tf.keras.layers.RandomContrast

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A preprocessing layer which randomly adjusts contrast during training.

Inherits From: Layer, Operation

tf.keras.layers.RandomContrast(
    factor, value_range=(0, 255), seed=None, **kwargs
)

This layer will randomly adjust the contrast of an image or images
by a random factor. Contrast is adjusted independently
for each channel of each image during training.

For each channel, this layer computes the mean of the image pixels in the
channel and then adjusts each component `x` of each pixel to
`(x - mean) * contrast_factor + mean`.

Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
in integer or floating point dtype.
By default, the layer will output floats.

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).

Input shape:
    3D (unbatched) or 4D (batched) tensor with shape:
    `(..., height, width, channels)`, in `"channels_last"` format.

Output shape:
    3D (unbatched) or 4D (batched) tensor with shape:
    `(..., height, width, channels)`, in `"channels_last"` format.

Args:
    factor: a positive float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound.
        When represented as a single float, lower = upper.
        The contrast factor will be randomly picked between
        `[1.0 - lower, 1.0 + upper]`. For any pixel x in the channel,
        the output will be `(x - mean) * factor + mean`
        where `mean` is the mean value of the channel.
    value_range: the range of values the incoming images will have.
        Represented as a two-number tuple written `[low, high]`. This is
        typically either `[0, 1]` or `[0, 255]` depending on how your
        preprocessing pipeline is set up.
    seed: Integer. Used to create a random seed.

Example:

layer = keras.layers.RandomContrast(value_range=(0, 255))
images = np.random.randint(0, 255, (8, 224, 224, 3), dtype="uint8")

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

segmentation_masks = np.random.randint(0, 3, (8, 224, 224, 1), dtype="uint8")

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

Attributes
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.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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

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transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images

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transform_images(
    images, transformation, training=True
)

transform_labels

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transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks

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transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box

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transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image

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transform_single_image(
    image, transformation, training=True
)

transform_single_label

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transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask

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transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)