Merge branch 'master' into segformer_tf

.github/ISSUE_TEMPLATE/bug.md

@@ -0,0 +1,44 @@
+---
+name: Bug
+about: File a bug/issue
+title: ''
+labels: bug
+assignees: ''
+
+---
+
+<!--
+Note: Please search to see if an issue already exists for the bug you encountered.
+-->
+
+### Current Behavior:
+<!-- A concise description of what you're experiencing. -->
+
+### Expected Behavior:
+<!-- A concise description of what you expected to happen. -->
+
+### Steps To Reproduce:
+<!--
+Please include a minimal colab gist replicating the issue you encountered. 
+THIS IS CRITICAL FOR US TO HELP YOU. It is very difficult to fix an issue from a 
+stack trace alone.
+
+To save a gist, choose "File > Save a copy as Github Gist" in colab. This 
+ensures that your colab is accessible to all users.
+
+Exception: Any issues related to installation outside of colab or where 
+replication is not important to addressing your concern.
+-->
+
+### Version:
+<!--
+Example:
+- HEAD
+- 0.6.1
+-->
+
+### Anything else:
+<!--
+Links? References? Anything that will give us more context about the issue that 
+you are encountering!
+-->

.github/workflows/release.yml

@@ -102,7 +102,7 @@ jobs:
         pip install tensorflow==2.13.0
         python -m pip install --upgrade pip setuptools wheel twine
         export BUILD_WITH_CUSTOM_OPS=false
-        python setup.py sdist bdist_wheel
+        python pip_build.py
     - name: Upload wheels
       env:
         TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}

examples/layers/preprocessing/segmentation/aug_mix_demo.py

@@ -0,0 +1,34 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""aug_mix_demo.py shows how to use the AugMix preprocessing layer.
+
+Uses the oxford iiit pet_dataset.  In this script the pets
+are loaded, then are passed through the preprocessing layers.
+Finally, they are shown using matplotlib.
+"""
+import demo_utils
+import tensorflow as tf
+
+from keras_cv.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_iiit_pet_dataset()
+    augmix = preprocessing.AugMix([0, 255])
+    ds = ds.map(augmix, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

examples/layers/preprocessing/segmentation/demo_utils.py

@@ -28,6 +28,7 @@
 def normalize(input_image, input_mask):
     input_image = tf.image.convert_image_dtype(input_image, tf.float32)
     input_image = (input_image - mean) / tf.maximum(std, backend.epsilon())
+    input_image = input_image / 255
     input_mask -= 1
     return input_image, input_mask
 

examples/layers/preprocessing/segmentation/fourier_mix_demo.py

@@ -0,0 +1,34 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""fourier_mix_demo.py shows how to use the FourierMix preprocessing layer.
+
+Uses the oxford iiit pet_dataset.  In this script the pets
+are loaded, then are passed through the preprocessing layers.
+Finally, they are shown using matplotlib.
+"""
+import demo_utils
+import tensorflow as tf
+
+from keras_cv.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_iiit_pet_dataset()
+    fouriermix = preprocessing.FourierMix(alpha=0.8)
+    ds = ds.map(fouriermix, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

examples/layers/preprocessing/segmentation/random_translation_demo.py

@@ -0,0 +1,33 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""random_translation_demo.py shows how to use the RandomTranslation
+preprocessing layer. Uses the oxford iiit pet_dataset. In this
+script the pets are loaded, then are passed through the
+preprocessing layers. Finally, they are shown using matplotlib.
+"""
+import demo_utils
+import tensorflow as tf
+
+from keras_cv.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_iiit_pet_dataset()
+    randomcutout = preprocessing.RandomTranslation(0.5, 0.5)
+    ds = ds.map(randomcutout, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

examples/layers/preprocessing/segmentation/resize_demo.py

@@ -31,7 +31,7 @@ def load_data():
     )
     return ds.map(
         lambda inputs: {
-            "images": tf.cast(inputs["image"], dtype=tf.float32) / 255.0,
+            "images": tf.cast(inputs["image"], dtype=tf.float32),
             "segmentation_masks": inputs["segmentation_mask"] - 1,
         }
     )

examples/visualization/plot_image_gallery.py

@@ -5,7 +5,7 @@
 Date created: 2022/10/16
 Last modified: 2022/06/24
 Description: Visualize ground truth and predicted bounding boxes for a given
-             dataset.
+            dataset.
 """
 
 """

keras_cv/api_export.py

@@ -0,0 +1,49 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import types
+
+from keras_cv.backend import keras
+
+try:
+    import namex
+except ImportError:
+    namex = None
+
+
+def maybe_register_serializable(symbol, package):
+    if isinstance(symbol, types.FunctionType) or hasattr(symbol, "get_config"):
+        keras.saving.register_keras_serializable(package=package)(symbol)
+
+
+if namex:
+
+    class keras_cv_export(namex.export):
+        def __init__(self, path, package="keras_cv"):
+            super().__init__(package="keras_cv", path=path)
+            self.package = package
+
+        def __call__(self, symbol):
+            maybe_register_serializable(symbol, self.package)
+            return super().__call__(symbol)
+
+else:
+
+    class keras_cv_export:
+        def __init__(self, path, package="keras_cv"):
+            self.package = package
+
+        def __call__(self, symbol):
+            maybe_register_serializable(symbol, self.package)
+            return symbol

keras_cv/bounding_box/converters.py

@@ -16,6 +16,7 @@
 
 import tensorflow as tf
 
+from keras_cv.api_export import keras_cv_export
 from keras_cv.backend import keras
 from keras_cv.backend import ops
 from keras_cv.backend.scope import tf_data
@@ -298,6 +299,7 @@ def _xyxy_to_rel_yxyx(boxes, images=None, image_shape=None):
 }
 
