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Add random_color_jitter processing layer (#20673)
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* Add implementations for random_saturation

* change parse_factor method to inner method.

* Add implementations for random_color_jitter

* Fix Randomhue (#20652)

* Small fix in random hue

* use self.backend for seed

* test: add test for class weights (py_dataset adapter) (#20638)

* test: add test for class weights (py_dataset adapter)

* "call _standardize_batch from enqueuer"

m

* add more tests, handle pytorch astype issue

m

* convert to numpy to ensure consistent handling of operations

* Fix paths for pytest in contribution guide (#20655)

* Add preliminary support of OpenVINO as Keras 3 backend (#19727)

* [POC][OV] Support OpenVINO as Keras 3 backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Mark all unsupported ops from numpy space

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Mark unsupported ops in core, image, and linalg spaces

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Mark unsupported ops in math, nn, random, and rnn spaces

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix sorting imports

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Format imports

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix sorting imports

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix sorting imports

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix inference

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Remove openvino specific code in common part

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix typo

* Clean-up code

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Recover imports

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* Sort imports properly

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Format source code

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Format the rest of source code

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* Continue format adjustment

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add OpenVINO dependency

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix inference using OV backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Support bert_base_en_uncased and mobilenet_v3_small from Keras Hub

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Remove extra openvino specific code from layer.py

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Apply code-style formatting

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Apply code-style formatting

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix remained code-style issue

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Run tests for OpenVINO backend in GHA

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add config file for openvino backend validation

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add import test for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix error in import_test.py

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add import_test for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add openvino specific integration tests in GHA

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Exclude coverage for OpenVINO

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* remove coverage for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Try layer tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Run layer tests for openvino backend selectively

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Mark enabled tests for openvino backend in a different way

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Update .github/workflows/actions.yml

* Fix import for BackendVariable

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix errors in layer tests for openvino backend

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* Add test for Elu via openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix sorted imports

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* Extend testing for attention

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Update keras/src/layers/attention/attention_test.py

* Switch on activation tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on attention tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Update keras/src/layers/attention/additive_attention_test.py

* Update keras/src/layers/attention/grouped_query_attention_test.py

* Run conv tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix convolution in openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Work around constant creation for tuple

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Work around constant creation in reshape

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Run depthwise conv tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix get_ov_output for other x types

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix elu translation

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix softmax and log_softmax for None axis

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Run nn tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix numpy operations for axis to be None

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Run operation_test for openvino_backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on math_test for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on image tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on linalg test for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Extend OpenVINOKerasTensor with new built-in methods and fix shape op

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on core tests for openvino backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Use different way of OpenVINO model creation that supports call method

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Unify integration test for openvino

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Support new operations abs, mod, etc.

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add support for more operations like squeeze, max

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Try to use excluded test files list

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Apply formatting for normalization_test.py

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Correct GHA yml file

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Test that openvino backend is used

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Revert testing change in excluded test files list

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Include testing group

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Include legacy test group

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Exclude legacy group of tests

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* Include initializers tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Skip tests for initializers group

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Remove export test group from ignore

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Include dtype_policies test group

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Reduce ignored tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix ops.cast

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add decorator for custom_gradient

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Shorten line in custom_gradient

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Ignore dtype_policy_map test

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Include callback tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on backend tests

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* Exclude failing tests

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* Correct paths to excluded tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on some layers tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Remove pytest.mark.openvino_backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Register mark requires_trainable_backend

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Ignore test files in a different way

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Try different way to ignore test files

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix GHA yml

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Support tuple axis for logsumexp

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on some ops tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Switch on some callbacks tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add openvino export

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Update sklearn tests

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* Add a comment to skipp numerical_test

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add custom requirements file for OpenVINO

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add reqs of openvino installation for api changes check

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix types of Variables and switch on some variables tests

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Fix nightly code check

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Make sklearn dependency optional (#20657)

* Add a condition to verify training status during image processing (#20650)

* Add a condition to verify training status during image processing

* resolve merge conflict

* fix transform_bounding_boxes logic

* add transform_bounding_boxes test

* Fix recurrent dropout for GRU. (#20656)

The simplified implementation, which used the same recurrent dropout masks for all the previous states didn't work and caused the training to not converge with large enough recurrent dropout values.

