View source on GitHub
|
A preprocessing layer which randomly translates images during training.
Inherits From: Layer, Operation
tf.keras.layers.RandomTranslation(
height_factor,
width_factor,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
data_format=None,
**kwargs
)
Used in the notebooks
| Used in the tutorials |
|---|
This layer will apply random translations to each image during training,
filling empty space according to `fill_mode`.
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,
or `(..., channels, height, width)`, in `"channels_first"` format.
Output shape:
3D (unbatched) or 4D (batched) tensor with shape:
`(..., target_height, target_width, channels)`,
or `(..., channels, target_height, target_width)`,
in `"channels_first"` format.
Args:
height_factor: a float represented as fraction of value, or a tuple of
size 2 representing lower and upper bound for shifting vertically. A
negative value means shifting image up, while a positive value means
shifting image down. When represented as a single positive float,
this value is used for both the upper and lower bound. For instance,
`height_factor=(-0.2, 0.3)` results in an output shifted by a random
amount in the range `[-20%, +30%]`. `height_factor=0.2` results in
an output height shifted by a random amount in the range
`[-20%, +20%]`.
width_factor: a float represented as fraction of value, or a tuple of
size 2 representing lower and upper bound for shifting horizontally.
A negative value means shifting image left, while a positive value
means shifting image right. When represented as a single positive
float, this value is used for both the upper and lower bound. For
instance, `width_factor=(-0.2, 0.3)` results in an output shifted
left by 20%, and shifted right by 30%. `width_factor=0.2` results
in an output height shifted left or right by 20%.
fill_mode: Points outside the boundaries of the input are filled
according to the given mode. Available methods are `"constant"`,
`"nearest"`, `"wrap"` and `"reflect"`. Defaults to `"constant"`.
- `"reflect"`: `(d c b a | a b c d | d c b a)`
The input is extended by reflecting about the edge of the last
pixel.
- `"constant"`: `(k k k k | a b c d | k k k k)`
The input is extended by filling all values beyond
the edge with the same constant value k specified by
`fill_value`.
- `"wrap"`: `(a b c d | a b c d | a b c d)`
The input is extended by wrapping around to the opposite edge.
- `"nearest"`: `(a a a a | a b c d | d d d d)`
The input is extended by the nearest pixel.
Note that when using torch backend, `"reflect"` is redirected to
`"mirror"` `(c d c b | a b c d | c b a b)` because torch does not
support `"reflect"`.
Note that torch backend does not support `"wrap"`.
interpolation: Interpolation mode. Supported values: `"nearest"`,
`"bilinear"`.
seed: Integer. Used to create a random seed.
fill_value: a float represents the value to be filled outside the
boundaries when `fill_mode="constant"`.
data_format: string, either `"channels_last"` or `"channels_first"`.
The ordering of the dimensions in the inputs. `"channels_last"`
corresponds to inputs with shape `(batch, height, width, channels)`
while `"channels_first"` corresponds to inputs with shape
`(batch, channels, height, width)`. It defaults to the
`image_data_format` value found in your Keras config file at
`~/.keras/keras.json`. If you never set it, then it will be
`"channels_last"`.
**kwargs: Base layer keyword arguments, such as `name` and `dtype`.
Example:
layer = keras.layers.RandomTranslation(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
)
Methods
convert_weight
convert_weight(
weight
)
Convert the weight if it is from the a different backend.
from_config
@classmethodfrom_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
get_random_transformation(
data, training=True, seed=None
)
get_shifted_bbox
get_shifted_bbox(
bounding_boxes, w_shift_factor, h_shift_factor
)
get_transformed_x_y
get_transformed_x_y(
x, y, transform
)
transform_bounding_boxes
transform_bounding_boxes(
bounding_boxes, transformation, training=True
)
transform_images
transform_images(
images, transformation, training=True
)
transform_labels
transform_labels(
labels, transformation, training=True
)
transform_segmentation_masks
transform_segmentation_masks(
segmentation_masks, transformation, training=True
)
transform_single_bounding_box
transform_single_bounding_box(
bounding_box, transformation, training=True
)
transform_single_image
transform_single_image(
image, transformation, training=True
)
transform_single_label
transform_single_label(
label, transformation, training=True
)
transform_single_segmentation_mask
transform_single_segmentation_mask(
segmentation_mask, transformation, training=True
)