Merge branch 'master' into aritra/port-od-layers-keras3

.github/dependabot.yml

@@ -21,3 +21,6 @@ updates:
       python:
         patterns:
           - "*"
+    ignore:
+        # TODO: ignore all updates for JAX GPU due to cuda version issue
+      - dependency-name: "jax[cuda12_pip]"

.github/workflows/actions.yml

@@ -38,6 +38,8 @@ jobs:
         pip install torch>=2.0.1+cpu
         pip install "jax[cpu]"
         pip install keras-core
+        pip install keras-nlp-nightly --no-deps
+        pip install tensorflow-text==2.15
         pip install -e ".[tests]" --progress-bar off --upgrade
     - name: Test with pytest
       env:
@@ -75,6 +77,7 @@ jobs:
       run: |
         pip install -r requirements.txt
         pip install -e ".[tests]" --progress-bar off --upgrade
+        pip install keras-nlp-nightly
     - name: Test with pytest
       env:
         TEST_CUSTOM_OPS: false # TODO(ianstenbit): test custom ops, or figure out what our story is here

.github/workflows/scorecard.yml

@@ -45,14 +45,14 @@ jobs:
       # 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@26f96dfa697d77e81fd5907df203aa23a56210a8 # v4.3.0
+        uses: actions/upload-artifact@5d5d22a31266ced268874388b861e4b58bb5c2f3 # v4.3.1
         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@b7bf0a3ed3ecfa44160715d7c442788f65f0f923 # v3.23.2
+        uses: github/codeql-action/upload-sarif@e675ced7a7522a761fc9c8eb26682c8b27c42b2b # v3.24.1
         with:
           sarif_file: results.sarif

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

@@ -20,6 +20,8 @@ nvcc --version
 
 cd "src/github/keras-cv"
 pip install -U pip setuptools
+# psutil is used by background log reader
+pip install -U psutil
 
 if [ "${KERAS2:-0}" == "1" ]
 then
@@ -29,21 +31,26 @@ then
    pip install --extra-index-url https://download.pytorch.org/whl/cpu torch==2.1.0+cpu
    pip install torchvision~=0.16.0
    pip install "jax[cpu]"
+   pip install keras-nlp-nightly --no-deps
+   pip install tensorflow-text==2.15
 
 elif [ "$KERAS_BACKEND" == "tensorflow" ]
 then
    echo "TensorFlow backend detected."
    pip install -r requirements-tensorflow-cuda.txt --progress-bar off
+   pip install keras-nlp-nightly
 
 elif [ "$KERAS_BACKEND" == "jax" ]
 then
    echo "JAX backend detected."
    pip install -r requirements-jax-cuda.txt --progress-bar off
+   pip install keras-nlp-nightly
 
 elif [ "$KERAS_BACKEND" == "torch" ]
 then
    echo "PyTorch backend detected."
    pip install -r requirements-torch-cuda.txt --progress-bar off
+   pip install keras-nlp-nightly
 fi
 
 pip install --no-deps -e "." --progress-bar off

LICENSE

@@ -1,3 +1,5 @@
+Files: keras_cv/*
+
 Copyright © 2023 The KerasCV Authors
 All code in this repository excluding the code located in
 keras_cv/layers/preprocessing_3d/waymo is licensed under the Apache License,
@@ -206,7 +208,58 @@ folder is licensed under terms appearing below.
    See the License for the specific language governing permissions and
    limitations under the License.
 