This new implementation is now the same as Keras 2. Note that recurrent dropout requires "implementation 1" to be turned on.

Fixes #20276

* Fix example title in probabilistic_metrics.py (#20662)

* Change recurrent dropout implementation for LSTM. (#20663)

This change is to make the implementation of recurrent dropout consistent with GRU (changed as of #20656 ) and Keras 2.

Also fixed a bug where the GRU fix would break when using CUDNN with a dropout and no recurrent dropout. The solution is to create multiple masks only when needed (implementation == 1).

Added coverage for the case when dropout is set and recurrent dropout is not set.

* Never pass enable_xla=False or native_serialization=False in tests (#20664)

These are invalid options in the latest version of jax2tf, they
will just immediately throw.

* Fix `PyDatasetAdapterTest::test_class_weight` test with Torch on GPU. (#20665)

The test was failing because arrays on device and on cpu were compared.

* Fix up torch GPU failing test for mix up (#20666)

We need to make sure to use get any tensors places on cpu before using
them in the tensorflow backend during preprocessing.

* Add random_color_jitter processing layer

* Add random_color_jitter test

* Update test cases

* Correct failed test case

* Correct failed test case

* Correct failed test case

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
Co-authored-by: IMvision12 <88665786+IMvision12@users.noreply.github.com>
Co-authored-by: Enrico <e.durso@live.com>
Co-authored-by: Marco <mmicu.github00@gmail.com>
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
Co-authored-by: Matt Watson <1389937+mattdangerw@users.noreply.github.com>
Co-authored-by: hertschuh <1091026+hertschuh@users.noreply.github.com>
Co-authored-by: Jasmine Dhantule <dhantule@google.com>
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3 changes: 3 additions & 0 deletions keras/api/_tf_keras/keras/layers/__init__.py
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from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
RandomBrightness,
)
from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
RandomColorJitter,
)
from keras.src.layers.preprocessing.image_preprocessing.random_contrast import (
RandomContrast,
)
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3 changes: 3 additions & 0 deletions keras/api/layers/__init__.py
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Expand Up @@ -155,6 +155,9 @@
from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
RandomBrightness,
)
from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
RandomColorJitter,
)
from keras.src.layers.preprocessing.image_preprocessing.random_contrast import (
RandomContrast,
)
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3 changes: 3 additions & 0 deletions keras/src/layers/__init__.py
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Expand Up @@ -99,6 +99,9 @@
from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
RandomBrightness,
)
from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
RandomColorJitter,
)
from keras.src.layers.preprocessing.image_preprocessing.random_contrast import (
RandomContrast,
)
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import keras.src.layers.preprocessing.image_preprocessing.random_brightness as random_brightness # noqa: E501
import keras.src.layers.preprocessing.image_preprocessing.random_contrast as random_contrast # noqa: E501
import keras.src.layers.preprocessing.image_preprocessing.random_hue as random_hue # noqa: E501
import keras.src.layers.preprocessing.image_preprocessing.random_saturation as random_saturation # noqa: E501
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.random.seed_generator import SeedGenerator
from keras.src.utils import backend_utils