 
+@keras_cv_export("keras_cv.bounding_box.convert_format")
 @tf_data
 def convert_format(
     boxes, source, target, images=None, image_shape=None, dtype="float32"

keras_cv/bounding_box/ensure_tensor.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from keras_cv.api_export import keras_cv_export
 from keras_cv.utils import preprocessing
 
 
+@keras_cv_export("keras_cv.bounding_box.ensure_tensor")
 def ensure_tensor(boxes, dtype=None):
     boxes = boxes.copy()
     for key in ["boxes", "classes", "confidence"]:

keras_cv/bounding_box/formats.py

@@ -15,7 +15,10 @@
 formats.py contains axis information for each supported format.
 """
 
+from keras_cv.api_export import keras_cv_export
 
+
+@keras_cv_export("keras_cv.bounding_box.XYXY")
 class XYXY:
     """XYXY contains axis indices for the XYXY format.
 
@@ -35,6 +38,7 @@ class XYXY:
     BOTTOM = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.REL_XYXY")
 class REL_XYXY:
     """REL_XYXY contains axis indices for the REL_XYXY format.
 
@@ -56,6 +60,7 @@ class REL_XYXY:
     BOTTOM = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.CENTER_XYWH")
 class CENTER_XYWH:
     """CENTER_XYWH contains axis indices for the CENTER_XYWH format.
 
@@ -75,6 +80,7 @@ class CENTER_XYWH:
     HEIGHT = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.XYWH")
 class XYWH:
     """XYWH contains axis indices for the XYWH format.
 
@@ -94,6 +100,7 @@ class XYWH:
     HEIGHT = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.REL_XYWH")
 class REL_XYWH:
     """REL_XYWH contains axis indices for the XYWH format.
 
@@ -113,6 +120,7 @@ class REL_XYWH:
     HEIGHT = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.YXYX")
 class YXYX:
     """YXYX contains axis indices for the YXYX format.
 
@@ -132,6 +140,7 @@ class YXYX:
     RIGHT = 3
 
 
+@keras_cv_export("keras_cv.bounding_box.REL_YXYX")
 class REL_YXYX:
     """REL_YXYX contains axis indices for the REL_YXYX format.
 

keras_cv/bounding_box/iou.py

@@ -15,6 +15,7 @@
 import math
 
 from keras_cv import bounding_box
+from keras_cv.api_export import keras_cv_export
 from keras_cv.backend import keras
 from keras_cv.backend import ops
 
@@ -60,6 +61,7 @@ def _compute_intersection(boxes1, boxes2):
     return intersect_height * intersect_width
 
 
+@keras_cv_export("keras_cv.bounding_box.compute_iou")
 def compute_iou(
     boxes1,
     boxes2,
@@ -170,6 +172,7 @@ def compute_iou(
     return iou_lookup_table
 
 
+@keras_cv_export("keras_cv.bounding_box.compute_ciou")
 def compute_ciou(box1, box2, bounding_box_format, eps=1e-7):
     """
     Computes the Complete IoU (CIoU) between two bounding boxes or between

keras_cv/bounding_box/mask_invalid_detections.py

@@ -13,11 +13,13 @@
 # limitations under the License.
 
 from keras_cv import backend
+from keras_cv.api_export import keras_cv_export
 from keras_cv.backend import ops
 from keras_cv.bounding_box.to_ragged import to_ragged
 from keras_cv.bounding_box.validate_format import validate_format
 
 
+@keras_cv_export("keras_cv.bounding_box.mask_invalid_detections")
 def mask_invalid_detections(bounding_boxes, output_ragged=False):
     """masks out invalid detections with -1s.
 

keras_cv/bounding_box/to_dense.py

@@ -14,6 +14,7 @@
 import tensorflow as tf
 
 import keras_cv.bounding_box.validate_format as validate_format
+from keras_cv.api_export import keras_cv_export
 from keras_cv.backend.scope import tf_data
 
 
@@ -36,6 +37,7 @@ def _classes_shape(batched, classes_shape, max_boxes):
     return [max_boxes] + classes_shape[2:]
 
 
+@keras_cv_export("keras_cv.bounding_box.to_dense")
 @tf_data
 def to_dense(bounding_boxes, max_boxes=None, default_value=-1):
     """to_dense converts bounding boxes to Dense tensors

keras_cv/bounding_box/to_ragged.py

@@ -15,9 +15,11 @@
 
 import keras_cv.bounding_box.validate_format as validate_format
 from keras_cv import backend
+from keras_cv.api_export import keras_cv_export
 from keras_cv.backend import keras
 
 
+@keras_cv_export("keras_cv.bounding_box.to_ragged")
 def to_ragged(bounding_boxes, sentinel=-1, dtype=tf.float32):
     """converts a Dense padded bounding box `tf.Tensor` to a `tf.RaggedTensor`.