CI/CD debug

* add log url

* fix: syntax error

* fix: syntax error

.cloud-build/execute_changed_notebooks_cli.py

@@ -18,6 +18,7 @@
 import argparse
 import pathlib
 import os
+import csv
 
 import execute_changed_notebooks_helper
 
@@ -37,7 +38,7 @@ parser = argparse.ArgumentParser(description="Run changed notebooks.")
 parser.add_argument(
     "--test_paths_file",
     type=pathlib.Path,
-    help="The path to the file that has newline-limited folders of notebooks that should be tested.",
+    help="The path to the file that has newline-delimited folders of notebooks that should be tested.",
     required=True,
 )
 parser.add_argument(
@@ -128,6 +129,13 @@ parser.add_argument(
     default=10,
     required=False,
 )
+parser.add_argument(
+    "--run_first_file",
+    type=pathlib.Path,
+    help="The path to the file that has newline-delimited of notebooks to run in the first batch",
+    default=None,
+    required=False,
+)
 parser.add_argument(
     "--dry_run",
     type=str2bool,
@@ -157,6 +165,29 @@ else:
     accumulative_results = execute_changed_notebooks_helper.load_results(results_bucket, results_file)
 
     notebooks = [changed_notebook for changed_notebook in changed_notebooks if execute_changed_notebooks_helper.select_notebook(changed_notebook, accumulative_results, args.test_percent)]
+    # cap the number of notebooks to the specified percentage
+    max_notebooks = int((len(changed_notebooks) * (args.test_percent/100)))
+    if (len(notebooks) > max_notebooks):
+        notebooks = notebooks[:max_notebooks]
+
+run_first = []
+if args.run_first_file:
+    if not os.path.isfile(args.run_first_file):
+        print("Error: file does not exist", args.run_first_file)
+    else:
+        with open(args.run_first_file, 'r') as csvfile:
+            reader = csv.reader(csvfile)
+            for row in reader:
+                notebook = row[0]
+                run_first.append(notebook)
+
+    for notebook in run_first:
+        if notebook in notebooks:
+            # remove from existing list
+            notebooks.remove(notebook)
+            # add back to the front of the list
+            notebooks.insert(0, notebook)
+            print(f"Run first: {notebook}")
 
 if args.dry_run:
     print("Dry run ...\n")

.cloud-build/execute_changed_notebooks_helper.py

@@ -45,6 +45,8 @@ PYTHON_VERSION = "3.9"  # Set default python version
 
 # rolling time window for accumulating build results for selecting notebooks
 MAX_RESULTS_AGE_SECONDS: int = (60 * 60) * 24 * 60  # 60 days
+# maximum time since last run to force a run on the current build
+MAX_AGE_BEFORE_FORCE_RUN: int = (60 * 60) * 24 * 30
 
 
 def format_timedelta(delta: datetime.timedelta) -> str:
@@ -118,8 +120,12 @@ def load_results(results_bucket: str,
                 if notebook in accumulative_results:
                     accumulative_results[notebook]['passed'] += build_results[notebook]['passed']
                     accumulative_results[notebook]['failed'] += build_results[notebook]['failed']
+                    if accumulative_results[notebook]['last_time_ran'] < time_created:
+                        accumulative_results[notebook]['last_time_ran'] = time_created
                 else:
                     accumulative_results[notebook] = build_results[notebook]
+                    accumulative_results[notebook]['failed_on_latest_run'] = build_results[notebook]['failed']
+                    accumulative_results[notebook]['last_time_ran'] = time_created
 
         print(accumulative_results)
     except Exception as e:
@@ -138,19 +144,37 @@ def select_notebook(changed_notebook: str,
     if changed_notebook in accumulative_results:
         pass_count = accumulative_results[changed_notebook]['passed']
         fail_count = accumulative_results[changed_notebook]['failed']
+        failed_on_latest_run = accumulative_results[changed_notebook]['failed_on_latest_run']
+        last_time_ran = accumulative_results[changed_notebook]['last_time_ran']
     else:
         pass_count = 1
         fail_count = 0
+        failed_on_latest_run = 0
+        last_time_ran = datetime.datetime.now().replace(tzinfo=None)
 
-    inferred_failure_rate = fail_count / (pass_count + fail_count)
+    # If notebook has not been ran in a long time, force running it
+    if (datetime.datetime.now().replace(tzinfo=None) - last_time_ran).total_seconds() > MAX_AGE_BEFORE_FORCE_RUN:
+        should_test_do_to_age = True
+    else:
+        should_test_do_to_age  = False
+
+
+    # if failed on the last time it was ran, select the notebook
+    if failed_on_latest_run:
+        inferred_failure_rate = 1
+    # otherwise, calculate the frequency of failure
+    else:
+        inferred_failure_rate = fail_count / (pass_count + fail_count)
 
     # If failure rate is high, the chance of testing should be higher
     should_test_due_to_failure = random.uniform(0, 1) <= inferred_failure_rate
 
+    #if accumulative_resultsi[changed_notebook]['latest_date_ran']
+
     # Additionally, only test a percentage of these
     should_test_due_to_random_subset = random.uniform(0, 1) <= (test_percent / 100)
 
-    if should_test_due_to_failure or should_test_due_to_random_subset:
+    if should_test_due_to_failure or should_test_due_to_random_subset or should_test_do_to_age:
         print(f"Selected: {changed_notebook}, {should_test_due_to_failure}, {should_test_due_to_random_subset}")
         return True
     else:
@@ -428,15 +452,39 @@ def _save_results(results: List[NotebookExecutionResult],
         else:
             pass_count = 0
             fail_count = 1
+        if result.error_message is None:
+            error_type = ''
+        elif '500 Internal' in result.error_message:
+            error_type = 'INTERNAL'
+        elif 'context deadline exceeded' in result.error_message:
+            error_type = 'TIMEOUT'
+        elif 'Quota' in result.error_message:
+            error_type = 'QUOTA'
+        elif 'ServiceUnavailable' in result.error_message:
+            error_type = 'SERVICEUNAVAILABLE'
+        elif 'ModuleNotFoundError' in result.error_message:
+            error_type = 'IMPORT'
+        elif result.is_pass:
+            error_type = ''
+        else:
+            error_type = 'undetermined'
+
+        if error_type != '':
+            log_url = result.log_url
+        else:
+            log_url = ''
+
         build_results[result.path] = {
                 'duration': result.duration.total_seconds(),
                 'start_time': str(result.start_time),
                 'passed': pass_count,
-                'failed': fail_count
+                'failed': fail_count,
+                'error_type': error_type,
+                'log_url': log_url
         }
         print(f"adding {result.path}")
 
-    print("Saving accumulative results ...")
+    print(f"Saving accumulative results to {results_file}, nentries {len(build_results)}")
     content = json.dumps(build_results)
 
     client = storage.Client()

.cloud-build/notebook-execution-test-cloudbuild.yaml

@@ -36,7 +36,7 @@ steps:
     - -c
     - |
       . workspace/env/bin/activate &&
-      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GPC_VPC_NETWORK_NAME}" `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi` --build_id ${BUILD_ID} --test_percent=${_TEST_PERCENT} --concurrent_notebooks=${_CONCURRENT_NOTEBOOKS}
+      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GCP_VPC_NETWORK_NAME}" `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi` --build_id ${BUILD_ID} --test_percent=${_TEST_PERCENT} --concurrent_notebooks=${_CONCURRENT_NOTEBOOKS} --run_first_file=${_RUN_FIRST_FILE}
     env:
     - 'IS_TESTING=1'
 timeout: 86400s

.cloud-build/run_first.csv

@@ -0,0 +1,7 @@
+notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb
+notebooks/official/generative_ai/rlhf_tune_llm.ipynb
+notebooks/official/generative_ai/tune_peft.ipynb
+notebooks/official/prediction/llm_streaming_prediction.ipynb
+notebooks/official/migration/sdk-automl-text-classification-batch-prediction.ipynb
+notebooks/official/vizier/get_started_vertex_vizier.ipynb
+notebooks/official/workbench/sentiment_analysis/Sentiment_Analysis.ipynb

.cloud-build/test_failing.txt

@@ -1,40 +0,0 @@
-notebooks/official/training/pytorch_gcs_data_training.ipynb
-notebooks/official/custom/custom_training_tensorboard_profiler.ipynb
-notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
-notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
-notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb
-notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb
-notebooks/official/tabnet/get_started_with_tabnet.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb
-notebooks/official/pipelines/multicontender_vs_champion_deployment_method.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb
-notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb
-notebooks/official/pipelines/challenger_vs_blessed_deployment_method.ipynb
-notebooks/official/matching_engine/sdk_matching_engine_create_stack_overflow_embeddings.ipynb
-notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb
-notebooks/official/matching_engine/sdk_matching_engine_create_text_to_image_embeddings.ipynb
-notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb
-notebooks/official/explainable_ai/xai_image_classification_feature_attributions.ipynb
-notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb
-notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb
-notebooks/official/model_registry/get_started_with_model_registry.ipynb
-notebooks/official/model_registry/bqml_vertexai_model_registry.ipynb
-notebooks/official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb
-notebooks/official/model_monitoring/batch_prediction_model_monitoring.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_custom.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_custom_tf_serving.ipynb
-notebooks/official/model_monitoring/model_monitoring.ipynb
-notebooks/official/tensorboard/tensorboard_profiler_custom_training_with_prebuilt_container.ipynb
-notebooks/official/tensorboard/tensorboard_hyperparameter_tuning_with_hparams.ipynb
-notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb
-notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb
-notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb
-notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb
-notebooks/official/experiments/comparing_local_trained_models.ipynb
-notebooks/official/automl/automl_image_classification_online_online_prediction.ipynb
-notebooks/official/automl/automl-text-classification.ipynb
-notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb
-notebooks/official/feature_store/sdk-feature-store-pandas.ipynb
-notebooks/official/prediction/custom_batch_prediction_feature_filter.ipynb
-notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb

.cloud-build/test_passing.txt

@@ -1,80 +0,0 @@
-notebooks/official/training/hyperparameter_tuning_tensorflow.ipynb
-notebooks/official/training/get_started_with_vertex_distributed_training.ipynb
-notebooks/official/training/hyperparameter_tuning_xgboost.ipynb
-notebooks/official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb
-notebooks/official/training/distributed_hyperparameter_tuning.ipynb
-notebooks/official/training/pytorch-text-sentiment-classification-custom-train-deploy.ipynb
-notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb
-notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb
-notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb
-notebooks/official/bigquery_ml/bqml-online-prediction.ipynb
-notebooks/official/custom/custom_training_container_and_model_registry.ipynb
-notebooks/official/custom/sdk-custom-image-classification-online.ipynb
-notebooks/official/custom/sdk-custom-image-classification-batch.ipynb
-notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb
-notebooks/official/custom/get_started_vertex_training_xgboost.ipynb
-notebooks/official/custom/get_started_with_vertex_endpoint_and_shared_vm.ipynb
-notebooks/official/custom/SDK_Custom_Container_Prediction.ipynb
-notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb
-notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb
-notebooks/official/vizier/get_started_vertex_vizier.ipynb
-notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
-notebooks/official/pipelines/get_started_with_hpt_pipeline_components.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb
-notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb
-notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb
-notebooks/official/pipelines/get_started_with_machine_management.ipynb
-notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb
-notebooks/official/pipelines/control_flow_kfp.ipynb
-notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb
-notebooks/official/pipelines/pipelines_intro_kfp.ipynb
-notebooks/official/pipelines/automl_tabular_classification_beans.ipynb
-notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb
-notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb
-notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb
-notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb
-notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain_get_metadata.ipynb
-notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb
-notebooks/official/tabular_workflows/prophet_on_vertex_pipelines.ipynb
-notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb
-notebooks/official/sdk/SDK_AutoML_Video_Classification.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_automl.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_batch.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_online.ipynb
-notebooks/official/model_monitoring/get_started_with_model_monitoring_xgboost.ipynb
-notebooks/official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb
-notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb
-notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb
-notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb
-notebooks/official/model_evaluation/get_started_with_custom_model_evaluation_import.ipynb
-notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb
-notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb
-notebooks/official/experiments/get_started_with_vertex_experiments.ipynb
-notebooks/official/experiments/comparing_pipeline_runs.ipynb
-notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb
-notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb
-notebooks/official/experiments/delete_outdated_tensorboard_experiments.ipynb
-notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb
-notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb
-notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb
-notebooks/official/automl/sdk_automl_forecasting_hierarchical_batch.ipynb
-notebooks/official/automl/automl_text_entity_extraction_batch_prediction.ipynb
-notebooks/official/automl/automl_image_classification_batch_prediction.ipynb
-notebooks/official/automl/automl_text_sentiment_analysis_batch_prediction.ipynb
-notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb
-notebooks/official/automl/get_started_automl_training.ipynb
-notebooks/official/automl/automl-tabular-classification.ipynb
-notebooks/official/automl/automl_image_object_detection_export_edge.ipynb
-notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb
-notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb
-notebooks/official/automl/sdk_automl_video_classification_batch.ipynb
-notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb
-notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb
-notebooks/official/automl/automl_image_object_detection_online_prediction.ipynb
-notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb
-notebooks/official/datasets/get_started_bq_datasets.ipynb
-notebooks/official/datasets/get_started_with_data_labeling.ipynb
-notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb

.cloud-build/utils/NotebookProcessors.py

@@ -35,7 +35,7 @@ class RemoveNoExecuteCells(Preprocessor):
 
 
 class UpdateVariablesPreprocessor(Preprocessor):
-    def __init__(self, replacement_map: Dict):
+    def __init__(self, replacement_map: Dict[str, str]):
         self._replacement_map = replacement_map
 
     @staticmethod

.cloud-build/utils/build_results_viewer.py

@@ -5,18 +5,51 @@ Cloud Storage location: gs://cloud-build-notebooks-presubmit/build_results/
 '''
 import argparse
 import json
+from  util import download_file
+import csv
 
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--file', dest='file',
                     default='build.json', type=str, help='build results file')
-import json
+args = parser.parse_args()
 
-with open('build.json', 'r') as f:
+investigate = {}
+with open('investigate.csv', 'r') as csvfile:
+    reader = csv.reader(csvfile)
+    for row in reader:
+        investigate[row[0][:-6]] = row[1]
+
+if args.file.startswith("gs://"):
+    path = args.file[5:]
+    bucket = path.split('/')[0]
+    file   = path[len(bucket)+1:]
+    download_file(bucket, file, "build.json")
+    args.file = "build.json"
+
+with open(args.file, 'r') as f:
     results = json.load(f)
 
 for item in results.items():
+    notebook = item[0][len("/notebooks/official/")-1:-6]
     if item[1]['passed']:
-        print(f"{item[0]},PASSED")
+        passed = "PASS"
     else:
-        print(f"{item[0]},FAILED")
+        if notebook in investigate:
+            passed = "INVG"
+        else:
+            passed = "FAIL"
+      
+    error = item[1]['error_type']
+  
+    if passed == "FAIL":
+        if error == '':
+            error = "undetermined"
+        if 'log_url' in item[1]:
+            log_url = item[1]['log_url']
+        else:
+            log_url = ''
+    else:
+        log_url = ''
+
+    print(f"{notebook:75} {passed} {error:10} {log_url}")

.cloud-build/utils/investigate.csv

@@ -0,0 +1,17 @@
+bigquery_ml/bqml-online-prediction.ipynb,flaky
+prediction/llm_streaming_prediction.ipynb,regr
+custom/get_started_with_vertex_endpoint_and_shared_vm.ipynb,wait_for_fix
+feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store.ipynb,regr
+pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb,wait_for_fix
+explainable_ai/sdk_custom_image_classification_batch_explain.ipynb,regr
+explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb,regr
+explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb,regr
+explainable_ai/xai_image_classification_feature_attributions.ipynb,regr
+matching_engine/sdk_matching_engine_create_multimodal_embeddings.ipynb,internal
+matching_engine/sdk_matching_engine_create_stack_overflow_embeddings.ipynb,internal
+matching_engine/sdk_matching_engine_for_indexing.ipynb,internal
+matching_engine,sdk_matching_engine_create_stack_overflow_embeddings_vertex.ipynb,regr
+migration/sdk-automl-image-classification-batch-online.ipynb,regr
+automl/automl_forecasting_bqml_arima_plus_comparison.ipynb,flaky
+experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb,regr
+model_evaluation/custom_tabular_regression_model_evaluation.ipynb,regr

.cloud-build/web-index/do_not_index.txt

@@ -0,0 +1,11 @@
+sdk2_remote_tabnet_training.ipynb
+remote_hyperparameter_tuning.ipynb
+remote_prediction.ipynb
+remote_training_bigframes_pytorch.ipynb
+remote_training_bigframes_sklearn.ipynb
+remote_training_bigframes_tensorflow.ipynb
+remote_training_lightning.ipynb
+remote_training_pytorch.ipynb
+remote_training_sklearn.ipynb
+remote_training_tensorflow_with_autologging.ipynb
+

.cloud-build/web-index/notebook-index-cloudbuild.yaml

@@ -15,7 +15,7 @@ steps:
     - -c
     - |
       . workspace/env/bin/activate &&
-      python3 notebooks/notebook_template_review.py --web --title --steps --desc --linkback --notebook-dir=notebooks/official >web.html
+      python3 notebooks/notebook_template_review.py --web --title --steps --desc --linkback --notebook-dir=notebooks/official --skip-file=${_DO_NOT_INDEX_FILE} >web.html
 artifacts:
   objects:
           location: gs://${_GCS_ARTIFACTS_BUCKET}/webdoc

.github/workflows/ci.yaml

@@ -11,7 +11,7 @@ jobs:
       with:
         python-version: '3.x'      
     - name: Fetch pull request branch
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         fetch-depth: 0      
     - name: Fetch base main branch

.github/workflows/linter/Dockerfile

@@ -4,7 +4,7 @@
 # 2. To lint specific notebooks:
 # docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
 
-FROM python:3.10
+FROM python:3.11
 
 WORKDIR setup
 

.github/workflows/linter/requirements.txt

@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==23.3.0
-pyupgrade==3.7.0
+black==23.10.1
+pyupgrade==3.15.0
 isort==5.12.0
-flake8==6.0.0
+flake8==6.1.0
 nbqa==1.7.0
 

community-content/CODEOWNERS

@@ -12,4 +12,15 @@
 /prediction_featurestore_integration @googleapis/vertex-prediction-team
 /vertex_vision_model_garden/model_oss/util @weigary
 /vertex_vision_model_garden/model_oss/diffusers @weigary
+/vertex_vision_model_garden/model_oss/keras @dstnluong-google
 /vertex_vision_model_garden/model_oss/transformers @dstnluong-google
+/vertex_vision_model_garden/model_oss/pic2word @jismailyan-google
+/vertex_vision_model_garden/model_oss/open_clip @lydhr
+/vertex_vision_model_garden/model_oss/movinet @KCFindstr
+/vertex_vision_model_garden/model_oss/data_converter @KCFindstr
+/vertex_vision_model_garden/model_oss/peft @weigary
+/vertex_vision_model_garden/model_oss/lm-evaluation-harness @kathyyu-google
+/vertex_vision_model_garden/model_oss/tfvision @dstnluong-google
+/vertex_vision_model_garden/model_oss/fvlm @minwoo33park
+/vertex_vision_model_garden/model_oss/imagebind @kathyyu-google
+

community-content/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines/Train_tabular_classification_model_using_XGBoost_and_import_to_Vertex_AI/pipeline.py

@@ -9,7 +9,7 @@ binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url
 split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
 train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
 xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/d5c9918850a6cc70004c4269dae066cfe2e664eb/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
 deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
 
 # %% Pipeline definition

community-content/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines/Train_tabular_classification_model_using_all_frameworks_and_import_to_Vertex_AI/pipeline.py

@@ -23,7 +23,7 @@ upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_comp
 # XGBoost
 train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
 xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/d5c9918850a6cc70004c4269dae066cfe2e664eb/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
 
 # Scikit-learn
 #train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")

community-content/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines/Train_tabular_regression_model_using_XGBoost_and_import_to_Vertex_AI/pipeline.py

@@ -8,7 +8,7 @@ fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_
 split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
 train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
 xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/d5c9918850a6cc70004c4269dae066cfe2e664eb/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
 deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
 
 # %% Pipeline definition

community-content/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines/Train_tabular_regression_model_using_all_frameworks_and_import_to_Vertex_AI/pipeline.py

@@ -22,7 +22,7 @@ upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_comp
 # XGBoost
 train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
 xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/d5c9918850a6cc70004c4269dae066cfe2e664eb/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
 
 # Scikit-learn
 train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")

community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml

@@ -64,8 +64,8 @@ implementation:
           labels["component-source"] = "github-com-ark-kun-pipeline-components"
 
           # The serving container decides the model type based on the model file extension.
-          # So we need to rename the mode file (e.g. /tmp/inputs/model/data) to *.pkl
-          _, renamed_model_path = tempfile.mkstemp(suffix=".pkl")
+          # So we need to rename the mode file (e.g. /tmp/inputs/model/data) to *.bst
+          _, renamed_model_path = tempfile.mkstemp(suffix=".bst")
           shutil.copyfile(src=model_path, dst=renamed_model_path)
 
           model = aiplatform.Model.upload_xgboost_model_file(

community-content/pipeline_components/image_ml_model_training/load_image_classification_model/component.yaml

@@ -87,7 +87,7 @@ outputs:
 - {name: image_size_path, type: HeightWidth}
 implementation:
   container:
-    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.1
+    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.2
     # command is a list of strings (command-line arguments).
     # The YAML language has two syntaxes for lists and you can use either of them.
     # Here we use the "flow syntax" - comma-separated strings inside square brackets.
@@ -109,4 +109,4 @@ implementation:
       {inputValue: l2_regularization_penalty},
       --image-size-path,
       {outputPath: image_size_path},
-    ]
+    ]

community-content/pipeline_components/image_ml_model_training/preprocess_image_data/component.yaml

@@ -34,7 +34,7 @@ outputs:
     path for the validation data,'}
 implementation:
   container:
-    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.1
+    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.2
     # command is a list of strings (command-line arguments).
     # The YAML language has two syntaxes for lists and you can use either of them.
     # Here we use the "flow syntax" - comma-separated strings inside square brackets.

community-content/pipeline_components/image_ml_model_training/train_image_classification_model/component.yaml

@@ -55,7 +55,7 @@ outputs:
     for the saved model,'}
 implementation:
   container:
-    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.1
+    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.2
     # command is a list of strings (command-line arguments).
     # The YAML language has two syntaxes for lists and you can use either of them.
     # Here we use the "flow syntax" - comma-separated strings inside square brackets.

community-content/pipeline_components/image_ml_model_training/transcode_tfrecord_image_dataset_from_csv/component.yaml

@@ -20,7 +20,7 @@ outputs:
     path for the TFRecord image data}
 implementation:
   container:
-    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.1
+    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.2
     # command is a list of strings (command-line arguments).
     # The YAML language has two syntaxes for lists and you can use either of them.
     # Here we use the "flow syntax" - comma-separated strings inside square brackets.

community-content/pipeline_components/image_ml_model_training/transcode_tfrecord_image_dataset_from_jsonl/component.yaml

@@ -22,7 +22,7 @@ outputs:
     path for the TFRecord image data}
 implementation:
   container:
-    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.1
+    image: us-docker.pkg.dev/vertex-ai/ready-to-go-image-classification/image-components:v0.2
     # command is a list of strings (command-line arguments).
     # The YAML language has two syntaxes for lists and you can use either of them.
     # Here we use the "flow syntax" - comma-separated strings inside square brackets.

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/logger/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-bigquery==2.20.0
 tensorflow==2.7.2
-pillow==9.0.1
+pillow==10.0.1
 tf-agents==0.8.0

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/prediction_container/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-pubsub==2.5.0
-pillow==9.0.1
+pillow==10.0.1
 tf-agents==0.8.0
 tensorflow==2.7.2

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/simulator/requirements.txt

@@ -1,5 +1,5 @@
 dataclasses==0.6
 google-cloud-aiplatform==1.8.1
 tensorflow==2.7.2
-pillow==9.0.1
+pillow==10.0.1
 tf-agents==0.8.0

community-content/vertex_vision_model_garden/model_oss/data_converter/common_lib.py

@@ -0,0 +1,623 @@
+"""Library with functions to use for data conversion."""
+
+import json
+import os
+import random
+from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+import uuid
+
+from absl import logging
+import apache_beam as beam
+import cv2
+import numpy as np
+import pandas as pd
+import PIL
+from PIL import Image
+import tensorflow as tf
+import yaml
+
+from util import constants
+from util import fileutils
+from apache_beam.options import pipeline_options
+
+REFORMATTED_CSV_SUFFIX = '-reformatted.csv'
+
+LABEL_MAP_NAME = 'label_map.yaml'
+
+_SPLIT_RATIO_ERROR_THRESHOLD = 1e-5
+# Internal constant. Only for distinguishing rows without ML use.
+ML_USE_UNASSIGNED = 'unassigned'
+ALL_ML_USES = (
+    constants.ML_USE_TRAINING,
+    constants.ML_USE_VALIDATION,
+    constants.ML_USE_TEST,
+    ML_USE_UNASSIGNED,
+)
+COLUMN_NAME_ML_USE = 'ml_use'
+COLUMN_NAME_GCS_FILE_PATH = 'gcs_file_path'
+COLUMN_NAME_LABEL = 'label'
+COLUMN_NAME_START_SEC = 'start_sec'
+COLUMN_NAME_END_SEC = 'end_sec'
+# Output filenames
+TRAIN_TFRECORD_NAME = 'train.tfrecord'
+VALIDATION_TFRECORD_NAME = 'val.tfrecord'
+TEST_TFRECORD_NAME = 'test.tfrecord'
+# Jsonl keys
+JSON_GCS_URI_KEY = 'imageGcsUri'
+JSON_RESOURCE_LABEL_KEY = 'dataItemResourceLabels'
+JSON_ML_USE_KEY = 'aiplatform.googleapis.com/ml_use'
+# I/O parameters
+READ_CHUNK_SIZE = 1024 * 1024 * 1024  # 1GB
+
+
+class WriteToTFRecord(beam.DoFn):
+  """DoFn to write TF examples to sharded TF record files."""
+
+  def __init__(
+      self,
+      output_prefix: str,
+      num_shards: int,
+      convert_fn: Callable[[Dict[str, Any]], tf.train.Example],
+  ):
+    self.output_prefix = output_prefix
+    self.num_shards = num_shards
+    self.writer: list[tf.io.TFRecordWriter] = []
+    self.sharded_files: list[str] = []
+    self.convert_fn = convert_fn
+    self.success_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Success'
+    )
+    self.failure_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Failure'
+    )
+
+  def start_bundle(self):
+    logging.info('Start writing TF Record to %s.', self.output_prefix)
+    unique_str = uuid.uuid4().hex
+    for i in range(self.num_shards):
+      uri = f'{self.output_prefix}-{i}-{unique_str}'
+      self.sharded_files.append(uri)
+      self.writer.append(tf.io.TFRecordWriter(uri))
+
+  def process(self, data: Dict[str, Any]) -> Iterable[Tuple[int, str]]:
+    try:
+      example = self.convert_fn(data)
+      data = example.SerializeToString()
+      idx = hash(data) % self.num_shards
+      self.writer[idx].write(data)
+      self.success_counter.inc()
+      yield (idx, self.sharded_files[idx])
+    # pylint: disable-next=broad-exception-caught
+    except Exception as err:
+      logging.error('Failed to process %s', data)
+      logging.exception(err)
+      self.failure_counter.inc()
+
+  def finish_bundle(self):
+    logging.info('Finish writing TF Record to %s.', self.output_prefix)
+    for writer in self.writer:
+      writer.close()
+    self.writer = []
+
+
+def convert_to_feature(
+    value: Union[List[Union[int, float, bytes]], int, float, bytes],
+    value_type: Optional[str] = None,
+) -> tf.train.Feature:
+  """Converts the given python object to a tf.train.Feature.
+
+  This is copied from tensorflow_models/official/vision/data/tfrecord_lib.py.
+
+  Args:
+    value: int, float, bytes or a list of them.
+    value_type: optional, if specified, forces the feature to be of the given
+      type. Otherwise, type is inferred automatically. Can be one of ['bytes',
+      'int64', 'float', 'bytes_list', 'int64_list', 'float_list']
+
+  Returns:
+    feature: A tf.train.Feature object.
+  """
+
+  if value_type is None:
+    element = value[0] if isinstance(value, list) else value
+
+    if isinstance(element, bytes):
+      value_type = 'bytes'
+
+    elif isinstance(element, (int, np.integer)):
+      value_type = 'int64'
+
+    elif isinstance(element, (float, np.floating)):
+      value_type = 'float'
+
+    else:
+      raise ValueError(
+          'Cannot convert type {} to feature'.format(type(element))
+      )
+
+    if isinstance(value, list):
+      value_type = value_type + '_list'
+
+  if value_type == 'int64':
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+  elif value_type == 'int64_list':
+    value = np.asarray(value).astype(np.int64).reshape(-1)
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+  elif value_type == 'float':
+    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
+
+  elif value_type == 'float_list':
+    value = np.asarray(value).astype(np.float32).reshape(-1)
+    return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+  elif value_type == 'bytes':
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+  elif value_type == 'bytes_list':
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
+
+  else:
+    raise ValueError('Unknown value_type parameter - {}'.format(value_type))
+
+
+def convert_to_string_feature(
+    value: str, encoding: str = 'utf-8'
+) -> tf.train.Feature:
+  """Returns a bytes_list from an encoded string."""
+  return convert_to_feature(value.encode(encoding))
+
+
+def convert_to_list_string_feature(
+    lst: list[str], encoding: str = 'utf-8'
+) -> tf.train.Feature:
+  """Returns a bytes_list from a list of encoded strings."""
+  return convert_to_feature([value.encode(encoding) for value in lst])
+
+
+def create_ml_use_array_with_split(
+    total_size: int,
+    split_ratio: Sequence[float],
+) -> list[str]:
+  """Create randomized list of 'training', 'validation', 'test'.
+
+  The list of will be of length total_size with ratios according to train_size,
+  validation_size, and test_size.
+
+  Args:
+    total_size: Length of sequence to return
+    split_ratio: Proportions to split into 'training', 'validation', and 'test'
+
+  Returns:
+    List containing 'training', 'validation', and 'test'
+  """
+  train_size, validation_size, _ = split_ratio
+  num_train = round(train_size * total_size)
+  num_validation = round(validation_size * total_size)
+  num_test = total_size - num_train - num_validation
+  ml_use_row = (
+      [constants.ML_USE_TRAINING] * num_train
+      + [constants.ML_USE_VALIDATION] * num_validation
+      + [constants.ML_USE_TEST] * num_test
+  )
+  random.shuffle(ml_use_row)
+  return ml_use_row
+
+
+def format_ml_use_column(df: pd.DataFrame):
+  df[COLUMN_NAME_ML_USE].replace(
+      # We need to support non-standard ML uses other than documented ones,
+      # since they are used by some existing datasets.
+      [r'(?i)^train(ing)?$', r'(?i)^test$', r'(?i)^validat(ion|e)$'],
+      [
+          constants.ML_USE_TRAINING,
+          constants.ML_USE_TEST,
+          constants.ML_USE_VALIDATION,
+      ],
+      inplace=True,
+      regex=True,
+  )
+
+
+def insert_missing_ml_use(df: pd.DataFrame) -> None:
+  """For every row that does not have ml_use as the first column, insert a column containing 'unassigned' to the front.
+
+  Args:
+    df: The DataFrame to process. The first column should be 'ml_use'.
+  """
+  df[COLUMN_NAME_ML_USE].fillna(ML_USE_UNASSIGNED, inplace=True)
+  rows_to_fill = ~df[COLUMN_NAME_ML_USE].isin(ALL_ML_USES)
+  df.loc[rows_to_fill] = df[rows_to_fill].shift(
+      axis=1, fill_value=ML_USE_UNASSIGNED
+  )
+
+
+def replace_unassigned_ml_use(
+    ml_uses: List[str],
+    split_ratio: Sequence[float],
+):
+  """Replace `unassigned` in ml_uses with `training`, `validation`, and `test` with ratios according to split_ratio.
+
+  Args:
+    ml_uses: List of ml_use string values.
+    split_ratio: Proportions to split into `training`, `validation`, and `test`.
+  """
+  unassigned_indices = [
+      i for i, ml_use in enumerate(ml_uses) if ml_use == ML_USE_UNASSIGNED
+  ]
+  ml_use_arr = create_ml_use_array_with_split(
+      len(unassigned_indices), split_ratio
+  )
+  for unassigned_index, ml_use in zip(unassigned_indices, ml_use_arr):
+    ml_uses[unassigned_index] = ml_use
+
+
+def merge_seq_into_dicts(
+    key: str, values: Sequence[Any], dicts: Sequence[Dict[Any, Any]]
+):
+  """Merges a list of values into a list of dicts, inserted with the given key.
+
+  Args:
+    key: Key to insert or overwrite in the dictionary.
+    values: A list of values to insert.
+    dicts: A list of dictionaries. Each value will be inserted into the
+      corresponding dictionary. The original value will be overwritten if the
+      key already existed.
+
+  Raises:
+    ValueError: The values and dicts have different lengths.
+  """
+  if len(values) != len(dicts):
+    raise ValueError(
+        f'Length of values and dicts must match, got {len(values)} and'
+        f' {len(dicts)}'
+    )
+  for val, d in zip(values, dicts):
+    d[key] = val
+
+
+def drop_invalid_rows(df: pd.DataFrame) -> int:
+  """Drops DataFrame rows missing the gcs_file_path column or the label column.
+
+  Args:
+    df: The DataFrame to process in place.
+
+  Returns:
+    The number of rows dropped.
+  """
+  original_rows = df.shape[0]
+  df.dropna(subset=[COLUMN_NAME_GCS_FILE_PATH, COLUMN_NAME_LABEL], inplace=True)
+  dropped_num = original_rows - df.shape[0]
+  if dropped_num > 0:
+    df.reset_index(drop=True, inplace=True)
+  return dropped_num
+
+
+def check_split_ratio(split_ratio: Sequence[float]):
+  """Checks if the give split ratio is valid.
+
+  Args:
+    split_ratio: Proportions to split into 'training', 'validation', and 'test'
+
+  Raises:
+    ValueError: Must have valid entries, correct length, and sum to 1.
+  """
+  if len(split_ratio) != 3:
+    raise ValueError('split_ratio must contain exactly 3 values.')
+  if abs(sum(split_ratio) - 1) > _SPLIT_RATIO_ERROR_THRESHOLD:
+    raise ValueError('split_ratio must sum to 1.')
+  if not all([0 <= val <= 1 for val in split_ratio]):
+    raise ValueError('Entries of split_ratio must be in the range [0, 1].')
+
+
+def check_num_shard(num_shard: Sequence[int]):
+  """Checks if the number of shards is valid.
+
+  Args:
+    num_shard: The number of shards for each tfrecord.
+
+  Raises:
+    ValueError: Must have valid entries and correct length.
+  """
+  if len(num_shard) != 3:
+    raise ValueError('num_shard must contain exactly 3 values.')
+  if not all([val >= 1 for val in num_shard]):
+    raise ValueError('Shards must be at least 1.')
+
+
+def create_label_map_yaml(meta_data_path: str, output_dir: str) -> None:
+  """Generate label_map.yaml from meta_data.yaml.
+
+  Args:
+    meta_data_path: Path to a meta_data.yaml file.
+    output_dir: Directory to output label_map.yaml.
+  """
+  tf.io.gfile.copy(
+      meta_data_path, os.path.join(output_dir, LABEL_MAP_NAME), overwrite=True
+  )
+
+
+def reformat_bbox(
+    bbox: Sequence[int], img_width: int, img_height: int
+) -> Tuple[float, float, float, float]:
+  """Converts XYWH unnormalized bounding box with to a normalized XYXY bounding box.
+
+  Args:
+    bbox: Relative bounding box with unnormalized coordinates as [x, y, width,
+      height].
+    img_width: Image's pixel width.
+    img_height: Image's pixel height.
+
+  Returns:
+    Absolute bounding box with normalized coordinates as
+      [xmin, ymin, xmax, ymax].
+  """
+  x, y, width, height = bbox
+  xmin = x / img_width
+  ymin = y / img_height
+  xmax = (x + width) / img_width
+  ymax = (y + height) / img_height
+  return xmin, ymin, xmax, ymax
+
+
+def encode_image(
+    filepath: str,
+    output_shape: Optional[Sequence[int]] = None,
+    image_format: str = 'png',
+) -> Tuple[bytes, Sequence[int]]:
+  """Encodes an image at the given path.
+
+  Args:
+    filepath: Path to the image.
+    output_shape: The output shape of the image, (height, width).
+    image_format: The format of the output image.
+
+  Returns:
+    The encoded image data in bytes and the shape of the image, (height, width).
+
+  Raises:
+    IOError: The image file is corrupt.
+  """
+  filepath = fileutils.force_gcs_fuse_path(filepath)
+  with open(filepath, 'rb') as f:
+    # If an output_shape is specified, resize the image and set data to the new
+    # bytes.
+    try:
+      img = Image.open(f)
+    except PIL.UnidentifiedImageError as e:
+      raise IOError(f'Failed to open {filepath}') from e
+
+    try:
+      if output_shape is not None:
+        rgb_img = img.resize((output_shape[1], output_shape[0])).convert('RGB')
+      else:
+        rgb_img = img.convert('RGB')
+      rgb_img = np.array(rgb_img)
+
+      _, data = cv2.imencode(f'.{image_format}', rgb_img)
+      data = data.tobytes()
+      return data, rgb_img.shape
+    except cv2.error as e:
+      raise IOError(f'Failed to encode {filepath}') from e
+    finally:
+      img.close()
+
+
+def encode_video(
+    filepath: str,
+    start_sec: float,
+    end_sec: float,
+    output_fps: int = 5,
+    output_shape: Optional[Sequence[int]] = None,
+    image_format: str = 'jpg',
+) -> Sequence[bytes]:
+  """Encodes a video clip at the given path with start and end timestamps.
+
+  Args:
+    filepath: Path to the video.
+    start_sec: Start timestamp of the video clip in seconds.
+    end_sec: End timestamp of the video clip in seconds.
+    output_fps: The output frame rate per second.
+    output_shape: The output shape of each frame, (height, width).
+    image_format: The format of the encoded frames.
+
+  Returns:
+    A list of the encoded frames data in bytes.
+
+  Raises:
+    IOError if the video file is corrupt.
+  """
+  filepath = fileutils.force_gcs_fuse_path(filepath)
+  video = None
+
+  try:
+    video = cv2.VideoCapture(filepath)
+    frames = []
+    frame_interval = 1 / output_fps
+    total_frames = video.get(cv2.CAP_PROP_FRAME_COUNT)
+    original_fps = video.get(cv2.CAP_PROP_FPS)
+    if not original_fps:
+      # 0 or None indicates the video is invalid
+      raise IOError(f'Failed to load {filepath}')
+    video_length = total_frames / original_fps
+    start_sec = max(start_sec, 0)
+    end_sec = min(end_sec, video_length)
+    for t in np.arange(start_sec, end_sec, frame_interval):
+      frame_idx = min(total_frames - 1, round(t * original_fps))
+      video.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+      ret, frame = video.read()
+      if not ret:
+        raise IOError(f'Failed to load {filepath} at frame {frame_idx}')
+      if output_shape is not None:
+        frame = cv2.resize(frame, (output_shape[1], output_shape[0]))
+      _, data = cv2.imencode(f'.{image_format}', frame)
+      frames.append(data.tobytes())
+  except cv2.error as e:
+    raise IOError(f'Failed to load {filepath}') from e
+  finally:
+    if video:
+      video.release()
+  return frames
+
+
+def create_label_map(
+    labels: Sequence[str],
+) -> Tuple[Sequence[int], Dict[int, str]]:
+  """Creates a label map from a sequence of label strings.
+
+  Args:
+    labels: The sequence of labels to create label map from. Must not contain
+      invalid values, which means data without labels should be filtered first.
+
+  Returns:
+    The integer labels and the mapping from integers to the original strings.
+  """
+  inverse_label_map: Dict[str, int] = dict()
+  num_labels = 0
+  for label in labels:
+    if label not in inverse_label_map:
+      num_labels += 1
+      inverse_label_map[label] = num_labels
+  int_labels = [inverse_label_map[label] for label in labels]
+  label_map = {value: key for key, value in inverse_label_map.items()}
+  return int_labels, label_map
+
+
+def write_label_map(output_file: str, label_map: Dict[int, str]) -> None:
+  """Writes a label map to the output file, which can be a GCS uri."""
+  with tf.io.gfile.GFile(output_file, 'w') as f:
+    yaml.dump({'label_map': label_map}, f)
+
+
+def detectron_json_to_image_rows(input_json: str) -> list[Dict[str, Any]]:
+  """Converts a Detectron JSON file to a list of image rows.
+
+  Args:
+    input_json: A path to a Detectron JSON or JSONL file.
+
+  Returns:
+    A list of dictionaries, where each dictionary contains Detectron format
+    entry.
+
+  Raises:
+    ValueError: If the input JSON is invalid.
+  """
+
+  image_rows = []
+  with tf.io.gfile.GFile(input_json, 'r') as f:
+    for line in f:
+      json_data = json.loads(line)
+      if isinstance(json_data, dict):
+        image_rows.append(json_data)
+      elif isinstance(json_data, list):
+        image_rows.extend(json_data)
+      else:
+        raise ValueError(
+            'The input JSON is invalid. Dict or list is expected, but got '
+            f'{type(json_data)}.'
+        )
+  return image_rows
+
+
+def coco_json_to_image_rows(
+    input_json: str,
+) -> List[Dict[str, Any]]:
+  """Converts a COCO JSON file to a list of image rows.
+
+  Args:
+    input_json: A path to a COCO JSON or JSONL file.
+
+  Returns:
+    A list of dictionaries, where each dictionary contains COCO format entry.
+
+  Raises:
+    ValueError: If the input JSON is invalid.
+  """
+
+  with tf.io.gfile.GFile(input_json, 'r') as f:
+    coco_json = json.load(f)
+  if 'annotations' not in coco_json:
+    raise ValueError('"annotations" is not in the dataset.')
+  if 'images' not in coco_json:
+    raise ValueError('"images" is not in the dataset.')
+
+  images = coco_json['images']
+  return images
+
+
+def partition_by_ml_use(element: Dict[str, Any], num_partitions: int) -> int:
+  """Beam partition function to split data by ml_use."""
+  del num_partitions
+  try:
+    partition = ALL_ML_USES.index(element[COLUMN_NAME_ML_USE])
+  except Exception as e:
+    raise ValueError(f'Invalid ML use: {element[COLUMN_NAME_ML_USE]}') from e
+  return partition
+
+
+def run_beam_pipeline(pipeline: Any) -> None:
+  """Runs a beam pipeline. Works in both internal and docker environment."""
+  options = pipeline_options.PipelineOptions([
+      '--runner=FlinkRunner',
+      '--faster_copy',
+      '--max_parallelism', '8',
+  ])
+  p = beam.Pipeline(options=options)
+  pipeline(p)
+  result = p.run()
+  result.wait_until_finish()
+  for counter in result.metrics().query()['counters']:
+    logging.info('%s counter: %s.', counter.key.metric.name, counter)
+  logging.info('Completing beam pipeline.')
+
+
+def beam_convert_tfexamples(
+    root: beam.Pipeline,
+    data_list: Sequence[Dict[str, Any]],
+    convert_fn: Callable[[Dict[str, Any]], tf.train.Example],
+    output_dir: str,
+    num_shards: Sequence[int],
+) -> None:
+  """Constructs beam pipelines to convert train, val, test TF Examples."""
+  names = [TRAIN_TFRECORD_NAME, VALIDATION_TFRECORD_NAME, TEST_TFRECORD_NAME]
+  split_data = (
+      root
+      | 'Create PCollection' >> beam.Create(data_list)
+      | 'Data split' >> beam.Partition(partition_by_ml_use, 3)
+  )
+  for i in range(3):
+    ml_use: str = ALL_ML_USES[i]
+    num_shard = num_shards[i]
+    output_prefix = os.path.join(output_dir, names[i])
+    _ = (
+        split_data[i]
+        | f'Convert {ml_use} TF Examples'
+        >> beam.ParDo(WriteToTFRecord(output_prefix, num_shard, convert_fn))
+        | f'Group {ml_use} TF Record files' >> beam.GroupBy(lambda x: x[0])
+        | f'Merge {ml_use} TF Record files'
+        >> beam.Map(merge_tfrecords_func(output_prefix, num_shard))
+    )
+
+
+def merge_tfrecords_func(output_prefix: str, num_shard: int) -> ...:
+  """Returns a function to merge sharded worker output into expected shards."""
+  output_prefix = fileutils.force_gcs_fuse_path(output_prefix)
+
+  def merge_tfrecords(worker_output: Tuple[int, Sequence[Tuple[int, str]]]):
+    idx = worker_output[0]
+    files: Sequence[str] = np.unique([x[1] for x in worker_output[1]])
+    output_file = f'{output_prefix}-{idx:05d}-of-{num_shard:05d}'
+    with open(output_file, 'wb') as f:
+      for file in files:
+        logging.info('Merging %s.', file)
+        file = fileutils.force_gcs_fuse_path(file)
+        with open(file, 'rb') as fin:
+          while True:
+            data = fin.read(READ_CHUNK_SIZE)
+            if not data:
+              break
+            f.write(data)
+        os.remove(file)
+
+  return merge_tfrecords

community-content/vertex_vision_model_garden/model_oss/data_converter/convert_coco_label_map_iod.py