@keras_export("keras.layers.RandomColorJitter")
class RandomColorJitter(BaseImagePreprocessingLayer):
"""RandomColorJitter class randomly apply brightness, contrast, saturation
and hue image processing operation sequentially and randomly on the
input.
Args:
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.
brightness_factor: Float or a list/tuple of 2 floats between -1.0
and 1.0. The factor is used to determine the lower bound and
upper bound of the brightness adjustment. A float value will
be chosen randomly between the limits. When -1.0 is chosen,
the output image will be black, and when 1.0 is chosen, the
image will be fully white. When only one float is provided,
eg, 0.2, then -0.2 will be used for lower bound and 0.2 will
be used for upper bound.
contrast_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.
saturation_factor: A tuple of two floats or a single float. `factor`
controls the extent to which the image saturation is impacted.
`factor=0.5` makes this layer perform a no-op operation.
`factor=0.0` makes the image fully grayscale. `factor=1.0`
makes the image fully saturated. Values should be between
`0.0` and `1.0`. If a tuple is used, a `factor` is sampled
between the two values for every image augmented. If a single
float is used, a value between `0.0` and the passed float is
sampled. To ensure the value is always the same, pass a tuple
with two identical floats: `(0.5, 0.5)`.
hue_factor: A single float or a tuple of two floats. `factor`
controls the extent to which the image hue is impacted.
`factor=0.0` makes this layer perform a no-op operation,
while a value of `1.0` performs the most aggressive contrast
adjustment available. If a tuple is used, a `factor` is
sampled between the two values for every image augmented.
If a single float is used, a value between `0.0` and the
passed float is sampled. In order to ensure the value is
always the same, please pass a tuple with two identical
floats: `(0.5, 0.5)`.
seed: Integer. Used to create a random seed.
"""

def __init__(
self,
value_range=(0, 255),
brightness_factor=None,
contrast_factor=None,
saturation_factor=None,
hue_factor=None,
seed=None,
data_format=None,
**kwargs,
):
super().__init__(data_format=data_format, **kwargs)
self.value_range = value_range
self.brightness_factor = brightness_factor
self.contrast_factor = contrast_factor
self.saturation_factor = saturation_factor
self.hue_factor = hue_factor
self.seed = seed
self.generator = SeedGenerator(seed)

self.random_brightness = None
self.random_contrast = None
self.random_saturation = None
self.random_hue = None

if self.brightness_factor is not None:
self.random_brightness = random_brightness.RandomBrightness(
factor=self.brightness_factor,
value_range=self.value_range,
seed=self.seed,
)

if self.contrast_factor is not None:
self.random_contrast = random_contrast.RandomContrast(
factor=self.contrast_factor,
value_range=self.value_range,
seed=self.seed,
)

if self.saturation_factor is not None:
self.random_saturation = random_saturation.RandomSaturation(
factor=self.saturation_factor,
value_range=self.value_range,
seed=self.seed,
)

if self.hue_factor is not None:
self.random_hue = random_hue.RandomHue(
factor=self.hue_factor,
value_range=self.value_range,
seed=self.seed,
)

def transform_images(self, images, transformation, training=True):
if training:
if backend_utils.in_tf_graph():
self.backend.set_backend("tensorflow")
images = self.backend.cast(images, self.compute_dtype)
if self.brightness_factor is not None:
if backend_utils.in_tf_graph():
self.random_brightness.backend.set_backend("tensorflow")
transformation = (
self.random_brightness.get_random_transformation(
images,
seed=self._get_seed_generator(self.backend._backend),
)
)
images = self.random_brightness.transform_images(
images, transformation
)
if self.contrast_factor is not None:
if backend_utils.in_tf_graph():
self.random_contrast.backend.set_backend("tensorflow")
transformation = self.random_contrast.get_random_transformation(
images, seed=self._get_seed_generator(self.backend._backend)
)
transformation["contrast_factor"] = self.backend.cast(
transformation["contrast_factor"], dtype=self.compute_dtype
)
images = self.random_contrast.transform_images(
images, transformation
)
if self.saturation_factor is not None:
if backend_utils.in_tf_graph():
self.random_saturation.backend.set_backend("tensorflow")
transformation = (
self.random_saturation.get_random_transformation(
images,
seed=self._get_seed_generator(self.backend._backend),
)
)
images = self.random_saturation.transform_images(
images, transformation
)
if self.hue_factor is not None:
if backend_utils.in_tf_graph():
self.random_hue.backend.set_backend("tensorflow")
transformation = self.random_hue.get_random_transformation(
images, seed=self._get_seed_generator(self.backend._backend)
)
images = self.random_hue.transform_images(
images, transformation
)
images = self.backend.cast(images, self.compute_dtype)
return images

def transform_labels(self, labels, transformation, training=True):
return labels

def transform_bounding_boxes(
self,
bounding_boxes,
transformation,
training=True,
):
return bounding_boxes

def transform_segmentation_masks(
self, segmentation_masks, transformation, training=True
):
return segmentation_masks

