Create patch release r0.8.2 (#2326)

* Move keras-cv markdown files to toplevel (#2291)

Keras, KerasNLP and KerasTuner all store things liek the CONTRIBUTING.md
in the toplevel of the repo. We should do the same here.

* Add `version()` API to unify with Keras and KerasNLP (#2199)

* Unify `version` API with keras and keras_nlp

* Formatting

* Update to keep `version` parity with KerasNLP, support nightly version string

* Update version_utils.py

* Update version_utils.py

* Update random_crop_and_zoom.py (#2294)

* Update random_crop_and_zoom.py

* Update description

* rename file

* bug fix (#2303)

* Add BASNet Segmentation Model (#2006) (#2271)

* BASNet model initial code structure

* adding test and initial preset details

* adding comments

* cleaning and formatting code

* keras 3 support added

* disabling preset test for BASNet

* Fix image.shape type (#2305)

Fixed image.shape issue for tensorflow backend

* Create workflow for auto assignment of issues and for stale issues (#2313)

* Create auto-assignment.yaml

* Create auto-assignment.js

* Create stale-issue-pr.yaml

* Rename auto-assignment.yaml to auto-assignment.yml

* Rename stale-issue-pr.yaml to stale-issue-pr.yml

* Fix format and Update Vectorized Base (#2323)

* Fix CI Test for Basnet OOM and PyCoCo Test Failure for JAX (#2322)

* Reduce memory consumption for BasNet tests (#2325)

---------

Co-authored-by: Matt Watson <1389937+mattdangerw@users.noreply.github.com>
Co-authored-by: Gabriel Rasskin <43894452+grasskin@users.noreply.github.com>
Co-authored-by: Sachin Prasad <sachinprasad@google.com>
Co-authored-by: Haifeng Jin <5476582+haifeng-jin@users.noreply.github.com>
Co-authored-by: Hamid Ali <hamidriasat@gmail.com>
Co-authored-by: Tirth Patel <tirthasheshpatel@gmail.com>

.github/workflows/auto-assignment.yml

@@ -0,0 +1,21 @@
+name: auto-assignment
+on:
+  issues:
+    types:
+      - opened
+
+permissions:
+  contents: read
+  issues: write
+  pull-requests: write
+
+jobs:
+  welcome:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/github-script@v7
+        with:
+          script: |
+            const script = require('./\.github/workflows/scripts/auto-assignment.js')
+            script({github, context})

.github/workflows/scripts/auto-assignment.js

@@ -0,0 +1,43 @@
+/** Automatically assign issues and PRs to users in the `assigneesList` 
+ *  on a rotating basis.
+
+  @param {!object}
+    GitHub objects can call GitHub APIs using their built-in library functions.
+    The context object contains issue and PR details.
+*/
+
+module.exports = async ({ github, context }) => {
+  let issueNumber;
+  let assigneesList;
+  // Is this an issue? If so, assign the issue number. Otherwise, assign the PR number.
+  if (context.payload.issue) {
+    //assignee List for issues. 
+    assigneesList = ["SuryanarayanaY", "sachinprasadhs"];
+    issueNumber = context.payload.issue.number;
+  } else {
+    //assignee List for PRs. 
+    assigneesList = [];
+    issueNumber = context.payload.number;
+  }
+  console.log("assignee list", assigneesList);
+  console.log("entered auto assignment for this issue:  ", issueNumber);
+  if (!assigneesList.length) {
+    console.log("No assignees found for this repo.");
+    return;
+  }
+  let noOfAssignees = assigneesList.length;
+  let selection = issueNumber % noOfAssignees;
+  let assigneeForIssue = assigneesList[selection];
+
+  console.log(
+    "issue Number = ",
+    issueNumber + " , assigning to: ",
+    assigneeForIssue
+  );
+  return github.rest.issues.addAssignees({
+    issue_number: context.issue.number,
+    owner: context.repo.owner,
+    repo: context.repo.repo,
+    assignees: [assigneeForIssue],
+  });
+};