@@ -0,0 +1,111 @@
+r"""Converts COCO labels as yamls for model garden playground (IOD).
+"""
+
+import os
+import urllib.request
+
+from absl import app
+from absl import flags
+import tensorflow as tf
+import yaml
+
+from object_detection.utils import label_map_util
+
+_CONVERT_LABEL_TYPE_COCO_80 = 'coco_80'
+_CONVERT_LABEL_TYPE_COCO_91 = 'coco_91'
+
+_CONVERT_LABEL_TYPE = flags.DEFINE_enum(
+    'convert_label_type',
+    None,
+    [
+        _CONVERT_LABEL_TYPE_COCO_80,
+        _CONVERT_LABEL_TYPE_COCO_91,
+    ],
+    'Different types of label type conversion.',
+    required=True,
+)
+
+_TEMPORARY_PATH = flags.DEFINE_string(
+    'temporary_path',
+    None,
+    'The tempory path.',
+    required=True,
+)
+
+_OUTPUT_YAML_FILEPATH = flags.DEFINE_string(
+    'output_yaml_filepath',
+    None,
+    'The output yaml filepath.',
+    required=True,
+)
+
+
+def convert_coco_label_map_91(
+    output_yaml_filepath: str,
+) -> None:
+  """Converts coco label map 91."""
+  input_proto_filepath = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt'
+  local_input_proto_filepath = os.path.join(
+      _TEMPORARY_PATH.value, 'mscoco_label_map.pbtxt'
+  )
+  with open(local_input_proto_filepath, 'w') as writer:
+    contents = (
+        urllib.request.urlopen(input_proto_filepath).read().decode('utf-8')
+    )
+    writer.write(contents)
+
+  label_map = label_map_util.load_labelmap(local_input_proto_filepath)
+  label_map_dict = label_map_util.get_label_map_dict(
+      label_map, use_display_name=True
+  )
+  swapped_label_map_dict = {v: k for k, v in label_map_dict.items()}
+  print(swapped_label_map_dict)
+
+  # Saves new label maps as yamls.
+  with tf.io.gfile.GFile(output_yaml_filepath, 'w') as writer:
+    writer.write(yaml.dump(swapped_label_map_dict))
+
+
+def convert_coco_label_map_80(
+    output_yaml_filepath: str,
+) -> None:
+  """Converts coco label map 80."""
+  # Loads label maps from texts.
+  input_text_filepath = 'https://gist.githubusercontent.com/AruniRC/7b3dadd004da04c80198557db5da4bda/raw/2f10965ace1e36c4a9dca76ead19b744f5eb7e88/ms_coco_classnames.txt'
+  local_input_text_filepath = os.path.join(
+      _TEMPORARY_PATH.value, 'ms_coco_classnames.txt'
+  )
+  with open(local_input_text_filepath, 'w') as writer:
+    contents = (
+        urllib.request.urlopen(input_text_filepath).read().decode('utf-8')
+    )
+    writer.write(contents)
+  with open(local_input_text_filepath, 'r') as file:
+    content = file.read()
+    label_map = yaml.safe_load(content)
+
+  # Removes background in label maps.
+  new_label_map = {}
+  for k, v in label_map.items():
+    if k == 0:
+      continue
+    new_label_map[k - 1] = v
+  print(new_label_map)
+  # Saves new label maps as yamls.
+  with tf.io.gfile.GFile(output_yaml_filepath, 'w') as writer:
+    writer.write(yaml.dump(new_label_map))
+
+
+def main(_) -> None:
+  if _CONVERT_LABEL_TYPE.value == _CONVERT_LABEL_TYPE_COCO_80:
+    convert_coco_label_map_80(_OUTPUT_YAML_FILEPATH.value)
+  elif _CONVERT_LABEL_TYPE.value == _CONVERT_LABEL_TYPE_COCO_91:
+    convert_coco_label_map_91(
+        _OUTPUT_YAML_FILEPATH.value,
+    )
+  else:
+    print('Not supported convert label type: ', _CONVERT_LABEL_TYPE.value)
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/data_converter/convert_imagenet_label_map.py

@@ -0,0 +1,86 @@
+r"""Converts ImageNet label texts as yamls for model garden playground.
+
+# ImageNet1K will have label maps with background.
+"""
+
+import urllib.request
+from absl import app
+from absl import flags
+import tensorflow as tf
+import yaml
+
+
+_INPUT_TEXT_FILEPATH = flags.DEFINE_string(
+    'input_text_filepath',
+    None,
+    'The input text filepath.',
+    required=True,
+)
+
+_ADD_BACKGROUND_LABEL = flags.DEFINE_boolean(
+    'add_background_label',
+    None,
+    'Whether or not add background labels.',
+    required=True,
+)
+
+_ADD_IDS = flags.DEFINE_boolean(
+    'add_ids',
+    None,
+    'Whether or not add ids.',
+    required=True,
+)
+
+_OUTPUT_YAML_FILEPATH = flags.DEFINE_string(
+    'output_yaml_filepath',
+    None,
+    'The output yaml filepath.',
+    required=True,
+)
+
+
+def convert_imagenet_label_map_from_text_to_yaml(
+    input_text_filepath: str,
+    add_background_label: bool,
+    add_ids: bool,
+    output_yaml_filepath: str,
+) -> None:
+  """Converts imagenet label map from text to yamls."""
+  label_map = {}
+
+  # Shifts all keys by 1, and add 0 as 'background'.
+  if add_background_label:
+    label_map = yaml.safe_load(
+        urllib.request.urlopen(input_text_filepath).read()
+    )
+    new_label_map = {}
+    for key, value in label_map.items():
+      new_label_map[key + 1] = value
+    new_label_map[0] = 'background'
+    label_map = new_label_map
+
+  # Adds maps from id to each line.
+  if add_ids:
+    lines = urllib.request.urlopen(input_text_filepath).readlines()
+    current_id = 0
+    for line in lines:
+      label_map[current_id] = line.decode('ascii').strip()
+      print(label_map[current_id])
+      current_id += 1
+
+  # Saves new label maps as yamls.
+  with tf.io.gfile.GFile(output_yaml_filepath, 'w') as writer:
+    writer.write(yaml.dump(label_map))
+
+
+def main(_) -> None:
+  convert_imagenet_label_map_from_text_to_yaml(
+      _INPUT_TEXT_FILEPATH.value,
+      _ADD_BACKGROUND_LABEL.value,
+      _ADD_IDS.value,
+      _OUTPUT_YAML_FILEPATH.value,
+  )
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/data_converter/data_converter_icn_lib.py

@@ -0,0 +1,199 @@
+"""Converts ICN CSV/JSONL files to TFRecord with apache beam."""
+
+import json
+from os import path
+from typing import Any, Dict, Sequence, Union, cast
+
+from absl import logging
+import apache_beam as beam
+import pandas as pd
+import tensorflow as tf
+
+from data_converter import common_lib
+
+
+_COLUMN_NAMES = [
+    common_lib.COLUMN_NAME_ML_USE,
+    common_lib.COLUMN_NAME_GCS_FILE_PATH,
+    common_lib.COLUMN_NAME_LABEL,
+]
+_JSON_GCS_URI_KEY = 'imageGcsUri'
+_JSON_CLASS_ANNOTATION_KEY = 'classificationAnnotation'
+_JSON_RESOURCE_LABEL_KEY = 'dataItemResourceLabels'
+_JSON_CLASS_NAME_KEY = 'displayName'
+_JSON_ML_USE_KEY = 'aiplatform.googleapis.com/ml_use'
+
+
+def build_tf_example(element: Dict[str, Union[str, int]]) -> tf.train.Example:
+  """Builds a TF Example from an image uri and label.
+
+  Args:
+    element: A dict with the keys gcs_file_path and label.
+
+  Returns:
+    The created TF Example.
+  """
+  image_uri = cast(str, element[common_lib.COLUMN_NAME_GCS_FILE_PATH])
+  label = cast(int, element[common_lib.COLUMN_NAME_LABEL])
+  image_bytes, shape = common_lib.encode_image(image_uri, image_format='jpeg')
+  features = tf.train.Features(
+      feature={
+          'image/encoded': common_lib.convert_to_feature(image_bytes),
+          'image/format': common_lib.convert_to_string_feature('jpeg'),
+          'image/height': common_lib.convert_to_feature(shape[0]),
+          'image/width': common_lib.convert_to_feature(shape[1]),
+          'image/class/label': common_lib.convert_to_feature(label),
+      },
+  )
+  return tf.train.Example(features=features)
+
+
+def _run_convert_pipeline(
+    output_dir: str, df: pd.DataFrame, num_shards: Sequence[int]
+) -> None:
+  """Starts a Beam pipeline to write DataFrame as TF Records.
+
+  Args:
+    output_dir: TF Records output directory.
+    df: DataFrame to convert from.
+    num_shards: Number of shards for train/validation/test TFRecord files.
+  """
+  images_list = df.to_dict('records')
+
+  def pipeline(root: beam.Pipeline):
+    common_lib.beam_convert_tfexamples(
+        root,
+        images_list,
+        build_tf_example,
+        output_dir,
+        num_shards,
+    )
+
+  common_lib.run_beam_pipeline(pipeline)
+
+
+def _convert_df_to_tfrecord(
+    df: pd.DataFrame,
+    output_dir: str,
+    split_ratio: Sequence[float],
+    num_shard: Sequence[int],
+) -> None:
+  """Converts a DataFrame into three separate tfrecords for training, validation, and testing into output_dir.
+
+  Args:
+    df: DataFrame to convert.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  # Replaces ml_use with common_lib string constants for consistency.
+  common_lib.format_ml_use_column(df)
+  common_lib.insert_missing_ml_use(df)
+
+  # Ignores invalid rows.
+  dropped_row_num = common_lib.drop_invalid_rows(df)
+  if dropped_row_num > 0:
+    logging.warning('Ignored %d invalid rows.', dropped_row_num)
+
+  common_lib.replace_unassigned_ml_use(
+      df[common_lib.COLUMN_NAME_ML_USE], split_ratio
+  )
+
+  # Converts labels to integers as required by training.
+  new_labels, label_map = common_lib.create_label_map(
+      df[common_lib.COLUMN_NAME_LABEL]
+  )
+  df[common_lib.COLUMN_NAME_LABEL] = new_labels
+  label_map_path = path.join(output_dir, common_lib.LABEL_MAP_NAME)
+  logging.info('Writing label map to %s.', label_map_path)
+  common_lib.write_label_map(label_map_path, label_map)
+
+  _run_convert_pipeline(output_dir, df, num_shard)
+
+
+def convert_csv_to_tfrecord(
+    input_csv: str,
+    output_dir: str,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_csv file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The csv format is shown in
+    https://cloud.google.com/vertex-ai/docs/image-data/classification/prepare-data#csv.
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_csv: Name of the csv file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  with tf.io.gfile.GFile(input_csv, 'r') as f:
+    df: pd.DataFrame = pd.read_csv(
+        f, header=None, names=_COLUMN_NAMES, on_bad_lines='warn'
+    )
+
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard)
+
+
+def convert_jsonl_to_tfrecord(
+    input_jsonl: str,
+    output_dir: str,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_jsonl file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The JSONL format is shown in
+    https://cloud.google.com/vertex-ai/docs/image-data/classification/prepare-data#json-lines.
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_jsonl: Name of the JSONL file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  df_rows = []
+  with tf.io.gfile.GFile(input_jsonl, 'r') as f:
+    lines = f.read().rstrip().splitlines()
+
+  for i, line in enumerate(lines, 1):
+    try:
+      item: Dict[str, Any] = json.loads(line)
+
+      gcs_uri = item.get(_JSON_GCS_URI_KEY)
+      label = item.get(_JSON_CLASS_ANNOTATION_KEY, {}).get(_JSON_CLASS_NAME_KEY)
+      if not gcs_uri or not label:
+        logging.warning('Invalid JSON at line %d, skipped.', i)
+        continue
+
+      ml_use = item.get(_JSON_RESOURCE_LABEL_KEY, {}).get(
+          _JSON_ML_USE_KEY, common_lib.ML_USE_UNASSIGNED
+      )
+    except (json.JSONDecodeError, AttributeError):
+      logging.warning('Invalid JSON at line %d, skipped.', i)
+      continue
+
+    df_rows.append([ml_use, gcs_uri, label])
+
+  df = pd.DataFrame(
+      data=df_rows,
+      columns=[
+          common_lib.COLUMN_NAME_ML_USE,
+          common_lib.COLUMN_NAME_GCS_FILE_PATH,
+          common_lib.COLUMN_NAME_LABEL,
+      ],
+  )
+
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard)

community-content/vertex_vision_model_garden/model_oss/data_converter/data_converter_iod_lib.py

@@ -0,0 +1,430 @@
+"""Converts IOD dataset files to TFRecord with apache beam."""
+
+import collections
+import json
+from os import path
+from typing import Any, Dict, Sequence
+
+from absl import logging
+import apache_beam as beam
+import pandas as pd
+import tensorflow as tf
+
+from data_converter import common_lib
+from util import constants
+
+COLUMN_NAME_LABEL_INT = 'label_int'
+_COLUMN_NAME_XMIN = 'X_MIN'
+_COLUMN_NAME_YMIN = 'Y_MIN'
+_COLUMN_NAME_XMAX = 'X_MAX'
+_COLUMN_NAME_YMAX = 'Y_MAX'
+COLUMN_NAMES = [
+    common_lib.COLUMN_NAME_ML_USE,
+    common_lib.COLUMN_NAME_GCS_FILE_PATH,
+    common_lib.COLUMN_NAME_LABEL,
+    _COLUMN_NAME_XMIN,
+    _COLUMN_NAME_YMIN,
+    'XMAX_NOT_USED',
+    'YMIN_NOT_USED',
+    _COLUMN_NAME_XMAX,
+    _COLUMN_NAME_YMAX,
+    'XMIN_NOT_USED',
+    'YMAX_NOT_USED',
+]
+_BOUNDING_BOX_COLUMNS = [
+    _COLUMN_NAME_XMIN,
+    _COLUMN_NAME_YMIN,
+    _COLUMN_NAME_XMAX,
+    _COLUMN_NAME_YMAX,
+]
+_JSON_BBOX_ANNOTATIONS_KEY = 'boundingBoxAnnotations'
+_JSON_DISPLAY_NAME_KEY = 'displayName'
+_JSON_X_MIN_KEY = 'xMin'
+_JSON_X_MAX_KEY = 'xMax'
+_JSON_Y_MIN_KEY = 'yMin'
+_JSON_Y_MAX_KEY = 'yMax'
+
+
+def build_tf_example(image_row: Dict[str, Any]) -> tf.train.Example:
+  """Builds a TF Example from an image row.
+
+  Args:
+    image_row: A dictionary containing information about the image, such as its
+      GCS uri, labels, and bounding box coordinates.
+
+  Returns:
+    A tf.train.Example containing the encoded image and optionally a
+      bounding box and label.
+  """
+  image_uri = image_row[common_lib.COLUMN_NAME_GCS_FILE_PATH]
+  image_bytes, shape = common_lib.encode_image(image_uri, image_format='jpeg')
+  feature = {
+      'image/encoded': common_lib.convert_to_feature(image_bytes),
+      'image/format': common_lib.convert_to_string_feature('jpeg'),
+      'image/height': common_lib.convert_to_feature(shape[0]),
+      'image/width': common_lib.convert_to_feature(shape[1]),
+      'image/source_id': common_lib.convert_to_string_feature(image_uri),
+      'image/object/bbox/xmin': common_lib.convert_to_feature(
+          image_row[_COLUMN_NAME_XMIN]
+      ),
+      'image/object/bbox/ymin': common_lib.convert_to_feature(
+          image_row[_COLUMN_NAME_YMIN]
+      ),
+      'image/object/bbox/xmax': common_lib.convert_to_feature(
+          image_row[_COLUMN_NAME_XMAX]
+      ),
+      'image/object/bbox/ymax': common_lib.convert_to_feature(
+          image_row[_COLUMN_NAME_YMAX]
+      ),
+      'image/object/class/text': common_lib.convert_to_list_string_feature(
+          image_row[common_lib.COLUMN_NAME_LABEL]
+      ),
+      'image/object/class/label': common_lib.convert_to_feature(
+          image_row[COLUMN_NAME_LABEL_INT]
+      ),
+  }
+  return tf.train.Example(features=tf.train.Features(feature=feature))
+
+
+def _run_convert_pipeline(
+    output_dir: str,
+    image_rows: Sequence[Dict[str, Any]],
+    num_shards: Sequence[int],
+) -> None:
+  """Starts a Beam pipeline to write DataFrame as TF Records.
+
+  Args:
+    output_dir: TF Records output directory.
+    image_rows: Contains all necessary information to create a TF Example.
+    num_shards: Number of shards for train/validation/test TFRecord files.
+  """
+
+  def pipeline(root: beam.Pipeline):
+    common_lib.beam_convert_tfexamples(
+        root,
+        image_rows,
+        build_tf_example,
+        output_dir,
+        num_shards,
+    )
+
+  common_lib.run_beam_pipeline(pipeline)
+
+
+def _convert_df_to_tfrecord(
+    df: pd.DataFrame,
+    output_dir: str,
+    split_ratio: Sequence[float],
+    num_shard: Sequence[int],
+) -> None:
+  """Converts a DataFrame into three separate tfrecords for training, validation, and testing into output_dir.
+
+  Args:
+    df: DataFrame to convert.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  # Replaces ml_use with common_lib string constants for consistency.
+  common_lib.format_ml_use_column(df)
+  common_lib.insert_missing_ml_use(df)
+
+  # Specify bounding box columns to be numeric.
+  df[_BOUNDING_BOX_COLUMNS] = df[_BOUNDING_BOX_COLUMNS].apply(pd.to_numeric)
+
+  # Ignores invalid rows.
+  dropped_row_num = common_lib.drop_invalid_rows(df)
+  dropped_row_num += drop_rows_without_bbox(df)
+  if dropped_row_num > 0:
+    logging.warning('Ignored %d invalid rows.', dropped_row_num)
+
+  # Converts labels to integers as required by training.
+  int_labels, label_map = common_lib.create_label_map(
+      df[common_lib.COLUMN_NAME_LABEL]
+  )
+  df[COLUMN_NAME_LABEL_INT] = int_labels
+  label_map_path = path.join(output_dir, common_lib.LABEL_MAP_NAME)
+  logging.info('Writing label map to %s.', label_map_path)
+  common_lib.write_label_map(label_map_path, label_map)
+
+  image_rows = _condense_bounding_boxes(df.to_dict(orient='records'))
+  ml_uses = [row[common_lib.COLUMN_NAME_ML_USE] for row in image_rows]
+  common_lib.replace_unassigned_ml_use(ml_uses, split_ratio)
+  common_lib.merge_seq_into_dicts(
+      common_lib.COLUMN_NAME_ML_USE, ml_uses, image_rows
+  )
+
+  _run_convert_pipeline(output_dir, image_rows, num_shard)
+
+
+def _condense_bounding_boxes(
+    image_rows: Sequence[Dict[str, Any]]
+) -> Sequence[Dict[str, Any]]:
+  """Gather all the bounding boxes in an image and put them in the same dictionary.
+
+  Args:
+    image_rows: List of dictionaries, each containing information about the
+      image, such as its GCS uri, labels, and bounding box coordinates.
+
+  Returns:
+    List of dictionaries such that each contains all the bounding boxes for a
+    given gcs_file_path.
+
+  Raises:
+    RuntimeError: This is raised when the input data contains images that have
+      annotations in different ml_use classes.
+  """
+  output = {}
+  for image_row in image_rows:
+    ml_use = image_row[common_lib.COLUMN_NAME_ML_USE]
+    gcs_file_path = image_row[common_lib.COLUMN_NAME_GCS_FILE_PATH]
+    label = image_row[common_lib.COLUMN_NAME_LABEL]
+    xmin = image_row[_COLUMN_NAME_XMIN]
+    ymin = image_row[_COLUMN_NAME_YMIN]
+    xmax = image_row[_COLUMN_NAME_XMAX]
+    ymax = image_row[_COLUMN_NAME_YMAX]
+    label_int = image_row[COLUMN_NAME_LABEL_INT]
+    if gcs_file_path in output:
+      d = output[gcs_file_path]
+      if ml_use != common_lib.ML_USE_UNASSIGNED:
+        if d[common_lib.COLUMN_NAME_ML_USE] == common_lib.ML_USE_UNASSIGNED:
+          d[common_lib.COLUMN_NAME_ML_USE] = ml_use
+        elif ml_use != d[common_lib.COLUMN_NAME_ML_USE]:
+          raise RuntimeError(
+              f'Image {gcs_file_path} can only be placed in one of'
+              f' training/validation/test. It is currently in {ml_use} and'
+              f' {d[common_lib.COLUMN_NAME_ML_USE]}.'
+          )
+      d[common_lib.COLUMN_NAME_LABEL].append(label)
+      d[_COLUMN_NAME_XMIN].append(xmin)
+      d[_COLUMN_NAME_YMIN].append(ymin)
+      d[_COLUMN_NAME_XMAX].append(xmax)
+      d[_COLUMN_NAME_YMAX].append(ymax)
+      d[COLUMN_NAME_LABEL_INT].append(label_int)
+    else:
+      output[gcs_file_path] = {
+          common_lib.COLUMN_NAME_ML_USE: ml_use,
+          common_lib.COLUMN_NAME_GCS_FILE_PATH: gcs_file_path,
+          common_lib.COLUMN_NAME_LABEL: [label],
+          _COLUMN_NAME_XMIN: [xmin],
+          _COLUMN_NAME_YMIN: [ymin],
+          _COLUMN_NAME_XMAX: [xmax],
+          _COLUMN_NAME_YMAX: [ymax],
+          COLUMN_NAME_LABEL_INT: [label_int],
+      }
+  return list(output.values())
+
+
+def convert_csv_to_tfrecord(
+    input_csv: str,
+    output_dir: str,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_csv file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The csv format is shown in
+    https://cloud.google.com/vertex-ai/docs/image-data/object-detection/prepare-data#csv.
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_csv: Name of the csv file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the train, validation, and test splits for
+      unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  with tf.io.gfile.GFile(input_csv, 'r') as f:
+    df: pd.DataFrame = pd.read_csv(
+        f, header=None, names=COLUMN_NAMES, on_bad_lines='warn'
+    )
+
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard)
+
+
+def drop_rows_without_bbox(df: pd.DataFrame) -> int:
+  """Drops DataFrame rows without bounding_boxes.
+
+  Args:
+    df: The DataFrame to process in place.
+
+  Returns:
+    The number of rows dropped.
+  """
+  invalid_rows = df.index[~(df[_BOUNDING_BOX_COLUMNS].notnull().all(axis=1))]
+  dropped_num = len(invalid_rows)
+  if dropped_num > 0:
+    invalid_df = df.loc[invalid_rows].to_dict(orient='records')
+    for entry in invalid_df:
+      logging.warning('Skipping entry due to missing bounding box: %s.', entry)
+    df.drop(invalid_rows, inplace=True)
+    df.reset_index(drop=True, inplace=True)
+  return dropped_num
+
+
+def convert_coco_json_categories_to_label_map(
+    categories: Sequence[Dict[str, Any]]
+) -> Dict[int, str]:
+  return {category['id']: category['name'] for category in categories}
+
+
+def convert_coco_json_to_tfrecord(
+    input_coco_json: str,
+    output_dir: str,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_csv file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The COCO json format is shown here: https://cocodataset.org/#format-data.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_coco_json: Name of coco json file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the train, validation, and test splits for
+      dataset.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  with tf.io.gfile.GFile(input_coco_json, 'r') as f:
+    coco_json = json.load(f)
+  # Writes label map from coco json categories.
+  label_map = convert_coco_json_categories_to_label_map(
+      coco_json[constants.COCO_JSON_CATEGORIES]
+  )
+  label_map_path = path.join(output_dir, common_lib.LABEL_MAP_NAME)
+  logging.info('Writes label map to %s.', label_map_path)
+  common_lib.write_label_map(label_map_path, label_map)
+
+  img_to_anns = collections.defaultdict(list)
+  imgs = {}
+  if constants.COCO_JSON_ANNOTATIONS in coco_json:
+    for ann in coco_json[constants.COCO_JSON_ANNOTATIONS]:
+      img_to_anns[ann[constants.COCO_JSON_ANNOTATION_IMAGE_ID]].append(ann)
+
+  if constants.COCO_JSON_IMAGES in coco_json:
+    for img in coco_json[constants.COCO_JSON_IMAGES]:
+      imgs[img[constants.COCO_JSON_IMAGE_ID]] = img
+
+  df_rows = []
+
+  for image_id, annotations in img_to_anns.items():
+    img = imgs[image_id]
+    for ann in annotations:
+      xmin, ymin, xmax, ymax = common_lib.reformat_bbox(
+          ann[constants.COCO_ANNOTATION_BBOX],
+          img[constants.COCO_JSON_IMAGE_WIDTH],
+          img[constants.COCO_JSON_IMAGE_HEIGHT],
+      )
+      df_rows.append([
+          common_lib.ML_USE_UNASSIGNED,
+          img[constants.COCO_JSON_IMAGE_COCO_URL],
+          label_map[ann[constants.COCO_JSON_ANNOTATION_CATEGORY_ID]],
+          xmin,
+          ymin,
+          xmax,
+          ymin,
+          xmax,
+          ymax,
+          xmin,
+          ymax,
+          ann[constants.COCO_JSON_ANNOTATION_CATEGORY_ID],
+      ])
+  df = pd.DataFrame(
+      data=df_rows,
+      columns=COLUMN_NAMES + [COLUMN_NAME_LABEL_INT],
+  )
+
+  # Replaces ml_use with common_lib string constants for consistency.
+  common_lib.format_ml_use_column(df)
+  common_lib.insert_missing_ml_use(df)
+
+  # Species bounding box columns to be numeric.
+  df[_BOUNDING_BOX_COLUMNS] = df[_BOUNDING_BOX_COLUMNS].apply(pd.to_numeric)
+
+  # Ignores invalid rows.
+  dropped_row_num = common_lib.drop_invalid_rows(df)
+  dropped_row_num += drop_rows_without_bbox(df)
+  if dropped_row_num > 0:
+    logging.warning('Ignored %d invalid rows.', dropped_row_num)
+
+  image_rows = _condense_bounding_boxes(df.to_dict(orient='records'))
+  ml_uses = [row[common_lib.COLUMN_NAME_ML_USE] for row in image_rows]
+  common_lib.replace_unassigned_ml_use(ml_uses, split_ratio)
+  common_lib.merge_seq_into_dicts(
+      common_lib.COLUMN_NAME_ML_USE, ml_uses, image_rows
+  )
+
+  _run_convert_pipeline(output_dir, image_rows, num_shard)
+
+
+def convert_jsonl_to_tfrecord(
+    input_jsonl: str,
+    output_dir: str,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_jsonl file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The JSONL format is shown in
+    https://cloud.google.com/vertex-ai/docs/image-data/object-detection/prepare-data#json-lines.
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_jsonl: Name of the JSONL file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  df_rows = []
+  with tf.io.gfile.GFile(input_jsonl, 'r') as f:
+    lines = f.read().rstrip().splitlines()
+
+  for i, line in enumerate(lines, start=1):
+    try:
+      item: Dict[str, Any] = json.loads(line)
+    except (json.JSONDecodeError, AttributeError):
+      logging.warning('Invalid JSON at line %d skipped.', i)
+      continue
+
+    gcs_uri = item.get(common_lib.JSON_GCS_URI_KEY)
+    if not gcs_uri:
+      logging.warning(
+          'Invalid JSON at line %d skipped. Missing gcs_uri_key.', i
+      )
+      continue
+    ml_use = item.get(common_lib.JSON_RESOURCE_LABEL_KEY, {}).get(
+        common_lib.JSON_ML_USE_KEY, common_lib.ML_USE_UNASSIGNED
+    )
+
+    for bbox in item.get(_JSON_BBOX_ANNOTATIONS_KEY, []):
+      label = bbox.get(_JSON_DISPLAY_NAME_KEY)
+      xmin = bbox.get(_JSON_X_MIN_KEY)
+      ymin = bbox.get(_JSON_Y_MIN_KEY)
+      xmax = bbox.get(_JSON_X_MAX_KEY)
+      ymax = bbox.get(_JSON_Y_MAX_KEY)
+
+      df_rows.append([ml_use, gcs_uri, label, xmin, ymin, xmax, ymax])
+
+  df = pd.DataFrame(
+      data=df_rows,
+      columns=[
+          common_lib.COLUMN_NAME_ML_USE,
+          common_lib.COLUMN_NAME_GCS_FILE_PATH,
+          common_lib.COLUMN_NAME_LABEL,
+          _COLUMN_NAME_XMIN,
+          _COLUMN_NAME_YMIN,
+          _COLUMN_NAME_XMAX,
+          _COLUMN_NAME_YMAX,
+      ],
+  )
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard)

community-content/vertex_vision_model_garden/model_oss/data_converter/data_converter_isg_lib.py

@@ -0,0 +1,328 @@
+"""Python script to convert different file formats for ISG to tfrecords."""
+
+import hashlib
+import os
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+
+from absl import logging
+import apache_beam as beam
+from apache_beam.io import tfrecordio
+import cv2
+import numpy as np
+from pycocotools import coco
+import tensorflow as tf
+import yaml
+
+from data_converter import common_lib
+from util import constants
+from util import fileutils
+
+_IMAGE_FORMAT = 'PNG'
+
+
+def build_tf_example(
+    image_info: dict[str, Union[str, int]],
+    segmentation_image: List[List[int]],
+    output_shape: Optional[Tuple[int, int]] = None,
+) -> tf.train.Example:
+  """Encodes an image and its segmentation mask into a tf.train.Example.
+
+  Args:
+    image_info: A dictionary containing information about the image, such as its
+      file name, height, and width.
+    segmentation_image: 2D image in list of lists having category ids.
+    output_shape: The desired output shape of the image. If None, the original
+      image shape will be used.
+
+  Returns:
+    A tf.train.Example containing the encoded image and segmentation mask.
+
+  Raises:
+    IOError: If image cannot be found in the path.
+  """
+  file_name = image_info[constants.COCO_JSON_FILE_NAME]
+  height = int(image_info[constants.COCO_JSON_IMAGE_HEIGHT])
+  width = int(image_info[constants.COCO_JSON_IMAGE_WIDTH])
+
+  segmentation_image = np.expand_dims(
+      np.asarray(segmentation_image, dtype=np.int32), axis=-1
+  )
+  _, encoded_seg = cv2.imencode(f'.{_IMAGE_FORMAT.lower()}', segmentation_image)
+  encoded_seg = encoded_seg.tobytes()
+
+  encoded_img, _ = common_lib.encode_image(
+      image_info[constants.COCO_JSON_IMAGE_COCO_URL],
+      output_shape=output_shape,
+      image_format=_IMAGE_FORMAT.lower(),
+  )
+
+  key = hashlib.sha256(encoded_img).hexdigest()
+
+  return tf.train.Example(
+      features=tf.train.Features(
+          feature={
+              'image/height': common_lib.convert_to_feature(height),
+              'image/width': common_lib.convert_to_feature(width),
+              'image/filename': common_lib.convert_to_string_feature(file_name),
+              'image/sha256': common_lib.convert_to_string_feature(key),
+              'image/encoded': common_lib.convert_to_feature(encoded_img),
+              'image/format': common_lib.convert_to_string_feature(
+                  _IMAGE_FORMAT
+              ),
+              'image/segmentation/class/encoded': common_lib.convert_to_feature(
+                  encoded_seg
+              ),
+              'image/segmentation/class/format': (
+                  common_lib.convert_to_string_feature(_IMAGE_FORMAT)
+              ),
+              'image/segmentation/class/height': common_lib.convert_to_feature(
+                  height
+              ),
+              'image/segmentation/class/width': common_lib.convert_to_feature(
+                  width
+              ),
+          }
+      )
+  )
+
+
+class AcquireTFExampleDoFn(beam.DoFn):
+  """Beam DoFn to build TF Examples from a single row of image_info data."""
+
+  # These tags will be used to tag the outputs of this DoFn.
+  output_tag_train = constants.ML_USE_TRAINING
+  output_tag_validation = constants.ML_USE_VALIDATION
+  output_tag_test = constants.ML_USE_TEST
+
+  valid_ml_use_set = set(
+      [output_tag_train, output_tag_validation, output_tag_test]
+  )
+
+  def __init__(self, output_shape: Optional[Tuple[int, int]] = None):
+    self.acquired_examples_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Success'
+    )
+    self.failure_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Failure'
+    )
+    self.output_shape = output_shape
+
+  def process(
+      self,
+      row: Tuple[str, Dict[str, Union[str, int]], List[List[int]]],
+  ) -> Iterator[tf.train.Example]:
+    ml_use, image_info, annotation_info = row
+    if ml_use not in self.valid_ml_use_set:
+      logging.warning('ml_use invalid: %s', ml_use)
+      self.failure_counter.inc()
+      return
+
+    try:
+      tf_example = build_tf_example(
+          image_info, annotation_info, self.output_shape
+      )
+    except IOError as e:
+      logging.warning('Failed to build TF Example: %s', e)
+      self.failure_counter.inc()
+    else:
+      self.acquired_examples_counter.inc()
+      yield beam.pvalue.TaggedOutput(ml_use, tf_example)
+
+
+def _define_data_conversion_pipeline(
+    root: beam.Pipeline,
+    ml_use_rows: List[str],
+    image_rows: List[Dict[str, Union[str, int]]],
+    segmentation_rows: List[List[List[int]]],
+    output_dir: str,
+    output_shape: Optional[Tuple[int, int]],
+    num_shard_list: List[int],
+):
+  """Define a data conversion pipeline.
+
+  Args:
+    root: A Beam pipeline.
+    ml_use_rows: List containing the ml_use.
+    image_rows: List of dictionaries containing information about the image,
+      such as its file name, height, and width.
+    segmentation_rows: List of 2D images of integers representing segmentation
+      masks.
+    output_dir: Directory where the output TFRecords will be written.
+    output_shape: Desired output shape of the image. If None, the original image
+      shape will be used.
+    num_shard_list: Number of shards to write to each output TFRecord.
+
+  Returns:
+    A Beam pipeline.
+  """
+  train, validation, test = (
+      root
+      | 'Load ml use and image rows to beam'
+      >> beam.Create(zip(ml_use_rows, image_rows, segmentation_rows))
+      | 'Build TF Examples'
+      >> beam.ParDo(AcquireTFExampleDoFn(output_shape)).with_outputs(
+          AcquireTFExampleDoFn.output_tag_train,
+          AcquireTFExampleDoFn.output_tag_validation,
+          AcquireTFExampleDoFn.output_tag_test,
+      )
+  )
+
+  # Save each split to TFRecord.
+  _ = train | 'Save train split to TFRecord' >> tfrecordio.WriteToTFRecord(
+      os.path.join(output_dir, common_lib.TRAIN_TFRECORD_NAME),
+      coder=beam.coders.ProtoCoder(tf.train.Example),
+      num_shards=num_shard_list[0],
+  )
+  _ = (
+      validation
+      | 'Save validation split to TFRecord'
+      >> tfrecordio.WriteToTFRecord(
+          os.path.join(output_dir, common_lib.VALIDATION_TFRECORD_NAME),
+          coder=beam.coders.ProtoCoder(tf.train.Example),
+          num_shards=num_shard_list[1],
+      )
+  )
+  _ = test | 'Save test split to TFRecord' >> tfrecordio.WriteToTFRecord(
+      os.path.join(output_dir, common_lib.TEST_TFRECORD_NAME),
+      coder=beam.coders.ProtoCoder(tf.train.Example),
+      num_shards=num_shard_list[2],
+  )
+
+
+def _image_info_to_segmentation_image(
+    img: Dict[str, Any],
+    coco_dataset: coco.COCO,
+    label_id_by_category_id: Dict[int, int],
+) -> List[List[int]]:
+  """Convert image information to a segmentation image.
+
+  Args:
+      img: The image information.
+      coco_dataset: The COCO dataset.
+      label_id_by_category_id: The mapping from label id used for training to
+        category_id defined in dataset.
+
+  Returns:
+      The segmentation image.
+
+  Raises:
+    ValueError: If the mask size does not match the image or if a pixel has
+      multiple labels.
+  """
+  seg_img = np.zeros(
+      shape=(
+          img[constants.COCO_JSON_IMAGE_HEIGHT],
+          img[constants.COCO_JSON_IMAGE_WIDTH],
+      ),
+      dtype=np.int32,
+  )
+  for ann in coco_dataset.imgToAnns[img[constants.COCO_JSON_IMAGE_ID]]:
+    new_category_id = ann[constants.COCO_JSON_ANNOTATION_CATEGORY_ID]
+    binary_mask = coco_dataset.annToMask(ann)
+    if seg_img.shape != binary_mask.shape:
+      raise ValueError(
+          'Binary mask does not have the same shape as image. image_id:'
+          f' {img["id"]}'
+      )
+    boolean_mask = binary_mask == 1
+    if (seg_img[boolean_mask] != 0).any():
+      raise ValueError(
+          'Error: Some pixels have more than one label in image_id:'
+          f' {img["id"]}.'
+      )
+    seg_img[boolean_mask] = label_id_by_category_id[new_category_id]
+
+  return seg_img.tolist()
+
+
+def get_input_rows(
+    coco_dataset: coco.COCO,
+    split_ratio: List[float],
+    label_id_by_category_id: Dict[int, int],
+) -> Tuple[List[str], List[Dict[str, Union[str, int]]], List[List[List[int]]]]:
+  """Get input rows for training and validation.
+
+  Args:
+      coco_dataset: The COCO dataset.
+      split_ratio: The split ratio for training and validation.
+      label_id_by_category_id: The mapping from label id used for training to
+        category_id defined in dataset.
+
+  Returns:
+      - A list of ml_use strings.
+      - A list of image informations.
+      - A list of segmentation images for the corresponding images.
+  """
+  image_rows = coco_dataset.dataset[constants.COCO_JSON_IMAGES]
+
+  segmentation_rows = [
+      _image_info_to_segmentation_image(
+          img, coco_dataset, label_id_by_category_id
+      )
+      for img in image_rows
+  ]
+
+  ml_use_rows = common_lib.create_ml_use_array_with_split(
+      len(image_rows), split_ratio
+  )
+  return ml_use_rows, image_rows, segmentation_rows
+
+
+def beam_build_tfrecord_from_coco_json(
+    input_json: str,
+    output_dir: str,
+    split_ratio: List[float],
+    num_shard_list: List[int],
+    output_shape: Optional[Tuple[int, int]] = None,
+) -> None:
+  """Builds TFRecord files from COCO dataset.
+
+  The output file names are `_TRAIN_TFRECORD_NAME`, `_VALIDATION_TFRECORD_NAME`,
+  and `_TEST_TFRECORD_NAME`.
+
+  Args:
+    input_json: Path to a COCO JSON or JSONL file.
+    output_dir: Directory to output the TFRecord files.
+    split_ratio: List of how to split entries to train, validation, and test
+      TFRecords.
+    num_shard_list: List of the number of shards for each TFRecord file.
+    output_shape: The desired output shape of the image. If None, the original
+      image shape will be used.
+  """
+  # `coco` cannot access gcs uri. Use gcsfuse, it is faster.
+  input_json = fileutils.force_gcs_fuse_path(input_json)
+  coco_dataset = coco.COCO(input_json)
+
+  label_map = {}
+  label_id_by_category_id = {}
+  for idx, category in enumerate(
+      coco_dataset.dataset[constants.COCO_JSON_CATEGORIES], start=1
+  ):
+    label_map[idx] = category[constants.COCO_JSON_CATEGORY_NAME]
+    label_id_by_category_id[category[constants.COCO_JSON_CATEGORY_ID]] = idx
+  label_map_path = os.path.join(output_dir, common_lib.LABEL_MAP_NAME)
+  logging.info('Writing label map to %s.', label_map_path)
+  common_lib.write_label_map(label_map_path, label_map)
+
+  with tf.io.gfile.GFile(
+      os.path.join(output_dir, 'label_id_by_category_id.yaml'), 'w'
+  ) as f:
+    yaml.dump(label_id_by_category_id, f)
+
+  ml_use_rows, image_rows, segmentation_rows = get_input_rows(
+      coco_dataset, split_ratio, label_id_by_category_id
+  )
+
+  def pipeline(root):
+    _define_data_conversion_pipeline(
+        root,
+        ml_use_rows,
+        image_rows,
+        segmentation_rows,
+        output_dir,
+        output_shape,
+        num_shard_list,
+    )
+
+  logging.info('Beginning beam pipeline to acquire tfrecords.')
+  common_lib.run_beam_pipeline(pipeline)

community-content/vertex_vision_model_garden/model_oss/data_converter/data_converter_main.py

