Added RandomZoom to preprocessing layers (#149)
* added random Zoom * added seed * update docstring * added name * added name
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divyasreepat
Francois Chollet
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e56c7650e2
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8a355940ed
@ -77,6 +77,7 @@ from keras_core.layers.preprocessing.random_contrast import RandomContrast
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from keras_core.layers.preprocessing.random_crop import RandomCrop
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from keras_core.layers.preprocessing.random_flip import RandomFlip
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from keras_core.layers.preprocessing.random_translation import RandomTranslation
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from keras_core.layers.preprocessing.random_zoom import RandomZoom
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from keras_core.layers.preprocessing.rescaling import Rescaling
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from keras_core.layers.preprocessing.resizing import Resizing
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from keras_core.layers.preprocessing.string_lookup import StringLookup
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116
keras_core/layers/preprocessing/random_zoom.py
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116
keras_core/layers/preprocessing/random_zoom.py
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import numpy as np
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import tensorflow as tf
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from keras_core import backend
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from keras_core.api_export import keras_core_export
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from keras_core.layers.layer import Layer
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@keras_core_export("keras_core.layers.RandomZoom")
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class RandomZoom(Layer):
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"""A preprocessing layer which randomly zooms images during training.
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This layer will randomly zoom in or out on each axis of an image
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independently, filling empty space according to `fill_mode`.
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Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
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of integer or floating point dtype.
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By default, the layer will output floats.
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Args:
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height_factor: a float represented as fraction of value,
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or a tuple of size 2 representing lower and upper bound
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for zooming vertically. When represented as a single float,
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this value is used for both the upper and
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lower bound. A positive value means zooming out,
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while a negative value
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means zooming in. For instance, `height_factor=(0.2, 0.3)`
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result in an output zoomed out by a random amount
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in the range `[+20%, +30%]`.
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`height_factor=(-0.3, -0.2)` result in an output zoomed
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in by a random amount in the range `[+20%, +30%]`.
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width_factor: a float represented as fraction of value,
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or a tuple of size 2 representing lower and upper bound
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for zooming horizontally. When
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represented as a single float, this value is used
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for both the upper and
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lower bound. For instance, `width_factor=(0.2, 0.3)`
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result in an output
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zooming out between 20% to 30%.
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`width_factor=(-0.3, -0.2)` result in an
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output zooming in between 20% to 30%. `None` means
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i.e., zooming vertical and horizontal directions
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by preserving the aspect ratio. Defaults to `None`.
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fill_mode: Points outside the boundaries of the input are
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filled according to the given mode
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(one of `{"constant", "reflect", "wrap", "nearest"}`).
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- *reflect*: `(d c b a | a b c d | d c b a)`
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The input is extended by reflecting about
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the edge of the last pixel.
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- *constant*: `(k k k k | a b c d | k k k k)`
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The input is extended by filling all values beyond
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the edge with the same constant value k = 0.
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- *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by
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wrapping around to the opposite edge.
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- *nearest*: `(a a a a | a b c d | d d d d)`
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The input is extended by the nearest pixel.
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interpolation: Interpolation mode. Supported values: `"nearest"`,
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`"bilinear"`.
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seed: Integer. Used to create a random seed.
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fill_value: a float represents the value to be filled outside
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the boundaries when `fill_mode="constant"`.
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Example:
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>>> input_img = np.random.random((32, 224, 224, 3))
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>>> layer = keras_core.layers.RandomZoom(.5, .2)
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>>> out_img = layer(input_img)
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Input shape:
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3D (unbatched) or 4D (batched) tensor with shape:
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`(..., height, width, channels)`, in `"channels_last"` format.
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Output shape:
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3D (unbatched) or 4D (batched) tensor with shape:
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`(..., height, width, channels)`, in `"channels_last"` format.
