tf.keras.layers.RandAugment

RandAugment performs the Rand Augment operation on input images.

Inherits From: Layer, Operation

tf.keras.layers.RandAugment(
    value_range=(0, 255),
    num_ops=2,
    factor=0.5,
    interpolation='bilinear',
    seed=None,
    data_format=None,
    **kwargs
)

This layer can be thought of as an all-in-one image augmentation layer. The
policy implemented by this layer has been benchmarked extensively and is
effective on a wide variety of datasets.

Note: This layer is safe to use inside a <a href="../../../tf/data"><code>tf.data</code></a> or `grain` pipeline
(independently of which backend you're using).

References:

    - [RandAugment](https://arxiv.org/abs/1909.13719)

Args:
    value_range: The range of values the input image can take.
        Default is `(0, 255)`. Typically, this would be `(0, 1)`
        for normalized images or `(0, 255)` for raw images.
    num_ops: The number of augmentation operations to apply sequentially
        to each image. Default is 2.
    factor: The strength of the augmentation as a normalized value
        between 0 and 1. Default is 0.5.
    interpolation: The interpolation method to use for resizing operations.
        Options include `nearest`, `bilinear`. Default is `bilinear`.
    seed: Integer. Used to create a random seed.

Example:

layer = keras.layers.RandAugment(bounding_box_format="xyxy")
images = np.random.randint(0, 255, (4, 224, 224, 3), dtype="uint8")

bounding_boxes = {
    "boxes": np.array([
        [[10, 20, 100, 150], [50, 60, 200, 250]],
        [[15, 25, 110, 160], [55, 65, 210, 260]],
        [[20, 30, 120, 170], [60, 70, 220, 270]],
        [[25, 35, 130, 180], [65, 75, 230, 280]],
    ], dtype="float32"),
    "labels": np.array([[0, 1], [1, 2], [2, 3], [0, 3]], dtype="int32")
}

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 3]),
    num_classes=4
)

segmentation_masks = np.random.randint(0, 3, (4, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "bounding_boxes": bounding_boxes,
        "labels": labels,
        "segmentation_masks": segmentation_masks
    },
    training=True
)

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

convert_weight

View source

convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_random_transformation

View source

get_random_transformation(
    data, training=True, seed=None
)

transform_bounding_boxes

View source

transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images

View source

transform_images(
    images, transformation, training=True
)

transform_labels

View source

transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks

View source

transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box

View source

transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image

View source

transform_single_image(
    image, transformation, training=True
)

transform_single_label

View source

transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask

View source

transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)