-# The following applies only to the code appearing in
-# keras_cv/layers/preprocessing_3d/waymo
-
-License: https://github.com/keras-team/keras-cv/blob/master/keras_cv/layers/preprocessing_3d/waymo/LICENSE
+---
+
+Files: keras_cv/layers/preprocessing_3d/waymo/*
+
+Copyright (c) 2023 Waymo LLC. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1. 	Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+2. 	Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following disclaimer
+in the documentation and/or other materials provided with the
+distribution.
+3. 	Neither the name of the copyright holder nor the names of its
+contributors may be used to endorse or promote products derived
+from this software without specific prior written permission.
+
+Additional IP Rights Grant (Patents)
+"Works" means the code located at keras_cv/layers/preprocessing_3d/waymo
+licensed from Waymo LLC ("Waymo") for inclusion in the KerasCV project at
+github.com/keras-team/keras-cv. “Patents" means the pending U.S. Patent App.
+No. 63/418,259 and any issued patents arising therefrom. Subject to the terms
+and conditions of this license, Waymo hereby grants to you a limited worldwide,
+non-exclusive, royalty-free, personal patent license to make, have made, use,
+and import the Works, where such license applies only to those Patent claims
+that are necessarily infringed by the Works executing the ”preprocessing_3d”
+augmentation library on 3D perception tasks using the
+“lidaraugment_keraspolicy.py” file. This grant does not include claims that
+would be infringed by combining the Works with other works, utilizing the Works
+on other tasks, or as a consequence of further modification of the Works. If
+you or your agent or exclusive licensee institute or order or agree to the
+institution of patent litigation or any other patent enforcement activity
+against any entity (including a cross-claim or counterclaim in a lawsuit)
+alleging that the Works or any activity using the Works to execute functions for
+3D perception tasks constitutes direct or contributory patent infringement, or
+inducement of patent infringement, then any patent rights granted to you under
+this license for the Works shall terminate as of the date such litigation is
+filed.
+
+DISCLAIMER
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

keras_cv/conftest.py → conftest.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import keras_core
 import pytest
 import tensorflow as tf
 from packaging import version
@@ -101,7 +100,7 @@ def pytest_collection_modifyitems(config, items):
         reason="This test is only supported on Keras 2",
     )
     skip_tf_only = pytest.mark.skipif(
-        keras_3() and keras_core.backend.backend() != "tensorflow",
+        keras_3() and backend_config.backend() != "tensorflow",
         reason="This test is only supported on TensorFlow",
     )
     for item in items:

keras_cv/custom_ops/BUILD

@@ -1,6 +1,7 @@
-licenses(["notice"])  # Apache 2.0
-
-package(default_visibility = ["//visibility:public"])
+package(
+    default_visibility = ["//visibility:public"],
+    licenses = ["notice"],
+)
 
 config_setting(
     name = "windows",
@@ -11,38 +12,68 @@ cc_library(
     name = "box_util",
     srcs = ["box_util.cc"],
     hdrs = ["box_util.h"],
+    copts = select({
+        ":windows": [
+            "/DEIGEN_STRONG_INLINE=inline",
+            "-DTENSORFLOW_MONOLITHIC_BUILD",
+            "/DPLATFORM_WINDOWS",
+            "/DEIGEN_HAS_C99_MATH",
+            "/DTENSORFLOW_USE_EIGEN_THREADPOOL",
+            "/DEIGEN_AVOID_STL_ARRAY",
+            "/Iexternal/gemmlowp",
+            "/wd4018",
+            "/wd4577",
+            "/DNOGDI",
+            "/UTF_COMPILE_LIBRARY",
+        ],
+        "//conditions:default": [
+            "-pthread",
+            "-std=c++17",
+        ],
+    }),
     deps = [
         "@local_config_tf//:libtensorflow_framework",
         "@local_config_tf//:tf_header_lib",
     ],
-    copts = select({
-        ":windows": ["/DEIGEN_STRONG_INLINE=inline", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"],
-        "//conditions:default": ["-pthread", "-std=c++17"],
-    }),
 )
 
