Merge remote-tracking branch 'upstream/master' into r0.8

Author: Divyashree Sreepathihalli

.github/workflows/scorecard.yml

@@ -0,0 +1,58 @@
+name: Scorecard supply-chain security
+on:
+  # For Branch-Protection check. Only the default branch is supported. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
+  branch_protection_rule:
+  # To guarantee Maintained check is occasionally updated. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained
+  schedule:
+    - cron: "37 4 * * 6"
+  push:
+    branches: [ "master" ]
+
+permissions: read-all
+
+jobs:
+  analysis:
+    name: Scorecard analysis
+    runs-on: ubuntu-latest
+    permissions:
+      # Needed to upload the results to code-scanning dashboard.
+      security-events: write
+      # Needed to publish results and get a badge (see publish_results below).
+      id-token: write
+
+    steps:
+      - name: "Checkout code"
+        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
+        with:
+          persist-credentials: false
+
+      - name: "Run analysis"
+        uses: ossf/scorecard-action@0864cf19026789058feabb7e87baa5f140aac736 # v2.3.1
+        with:
+          results_file: results.sarif
+          results_format: sarif
+          # (Optional) "write" PAT token. Uncomment the `repo_token` line below if
+          # you want to enable the Branch-Protection check on a *public* repository.
+          # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action#authentication-with-pat.
+          # repo_token: ${{ secrets.SCORECARD_TOKEN }}
+
+          # Publish results to OpenSSF REST API for easy access by consumers.
+          # See https://github.com/ossf/scorecard-action#publishing-results.
+          publish_results: true
+
+      # Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF
+      # format to the repository Actions tab.
+      - name: "Upload artifact"
+        uses: actions/upload-artifact@c7d193f32edcb7bfad88892161225aeda64e9392 # v4.0.0
+        with:
+          name: SARIF file
+          path: results.sarif
+          retention-days: 5
+
+      # Upload the results to GitHub's code scanning dashboard.
+      - name: "Upload to code-scanning"
+        uses: github/codeql-action/upload-sarif@012739e5082ff0c22ca6d6ab32e07c36df03c4a4 # v3.22.12
+        with:
+          sarif_file: results.sarif

keras_cv/__init__.py

@@ -42,4 +42,4 @@
 from keras_cv.core import NormalFactorSampler
 from keras_cv.core import UniformFactorSampler
 
-__version__ = "0.8.0"
+__version__ = "0.8.1.dev0"

keras_cv/layers/preprocessing/base_image_augmentation_layer.py

@@ -23,6 +23,7 @@
 
 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
@@ -411,14 +412,16 @@ def get_random_transformation(
     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
             )
@@ -444,13 +447,15 @@ 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,
-            )
+        # Skip this step for TF backend or if in `tf.graph` like `tf.data`.
+        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 _augment(self, inputs):

keras_cv/layers/preprocessing/base_image_augmentation_layer_test.py

@@ -265,3 +265,11 @@ def in_tf_function(inputs):
         self.assertNotAllClose(
             segmentation_mask_diff[0], segmentation_mask_diff[1]
         )
+
+    def test_augment_tf_data_pipeline(self):
+        image = np.random.random(size=(1, 8, 8, 3)).astype("float32")
+        tf_dataset = tf.data.Dataset.from_tensor_slices(image).map(
+            RandomAddLayer(fixed_value=2.0)
+        )
+        output = iter(tf_dataset).get_next()
+        self.assertAllClose(image[0] + 2.0, output)

keras_cv/models/object_detection/yolo_v8/yolo_v8_detector.py

@@ -641,10 +641,31 @@ def get_config(self):
             "bounding_box_format": self.bounding_box_format,
             "fpn_depth": self.fpn_depth,
             "backbone": keras.saving.serialize_keras_object(self.backbone),
-            "label_encoder": self.label_encoder,
-            "prediction_decoder": self._prediction_decoder,
+            "label_encoder": keras.saving.serialize_keras_object(
+                self.label_encoder
+            ),
+            "prediction_decoder": keras.saving.serialize_keras_object(
+                self._prediction_decoder
+            ),
         }
 
+    @classmethod
+    def from_config(cls, config):
+        config["backbone"] = keras.saving.deserialize_keras_object(
+            config["backbone"]
+        )
+        label_encoder = config.get("label_encoder")
+        if label_encoder is not None:
+            config["label_encoder"] = keras.saving.deserialize_keras_object(
+                label_encoder
+            )
+        prediction_decoder = config.get("prediction_decoder")
+        if prediction_decoder is not None:
+            config[
+                "prediction_decoder"
+            ] = keras.saving.deserialize_keras_object(prediction_decoder)
+        return cls(**config)
+
     @classproperty
     def presets(cls):
         """Dictionary of preset names and configurations."""

requirements-jax-cuda.txt

@@ -1,5 +1,5 @@
 # Tensorflow cpu-only version.
-tf-nightly-cpu==2.16.0.dev20231221  # Pin a working nightly until rc0.
+tf-nightly-cpu==2.16.0.dev20240104  # Pin a working nightly until rc0.
 
 # Torch cpu-only version.
 --extra-index-url https://download.pytorch.org/whl/cpu

requirements-tensorflow-cuda.txt

@@ -1,5 +1,5 @@
 # Tensorflow with cuda support.
-tf-nightly[and-cuda]==2.16.0.dev20231221  # Pin a working nightly until rc0.
+tf-nightly[and-cuda]==2.16.0.dev20240104  # Pin a working nightly until rc0.
 
 # Torch cpu-only version.
 --extra-index-url https://download.pytorch.org/whl/cpu

requirements-torch-cuda.txt

@@ -1,10 +1,10 @@
 # Tensorflow cpu-only version.
-tf-nightly-cpu==2.16.0.dev20231221  # Pin a working nightly until rc0.
+tf-nightly-cpu==2.16.0.dev20240104  # Pin a working nightly until rc0.
 
 # Torch with cuda support.
 --extra-index-url https://download.pytorch.org/whl/cu121
-torch==2.1.2
-torchvision==0.16.2
+torch==2.1.2+cu121
+torchvision==0.16.2+cu121
 
 # Jax cpu-only version.
 jax[cpu]

requirements.txt

@@ -1,5 +1,5 @@
 # Tensorflow.
-tf-nightly-cpu==2.16.0.dev20231221  # Pin a working nightly until rc0.
+tf-nightly-cpu==2.16.0.dev20240104  # Pin a working nightly until rc0.
 
 # Torch.
 --extra-index-url https://download.pytorch.org/whl/cpu