.github/workflows/stale-issue-pr.yml

@@ -0,0 +1,50 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "30 1 * * *"
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - name: Awaiting response issues
+        uses: actions/stale@v9
+        with:
+          days-before-issue-stale: 14
+          days-before-issue-close: 14
+          stale-issue-label: "stale"
+          # reason for closed the issue default value is not_planned
+          close-issue-reason: completed
+          only-labels: "stat:awaiting response from contributor"
+          stale-issue-message: > 
+            This issue is stale because it has been open for 14 days with no activity.
+            It will be closed if no further activity occurs. Thank you.
+          # List of labels to remove when issues/PRs unstale. 
+          labels-to-remove-when-unstale: "stat:awaiting response from contributor"
+          close-issue-message: >
+            This issue was closed because it has been inactive for 28 days.
+            Please reopen if you'd like to work on this further.
+          days-before-pr-stale: 14
+          days-before-pr-close: 14
+          stale-pr-message: "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you."
+          close-pr-message: "This PR was closed because it has been inactive for 28 days. Please reopen if you'd like to work on this further."
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+      - name: Contribution issues
+        uses: actions/stale@v9
+        with:
+          days-before-issue-stale: 180
+          days-before-issue-close: 365
+          stale-issue-label: "stale"
+          # reason for closed the issue default value is not_planned
+          close-issue-reason: not_planned
+          any-of-labels: "stat:contributions welcome,good first issue"
+          # List of labels to remove when issues/PRs unstale. 
+          labels-to-remove-when-unstale: "stat:contributions welcome,good first issue"
+          stale-issue-message: > 
+            This issue is stale because it has been open for 180 days with no activity.
+            It will be closed if no further activity occurs. Thank you.
+          close-issue-message: >
+            This issue was closed because it has been inactive for more than 1 year.
+          repo-token: ${{ secrets.GITHUB_TOKEN }}

.kokoro/github/ubuntu/gpu/build.sh

@@ -51,7 +51,7 @@ pip install --no-deps -e "." --progress-bar off
 # Run Extra Large Tests for Continuous builds
 if [ "${RUN_XLARGE:-0}" == "1" ]
 then
-   pytest --check_gpu --run_large --run_extra_large --durations 0 \
+   pytest --cache-clear --check_gpu --run_large --run_extra_large --durations 0 \
       keras_cv/bounding_box \
       keras_cv/callbacks \
       keras_cv/losses \
@@ -65,7 +65,7 @@ then
       keras_cv/models/segmentation \
       keras_cv/models/stable_diffusion
 else
-   pytest --check_gpu --run_large --durations 0 \
+   pytest --cache-clear --check_gpu --run_large --durations 0 \
       keras_cv/bounding_box \
       keras_cv/callbacks \
       keras_cv/losses \

benchmarks/vectorized_randomly_zoomed_crop.py

@@ -249,10 +249,10 @@ def from_config(cls, config):
                 config["zoom_factor"]
             )
         if isinstance(config["aspect_ratio_factor"], dict):
-            config[
-                "aspect_ratio_factor"
-            ] = keras.utils.deserialize_keras_object(
-                config["aspect_ratio_factor"]
+            config["aspect_ratio_factor"] = (
+                keras.utils.deserialize_keras_object(
+                    config["aspect_ratio_factor"]
+                )
             )
         return cls(**config)
 

examples/layers/preprocessing/classification/random_crop_and_zoom.py → examples/layers/preprocessing/classification/random_crop_and_resize_demo.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""random_resized_crop_demo.py.py shows how to use the RandomResizedCrop
-preprocessing layer. Operates on an image of elephant. In this script the image
+"""This demo example shows how to use the RandomCropAndResize preprocessing
+layer. Operates on an image of elephant. In this script the image
 is loaded, then are passed through the preprocessing layers.
 Finally, they are shown using matplotlib.
 """

keras_cv/__init__.py

@@ -41,5 +41,5 @@
 from keras_cv.core import FactorSampler
 from keras_cv.core import NormalFactorSampler
 from keras_cv.core import UniformFactorSampler
-
-__version__ = "0.8.1"
+from keras_cv.version_utils import __version__
+from keras_cv.version_utils import version

keras_cv/layers/object_detection/anchor_generator.py

@@ -172,7 +172,7 @@ def __call__(self, image=None, image_shape=None):
                     "Expected `image` to be a Tensor of rank 3. Got "
                     f"image.shape.rank={len(image.shape)}"
                 )
-            image_shape = image.shape
+            image_shape = tuple(image.shape)
 
         results = {}
         for key, generator in self.anchor_generators.items():

keras_cv/layers/preprocessing/base_image_augmentation_layer.py

@@ -236,15 +236,15 @@ def _compute_output_signature(self, inputs):
         bounding_boxes = inputs.get(BOUNDING_BOXES, None)
 
         if bounding_boxes is not None:
-            fn_output_signature[
-                BOUNDING_BOXES
-            ] = self._compute_bounding_box_signature(bounding_boxes)
+            fn_output_signature[BOUNDING_BOXES] = (
+                self._compute_bounding_box_signature(bounding_boxes)
+            )
 