 cc_binary(
-    name = '_keras_cv_custom_ops.so',
+    name = "_keras_cv_custom_ops.so",
     srcs = [
         "kernels/pairwise_iou_kernel.cc",
-        "ops/pairwise_iou_op.cc",
-        "kernels/withinbox_op.cc",
-        "ops/withinbox_op.cc",
         "kernels/within_any_box_op.cc",
+        "kernels/withinbox_op.cc",
+        "ops/pairwise_iou_op.cc",
         "ops/within_any_box_op.cc",
+        "ops/withinbox_op.cc",
     ],
+    copts = select({
+        ":windows": [
+            "/DEIGEN_STRONG_INLINE=inline",
+            "-DTENSORFLOW_MONOLITHIC_BUILD",
+            "/DPLATFORM_WINDOWS",
+            "/DEIGEN_HAS_C99_MATH",
+            "/DTENSORFLOW_USE_EIGEN_THREADPOOL",
+            "/DEIGEN_AVOID_STL_ARRAY",
+            "/Iexternal/gemmlowp",
+            "/wd4018",
+            "/wd4577",
+            "/DNOGDI",
+            "/UTF_COMPILE_LIBRARY",
+        ],
+        "//conditions:default": [
+            "-pthread",
+            "-std=c++17",
+        ],
+    }),
+    features = select({
+        ":windows": ["windows_export_all_symbols"],
+        "//conditions:default": [],
+    }),
     linkshared = 1,
     deps = [
+        ":box_util",
         "@local_config_tf//:libtensorflow_framework",
         "@local_config_tf//:tf_header_lib",
-        ":box_util",
     ],
-    features = select({
-        ":windows": ["windows_export_all_symbols"],
-        "//conditions:default": [],
-    }),
-    copts = select({
-        ":windows": ["/DEIGEN_STRONG_INLINE=inline", "-DTENSORFLOW_MONOLITHIC_BUILD", "/DPLATFORM_WINDOWS", "/DEIGEN_HAS_C99_MATH", "/DTENSORFLOW_USE_EIGEN_THREADPOOL", "/DEIGEN_AVOID_STL_ARRAY", "/Iexternal/gemmlowp", "/wd4018", "/wd4577", "/DNOGDI", "/UTF_COMPILE_LIBRARY"],
-        "//conditions:default": ["-pthread", "-std=c++17"],
-    }),
 )

keras_cv/layers/object_detection/roi_pool.py

@@ -110,11 +110,12 @@ def _pool_single_sample(self, args):
           feature_map: [H, W, C] float Tensor
           rois: [N, 4] float Tensor
         Returns:
-          pooled_feature_map: [target_size, C] float Tensor
+          pooled_feature_map: [N, target_height, target_width, C] float Tensor
         """
         feature_map, rois = args
-        num_rois = ops.shape(rois)[0]
-        height, width, channel = ops.shape(feature_map)
+        num_rois = rois.get_shape().as_list()[0]
+        height, width, channel = feature_map.get_shape().as_list()
+        regions = []
         # TODO (consider vectorize it for better performance)
         for n in range(num_rois):
             # [4]
@@ -125,7 +126,7 @@ def _pool_single_sample(self, args):
             region_width = width * (roi[3] - roi[1])
             h_step = region_height / self.target_height
             w_step = region_width / self.target_width
-            regions = []
+            region_steps = []
             for i in range(self.target_height):
                 for j in range(self.target_width):
                     height_start = y_start + i * h_step
@@ -145,16 +146,18 @@ def _pool_single_sample(self, args):
                         1, width_end - width_start
                     )
                     # [h_step, w_step, C]
-                    region = feature_map[
+                    region_step = feature_map[
                         height_start:height_end, width_start:width_end, :
                     ]
                     # target_height * target_width * [C]
-                    regions.append(ops.max(region, axis=[0, 1]))
-            regions = ops.reshape(
-                ops.stack(regions),
-                [self.target_height, self.target_width, channel],
+                    region_steps.append(ops.max(region_step, axis=[0, 1]))
+            regions.append(
+                ops.reshape(
+                    ops.stack(region_steps),
+                    [self.target_height, self.target_width, channel],
+                )
             )
-            return regions
+        return ops.stack(regions)
 
     def get_config(self):
         config = {