the dataset directory
force_rgb:`False`	whether to force convert all images to RGB
num_parallel_calls:`None`	the number of samples to read asynchronously in parallel. See https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map for details
Returns
a tuple of	dataset: a tf.data.Dataset` that emits ``(img, label) pairs classes: a list of class label strings

def from_image_paths_and_labels(image_paths, labels, force_rgb=False, num_parallel_calls=None): (source) ¶

Creates a tf.data.Dataset for an image classification dataset stored as a list of image paths and labels.

Parameters
image_paths	an iterable of image paths
labels	an iterable of labels
force_rgb:`False`	whether to force convert all images to RGB
num_parallel_calls:`None`	the number of samples to read asynchronously in parallel. See https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map for details
Returns
a `tf.data.Dataset` that emits `(img, label)` pairs

def from_images(image_paths, force_rgb=False, num_parallel_calls=None): (source) ¶

Creates a tf.data.Dataset for the given list of images.

Parameters
image_paths	an iterable of image paths
force_rgb:`False`	whether to force convert all images to RGB
num_parallel_calls:`None`	the number of samples to read asynchronously in parallel. See https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map for details
Returns
a `tf.data.Dataset` that emits decoded images

def from_images_dir(images_dir, recursive=True, force_rgb=False, num_parallel_calls=None): (source) ¶

Creates a tf.data.Dataset for the given directory of images.

Parameters
images_dir	a directory of images
recursive:`True`	whether to recursively traverse subdirectories
force_rgb:`False`	whether to force convert all images to RGB
num_parallel_calls:`None`	the number of samples to read asynchronously in parallel. See https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map for details
Returns
a `tf.data.Dataset` that emits decoded images

def from_images_patt(images_patt, force_rgb=False, num_parallel_calls=None): (source) ¶

Creates a tf.data.Dataset for the given glob pattern of images.

Parameters
images_patt	a glob pattern of images like `/path/to/images/*.jpg`
force_rgb:`False`	whether to force convert all images to RGB
num_parallel_calls:`None`	the number of samples to read asynchronously in parallel. See https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map for details
Returns
a `tf.data.Dataset` that emits decoded images

def from_tf_records(tf_records_patt, buffer_size=None, num_parallel_reads=None): (source) ¶

Creates a tf.data.Dataset for the TFRecords at the given path(s).

Parameters
tf_records_patt	the path (or glob pattern of paths) to the TFRecords file(s) to load
buffer_size:`None`	an optional buffer size, in bytes, to use when reading the records. Reasonable values are 1-100MBs
num_parallel_reads:`None`	an optional number of files to read in parallel. If a negative value is passed, this parameter is set to the number of CPU cores on the host machine. By default, the files are read in series
Returns
a `tf.data.Dataset` that emits `tf.train.Example` protos

def write_tf_records(examples, tf_records_path, num_shards=None): (source) ¶

Writes the given tf.train.Example protos to disk as TFRecords.

Parameters
examples	an iterable that emits `tf.train.Example` protos
tf_records_path	the path to write the `.tfrecords` file. If sharding is requested `-%%05d-of-%%05d` is appended to the path
num_shards:`None`	an optional number of shards to split the records into (using a round robin strategy)