tf.keras.layers.RandomCrop

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A preprocessing layer which randomly crops images during training.

Inherits From: Layer, Operation

tf.keras.layers.RandomCrop(
    height, width, seed=None, data_format=None, name=None, **kwargs
)

During training, this layer will randomly choose a location to crop images
down to a target size. The layer will crop all the images in the same batch
to the same cropping location.

At inference time, and during training if an input image is smaller than the
target size, the input will be resized and cropped so as to return the
largest possible window in the image that matches the target aspect ratio.
If you need to apply random cropping at inference time, set `training` to
True when calling the layer.

Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
of 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:
    `(..., target_height, target_width, channels)`.

Args:
    height: Integer, the height of the output shape.
    width: Integer, the width of the output shape.
    seed: Integer. Used to create a random seed.
    **kwargs: Base layer keyword arguments, such as
        `name` and `dtype`.

Example:

layer = keras.layers.RandomCrop(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
)

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

View source

transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

bounding_boxes = { "boxes": (batch, num_boxes, 4), # left-top-right-bottom (xyxy) "labels": (batch, num_boxes, num_classes), } or bounding_boxes = { "boxes": (num_boxes, 4), "labels": (num_boxes, num_classes), }

transform_images

View source

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
)