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"""
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def __init__(
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self,
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height_factor,
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width_factor=None,
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fill_mode="reflect",
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interpolation="bilinear",
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seed=None,
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fill_value=0.0,
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name=None,
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**kwargs,
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):
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super().__init__(name=name, **kwargs)
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self.seed = seed or backend.random.make_default_seed()
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self.layer = tf.keras.layers.RandomZoom(
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height_factor=height_factor,
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width_factor=width_factor,
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fill_mode=fill_mode,
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interpolation=interpolation,
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seed=self.seed,
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name=name,
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fill_value=fill_value,
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**kwargs,
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)
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def call(self, inputs, training=True):
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if not isinstance(inputs, (tf.Tensor, np.ndarray, list, tuple)):
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inputs = tf.convert_to_tensor(np.array(inputs))
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outputs = self.layer.call(inputs)
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if backend.backend() != "tensorflow":
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outputs = backend.convert_to_tensor(outputs)
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return outputs
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def compute_output_shape(self, input_shape):
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return tuple(self.layer.compute_output_shape(input_shape))
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def get_config(self):
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config = self.layer.get_config()
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config.update({"seed": self.seed})
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return config
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83
keras_core/layers/preprocessing/random_zoom_test.py
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83
keras_core/layers/preprocessing/random_zoom_test.py
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import numpy as np
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from absl.testing import parameterized
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from keras_core import backend
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from keras_core import layers
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from keras_core import testing
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class RandomZoomTest(testing.TestCase, parameterized.TestCase):
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@parameterized.named_parameters(
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("random_zoom_in_4_by_6", -0.4, -0.6),
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("random_zoom_in_2_by_3", -0.2, -0.3),
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("random_zoom_in_tuple_factor", (-0.4, -0.5), (-0.2, -0.3)),
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("random_zoom_out_4_by_6", 0.4, 0.6),
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("random_zoom_out_2_by_3", 0.2, 0.3),
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("random_zoom_out_tuple_factor", (0.4, 0.5), (0.2, 0.3)),
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)
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def test_random_zoom(self, height_factor, width_factor):
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self.run_layer_test(
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layers.RandomZoom,
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init_kwargs={
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"height_factor": height_factor,
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"width_factor": width_factor,
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},
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input_shape=(2, 3, 4),
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expected_output_shape=(2, 3, 4),
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supports_masking=False,
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)
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def test_random_zoom_out_correctness(self):
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input_image = np.reshape(np.arange(0, 25), (1, 5, 5, 1))
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expected_output = np.asarray(
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[
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[0, 0, 0, 0, 0],
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[0, 5, 7, 9, 0],
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[0, 10, 12, 14, 0],
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[0, 20, 22, 24, 0],
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[0, 0, 0, 0, 0],
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]
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)
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expected_output = backend.convert_to_tensor(
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np.reshape(expected_output, (1, 5, 5, 1))
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)
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self.run_layer_test(
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layers.RandomZoom,
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init_kwargs={
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"height_factor": (0.5, 0.5),
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"width_factor": (0.8, 0.8),
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"interpolation": "nearest",
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"fill_mode": "constant",
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},
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input_shape=None,
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input_data=input_image,
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expected_output=expected_output,
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supports_masking=False,
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)
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def test_random_zoom_in_correctness(self):
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input_image = np.reshape(np.arange(0, 25), (1, 5, 5, 1))
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expected_output = np.asarray(
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[
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[6, 7, 7, 8, 8],
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[11, 12, 12, 13, 13],
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[11, 12, 12, 13, 13],
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[16, 17, 17, 18, 18],
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[16, 17, 17, 18, 18],
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]
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)
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expected_output = backend.convert_to_tensor(
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np.reshape(expected_output, (1, 5, 5, 1))
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)
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self.run_layer_test(
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layers.RandomZoom,
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init_kwargs={
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"height_factor": (-0.5, -0.5),
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"width_factor": (-0.5, -0.5),
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"interpolation": "nearest",
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},
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input_shape=None,
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input_data=input_image,
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expected_output=expected_output,
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supports_masking=False,
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)
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