@@ -0,0 +1,166 @@
+r"""Python script to convert user input data to training docker format.
+
+
+Note: the training format is designed to be tfrecord as in the design doc.
+If there are training efficiency issues for pytorch algorithms, we will also
+support pytorch formats as well.
+"""
+
+from absl import app
+from absl import flags
+from absl import logging
+from data_converter import common_lib
+from data_converter import data_converter_icn_lib
+from data_converter import data_converter_iod_lib
+from data_converter import data_converter_isg_lib
+from data_converter import data_converter_vcn_lib
+from util import constants
+
+
+_INPUT_FILE_PATH = flags.DEFINE_string(
+    'input_file_path',
+    None,
+    'Input file path.',
+    required=True,
+)
+_INPUT_FILE_TYPE = flags.DEFINE_enum(
+    'input_file_type',
+    None,
+    [
+        constants.INPUT_FILE_TYPE_CSV,
+        constants.INPUT_FILE_TYPE_JSONL,
+        constants.INPUT_FILE_TYPE_COCO_JSON,
+    ],
+    'Input file type.',
+    required=True,
+)
+_OBJECTIVE = flags.DEFINE_enum(
+    'objective',
+    None,
+    [
+        constants.OBJECTIVE_IMAGE_CLASSIFICATION,
+        constants.OBJECTIVE_IMAGE_OBJECT_DETECTION,
+        constants.OBJECTIVE_IMAGE_SEGMENTATION,
+        constants.OBJECTIVE_VIDEO_CLASSIFICATION,
+    ],
+    'The objective of this training job.',
+    required=True,
+)
+_OUTPUT_DIR = flags.DEFINE_string(
+    'output_dir',
+    None,
+    'The output directory for converted data and label map files.',
+    required=True,
+)
+_SPLIT_RATIO = flags.DEFINE_list(
+    'split_ratio',
+    '0.8,0.1,0.1',
+    'Proportion of data to split into train/validation/test.',
+)
+_NUM_SHARD = flags.DEFINE_list(
+    'num_shard', '10,10,10', 'The number of shards for train/validation/test.'
+)
+_OUTPUT_FPS = flags.DEFINE_integer(
+    'output_fps', 5, 'For videos only. The output frames rate per second.'
+)
+
+
+def main(_) -> None:
+  logging.info(
+      (
+          'Start data converter on: %s (type: %s) with split: %s for %s'
+          ' (shard=%s), and output to %s.'
+      ),
+      _INPUT_FILE_PATH.value,
+      _INPUT_FILE_TYPE.value,
+      _SPLIT_RATIO.value,
+      _OBJECTIVE.value,
+      _NUM_SHARD.value,
+      _OUTPUT_DIR.value,
+  )
+  split_ratio = list(map(float, _SPLIT_RATIO.value))
+  num_shard = list(map(int, _NUM_SHARD.value))
+  common_lib.check_split_ratio(split_ratio)
+  common_lib.check_num_shard(num_shard)
+  if (
+      _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_CSV
+  ):
+    data_converter_iod_lib.convert_csv_to_tfrecord(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        split_ratio,
+        num_shard,
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_JSONL
+  ):
+    data_converter_iod_lib.convert_jsonl_to_tfrecord(
+        _INPUT_FILE_PATH.value, _OUTPUT_DIR.value, split_ratio, num_shard
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_COCO_JSON
+  ):
+    data_converter_iod_lib.convert_coco_json_to_tfrecord(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        split_ratio,
+        num_shard,
+    )
+  elif _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_SEGMENTATION:
+    data_converter_isg_lib.beam_build_tfrecord_from_coco_json(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        split_ratio,
+        num_shard,
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_CLASSIFICATION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_CSV
+  ):
+    data_converter_icn_lib.convert_csv_to_tfrecord(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        split_ratio,
+        num_shard,
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_CLASSIFICATION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_JSONL
+  ):
+    data_converter_icn_lib.convert_jsonl_to_tfrecord(
+        _INPUT_FILE_PATH.value, _OUTPUT_DIR.value, split_ratio, num_shard
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_VIDEO_CLASSIFICATION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_CSV
+  ):
+    data_converter_vcn_lib.convert_csv_to_tfrecord(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        _OUTPUT_FPS.value,
+        split_ratio,
+        num_shard,
+    )
+  elif (
+      _OBJECTIVE.value == constants.OBJECTIVE_VIDEO_CLASSIFICATION
+      and _INPUT_FILE_TYPE.value == constants.INPUT_FILE_TYPE_JSONL
+  ):
+    data_converter_vcn_lib.convert_jsonl_to_tfrecord(
+        _INPUT_FILE_PATH.value,
+        _OUTPUT_DIR.value,
+        _OUTPUT_FPS.value,
+        split_ratio,
+        num_shard,
+    )
+  else:
+    raise NotImplementedError(
+        f'File format {_INPUT_FILE_TYPE.value} is not supported for'
+        f' {_OBJECTIVE.value}.'
+    )
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/data_converter/data_converter_vcn_lib.py

@@ -0,0 +1,289 @@
+"""Converts VCN CSV/JSONL files to TFRecord with apache beam."""
+
+import json
+from os import path
+from typing import Any, Dict, Iterator, Sequence, Union, cast
+
+from absl import logging
+import apache_beam as beam
+from apache_beam.io import tfrecordio
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+from data_converter import common_lib
+from util import constants
+
+
+_COLUMN_NAMES = [
+    common_lib.COLUMN_NAME_ML_USE,
+    common_lib.COLUMN_NAME_GCS_FILE_PATH,
+    common_lib.COLUMN_NAME_LABEL,
+    common_lib.COLUMN_NAME_START_SEC,
+    common_lib.COLUMN_NAME_END_SEC,
+]
+_JSON_GCS_URI_KEY = 'videoGcsUri'
+_JSON_CLASS_ANNOTATION_KEY = 'timeSegmentAnnotations'
+_JSON_CLASS_NAME_KEY = 'displayName'
+_JSON_START_TIME_KEY = 'startTime'
+_JSON_END_TIME_KEY = 'endTime'
+_JSON_RESOURCE_LABEL_KEY = 'dataItemResourceLabels'
+_JSON_ML_USE_KEY = 'aiplatform.googleapis.com/ml_use'
+
+
+def build_tf_example(
+    video_uri: str,
+    label: int,
+    start_sec: float,
+    end_sec: float,
+    output_fps: int,
+) -> tf.train.SequenceExample:
+  """Builds a TF Example from a video clip.
+
+  Args:
+    video_uri: GCS URI to the video file.
+    label: Class label as an integer.
+    start_sec: Start timestamp of the video clip in seconds.
+    end_sec: End timestamp of the video clip in seconds.
+    output_fps: The output frame rate per second.
+
+  Returns:
+    The created TF Example.
+  """
+  frame_bytes = common_lib.encode_video(
+      video_uri, start_sec, end_sec, output_fps, image_format='jpg'
+  )
+  seq_example = tf.train.SequenceExample()
+  seq_example.context.feature['clip/label/index'].int64_list.value[:] = [label]
+  for frame in frame_bytes:
+    seq_example.feature_lists.feature_list.get_or_create(
+        'image/encoded'
+    ).feature.add().bytes_list.value[:] = [frame]
+
+  return seq_example
+
+
+class AcquireTFExampleDoFn(beam.DoFn):
+  """Beam DoFn to build TF Examples from a DataFrame row dict for VCN."""
+
+  def __init__(self, output_fps: int):
+    self._success_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Success'
+    )
+    self._failure_counter = beam.metrics.Metrics.counter(
+        self.__class__.__name__, 'Failure'
+    )
+    self._output_fps = output_fps
+
+  def process(
+      self, element: Dict[str, Union[float, int, str]]
+  ) -> Iterator[tf.train.SequenceExample]:
+    ml_use: str = cast(str, element[common_lib.COLUMN_NAME_ML_USE])
+    video_uri: str = cast(str, element[common_lib.COLUMN_NAME_GCS_FILE_PATH])
+
+    try:
+      label: int = int(element[common_lib.COLUMN_NAME_LABEL])
+      start_sec: float = float(element[common_lib.COLUMN_NAME_START_SEC])
+      end_sec: float = float(element[common_lib.COLUMN_NAME_END_SEC])
+
+      tf_example = build_tf_example(
+          video_uri,
+          label,
+          start_sec,
+          end_sec,
+          self._output_fps,
+      )
+      self._success_counter.inc()
+      yield beam.pvalue.TaggedOutput(ml_use, tf_example)
+    except (ValueError, IOError) as err:
+      logging.error('Failed to process %s', video_uri)
+      logging.exception(err)
+      self._failure_counter.inc()
+
+
+def _run_convert_pipeline(
+    output_dir: str,
+    df: pd.DataFrame,
+    num_shards: Sequence[int],
+    output_fps: int,
+) -> None:
+  """Starts a Beam pipeline to write DataFrame as TF Records.
+
+  Args:
+    output_dir: TF Records output directory.
+    df: DataFrame to convert from.
+    num_shards: Number of shards for train/validation/test TFRecord files.
+    output_fps: The output frame rate per second.
+  """
+  clip_list = df.to_dict('records')
+
+  def pipeline(root):
+    train, val, test = (
+        root
+        | 'Create PCollection' >> beam.Create(clip_list)
+        | 'Convert to TF Example'
+        >> beam.ParDo(AcquireTFExampleDoFn(output_fps)).with_outputs(
+            constants.ML_USE_TRAINING,
+            constants.ML_USE_VALIDATION,
+            constants.ML_USE_TEST,
+        )
+    )
+    _ = train | 'Save train TF Record' >> tfrecordio.WriteToTFRecord(
+        path.join(output_dir, common_lib.TRAIN_TFRECORD_NAME),
+        coder=beam.coders.ProtoCoder(tf.train.Example),
+        num_shards=num_shards[0],
+    )
+    _ = val | 'Save val TF Record' >> tfrecordio.WriteToTFRecord(
+        path.join(output_dir, common_lib.VALIDATION_TFRECORD_NAME),
+        coder=beam.coders.ProtoCoder(tf.train.Example),
+        num_shards=num_shards[1],
+    )
+    _ = test | 'Save test TF Record' >> tfrecordio.WriteToTFRecord(
+        path.join(output_dir, common_lib.TEST_TFRECORD_NAME),
+        coder=beam.coders.ProtoCoder(tf.train.Example),
+        num_shards=num_shards[2],
+    )
+
+  common_lib.run_beam_pipeline(pipeline)
+
+
+def _convert_df_to_tfrecord(
+    df: pd.DataFrame,
+    output_dir: str,
+    split_ratio: Sequence[float],
+    num_shard: Sequence[int],
+    output_fps: int,
+) -> None:
+  """Converts a DataFrame into three separate tfrecords for training, validation, and testing into output_dir.
+
+  Args:
+    df: DataFrame to convert.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+    output_fps: The output frame rate per second.
+  """
+  # Replaces ml_use with common_lib string constants for consistency.
+  common_lib.format_ml_use_column(df)
+  common_lib.insert_missing_ml_use(df)
+
+  # Ignores invalid rows.
+  dropped_row_num = common_lib.drop_invalid_rows(df)
+  if dropped_row_num > 0:
+    logging.warning('Ignored %d invalid rows.', dropped_row_num)
+
+  common_lib.replace_unassigned_ml_use(
+      df[common_lib.COLUMN_NAME_ML_USE], split_ratio
+  )
+
+  # Converts labels to integers as required by training.
+  new_labels, label_map = common_lib.create_label_map(
+      df[common_lib.COLUMN_NAME_LABEL]
+  )
+  df[common_lib.COLUMN_NAME_LABEL] = new_labels
+  label_map_path = path.join(output_dir, common_lib.LABEL_MAP_NAME)
+  logging.info('Writing label map to %s.', label_map_path)
+  common_lib.write_label_map(label_map_path, label_map)
+
+  # Missing start / end times are treated as 0, inf, respectively.
+  df[common_lib.COLUMN_NAME_START_SEC].fillna(0, inplace=True)
+  df[common_lib.COLUMN_NAME_END_SEC].fillna(np.inf, inplace=True)
+
+  _run_convert_pipeline(output_dir, df, num_shard, output_fps)
+
+
+def convert_csv_to_tfrecord(
+    input_csv: str,
+    output_dir: str,
+    output_fps: int,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_csv file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The csv format is shown in
+    https://cloud.google.com/vertex-ai/docs/video-data/classification/prepare-data#csv
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_csv: Name of the csv file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    output_fps: The output frame rate per second.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  with tf.io.gfile.GFile(input_csv, 'r') as f:
+    df: pd.DataFrame = pd.read_csv(
+        f, header=None, names=_COLUMN_NAMES, on_bad_lines='warn'
+    )
+
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard, output_fps)
+
+
+def convert_jsonl_to_tfrecord(
+    input_jsonl: str,
+    output_dir: str,
+    output_fps: int,
+    split_ratio: Sequence[float] = (0.8, 0.1, 0.1),
+    num_shard: Sequence[int] = (10, 10, 10),
+) -> None:
+  """Parses input_jsonl file into three separate tfrecords for training, validation, and testing into output_dir.
+
+  The JSONL format is shown in
+    https://cloud.google.com/vertex-ai/docs/video-data/classification/prepare-data#jsonl.
+
+  If an ml_use column is not provided, one will be created.
+
+  label_map.yaml containing the label map will be placed in output_dir.
+
+  Args:
+    input_jsonl: Name of the JSONL file.
+    output_dir: The directory to save TFRecords and label_map.yaml.
+    output_fps: The output frame rate per second.
+    split_ratio: List specifying the training, validation, and testing splits
+      for unassigned TFRecords.
+    num_shard: Number of shards for train/validation/test TFRecord files.
+  """
+  df_rows = []
+  with tf.io.gfile.GFile(input_jsonl, 'r') as f:
+    lines = f.read().rstrip().splitlines()
+
+  for i, line in enumerate(lines, 1):
+    try:
+      item: Dict[str, Any] = json.loads(line)
+
+      gcs_uri = item.get(_JSON_GCS_URI_KEY)
+      if not gcs_uri:
+        logging.warning('Invalid JSON at line %d, skipped.', i)
+        continue
+
+      annotations = item.get(_JSON_CLASS_ANNOTATION_KEY, [])
+      ml_use = item.get(_JSON_RESOURCE_LABEL_KEY, {}).get(
+          _JSON_ML_USE_KEY, common_lib.ML_USE_UNASSIGNED
+      )
+
+      for j, annotation in enumerate(annotations):
+        label = annotation.get(_JSON_CLASS_NAME_KEY)
+        if not label:
+          logging.warning('Invalid annotation #%d at line %d, skipped.', j, i)
+          continue
+        # The example in external documentation uses strings like "1.0s", so we
+        # need to remove the "s" suffix.
+        start_time = annotation.get(_JSON_START_TIME_KEY, '0').removesuffix('s')
+        end_time = annotation.get(_JSON_END_TIME_KEY, 'inf').removesuffix('s')
+        df_rows.append([ml_use, gcs_uri, label, start_time, end_time])
+    except (json.JSONDecodeError, AttributeError):
+      logging.warning('Invalid JSON at line %d, skipped.', i)
+      continue
+
+  df = pd.DataFrame(
+      data=df_rows,
+      columns=_COLUMN_NAMES,
+  )
+
+  _convert_df_to_tfrecord(df, output_dir, split_ratio, num_shard, output_fps)

community-content/vertex_vision_model_garden/model_oss/data_converter/dockerfile/base.Dockerfile

@@ -0,0 +1,50 @@
+FROM python:3.9
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        python3-opencv \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        screen \
+        libportaudio2 \
+        libusb-1.0-0-dev \
+        openjdk-17-jre
+
+# Add gcsfuse distribution URL as a package source and import its public key.
+RUN echo "deb https://packages.cloud.google.com/apt gcsfuse-`lsb_release -c -s` main" | sudo tee /etc/apt/sources.list.d/gcsfuse.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
+
+# Install gcsfuse.
+RUN apt-get update && apt-get install -y --no-install-recommends gcsfuse
+
+# Install google cloud SDK.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN tar xzf google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install pyyaml==5.4.1
+RUN pip install pycocotools==2.0.6
+RUN pip install opencv-python-headless==4.7.0.72
+RUN pip install numpy==1.24.2
+RUN pip install pandas==1.5.3
+RUN pip install Pillow==9.4.0
+RUN pip install apache-beam[gcp]==2.45.0
+RUN pip install object-detection==0.0.3
+RUN pip install google-cloud-storage==1.42.3
+RUN pip install gcsfs==2021.10.1
+RUN pip install pylint==2.17.2

community-content/vertex_vision_model_garden/model_oss/data_converter/dockerfile/run.Dockerfile

@@ -0,0 +1,23 @@
+FROM gcr.io/automl-migration-test/automl-vision-data-converter-base:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+COPY model_oss/data_converter /automl_vision/data_converter
+COPY model_oss/util /automl_vision/util
+
+WORKDIR /automl_vision
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision"
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","data_converter/data_converter_main.py"]
+
+CMD ["--input_file_path=YOUR_INPUT_FILE",\
+"--input_file_type=csv",\
+"--objective=iod",\
+"--output_dir=YOUR_OUTPUT_DIR",\
+"--num_shard=10,10,10",\
+"--split_ratio=0.8,0.1,0.1"]

community-content/vertex_vision_model_garden/model_oss/detectron2/dockerfile/serving.Dockerfile

@@ -0,0 +1,90 @@
+# Dockerfile for Detectron2 serving.
+#
+# To build:
+# docker build -f model_oss/detectron2/dockerfile/serving.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+FROM pytorch/torchserve:0.7.0-cpu
+
+USER root
+
+# Install tools.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim
+
+# run and update some basic packages software packages, including security libs
+RUN apt-get update &&  apt-get install -y \
+    software-properties-common && \
+    add-apt-repository -y ppa:ubuntu-toolchain-r/test && \
+    apt-get update && apt-get install -y \
+    gcc-9 g++-9 apt-transport-https ca-certificates gnupg curl
+
+# Install gcloud tools for gsutil as well as debugging
+RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | \
+    tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
+    apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
+    apt-get update -y && apt-get install google-cloud-sdk -y
+
+USER model-server
+
+# install detectron2 dependencies
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --user numpy==1.24.2
+RUN python3 -m pip install --user opencv-python==4.7.0.72
+RUN python3 -m pip install --user 'git+https://github.com/facebookresearch/detectron2.git@v0.6'
+
+# Install GCS storage library.
+RUN pip install google-cloud-storage==2.6.0
+
+# For mask encoding.
+RUN pip install --upgrade pycocotools==2.0.6
+
+ARG MODEL_NAME=detectron2_serving
+ENV MODEL_NAME="${MODEL_NAME}"
+
+# health and prediction listener ports
+ARG AIP_HTTP_PORT=7080
+ENV AIP_HTTP_PORT="${AIP_HTTP_PORT}"
+
+ARG MODEL_MGMT_PORT=7081
+
+# expose health and prediction listener ports from the image
+EXPOSE "${AIP_HTTP_PORT}"
+EXPOSE "${MODEL_MGMT_PORT}"
+EXPOSE 8080 8081 8082 7070 7071
+
+# create torchserve configuration file
+USER root
+RUN echo "service_envelope=json\n" \
+    "inference_address=http://0.0.0.0:${AIP_HTTP_PORT}\n"  \
+    "management_address=http://0.0.0.0:${MODEL_MGMT_PORT}" >> /home/model-server/config.properties
+USER model-server
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Copy model artifacts.
+COPY ./model_oss/detectron2/handler.py /home/model-server/handler.py
+WORKDIR /home/model-server/
+
+# Create model archive file packaging model artifacts and dependencies.
+# Note(lavrai): The model `.pth` file and `cfg.yaml` file will be set by the
+# customer as an environment variable and will be later loaded by the
+# `handler.py` file.
+RUN torch-model-archiver \
+  --model-name="${MODEL_NAME}" \
+  --version=1.0 \
+  --handler=/home/model-server/handler.py \
+  --export-path=/home/model-server/model-store \
+  -f
+
+# run Torchserve HTTP serve to respond to prediction requests
+CMD ["ls", "-ltr", "/home/model-server/model-store/", ";", \
+    "torchserve", "--start", "--ts-config=/home/model-server/config.properties", \
+    "--models", "${MODEL_NAME}=${MODEL_NAME}.mar", \
+    "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/detectron2/dockerfile/train.Dockerfile

@@ -0,0 +1,100 @@
+# Dockerfile for Detectron2 training.
+#
+# To build:
+# docker build -f model_oss/detectron2/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
+# Using an older system (18.04) to avoid opencv incompatibility (issue#3524).
+
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt-get update && apt-get install -y \
+    python3.7 python3.7-dev python3.7-distutils \
+    python3-opencv ca-certificates git wget sudo ninja-build \
+    curl wget vim
+
+# Make python3 available for python3.7.
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 2
+RUN update-alternatives --config python3
+# Make python available for python3.7.
+RUN ln -sv /usr/bin/python3.7 /usr/bin/python
+
+# Create a non-root user.
+ARG USER_ID=1000
+RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+USER appuser
+WORKDIR /home/appuser
+
+ENV PATH="/home/appuser/.local/bin:${PATH}"
+RUN wget https://bootstrap.pypa.io/pip/get-pip.py && \
+    python3.7 get-pip.py --user && \
+    rm get-pip.py
+
+# Important! Otherwise, it uses existing numpy from host-modules
+# which throws error.
+RUN pip install --user numpy==1.20.3
+
+# Install dependencies:
+# See https://pytorch.org/ for other options if you use
+# a different version of CUDA.
+RUN pip install --user tensorboard==2.11.0
+# cmake from apt-get is too old.
+RUN pip install --user cmake==3.25.2
+RUN pip install --user torch==1.10.0+cu111 torchvision==0.11.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
+RUN pip install --user setuptools==59.5.0
+RUN pip install --user opencv-python==4.7.0.72
+RUN pip install --user cloudml-hypertune==0.1.0.dev6
+RUN pip install --user fvcore==0.1.5.post20221221
+# Install detectron2.
+RUN git clone -b v0.6 https://github.com/facebookresearch/detectron2 detectron2_repo
+# Set FORCE_CUDA because during `docker build` cuda is not accessible.
+ENV FORCE_CUDA="1"
+# This will by default build detectron2 for all common cuda
+# architectures and take a lot more time,
+# because inside `docker build`, there is no way to tell
+# which architecture will be used.
+ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
+ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
+RUN pip install --user -e detectron2_repo
+
+# Set a fixed model cache directory.
+ENV FVCORE_CACHE="/tmp"
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Copy model-garden detectron2 files to '/home/appuser/trainer' folder.
+ADD ./model_oss/detectron2 /home/appuser/trainer
+
+################ Copy plain_train_net.py to task.py and
+# then modify it using sed commands. ###################
+# Src: https://github.com/facebookresearch/detectron2/blob/v0.6/tools/plain_train_net.py
+RUN sudo cp /home/appuser/detectron2_repo/tools/plain_train_net.py /home/appuser/trainer/task.py
+# Make additional changes to task.py.
+# Note(lavrai): Start adding SED commands from end of file towards the top
+# so that the line numbers do not keep changing for the source file.
+# For entry-point:
+RUN sudo sed -i "214 d" /home/appuser/trainer/task.py
+RUN sudo sed -i "213 a\    default_arg_parser = default_argument_parser()" /home/appuser/trainer/task.py
+RUN sudo sed -i "214 a\    extended_parser = trainer_utils.extend_parser_arguments(default_arg_parser)" /home/appuser/trainer/task.py
+RUN sudo sed -i "215 a\    args = extended_parser.parse_args()" /home/appuser/trainer/task.py
+# For main() function:
+RUN sudo sed -i "192 a\    trainer_utils.register_dataset(args)" /home/appuser/trainer/task.py
+# For setup() function:
+RUN sudo sed -i "184 a\    cfg.SOLVER.BASE_LR = args.lr" /home/appuser/trainer/task.py
+RUN sudo sed -i "185 a\    cfg.OUTPUT_DIR = args.output_dir" /home/appuser/trainer/task.py
+RUN sudo sed -i "186 a\    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_file_copy)" /home/appuser/trainer/task.py
+RUN sudo sed -i "182 a\    config_file_copy = args.config_file" /home/appuser/trainer/task.py
+RUN sudo sed -i "183 a\    args.config_file = model_zoo.get_config_file(args.config_file)" /home/appuser/trainer/task.py
+# For new import:
+RUN sudo sed -i "27 a\from detectron2 import model_zoo" /home/appuser/trainer/task.py
+RUN sudo sed -i "21 a\import trainer_utils" /home/appuser/trainer/task.py
+
+ENV PYTHONPATH /home/appuser/trainer
+
+ENTRYPOINT ["python", "-m", "trainer.task"]

community-content/vertex_vision_model_garden/model_oss/detectron2/handler.py

@@ -0,0 +1,154 @@
+"""Custom handler for Detectron2 serving."""
+
+import io
+import json
+import os
+from typing import Any, List, Tuple
+
+import cv2
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from google.cloud import storage
+import numpy as np
+import pycocotools.mask as mask_util
+import torch
+
+
+def get_bucket_and_blob_name(gcs_filepath: str) -> Tuple[str, str]:
+  """Gets bucket and blob name from gcs path."""
+  # The gcs path is of the form gs://<bucket-name>/<blob-name>
+  gs_suffix = gcs_filepath.split("gs://", 1)[1]
+  return tuple(gs_suffix.split("/", 1))
+
+
+def download_gcs_file(src_file_path: str, dst_file_path: str):
+  """Downloads gcs-file to local folder."""
+  src_bucket_name, src_blob_name = get_bucket_and_blob_name(src_file_path)
+  client = storage.Client()
+  src_bucket = client.get_bucket(src_bucket_name)
+  src_blob = src_bucket.blob(src_blob_name)
+  src_blob.download_to_filename(dst_file_path)
+
+
+class ModelHandler:
+  """Custom model handler for Detectron2."""
+
+  def __init__(self):
+    self.error = None
+    self._batch_size = 0
+    self.initialized = False
+    self.predictor = None
+    self.test_threshold = 0.5
+
+  def initialize(self, context: Any):
+    """Initialize."""
+    print("context.system_properties: ", context.system_properties)
+    print("context.manifest: ", context.manifest)
+    self.manifest = context.manifest
+    properties = context.system_properties
+    # Get threshold from environment variable.
+    # This will be set by customer.
+    self.test_threshold = float(os.environ.get("TEST_THRESHOLD"))
+    print("test_threshold: ", self.test_threshold)
+    # Get model and config file location from environment variables.
+    # These will be set by customer when doing model upload.
+    gcs_model_file = os.environ["MODEL_PTH_FILE"]
+    gcs_config_file = os.environ["CONFIG_YAML_FILE"]
+    print("Copying gcs_model_file: ", gcs_model_file)
+    print("Copying gcs_config_file: ", gcs_config_file)
+    # Copy these files from GCS location to local file.
+    # Note(lavrai): GCSFuse path does not seem to work here for now.
+    model_file = "./model.pth"
+    config_file = "./cfg.yaml"
+    download_gcs_file(src_file_path=gcs_model_file, dst_file_path=model_file)
+    if not os.path.exists(model_file):
+      raise RuntimeError("Missing model_file: %s" % model_file)
+    download_gcs_file(src_file_path=gcs_config_file, dst_file_path=config_file)
+    if not os.path.exists(config_file):
+      raise RuntimeError("Missing config_file: %s" % config_file)
+
+    # Set up config file.
+    cfg = get_cfg()
+    cfg.merge_from_file(config_file)
+    cfg.MODEL.WEIGHTS = model_file
+    cfg.MODEL.DEVICE = (
+        cfg.MODEL.DEVICE + str(properties.get("gpu_id"))
+        if torch.cuda.is_available()
+        else "cpu"
+    )
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = self.test_threshold
+
+    # Build predictor from config.
+    self.predictor = DefaultPredictor(cfg)
+    self._batch_size = context.system_properties["batch_size"]
+    self.initialized = True
+
+  def preprocess(self, batch: List[Any]) -> List[Any]:
+    """Preprocess raw input and return as list of images."""
+    print("Running pre-processing.")
+    images = []
+    for request in batch:
+      request_data = request.get("data")
+      input_bytes = io.BytesIO(request_data)
+      img = cv2.imdecode(np.fromstring(input_bytes.read(), np.uint8), 1)
+      images.append(img)
+    return images
+
+  def inference(self, model_input: List[Any]) -> List[Any]:
+    """Runs inference."""
+    print("Running model-inference.")
+    return [self.predictor(image) for image in model_input]
+
+  def postprocess(self, inference_result: List[Any]) -> List[Any]:
+    """Post process inference result."""
+    response_list = []
+    print("Num inference_items are:", len(inference_result))
+    for inference_item in inference_result:
+      predictions = inference_item["instances"].to("cpu")
+      print("Predictions are:", predictions)
+      boxes = None
+      if predictions.has("pred_boxes"):
+        boxes = predictions.pred_boxes.tensor.numpy().tolist()
+      scores = None
+      if predictions.has("scores"):
+        scores = predictions.scores.numpy().tolist()
+      classes = None
+      if predictions.has("pred_classes"):
+        classes = predictions.pred_classes.numpy().tolist()
+      masks_rle = None
+      if predictions.has("pred_masks"):
+        # Do run length encoding, else the mask output becomes huge.
+        masks_rle = [
+            mask_util.encode(np.asfortranarray(mask))
+            for mask in predictions.pred_masks
+        ]
+        for rle in masks_rle:
+          rle["counts"] = rle["counts"].decode("utf-8")
+      response = {
+          "classes": classes,
+          "scores": scores,
+          "boxes": boxes,
+          "masks_rle": masks_rle,
+      }
+      response_list.append(json.dumps(response))
+    print("response_list: ", response_list)
+    return response_list
+
+  def handle(self, data: Any, context: Any) -> List[Any]:  # pylint: disable=unused-argument
+    """Runs preprocess, inference, and post-processing."""
+    model_input = self.preprocess(data)
+    model_out = self.inference(model_input)
+    output = self.postprocess(model_out)
+    print("Done handling input.")
+    return output
+
+
+_service = ModelHandler()
+
+
+def handle(data: Any, context: Any) -> List[Any]:
+  if not _service.initialized:
+    _service.initialize(context)
+  if data is None:
+    return None
+  return _service.handle(data, context)

community-content/vertex_vision_model_garden/model_oss/detectron2/trainer_utils.py

@@ -0,0 +1,97 @@
+"""Detectron2 trainer helper functions."""
+
+import argparse
+from detectron2.data.datasets import register_coco_instances
+
+
+def extend_parser_arguments(
+    parser: argparse.ArgumentParser,
+) -> argparse.ArgumentParser:
+  """Adds additional model-garden related arguments."""
+  parser.add_argument(
+      "--train_dataset_name",
+      required=False,
+      default="",
+      type=str,
+      help=(
+          "The training dataset name for registration. "
+          "For example: 'balloon_train'."
+      ),
+  )
+  parser.add_argument(
+      "--train_coco_json_file",
+      required=False,
+      default="",
+      type=str,
+      help="The path to the training coco-json format file.",
+  )
+  parser.add_argument(
+      "--train_image_root",
+      required=False,
+      default="",
+      type=str,
+      help="The path to the root folder containing the training images.",
+  )
+  parser.add_argument(
+      "--val_dataset_name",
+      required=False,
+      default="",
+      type=str,
+      help=(
+          "The validation dataset name for registration. "
+          "For example: 'balloon_val'."
+      ),
+  )
+  parser.add_argument(
+      "--val_coco_json_file",
+      required=False,
+      default="",
+      type=str,
+      help="The path to the validation coco-json format file.",
+  )
+  parser.add_argument(
+      "--val_image_root",
+      required=False,
+      default="",
+      type=str,
+      help="The path to the root folder containing the validation images.",
+  )
+  parser.add_argument(
+      "--output_dir",
+      required=True,
+      type=str,
+      help="The path to the output directory.",
+  )
+  # Add hyper-parameter tuning related variables.
+  parser.add_argument(
+      "--lr",
+      type=float,
+      default=0.00025,
+      help="The learning rate to be tuned.",
+  )
+  parser.add_argument(
+      "--hp_eval_task",
+      type=str,
+      choices=["bbox", "segm"],
+      default="bbox",
+      help="The task choice for HP tuning.",
+  )
+  return parser
+
+
+def register_dataset(args: argparse.Namespace):
+  """Register the input dataset in Detectron2 Coco format."""
+  if args.train_dataset_name:
+    register_coco_instances(
+        name=args.train_dataset_name,
+        metadata={},
+        json_file=args.train_coco_json_file,
+        image_root=args.train_image_root,
+    )
+  if args.val_dataset_name:
+    register_coco_instances(
+        name=args.val_dataset_name,
+        metadata={},
+        json_file=args.val_coco_json_file,
+        image_root=args.val_image_root,
+    )

community-content/vertex_vision_model_garden/model_oss/diffusers/handler.py

@@ -4,9 +4,10 @@
 # pylint: disable=logging-fstring-interpolation
 
 import base64
+import io
 import logging
 import os
-from typing import Any, List, Tuple
+from typing import Any, List, Sequence, Tuple
 
 from diffusers import ControlNetModel
 from diffusers import DiffusionPipeline
@@ -20,6 +21,7 @@ from diffusers import StableDiffusionPipeline
 from diffusers import StableDiffusionUpscalePipeline
 from diffusers import TextToVideoZeroPipeline
 from diffusers import UniPCMultistepScheduler
+import imageio
 import numpy as np
 from PIL import Image
 import torch
@@ -43,6 +45,13 @@ TEXT_TO_VIDEO_ZERO_SHOT = "text-to-video-zero-shot"
 TEXT_TO_VIDEO = "text-to-video"
 
 
+def frames_to_video_bytes(frames: Sequence[np.ndarray], fps: int) -> bytes:
+  images = [Image.fromarray(array) for array in frames]
+  io_obj = io.BytesIO()
+  imageio.mimsave(io_obj, images, format=".mp4", fps=fps)
+  return io_obj.getvalue()
+
+
 class DiffusersHandler(BaseHandler):
   """Custom handler for TIMM models."""
 