         segmentation_masks = inputs.get(SEGMENTATION_MASKS, None)
         if segmentation_masks is not None:
-            fn_output_signature[
-                SEGMENTATION_MASKS
-            ] = self.compute_image_signature(segmentation_masks)
+            fn_output_signature[SEGMENTATION_MASKS] = (
+                self.compute_image_signature(segmentation_masks)
+            )
 
         keypoints = inputs.get(KEYPOINTS, None)
         if keypoints is not None:

keras_cv/layers/preprocessing/random_crop_and_resize.py

@@ -272,10 +272,10 @@ def from_config(cls, config):
                 config["crop_area_factor"]
             )
         if isinstance(config["aspect_ratio_factor"], dict):
-            config[
-                "aspect_ratio_factor"
-            ] = keras.utils.deserialize_keras_object(
-                config["aspect_ratio_factor"]
+            config["aspect_ratio_factor"] = (
+                keras.utils.deserialize_keras_object(
+                    config["aspect_ratio_factor"]
+                )
             )
         return cls(**config)
 

keras_cv/layers/preprocessing/vectorized_base_image_augmentation_layer.py

@@ -17,6 +17,7 @@
 
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
+from keras_cv.backend import config
 from keras_cv.backend import keras
 from keras_cv.backend import ops
 from keras_cv.backend import scope
@@ -412,14 +413,16 @@ def _batch_augment(self, inputs):
     def call(self, inputs):
         # try to convert a given backend native tensor to TensorFlow tensor
         # before passing it over to TFDataScope
+        is_tf_backend = config.backend() == "tensorflow"
+        is_in_tf_graph = not tf.executing_eagerly()
         contains_ragged = lambda y: any(
             tree.map_structure(
                 lambda x: isinstance(x, (tf.RaggedTensor, tf.SparseTensor)),
                 tree.flatten(y),
             )
         )
         inputs_contain_ragged = contains_ragged(inputs)
-        if not inputs_contain_ragged:
+        if not is_tf_backend and not inputs_contain_ragged:
             inputs = tree.map_structure(
                 lambda x: tf.convert_to_tensor(x), inputs
             )
@@ -443,13 +446,14 @@ def call(self, inputs):
         # backend native tensors. This is to avoid breaking TF data
         # pipelines that can't easily be ported to become backend
         # agnostic.
-        if not inputs_contain_ragged and not contains_ragged(outputs):
-            outputs = tree.map_structure(
-                # some layers return None, handle that case when
-                # converting to tensors
-                lambda x: ops.convert_to_tensor(x) if x is not None else x,
-                outputs,
-            )
+        if not is_tf_backend and not is_in_tf_graph:
+            if not inputs_contain_ragged and not contains_ragged(outputs):
+                outputs = tree.map_structure(
+                    # some layers return None, handle that case when
+                    # converting to tensors
+                    lambda x: ops.convert_to_tensor(x) if x is not None else x,
+                    outputs,
+                )
         return outputs
 
     def _format_inputs(self, inputs):

keras_cv/layers/preprocessing/vectorized_base_image_augmentation_layer_test.py

@@ -549,3 +549,15 @@ def test_converts_ragged_to_dense_segmentation_masks(self):
             {"images": images, "segmentation_masks": segmentation_masks}
         )
         self.assertTrue(isinstance(result["segmentation_masks"], tf.Tensor))
+
+    def test_in_tf_data_pipeline(self):
+        images = np.random.randn(4, 100, 100, 3).astype("float32")
+        train_ds = tf.data.Dataset.from_tensor_slices(images)
+        train_ds = train_ds.map(lambda x: {"images": x})
+        train_ds = train_ds.map(
+            VectorizedRandomAddLayer(fixed_value=2.0)
+        ).batch(4)
+        for output in train_ds.take(1):
+            pass
+        self.assertTrue(isinstance(output["images"], tf.Tensor))
+        self.assertAllClose(output["images"], images + 2.0)

keras_cv/layers/regularization/squeeze_excite.py

@@ -118,10 +118,10 @@ def get_config(self):
     @classmethod
     def from_config(cls, config):
         if isinstance(config["squeeze_activation"], dict):
-            config[
-                "squeeze_activation"
-            ] = keras.saving.deserialize_keras_object(
-                config["squeeze_activation"]
+            config["squeeze_activation"] = (
+                keras.saving.deserialize_keras_object(
+                    config["squeeze_activation"]
+                )
             )
         if isinstance(config["excite_activation"], dict):
             config["excite_activation"] = keras.saving.deserialize_keras_object(