def compute_output_shape(self, input_shape):
return input_shape

def get_config(self):
config = {
"value_range": self.value_range,
"brightness_factor": self.brightness_factor,
"contrast_factor": self.contrast_factor,
"saturation_factor": self.saturation_factor,
"hue_factor": self.hue_factor,
"seed": self.seed,
}
base_config = super().get_config()
return {**base_config, **config}
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import numpy as np
import pytest
from tensorflow import data as tf_data

from keras.src import backend
from keras.src import layers
from keras.src import testing


class RandomColorJitterTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
layers.RandomColorJitter,
init_kwargs={
"value_range": (20, 200),
"brightness_factor": 0.2,
"contrast_factor": 0.2,
"saturation_factor": 0.2,
"hue_factor": 0.2,
"seed": 1,
},
input_shape=(8, 3, 4, 3),
supports_masking=False,
expected_output_shape=(8, 3, 4, 3),
)

def test_random_color_jitter_inference(self):
seed = 3481
layer = layers.RandomColorJitter(
value_range=(0, 1),
brightness_factor=0.1,
contrast_factor=0.2,
saturation_factor=0.9,
hue_factor=0.1,
)

np.random.seed(seed)
inputs = np.random.randint(0, 255, size=(224, 224, 3))
output = layer(inputs, training=False)
self.assertAllClose(inputs, output)

def test_brightness_only(self):
seed = 2390
np.random.seed(seed)

data_format = backend.config.image_data_format()
if data_format == "channels_last":
inputs = np.random.random((12, 8, 16, 3))
else:
inputs = np.random.random((12, 3, 8, 16))

layer = layers.RandomColorJitter(
brightness_factor=[0.5, 0.5], seed=seed
)
output = backend.convert_to_numpy(layer(inputs))

layer = layers.RandomBrightness(factor=[0.5, 0.5], seed=seed)
sub_output = backend.convert_to_numpy(layer(inputs))

self.assertAllClose(output, sub_output)

def test_saturation_only(self):
seed = 2390
np.random.seed(seed)

data_format = backend.config.image_data_format()
if data_format == "channels_last":
inputs = np.random.random((12, 8, 16, 3))
else:
inputs = np.random.random((12, 3, 8, 16))

layer = layers.RandomColorJitter(
saturation_factor=[0.5, 0.5], seed=seed
)
output = layer(inputs)

layer = layers.RandomSaturation(factor=[0.5, 0.5], seed=seed)
sub_output = layer(inputs)

self.assertAllClose(output, sub_output)

def test_hue_only(self):
seed = 2390
np.random.seed(seed)

data_format = backend.config.image_data_format()
if data_format == "channels_last":
inputs = np.random.random((12, 8, 16, 3))
else:
inputs = np.random.random((12, 3, 8, 16))

layer = layers.RandomColorJitter(hue_factor=[0.5, 0.5], seed=seed)
output = layer(inputs)

layer = layers.RandomHue(factor=[0.5, 0.5], seed=seed)
sub_output = layer(inputs)

self.assertAllClose(output, sub_output)

def test_contrast_only(self):
seed = 2390
np.random.seed(seed)

data_format = backend.config.image_data_format()
if data_format == "channels_last":
inputs = np.random.random((12, 8, 16, 3))
else:
inputs = np.random.random((12, 3, 8, 16))

layer = layers.RandomColorJitter(contrast_factor=[0.5, 0.5], seed=seed)
output = layer(inputs)

layer = layers.RandomContrast(factor=[0.5, 0.5], seed=seed)
sub_output = layer(inputs)

self.assertAllClose(output, sub_output)

def test_tf_data_compatibility(self):
data_format = backend.config.image_data_format()
if data_format == "channels_last":
input_data = np.random.random((2, 8, 8, 3))
else:
input_data = np.random.random((2, 3, 8, 8))
layer = layers.RandomColorJitter(
value_range=(0, 1),
brightness_factor=0.1,
contrast_factor=0.2,
saturation_factor=0.9,
hue_factor=0.1,
)

ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
for output in ds.take(1):
output.numpy()

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