@@ -214,7 +223,7 @@ class DiffusersHandler(BaseHandler):
         numpy_arrays = self.pipeline(prompt=prompt).images
         numpy_arrays = [(i * 255).astype("uint8") for i in numpy_arrays]
         videos.append(
-            video_format_converter.frames_to_video_bytes(numpy_arrays, fps=4)
+            frames_to_video_bytes(numpy_arrays, fps=4)
         )
       return videos
     elif self.task == TEXT_TO_VIDEO:
@@ -224,7 +233,7 @@ class DiffusersHandler(BaseHandler):
       # Therefore we need to split the output into different videos.
       predicted_images = np.array_split(predicted_images, len(prompts), axis=2)
       videos = [
-          video_format_converter.frames_to_video_bytes(images, fps=8)
+          frames_to_video_bytes(images, fps=8)
           for images in predicted_images
       ]
       return videos

community-content/vertex_vision_model_garden/model_oss/imagebind/config.properties

@@ -0,0 +1,21 @@
+number_of_netty_threads=32
+job_queue_size=1000
+model_store=/home/model-server/model-store
+workflow_store=/home/model-server/wf-store
+default_response_timeout=1800
+service_envelope=json
+inference_address=http://0.0.0.0:7080
+management_address=http://0.0.0.0:7081
+metrics_address=http://0.0.0.0:7082
+
+models={\
+  "imagebind_serving": {\
+    "1.0": {\
+        "defaultVersion": true,\
+        "marName": "imagebind_serving.mar",\
+        "minWorkers": 1,\
+        "maxWorkers": 1,\
+        "batchSize": 1\
+    }\
+  }\
+}

community-content/vertex_vision_model_garden/model_oss/imagebind/dockerfile/serve.Dockerfile

@@ -0,0 +1,74 @@
+# Dockerfile for the serving docker for ImageBind.
+#
+# To build:
+# docker build -f model_oss/imagebind/dockerfile/serve.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM pytorch/torchserve:0.7.0-gpu
+
+USER root
+
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="imagebind_serving"
+ENV PATH="/home/model-server/:${PATH}"
+
+# Install tools.
+RUN apt-get update && apt-get -y upgrade && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim \
+        git \
+        libgeos-dev
+
+# Install libraries.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN python3 -m pip install --upgrade pip
+RUN pip install absl-py==1.4.0
+RUN pip install google-cloud-storage==2.7.0
+
+# Install ImageBind and dependencies.
+RUN git clone https://github.com/facebookresearch/ImageBind.git
+WORKDIR ImageBind
+# Pin the commit at 07/14/2023.
+RUN git reset --hard 95d27c7fd5a8362f3527e176c3a80ae5a4d880c0
+# Modify tokenizer file path from ImageBind repo to work with the server.
+RUN sed -i '25d' imagebind/data.py
+RUN sed -i '25 i\BPE_PATH = "/home/model-server/ImageBind/bpe/bpe_simple_vocab_16e6.txt.gz"' imagebind/data.py
+RUN pip install .
+
+WORKDIR /home/model-server
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Copy model artifacts.
+COPY model_oss/imagebind/handler.py /home/model-server/handler.py
+COPY model_oss/imagebind/config.properties /home/model-server/config.properties
+COPY model_oss/util/ /home/model-server/util/
+ENV PYTHONPATH /home/model-server/
+
+# Expose ports.
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
+
+# Archive model artifacts and dependencies.
+# Do not set --model-file and --serialized-file because model and checkpoint
+# will be dynamically loaded in handler.py.
+RUN torch-model-archiver \
+--model-name=${model_name} \
+--version=1.0 \
+--handler=/home/model-server/handler.py \
+--runtime=python3 \
+--export-path=/home/model-server/model-store \
+--archive-format=default \
+--force
+
+# Run Torchserve HTTP serve to respond to prediction requests.
+CMD ["torchserve", "--start", \
+     "--ts-config", "/home/model-server/config.properties", \
+     "--models", "${model_name}=${model_name}.mar", \
+     "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/imagebind/handler.py

@@ -0,0 +1,277 @@
+"""Custom handler for the ImageBind model."""
+
+import logging
+import os
+from typing import Any, Dict, List
+
+from imagebind import data as data_util
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+from PIL import Image
+import torch
+from torchvision import transforms
+from ts.torch_handler import base_handler
+
+from util import constants
+from util import fileutils
+
+
+_VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE = "video"
+
+
+class ImageBindHandler(base_handler.BaseHandler):
+  """Custom handler for the ImageBind model.
+
+  Attributes:
+    map_location: Mapping storage location.
+    device: Device on which to run inference.
+    manifest: TorchServe manifest.
+    task: Task for which to run the ImageBind model.
+    model: ImageBind model instance.
+  """
+
+  def initialize(self, context: Any) -> None:
+    """Initializes the ImageBind model handler.
+
+    Args:
+      context: TorchServe context, which contains system information and the
+        manifest.
+
+    Raises:
+      ValueError: A task that is unsupported by the handler.
+    """
+    properties = context.system_properties
+    self.map_location = (
+        "cuda"
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else "cpu"
+    )
+    self.device = torch.device(
+        self.map_location + ":" + str(properties.get("gpu_id"))
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else self.map_location
+    )
+    self.manifest = context.manifest
+
+    self.task = os.environ.get("TASK", constants.FEATURE_EMBEDDING_GENERATION)
+    if self.task not in [
+        constants.FEATURE_EMBEDDING_GENERATION,
+        constants.ZERO_SHOT_CLASSIFICATION,
+    ]:
+      raise ValueError(f"Invalid task: {self.task}.")
+    logging.info(
+        "Handler initializing ImageBind pretrained model for task %s.",
+        self.task,
+    )
+
+    self.model = imagebind_model.imagebind_huge(pretrained=True)
+    self.model.eval()
+    self.model.to(self.device)
+
+    logging.info("Initialized ImageBind pretrained model.")
+    self.initialized = True
+
+  def preprocess(self, data: Any) -> List[Dict[str, Any]]:
+    """Preprocesses input data, including text, image, audio and video data.
+
+    Args:
+      data: Input data.
+
+    Returns:
+      A list of processed data samples, with each sample being a dictionary of
+      modality (key): input (value) pairs.
+    """
+    logging.info("Preprocessing: %d instances received.", len(data))
+    preprocessed_sample_list = []
+    for item in data:
+      preprocessed_sample = {}
+      if ModalityType.TEXT in item:
+        preprocessed_sample[ModalityType.TEXT] = (
+            data_util.load_and_transform_text(
+                item[ModalityType.TEXT], self.device
+            )
+        )
+      for image_modality in [
+          ModalityType.VISION,
+          ModalityType.DEPTH,
+          ModalityType.THERMAL,
+      ]:
+        if image_modality in item:
+          image_paths = item[image_modality]
+          local_image_paths = fileutils.download_gcs_file_list_to_local(
+              image_paths, constants.LOCAL_DATA_DIR
+          )
+          is_depth_or_thermal = image_modality in [
+              ModalityType.DEPTH,
+              ModalityType.THERMAL,
+          ]
+          preprocessed_sample[image_modality] = (
+              self._load_and_transform_image_data(
+                  local_image_paths,
+                  self.device,
+                  is_depth_or_thermal=is_depth_or_thermal,
+              )
+          )
+      if ModalityType.AUDIO in item:
+        audio_paths = item[ModalityType.AUDIO]
+        local_audio_paths = fileutils.download_gcs_file_list_to_local(
+            audio_paths, constants.LOCAL_DATA_DIR
+        )
+        preprocessed_sample[ModalityType.AUDIO] = (
+            data_util.load_and_transform_audio_data(
+                local_audio_paths, self.device
+            )
+        )
+      if _VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE in item:
+        video_paths = item[_VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE]
+        local_video_paths = fileutils.download_gcs_file_list_to_local(
+            video_paths, constants.LOCAL_DATA_DIR
+        )
+        preprocessed_sample[_VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE] = (
+            data_util.load_and_transform_video_data(
+                local_video_paths, self.device
+            )
+        )
+      if ModalityType.IMU in item:
+        # Input data in the IMU modality are expected in shape [B, 6, 2000].
+        preprocessed_sample[ModalityType.IMU] = torch.tensor(
+            item[ModalityType.IMU], dtype=torch.float32, device=self.device
+        )
+      if preprocessed_sample:
+        preprocessed_sample_list.append(preprocessed_sample)
+    return preprocessed_sample_list
+
+  def _load_and_transform_image_data(
+      self,
+      image_paths: List[str],
+      device: torch.device,
+      is_depth_or_thermal: bool = False,
+  ) -> torch.Tensor:
+    """Loads and transforms 3-channel images, depth images and thermal images.
+
+    Args:
+      image_paths: A list of image paths.
+      device: Device onto which to load images.
+      is_depth_or_thermal: Whether the images are depth or thermal images.
+
+    Returns:
+      A list of processed tensors corresponding to the input images.
+
+    Raises:
+      ValueError: The input image_paths is None.
+    """
+    if image_paths is None:
+      raise ValueError("image_paths must not be None.")
+
+    image_outputs = []
+    for image_path in image_paths:
+      transforms_list = [
+          transforms.Resize(
+              224, interpolation=transforms.InterpolationMode.BICUBIC
+          ),
+          transforms.CenterCrop(224),
+          transforms.ToTensor(),
+      ]
+      if not is_depth_or_thermal:
+        transforms_list.append(
+            transforms.Normalize(
+                mean=(0.48145466, 0.4578275, 0.40821073),
+                std=(0.26862954, 0.26130258, 0.27577711),
+            )
+        )
+      data_transform = transforms.Compose(transforms_list)
+      with open(image_path, "rb") as fopen:
+        if is_depth_or_thermal:
+          image = Image.open(fopen).convert("L")
+        else:
+          image = Image.open(fopen).convert("RGB")
+
+      image = data_transform(image).to(device)
+      image_outputs.append(image)
+    return torch.stack(image_outputs, dim=0)
+
+  def inference(
+      self, data: List[Dict[str, Any]], *args, **kwargs
+  ) -> List[Dict[str, Any]]:
+    """Runs inference using the ImageBind model.
+
+    Args:
+      data: A list of processed data samples, with each sample being a
+        dictionary of modality (key): input (value) pairs.
+      *args: Additional inference args.
+      **kwargs: Additional inference kwargs.
+
+    Returns:
+      A list of model outputs, with each output being a dictionary of
+      modality (key): embedding (value) pairs.
+    """
+    output_list = []
+    with torch.no_grad():
+      for inputs in data:
+        if _VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE in inputs:
+          # Allows inference on both image and video data, which both fall under
+          # ModalityType.VISION.
+          video_inputs = {
+              ModalityType.VISION: inputs[
+                  _VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE
+              ]
+          }
+          video_embeddings = self.model(video_inputs)
+          video_embeddings[_VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE] = (
+              video_embeddings[ModalityType.VISION]
+          )
+          del video_embeddings[ModalityType.VISION]
+          del inputs[_VIDEO_KEY_TO_AVOID_CONFLICT_WITH_IMAGE]
+        else:
+          video_embeddings = {}
+        embeddings = self.model(inputs)
+        embeddings.update(video_embeddings)
+        output_list.append(embeddings)
+    return output_list
+
+  def postprocess(self, output_list: List[Dict[str, Any]]) -> List[Any]:
+    """Postprocesses model outputs for the task of interest.
+
+    For feature embedding generation, returns the embeddings for each modality
+    for each input.
+    For zero-shot classification, generates classification probabilities
+    between the inputs of a pair of modalities for all possible pairings.
+
+    Args:
+      output_list: A list of model outputs, with each output being a dictionary
+        of modality (key): embedding (value) pairs.
+
+    Returns:
+      A list of postprocessed model outputs for the task of interest, with each
+      output corresponding to an input.
+
+    Raises:
+      ValueError: Fewer than two modalities are provided for zero-shot
+      classification, or the task is not supported.
+    """
+    preds = []
+    if self.task == constants.FEATURE_EMBEDDING_GENERATION:
+      for item in output_list:
+        preds.append({k: v.tolist() for k, v in item.items()})
+    elif self.task == constants.ZERO_SHOT_CLASSIFICATION:
+      for item in output_list:
+        modalities = list(item.keys())
+        if len(modalities) < 2:
+          raise ValueError(
+              "Two or more modalities are needed for task"
+              f" {constants.ZERO_SHOT_CLASSIFICATION}."
+          )
+        pairwise_probs = {}
+        for m1 in modalities:
+          for m2 in modalities:
+            if m1 == m2:
+              continue
+            probs = torch.softmax(item[m1] @ item[m2].T, dim=-1)
+            pairwise_probs[
+                f"Classify each input in {m1} (row) against inputs in"
+                f" {m2} (column)"
+            ] = probs.tolist()
+        preds.append(pairwise_probs)
+    else:
+      raise ValueError(f"Task {self.task} is not supported by the handler.")
+    return preds

community-content/vertex_vision_model_garden/model_oss/jax_vision_transformer/dockerfile/jax_vit_model_conversion.Dockerfile

@@ -0,0 +1,151 @@
+# This Dockerfile converts JAX vision transformer model to
+# tensorflow saved model format.
+# Here is an example to build this dockerfile:
+# PROJECT="your gcp project"
+# IMAGE_TAG="jax-vit-model-conversion:${USER}-test"
+# docker build -f model_oss/jax_vision_transformer/dockerfile/jax_vit_model_conversion.Dockerfile . -t "${IMAGE_TAG}"
+# docker tag "${IMAGE_TAG}" "gcr.io/${PROJECT}/${IMAGE_TAG}"
+# docker push "gcr.io/${PROJECT}/${IMAGE_TAG}"
+
+
+FROM tensorflow/tensorflow:2.12.0-gpu
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install basic libs
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git
+
+# Copy Apache license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Get 'vision_transformer' repository from github.
+RUN git clone https://github.com/google-research/vision_transformer
+# Set current directory to the downloaded 'vision_transformer' repository.
+WORKDIR ./vision_transformer
+# Using git reset command to pin it down to a specific version.
+RUN git reset --hard e66b4732d44504251197a3da3f5949f3f3ce9ca6
+
+# Install required libs
+RUN pip install --upgrade pip
+# The following pip installs are pinned down versions of those inside
+# vit_jax/requirements.txt file.
+# NOTE: Using `no-deps` flag to avoid overwriting of
+# dependent library versions. For example,
+# both `chex` and `jax` can overwrite each others
+# `jax-lib` version.
+RUN pip install --no-deps absl-py==1.4.0
+RUN pip install --no-deps aqtp==0.0.10
+RUN pip install --no-deps array-record==0.2.0
+RUN pip install --no-deps astunparse==1.6.3
+RUN pip install --no-deps cached-property==1.5.2
+RUN pip install --no-deps cachetools==5.3.0
+RUN pip install --no-deps certifi==2019.11.28
+RUN pip install --no-deps chardet==3.0.4
+RUN pip install --no-deps chex==0.1.7
+RUN pip install --no-deps click==8.1.3
+RUN pip install --no-deps cloudpickle==2.2.1
+RUN pip install --no-deps clu==0.0.9
+RUN pip install --no-deps contextlib2==21.6.0
+RUN pip install --no-deps dacite==1.8.1
+RUN pip install --no-deps dbus-python==1.2.16
+RUN pip install --no-deps decorator==5.1.1
+RUN pip install --no-deps dm-tree==0.1.8
+RUN pip install --no-deps einops==0.6.1
+RUN pip install --no-deps etils==1.3.0
+RUN pip install --no-deps flatbuffers==23.3.3
+RUN pip install --no-deps flax==0.6.10
+RUN pip install --no-deps git+https://github.com/google/flaxformer@9adaa4467cf17703949b9f537c3566b99de1b416
+RUN pip install --no-deps gast==0.4.0
+RUN pip install --no-deps google-auth==2.16.2
+RUN pip install --no-deps google-auth-oauthlib==0.4.6
+RUN pip install --no-deps google-pasta==0.2.0
+RUN pip install --no-deps googleapis-common-protos==1.59.0
+RUN pip install --no-deps grpcio==1.51.3
+RUN pip install --no-deps h5py==3.8.0
+RUN pip install --no-deps idna==2.8
+RUN pip install --no-deps importlib-metadata==6.1.0
+RUN pip install --no-deps importlib-resources==5.12.0
+RUN pip install --no-deps keras==2.12.0
+RUN pip install --no-deps libclang==16.0.0
+RUN pip install --no-deps Markdown==3.4.3
+RUN pip install --no-deps markdown-it-py==2.2.0
+RUN pip install --no-deps MarkupSafe==2.1.2
+RUN pip install --no-deps mdurl==0.1.2
+RUN pip install --no-deps ml-collections==0.1.1
+RUN pip install --no-deps msgpack==1.0.5
+RUN pip install --no-deps nest-asyncio==1.5.6
+RUN pip install --no-deps numpy==1.23.5
+RUN pip install --no-deps oauthlib==3.2.2
+RUN pip install --no-deps opt-einsum==3.3.0
+RUN pip install --no-deps optax==0.1.5
+RUN pip install --no-deps orbax-checkpoint==0.1.6
+RUN pip install --no-deps packaging==23.0
+RUN pip install --no-deps pandas==2.0.1
+RUN pip install --no-deps pip==23.1.2
+RUN pip install --no-deps promise==2.3
+RUN pip install --no-deps protobuf==4.22.1
+RUN pip install --no-deps psutil==5.9.5
+RUN pip install --no-deps pyasn1==0.4.8
+RUN pip install --no-deps pyasn1-modules==0.2.8
+RUN pip install --no-deps Pygments==2.15.1
+RUN pip install --no-deps PyGObject==3.36.0
+RUN pip install --no-deps python-apt==2.0.1+ubuntu0.20.4.1
+RUN pip install --no-deps python-dateutil==2.8.2
+RUN pip install --no-deps pytz==2023.3
+RUN pip install --no-deps PyYAML==6.0
+RUN pip install --no-deps requests==2.22.0
+RUN pip install --no-deps requests-oauthlib==1.3.1
+RUN pip install --no-deps requests-unixsocket==0.2.0
+RUN pip install --no-deps rich==13.3.5
+RUN pip install --no-deps rsa==4.9
+RUN pip install --no-deps scipy==1.10.1
+RUN pip install --no-deps setuptools==67.6.0
+RUN pip install --no-deps six==1.14.0
+RUN pip install --no-deps tensorboard==2.12.0
+RUN pip install --no-deps tensorboard-data-server==0.7.0
+RUN pip install --no-deps tensorboard-plugin-wit==1.8.1
+RUN pip install --no-deps tensorflow==2.12.0
+RUN pip install --no-deps tensorflow-cpu==2.12.0
+RUN pip install --no-deps tensorflow-datasets==4.9.2
+RUN pip install --no-deps tensorflow-estimator==2.12.0
+RUN pip install --no-deps tensorflow-hub==0.13.0
+RUN pip install --no-deps tensorflow-io-gcs-filesystem==0.31.0
+RUN pip install --no-deps tensorflow-metadata==1.13.1
+RUN pip install --no-deps tensorflow-probability==0.20.0
+RUN pip install --no-deps tensorflow-text==2.12.1
+RUN pip install --no-deps tensorstore==0.1.36
+RUN pip install --no-deps termcolor==2.2.0
+RUN pip install --no-deps toml==0.10.2
+RUN pip install --no-deps toolz==0.12.0
+RUN pip install --no-deps tqdm==4.65.0
+RUN pip install --no-deps typing_extensions==4.5.0
+RUN pip install --no-deps tzdata==2023.3
+RUN pip install --no-deps urllib3==1.25.8
+RUN pip install --no-deps Werkzeug==2.2.3
+RUN pip install --no-deps wheel==0.40.0
+RUN pip install --no-deps wrapt==1.14.1
+RUN pip install --no-deps zipp==3.15.0
+# Installing jax at the very end with GPU support.
+# NOTE: Not using `no-deps` flag here because
+# we need CUDA support.
+RUN pip install jax[cuda11_cudnn82]==0.4.6 \
+--find-links https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+
+ENV PYTHONPATH ./vit_jax
+
+COPY ./model_oss/jax_vision_transformer/vit_jax2tf.py ./
+COPY ./model_oss/jax_vision_transformer/vit_config_without_data.py vit_jax/configs/vit.py
+
+ENTRYPOINT ["python", "vit_jax2tf.py"]

community-content/vertex_vision_model_garden/model_oss/jax_vision_transformer/dockerfile/train_vit_gpu.Dockerfile

@@ -0,0 +1,149 @@
+# This Dockerfile runs the JAX based Vision transformer training on GPU.
+# See https://github.com/google-research/vision_transformer#running-on-cloud
+# for more details.
+# Here is an example to build this dockerfile:
+# PROJECT="your gcp project"
+# IMAGE_TAG="trainn_vit_gpu:${USER}-test"
+# docker build -f model_oss/jax_vision_transformer/dockerfile/train_vit_gpu.Dockerfile . -t "${IMAGE_TAG}"
+# docker tag "${IMAGE_TAG}" "gcr.io/${PROJECT}/${IMAGE_TAG}"
+# docker push "gcr.io/${PROJECT}/${IMAGE_TAG}"
+
+FROM tensorflow/tensorflow:2.12.0-gpu
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install basic libs
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git
+
+# Copy Apache license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Get 'vision_transformer' repository from github.
+RUN git clone https://github.com/google-research/vision_transformer
+# Ser current directory to the downloaded 'vision_transformer' repository.
+WORKDIR ./vision_transformer
+# Using git reset command to pin it down to a specific version.
+RUN git reset --hard e66b4732d44504251197a3da3f5949f3f3ce9ca6
+
+# Install required libs
+RUN pip install --upgrade pip
+# The following pip installs are pinned down versions of those inside
+# vit_jax/requirements.txt file.
+# NOTE: Using `no-deps` flag to avoid overwriting of
+# dependent library versions. For example,
+# both `chex` and `jax` can overwrite each others
+# `jax-lib` version.
+RUN pip install --no-deps absl-py==1.4.0
+RUN pip install --no-deps aqtp==0.0.10
+RUN pip install --no-deps array-record==0.2.0
+RUN pip install --no-deps astunparse==1.6.3
+RUN pip install --no-deps cached-property==1.5.2
+RUN pip install --no-deps cachetools==5.3.0
+RUN pip install --no-deps certifi==2019.11.28
+RUN pip install --no-deps chardet==3.0.4
+RUN pip install --no-deps chex==0.1.7
+RUN pip install --no-deps click==8.1.3
+RUN pip install --no-deps cloudpickle==2.2.1
+RUN pip install --no-deps clu==0.0.9
+RUN pip install --no-deps contextlib2==21.6.0
+RUN pip install --no-deps dacite==1.8.1
+RUN pip install --no-deps dbus-python==1.2.16
+RUN pip install --no-deps decorator==5.1.1
+RUN pip install --no-deps dm-tree==0.1.8
+RUN pip install --no-deps einops==0.6.1
+RUN pip install --no-deps etils==1.3.0
+RUN pip install --no-deps flatbuffers==23.3.3
+RUN pip install --no-deps flax==0.6.10
+RUN pip install --no-deps git+https://github.com/google/flaxformer@9adaa4467cf17703949b9f537c3566b99de1b416
+RUN pip install --no-deps gast==0.4.0
+RUN pip install --no-deps google-auth==2.16.2
+RUN pip install --no-deps google-auth-oauthlib==0.4.6
+RUN pip install --no-deps google-pasta==0.2.0
+RUN pip install --no-deps googleapis-common-protos==1.59.0
+RUN pip install --no-deps grpcio==1.51.3
+RUN pip install --no-deps h5py==3.8.0
+RUN pip install --no-deps idna==2.8
+RUN pip install --no-deps importlib-metadata==6.1.0
+RUN pip install --no-deps importlib-resources==5.12.0
+RUN pip install --no-deps keras==2.12.0
+RUN pip install --no-deps libclang==16.0.0
+RUN pip install --no-deps Markdown==3.4.3
+RUN pip install --no-deps markdown-it-py==2.2.0
+RUN pip install --no-deps MarkupSafe==2.1.2
+RUN pip install --no-deps mdurl==0.1.2
+RUN pip install --no-deps ml-collections==0.1.1
+RUN pip install --no-deps msgpack==1.0.5
+RUN pip install --no-deps nest-asyncio==1.5.6
+RUN pip install --no-deps numpy==1.23.5
+RUN pip install --no-deps oauthlib==3.2.2
+RUN pip install --no-deps opt-einsum==3.3.0
+RUN pip install --no-deps optax==0.1.5
+RUN pip install --no-deps orbax-checkpoint==0.1.6
+RUN pip install --no-deps packaging==23.0
+RUN pip install --no-deps pandas==2.0.1
+RUN pip install --no-deps pip==23.1.2
+RUN pip install --no-deps promise==2.3
+RUN pip install --no-deps protobuf==4.22.1
+RUN pip install --no-deps psutil==5.9.5
+RUN pip install --no-deps pyasn1==0.4.8
+RUN pip install --no-deps pyasn1-modules==0.2.8
+RUN pip install --no-deps Pygments==2.15.1
+RUN pip install --no-deps PyGObject==3.36.0
+RUN pip install --no-deps python-apt==2.0.1+ubuntu0.20.4.1
+RUN pip install --no-deps python-dateutil==2.8.2
+RUN pip install --no-deps pytz==2023.3
+RUN pip install --no-deps PyYAML==6.0
+RUN pip install --no-deps requests==2.22.0
+RUN pip install --no-deps requests-oauthlib==1.3.1
+RUN pip install --no-deps requests-unixsocket==0.2.0
+RUN pip install --no-deps rich==13.3.5
+RUN pip install --no-deps rsa==4.9
+RUN pip install --no-deps scipy==1.10.1
+RUN pip install --no-deps setuptools==67.6.0
+RUN pip install --no-deps six==1.14.0
+RUN pip install --no-deps tensorboard==2.12.0
+RUN pip install --no-deps tensorboard-data-server==0.7.0
+RUN pip install --no-deps tensorboard-plugin-wit==1.8.1
+RUN pip install --no-deps tensorflow==2.12.0
+RUN pip install --no-deps tensorflow-cpu==2.12.0
+RUN pip install --no-deps tensorflow-datasets==4.9.2
+RUN pip install --no-deps tensorflow-estimator==2.12.0
+RUN pip install --no-deps tensorflow-hub==0.13.0
+RUN pip install --no-deps tensorflow-io-gcs-filesystem==0.31.0
+RUN pip install --no-deps tensorflow-metadata==1.13.1
+RUN pip install --no-deps tensorflow-probability==0.20.0
+RUN pip install --no-deps tensorflow-text==2.12.1
+RUN pip install --no-deps tensorstore==0.1.36
+RUN pip install --no-deps termcolor==2.2.0
+RUN pip install --no-deps toml==0.10.2
+RUN pip install --no-deps toolz==0.12.0
+RUN pip install --no-deps tqdm==4.65.0
+RUN pip install --no-deps typing_extensions==4.5.0
+RUN pip install --no-deps tzdata==2023.3
+RUN pip install --no-deps urllib3==1.25.8
+RUN pip install --no-deps Werkzeug==2.2.3
+RUN pip install --no-deps wheel==0.40.0
+RUN pip install --no-deps wrapt==1.14.1
+RUN pip install --no-deps zipp==3.15.0
+# Installing jax at the very end with GPU support.
+# NOTE: Not using `no-deps` flag here because
+# we need CUDA support.
+RUN pip install jax[cuda11_cudnn82]==0.4.6 \
+--find-links https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+
+COPY ./model_oss/jax_vision_transformer/vit_config_without_data.py vit_jax/configs/vit.py
+
+ENV PYTHONPATH ./vit_jax
+ENTRYPOINT ["python", "-m", "vit_jax.main"]

community-content/vertex_vision_model_garden/model_oss/jax_vision_transformer/vit_config_without_data.py

@@ -0,0 +1,27 @@
+"""Returns a config for a Vision Transformer model without asking for data."""
+import ml_collections
+from vit_jax.configs import common
+from vit_jax.configs import models
+
+
+def get_config(model: str) -> ml_collections.ConfigDict:
+  """Returns default parameters for finetuning ViT `model`."""
+  config = common.get_config()
+
+  get_model_config = getattr(models, f'get_{model}_config')
+  config.model = get_model_config()
+
+  # These values are often overridden on the command line.
+  config.base_lr = 0.03
+  config.total_steps = 500
+  config.warmup_steps = 100
+  config.pp = ml_collections.ConfigDict()
+  config.pp.train = 'train'
+  config.pp.test = 'test'
+  config.pp.resize = 448
+  config.pp.crop = 384
+
+  # This value MUST be overridden on the command line.
+  config.dataset = ''
+
+  return config

community-content/vertex_vision_model_garden/model_oss/keras/dockerfile/serve.Dockerfile

@@ -0,0 +1,118 @@
+# Dockerfile for basic serving dockers with Keras.
+#
+# To build:
+# docker build -f model_oss/keras/dockerfile/serve.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM tensorflow/tensorflow:2.12.0-gpu
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# This is added to fix docker build error related to Nvidia key update.
+RUN rm -f /etc/apt/sources.list.d/cuda.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        screen \
+        libtcmalloc-minimal4
+
+
+# Install google cloud SDK.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN tar xzf google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install cloud-tpu-client==0.10
+RUN pip install pyyaml==5.4.1
+RUN pip install fsspec==2021.10.1
+RUN pip install gcsfs==2021.10.1
+RUN pip install tensorflow-text==2.11.0
+RUN pip install pyglove==0.1.0
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install pylint==2.17.2
+RUN pip install keras-cv==0.4.0
+RUN pip install tensorflow-datasets==4.8.3
+RUN pip install protobuf==3.20.3
+RUN pip install Pillow==9.5.0
+RUN pip install flask==2.3.2
+RUN pip install waitress==2.1.2
+
+# Installs Reduction Server NCCL plugin.
+RUN echo "deb https://packages.cloud.google.com/apt google-fast-socket main" | tee /etc/apt/sources.list.d/google-fast-socket.list \
+&&  curl -s -L https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+&&  apt update && apt install -y google-reduction-server
+
+# Downloading gcloud package
+RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz
+
+# Installing the package
+RUN mkdir -p /usr/local/gcloud \
+  && tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \
+  && /usr/local/gcloud/google-cloud-sdk/install.sh
+
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Adding the package path to local
+ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin
+
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+
+# Lower the memory fragmentation, and speed up the training.
+# https://github.com/tensorflow/tensorflow/issues/44176#issuecomment-783768033
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4
+
+# Enable userspace DNS cache
+ENV GCS_RESOLVE_REFRESH_SECS=60
+ENV GCS_REQUEST_CONNECTION_TIMEOUT_SECS=300
+ENV GCS_METADATA_REQUEST_TIMEOUT_SECS=300
+ENV GCS_READ_REQUEST_TIMEOUT_SECS=300
+ENV GCS_WRITE_REQUEST_TIMEOUT_SECS=600
+# Each opened GCS file takes GCS_READ_CACHE_BLOCK_SIZE_MB of RAM, reduce the
+# value from the default 64MB to 8MB to decrease memory footprint.
+ENV GCS_READ_CACHE_BLOCK_SIZE_MB=8
+
+EXPOSE 8501
+
+WORKDIR /usr/local/lib/python3.8/dist-packages/official/vision
+
+COPY model_oss/keras /automl_vision/keras
+COPY model_oss/util /automl_vision/util
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+ENV MODEL_PATH ""
+ENV IMAGE_WIDTH "512"
+ENV IMAGE_HEIGHT "512"
+
+COPY model_oss/keras/serve.py ./app.py
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["flask","run"]
+CMD ["--host=0.0.0.0", "--port=8501"]

community-content/vertex_vision_model_garden/model_oss/keras/dockerfile/train.Dockerfile

@@ -0,0 +1,111 @@
+# Dockerfile for basic training dockers with Keras.
+#
+# To build:
+# docker build -f model_oss/keras/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM tensorflow/tensorflow:2.12.0-gpu
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# This is added to fix docker build error related to Nvidia key update.
+RUN rm -f /etc/apt/sources.list.d/cuda.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        screen \
+        libtcmalloc-minimal4
+
+
+# Install google cloud SDK.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN tar xzf google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install cloud-tpu-client==0.10
+RUN pip install pyyaml==5.4.1
+RUN pip install fsspec==2021.10.1
+RUN pip install gcsfs==2021.10.1
+RUN pip install tensorflow-text==2.11.0
+RUN pip install pyglove==0.1.0
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install pylint==2.17.2
+RUN pip install keras-cv==0.4.0
+RUN pip install tensorflow-datasets==4.8.3
+RUN pip install tensorflow-estimator==2.12.0
+RUN pip install tensorflow-gcs-config==2.12.0
+RUN pip install tensorflow-hub==0.13.0
+RUN pip install tensorflow-io-gcs-filesystem==0.32.0
+RUN pip install tensorflow-metadata==1.13.1
+RUN pip install tensorflow-probability==0.19.0
+RUN pip install tensorboard==2.12.2
+RUN pip install tensorboard-data-server==0.7.0
+RUN pip install tensorboard-plugin-wit==1.8.1
+RUN pip install protobuf==3.20.3
+RUN pip install pandas==1.5.3
+RUN pip install pandas-datareader==0.10.0
+RUN pip install pandas-gbq==0.17.9
+RUN pip install pycocotools==2.0.6
+
+# Installs Reduction Server NCCL plugin.
+RUN echo "deb https://packages.cloud.google.com/apt google-fast-socket main" | tee /etc/apt/sources.list.d/google-fast-socket.list \
+&&  curl -s -L https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+&&  apt update && apt install -y google-reduction-server
+
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+
+# Lower the memory fragmentation, and speed up the training.
+# https://github.com/tensorflow/tensorflow/issues/44176#issuecomment-783768033
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4
+
+# Enable userspace DNS cache
+ENV GCS_RESOLVE_REFRESH_SECS=60
+ENV GCS_REQUEST_CONNECTION_TIMEOUT_SECS=300
+ENV GCS_METADATA_REQUEST_TIMEOUT_SECS=300
+ENV GCS_READ_REQUEST_TIMEOUT_SECS=300
+ENV GCS_WRITE_REQUEST_TIMEOUT_SECS=600
+# Each opened GCS file takes GCS_READ_CACHE_BLOCK_SIZE_MB of RAM, reduce the
+# value from the default 64MB to 8MB to decrease memory footprint.
+ENV GCS_READ_CACHE_BLOCK_SIZE_MB=8
+
+WORKDIR /usr/local/lib/python3.8/dist-packages/official/vision
+
+COPY model_oss/keras /automl_vision/keras
+COPY model_oss/util /automl_vision/util
+
+WORKDIR /automl_vision
+
+# Keras stable diffusion training codes set width and height as RESOLUTION.
+ENV RESOLUTION "512"
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","keras/train.py"]

community-content/vertex_vision_model_garden/model_oss/keras/serve.py

@@ -0,0 +1,184 @@
+r"""Servers Keras Stable Diffusion models.
+
+python serve.py --model_path=<model path in gcs>
+
+curl -d \
+'{"prompt":"Hello Kitty"}' \
+-H "Content-Type: application/json" \
+-X POST http://localhost:8501/predict
+"""
+
+import base64
+import io
+import json
+import os
+from typing import List, Tuple
+
+from absl import app
+# The docker builds could not find flask and waitress.
+# pylint: disable=import-error
+from flask import Flask
+from flask import request
+from flask import Response
+import keras_cv
+from PIL import Image
+from waitress import serve
+
+from util import constants
+from util import fileutils
+
+
+flask_app = Flask(__name__)
+
+stable_diffusion_model = None
+
+
+model_path = os.environ.get('MODEL_PATH', '')
+if model_path.startswith(constants.GCS_URI_PREFIX):
+  print('Downloading models from gcs to local.')
+  os.makedirs(constants.LOCAL_MODEL_DIR, exist_ok=True)
+  fileutils.download_gcs_dir_to_local(
+      os.path.dirname(model_path), constants.LOCAL_MODEL_DIR
+  )
+  model_path = os.path.join(
+      constants.LOCAL_MODEL_DIR, os.path.basename(model_path)
+  )
+
+image_width = int(os.environ.get('IMAGE_WIDTH', 512))
+image_height = int(os.environ.get('IMAGE_HEIGHT', 512))
+
+print('image_width=', image_width, 'image_height=', image_height)
+print('Create Keras stable diffusion models.')
+stable_diffusion_model = keras_cv.models.StableDiffusion(
+    img_width=image_width,
+    img_height=image_height,
+    jit_compile=True,
+)
+
+if model_path:
+  # We just reload the weights of the fine-tuned diffusion model.
+  print('Initialize finetuned models from: ', model_path)
+  stable_diffusion_model.diffusion_model.load_weights(model_path)
+
+
+def error(message: str) -> str:
+  """Returns a JSON representing an error response."""
+  return json.dumps({
+      'success': False,
+      'error': message,
+  })
+
+
+def check_key_in_json(content: str, keys: List[str]) -> str:
+  for key in keys:
+    if key not in content:
+      return error('No {} in request {}.'.format(key, content))
+  return None
+
+
+def validate_json_key(json_key_string: str) -> Tuple[str, bool]:
+  try:
+    json_key = json.loads(json_key_string)
+  except (ValueError, TypeError):
+    return (error('Invalid key found in request'), False)
+  return (json_key, True)
+
+
+# The health check route is required for docker deployment in google cloud.
+@flask_app.route('/ping')
+def ping() -> Response:
+  """Health checks."""
+  return Response(status=200)
+
+
+# The return should be `Response` for docker deployment in google cloud.
+@flask_app.route('/predict', methods=['GET', 'POST'])
+def predict_model() -> Response:
+  """Predictions."""
+  if request.method == 'POST':
+    contents = request.get_json(force=True)
+
+    print('The input contents are:', contents)
+    batch_size = 1
+    num_steps = 25
+    seed = 1234
+    if 'parameters' in contents:
+      parameters = contents['parameters']
+      if 'batch_size' in parameters:
+        batch_size = int(parameters['batch_size'])
+      if 'num_steps' in parameters:
+        num_steps = int(parameters['num_steps'])
+      if 'seed' in parameters:
+        seed = int(parameters['seed'])
+    print('batch_size=', batch_size, 'num_steps=', num_steps, 'seed=', seed)
+    if batch_size < 1:
+      return Response(
+          response=error('The batch size must be a positive integar.'),
+          status=200,
+          mimetype='text/plain',
+      )
+    if num_steps < 1:
+      return Response(
+          response=error('The num steps must be a positive integar.'),
+          status=200,
+          mimetype='text/plain',
+      )
+    predictions = []
+    for content in contents['instances']:
+      print('Processing:', content)
+      prompt = content['prompt']
+      generated_image_array = stable_diffusion_model.text_to_image(
+          prompt=prompt,
+          batch_size=batch_size,
+          num_steps=num_steps,
+          seed=seed,
+      )
+
+      generated_image_bytes_array = []
+      for i in range(batch_size):
+        generated_image = Image.fromarray(generated_image_array[i])
+        # Converts the image to a base64-encoded string.
+        buffered_image = io.BytesIO()
+        generated_image.save(buffered_image, format='JPEG')
+        generated_image_bytes = base64.b64encode(
+            buffered_image.getvalue()
+        ).decode('utf-8')
+        generated_image_bytes_array.append(generated_image_bytes)
+      prediction = {
+          'prompt': prompt,
+          'predicted_image': generated_image_bytes_array,
+      }
+      predictions.append(prediction)
+
+    return Response(
+        response=json.dumps({
+            'success': True,
+            'predictions': predictions,
+        }),
+        status=200,
+        mimetype='text/plain',
+    )
+  else:
+    return Response(
+        response=json.dumps({
+            'success': True,
+            'isalive': stable_diffusion_model is not None,
+        }),
+        status=200,
+        mimetype='text/plain',
+    )
+
+
+def serve_main(unused_argv):
+  """The main function to serve Keras models."""
+  del unused_argv
+  # This is used when running locally only. When deploying to Google App
+  # Engine, a webserver process such as Gunicorn will serve the app.
+  # # Debug deployment.
+  # flask_app.run(host='0.0.0.0', port=8501, debug=True)
+  # Prod deployment.
+  serve(flask_app, host='0.0.0.0', port=8501)
+
+
+if __name__ == '__main__':
+  app.run(serve_main)