keras_cv/layers/vit_det_layers.py

@@ -430,9 +430,9 @@ def __init__(
             key_dim=self.project_dim // self.num_heads,
             use_bias=use_bias,
             use_rel_pos=use_rel_pos,
-            input_size=input_size
-            if window_size == 0
-            else (window_size, window_size),
+            input_size=(
+                input_size if window_size == 0 else (window_size, window_size)
+            ),
         )
         self.mlp_block = MLP(
             mlp_dim,

keras_cv/metrics/coco/pycoco_wrapper.py

@@ -125,6 +125,9 @@ def _convert_predictions_to_coco_annotations(predictions):
     num_batches = len(predictions["source_id"])
     for i in range(num_batches):
         batch_size = predictions["source_id"][i].shape[0]
+        predictions["detection_boxes"][i] = predictions["detection_boxes"][
+            i
+        ].copy()
         for j in range(batch_size):
             max_num_detections = predictions["num_detections"][i][j]
             predictions["detection_boxes"][i][j] = _yxyx_to_xywh(

keras_cv/metrics/object_detection/box_coco_metrics.py

@@ -212,9 +212,9 @@ def result_fn(self, force=False):
             )
             result = {}
             for i, key in enumerate(METRIC_NAMES):
-                result[
-                    self.name_prefix() + METRIC_MAPPING[key]
-                ] = py_func_result[i]
+                result[self.name_prefix() + METRIC_MAPPING[key]] = (
+                    py_func_result[i]
+                )
             return result
 
         obj.result = types.MethodType(result_fn, obj)

keras_cv/models/__init__.py

@@ -190,6 +190,7 @@
 from keras_cv.models.object_detection.yolo_v8.yolo_v8_detector import (
     YOLOV8Detector,
 )
+from keras_cv.models.segmentation import BASNet
 from keras_cv.models.segmentation import DeepLabV3Plus
 from keras_cv.models.segmentation import SAMMaskDecoder
 from keras_cv.models.segmentation import SAMPromptEncoder

keras_cv/models/backbones/densenet/densenet_backbone.py

@@ -119,9 +119,9 @@ def __init__(
             name=f"conv{len(stackwise_num_repeats) + 1}",
         )
 
-        pyramid_level_inputs[
-            f"P{len(stackwise_num_repeats) + 1}"
-        ] = utils.get_tensor_input_name(x)
+        pyramid_level_inputs[f"P{len(stackwise_num_repeats) + 1}"] = (
+            utils.get_tensor_input_name(x)
+        )
         x = keras.layers.BatchNormalization(
             axis=BN_AXIS, epsilon=BN_EPSILON, name="bn"
         )(x)

keras_cv/models/backbones/resnet_v1/resnet_v1_backbone.py

@@ -130,9 +130,9 @@ def __init__(
                 first_shortcut=(block_type == "block" or stack_index > 0),
                 name=f"v2_stack_{stack_index}",
             )
-            pyramid_level_inputs[
-                f"P{stack_index + 2}"
-            ] = utils.get_tensor_input_name(x)
+            pyramid_level_inputs[f"P{stack_index + 2}"] = (
+                utils.get_tensor_input_name(x)
+            )
 
         # Create model.
         super().__init__(inputs=inputs, outputs=x, **kwargs)

keras_cv/models/backbones/resnet_v2/resnet_v2_backbone.py

@@ -136,9 +136,9 @@ def __init__(
                 first_shortcut=(block_type == "block" or stack_index > 0),
                 name=f"v2_stack_{stack_index}",
             )
-            pyramid_level_inputs[
-                f"P{stack_index + 2}"
-            ] = utils.get_tensor_input_name(x)
+            pyramid_level_inputs[f"P{stack_index + 2}"] = (
+                utils.get_tensor_input_name(x)
+            )
 
         x = keras.layers.BatchNormalization(
             axis=BN_AXIS, epsilon=BN_EPSILON, name="post_bn"

keras_cv/models/backbones/vit_det/vit_det_backbone.py

@@ -144,9 +144,9 @@ def __init__(
                 num_heads=num_heads,
                 use_bias=use_bias,
                 use_rel_pos=use_rel_pos,
-                window_size=window_size
-                if i not in global_attention_indices
-                else 0,
+                window_size=(
+                    window_size if i not in global_attention_indices else 0
+                ),
                 input_size=(img_size // patch_size, img_size // patch_size),
             )(x)
         x = keras.models.Sequential(

keras_cv/models/legacy/darknet.py

@@ -76,7 +76,6 @@
 
 @keras.utils.register_keras_serializable(package="keras_cv.models")
 class DarkNet(keras.Model):
-
     """Represents the DarkNet architecture.
 
     The DarkNet architecture is commonly used for detection tasks. It is