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sampathwebmattdangerwgrasskinsachinprasadhshaifeng-jin
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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 <[email protected]> Co-authored-by: Gabriel Rasskin <[email protected]> Co-authored-by: Sachin Prasad <[email protected]> Co-authored-by: Haifeng Jin <[email protected]> Co-authored-by: Hamid Ali <[email protected]> Co-authored-by: Tirth Patel <[email protected]>
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‎.github/workflows/auto-assignment.yml‎

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

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

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

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

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

‎.kokoro/github/ubuntu/gpu/build.sh‎

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# Run Extra Large Tests for Continuous builds
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if [ "${RUN_XLARGE:-0}" == "1" ]
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then
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pytest --check_gpu --run_large --run_extra_large --durations 0 \
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pytest --cache-clear --check_gpu --run_large --run_extra_large --durations 0 \
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keras_cv/bounding_box \
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keras_cv/callbacks \
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keras_cv/losses \
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keras_cv/models/segmentation \
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keras_cv/models/stable_diffusion
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else
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pytest --check_gpu --run_large --durations 0 \
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pytest --cache-clear --check_gpu --run_large --durations 0 \
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keras_cv/bounding_box \
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keras_cv/callbacks \
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keras_cv/losses \

‎.github/API_DESIGN.md‎ renamed to ‎API_DESIGN.md‎

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‎.github/CALL_FOR_CONTRIBUTIONS.md‎ renamed to ‎CALL_FOR_CONTRIBUTIONS.md‎

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‎.github/CONTRIBUTING.md‎ renamed to ‎CONTRIBUTING.md‎

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‎benchmarks/vectorized_randomly_zoomed_crop.py‎

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config["zoom_factor"]
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)
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if isinstance(config["aspect_ratio_factor"], dict):
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config[
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"aspect_ratio_factor"
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] = keras.utils.deserialize_keras_object(
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config["aspect_ratio_factor"]
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config["aspect_ratio_factor"] = (
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keras.utils.deserialize_keras_object(
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config["aspect_ratio_factor"]
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)
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)
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return cls(**config)
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‎examples/layers/preprocessing/classification/random_crop_and_zoom.py‎ renamed to ‎examples/layers/preprocessing/classification/random_crop_and_resize_demo.py‎

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

‎keras_cv/__init__.py‎

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from keras_cv.core import FactorSampler
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from keras_cv.core import NormalFactorSampler
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from keras_cv.core import UniformFactorSampler
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__version__ = "0.8.1"
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from keras_cv.version_utils import __version__
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from keras_cv.version_utils import version

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