community-content/vertex_vision_model_garden/model_oss/keras/train.py

@@ -0,0 +1,363 @@
+"""Train Keras Stable Diffusion.
+
+Most the codes below are from
+https://keras.io/examples/generative/finetune_stable_diffusion/.
+"""
+import os
+
+from absl import app
+from absl import flags
+from absl import logging
+import keras_cv
+# pylint: disable=g-importing-member
+from keras_cv.models.stable_diffusion.clip_tokenizer import SimpleTokenizer
+from keras_cv.models.stable_diffusion.diffusion_model import DiffusionModel
+from keras_cv.models.stable_diffusion.image_encoder import ImageEncoder
+from keras_cv.models.stable_diffusion.noise_scheduler import NoiseScheduler
+from keras_cv.models.stable_diffusion.text_encoder import TextEncoder
+import numpy as np
+# The docker builds could not find pandas.
+# pylint: disable=import-error
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+import tensorflow.experimental.numpy as tnp
+
+from util import constants
+from util import fileutils
+
+_INPUT_CSV_PATH = flags.DEFINE_string(
+    'input_csv_path',
+    None,
+    'The input csv path.',
+    required=True,
+)
+
+_USE_MP = flags.DEFINE_bool(
+    'use_mp',
+    True,
+    'Enable mixed-precision training if the underlying GPU has tensor cores.',
+)
+
+_EPOCHS = flags.DEFINE_integer('epochs', 1, 'The number of epochs.')
+
+_OUTPUT_MODEL_DIR = flags.DEFINE_string(
+    'output_model_dir',
+    None,
+    'The output model dir.',
+    required=True,
+)
+
+# These hyperparameters defaults come from this tutorial by Hugging Face:
+# https://huggingface.co/docs/diffusers/training/text2image
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate', 1e-5, 'The learning rate parameter for AdamW optimizer.'
+)
+
+_BETA_1 = flags.DEFINE_float(
+    'beta_1', 0.9, 'The beta_1 parameter for AdamW optimizer.'
+)
+
+_BETA_2 = flags.DEFINE_float(
+    'beta_2', 0.999, 'The beta_2 parameter for AdamW optimizer.'
+)
+
+_WEIGHT_DECAY = flags.DEFINE_float(
+    'weight_decay', 1e-2, 'The weight decay parameter for AdamW optimizer.'
+)
+
+_EPSILON = flags.DEFINE_float(
+    'epsilon', 1e-08, 'The epsilon parameter for AdamW optimizer.'
+)
+
+RESOLUTION = int(os.environ.get('RESOLUTION', 512))
+
+# The padding token and maximum prompt length are specific to the text encoder.
+# If you're using a different text encoder be sure to change them accordingly.
+PADDING_TOKEN = 49407
+MAX_PROMPT_LENGTH = 77
+
+AUTO = tf.data.AUTOTUNE
+POS_IDS = tf.convert_to_tensor([list(range(MAX_PROMPT_LENGTH))], dtype=tf.int32)
+
+
+augmenter = keras.Sequential(
+    layers=[
+        keras_cv.layers.CenterCrop(RESOLUTION, RESOLUTION),
+        keras_cv.layers.RandomFlip(),
+        tf.keras.layers.Rescaling(scale=1.0 / 127.5, offset=-1),
+    ]
+)
+text_encoder = TextEncoder(MAX_PROMPT_LENGTH)
+
+
+def process_image(image_path, tokenized_text):
+  image = tf.io.read_file(image_path)
+  image = tf.io.decode_png(image, 3)
+  image = tf.image.resize(image, (RESOLUTION, RESOLUTION))
+  return image, tokenized_text
+
+
+def apply_augmentation(image_batch, token_batch):
+  return augmenter(image_batch), token_batch
+
+
+def run_text_encoder(image_batch, token_batch):
+  return (
+      image_batch,
+      token_batch,
+      text_encoder([token_batch, POS_IDS], training=False),
+  )
+
+
+def prepare_dict(image_batch, token_batch, encoded_text_batch):
+  return {
+      'images': image_batch,
+      'tokens': token_batch,
+      'encoded_text': encoded_text_batch,
+  }
+
+
+def prepare_dataset(image_paths, tokenized_texts, batch_size=1):
+  dataset = tf.data.Dataset.from_tensor_slices((image_paths, tokenized_texts))
+  dataset = dataset.shuffle(batch_size * 10)
+  dataset = dataset.map(process_image, num_parallel_calls=AUTO).batch(
+      batch_size
+  )
+  dataset = dataset.map(apply_augmentation, num_parallel_calls=AUTO)
+  dataset = dataset.map(run_text_encoder, num_parallel_calls=AUTO)
+  dataset = dataset.map(prepare_dict, num_parallel_calls=AUTO)
+  return dataset.prefetch(AUTO)
+
+
+def prepare_training_dataset(dataset_csv):
+  """Prepares training datasets."""
+  if dataset_csv.startswith(constants.GCS_URI_PREFIX):
+    if not os.path.exists(constants.LOCAL_DATA_DIR):
+      os.makedirs(constants.LOCAL_DATA_DIR)
+    logging.info(
+        'Start to download data from %s to %s.',
+        os.path.dirname(dataset_csv),
+        constants.LOCAL_DATA_DIR,
+    )
+    fileutils.download_gcs_dir_to_local(
+        os.path.dirname(dataset_csv), constants.LOCAL_DATA_DIR
+    )
+    data_frame = pd.read_csv(
+        os.path.join(constants.LOCAL_DATA_DIR, os.path.basename(dataset_csv))
+    )
+    data_frame['image_path'] = data_frame['image_path'].apply(
+        lambda x: os.path.join(constants.LOCAL_DATA_DIR, x)
+    )
+  else:
+    # Keeps the following codes for experiments with
+    # https://keras.io/examples/generative/finetune_stable_diffusion/.
+    data_path = tf.keras.utils.get_file(origin=dataset_csv, untar=True)
+    data_frame = pd.read_csv(os.path.join(data_path, 'data.csv'))
+    data_frame['image_path'] = data_frame['image_path'].apply(
+        lambda x: os.path.join(data_path, x)
+    )
+  data_frame.head()
+
+  # Load the tokenizer.
+  tokenizer = SimpleTokenizer()
+
+  #  Method to tokenize and pad the tokens.
+  def process_text(caption):
+    tokens = tokenizer.encode(caption)
+    tokens = tokens + [PADDING_TOKEN] * (MAX_PROMPT_LENGTH - len(tokens))
+    return np.array(tokens)
+
+  # Collate the tokenized captions into an array.
+  tokenized_texts = np.empty((len(data_frame), MAX_PROMPT_LENGTH))
+
+  all_captions = list(data_frame['caption'].values)
+  for i, caption in enumerate(all_captions):
+    tokenized_texts[i] = process_text(caption)
+
+  # Prepare the dataset.
+  training_dataset = prepare_dataset(
+      np.array(data_frame['image_path']), tokenized_texts, batch_size=4
+  )
+
+  return training_dataset
+
+
+class Trainer(tf.keras.Model):
+  """The trainer for Keras Stable Diffusion."""
+
+  # Reference:
+  # https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
+
+  def __init__(
+      self,
+      diffusion_model,
+      vae,
+      noise_scheduler,
+      use_mixed_precision=False,
+      max_grad_norm=1.0,
+      **kwargs,
+  ):
+    super().__init__(**kwargs)
+
+    self.diffusion_model = diffusion_model
+    self.vae = vae
+    self.noise_scheduler = noise_scheduler
+    self.max_grad_norm = max_grad_norm
+
+    self.use_mixed_precision = use_mixed_precision
+    self.vae.trainable = False
+
+  def train_step(self, inputs):
+    images = inputs['images']
+    encoded_text = inputs['encoded_text']
+    batch_size = tf.shape(images)[0]
+
+    with tf.GradientTape() as tape:
+      # Project image into the latent space and sample from it.
+      latents = self.sample_from_encoder_outputs(
+          self.vae(images, training=False)
+      )
+      # Know more about the magic number here:
+      # https://keras.io/examples/generative/fine_tune_via_textual_inversion/
+      latents = latents * 0.18215
+
+      # Sample noise that we'll add to the latents.
+      noise = tf.random.normal(tf.shape(latents))
+
+      # Sample a random timestep for each image.
+      timesteps = tnp.random.randint(
+          0, self.noise_scheduler.train_timesteps, (batch_size,)
+      )
+
+      # Add noise to the latents according to the noise magnitude at each
+      # timestep (this is the forward diffusion process).
+      noisy_latents = self.noise_scheduler.add_noise(
+          tf.cast(latents, noise.dtype), noise, timesteps
+      )
+
+      # Get the target for loss depending on the prediction type
+      # just the sampled noise for now.
+      target = noise  # noise_schedule.predict_epsilon == True
+
+      # Predict the noise residual and compute loss.
+      # pylint: disable=unnecessary-lambda
+      timestep_embedding = tf.map_fn(
+          lambda t: self.get_timestep_embedding(t), timesteps, dtype=tf.float32
+      )
+      timestep_embedding = tf.squeeze(timestep_embedding, 1)
+      model_pred = self.diffusion_model(
+          [noisy_latents, timestep_embedding, encoded_text], training=True
+      )
+      loss = self.compiled_loss(target, model_pred)
+      if self.use_mixed_precision:
+        loss = self.optimizer.get_scaled_loss(loss)
+
+    # Update parameters of the diffusion model.
+    trainable_vars = self.diffusion_model.trainable_variables
+    gradients = tape.gradient(loss, trainable_vars)
+    if self.use_mixed_precision:
+      gradients = self.optimizer.get_unscaled_gradients(gradients)
+    gradients = [tf.clip_by_norm(g, self.max_grad_norm) for g in gradients]
+    self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+
+    return {m.name: m.result() for m in self.metrics}
+
+  def get_timestep_embedding(self, timestep, dim=320, max_period=10000):
+    half = dim // 2
+    log_max_preiod = tf.math.log(tf.cast(max_period, tf.float32))
+    # The docker builds could not support unary `-`.
+    # pylint: disable=invalid-unary-operand-type
+    freqs = tf.math.exp(
+        -log_max_preiod * tf.range(0, half, dtype=tf.float32) / half
+    )
+    args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
+    embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
+    embedding = tf.reshape(embedding, [1, -1])
+    return embedding
+
+  def sample_from_encoder_outputs(self, outputs):
+    mean, logvar = tf.split(outputs, 2, axis=-1)
+    logvar = tf.clip_by_value(logvar, -30.0, 20.0)
+    std = tf.exp(0.5 * logvar)
+    sample = tf.random.normal(tf.shape(mean), dtype=mean.dtype)
+    return mean + std * sample
+
+  def save_weights(
+      self, filepath, overwrite=True, save_format=None, options=None
+  ):
+    # Overriding this method will allow us to use the `ModelCheckpoint`
+    # callback directly with this trainer class. In this case, it will
+    # only checkpoint the `diffusion_model` since that's what we're training
+    # during fine-tuning.
+    self.diffusion_model.save_weights(
+        filepath=filepath,
+        overwrite=overwrite,
+        save_format=save_format,
+        options=options,
+    )
+
+
+def main(_) -> None:
+  # _INPUT_CSV_PATH and _OUTPUT_MODEL_DIR should have the format as
+  # gs://<bucket_name>/<object_name>.
+  if _INPUT_CSV_PATH.value:
+    if not _INPUT_CSV_PATH.value.startswith(constants.GCS_URI_PREFIX):
+      raise ValueError('The input csv path should be a gcs path like gs://<>')
+  if _OUTPUT_MODEL_DIR.value:
+    if not _OUTPUT_MODEL_DIR.value.startswith(constants.GCS_URI_PREFIX):
+      raise ValueError('The output model dir should be a gcs path like gs://<>')
+
+  if _USE_MP.value:
+    keras.mixed_precision.set_global_policy('mixed_float16')
+
+  image_encoder = ImageEncoder(RESOLUTION, RESOLUTION)
+  diffusion_ft_trainer = Trainer(
+      diffusion_model=DiffusionModel(RESOLUTION, RESOLUTION, MAX_PROMPT_LENGTH),
+      # Remove the top layer from the encoder, which cuts off the variance and
+      # only returns the mean.
+      vae=tf.keras.Model(
+          image_encoder.input,
+          image_encoder.layers[-2].output,
+      ),
+      noise_scheduler=NoiseScheduler(),
+      use_mixed_precision=_USE_MP.value,
+  )
+
+  optimizer = tf.keras.optimizers.experimental.AdamW(
+      learning_rate=_LEARNING_RATE.value,
+      weight_decay=_WEIGHT_DECAY.value,
+      beta_1=_BETA_1.value,
+      beta_2=_BETA_2.value,
+      epsilon=_EPSILON.value,
+  )
+  diffusion_ft_trainer.compile(optimizer=optimizer, loss='mse')
+
+  training_dataset = prepare_training_dataset(_INPUT_CSV_PATH.value)
+
+  # Note: gcsfuse does not work for Keras. We saves the trained models locally
+  # first, and then copy to gcs storages.
+  if not os.path.exists(constants.LOCAL_MODEL_DIR):
+    os.makedirs(constants.LOCAL_MODEL_DIR)
+  # The default saved model is in HDF5.
+  ckpt_path = os.path.join(constants.LOCAL_MODEL_DIR, 'saved_model.h5')
+  ckpt_callback = tf.keras.callbacks.ModelCheckpoint(
+      ckpt_path,
+      save_weights_only=True,
+      monitor='loss',
+      mode='min',
+  )
+  diffusion_ft_trainer.fit(
+      training_dataset, epochs=_EPOCHS.value, callbacks=[ckpt_callback]
+  )
+
+  # Copies the files in constants.LOCAL_MODEL_DIR to output_model_dir.
+  fileutils.upload_local_dir_to_gcs(
+      constants.LOCAL_MODEL_DIR, _OUTPUT_MODEL_DIR.value
+  )
+
+  return
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/lm-evaluation-harness/dockerfile/eval.Dockerfile

@@ -0,0 +1,40 @@
+# Dockerfile for lm-evaluation-harness evaluation.
+#
+# To build:
+# docker build -f model_oss/lm-evaluation-harness/dockerfile/eval.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/{YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/{YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+
+USER root
+
+# Install tools.
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update
+RUN apt-get install -y --no-install-recommends apt-utils
+RUN apt-get install -y --no-install-recommends curl
+RUN apt-get install -y --no-install-recommends wget
+RUN apt-get install -y --no-install-recommends git
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Install libraries.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN python3 -m pip install --upgrade pip
+RUN pip install google-cloud-storage==2.7.0
+RUN pip install absl-py==1.4.0
+
+# Install lm-evaluation-harness
+RUN git clone https://github.com/EleutherAI/lm-evaluation-harness
+WORKDIR lm-evaluation-harness
+# Pin version up to date 08/08/2023
+RUN git reset --hard b952a206de210b72b1bf750fbab38c26121e0dc0
+# Edit tokenizer loading function to avoid using fast tokenizer for OpenLLaMA
+RUN sed -i '355 i\        use_fast = not pretrained.startswith("openlm-research/open_llama")' lm_eval/models/huggingface.py
+RUN sed -i '360 i\            use_fast=use_fast,' lm_eval/models/huggingface.py
+# Install from source while including the sentencepiece dependency
+RUN pip install -e ".[sentencepiece]"

community-content/vertex_vision_model_garden/model_oss/movinet/dockerfile/base.Dockerfile

@@ -0,0 +1,64 @@
+FROM tensorflow/build:2.12-python3.9
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# This is added to fix docker build error related to Nvidia key update.
+RUN rm -f /etc/apt/sources.list.d/cuda.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        libtcmalloc-minimal4
+
+
+# Install google cloud CLI.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-430.0.0-linux-x86.tar.gz
+RUN tar xzf google-cloud-cli-430.0.0-linux-x86.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install cloud-tpu-client==0.10
+RUN pip install pyyaml==6.0
+RUN pip install fsspec==2023.4.0
+RUN pip install gcsfs==2023.4.0
+RUN pip install tf-models-official==2.12.0
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install pylint==2.17.3
+
+# Installs Reduction Server NCCL plugin.
+RUN echo "deb https://packages.cloud.google.com/apt google-fast-socket main" | tee /etc/apt/sources.list.d/google-fast-socket.list \
+&&  curl -s -L https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+&&  apt update && apt install -y google-reduction-server
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+
+# Lower the memory fragmentation, and speed up the training.
+# https://github.com/tensorflow/tensorflow/issues/44176#issuecomment-783768033
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4
+
+# Enable userspace DNS cache
+ENV GCS_RESOLVE_REFRESH_SECS=60
+ENV GCS_REQUEST_CONNECTION_TIMEOUT_SECS=300
+ENV GCS_METADATA_REQUEST_TIMEOUT_SECS=300
+ENV GCS_READ_REQUEST_TIMEOUT_SECS=300
+ENV GCS_WRITE_REQUEST_TIMEOUT_SECS=600
+# Each opened GCS file takes GCS_READ_CACHE_BLOCK_SIZE_MB of RAM, reduce the
+# value from the default 64MB to 8MB to decrease memory footprint.
+ENV GCS_READ_CACHE_BLOCK_SIZE_MB=8

community-content/vertex_vision_model_garden/model_oss/movinet/dockerfile/model_export.Dockerfile

@@ -0,0 +1,13 @@
+FROM gcr.io/automl-migration-test/movinet-base:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN wget https://raw.githubusercontent.com/tensorflow/models/954dd73bffd43174bd3ca26a4a34abebe4147570/official/projects/movinet/tools/export_saved_model.py \
+  -O /usr/local/lib/python3.9/dist-packages/official/projects/movinet/tools/export_saved_model.py
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+ENTRYPOINT ["python3", "-m", "official.projects.movinet.tools.export_saved_model"]

community-content/vertex_vision_model_garden/model_oss/movinet/dockerfile/serve.Dockerfile

@@ -0,0 +1,18 @@
+FROM gcr.io/automl-migration-test/movinet-base:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN pip install flask==2.3.2
+RUN pip install waitress==2.1.2
+
+RUN mkdir -p /automl_vision/movinet/serving
+COPY model_oss/movinet/serving /automl_vision/movinet/serving
+COPY model_oss/util /automl_vision/util
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+ENTRYPOINT ["flask", "--app", "movinet.serving.serving_main", "run"]
+CMD ["--host=0.0.0.0", "--port=8501"]

community-content/vertex_vision_model_garden/model_oss/movinet/dockerfile/train.Dockerfile

@@ -0,0 +1,18 @@
+FROM gcr.io/automl-migration-test/movinet-base:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN mkdir -p /automl_vision/movinet
+COPY model_oss/movinet/*.py /automl_vision/movinet/
+COPY model_oss/util /automl_vision/util
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","movinet/train.py"]

community-content/vertex_vision_model_garden/model_oss/movinet/serving/serving_main.py

@@ -0,0 +1,142 @@
+"""Main executable for MoViNet online / batch predictions."""
+
+from collections.abc import Sequence
+import json
+import os
+
+from absl import app
+from absl import logging
+import flask
+import tensorflow as tf
+import waitress
+
+from movinet.serving import video_serving_lib
+from util import constants
+
+
+flask_app = flask.Flask(__name__)
+logging.set_verbosity(logging.INFO)
+
+movinet_model = None
+
+_BATCH_SIZE = int(os.environ.get('BATCH_SIZE', '1'))
+_NUM_FRAMES = int(os.environ.get('NUM_FRAMES', '32'))
+_FPS = float(os.environ.get('FPS', '5'))
+_OVERLAP_FRAMES = int(os.environ.get('OVERLAP_FRAMES', '24'))
+_OBJECTIVE = os.environ.get(
+    'OBJECTIVE', constants.OBJECTIVE_VIDEO_CLASSIFICATION
+).lower()
+
+# VAR parameters.
+_CONFIDENCE_THRESHOLD = float(os.environ.get('CONFIDENCE_THRESHOLD', '0.5'))
+_MIN_GAP_TIME = float(os.environ.get('MIN_GAP_TIME', '1.5'))
+
+
+def load_movinet_model() -> None:
+  model_path = os.environ.get('MODEL_PATH')
+
+  if not model_path:
+    raise app.UsageError('Missing MODEL_PATH environment variable.')
+
+  # We just reload the weights of the fine-tuned diffusion model.
+  logging.info('Initialize finetuned models from: %s', model_path)
+  global movinet_model
+  movinet_model = tf.saved_model.load(model_path)
+
+
+load_movinet_model()
+
+
+def error(message: str) -> str:
+  """Returns a JSON representing an error response."""
+  return json.dumps({
+      'success': False,
+      'error': message,
+  })
+
+
+# The health check route is required for docker deployment in google cloud.
+@flask_app.route('/ping')
+def ping() -> flask.Response:
+  """Health checks."""
+  return flask.Response(status=200)
+
+
+# The return should be `Response` for docker deployment in google cloud.
+@flask_app.route('/predict', methods=['GET', 'POST'])
+def predict_model() -> flask.Response:
+  """Predictions."""
+  if flask.request.method == 'POST':
+    contents = flask.request.get_json(force=True)
+
+    logging.info('The input contents are: %s', contents)
+    instances = contents.get('instances', [])
+
+    try:
+      predictions = []
+      for instance in instances:
+        executor = video_serving_lib.parse_request(instance)
+        prediction = executor.get_prediction(
+            movinet_model,
+            _BATCH_SIZE,
+            _FPS,
+            _NUM_FRAMES,
+            _OVERLAP_FRAMES,
+            _OBJECTIVE,
+        )
+        if _OBJECTIVE == constants.OBJECTIVE_VIDEO_CLASSIFICATION:
+          prediction = video_serving_lib.postprocess_vcn(prediction)
+        elif _OBJECTIVE == constants.OBJECTIVE_VIDEO_ACTION_RECOGNITION:
+          prediction = video_serving_lib.postprocess_var(
+              executor.windows, prediction, _CONFIDENCE_THRESHOLD, _MIN_GAP_TIME
+          )
+        predictions.append(prediction)
+    except ValueError as e:
+      return flask.Response(
+          error(str(e)), status=500, mimetype='application/json'
+      )
+
+    return flask.Response(
+        response=json.dumps({
+            'success': True,
+            'predictions': predictions,
+        }),
+        status=200,
+        mimetype='application/json',
+    )
+  else:
+    return flask.Response(
+        response=json.dumps({
+            'success': True,
+            'isalive': movinet_model is not None,
+        }),
+        status=200,
+        mimetype='application/json',
+    )
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError('Too many command-line arguments.')
+  # This is used when running locally only. When deploying to Google App
+  # Engine, a webserver process such as Gunicorn will serve the app.
+  # # Debug deployment.
+  # flask_app.run(host='0.0.0.0', port=8501, debug=True)
+  # Prod deployment.
+  if _OBJECTIVE not in [
+      constants.OBJECTIVE_VIDEO_CLASSIFICATION,
+      constants.OBJECTIVE_VIDEO_ACTION_RECOGNITION,
+  ]:
+    raise app.UsageError('Objective must be vcn or var.')
+  logging.info(
+      'Env: batch_size: %s, num_frames: %s, fps: %s, overlap_frames: %s',
+      _BATCH_SIZE,
+      _NUM_FRAMES,
+      _FPS,
+      _OVERLAP_FRAMES,
+  )
+  waitress.serve(flask_app, host='0.0.0.0', port=8501)
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/movinet/serving/video_serving_lib.py

@@ -0,0 +1,462 @@
+"""Lib for handling video prediction requests.
+
+The VCN inference algorithm is as follows:
+1. Find all video frames within the given clip according to the sampling FPS.
+2. Create possibly overlapping sliding windows according to the num_frames and
+overlap_frames parameters. The last window might have a larger overlap if it
+doesn't exactly fit.
+3. Run model inference on each sliding window and compute softmax to obtain
+probabilities.
+4. Average the probabilities over all sliding windows.
+
+The VAR inference algorithm is very similar to VCN, with a few differences:
+1. The last sliding window is discarded if it does not exactly fit.
+2. Instead of averaging, the postprocessing consists of temporal nonmaximal
+suppression and removing background and low-confidence labels.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import os
+from typing import Any, Dict, Optional, Sequence, Union, cast
+
+from absl import logging
+import cv2
+import numpy as np
+import tensorflow as tf
+
+from util import constants
+from util import fileutils
+
+
+_JSON_LABEL_KEY = 'label'
+_JSON_GCS_URI_KEY = 'content'
+_JSON_CONFIDENCE_KEY = 'confidence'
+_JSON_START_TIME_KEY = 'timeSegmentStart'
+_JSON_END_TIME_KEY = 'timeSegmentEnd'
+_BACKGROUND_LABEL = 0
+_JSON_REQUIRED_KEYS = [
+    _JSON_GCS_URI_KEY,
+    _JSON_START_TIME_KEY,
+    _JSON_END_TIME_KEY,
+]
+_IMAGE_WIDTH = int(os.environ.get('IMAGE_WIDTH', '172'))
+_IMAGE_HEIGHT = int(os.environ.get('IMAGE_HEIGHT', '172'))
+
+
+@dataclasses.dataclass
+class DetectionOutput:
+  timestamp: float
+  label: int
+  confidence: float
+
+  def to_json_obj(self) -> Dict[str, Union[int, float]]:
+    """Encodes self as a dict for JSON serialization."""
+    return {
+        _JSON_LABEL_KEY: self.label,
+        _JSON_START_TIME_KEY: self.timestamp,
+        _JSON_END_TIME_KEY: self.timestamp,
+        _JSON_CONFIDENCE_KEY: self.confidence,
+    }
+
+
+def create_detection_output(
+    timestamp: float, predictions: np.ndarray
+) -> DetectionOutput:
+  label = np.argmax(predictions).item()
+  confidence: float = predictions[label].item()
+  return DetectionOutput(timestamp, label, confidence)
+
+
+class SlidingWindow:
+  """Represents a sliding window with start / end timestamps."""
+
+  def __init__(self, fps: float, frames: Sequence[int]):
+    if not frames:
+      raise ValueError('Sliding window cannot be empty.')
+    self.frames = frames
+    self.start_time = frames[0] / fps
+    self.end_time = frames[-1] / fps
+    self.frame_data: list[Optional[np.ndarray]] = []
+    self.clear_frame_data()
+
+  def load_cache_from(self, other: SlidingWindow) -> int:
+    """Loads cache from another sliding window if possible."""
+    cache_count = 0
+    for i, frame in enumerate(self.frames):
+      try:
+        other_idx = other.frames.index(frame)
+        self.frame_data[i] = other.frame_data[other_idx]
+        cache_count += 1
+      except ValueError:
+        # Cache miss.
+        pass
+    return cache_count
+
+  def load_frames(self, video: Any) -> Sequence[np.ndarray]:
+    """Loads frames of this sliding window from a video."""
+    for i, frame in enumerate(self.frames):
+      if self.frame_data[i] is None:
+        video.set(cv2.CAP_PROP_POS_FRAMES, frame)
+        ret, frame = video.read()
+        if not ret:
+          raise IOError(f'Failed to read video at frame {frame}.')
+        self.frame_data[i] = cv2.resize(frame, (_IMAGE_WIDTH, _IMAGE_HEIGHT))
+    return cast(Sequence[np.ndarray], self.frame_data)
+
+  def clear_frame_data(self) -> None:
+    """Clears frame data of this sliding window to reduce memory usage."""
+    self.frame_data: list[Optional[np.ndarray]] = [None] * len(self)
+
+  def __len__(self) -> int:
+    return len(self.frames)
+
+  @property
+  def middle_timestamp(self) -> float:
+    return (self.start_time + self.end_time) / 2
+
+
+def _get_sliding_windows(
+    frames: Sequence[int],
+    original_fps: float,
+    window_size: int,
+    overlap: int,
+    flush_last_window: bool,
+) -> Sequence[SlidingWindow]:
+  """Computes a list of sliding windows from frames.
+
+  Args:
+    frames: A list of frame indices.
+    original_fps: Frames per second of the original video.
+    window_size: Number of frames in a single window.
+    overlap: Number of overlapping frames in adjacent windows.
+    flush_last_window: Where to flush the last window if there are not enough
+      frames left.
+
+  Returns:
+    A list of sliding windows, each has a list of frame indices. The last two
+    windows might have a larger overlap if the last window does not exactly fit
+    and flush_last_window is set to True.
+
+  Raises:
+    ValueError: Arguments are invalid.
+  """
+  if window_size <= overlap:
+    raise ValueError(f'Window size {window_size} <= overlap {overlap}')
+  total_frames = len(frames)
+  windows: list[SlidingWindow] = []
+  for i in range(0, total_frames, window_size - overlap):
+    if i == 0 or i + window_size <= total_frames:
+      windows.append(SlidingWindow(original_fps, frames[i : i + window_size]))
+    elif i + overlap < total_frames and flush_last_window:
+      # Some frames in this window are not covered by the previous window.
+      windows.append(
+          SlidingWindow(
+              original_fps, frames[total_frames - window_size : total_frames]
+          )
+      )
+  return windows
+
+
+def _sample_frame_indices(
+    start_time: float,
+    end_time: float,
+    original_fps: float,
+    sample_fps: float,
+    max_frames: int,
+    padding_left: int = 0,
+    padding_right: int = 0,
+) -> Sequence[int]:
+  """Samples frames from start_time to end_time by sample_fps.
+
+  Args:
+    start_time: Start timestamp in seconds.
+    end_time: End timestamp in seconds.
+    original_fps: Frames per second of the original video.
+    sample_fps: Number of frames to sample per second.
+    max_frames: Total number of frames in the video.
+    padding_left: Padding to add to the start in frames. Padded frames will be
+      duplicates of the first frame.
+    padding_right: Padding to add to the end in frames. Padded frames will be
+      duplicates of the last frame.
+
+  Returns:
+    A list of sampled frame indices.
+  """
+  ret = [
+      min(max_frames - 1, round(t * original_fps))
+      for t in np.arange(start_time, end_time, 1 / sample_fps)
+  ]
+  if ret:
+    ret = [ret[0]] * padding_left + ret + [ret[-1]] * padding_right
+  return ret
+
+
+class VideoPredictionExecutor:
+  """Represents a Video prediction request with a video clip."""
+
+  def __init__(self, gcs_uri: str, start_time: float, end_time: float):
+    self._gcs_uri = gcs_uri
+    self._start_time = start_time
+    self._end_time = end_time
+    self.windows: Sequence[SlidingWindow] = []
+    self._last_window: SlidingWindow = None
+
+  def _read_frames_from_window(
+      self, video: Any, new_window: SlidingWindow
+  ) -> Sequence[np.ndarray]:
+    """Reads video frames from the new window.
+
+    Args:
+      video: Video loaded with cv2.
+      new_window: A list of sorted frame indices in the new window.
+
+    Returns:
+      Frame data from the video as a list of numpy arrays.
+
+    Raises:
+      IOError: Failed to read video.
+    """
+    # Caches frames as much as possible.
+    if self._last_window is not None:
+      cache_count = new_window.load_cache_from(self._last_window)
+      logging.info('Cached %d frames.', cache_count)
+      self._last_window.clear_frame_data()
+    self._last_window = new_window
+    return new_window.load_frames(video)
+
+  def _predict(
+      self, model: Any, video: Any, batched_windows: Sequence[SlidingWindow]
+  ) -> np.ndarray:
+    """Run model inference on specific frames of a video.
+
+    Args:
+      model: MoViNet model.
+      video: Video loaded with cv2.
+      batched_windows: A batch of sliding windows to predict. Each element is an
+        integer frame index. Must have equal number of frames in each window.
+
+    Returns:
+      Prediction results.
+
+    Raises:
+      ValueError: Batched windows are not sorted, or do not have equal number of
+        frames in each window.
+      IOError: Failed to read video.
+    """
+    if any(
+        (
+            len(window) != len(batched_windows[0])
+            for window in batched_windows[1:]
+        )
+    ):
+      raise ValueError(
+          'Batched windows do not have equal number of frames in each window.'
+      )
+    batch = []
+    logging.info('Loading video frames...')
+    for window in batched_windows:
+      logging.info('Predict frames: %s', window.frames)
+      frames = self._read_frames_from_window(video, window)
+      batch.append(frames)
+    input_tensor = tf.convert_to_tensor(batch, dtype=tf.float32) / 255.0
+    logging.info('Predict: Input tensor shape %s', input_tensor.shape)
+    predictions = model({'image': input_tensor})
+    logging.info('Running softmax on predictions...')
+    predictions = tf.nn.softmax(predictions, axis=1)
+    return predictions.numpy()
+
+  def get_prediction(
+      self,
+      model: Any,
+      batch_size: int,
+      fps: float,
+      num_frames: int,
+      overlap_frames: int,
+      objective: str,
+  ) -> Sequence[np.ndarray]:
+    """Predicts the video clip with the model.
+
+    Args:
+      model: The loaded MoViNet model.
+      batch_size: Batch size for prediction.
+      fps: Video sampling FPS.
+      num_frames: Number of frames in a single predictions. If the model is
+        exported with a fixed input shape, this must match its num_frames
+        dimension.
+      overlap_frames: Number of overlapping frames of consecutive sliding
+        windows.
+      objective: A string `vcn` or `var`.
+
+    Returns:
+      A list of floats as the prediction response.
+
+    Raises:
+      IOError: The video fails to load.
+      ValueError: Some arguments are invalid.
+    """
+    if objective not in [
+        constants.OBJECTIVE_VIDEO_CLASSIFICATION,
+        constants.OBJECTIVE_VIDEO_ACTION_RECOGNITION,
+    ]:
+      raise ValueError(f'{objective} objective is not supported.')
+
+    # cv2 expects a local path so we need to download the video from GCS.
+    local_file_path = fileutils.generate_tmp_path(
+        os.path.splitext(self._gcs_uri)[1]
+    )
+    logging.info('Downloading %s to %s...', self._gcs_uri, local_file_path)
+    fileutils.download_gcs_file_to_local(self._gcs_uri, local_file_path)
+    logging.info('Download %s complete.', self._gcs_uri)
+
+    # Loads video.
+    video = cv2.VideoCapture(local_file_path)
+    total_frames = video.get(cv2.CAP_PROP_FRAME_COUNT)
+    original_fps = video.get(cv2.CAP_PROP_FPS)
+    if not original_fps:
+      # 0 or None indicates the video is invalid.
+      raise IOError(f'Failed to load {self._gcs_uri}.')
+    video_length = total_frames / original_fps
+    self._start_time = max(0, self._start_time)
+    self._end_time = min(video_length, self._end_time)
+    padding = (
+        (num_frames // 2)
+        if objective == constants.OBJECTIVE_VIDEO_ACTION_RECOGNITION
+        else 0
+    )
+
+    # Computes sliding windows.
+    frame_indices = _sample_frame_indices(
+        self._start_time,
+        self._end_time,
+        original_fps,
+        fps,
+        total_frames,
+        padding,
+        padding,
+    )
+    logging.info('Frame indices: %s', frame_indices)
+    self.windows = _get_sliding_windows(
+        frame_indices,
+        original_fps,
+        num_frames,
+        overlap_frames,
+        objective != 'var',
+    )
+    if not self.windows:
+      raise ValueError(
+          f'No sliding windows found from {self._start_time} to'
+          f' {self._end_time}.'
+      )
+    self._last_window = None
+
+    # Runs inference.
+    predictions = []
+    for i in range(0, len(self.windows), batch_size):
+      predictions.extend(
+          self._predict(model, video, self.windows[i : i + batch_size])
+      )
+    return predictions
+
+
+def parse_request(req_json: Any) -> VideoPredictionExecutor:
+  """Parses VideoPredictionExecutor from request JSON object.
+
+  Args:
+    req_json: Request JSON object.
+
+  Returns:
+    Parsed VideoPredictionExecutor.
+
+  Raises:
+    ValueError: Request JSON object is invalid.
+  """
+  for key in _JSON_REQUIRED_KEYS:
+    if key not in req_json:
+      raise ValueError(f'{key} not found in {req_json}.')
+  gcs_uri = req_json[_JSON_GCS_URI_KEY]
+  start_time = float(req_json[_JSON_START_TIME_KEY].removesuffix('s'))
+  end_time = float(req_json[_JSON_END_TIME_KEY].removesuffix('s'))
+  return VideoPredictionExecutor(gcs_uri, start_time, end_time)
+
+
+def postprocess_vcn(predictions: Sequence[np.ndarray]) -> Sequence[float]:
+  """Aggregates VCN predictions of sliding windows."""
+  return np.mean(predictions, axis=0).tolist()
+
+
+def temporal_nonmaximal_suppression(
+    detections: Sequence[DetectionOutput], min_gap_time: float
+) -> Sequence[DetectionOutput]:
+  """Nonmaximal suppression for key frame detection.
+
+  For consecutive packets of the same label within a pre-defined duration, we
+  only keep the one with the highest confidence score. Such duration can be
+  determined by performing data analysis on users' dataset.
+
+  Args:
+    detections: A list of DetectionOutputs.
+    min_gap_time: Minimum time between consecutive key frames of the same label
+      in seconds.
+
+  Returns:
+    DetectionOutput after nonmaximal suppression sorted in ascending timestamps.
+  """
+  max_label = max([detection.label for detection in detections])
+  prev_detections: list[Optional[DetectionOutput]] = [None] * (max_label + 1)
+  ret: list[DetectionOutput] = []
+  by_time = lambda x: x.timestamp
+  for detection in sorted(detections, key=by_time):
+    prev_detection = prev_detections[detection.label]
+    prev_detections[detection.label] = detection
+    if not prev_detection:
+      continue
+    if detection.timestamp - prev_detection.timestamp > min_gap_time:
+      ret.append(prev_detection)
+      continue
+    detection.confidence = max(detection.confidence, prev_detection.confidence)
+  ret.extend((d for d in prev_detections if d is not None))
+  return sorted(ret, key=by_time)
+
+
+def postprocess_var(
+    windows: Sequence[SlidingWindow],
+    predictions: Sequence[np.ndarray],
+    confidence_threshold: float,
+    min_gap_time: float,
+) -> Sequence[Dict[str, Any]]:
+  """Generates a list of detected keyframes from sliding window predictions.
+
+  Args:
+    windows: Sliding windows.
+    predictions: A list of predictions of sliding windows.
+    confidence_threshold: Only probabilities greater than this threshold will
+      contribute to the final result.
+    min_gap_time: Minimum time between consecutive key frames of the same label
+      in seconds. Used in temporal nonmaximal suppression.
+
+  Returns:
+    A sequence of dictionaries, each item has the following keys:
+    - label: Integer label of the detection result.
+    - timeSegmentStart: Start timestamp in seconds.
+    - timeSegmentEnd: End timestamp in seconds. Always equals timeSegmentStart.
+  """
+  if len(windows) != len(predictions):
+    raise ValueError('Mismatched # of windows with # of predictions.')
+
+  # Creates detection results from windows, filtering out the background label.
+  detections = [
+      create_detection_output(window.middle_timestamp, predictions[i])
+      for i, window in enumerate(windows)
+  ]
+
+  # Temporal nonmaximal suppression.
+  detections = temporal_nonmaximal_suppression(detections, min_gap_time)
+
+  # Filters out ones with low confidence and the background label.
+  return [
+      x.to_json_obj()
+      for x in detections
+      if x.label != _BACKGROUND_LABEL and x.confidence > confidence_threshold
+  ]

community-content/vertex_vision_model_garden/model_oss/movinet/train.py

@@ -0,0 +1,210 @@
+"""Main executable for MoViNet docker."""
+
+import json
+import os
+from typing import Sequence, Any
+
+from absl import app
+from absl import flags
+from absl import logging
+import gin
+import hypertune
+import tensorflow as tf
+
+from util import constants
+from util import hypertune_utils
+from official.common import distribute_utils
+from official.common import flags as tfm_flags
+from official.core import task_factory
+from official.core import train_lib
+from official.core import train_utils
+from official.modeling import performance
+# Import movinet libraries to register the backbone and model into tf.vision
+# model garden factory.
+# pylint: disable=unused-import
+from official.projects.movinet.modeling import movinet
+from official.projects.movinet.modeling import movinet_model
+from official.vision import registry_imports
+# pylint: enable=unused-import
+
+
+FLAGS = flags.FLAGS
+
+_FILE_TYPE_TFRECORD = 'tfrecord'
+
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate', None, 'The learning rate of this training job.'
+)
+
+_NUM_CLASSES = flags.DEFINE_integer(
+    'num_classes', None, 'The number of classes.'
+)
+
+_INIT_CHECKPOINT = flags.DEFINE_string(
+    'init_checkpoint', None, 'The initial checkpoint of this training job.'
+)
+
+_INPUT_TRAIN_DATA_PATH = flags.DEFINE_string(
+    'input_train_data_path', None, 'Input train data path.'
+)
+
+_INPUT_VALIDATION_DATA_PATH = flags.DEFINE_string(
+    'input_validation_data_path', None, 'Input validation data path.'
+)
+
+_GLOBAL_BATCH_SIZE = flags.DEFINE_integer(
+    'global_batch_size', None, 'Global batch size.'
+)
+
+_PREFETCH_BUFFER_SIZE = flags.DEFINE_integer(
+    'prefetch_buffer_size', None, 'Prefetch buffer size.'
+)
+
+_SHUFFLE_BUFFER_SIZE = flags.DEFINE_integer(
+    'shuffle_buffer_size', None, 'Shuffle buffer size.'
+)
+
+_TRAIN_STEPS = flags.DEFINE_integer('train_steps', None, 'Train steps.')
+_LOG_LEVEL = flags.DEFINE_enum(
+    'log_level',
+    'INFO',
+    ['FATAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG'],
+    'Log level.',
+)
+
+
+def parse_params() -> Any:
+  """Parses parameters."""
+  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
+  params = train_utils.parse_configuration(FLAGS, lock_return=False)
+  if _INIT_CHECKPOINT.value:
+    params.task.init_checkpoint = _INIT_CHECKPOINT.value
+    params.task.init_checkpoint_modules = 'backbone'
+  if _NUM_CLASSES.value:
+    params.task.model.num_classes = _NUM_CLASSES.value
+    params.task.train_data.num_classes = _NUM_CLASSES.value
+    params.task.validation_data.num_classes = _NUM_CLASSES.value
+  # If users set input train/validation data path, we assume the data are
+  # converted from data converter as tfrecord. Users can use tfds by writing
+  # their own config directly, and no need to override this parameter.
+  if _INPUT_TRAIN_DATA_PATH.value:
+    params.task.train_data.input_path = _INPUT_TRAIN_DATA_PATH.value
+    params.task.train_data.file_type = _FILE_TYPE_TFRECORD
+    params.task.train_data.tfds_name = ''
+  if _INPUT_VALIDATION_DATA_PATH.value:
+    params.task.validation_data.input_path = _INPUT_VALIDATION_DATA_PATH.value
+    params.task.validation_data.file_type = _FILE_TYPE_TFRECORD
+    params.task.validation_data.tfds_name = ''
+  if _GLOBAL_BATCH_SIZE.value:
+    params.task.train_data.global_batch_size = _GLOBAL_BATCH_SIZE.value
+    params.task.validation_data.global_batch_size = _GLOBAL_BATCH_SIZE.value
+  if _PREFETCH_BUFFER_SIZE.value:
+    params.task.train_data.prefetch_buffer_size = _PREFETCH_BUFFER_SIZE.value
+    params.task.validation_data.prefetch_buffer_size = (
+        _PREFETCH_BUFFER_SIZE.value
+    )
+  if _SHUFFLE_BUFFER_SIZE.value:
+    params.task.train_data.shuffle_buffer_size = _SHUFFLE_BUFFER_SIZE.value
+  if _TRAIN_STEPS.value:
+    params.trainer.train_steps = _TRAIN_STEPS.value
+  if _LEARNING_RATE.value:
+    logging.info('Updating learning_rate: %s', _LEARNING_RATE.value)
+    # Use `get` method of train_utils.hyperparams.OneOfConfig to get learning
+    # rate config.
+    learning_rate = params.trainer.optimizer_config.learning_rate.get()
+    if hasattr(learning_rate, 'initial_learning_rate'):
+      learning_rate.initial_learning_rate = _LEARNING_RATE.value
+    else:
+      logging.warning('Cannot set learning rate for %s', learning_rate)
+  # Set default params for best checkpoints.
+  params.trainer.best_checkpoint_export_subdir = constants.BEST_CKPT_DIRNAME
+  params.trainer.best_checkpoint_metric_comp = constants.BEST_CKPT_METRIC_COMP
+  params.trainer.best_checkpoint_eval_metric = (
+      constants.VIDEO_CLASSIFICATION_BEST_EVAL_METRIC
+  )
+  return params
+
+
+def main(argv: Sequence[str]) -> None:
+  logging.set_verbosity(_LOG_LEVEL.value)
+  if len(argv) > 1:
+    raise app.UsageError('Too many command-line arguments.')
+  params = parse_params()
+  logging.info('The actual training parameters are:\n%s', params.as_dict())
+  model_dir: str = os.path.join(
+      FLAGS.model_dir,
+      constants.TRIAL_PREFIX + hypertune_utils.get_trial_id_from_environment(),
+  )
+  logging.info('model_dir: %s', model_dir)
+
+  if 'train' in FLAGS.mode:
+    # Pure eval modes do not output yaml files. Otherwise continuous eval job
+    # may race against the train job for writing the same file.
+    train_utils.serialize_config(params, model_dir)
+
+  # Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
+  # can have significant impact on model speeds by utilizing float16 in case of
+  # GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
+  # dtype is float16
+  if params.runtime.mixed_precision_dtype:
+    performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype)
+  distribution_strategy = distribute_utils.get_distribution_strategy(
+      distribution_strategy=params.runtime.distribution_strategy,
+      all_reduce_alg=params.runtime.all_reduce_alg,
+      num_gpus=params.runtime.num_gpus,
+      tpu_address=params.runtime.tpu,
+  )
+
+  # Create task and run experiment.
+  with distribution_strategy.scope():
+    task = task_factory.get_task(params.task, logging_dir=model_dir)
+
+  train_lib.run_experiment(
+      distribution_strategy=distribution_strategy,
+      task=task,
+      mode=FLAGS.mode,
+      params=params,
+      model_dir=model_dir,
+  )
+
+  train_utils.save_gin_config(FLAGS.mode, model_dir)
+
+  eval_metric_name = constants.VIDEO_CLASSIFICATION_BEST_EVAL_METRIC
+
+  eval_filepath = os.path.join(
+      model_dir, constants.BEST_CKPT_DIRNAME, constants.BEST_CKPT_EVAL_FILENAME
+  )
+  logging.info('Load eval metrics from: %s.', eval_filepath)
+
+  with tf.io.gfile.GFile(eval_filepath, 'rb') as f:
+    eval_metric_results = json.load(f)
+  logging.info('eval metrics are: %s.', eval_metric_results)
+  if (
+      eval_metric_name in eval_metric_results
+      and constants.BEST_CKPT_STEP_NAME in eval_metric_results
+  ):
+    hp_metric = eval_metric_results[eval_metric_name]
+    hp_step = int(eval_metric_results[constants.BEST_CKPT_STEP_NAME])
+    hpt = hypertune.HyperTune()
+    hpt.report_hyperparameter_tuning_metric(
+        hyperparameter_metric_tag=constants.HP_METRIC_TAG,
+        metric_value=hp_metric,
+        global_step=hp_step,
+    )
+    logging.info(
+        'Send HP metric: %f and steps %d to hyperparameter tuning.',
+        hp_metric,
+        hp_step,
+    )
+  else:
+    logging.info(
+        'Either %s or %s is not included in the evaluation results: %s.',
+        eval_metric_name,
+        constants.BEST_CKPT_STEP_NAME,
+        eval_metric_results,
+    )
+
+
+if __name__ == '__main__':
+  tfm_flags.define_flags()
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/open_clip/dockerfile/serve.Dockerfile

@@ -0,0 +1,67 @@
+# Dockerfile for basic serving dockers for OpenCLIP.
+#
+# To build:
+# docker build -f model_oss/open_clip/dockerfile/serve.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+# Switch to this base image for gpu serve.
+FROM pytorch/torchserve:0.7.1-gpu
+
+USER root
+
+# Install tools.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="transformers_serving"
+ENV PATH="/home/model-server/:${PATH}"
+
+# Install libraries.
+RUN python3 -m pip install --upgrade pip
+RUN pip install torch==1.13.1
+RUN pip install open_clip_torch==2.20.0
+RUN pip install pillow==9.5.0
+RUN pip install google-cloud-storage==2.7.0
+
+# Copy model artifacts.
+COPY model_oss/open_clip/handler.py /home/model-server/handler.py
+COPY model_oss/util/ /home/model-server/util/
+ENV PYTHONPATH /home/model-server/
+
+# Create torchserve configuration file.
+RUN echo \
+    "default_response_timeout=1800\n" \
+    "service_envelope=json\n" \
+    "inference_address=http://0.0.0.0:${infer_port}\n"  \
+    "management_address=http://0.0.0.0:${mng_port}" >> /home/model-server/config.properties
+
+# Expose ports.
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
+
+# Archive model artifacts and dependencies.
+# Do not set --model-file and --serialized-file because model and checkpoint will be dynamically loaded in handler.py.
+RUN torch-model-archiver \
+--model-name=${model_name} \
+--version=1.0 \
+--handler=/home/model-server/handler.py \
+--runtime=python3 \
+--export-path=/home/model-server/model-store \
+--archive-format=default \
+--force
+
+# Run Torchserve HTTP serve to respond to prediction requests.
+CMD ["torchserve", "--start", \
+     "--ts-config", "/home/model-server/config.properties", \
+     "--models", "${model_name}=${model_name}.mar", \
+     "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/open_clip/dockerfile/train.Dockerfile

@@ -0,0 +1,53 @@
+# Dockerfile for training dockers with OpenCLIP.
+#
+# To build:
+# docker build -f model_oss/open_clilp/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+
+# Install tools.
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update
+RUN apt-get install -y --no-install-recommends apt-utils
+RUN apt-get install -y --no-install-recommends curl
+RUN apt-get install -y --no-install-recommends wget
+RUN apt-get install -y --no-install-recommends git
+RUN apt-get install -y --no-install-recommends jq
+RUN apt-get install -y --no-install-recommends gnupg
+RUN apt-get install -y --no-install-recommends build-essential
+
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Prepare artifacts.
+WORKDIR /workspace
+RUN git clone --branch main https://github.com/mlfoundations/open_clip.git
+WORKDIR ./open_clip
+RUN git reset --hard 67e5e5ec8741281eb9b30f640c26f91c666308b7
+
+# Install libraries.
+RUN pip install webdataset==0.2.5
+RUN pip install regex==2023.6.3
+RUN pip install ftfy==6.1.1
+RUN pip install pandas==2.0.3
+RUN pip install braceexpand==0.1.7
+RUN pip install huggingface_hub==0.16.4
+RUN pip install transformers==4.31.0
+RUN pip install timm==0.9.2
+RUN pip install fsspec==2023.6.0
+RUN pip install sentencepiece==0.1.99
+RUN pip install protobuf==3.20.3
+RUN pip install tensorboard==2.12.2
+
+# Switch work folder for training.
+WORKDIR ./src

community-content/vertex_vision_model_garden/model_oss/open_clip/handler.py

@@ -0,0 +1,142 @@
+"""Custom handler for OpenCLIP model."""
+
+# pylint:disable=g-importing-member
+import enum
+import logging
+import os
+from typing import Any, Dict, List
+
+import open_clip
+import torch
+from ts.torch_handler.base_handler import BaseHandler
+
+from google3.cloud.ml.applications.vision.model_garden.model_oss.util import constants
+from google3.cloud.ml.applications.vision.model_garden.model_oss.util import fileutils
+from google3.cloud.ml.applications.vision.model_garden.model_oss.util import image_format_converter
+
+
+@enum.unique
+class Precision(enum.Enum):
+  AMP = "amp"
+  AMP_BF16 = "amp_bf16"
+  AMP_BFLOAT16 = "amp_bfloat16"
+  BF16 = "bf16"
+  FP16 = "fp16"
+  PURE_BF16 = "pure_bf16"
+  PURE_FP16 = "pure_fp16"
+  FP32 = "fp32"
+
+
+# Supported checkpoint&model pairs:
+# https://github.com/mlfoundations/open_clip#pretrained-model-interface
+_DEFAULT_CHECKPOINT = "openai"
+_DEFAULT_MODEL = "RN50"
+_DEFAULT_PRECISION = Precision.AMP
+_ZERO_CLASSIFICATION = "zero-shot-image-classification"
+_FEATURE_EMBEDDING = "feature-embedding"
+_VALID_TASKS = frozenset([_ZERO_CLASSIFICATION, _FEATURE_EMBEDDING])
+
+_IMAGE_KEY = "image"
+_TEXT_KEY = "text"
+_IMAGE_FEATURES_KEY = "image_features"
+_TEXT_FEATURES_KEY = "text_features"
+
+
+class OpenclipHandler(BaseHandler):
+  """Custom handler for OpenCLIP."""
+
+  def initialize(self, context: Any):
+    """Custom initialize."""
+
+    properties = context.system_properties
+    self.map_location = (
+        "cuda"
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else "cpu"
+    )
+    self.device = torch.device(
+        self.map_location + ":" + str(properties.get("gpu_id"))
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else self.map_location
+    )
+    self.manifest = context.manifest
+
+    model_name = os.environ.get("MODEL", _DEFAULT_MODEL)
+    precision = os.environ.get("PRECISION", _DEFAULT_PRECISION)
+    checkpoint = os.environ.get("CHECKPOINT", _DEFAULT_CHECKPOINT)
+    self.task = os.environ.get("TASK", _FEATURE_EMBEDDING)
+    if self.task not in _VALID_TASKS:
+      raise ValueError(f"Invalid task: {self.task}.")
+    logging.info(
+        "Handler initializing task:%s, model:%s, precision:%s, checkpoint:%s",
+        self.task,
+        model_name,
+        precision,
+        checkpoint,
+    )
+
+    if checkpoint != _DEFAULT_CHECKPOINT:
+      local_fname = os.path.join(constants.LOCAL_MODEL_DIR, "model.pt")
+      fileutils.download_gcs_file_to_local(checkpoint, local_fname)
+      checkpoint = local_fname
+
+    self.model, _, self.preprocessor = open_clip.create_model_and_transforms(
+        model_name, pretrained=checkpoint, precision=precision
+    )
+    self.tokenizer = open_clip.get_tokenizer(model_name)
+
+    self.initialized = True
+
+  def preprocess(self, data: Any) -> List[Dict[str, Any]]:
+    """Preprocess input data."""
+    logging.info("preprocessing: %d instances received.", len(data))
+    processed_list = []
+    for item in data:
+      sample = {}
+      if _IMAGE_KEY in item:
+        sample[_IMAGE_KEY] = self.preprocessor(
+            image_format_converter.base64_to_image(item[_IMAGE_KEY])
+        ).unsqueeze(0)
+      if _TEXT_KEY in item:
+        sample[_TEXT_KEY] = self.tokenizer(item[_TEXT_KEY])
+      processed_list.append(sample)
+    return processed_list
+
+  def inference(
+      self, data: List[Dict[str, Any]], *args, **kwargs
+  ) -> List[Dict[str, Any]]:
+    feature_list = []
+    with torch.no_grad(), torch.cuda.amp.autocast():
+      for item in data:
+        sample = {}
+        if _IMAGE_KEY in item:
+          sample[_IMAGE_FEATURES_KEY] = self.model.encode_image(
+              item[_IMAGE_KEY]
+          )
+        if _TEXT_KEY in item:
+          sample[_TEXT_FEATURES_KEY] = self.model.encode_text(item[_TEXT_KEY])
+        feature_list.append(sample)
+    return feature_list
+
+  def postprocess(self, features: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Postprocess the image/text featreus for downstream task."""
+    preds = []
+    if self.task == _FEATURE_EMBEDDING:
+      for item in features:
+        preds.append({k: v.tolist() for k, v in item.items()})
+    elif self.task == _ZERO_CLASSIFICATION:
+      for item in features:
+        image_features = item.get(_IMAGE_FEATURES_KEY, None)
+        text_features = item.get(_TEXT_FEATURES_KEY, None)
+        if image_features is None or text_features is None:
+          raise ValueError(
+              "Missing input for {} task. {} received.".format(
+                  _ZERO_CLASSIFICATION, item.keys()
+              )
+          )
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+        text_features /= text_features.norm(dim=-1, keepdim=True)
+        text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+        preds.append(text_probs.tolist())
+
+    return preds

community-content/vertex_vision_model_garden/model_oss/peft/causal_language_modeling_lora.py

@@ -0,0 +1,142 @@
+"""Causal language modeling with LoRA models."""
+
+# pylint: disable=g-importing-member
+
+from datasets import load_dataset
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+from torch import nn
+import transformers
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from transformers import TrainingArguments
+from util import constants
+
+
+def finetune_causal_language_modeling(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    precision_mode: str = None,
+    lora_rank: int = 16,
+    lora_alpha: int = 32,
+    lora_dropout: float = 0.05,
+    warmup_steps: int = 10,
+    max_steps: int = 10,
+    learning_rate: float = 2e-4,
+    local_pretrained_model_id: str = None,
+) -> None:
+  """Finetunes causal language modelings."""
+  if precision_mode == constants.PRECISION_MODE_32:
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.float32,
+        device_map="auto",
+    )
+  elif precision_mode == constants.PRECISION_MODE_16:
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+  elif precision_mode == constants.PRECISION_MODE_8:
+    quantization_config = BitsAndBytesConfig(
+        load_in_8bit=True, int8_threshold=0
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        quantization_config=quantization_config,
+    )
+  else:
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        device_map="auto",
+        torch_dtype=torch.bfloat16,
+        quantization_config=quantization_config,
+    )
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      local_pretrained_model_id
+      if local_pretrained_model_id
+      else pretrained_model_id
+  )
+  if "llama" in pretrained_model_id:
+    tokenizer.pad_token = "[PAD]"
+
+  for param in model.parameters():
+    # Freezes the model - train adapters later.
+    param.requires_grad = False
+    if param.ndim == 1:
+      # Casts the small parameters (e.g. layernorm) to fp32 for stability.
+      param.data = param.data.to(torch.float32)
+
+  # Reduces the number of stored activations.
+  model.gradient_checkpointing_enable()
+  model.enable_input_require_grads()
+
+  class CastOutputToFloat(nn.Sequential):
+
+    def forward(self, x):
+      return super().forward(x).to(torch.float32)
+
+  model.lm_head = CastOutputToFloat(model.lm_head)
+
+  config = LoraConfig(
+      r=lora_rank,
+      lora_alpha=lora_alpha,
+      target_modules=["q_proj", "v_proj"],
+      lora_dropout=lora_dropout,
+      bias="none",
+      task_type="CAUSAL_LM",
+  )
+
+  model = get_peft_model(model, config)
+  model.print_trainable_parameters()
+
+  data = load_dataset(dataset_name)
+  data = data.map(
+      lambda samples: tokenizer(samples["quote"]),
+      batched=True,
+  )
+
+  trainer = transformers.Trainer(
+      model=model,
+      train_dataset=data["train"],
+      args=TrainingArguments(
+          per_device_train_batch_size=4,
+          gradient_accumulation_steps=4,
+          warmup_steps=warmup_steps,
+          max_steps=max_steps,
+          learning_rate=learning_rate,
+          fp16=True,
+          logging_steps=1,
+          output_dir=output_dir,
+          ddp_find_unused_parameters=False,
+      ),
+      data_collator=transformers.DataCollatorForLanguageModeling(
+          tokenizer,
+          mlm=False,
+      ),
+  )
+  # Silence the warnings. Please re-enable for inference!
+  model.config.use_cache = False
+  trainer.train()
+
+  model.save_pretrained(output_dir)

community-content/vertex_vision_model_garden/model_oss/peft/config.properties

@@ -0,0 +1,21 @@
+number_of_netty_threads=32
+job_queue_size=1000
+model_store=/home/model-server/model-store
+workflow_store=/home/model-server/wf-store
+default_response_timeout=1800
+service_envelope=json
+inference_address=http://0.0.0.0:7080
+management_address=http://0.0.0.0:7081
+metrics_address=http://0.0.0.0:7082
+
+models={\
+  "peft_serving": {\
+    "1.0": {\
+        "defaultVersion": true,\
+        "marName": "peft_serving.mar",\
+        "minWorkers": 1,\
+        "maxWorkers": 1,\
+        "batchSize": 1\
+    }\
+  }\
+}

community-content/vertex_vision_model_garden/model_oss/peft/dockerfile/serve.Dockerfile

@@ -0,0 +1,107 @@
+# Dockerfile for PEFT Serving.
+#
+# To build:
+# docker build -f model_oss/peft/dockerfile/serve.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM pytorch/torchserve:0.7.0-gpu
+
+USER root
+
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="peft_serving"
+ENV PATH="/home/model-server/:${PATH}"
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim \
+        git \
+        git-lfs
+RUN git lfs install
+
+# Install libraries.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN python3 -m pip install --upgrade pip
+RUN pip install --upgrade torch==2.0.1
+RUN pip install torchvision==0.15.2
+RUN pip install tokenizers==0.13.3
+RUN pip install accelerate==0.21.0
+RUN pip install sentencepiece==0.1.99
+RUN pip install grpcio-status==1.33.2
+RUN pip install protobuf==3.19.6
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
+RUN pip install datasets==2.14.4
+RUN pip install triton==2.0.0.dev20221120
+RUN pip install xformers==0.0.20
+RUN pip install google-cloud-storage==2.7.0
+RUN pip install absl-py==1.4.0
+RUN pip install scipy==1.10.1
+RUN pip install evaluate==0.4.0
+RUN pip install scikit-learn==1.2.2
+RUN pip install loralib==0.1.1
+RUN pip install bitsandbytes==0.39.0
+RUN pip install trl==0.4.4
+RUN pip install einops==0.6.1
+
+# Install diffusers from source.
+RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
+WORKDIR diffusers
+RUN pip install -e .
+WORKDIR /home/model-server
+
+# Install transformers from source.
+RUN git clone --depth 1 --branch v4.31.0 https://github.com/huggingface/transformers.git
+# The patch is used to change the transformers loading model behavior:
+# 1) For models on Huggingface hub: if the model has multiple shards, each shard
+#    will be downloaded separately and get deleted after loading to GPU.
+# 2) For models on local disk: if a model bin file is actually a text file
+#    recording a GCS path, the model file will be downloaded and get deleted
+#    after loading to GPU.
+COPY model_oss/peft/hf_transformers_lazy_download.patch /home/model-server/hf_transformers_lazy_download.patch
+WORKDIR transformers
+RUN git apply /home/model-server/hf_transformers_lazy_download.patch
+RUN pip install -e .
+WORKDIR /home/model-server
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Copy model artifacts.
+COPY model_oss/peft/handler.py /home/model-server/handler.py
+COPY model_oss/peft/config.properties /home/model-server/config.properties
+COPY model_oss/util/ /home/model-server/util/
+ENV PYTHONPATH /home/model-server/
+
+# Expose ports.
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
+
+# Set environments.
+ENV TASK "causal-language-modeling-lora"
+ENV BASE_MODEL_ID "openlm-research/open_llama_7b"
+ENV PRECISION_LOADING_MODE "float16"
+ENV FINETUNED_LORA_MODEL_PATH ""
+
+
+# Archive model artifacts and dependencies.
+# Do not set --model-file and --serialized-file because model and checkpoint
+# will be dynamically loaded in handler.py.
+RUN torch-model-archiver \
+--model-name=${model_name} \
+--version=1.0 \
+--handler=/home/model-server/handler.py \
+--runtime=python3 \
+--export-path=/home/model-server/model-store \
+--archive-format=default \
+--force
+
+# Run Torchserve HTTP serve to respond to prediction requests.
+CMD ["torchserve", "--start", \
+     "--ts-config", "/home/model-server/config.properties", \
+     "--models", "${model_name}=${model_name}.mar", \
+     "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/peft/dockerfile/train.Dockerfile

@@ -0,0 +1,111 @@
+# Dockerfile for PEFT Training.
+#
+# To build:
+# docker build -f model_oss/peft/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+# Builds GPU docker image of PyTorch
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+ENV PYTHON_VERSION=3.8
+# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN apt-get update && \
+    apt-get install -y curl git wget software-properties-common git-lfs && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Install audio-related libraries
+RUN apt-get update && \
+    apt install -y ffmpeg
+
+RUN apt install -y libsndfile1-dev
+RUN git lfs install
+
+# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH /opt/conda/envs/peft/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+# Activate the conda env and install transformers + accelerate from source
+RUN source activate peft
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/transformers
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft#egg=peft[test]
+RUN python3 -m pip install --no-cache-dir bitsandbytes
+
+# Stage 2
+FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 AS build-image
+COPY --from=compile-image /opt/conda /opt/conda
+ENV PATH /opt/conda/bin:$PATH
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget vim && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN echo "source activate peft" >> ~/.profile
+
+# Install libraries.
+RUN pip install --upgrade torch==2.0.1
+RUN pip install torchvision==0.15.2
+RUN pip install git+https://github.com/huggingface/transformers@de9255de27abfcae4a1f816b904915f0b1e23cd9
+RUN pip install transformers -U
+RUN pip install accelerate==0.21.0
+RUN pip install sentencepiece==0.1.99
+RUN pip install grpcio-status==1.33.2
+RUN pip install protobuf==3.19.6
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
+RUN pip install datasets==2.9.0
+RUN pip install triton==2.0.0.dev20221120
+RUN pip install xformers==0.0.20
+RUN pip install Jinja2==3.1.2
+RUN pip install ftfy==6.1.1
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install tensorboard==2.12.0
+RUN pip install scipy==1.10.1
+RUN pip install evaluate==0.4.0
+RUN pip install scikit-learn==1.2.2
+RUN pip install loralib==0.1.1
+RUN pip install bitsandbytes==0.39.0
+RUN pip install trl==0.4.4
+RUN pip install einops==0.6.1
+RUN pip install google-cloud-storage==2.7.0
+
+RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
+WORKDIR diffusers
+RUN pip install -e .
+
+# Switch to diffusers examples folder.
+WORKDIR examples
+
+# NOTE: use 'sed' to modify train_text_to_image_lora.py to
+#       fix the bug for accelerator.
+RUN sed -i \
+"s#logging_dir=logging_dir#project_dir=logging_dir#g" \
+text_to_image/train_text_to_image_lora.py
+
+# Config accelerate.
+RUN mkdir -p ./vertex_vision_model_garden_peft/
+COPY model_oss/peft/train.sh ./vertex_vision_model_garden_peft/train.sh
+COPY model_oss/peft/*.py     ./vertex_vision_model_garden_peft/
+COPY model_oss/util /diffusers/examples/util
+ENV PYTHONPATH /diffusers/examples/
+
+# Generate accelerate config at the beginning of docker run.
+ENTRYPOINT ["python3", "vertex_vision_model_garden_peft/main.py"]

community-content/vertex_vision_model_garden/model_oss/peft/handler.py

@@ -0,0 +1,250 @@
+"""Custom handler for huggingface/peft models."""
+
+# pylint: disable=g-importing-member
+# pylint: disable=logging-fstring-interpolation
+
+import logging
+import os
+from typing import Any, List
+
+from absl import logging
+from diffusers import DPMSolverMultistepScheduler
+from diffusers import StableDiffusionPipeline
+from peft import PeftModel
+from PIL import Image
+import torch
+import transformers
+from transformers import AutoModelForCausalLM
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from ts.torch_handler.base_handler import BaseHandler
+
+from util import constants
+from util import fileutils
+from util import image_format_converter
+
+# Tasks
+TEXT_TO_IMAGE_LORA = "text-to-image-lora"
+SEQUENCE_CLASSIFICATION_LORA = "sequence-classification-lora"
+CAUSAL_LANGUAGE_MODELING_LORA = "causal-language-modeling-lora"
+INSTRUCT_LORA = "instruct-lora"
+
+# Inference parameters.
+_NUM_INFERENCE_STEPS = 25
+_MAX_LENGTH_DEFAULT = 200
+_TOP_K_DEFAULT = 10
+
+
+class PeftHandler(BaseHandler):
+  """Custom handler for Peft models."""
+
+  def initialize(self, context: Any):
+    """Initializes the handler."""
+    logging.info("Start to initialize the PEFT handler.")
+    properties = context.system_properties
+    self.map_location = (
+        "cuda"
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else "cpu"
+    )
+
+    self.device = torch.device(
+        self.map_location + ":" + str(properties.get("gpu_id"))
+        if torch.cuda.is_available() and properties.get("gpu_id") is not None
+        else self.map_location
+    )
+    self.manifest = context.manifest
+    self.precision_mode = os.environ.get(
+        "PRECISION_LOADING_MODE", constants.PRECISION_MODE_16
+    )
+    self.task = os.environ.get("TASK", CAUSAL_LANGUAGE_MODELING_LORA)
+    self.base_model_id = os.environ.get(
+        "BASE_MODEL_ID", "openlm-research/open_llama_7b"
+    )
+    if fileutils.is_gcs_path(self.base_model_id):
+      fileutils.download_gcs_dir_to_local(
+          self.base_model_id,
+          constants.LOCAL_BASE_MODEL_DIR,
+          skip_hf_model_bin=True,
+      )
+      self.base_model_id = constants.LOCAL_BASE_MODEL_DIR
+    self.finetuned_lora_model_path = os.environ.get(
+        "FINETUNED_LORA_MODEL_PATH", ""
+    )
+    if fileutils.is_gcs_path(self.finetuned_lora_model_path):
+      fileutils.download_gcs_dir_to_local(
+          self.finetuned_lora_model_path, constants.LOCAL_MODEL_DIR
+      )
+      self.finetuned_lora_model_path = constants.LOCAL_MODEL_DIR
+
+    logging.info(
+        f"Using task:{self.task}, base model:{self.base_model_id}, lora model:"
+        f" {self.finetuned_lora_model_path}, and precision"
+        f" {self.precision_mode}."
+    )
+
+    self.pipeline = None
+    self.model = None
+    self.tokenizer = None
+    if self.task == TEXT_TO_IMAGE_LORA:
+      pipeline = StableDiffusionPipeline.from_pretrained(
+          self.base_model_id, torch_dtype=torch.float16
+      )
+      logging.debug("Initialized the base model for text to image.")
+      pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+          pipeline.scheduler.config
+      )
+      logging.debug("Initialized the scheduler for text to image.")
+      if self.finetuned_lora_model_path:
+        pipeline.unet.load_attn_procs(self.finetuned_lora_model_path)
+      logging.debug("Initialized the LoRA model for text to image.")
+      # This is to reduce GPU memory requirements.
+      pipeline.enable_xformers_memory_efficient_attention()
+      pipeline = pipeline.to(self.map_location)
+      # Reduces memory footprint.
+      pipeline.enable_attention_slicing()
+      self.pipeline = pipeline
+      logging.info("Initialized the text to image pipelines.")
+    elif self.task == SEQUENCE_CLASSIFICATION_LORA:
+      tokenizer = AutoTokenizer.from_pretrained(self.base_model_id)
+      logging.debug("Initialized the tokenizer for sequence classification.")
+      model = AutoModelForSequenceClassification.from_pretrained(
+          self.base_model_id, torch_dtype=torch.float16
+      )
+      logging.debug("Initialized the base model for sequence classification.")
+      if self.finetuned_lora_model_path:
+        model = PeftModel.from_pretrained(model, self.finetuned_lora_model_path)
+        logging.debug("Initialized the LoRA model for sequence classification.")
+      model.to(self.map_location)
+      self.model = model
+      self.tokenizer = tokenizer
+    elif (
+        self.task == CAUSAL_LANGUAGE_MODELING_LORA or self.task == INSTRUCT_LORA
+    ):
+      tokenizer = AutoTokenizer.from_pretrained(self.base_model_id)
+      logging.debug("Initialized the tokenizer.")
+      if self.task == CAUSAL_LANGUAGE_MODELING_LORA:
+        if self.precision_mode == constants.PRECISION_MODE_32:
+          model = AutoModelForCausalLM.from_pretrained(
+              self.base_model_id,
+              return_dict=True,
+              torch_dtype=torch.float32,
+              device_map="auto",
+          )
+        elif self.precision_mode == constants.PRECISION_MODE_16:
+          model = AutoModelForCausalLM.from_pretrained(
+              self.base_model_id,
+              return_dict=True,
+              torch_dtype=torch.bfloat16,
+              device_map="auto",
+          )
+        elif self.precision_mode == constants.PRECISION_MODE_8:
+          quantization_config = BitsAndBytesConfig(
+              load_in_8bit=True, int8_threshold=0
+          )
+          model = AutoModelForCausalLM.from_pretrained(
+              self.base_model_id,
+              return_dict=True,
+              torch_dtype=torch.float16,
+              device_map="auto",
+              quantization_config=quantization_config,
+          )
+        else:
+          quantization_config = BitsAndBytesConfig(
+              load_in_4bit=True,
+              bnb_4bit_quant_type="nf4",
+              bnb_4bit_compute_dtype=torch.bfloat16,
+          )
+          model = AutoModelForCausalLM.from_pretrained(
+              self.base_model_id,
+              return_dict=True,
+              device_map="auto",
+              torch_dtype=torch.bfloat16,
+              quantization_config=quantization_config,
+          )
+      else:
+        model = AutoModelForCausalLM.from_pretrained(
+            self.base_model_id,
+            return_dict=True,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+            device_map="auto",
+        )
+      logging.debug("Initialized the base model.")
+      if self.finetuned_lora_model_path:
+        model = PeftModel.from_pretrained(model, self.finetuned_lora_model_path)
+        logging.debug("Initialized the LoRA model.")
+      pipeline = transformers.pipeline(
+          "text-generation",
+          model=model,
+          tokenizer=tokenizer,
+      )
+      self.tokenizer = tokenizer
+      self.pipeline = pipeline
+    else:
+      raise ValueError(f"Invalid TASK: {self.task}")
+
+    self.initialized = True
+    logging.info("The PEFT handler was initialized.")
+
+  def preprocess(self, data: Any) -> Any:
+    """Preprocesses input data."""
+    # Assumes that the parameters are same in one request. We parse the
+    # parameters from the first instance for all instances in one request.
+    max_length = _MAX_LENGTH_DEFAULT
+    top_k = _TOP_K_DEFAULT
+
+    prompts = [item["prompt"] for item in data]
+    if "max_length" in data[0]:
+      max_length = data[0]["max_length"]
+    if "top_k" in data[0]:
+      top_k = data[0]["top_k"]
+
+    return prompts, max_length, top_k
+
+  def inference(self, data: Any, *args, **kwargs) -> List[Image.Image]:
+    """Runs the inference."""
+    prompts, max_length, top_k = data
+    logging.debug(
+        f"Inference prompts={prompts}, max_length={max_length}, top_k={top_k}."
+    )
+    if self.task == TEXT_TO_IMAGE_LORA:
+      predicted_results = self.pipeline(
+          prompt=prompts, num_inference_steps=_NUM_INFERENCE_STEPS
+      ).images
+    elif self.task == SEQUENCE_CLASSIFICATION_LORA:
+      encoded_input = self.tokenizer(prompts, return_tensors="pt")
+      encoded_input.to(self.map_location)
+      with torch.no_grad():
+        outputs = self.model(**encoded_input)
+      predictions = outputs.logits.argmax(dim=-1)
+      predicted_results = predictions.tolist()
+    elif (
+        self.task == CAUSAL_LANGUAGE_MODELING_LORA or self.task == INSTRUCT_LORA
+    ):
+      predicted_results = self.pipeline(
+          prompts,
+          max_length=max_length,
+          do_sample=True,
+          top_k=top_k,
+          num_return_sequences=1,
+          eos_token_id=self.tokenizer.eos_token_id,
+      )
+    else:
+      raise ValueError(f"Invalid TASK: {self.task}")
+    return predicted_results
+
+  def postprocess(self, data: Any) -> List[str]:
+    """Postprocesses output data."""
+    if self.task == TEXT_TO_IMAGE_LORA:
+      # Converts the images to base64 string.
+      outputs = [
+          image_format_converter.image_to_base64(image) for image in data
+      ]
+    else:
+      outputs = data
+    return outputs
+
+
+# pylint: enable=logging-fstring-interpolation

community-content/vertex_vision_model_garden/model_oss/peft/hf_transformers_lazy_download.patch

@@ -0,0 +1,131 @@
+diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
+index 45459ed..32527f4 100644
+--- a/src/transformers/modeling_utils.py
++++ b/src/transformers/modeling_utils.py
+@@ -32,6 +32,8 @@ import torch
+ from packaging import version
+ from torch import Tensor, nn
+ from torch.nn import CrossEntropyLoss
++from huggingface_hub import hf_hub_download
++from google.cloud import storage
+
+ from .activations import get_activation
+ from .configuration_utils import PretrainedConfig
+@@ -442,6 +444,29 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+     """
+     Reads a PyTorch checkpoint file, returning properly formatted errors if they arise.
+     """
++    delete_download = False
++    tmp_dir = "/tmp/model"
++    os.makedirs(tmp_dir, exist_ok=True)
++    if isinstance(checkpoint_file, dict):
++        # Download model file from huggingface
++        print(f"==> Download model from HF: {checkpoint_file}")
++        checkpoint_file = hf_hub_download(
++            local_dir=tmp_dir, local_dir_use_symlinks=False, force_download=True, resume_download=True, **checkpoint_file)
++        delete_download = True
++    else:
++        with open(checkpoint_file, "rb") as f:
++            is_gcs_file = (f.read(2) == b"gs")
++        if is_gcs_file:
++            # Download model file from GCS
++            with open(checkpoint_file, "r") as f:
++                gcs_file = f.read()
++            checkpoint_file = os.path.join(tmp_dir, gcs_file.split("/")[-1])
++            print(f"==> Download model from GCS: {gcs_file} to: {checkpoint_file}")
++            client = storage.Client()
++            with open(checkpoint_file, 'wb') as f:
++                client.download_blob_to_file(gcs_file, f)
++            delete_download = True
++
+     if checkpoint_file.endswith(".safetensors") and is_safetensors_available():
+         # Check format of the archive
+         with safe_open(checkpoint_file, framework="pt") as f:
+@@ -455,9 +480,9 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+             raise NotImplementedError(
+                 f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet."
+             )
+-        return safe_load_file(checkpoint_file)
++        state_dict = safe_load_file(checkpoint_file)
+     try:
+-        return torch.load(checkpoint_file, map_location="cpu")
++        state_dict = torch.load(checkpoint_file, map_location="cpu")
+     except Exception as e:
+         try:
+             with open(checkpoint_file) as f:
+@@ -478,6 +503,10 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+                 f"at '{checkpoint_file}'. "
+                 "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
+             )
++    if delete_download:
++        print(f"==> Delete downloaded model: {checkpoint_file}")
++        os.remove(checkpoint_file)
++    return state_dict
+
+
+ def set_initialized_submodules(model, state_dict_keys):
+@@ -3179,7 +3208,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
+             return mismatched_keys
+
+         if resolved_archive_file is not None:
+-            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
++            if isinstance(resolved_archive_file, str):
++                folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
++            else:
++                folder = None
+         else:
+             folder = None
+         if device_map is not None and is_safetensors:
+diff --git a/src/transformers/utils/hub.py b/src/transformers/utils/hub.py
+index ffed743..4b15770 100644
+--- a/src/transformers/utils/hub.py
++++ b/src/transformers/utils/hub.py
+@@ -414,20 +414,34 @@ def cached_file(
+     user_agent = http_user_agent(user_agent)
+     try:
+         # Load from URL or cache if already cached
+-        resolved_file = hf_hub_download(
+-            path_or_repo_id,
+-            filename,
+-            subfolder=None if len(subfolder) == 0 else subfolder,
+-            repo_type=repo_type,
+-            revision=revision,
+-            cache_dir=cache_dir,
+-            user_agent=user_agent,
+-            force_download=force_download,
+-            proxies=proxies,
+-            resume_download=resume_download,
+-            use_auth_token=use_auth_token,
+-            local_files_only=local_files_only,
+-        )
++        if filename.endswith(".bin"):
++            # NOTE: To save disk we do not download bin file eagerly. Do not support safetensors.
++            resolved_file = dict(
++                repo_id=path_or_repo_id,
++                filename=filename,
++                subfolder=None if len(subfolder) == 0 else subfolder,
++                repo_type=repo_type,
++                revision=revision,
++                user_agent=user_agent,
++                proxies=proxies,
++                use_auth_token=use_auth_token,
++            )
++            print(f"--> Apply lazy download to bin file: {resolved_file}")
++        else:
++            resolved_file = hf_hub_download(
++                path_or_repo_id,
++                filename,
++                subfolder=None if len(subfolder) == 0 else subfolder,
++                repo_type=repo_type,
++                revision=revision,
++                cache_dir=cache_dir,
++                user_agent=user_agent,
++                force_download=force_download,
++                proxies=proxies,
++                resume_download=resume_download,
++                use_auth_token=use_auth_token,
++                local_files_only=local_files_only,
++            )
+
+     except RepositoryNotFoundError:
+         raise EnvironmentError(

community-content/vertex_vision_model_garden/model_oss/peft/instruct_lora.py

@@ -0,0 +1,97 @@
+"""Instruct/Chat with LoRA models."""
+
+# pylint: disable=g-importing-member
+from datasets import load_dataset
+from peft import LoraConfig
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from transformers import TrainingArguments
+from trl import SFTTrainer
+
+
+def finetune_instruct(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    lora_rank: int = 64,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    warmup_ratio: int = 0.03,
+    max_steps: int = 10,
+    max_seq_length: int = 512,
+    learning_rate: float = 2e-4,
+) -> None:
+  """Finetunes instruct."""
+  dataset = load_dataset(dataset_name, split="train")
+
+  bnb_config = BitsAndBytesConfig(
+      load_in_4bit=True,
+      bnb_4bit_quant_type="nf4",
+      bnb_4bit_compute_dtype=torch.float16,
+  )
+
+  model = AutoModelForCausalLM.from_pretrained(
+      pretrained_model_id,
+      quantization_config=bnb_config,
+      trust_remote_code=True,
+  )
+  model.config.use_cache = False
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      pretrained_model_id, trust_remote_code=True
+  )
+  tokenizer.pad_token = tokenizer.eos_token
+
+  peft_config = LoraConfig(
+      lora_alpha=lora_alpha,
+      lora_dropout=lora_dropout,
+      r=lora_rank,
+      bias="none",
+      task_type="CAUSAL_LM",
+      target_modules=[
+          "query_key_value",
+          "dense",
+          "dense_h_to_4h",
+          "dense_4h_to_h",
+      ],
+  )
+
+  per_device_train_batch_size = 4
+  gradient_accumulation_steps = 4
+  optim = "paged_adamw_32bit"
+  save_steps = 10
+  logging_steps = 10
+  max_grad_norm = 0.3
+  lr_scheduler_type = "constant"
+
+  training_arguments = TrainingArguments(
+      output_dir=output_dir,
+      per_device_train_batch_size=per_device_train_batch_size,
+      gradient_accumulation_steps=gradient_accumulation_steps,
+      optim=optim,
+      save_steps=save_steps,
+      logging_steps=logging_steps,
+      learning_rate=learning_rate,
+      fp16=True,
+      max_grad_norm=max_grad_norm,
+      max_steps=max_steps,
+      warmup_ratio=warmup_ratio,
+      group_by_length=True,
+      lr_scheduler_type=lr_scheduler_type,
+  )
+
+  trainer = SFTTrainer(
+      model=model,
+      train_dataset=dataset,
+      peft_config=peft_config,
+      dataset_text_field="text",
+      max_seq_length=max_seq_length,
+      tokenizer=tokenizer,
+      args=training_arguments,
+  )
+  for name, module in trainer.model.named_modules():
+    if "norm" in name:
+      module = module.to(torch.float32)
+  trainer.train()

community-content/vertex_vision_model_garden/model_oss/peft/main.py

@@ -0,0 +1,177 @@
+"""Main function to start PEFT finetuning."""
+import subprocess
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from peft import causal_language_modeling_lora
+from peft import instruct_lora
+from peft import sequence_classification_lora
+from util import constants
+from util import fileutils
+
+_TASK = flags.DEFINE_string(
+    'task',
+    constants.CAUSAL_LANGUAGE_MODELING_LORA,
+    'The supported PEFT tasks.',
+)
+
+_PRETRAINED_MODEL_ID = flags.DEFINE_string(
+    'pretrained_model_id',
+    None,
+    'The pretrained model id. Supported models can be causal language modeling'
+    ' models from https://github.com/huggingface/peft/tree/main.',
+    required=True,
+)
+
+_DATASET_NAME = flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The dataset name in huggingface.',
+    required=True,
+)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    'output_dir',
+    None,
+    'The output directory.',
+    required=True,
+)
+
+_PRECISION_MODE = flags.DEFINE_string(
+    'precision_mode',
+    constants.PRECISION_MODE_16,
+    'Supported finetuning precision_modes are `{}` and `{}`.'.format(
+        constants.PRECISION_MODE_8, constants.PRECISION_MODE_16
+    ),
+)
+
+_LORA_RANK = flags.DEFINE_integer(
+    'lora_rank',
+    16,
+    'The rank of the update matrices, expressed in int. Lower rank results in'
+    ' smaller update matrices with fewer trainable parameters, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_ALPHA = flags.DEFINE_integer(
+    'lora_alpha',
+    32,
+    'LoRA scaling factor, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_DROPOUT = flags.DEFINE_float(
+    'lora_dropout',
+    0.05,
+    'dropout probability of the LoRA layers, referring to'
+    ' https://huggingface.co/docs/peft/task_guides/token-classification-lora.',
+)
+
+_WARMUP_STEPS = flags.DEFINE_integer(
+    'warmup_steps',
+    10,
+    'Number of steps for the warmup in the learning rate scheduler.',
+)
+
+_WARMUP_RATIO = flags.DEFINE_float(
+    'warmup_ratio',
+    0.03,
+    'The warmup ratio in the learning rate scheduler.',
+)
+
+_MAX_STEPS = flags.DEFINE_integer(
+    'max_steps',
+    10,
+    'Total number of training steps.',
+)
+
+_MAX_SEQ_LENGTH = flags.DEFINE_integer(
+    'max_seq_length',
+    512,
+    'The maximum sequence length.',
+)
+
+_NUM_EPOCHS = flags.DEFINE_integer(
+    'num_epochs',
+    20,
+    'The number of training epochs.',
+)
+
+_BATCH_SIZE = flags.DEFINE_integer(
+    'batch_size',
+    32,
+    'The batch size.',
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate',
+    2e-4,
+    'The learning rate after the potential warmup period.',
+)
+
+
+def main(_) -> None:
+  task = _TASK.value
+  pretrained_model_id = _PRETRAINED_MODEL_ID.value
+  local_pretrained_model_id = None
+  if pretrained_model_id.startswith(constants.GCS_URI_PREFIX):
+    logging.info(
+        'Start to copy pretrained models locally: %s.', pretrained_model_id
+    )
+    fileutils.download_gcs_dir_to_local(
+        pretrained_model_id, constants.LOCAL_BASE_MODEL_DIR
+    )
+    local_pretrained_model_id = constants.LOCAL_BASE_MODEL_DIR
+    logging.info(
+        'Finished copying pretrained models locally to: %s.',
+        local_pretrained_model_id,
+    )
+  if task == constants.TEXT_TO_IMAGE_LORA:
+    subprocess.run(['/bin/bash', 'train.sh'], check=True)
+  elif task == constants.SEQUENCE_CLASSIFICATION_LORA:
+    sequence_classification_lora.finetune_sequence_classification(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        num_epochs=_NUM_EPOCHS.value,
+        batch_size=_BATCH_SIZE.value,
+        learning_rate=_LEARNING_RATE.value,
+    )
+  elif task == constants.CAUSAL_LANGUAGE_MODELING_LORA:
+    causal_language_modeling_lora.finetune_causal_language_modeling(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        precision_mode=_PRECISION_MODE.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        warmup_steps=_WARMUP_STEPS.value,
+        max_steps=_MAX_STEPS.value,
+        learning_rate=_LEARNING_RATE.value,
+        local_pretrained_model_id=local_pretrained_model_id,
+    )
+  elif task == constants.INSTRUCT_LORA:
+    instruct_lora.finetune_instruct(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        warmup_ratio=_WARMUP_RATIO.value,
+        max_steps=_MAX_STEPS.value,
+        max_seq_length=_MAX_SEQ_LENGTH.value,
+        learning_rate=_LEARNING_RATE.value,
+    )
+  else:
+    raise ValueError('The task {} is not supported.'.format(task))
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/peft/sequence_classification_lora.py

@@ -0,0 +1,133 @@
+"""Sequence classification with LoRA models."""
+
+# pylint: disable=g-importing-member
+
+from datasets import load_dataset
+import evaluate
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import get_linear_schedule_with_warmup
+
+
+def finetune_sequence_classification(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    lora_rank: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    num_epochs: int = 20,
+    batch_size: int = 32,
+    learning_rate: float = 3e-4,
+) -> None:
+  """Finetunes sequence classification."""
+  task = "mrpc"
+  device = "cuda"
+
+  peft_config = LoraConfig(
+      task_type="SEQ_CLS",
+      inference_mode=False,
+      r=lora_rank,
+      lora_alpha=lora_alpha,
+      lora_dropout=lora_dropout,
+  )
+  if any(k in pretrained_model_id for k in ("gpt", "opt", "bloom")):
+    padding_side = "left"
+  else:
+    padding_side = "right"
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      pretrained_model_id, padding_side=padding_side
+  )
+  if getattr(tokenizer, "pad_token_id") is None:
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+  datasets = load_dataset(dataset_name, task)
+  metric = evaluate.load(dataset_name, task)
+
+  def tokenize_function(examples):
+    # max_length=None => use the model max length (it's actually the default)
+    outputs = tokenizer(
+        examples["sentence1"],
+        examples["sentence2"],
+        truncation=True,
+        max_length=None,
+    )
+    return outputs
+
+  tokenized_datasets = datasets.map(
+      tokenize_function,
+      batched=True,
+      remove_columns=["idx", "sentence1", "sentence2"],
+  )
+
+  # We also rename the 'label' column to 'labels' which is the expected name for
+  # labels by the models of the transformers library.
+  tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+  def collate_fn(examples):
+    return tokenizer.pad(examples, padding="longest", return_tensors="pt")
+
+  # Instantiate dataloaders.
+  train_dataloader = DataLoader(
+      tokenized_datasets["train"],
+      shuffle=True,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+  eval_dataloader = DataLoader(
+      tokenized_datasets["validation"],
+      shuffle=False,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+
+  model = AutoModelForSequenceClassification.from_pretrained(
+      pretrained_model_id, return_dict=True
+  )
+  model = get_peft_model(model, peft_config)
+  model.print_trainable_parameters()
+
+  optimizer = AdamW(params=model.parameters(), lr=learning_rate)
+
+  # Instantiate scheduler
+  lr_scheduler = get_linear_schedule_with_warmup(
+      optimizer=optimizer,
+      num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),
+      num_training_steps=(len(train_dataloader) * num_epochs),
+  )
+
+  model.to(device)
+  for epoch in range(num_epochs):
+    model.train()
+    for _, batch in enumerate(tqdm(train_dataloader)):
+      batch.to(device)
+      outputs = model(**batch)
+      loss = outputs.loss
+      loss.backward()
+      optimizer.step()
+      lr_scheduler.step()
+      optimizer.zero_grad()
+
+    model.eval()
+    for _, batch in enumerate(tqdm(eval_dataloader)):
+      batch.to(device)
+      with torch.no_grad():
+        outputs = model(**batch)
+      predictions = outputs.logits.argmax(dim=-1)
+      references = batch["labels"]
+      metric.add_batch(
+          predictions=predictions,
+          references=references,
+      )
+
+    eval_metric = metric.compute()
+    print(f"epoch {epoch}:", eval_metric)
+
+  model.save_pretrained(output_dir)

community-content/vertex_vision_model_garden/model_oss/peft/train.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+# Setup accelerate config before running trainer.
+python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
+
+accelerate launch "$@"

community-content/vertex_vision_model_garden/model_oss/pic2word/dockerfile/serve.Dockerfile

@@ -0,0 +1,115 @@
+FROM pytorch/torchserve:0.7.1-gpu
+
+USER root
+
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="pic2word"
+ENV PATH="/home/model-server/:${PATH}"
+
+# Copy license.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        wget
+
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Install dependencies.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN python3 -m pip install --upgrade pip
+RUN pip install google-cloud-storage==2.7.0
+RUN pip install open_clip_torch==2.20.0
+RUN pip install numpy==1.22.0
+RUN pip install scikit-image==0.21.0
+RUN pip install scikit-learn==1.0.2
+RUN pip install torch==2.0.0
+RUN pip install torchvision==0.15.2
+RUN pip install tensorboard==2.13.0
+RUN pip install ase==3.21.1
+RUN pip install braceexpand==0.1.7
+RUN pip install cached-property==1.5.2
+RUN pip install configparser==5.0.2
+RUN pip install cycler==0.10.0
+RUN pip install decorator==4.4.2
+RUN pip install docker-pycreds==0.4.0
+RUN pip install gitdb==4.0.7
+RUN pip install gitpython==3.1.30
+RUN pip install googledrivedownloader==0.4
+RUN pip install h5py==3.1.0
+RUN pip install isodate==0.6.0
+RUN pip install jinja2==3.0.1
+RUN pip install kiwisolver==1.3.1
+RUN pip install littleutils==0.2.2
+RUN pip install llvmlite==0.36.0
+RUN pip install markupsafe==2.0.1
+RUN pip install matplotlib==3.3.4
+RUN pip install networkx==2.5.1
+RUN pip install numba==0.53.1
+RUN pip install ogb==1.3.1
+RUN pip install outdated==0.2.1
+RUN pip install pathtools==0.1.2
+RUN pip install promise==2.3
+RUN pip install psutil==5.8.0
+RUN pip install pyarrow==4.0.0
+RUN pip install pyparsing==2.4.7
+RUN pip install python-louvain==0.15
+RUN pip install pyyaml==5.4.1
+RUN pip install rdflib==5.0.0
+RUN pip install sentry-sdk==1.14.0
+RUN pip install shortuuid==1.0.1
+RUN pip install sklearn==0.0
+RUN pip install smmap==4.0.0
+RUN pip install subprocess32==3.5.4
+RUN pip install torch-geometric==1.7.0
+RUN pip install wandb==0.10.30
+RUN pip install wilds==1.1.0
+RUN pip install ftfy==6.1.1
+RUN pip install regex==2023.6.3
+RUN pip install webdataset==0.2.48
+RUN pip install requests==2.31.0
+RUN pip install hydra-core==1.3.2
+RUN pip install omegaconf==2.3.0
+RUN pip install fairseq==0.10.0
+RUN pip install bitarray==2.7.6
+
+# Get 'composed_image_retrieval' repository from github.
+RUN git clone https://github.com/google-research/composed_image_retrieval
+# Set workdir to composed_image_retrieval.
+WORKDIR ./composed_image_retrieval
+# Using git reset command to pin it down to a specific version.
+RUN git reset --hard 8c053297c2fae9cd17ddcded48445a4f47208dbd
+
+# Fix issue introduced by installing composed_image_retrieval
+# https://github.com/huggingface/transformers/issues/8638#issuecomment-790772391
+RUN pip uninstall dataclasses -y
+
+# Copy model artifacts.
+COPY model_oss/pic2word/handler.py /home/model-server/handler.py
+
+# Create torchserve configuration file.
+RUN echo \
+    "default_response_timeout=1800\n" \
+    "service_envelope=json\n" \
+    "inference_address=http://0.0.0.0:${infer_port}\n"  \
+    "management_address=http://0.0.0.0:${mng_port}" >> /home/model-server/config.properties
+
+# Expose ports.
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
+
+# Archive model artifacts and dependencies.
+# Do not set --model-file and --serialized-file because model and checkpoint
+# will be dynamically loaded in handler.py.
+RUN torch-model-archiver \
+--model-name=${model_name} \
+--version=1.0 \
+--handler=/home/model-server/handler.py \
+--runtime=python3 \
+--export-path=/home/model-server/model-store \
+--archive-format=default \
+--force
+
+# Run Torchserve HTTP serve to respond to prediction requests.
+CMD ["torchserve", "--start", \
+     "--ts-config", "/home/model-server/config.properties", \
+     "--models", "${model_name}=${model_name}.mar", \
+     "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/pic2word/handler.py

@@ -0,0 +1,167 @@
+"""Custom handler for Pic2Word."""
+
+from argparse import Namespace  # pylint: disable=g-importing-member
+import os
+from typing import Any
+
+from absl import logging
+from data import CustomFolder
+from eval_utils import visualize_results
+from model.clip import load
+from model.model import convert_weights
+from model.model import IM2TEXT
+from params import get_project_root
+import torch
+from torch.utils.data import DataLoader
+from ts.torch_handler.base_handler import BaseHandler
+
+from util import fileutils
+
+# The COCO dataset is stored in a publicly accessible bucket.
+_COCO_STORAGE_DIR = "gs://pic2word-bucket/data/coco/"
+_COCO_LOCAL_DIR = "/home/model-server/composed_image_retrieval/data/coco/"
+_COCO_VAL2017_PATH = "coco/val2017"
+_COCO_DATASET_NAME = "coco"
+_MODEL_NAME = "ViT-L/14"
+_LOCAL_QUERY_PATH = "./query/"
+_IMAGE_OUTPUT_LOCAL_DIR = "demo_out/images"
+_OUTPUT_LOCAL_DIR = "/demo_out/"
+_DATA_DIR = "data"
+_CHECKPOINT_DIR = "checkpoint/pic2word_model.pt"
+_REQUEST_PROMPTS = "prompts"
+_REQUEST_OUTPUT_STORAGE_DIR = "output_storage_dir"
+_REQUEST_IMAGE_PATH = "image_path"
+_REQUEST_IMAGE_FILE_NAME = "image_file_name"
+_RESPONSE_MSG = "Successfully retrieved images."
+
+
+class ModelHandler(BaseHandler):
+  """A custom model handler implementation."""
+
+  def __init__(self):
+    self.initialized = False
+    self.gpu = 0
+    self.model = None
+    self.dataloader = None
+    self.prompt = None
+    self.output_storage_dir = None
+
+  def initialize(self, context: Any):
+    """Initialize."""
+    logging.info("Initializing pic2word.")
+
+    # Download COCO dataset. The model looks for this folder specifically
+    # during image retrieval to generate a response for each request.
+    # This is a publicly accessible bucket.
+    fileutils.download_gcs_dir_to_local(
+        _COCO_STORAGE_DIR,
+        _COCO_LOCAL_DIR,
+    )
+
+    # Load the model.
+
+    self.initialized = True
+
+    torch.cuda.set_device(self.gpu)
+    model, _, preprocess_val = load(_MODEL_NAME, jit=False)
+
+    img2text = IM2TEXT(
+        embed_dim=model.embed_dim,
+        output_dim=model.token_embedding.weight.shape[1],
+    )
+
+    model.cuda(self.gpu)
+    img2text.cuda(self.gpu)
+
+    convert_weights(model)
+    convert_weights(img2text)
+
+    self.model = model
+    self.img2text = img2text
+
+    # Load the dataset
+    logging.info("Loading dataset.")
+
+    root_project = os.path.join(get_project_root(), _DATA_DIR)
+    dataset = CustomFolder(
+        os.path.join(root_project, _COCO_VAL2017_PATH), transform=preprocess_val
+    )
+
+    # Initialize the dataloader. This is used to create the pickle file from
+    # the dataset.
+    dataloader = DataLoader(
+        dataset,
+        batch_size=64,
+        shuffle=False,
+        num_workers=1,
+        pin_memory=True,
+        drop_last=False,
+    )
+
+    self.dataloader = dataloader
+
+    logging.info("Finished initializing Pic2Word server.")
+
+  def preprocess(self, data: Any) -> str:
+    """Preprocess input data."""
+    logging.info("Preprocessing Pic2Word inference request.")
+    query = data[0]
+
+    self.output_storage_dir = query[_REQUEST_OUTPUT_STORAGE_DIR]
+    prompts = query[_REQUEST_PROMPTS]
+    prompts = prompts.split(",")
+    self.prompt = prompts
+
+    image_path = query[_REQUEST_IMAGE_PATH]
+    # The query image is only supported via GCS bucket upload.
+    fileutils.download_gcs_dir_to_local(image_path, _LOCAL_QUERY_PATH)
+    image_file_name = query[_REQUEST_IMAGE_FILE_NAME]
+
+    query_file = f"./query/{image_file_name}"
+
+    logging.info("Setting model args.")
+
+    args = {
+        "openai-pretrained": True,
+        "resume": _CHECKPOINT_DIR,
+        "retrieval_data": _COCO_DATASET_NAME,
+        "query_file": query_file,
+        "demo_out": _OUTPUT_LOCAL_DIR,
+        "prompts": prompts,
+        "distributed": False,
+        "dp": False,
+        "gpu": 0,
+        "model": _MODEL_NAME,
+        "world_size": 1,
+    }
+    model_input = Namespace(**args)
+
+    logging.info("Finished preprocessing Pic2Word inference request.")
+    return model_input
+
+  def inference(self, model_input: Any):
+    """Runs inference."""
+    logging.info("Running model-inference.")
+    visualize_results(
+        model=self.model,
+        img2text=self.img2text,
+        args=model_input,
+        prompt=self.prompt,
+        dataloader=self.dataloader,
+    )
+
+  def postprocess(self):
+    """Upload the output images to the bucket."""
+    logging.info("Running request postprocess.")
+    fileutils.upload_local_dir_to_gcs(
+        _IMAGE_OUTPUT_LOCAL_DIR, self.output_storage_dir
+    )
+
+  def handle(self, data: Any, context: Any) -> str:  # pylint: disable=unused-argument
+    """Runs preprocess, inference, and post-processing."""
+    logging.info("Received Pic2Word inference request")
+    model_input = self.preprocess(data)
+    self.inference(model_input)
+    self.postprocess()
+    logging.info("Done handling input.")
+    return _RESPONSE_MSG

community-content/vertex_vision_model_garden/model_oss/tfvision/configs/__init__.py

@@ -0,0 +1,4 @@
+"""AutoML Vision Tfvision configs package definition."""
+
+from tfvision.configs import backbones
+from tfvision.configs import hub_model

community-content/vertex_vision_model_garden/model_oss/tfvision/configs/backbones.py

@@ -0,0 +1,28 @@
+"""Backbones configurations."""
+import dataclasses
+from typing import Optional
+
+from official.modeling import hyperparams
+
+
+@dataclasses.dataclass
+class HubModel(hyperparams.Config):
+  """Tf-hub model config."""
+  handle: Optional[str] = None
+  trainable: bool = True
+  mean_rgb: Optional[float] = None
+  stddev_rgb: Optional[float] = None
+  signature: Optional[str] = None
+  output_key: Optional[str] = None
+
+
+@dataclasses.dataclass
+class Backbone(hyperparams.OneOfConfig):
+  """Configuration for backbones.
+
+  Attributes:
+    type: The type of a backbone, such as 'hub_model'.
+    hub_model: hub model backbone config.
+  """
+  type: Optional[str] = 'hub_model'
+  hub_model: HubModel = dataclasses.field(default_factory=HubModel)

community-content/vertex_vision_model_garden/model_oss/tfvision/configs/hub_model.py

@@ -0,0 +1,166 @@
+"""Tf-hub model configuration definition for AutoML Vision ICN.."""
+
+import os
+
+from tfvision.configs import backbones
+from official.core import config_definitions as cfg
+from official.core import exp_factory
+from official.modeling import optimization
+from official.vision.configs import image_classification
+
+_HANDLE = 'https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_m/feature_vector/2'  # pylint: disable=line-too-long
+_COCA_HANDLE = None
+_INPUT_SIZE = [480, 480, 3]
+_MEAN_RGB = 0.0
+_STDDEV_RGB = 255.0
+
+
+# pylint is unable to handle dataclasses constructor arguments correctly.
+# pylint: disable=unexpected-keyword-arg
+@exp_factory.register_config_factory('hub_model')
+def hub_model() -> cfg.ExperimentConfig:
+  """Gets experimental configs for tf-hub models."""
+
+  batch_size = 8
+  train_steps = 625000
+  steps_per_loop = 1250
+  return cfg.ExperimentConfig(
+      task=image_classification.ImageClassificationTask(
+          model=image_classification.ImageClassificationModel(
+              num_classes=1000,
+              input_size=_INPUT_SIZE,
+              backbone=backbones.Backbone(
+                  type='hub_model',
+                  hub_model=backbones.HubModel(
+                      handle=_HANDLE, mean_rgb=_MEAN_RGB, stddev_rgb=_STDDEV_RGB
+                  ),
+              ),
+              dropout_rate=0.0,
+          ),
+          losses=image_classification.Losses(
+              l2_weight_decay=0.0, label_smoothing=0.1, one_hot=True
+          ),
+          train_data=image_classification.DataConfig(
+              input_path=os.path.join(
+                  image_classification.IMAGENET_INPUT_PATH_BASE, 'train*'
+              ),
+              aug_type=None,
+              dtype='float32',
+              global_batch_size=batch_size,
+              is_training=True,
+              decode_jpeg_only=False,
+          ),
+          validation_data=image_classification.DataConfig(
+              input_path=os.path.join(
+                  image_classification.IMAGENET_INPUT_PATH_BASE, 'valid*'
+              ),
+              dtype='float32',
+              global_batch_size=batch_size,
+              is_training=False,
+              decode_jpeg_only=False,
+              drop_remainder=False,
+          ),
+      ),
+      trainer=cfg.TrainerConfig(
+          best_checkpoint_eval_metric='accuracy',
+          best_checkpoint_export_subdir='best_ckpt',
+          best_checkpoint_metric_comp='higher',
+          optimizer_config=optimization.OptimizationConfig(
+              learning_rate=optimization.LrConfig(
+                  type='cosine',
+                  cosine=optimization.lr_cfg.CosineLrConfig(
+                      decay_steps=train_steps, initial_learning_rate=0.001
+                  ),
+              ),
+              optimizer=optimization.OptimizerConfig(
+                  type='sgd', sgd=optimization.SGDConfig(momentum=0.9)
+              ),
+          ),
+          checkpoint_interval=steps_per_loop,
+          steps_per_loop=steps_per_loop,
+          summary_interval=steps_per_loop,
+          validation_interval=steps_per_loop,
+          train_steps=train_steps,
+          validation_steps=-1,
+      ),
+      restrictions=[
+          'task.train_data.is_training != None',
+          'task.validation_data.is_training != None',
+      ],
+  )
+
+
+@exp_factory.register_config_factory('coca')
+def coca() -> cfg.ExperimentConfig:
+  """Gets experimental configs for tf-hub models."""
+
+  batch_size = 8
+  train_steps = 625000
+  steps_per_loop = 1250
+  return cfg.ExperimentConfig(
+      task=image_classification.ImageClassificationTask(
+          model=image_classification.ImageClassificationModel(
+              num_classes=1000,
+              input_size=[288, 288, 3],
+              backbone=backbones.Backbone(
+                  type='hub_model',
+                  hub_model=backbones.HubModel(
+                      handle=_COCA_HANDLE,
+                      trainable=False,
+                      mean_rgb=0.0,
+                      stddev_rgb=255.0,
+                  ),
+              ),
+              dropout_rate=0.0,
+          ),
+          losses=image_classification.Losses(
+              l2_weight_decay=0.0, label_smoothing=0.1, one_hot=True
+          ),
+          train_data=image_classification.DataConfig(
+              input_path=os.path.join(
+                  image_classification.IMAGENET_INPUT_PATH_BASE, 'train*'
+              ),
+              aug_type=None,
+              dtype='float32',
+              global_batch_size=batch_size,
+              is_training=True,
+              decode_jpeg_only=False,
+          ),
+          validation_data=image_classification.DataConfig(
+              input_path=os.path.join(
+                  image_classification.IMAGENET_INPUT_PATH_BASE, 'valid*'
+              ),
+              dtype='float32',
+              global_batch_size=batch_size,
+              is_training=False,
+              decode_jpeg_only=False,
+              drop_remainder=False,
+          ),
+      ),
+      trainer=cfg.TrainerConfig(
+          best_checkpoint_eval_metric='accuracy',
+          best_checkpoint_export_subdir='best_ckpt',
+          best_checkpoint_metric_comp='higher',
+          optimizer_config=optimization.OptimizationConfig(
+              learning_rate=optimization.LrConfig(
+                  type='cosine',
+                  cosine=optimization.lr_cfg.CosineLrConfig(
+                      decay_steps=train_steps, initial_learning_rate=0.001
+                  ),
+              ),
+              optimizer=optimization.OptimizerConfig(
+                  type='sgd', sgd=optimization.SGDConfig(momentum=0.9)
+              ),
+          ),
+          checkpoint_interval=steps_per_loop,
+          steps_per_loop=steps_per_loop,
+          summary_interval=steps_per_loop,
+          validation_interval=steps_per_loop,
+          train_steps=train_steps,
+          validation_steps=-1,
+      ),
+      restrictions=[
+          'task.train_data.is_training != None',
+          'task.validation_data.is_training != None',
+      ],
+  )

community-content/vertex_vision_model_garden/model_oss/tfvision/dockerfile/base.Dockerfile

@@ -0,0 +1,86 @@
+# Dockerfile for basic training dockers with tfvision.
+#
+# To build:
+# docker build -f model_oss/tfvision/dockerfile/base.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM tensorflow/tensorflow:2.11.0-gpu
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# This is added to fix docker build error related to Nvidia key update.
+RUN rm -f /etc/apt/sources.list.d/cuda.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        screen \
+        libtcmalloc-minimal4
+
+
+# Install google cloud SDK.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN tar xzf google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install cloud-tpu-client==0.10
+RUN pip install pyyaml==5.4.1
+RUN pip install fsspec==2021.10.1
+RUN pip install gcsfs==2021.10.1
+RUN pip install tensorflow-text==2.11.0
+RUN pip install tf-models-official==2.11.3
+RUN pip install pyglove==0.1.0
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install object-detection==0.0.3
+RUN pip install pylint==2.17.2
+
+# Installs Reduction Server NCCL plugin.
+RUN echo "deb https://packages.cloud.google.com/apt google-fast-socket main" | tee /etc/apt/sources.list.d/google-fast-socket.list \
+&&  curl -s -L https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+&&  apt update && apt install -y google-reduction-server
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+
+# Lower the memory fragmentation, and speed up the training.
+# https://github.com/tensorflow/tensorflow/issues/44176#issuecomment-783768033
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4
+
+# Enable userspace DNS cache
+ENV GCS_RESOLVE_REFRESH_SECS=60
+ENV GCS_REQUEST_CONNECTION_TIMEOUT_SECS=300
+ENV GCS_METADATA_REQUEST_TIMEOUT_SECS=300
+ENV GCS_READ_REQUEST_TIMEOUT_SECS=300
+ENV GCS_WRITE_REQUEST_TIMEOUT_SECS=600
+# Each opened GCS file takes GCS_READ_CACHE_BLOCK_SIZE_MB of RAM, reduce the
+# value from the default 64MB to 8MB to decrease memory footprint.
+ENV GCS_READ_CACHE_BLOCK_SIZE_MB=8
+
+WORKDIR /usr/local/lib/python3.8/dist-packages/official/vision
+
+ENTRYPOINT ["python3","train.py"]
+
+CMD ["--experiment=YOUR_EXPERIMENT",\
+"--config_file=YOUR_CONFIG_FILE",\
+"--mode=YOUR_MODE",\
+"--model_dir=YOUR_MODEL_DIR"]

community-content/vertex_vision_model_garden/model_oss/tfvision/dockerfile/base_v2.Dockerfile

@@ -0,0 +1,86 @@
+# Dockerfile for basic training dockers with tfvision.
+#
+# To build:
+# docker build -f model_oss/tfvision/dockerfile/base_v2.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM tensorflow/build:2.12-python3.9
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# This is added to fix docker build error related to Nvidia key update.
+RUN rm -f /etc/apt/sources.list.d/cuda.list
+RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
+
+# Install basic libs.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        vim \
+        screen \
+        libtcmalloc-minimal4
+
+
+# Install google cloud SDK.
+RUN wget -q https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN tar xzf google-cloud-sdk-359.0.0-linux-x86_64.tar.gz
+RUN ./google-cloud-sdk/install.sh -q
+# Make sure gsutil will use the default service account.
+RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg
+
+
+# Install required libs.
+RUN pip install --upgrade pip
+RUN pip install cloud-tpu-client==0.10
+RUN pip install pyyaml==5.4.1
+RUN pip install fsspec==2021.10.1
+RUN pip install gcsfs==2021.10.1
+RUN pip install tensorflow-text==2.12.1
+RUN pip install tf-models-official==2.12.0
+RUN pip install pyglove==0.1.0
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install object-detection==0.0.3
+RUN pip install pylint==2.17.2
+
+# Installs Reduction Server NCCL plugin.
+RUN echo "deb https://packages.cloud.google.com/apt google-fast-socket main" | tee /etc/apt/sources.list.d/google-fast-socket.list \
+&&  curl -s -L https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+&&  apt update && apt install -y google-reduction-server
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+
+# Lower the memory fragmentation, and speed up the training.
+# https://github.com/tensorflow/tensorflow/issues/44176#issuecomment-783768033
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4
+
+# Enable userspace DNS cache
+ENV GCS_RESOLVE_REFRESH_SECS=60
+ENV GCS_REQUEST_CONNECTION_TIMEOUT_SECS=300
+ENV GCS_METADATA_REQUEST_TIMEOUT_SECS=300
+ENV GCS_READ_REQUEST_TIMEOUT_SECS=300
+ENV GCS_WRITE_REQUEST_TIMEOUT_SECS=600
+# Each opened GCS file takes GCS_READ_CACHE_BLOCK_SIZE_MB of RAM, reduce the
+# value from the default 64MB to 8MB to decrease memory footprint.
+ENV GCS_READ_CACHE_BLOCK_SIZE_MB=8
+
+WORKDIR /usr/local/lib/python3.9/dist-packages/official/vision
+
+ENTRYPOINT ["python3","train.py"]
+
+CMD ["--experiment=YOUR_EXPERIMENT",\
+"--config_file=YOUR_CONFIG_FILE",\
+"--mode=YOUR_MODE",\
+"--model_dir=YOUR_MODEL_DIR"]

community-content/vertex_vision_model_garden/model_oss/tfvision/dockerfile/model_export.Dockerfile

@@ -0,0 +1,68 @@
+# Dockerfile for AutoML vision model export dockers with tfvision.
+#
+# To build:
+# docker build -f model_oss/tfvision/dockerfile/model_export.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/tfvision-base-v2:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN PROTOC_ZIP=protoc-3.9.2-linux-x86_64.zip && \
+  curl -OL https://github.com/google/protobuf/releases/download/v3.9.2/$PROTOC_ZIP && \
+  unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
+  unzip -o $PROTOC_ZIP -d /usr/local include/* && \
+  rm -f $PROTOC_ZIP
+
+COPY model_oss/tfvision /automl_vision/tfvision
+COPY model_oss/util /automl_vision/util
+
+# Install tensorflow models following:
+# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md.
+# https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/object_detection_tutorial.ipynb.
+RUN cd /automl_vision && \
+  git clone --depth 1 https://github.com/tensorflow/models && \
+  cd models/research && \
+  protoc object_detection/protos/*.proto --python_out=. && \
+  cp object_detection/packages/tf2/setup.py . && \
+  pip install . && \
+  cd /automl_vision && \
+  rm -rf ./models
+
+RUN pip install tensorflow-io==0.25.0
+
+RUN pip install "opencv-python-headless<4.3"
+RUN pip install google-cloud-aiplatform==1.23.0
+
+# Install yolov4, yolov7, and maxvit
+RUN mkdir /tmp/buffer && \
+    cd /tmp/buffer && \
+    git clone https://github.com/tensorflow/models.git && \
+    cd models && \
+    git reset --hard 6138633a41097a3c0f320bd895ac5da65c33016f && \
+    cd /usr/local/lib/python3.9/dist-packages/official/projects/ && \
+    cp -R /tmp/buffer/models/official/projects/yolo/ ./ && \
+    cp -R /tmp/buffer/models/official/projects/maxvit/ ./ && \
+    rm -rf /tmp/buffer
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/tfvision"
+
+WORKDIR /automl_vision
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","tfvision/serving/export_oss_saved_model.py"]
+
+CMD ["--experiment=YOUR_EXPERIMENT",\
+"--objective=YOUR_OBJECTIVE",\
+"--config_file=YOUR_CONFIG_FILE",\
+"--checkpoint_path=YOUR_CHECKPOINT_DIR",\
+"--label_map_path=YOUR_LABEL_MAP_PATH",\
+"--input_image_size=YOUR_INPUT_IMAGE_SIZE",\
+"--export_dir=YOUR_EXPORT_DIR"]

community-content/vertex_vision_model_garden/model_oss/tfvision/dockerfile/train_oss.Dockerfile

@@ -0,0 +1,52 @@
+# Dockerfile for AutoML vision training dockers with tfvision.
+#
+# To build:
+# docker build -f model_oss/tfvision/dockerfile/train_oss.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/tfvision-base:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Fix yolo and retinanet issues.
+RUN mkdir /tmp/buffer && \
+    cd /tmp/buffer && \
+    git clone https://github.com/tensorflow/models.git && \
+    cd models && \
+    git checkout fbd4c57fd7e9f7d73da30ed3fc755b8c4c682df7 && \
+    cd /usr/local/lib/python3.8/dist-packages/official/projects/yolo && \
+    cp /tmp/buffer/models/official/projects/yolo/optimization/optimizer_factory.py ./optimization/ && \
+    cp /tmp/buffer/models/official/projects/yolo/configs/yolo.py ./configs && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/factory.py ./modeling && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/layers/detection_generator.py ./modeling/layers && \
+    cd /usr/local/lib/python3.8/dist-packages/official/vision && \
+    cp /tmp/buffer/models/official/vision/configs/retinanet.py ./configs && \
+    cp /tmp/buffer/models/official/vision/modeling/layers/detection_generator.py ./modeling/layers && \
+    cp /tmp/buffer/models/official/vision/modeling/layers/edgetpu.py ./modeling/layers && \
+    rm -rf /tmp/buffer
+
+COPY model_oss/tfvision /automl_vision/tfvision
+COPY model_oss/util /automl_vision/util
+RUN rm -rf /automl_vision/tfvision/serving
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","tfvision/train_hpt_oss.py"]
+
+CMD ["--experiment=YOUR_EXPERIMENT",\
+"--config_file=",\
+"--mode=YOUR_MODE",\
+"--model_dir=YOUR_MODEL_DIR",\
+"--objective=YOUR_OBJECTIVE",\
+"--learning_rate=",\
+"--anchor_size="]

community-content/vertex_vision_model_garden/model_oss/tfvision/dockerfile/train_oss_v2.Dockerfile

@@ -0,0 +1,80 @@
+# Dockerfile for AutoML vision training dockers with tfvision.
+#
+# To build:
+# docker build -f model_oss/tfvision/dockerfile/train_oss_v2.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/tfvision-base-v2:latest
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Fix yolo and retinanet issues.
+RUN mkdir /tmp/buffer && \
+    cd /tmp/buffer && \
+    git clone https://github.com/tensorflow/models.git && \
+    cd models && \
+    # Add support for newly added config options.
+    git reset --hard ed6d4d220b86237980d3f7563d261d19e040ef1a && \
+    cd /usr/local/lib/python3.9/dist-packages/official/projects/yolo && \
+    cp /tmp/buffer/models/official/projects/yolo/dataloaders/yolo_input.py ./dataloaders/ && \
+    cp /tmp/buffer/models/official/projects/yolo/optimization/optimizer_factory.py ./optimization/ && \
+    cp /tmp/buffer/models/official/projects/yolo/configs/yolo.py ./configs && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/factory.py ./modeling && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/layers/detection_generator.py ./modeling/layers && \
+    cp /tmp/buffer/models/official/projects/yolo/common/registry_imports.py ./common && \
+    cp /tmp/buffer/models/official/projects/yolo/configs/yolov7.py ./configs && \
+    cp /tmp/buffer/models/official/projects/yolo/configs/decoders.py ./configs && \
+    cp /tmp/buffer/models/official/projects/yolo/configs/backbones.py ./configs && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/yolov7_model.py ./modeling && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/backbones/yolov7.py ./modeling/backbones && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/decoders/yolov7.py ./modeling/decoders && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/heads/yolov7_head.py ./modeling/heads && \
+    cp /tmp/buffer/models/official/projects/yolo/modeling/layers/nn_blocks.py ./modeling/layers && \
+    cp /tmp/buffer/models/official/projects/yolo/losses/yolov7_loss.py ./losses && \
+    cp /tmp/buffer/models/official/projects/yolo/tasks/yolov7.py ./tasks && \
+    cp /tmp/buffer/models/official/projects/yolo/ops/initializer_ops.py ./ops && \
+    cp /tmp/buffer/models/official/projects/yolo/ops/mosaic.py ./ops && \
+    cd /usr/local/lib/python3.9/dist-packages/official/vision && \
+    cp /tmp/buffer/models/official/vision/configs/retinanet.py ./configs && \
+    cp /tmp/buffer/models/official/vision/modeling/layers/detection_generator.py ./modeling/layers && \
+    cp /tmp/buffer/models/official/vision/modeling/layers/edgetpu.py ./modeling/layers && \
+    cp /tmp/buffer/models/official/vision/ops/augment.py ./ops && \
+    rm -rf /tmp/buffer
+
+# Add MaxViT
+RUN mkdir /tmp/buffer && \
+    cd /tmp/buffer && \
+    git clone https://github.com/tensorflow/models.git && \
+    cd models && \
+    git reset --hard 6138633a41097a3c0f320bd895ac5da65c33016f && \
+    cd /usr/local/lib/python3.9/dist-packages/official/projects/ && \
+    cp -R /tmp/buffer/models/official/projects/maxvit/ ./ && \
+    rm -rf /tmp/buffer
+ENV ENABLE_MAX_VIT "True"
+
+
+COPY model_oss/tfvision /automl_vision/tfvision
+COPY model_oss/util /automl_vision/util
+RUN rm -rf /automl_vision/tfvision/serving
+
+WORKDIR /automl_vision
+
+ENV PYTHONPATH "${PYTHONPATH}:/automl_vision/util"
+
+# Run pylint to validate code.
+COPY .pylintrc /automl_vision/.pylintrc
+RUN find . -type f -name "*.py" | xargs pylint --rcfile=./.pylintrc --errors-only
+
+ENTRYPOINT ["python3","tfvision/train_hpt_oss.py"]
+
+CMD ["--experiment=YOUR_EXPERIMENT",\
+"--config_file=",\
+"--mode=YOUR_MODE",\
+"--model_dir=YOUR_MODEL_DIR",\
+"--objective=YOUR_OBJECTIVE",\
+"--learning_rate=",\
+"--anchor_size="]

community-content/vertex_vision_model_garden/model_oss/tfvision/modeling/backbones/__init__.py

@@ -0,0 +1,3 @@
+"""Backbones package definition."""
+
+from tfvision.modeling.backbones import hub_model

community-content/vertex_vision_model_garden/model_oss/tfvision/modeling/backbones/hub_model.py

@@ -0,0 +1,165 @@
+"""Loads a tf-hub model."""
+
+from __future__ import annotations
+
+from typing import Any, Mapping, Optional
+
+from absl import logging
+import tensorflow as tf
+import tensorflow_hub as hub
+
+from official.modeling import hyperparams
+from official.vision.modeling.backbones import factory
+from official.vision.ops import preprocess_ops
+
+layers = tf.keras.layers
+
+
+@tf.keras.utils.register_keras_serializable(package='Vision')
+class HubModel(tf.keras.Model):
+  """A tf-hub model wrapper."""
+
+  def __init__(
+      self,
+      handle: str,
+      input_specs: tf.keras.layers.InputSpec = layers.InputSpec(
+          shape=[None, None, None, 3]
+      ),
+      trainable: bool = True,
+      mean_rgb: Optional[float] = None,
+      stddev_rgb: Optional[float] = None,
+      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
+      signature: Optional[str] = None,
+      output_key: Optional[str] = None,
+      **kwargs,
+  ):
+    """Initializes a tf-hub model.
+
+    Args:
+      handle: A handle to load a saved model via hub.load().
+      input_specs: A input_spec of the input tensor.
+      trainable: Controls whether this layer is trainable. Must not be set to
+        True when using a signature (raises ValueError), including the use of
+        legacy TF1 Hub format.
+      mean_rgb: The mean rgb value used for normalization.
+      stddev_rgb: The standard deviation of rgb values used for normalization.
+      kernel_regularizer: A regularizer object for kernel weights.
+      signature: Optional. If set, KerasLayer will use the requested signature.
+        For legacy models in TF1 Hub format leaving unset means to use the
+        `default` signature. When using a signature, output_key have to set.
+      output_key: Name of the output item to return if the layer returns a dict.
+        For legacy models in TF1 Hub format leaving unset means to return the
+        `default` output.
+      **kwargs: Additional keyword arguments to be passed.
+    """
+    self._handle = handle
+    self._mean_rgb = mean_rgb
+    self._stddev_rgb = stddev_rgb
+    self._kernel_regularizer = kernel_regularizer
+    self._signature = signature
+    self._output_key = output_key
+
+    inputs = tf.keras.Input(shape=input_specs.shape[1:])
+    x = inputs
+    if mean_rgb or stddev_rgb:
+      x = layers.Lambda(self.re_normalize)(x)
+
+    model = hub.KerasLayer(
+        handle=handle,
+        trainable=trainable,
+        signature=signature,
+        output_key=output_key,
+    )
+    if trainable and kernel_regularizer:
+      if hasattr(model, 'regularization_losses'):
+        logging.warning('regularization_losses already defined in the model.')
+
+      def reg_loss(x):
+        return lambda: kernel_regularizer(x)
+
+      for v in model.trainable_variables:
+        if 'kernel' in v.name:
+          model.add_loss(reg_loss(v))
+    x = model(x)
+    if not trainable:
+      # Solves backpropagation errors when loading CoCa.
+      x = tf.stop_gradient(x)
+    endpoints = {'0': x[:, tf.newaxis, tf.newaxis, :]}
+
+    self._output_specs = {l: endpoints[l].get_shape() for l in endpoints}
+
+    super().__init__(
+        inputs=inputs, outputs=endpoints, trainable=trainable, **kwargs
+    )
+
+  def re_normalize(self, x: tf.Tensor) -> tf.Tensor:
+    """Re-normalizes the input image.
+
+    Tf-vision normalizes the images from [0, 255] to normal distribution. The
+    tf-hub models are usually normalized to [0.0, 1.0]. This function converts
+    the input image to proper scale.
+
+    Args:
+      x: The input image.
+
+    Returns:
+      The re-normalized image.
+    """
+    offset = tf.constant(preprocess_ops.MEAN_RGB)
+    scale = tf.constant(preprocess_ops.STDDEV_RGB)
+    x = x * scale + offset
+
+    if self._mean_rgb:
+      x -= self._mean_rgb
+    if self._stddev_rgb:
+      x /= self._stddev_rgb
+    return x
+
+  def get_config(self) -> Mapping[str, Any]:
+    config_dict = {
+        'handle': self._handle,
+        'trainable': self.trainable,
+        'mean_rgb': self._mean_rgb,
+        'stddev_rgb': self._stddev_rgb,
+        'kernel_regularizer': self._kernel_regularizer,
+        'signature': self._signature,
+        'output_key': self._output_key,
+    }
+    return config_dict
+
+  @classmethod
+  def from_config(cls,
+                  config: Mapping[str, Any],
+                  custom_objects: Optional[Any] = None) -> HubModel:
+    return cls(**config)
+
+  @property
+  def output_specs(self) -> Mapping[str, tf.TensorShape]:
+    """A dict of {level: TensorShape} pairs for the model output."""
+    return self._output_specs
+
+
+@factory.register_backbone_builder('hub_model')
+def build_hub_model(
+    input_specs: tf.keras.layers.InputSpec,
+    backbone_config: hyperparams.Config,
+    l2_regularizer: tf.keras.regularizers.Regularizer = None,
+    **kwargs: Any,
+) -> tf.keras.Model:  # pytype: disable=annotation-type-mismatch  # typed-keras
+  """Builds ResNet backbone from a config."""
+  del kwargs
+  backbone_type = backbone_config.type
+  backbone_cfg = backbone_config.get()
+  assert backbone_type == 'hub_model', (f'Inconsistent backbone type '
+                                        f'{backbone_type}')
+
+  return HubModel(
+      input_specs=input_specs,
+      handle=backbone_cfg.handle,
+      trainable=backbone_cfg.trainable,
+      mean_rgb=backbone_cfg.mean_rgb,
+      stddev_rgb=backbone_cfg.stddev_rgb,
+      kernel_regularizer=l2_regularizer,
+      signature=backbone_cfg.signature,
+      output_key=backbone_cfg.output_key,
+  )

community-content/vertex_vision_model_garden/model_oss/tfvision/registry_imports.py

@@ -0,0 +1,4 @@
+"""AutoML Vision tf-vision custom code import."""
+# pylint: disable=unused-import
+from tfvision import configs
+from tfvision.modeling import backbones

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/automl_constants.py

@@ -0,0 +1,25 @@
+"""AutoML tfvision saved_model constants."""
+
+# Tfvision training artifact marcos.
+# Exported parameter.yaml in the model directory.
+CFG_FILENAME = 'params.yaml'
+
+# Common automl saved_model marcos.
+## Type of input to automl saved_model, fixed as image bytes string.
+INPUT_TYPE = 'image_bytes'
+IMAGE_TENSOR = 'image_tensor'
+## Automl IOD saved_model signature input image argument name.
+IOD_INPUT_NAME = 'encoded_image'
+## ICN saved_model input name.
+ICN_INPUT_NAME = 'image_bytes'
+## Automl saved_model signature input key argument name.
+INPUT_KEY_NAME = 'key'
+OUTPUT_KEY_NAME = 'key'
+
+# IOD saved_model marcos.
+## IOD class as text output
+DETECTION_CLASSES_AS_TEXT = 'detection_classes_as_text'
+## Default value for labelmap text lookup table.
+LOOKUP_DEFAULT_VALUE = 'unknown'
+## Suffix for signature def without input key tensor.
+NO_KEY_SIG_DEF_SUFFIX = '_without_key'

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/detection.py

@@ -0,0 +1,475 @@
+"""Detection input and model functions for serving/inference."""
+
+import functools
+import heapq
+from typing import Any, Callable, Dict, List, Optional, Text
+
+import tensorflow as tf
+
+from tfvision.serving import automl_constants
+from object_detection.utils import label_map_util
+from official.core import config_definitions as cfg
+from official.projects.yolo.modeling import factory as yolo_factory
+from official.projects.yolo.modeling.decoders import yolo_decoder  # pylint: disable=unused-import
+from official.projects.yolo.serving import model_fn as yolo_model_fn
+from official.vision import configs
+from official.vision.ops import box_ops
+from official.vision.serving import detection as detection_module
+
+
+def load_label_map_to_string_list(label_map_path: str,
+                                  fill_in_gaps_and_background: bool = True
+                                 ) -> List[str]:
+  """Loads class labels as string list ordered by class id.
+
+  Args:
+    label_map_path: the path to label_map.pbtxt with string_int_label_map_pb2
+      proto format.
+    fill_in_gaps_and_background: whether to fill in gaps and background with
+      respect to the id field in the proto. The id: 0 is reserved for the
+      'background' class and will be added if it is missing. All other missing
+      ids in range(1, max(id)) will be added with a dummy class name
+      ("class_<id>") if they are missing.
+
+  Returns:
+    The class labels as text string lists in the order of the class numeric id.
+  """
+
+  labelmap = label_map_util.get_label_map_dict(
+      label_map_path, fill_in_gaps_and_background=fill_in_gaps_and_background)
+  heap = []
+  for label_name, label_id in labelmap.items():
+    heapq.heappush(heap, (label_id, label_name))
+  label_list = [heapq.heappop(heap)[1] for _ in range(len(heap))]
+
+  return label_list
+
+
+class DetectionModule(detection_module.DetectionModule):
+  """Detection Module."""
+
+  def __init__(self,
+               params: cfg.ExperimentConfig,
+               *,
+               batch_size: int,
+               input_image_size: List[int],
+               input_type: str = automl_constants.INPUT_TYPE,
+               num_channels: int = 3,
+               model: Optional[tf.keras.Model] = None,
+               label_map_path: Optional[str] = None,
+               input_name: str = automl_constants.IOD_INPUT_NAME,
+               key_name: str = automl_constants.INPUT_KEY_NAME):
+    """Initializes a module for export.
+
+    Args:
+      params: Experiment params.
+      batch_size: The batch size of the model input. Can be `int` or None.
+      input_image_size: List or Tuple of size of the input image. For 2D image,
+        it is [height, width].
+      input_type: The input signature type.
+      num_channels: The number of the image channels.
+      model: A tf.keras.Model instance to be exported.
+      label_map_path: A labelmap proto file path.
+      input_name: A customized input tensor name. This will be used as the
+        signature's input image argument name.
+      key_name: A name to the automl model input key.
+    """
+    self._key_name = key_name
+    if label_map_path is not None:
+      self._label_map_table = self._generate_label_map_list(label_map_path)
+    else:
+      self._label_map_table = None
+    super().__init__(
+        params=params,
+        model=model,
+        batch_size=batch_size,
+        input_image_size=input_image_size,
+        input_name=input_name,
+        input_type=input_type)
+
+  def _generate_label_map_list(self, label_map_path: str) -> tf.Tensor:
+    """Generates a list of label texts from a labelmap path."""
+    mapping_string = tf.convert_to_tensor(
+        load_label_map_to_string_list(label_map_path))
+    return tf.lookup.index_to_string_table_from_tensor(
+        mapping_string, default_value=automl_constants.LOOKUP_DEFAULT_VALUE)
+
+  def _generate_class_text_output(self, detection_classes) -> tf.Tensor:
+    """Converts class index to class text."""
+    if self._label_map_table is None:
+      raise ValueError('_label_map_table is None.')
+    indices = tf.cast(detection_classes, tf.int64)
+    indices = tf.reshape(indices, [-1])
+    values = self._label_map_table.lookup(indices)
+    return tf.reshape(
+        values, [-1, tf.array_ops.shape(detection_classes)[1]],
+        name=automl_constants.DETECTION_CLASSES_AS_TEXT)
+
+  def serve(self,
+            images: tf.Tensor,
+            key: Optional[tf.Tensor] = None) -> Dict[Text, tf.Tensor]:
+    """Cast image to float and run inference.
+
+    Args:
+      images: uint8 Tensor of input images. For input type image tensor, the
+        shape is [batch_size, None, None, 3], for image_bytes, the shape is
+        [batch_size].
+      key: Optional string Tensor of shape [batch_size]. If not provided
+        output tensors will not contain it as well.
+
+    Returns:
+      Tensor holding detection output logits.
+    """
+
+    images, anchor_boxes, image_info = self.preprocess(images)
+    input_image_shape = image_info[:, 1, :]
+
+    # To overcome keras.Model extra limitation to save a model with layers that
+    # have multiple inputs, we use `model.call` here to trigger the forward
+    # path. Note that, this disables some keras magics happens in `__call__`.
+    detections = self.model.call(
+        images=images,
+        image_shape=input_image_shape,
+        anchor_boxes=anchor_boxes,
+        training=False)
+
+    if self.params.task.model.detection_generator.apply_nms:
+      # For RetinaNet model, apply export_config.
+      if isinstance(self.params.task.model, configs.retinanet.RetinaNet):
+        export_config = self.params.task.export_config
+        # Normalize detection box coordinates to [0, 1].
+        if export_config.output_normalized_coordinates:
+          detection_boxes = (
+              detections['detection_boxes'] /
+              tf.tile(image_info[:, 2:3, :], [1, 1, 2]))
+          detections['detection_boxes'] = box_ops.normalize_boxes(
+              detection_boxes, image_info[:, 0:1, :])
+
+        # Cast num_detections and detection_classes to float. This allows the
+        # model inference to work on chain (go/chain) as chain requires floating
+        # point outputs.
+        if export_config.cast_num_detections_to_float:
+          detections['num_detections'] = tf.cast(
+              detections['num_detections'], dtype=tf.float32)
+        if export_config.cast_detection_classes_to_float:
+          detections['detection_classes'] = tf.cast(
+              detections['detection_classes'], dtype=tf.float32)
+
+      final_outputs = {
+          'detection_boxes': detections['detection_boxes'],
+          'detection_scores': detections['detection_scores'],
+          'detection_classes': detections['detection_classes'],
+          'num_detections': detections['num_detections']
+      }
+    else:
+      final_outputs = {
+          'decoded_boxes': detections['decoded_boxes'],
+          'decoded_box_scores': detections['decoded_box_scores']
+      }
+
+    if 'detection_masks' in detections.keys():
+      final_outputs['detection_masks'] = detections['detection_masks']
+
+    # Adding AutoML specific outputs.
+    if self._label_map_table is not None:
+      final_outputs.update({
+          automl_constants.DETECTION_CLASSES_AS_TEXT:
+              self._generate_class_text_output(detections['detection_classes'])
+      })
+
+    final_outputs.update({'image_info': image_info})
+    if key is not None:
+      final_outputs.update({automl_constants.OUTPUT_KEY_NAME: key})
+
+    return final_outputs
+
+  @tf.function
+  def inference_from_image_bytes(
+      self,
+      inputs: tf.Tensor,
+      key: tf.Tensor,
+  ) -> Dict[Text, tf.Tensor]:
+    """Entry point for model input.
+
+    Raw image tensor will be decoded to the desired image format.
+
+    Args:
+      inputs: Image tensor to be feed to the model.
+      key: AutoML specific input key to track image names or image ids.
+
+    Returns:
+      A dictionary of Tensor that contains model outputs.
+    """
+    with tf.device('cpu:0'):
+      images = tf.nest.map_structure(
+          tf.identity,
+          tf.map_fn(
+              self._decode_image,
+              elems=inputs,
+              fn_output_signature=tf.TensorSpec(
+                  shape=[None] * len(self._input_image_size) +
+                  [self._num_channels],
+                  dtype=tf.uint8),
+              parallel_iterations=32))
+      images = tf.stack(images)
+
+    return self.serve(images, key)
+
+  @tf.function
+  def inference_from_image_bytes_wo_key(
+      self, inputs: tf.Tensor) -> Dict[Text, tf.Tensor]:
+    """Entry point for model inference without input key tensor.
+
+    Raw image tensor will be decoded to the desired image format.
+
+    Args:
+      inputs: Image tensor to be feed to the model.
+
+    Returns:
+      A dictionary of Tensor that contains model outputs.
+    """
+    with tf.device('cpu:0'):
+      images = tf.nest.map_structure(
+          tf.identity,
+          tf.map_fn(
+              self._decode_image,
+              elems=inputs,
+              fn_output_signature=tf.TensorSpec(
+                  shape=[None] * len(self._input_image_size) +
+                  [self._num_channels],
+                  dtype=tf.uint8),
+              parallel_iterations=32))
+      images = tf.stack(images)
+
+    return self.serve(images)
+
+  def get_inference_signatures(
+      self, function_keys: Dict[Text, Text]
+  ) -> Dict[Text, Callable[[tf.Tensor, tf.Tensor], Dict[Text, tf.Tensor]]]:
+    """Gets defined function signatures.
+
+    Args:
+      function_keys: A dictionary with keys as the function to create signature
+        for and values as the signature keys when returns.
+
+    Returns:
+      A dictionary with key as signature key and value as concrete functions
+        that can be used for tf.saved_model.save.
+    """
+    signatures = {}
+    for key, def_name in function_keys.items():
+      # Adds input string 'key' to image_bytes input type.
+      if key == automl_constants.INPUT_TYPE:
+        input_images = tf.TensorSpec(
+            shape=[self._batch_size], dtype=tf.string, name=self._input_name)
+        input_key = tf.TensorSpec(
+            shape=[self._batch_size], dtype=tf.string, name=self._key_name)
+        signatures[
+            def_name] = self.inference_from_image_bytes.get_concrete_function(
+                input_images, input_key)
+        # For each input type, create a signature without input key tensor.
+        def_name_wo_key = def_name + automl_constants.NO_KEY_SIG_DEF_SUFFIX
+        signatures[def_name_wo_key] = (
+            self.inference_from_image_bytes_wo_key.get_concrete_function(
+                input_images))
+      else:
+        raise ValueError('Unrecognized `input_type`')
+    return signatures
+
+
+class YoloDetectionModule(DetectionModule):
+  """Yolo detection module for Model Garden."""
+
+  def __init__(
+      self,
+      params: cfg.ExperimentConfig,
+      *,
+      batch_size: int,
+      input_image_size: List[int],
+      preprocessor: Callable[..., Any],
+      inference_step: Callable[..., Any],
+      input_type: str = automl_constants.INPUT_TYPE,
+      num_channels: int = 3,
+      model: Optional[tf.keras.Model] = None,
+      label_map_path: Optional[str] = None,
+      input_name: str = automl_constants.IOD_INPUT_NAME,
+      key_name: str = automl_constants.INPUT_KEY_NAME,
+  ):
+    """Initializes a module for export.
+
+    Args:
+      params: Experiment params.
+      batch_size: The batch size of the model input. Can be `int` or None.
+      input_image_size: List or Tuple of size of the input image. For 2D image,
+        it is [height, width].
+      preprocessor: An optional callable to preprocess the inputs.
+      inference_step: An optional callable to forward-pass the model.
+      input_type: The input signature type.
+      num_channels: The number of the image channels.
+      model: A tf.keras.Model instance to be exported.
+      label_map_path: A labelmap proto file path.
+      input_name: A customized input tensor name. This will be used as the
+        signature's input image argument name.
+      key_name: A name to the automl model input key.
+    """
+    super().__init__(
+        params=params,
+        batch_size=batch_size,
+        input_image_size=input_image_size,
+        input_type=input_type,
+        num_channels=num_channels,
+        model=model,
+        label_map_path=label_map_path,
+        input_name=input_name,
+        key_name=key_name,
+    )
+
+    self.preprocessor = preprocessor
+    self.inference_step = functools.partial(inference_step, model=self.model)
+
+  def preprocess(self, images: tf.Tensor) -> None:
+    raise NotImplementedError('Use self.preprocessor instead.')
+
+  def serve(
+      self, images: tf.Tensor, key: Optional[tf.Tensor] = None
+  ) -> Dict[Text, tf.Tensor]:
+    """Cast image to float and run inference.
+
+    Args:
+      images: uint8 Tensor of input images. For input type image tensor, the
+        shape is [batch_size, None, None, 3], for image_bytes, the shape is
+        [batch_size].
+      key: Optional string Tensor of shape [batch_size]. If not provided output
+        tensors will not contain it as well.
+
+    Returns:
+      Tensor holding detection output logits.
+    """
+    images, image_info = self.preprocessor(images)
+    final_outputs = self.inference_step((images, image_info))
+
+    # Normalize detection box coordinates to [0, 1].
+    detection_boxes = final_outputs['detection_boxes'] / tf.tile(
+        image_info[:, 2:3, :], [1, 1, 2]
+    )
+    final_outputs['detection_boxes'] = box_ops.normalize_boxes(
+        detection_boxes, image_info[:, 0:1, :]
+    )
+
+    # Cast num_detections and detection_classes to float. This allows the
+    # model inference to work on chain (go/chain) as chain requires floating
+    # point outputs.
+    final_outputs['num_detections'] = tf.cast(
+        final_outputs['num_detections'], dtype=tf.float32
+    )
+    final_outputs['detection_classes'] = tf.cast(
+        final_outputs['detection_classes'], dtype=tf.float32
+    )
+
+    # Adding AutoML specific outputs.
+    if self._label_map_table is not None:
+      final_outputs.update(
+          {
+              automl_constants.DETECTION_CLASSES_AS_TEXT: (
+                  self._generate_class_text_output(
+                      final_outputs['detection_classes']
+                  )
+              )
+          }
+      )
+
+    final_outputs.update({'image_info': image_info})
+    if key is not None:
+      final_outputs.update({automl_constants.OUTPUT_KEY_NAME: key})
+
+    return final_outputs
+
+
+def create_yolov7_export_module(
+    params: cfg.ExperimentConfig,
+    input_type: str,
+    batch_size: int,
+    input_image_size: List[int],
+    num_channels: int = 3,
+    input_name: Optional[str] = None,
+    label_map_path: Optional[str] = None,
+) -> YoloDetectionModule:
+  """Creates YOLO export module for Model Garden."""
+  input_specs = tf.keras.layers.InputSpec(
+      shape=[batch_size] + input_image_size + [num_channels]
+  )
+  model = yolo_factory.build_yolov7(
+      input_specs=input_specs,
+      model_config=params.task.model,
+      l2_regularization=None,
+  )
+
+  def preprocess_fn(image_tensor):
+    def normalize_image_fn(inputs):
+      image = tf.cast(inputs, dtype=tf.float32)
+      return image / 255.0
+
+    # If input_type is `tflite`, do not apply image preprocessing. Only apply
+    # normalization.
+    if input_type == 'tflite':
+      return normalize_image_fn(image_tensor), None
+
+    def preprocess_image_fn(inputs):
+      image = normalize_image_fn(inputs)
+      (image, image_info) = yolo_model_fn.letterbox(
+          image,
+          input_image_size,
+          letter_box=params.task.validation_data.parser.letter_box,
+      )
+      return image, image_info
+
+    images_spec = tf.TensorSpec(shape=input_image_size + [3], dtype=tf.float32)
+
+    image_info_spec = tf.TensorSpec(shape=[4, 2], dtype=tf.float32)
+
+    images, image_info = tf.nest.map_structure(
+        tf.identity,
+        tf.map_fn(
+            preprocess_image_fn,
+            elems=image_tensor,
+            fn_output_signature=(images_spec, image_info_spec),
+            parallel_iterations=32,
+        ),
+    )
+
+    return images, image_info
+
+  def inference_steps(inputs, model):
+    images, image_info = inputs
+    detection = model.call(images, training=False)
+    if input_type != 'tflite':
+      detection['bbox'] = yolo_model_fn.undo_info(
+          detection['bbox'],
+          detection['num_detections'],
+          image_info,
+          expand=False,
+      )
+
+    final_outputs = {
+        'detection_boxes': detection['bbox'],
+        'detection_scores': detection['confidence'],
+        'detection_classes': detection['classes'],
+        'num_detections': detection['num_detections'],
+    }
+
+    return final_outputs
+
+  export_module = YoloDetectionModule(
+      params=params,
+      model=model,
+      batch_size=batch_size,
+      input_image_size=input_image_size,
+      input_type=input_type,
+      num_channels=num_channels,
+      input_name=input_name,
+      label_map_path=label_map_path,
+      preprocessor=preprocess_fn,
+      inference_step=inference_steps,
+  )
+
+  return export_module

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/export_oss_saved_model.py

@@ -0,0 +1,293 @@
+"""Export OSS TfVision models."""
+import os
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from google.cloud import aiplatform as aip
+
+# pylint: disable=line-too-long,unused-import
+from tfvision import registry_imports as vision_registry_imports
+from tfvision.serving import automl_constants
+from tfvision.serving import export_oss_saved_model_lib as export_automl_oss_saved_model_lib
+from util import constants
+from official.core import exp_factory
+from official.modeling import hyperparams
+from official.projects.maxvit import registry_imports as maxvit_imports
+from official.projects.yolo.common import registry_imports as yolo_imports
+from official.vision import registry_imports
+from official.vision.serving import export_saved_model_lib as export_oss_saved_model_lib
+# pylint: enable=line-too-long, unused-import
+
+_PARAMS_OVERRIDE_IOD = """
+task:
+  export_config:
+    output_normalized_coordinates: true
+    cast_num_detections_to_float: true
+    cast_detection_classes_to_float: true
+  model:
+    detection_generator:
+      nms_version: batched"""
+
+_PARAMS_OVERRIDE_YOLO = """
+task:
+  export_config:
+    output_normalized_coordinates: true
+    cast_num_detections_to_float: true
+    cast_detection_classes_to_float: true
+  model:
+    detection_generator:
+      nms_version: v2"""
+
+
+_PARAMS_OVERRIDE_ISG = """
+task:
+  export_config:
+    rescale_output: true"""
+
+_YOLO_KEY = 'yolo'
+
+_OBJECTIVE = flags.DEFINE_enum(
+    'objective',
+    None,
+    [
+        constants.OBJECTIVE_IMAGE_CLASSIFICATION,
+        constants.OBJECTIVE_IMAGE_OBJECT_DETECTION,
+        constants.OBJECTIVE_IMAGE_SEGMENTATION,
+    ],
+    'The objective of this training job.',
+)
+
+# Cloud AI platform HPT related parameter
+_PROJECT_NAME = flags.DEFINE_string(
+    'project_name', None, 'Training vizier study name.'
+)
+_LOCATION = flags.DEFINE_string('location', None, 'Vizier study owner.')
+_HPT_JOB_ID = flags.DEFINE_string('hpt_job_id', None, 'HPT job id.')
+_HPT_RESULT_DIR = flags.DEFINE_string(
+    'hpt_result_dir', None, 'HPT job result directory.'
+)
+_USE_BIGSTORE = flags.DEFINE_bool(
+    'use_bigstore', None, 'Whether to use bigstore in hub model path.'
+)
+
+# TfVision related inputs.
+_EXPERIMENT = flags.DEFINE_string(
+    'experiment', None, 'experiment type, e.g. retinanet_resnetfpn_coco')
+_EXPORT_DIR = flags.DEFINE_string('export_dir', None, 'The export directory.')
+_CHECKPOINT_PATH = flags.DEFINE_string('checkpoint_path', None,
+                                       'Checkpoint path.')
+_LABEL_MAP_PATH = flags.DEFINE_string('label_map_path', None,
+                                      'Path to the labelmap proto file.')
+_LABEL_PATH = flags.DEFINE_string(
+    'label_path', None, 'Path to the image classification label file.')
+_CONFIG_FILE = flags.DEFINE_multi_string(
+    'config_file',
+    default=None,
+    help=(
+        'YAML/JSON files which specifies overrides. The override order follows'
+        ' the order of args. Note that each file can be used as an override'
+        ' template to override the default parameters specified in Python. If'
+        ' the same parameter is specified in both `--config_file`.'
+    ),
+)
+_INPUT_IMAGE_SIZE = flags.DEFINE_string(
+    'input_image_size', '224,224',
+    'The comma-separated string of two integers representing the height,width '
+    'of the input to the model.')
+
+# Fixed inputs.
+_IMAGE_TYPE = flags.DEFINE_string(
+    'input_type',
+    'image_bytes',
+    'One of `image_tensor`, `image_bytes`, `tf_example` and `tflite`.',
+)
+_EXPORT_SAVED_MODEL_SUBDIR = flags.DEFINE_string(
+    'export_saved_model_subdir', 'saved_model',
+    'The subdirectory for saved model.')
+_BATCH_SIZE = flags.DEFINE_integer('batch_size', 1, 'The batch size.')
+_INPUT_NAME = flags.DEFINE_string(
+    'input_name',
+    'encoded_image',
+    (
+        'Input tensor name in signature def. Default at None which'
+        'produces input tensor name `inputs`.'
+    ),
+)
+_MAX_TRIAL_COUNT = flags.DEFINE_integer(
+    'max_trial_count', None, 'The desired total number of trials.'
+)
+_EVALUATION_METRIC = flags.DEFINE_string(
+    'evaluation_metric',
+    None,
+    'The evaluation metric to use (e.g. accuracy).',
+)
+
+
+def change_handle(params: hyperparams.ParamsDict) -> hyperparams.ParamsDict:
+  """Changes the prefix of the `handle` path in the `model.backbone.hub_model` sub-dictionary from gs:// to /bigstore/.
+
+  Args:
+    params: hyperparams.ParamsDict object containing experiment config
+      information.
+
+  Returns:
+    params: hyperparams.ParamsDict.
+  """
+
+  params.task.model.backbone.hub_model.handle = (
+      params.task.model.backbone.hub_model.handle.replace(
+          'gs://', '/bigstore/', 1
+      )
+  )
+
+  return params
+
+
+def get_best_hpt_trials(
+    project: str, location: str, hpt_job_id: str, hpt_result_dir: str
+) -> str:
+  """Select best trials by cloud ai platorm hyperparameter tuning.
+
+  Args:
+   project: GCP project name.
+   location: Hyperparameter job location.
+   hpt_job_id: Hyperparameter job id.
+   hpt_result_dir: HPT job result GCS directory.
+
+  Returns:
+    Trial Id of the best performing trial.
+  """
+
+  aip.init(project=project, location=location)
+  job_response = aip.HyperparameterTuningJob.get(resource_name=hpt_job_id)
+  max_value = -1
+  best_trial_id = -1
+  trials = list(job_response._gca_resource.trials)  # pylint: disable=protected-access
+  for trial in trials:
+    if trial.final_measurement.metrics[0].metric_id != constants.HP_METRIC_TAG:
+      continue
+    if trial.final_measurement.metrics[0].value > max_value:
+      best_trial_id = trial.id
+      max_value = trial.final_measurement.metrics[0].value
+  if best_trial_id == -1:
+    raise ValueError('No valid completed trials.')
+  best_model_dir = os.path.join(
+      hpt_result_dir, constants.TRIAL_PREFIX + str(best_trial_id)
+  )
+  logging.info(
+      'Best model directory: %s with performance: %s', best_model_dir, max_value
+  )
+  return best_model_dir
+
+
+def main(_) -> None:
+  if (
+      _MAX_TRIAL_COUNT.present
+      and _EVALUATION_METRIC.present
+      and _CONFIG_FILE.present
+  ):
+    best_ckpt_dir, _ = export_automl_oss_saved_model_lib.get_best_oss_trial(
+        _CHECKPOINT_PATH.value, _MAX_TRIAL_COUNT.value, _EVALUATION_METRIC.value
+    )
+    config_filepath = _CONFIG_FILE.value
+  elif _CHECKPOINT_PATH.present and _CONFIG_FILE.present:
+    best_ckpt_dir = _CHECKPOINT_PATH.value
+    config_filepath = _CONFIG_FILE.value
+  elif (
+      _PROJECT_NAME.present
+      and _LOCATION.present
+      and _HPT_JOB_ID.present
+      and _HPT_RESULT_DIR.present
+  ):
+    # Reads HPT results by project and location and hpt_job_id.
+    best_ckpt_dir = get_best_hpt_trials(
+        _PROJECT_NAME.value,
+        _LOCATION.value,
+        _HPT_JOB_ID.value,
+        _HPT_RESULT_DIR.value,
+    )
+    config_filepath = [
+        os.path.join(best_ckpt_dir, automl_constants.CFG_FILENAME)
+    ]
+  else:
+    raise ValueError('No checkpoint path or HTP Job parameters given.')
+
+  params = exp_factory.get_exp_config(_EXPERIMENT.value)
+  for config_file in config_filepath or []:
+    params = hyperparams.override_params_dict(
+        params, config_file, is_strict=False
+    )
+  if _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION:
+    if _YOLO_KEY in _EXPERIMENT.value:
+      params = hyperparams.override_params_dict(
+          params, _PARAMS_OVERRIDE_YOLO, is_strict=False
+      )
+    else:
+      params = hyperparams.override_params_dict(
+          params, _PARAMS_OVERRIDE_IOD, is_strict=False
+      )
+  elif _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_SEGMENTATION:
+    params = hyperparams.override_params_dict(
+        params, _PARAMS_OVERRIDE_ISG, is_strict=True
+    )
+
+  if _USE_BIGSTORE.value:
+    params = change_handle(params)
+
+  params.validate()
+  params.lock()
+
+  if best_ckpt_dir and not best_ckpt_dir.endswith(
+      params.trainer.best_checkpoint_export_subdir
+  ):
+    best_ckpt_dir = os.path.join(
+        best_ckpt_dir, params.trainer.best_checkpoint_export_subdir
+    )
+
+  if (
+      _LABEL_MAP_PATH.value
+      or _LABEL_PATH.value
+      or _OBJECTIVE.value == constants.OBJECTIVE_IMAGE_SEGMENTATION
+  ):
+    export_automl_oss_saved_model_lib.export_inference_graph(
+        input_type=_IMAGE_TYPE.value,
+        batch_size=_BATCH_SIZE.value,
+        input_image_size=[int(x) for x in _INPUT_IMAGE_SIZE.value.split(',')],
+        params=params,
+        checkpoint_path=best_ckpt_dir,
+        label_map_path=_LABEL_MAP_PATH.value,
+        label_path=_LABEL_PATH.value,
+        export_dir=_EXPORT_DIR.value,
+        export_saved_model_subdir=_EXPORT_SAVED_MODEL_SUBDIR.value,
+        input_name=_INPUT_NAME.value,
+        objective=_OBJECTIVE.value,
+    )
+  elif _YOLO_KEY in _EXPERIMENT.value:
+    export_automl_oss_saved_model_lib.export_inference_graph(
+        input_type=_IMAGE_TYPE.value,
+        batch_size=_BATCH_SIZE.value,
+        input_image_size=[int(x) for x in _INPUT_IMAGE_SIZE.value.split(',')],
+        params=params,
+        checkpoint_path=best_ckpt_dir,
+        export_dir=_EXPORT_DIR.value,
+        export_saved_model_subdir=_EXPORT_SAVED_MODEL_SUBDIR.value,
+        input_name=_INPUT_NAME.value,
+        objective=_OBJECTIVE.value,
+    )
+  else:
+    export_oss_saved_model_lib.export_inference_graph(
+        input_type=_IMAGE_TYPE.value,
+        batch_size=_BATCH_SIZE.value,
+        input_image_size=[int(x) for x in _INPUT_IMAGE_SIZE.value.split(',')],
+        params=params,
+        checkpoint_path=best_ckpt_dir,
+        export_dir=_EXPORT_DIR.value,
+        export_saved_model_subdir=_EXPORT_SAVED_MODEL_SUBDIR.value,
+        input_name=_INPUT_NAME.value,
+    )
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/export_oss_saved_model_lib.py

@@ -0,0 +1,169 @@
+r"""Vision models export utility function for serving/inference."""
+
+import json
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import tensorflow as tf
+
+from tfvision.serving import detection
+from tfvision.serving import image_classification
+from tfvision.serving import semantic_segmentation_export_module_lib as isg_export_lib
+from util import constants
+from official.core import config_definitions as cfg
+from official.core import export_base
+from official.projects.yolo.configs import yolo as yolo_config
+from official.projects.yolo.configs import yolov7 as yolov7_config
+from official.projects.yolo.serving import export_module_factory as yolo_export_module_factory
+
+
+def export_inference_graph(
+    input_type: str,
+    batch_size: Optional[int],
+    input_image_size: List[int],
+    params: cfg.ExperimentConfig,
+    checkpoint_path: str,
+    export_dir: str,
+    label_map_path: Optional[str] = None,
+    label_path: Optional[str] = None,
+    num_channels: Optional[int] = 3,
+    export_module: Optional[export_base.ExportModule] = None,
+    export_saved_model_subdir: Optional[str] = None,
+    save_options: Optional[tf.saved_model.SaveOptions] = None,
+    checkpoint: Optional[tf.train.Checkpoint] = None,
+    input_name: Optional[str] = None,
+    function_keys: Optional[Union[List[str], Dict[str, str]]] = None,
+    objective: Optional[str] = None,
+):
+  """Exports inference graph for the model specified in the exp config.
+
+  Saved model is stored at export_dir/saved_model, checkpoint is saved
+  at export_dir/checkpoint, and params is saved at export_dir/params.yaml.
+
+  Args:
+    input_type: Input type must be `image_bytes`.
+    batch_size: 'int', or None.
+    input_image_size: List or Tuple of height and width.
+    params: Experiment params.
+    checkpoint_path: Trained checkpoint path or directory.
+    export_dir: CNS export directory path.
+    label_map_path: Labelmap proto file path.
+    label_path: Label file path.
+    num_channels: The number of input image channels.
+    export_module: Optional export module to be used instead of using params to
+      create one. If None, the params will be used to create an export module.
+    export_saved_model_subdir: Optional subdirectory under export_dir to store
+      saved model.
+    save_options: `SaveOptions` for `tf.saved_model.save`.
+    checkpoint: An optional tf.train.Checkpoint. If provided, the export module
+      will use it to read the weights.
+    input_name: The input tensor name, default at `None` which produces input
+      tensor name `inputs`.
+    function_keys: a list of string keys to retrieve pre-defined serving
+      signatures. The signaute keys will be set with defaults. If a dictionary
+      is provided, the values will be used as signature keys.
+    objective: The objective of the training job.
+  """
+  if export_saved_model_subdir:
+    output_saved_model_directory = os.path.join(export_dir,
+                                                export_saved_model_subdir)
+  else:
+    output_saved_model_directory = export_dir
+
+  if not export_module:
+    if objective == constants.OBJECTIVE_IMAGE_CLASSIFICATION:
+      export_module = image_classification.ClassificationModule(
+          params=params,
+          batch_size=batch_size,
+          input_image_size=input_image_size,
+          input_type=input_type,
+          num_channels=num_channels,
+          input_name=input_name,
+          label_path=label_path,
+      )
+    elif objective == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION:
+      # If experiment is YOLO object detection, loads Yolo detection module.
+      if isinstance(
+          params.task, (yolo_config.YoloTask, yolov7_config.YoloV7Task)
+      ):
+        export_module = yolo_export_module_factory.get_export_module(
+            params=params,
+            batch_size=batch_size,
+            input_image_size=input_image_size,
+            input_type=input_type,
+            num_channels=num_channels,
+            input_name=input_name,
+        )
+      else:
+        export_module = detection.DetectionModule(
+            params=params,
+            batch_size=batch_size,
+            input_image_size=input_image_size,
+            input_type=input_type,
+            num_channels=num_channels,
+            input_name=input_name,
+            label_map_path=label_map_path,
+        )
+    elif objective == constants.OBJECTIVE_IMAGE_SEGMENTATION:
+      export_module = isg_export_lib.OssSegmentationModule(
+          params=params,
+          batch_size=batch_size,
+          input_image_size=input_image_size,
+          input_type=input_type,
+          num_channels=num_channels,
+          input_name=input_name,
+      )
+    else:
+      raise ValueError(
+          'Export module not implemented for objective {}.'.format(objective)
+      )
+
+  export_base.export(
+      export_module,
+      function_keys=function_keys if function_keys else [input_type],
+      export_savedmodel_dir=output_saved_model_directory,
+      checkpoint=checkpoint,
+      checkpoint_path=checkpoint_path,
+      timestamped=False,
+      save_options=save_options)
+
+
+def get_best_oss_trial(
+    model_dir: str, max_trial_count: int, evaluation_metric: str
+) -> Tuple[str, Dict[str, Any]]:
+  """Export models from TF checkpoints to TF saved model format.
+
+  Args:
+    model_dir: Path of directory to store checkpoints and metric summaries.
+    max_trial_count: The desired total number of trials.
+    evaluation_metric: The evaluation metric to use (ie. accuracy).
+
+  Returns:
+  """
+  best_trial_dir = ''
+  best_trial_evaluation_results = {}
+  best_performance = -1
+  trial_file_count = 0
+  for i in range(max_trial_count):
+    current_trial = i + 1
+    current_trial_dir = os.path.join(model_dir, 'trial_' + str(current_trial))
+    current_trial_best_ckpt_dir = os.path.join(current_trial_dir, 'best_ckpt')
+    current_trial_best_ckpt_evaluation_filepath = os.path.join(
+        current_trial_best_ckpt_dir, 'info.json'
+    )
+    if tf.io.gfile.exists(current_trial_best_ckpt_evaluation_filepath):
+      trial_file_count += 1
+      with tf.io.gfile.GFile(
+          current_trial_best_ckpt_evaluation_filepath, 'rb'
+      ) as f:
+        eval_metric_results = json.load(f)
+        current_performance = eval_metric_results[evaluation_metric]
+        if current_performance > best_performance:
+          best_performance = current_performance
+          best_trial_dir = current_trial_dir
+          best_trial_evaluation_results = eval_metric_results
+
+  if not trial_file_count:
+    raise ValueError('None of the best checkpoint paths exist.')
+
+  return best_trial_dir, best_trial_evaluation_results

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/image_classification.py

@@ -0,0 +1,159 @@
+"""Image classification input and model functions for serving/inference."""
+
+from typing import Callable, List, Mapping, Optional
+
+import tensorflow as tf
+from tensorflow.io import gfile
+
+from tfvision.serving import automl_constants
+from official.core import config_definitions as cfg
+from official.vision.serving import image_classification
+
+
+class ClassificationModule(image_classification.ClassificationModule):
+  """classification Module."""
+
+  def __init__(self,
+               params: cfg.ExperimentConfig,
+               *,
+               batch_size: Optional[int] = None,
+               input_image_size: List[int],
+               input_type: str = automl_constants.INPUT_TYPE,
+               num_channels: int = 3,
+               model: Optional[tf.keras.Model] = None,
+               input_name: str = automl_constants.ICN_INPUT_NAME,
+               label_path: Optional[str] = None,
+               key_name: str = automl_constants.INPUT_KEY_NAME):
+    """Initializes a module for export.
+
+    Args:
+      params: Experiment params.
+      batch_size: The batch size of the model input. Can be `int` or None.
+      input_image_size: List or Tuple of size of the input image. For 2D image,
+        it is [height, width].
+      input_type: The input signature type.
+      num_channels: The number of the image channels.
+      model: A tf.keras.Model instance to be exported.
+      input_name: A customized input tensor name.
+      label_path: A label file path.
+      key_name: A name to the automl model input key.
+    """
+    super().__init__(
+        params=params,
+        model=model,
+        batch_size=batch_size,
+        input_image_size=input_image_size,
+        num_channels=num_channels,
+        input_name=input_name,
+        input_type=input_type,
+    )
+
+    self._key_name = key_name
+    if label_path is not None:
+      self._label = self._read_label(label_path)
+    else:
+      self._label = None
+
+  def _read_label(self, label_path: str) -> tf.Tensor:
+    """Reads the labels from a label file."""
+    with gfile.GFile(label_path, 'r') as f:
+      labels = [i.strip() for i in f.readlines()]
+    labels = tf.convert_to_tensor([labels])
+    return labels
+
+  def serve(self, images: tf.Tensor, key: tf.Tensor) -> Mapping[str, tf.Tensor]:
+    """Cast image to float and run inference.
+
+    Args:
+      images: uint8 Tensor of shape [batch_size, None, None, 3]
+      key: string Tensor of shape [batch_size].
+
+    Returns:
+      Dictionary holding classification outputs.
+    """
+    with tf.device('cpu:0'):
+      images = tf.cast(images, dtype=tf.float32)
+
+      images = tf.nest.map_structure(
+          tf.identity,
+          tf.map_fn(
+              self._build_inputs,
+              elems=images,
+              fn_output_signature=tf.TensorSpec(
+                  shape=self._input_image_size + [3], dtype=tf.float32),
+              parallel_iterations=32))
+
+    logits = self.inference_step(images)
+    if self.params.task.train_data.is_multilabel:
+      probs = tf.math.sigmoid(logits)
+    else:
+      probs = tf.nn.softmax(logits)
+
+    outputs = {'scores': probs, automl_constants.OUTPUT_KEY_NAME: key}
+    if self._label is not None:
+      outputs['labels'] = tf.tile(self._label, [tf.shape(images)[0], 1])
+    return outputs
+
+  @tf.function
+  def inference_from_image_bytes(self, inputs: tf.Tensor,
+                                 key: tf.Tensor) -> Mapping[str, tf.Tensor]:
+    with tf.device('cpu:0'):
+      images = tf.nest.map_structure(
+          tf.identity,
+          tf.map_fn(
+              self._decode_image,
+              elems=inputs,
+              fn_output_signature=tf.TensorSpec(
+                  shape=[None] * len(self._input_image_size) +
+                  [self._num_channels],
+                  dtype=tf.uint8),
+              parallel_iterations=32))
+      images = tf.stack(images)
+    return self.serve(images, key)
+
+  @tf.function
+  def inference_from_image_tensors(
+      self, inputs: tf.Tensor
+  ) -> Mapping[str, tf.Tensor]:
+    return self.serve(inputs, tf.zeros(tf.shape(inputs)[0], dtype=tf.string))
+
+  def get_inference_signatures(
+      self, function_keys: Mapping[str, str]
+  ) -> Mapping[str, Callable[[tf.Tensor, tf.Tensor], Mapping[str, tf.Tensor]]]:
+    """Gets defined function signatures.
+
+    Args:
+      function_keys: A dictionary with keys as the function to create signature
+        for and values as the signature keys when returns.
+
+    Returns:
+      A dictionary with key as signature key and value as concrete functions
+        that can be used for tf.saved_model.save.
+    """
+    signatures = {}
+    for key, def_name in function_keys.items():
+      # Adds input string 'key' to image_bytes input type.
+      if key == automl_constants.INPUT_TYPE:
+        input_images = tf.TensorSpec(
+            shape=[self._batch_size], dtype=tf.string, name=self._input_name)
+        input_key = tf.TensorSpec(
+            shape=[self._batch_size], dtype=tf.string, name=self._key_name)
+        signatures[
+            def_name] = self.inference_from_image_bytes.get_concrete_function(
+                input_images, input_key)
+      elif key == automl_constants.IMAGE_TENSOR:
+        input_signature = tf.TensorSpec(
+            shape=[self._batch_size]
+            + [None] * len(self._input_image_size)
+            + [self._num_channels],
+            dtype=tf.uint8,
+            name=self._input_name,
+        )
+        signatures[def_name] = (
+            self.inference_from_image_tensors.get_concrete_function(
+                input_signature
+            )
+        )
+      else:
+        raise ValueError('Unrecognized `input_type`')
+    return signatures

community-content/vertex_vision_model_garden/model_oss/tfvision/serving/semantic_segmentation_export_module_lib.py

@@ -0,0 +1,52 @@
+"""Semantic segmentation input and model functions for serving/inference."""
+
+
+import tensorflow as tf
+
+from official.vision.serving import semantic_segmentation
+
+
+class OssSegmentationModule(semantic_segmentation.SegmentationModule):
+  """OSS Segmentation Module."""
+
+  def serve(self, images):
+    """Cast image to float and run inference.
+
+    Overrides the method in the super class, and changes the output format.
+
+    Args:
+      images: uint8 Tensor of shape [batch_size, None, None, 3]
+
+    Returns:
+      Dict containing the following key value pairs:
+        category_bytes: Encoded PNG image of grayscale output categories.
+        score_bytes: Encoded PNG image of grayscale probability scores mapped to
+          [0, 255].
+    """
+    result = super().serve(images)
+    logits = result['logits']
+
+    probabilities = tf.nn.softmax(logits)
+    scores = tf.reduce_max(probabilities, 3, keepdims=True)
+    scores = tf.cast(tf.minimum(scores * 255.0, 255), dtype=tf.uint8)
+
+    categories = tf.cast(
+        tf.expand_dims(tf.argmax(logits, 3), -1), dtype=tf.int32
+    )
+
+    score_bytes = tf.map_fn(
+        tf.image.encode_png, scores, back_prop=False, dtype=tf.string
+    )
+    category_bytes = tf.map_fn(
+        tf.image.encode_png,
+        tf.cast(categories, dtype=tf.uint8),
+        back_prop=False,
+        dtype=tf.string,
+    )
+
+    outputs = {
+        'category_bytes': tf.identity(category_bytes, name='category_bytes'),
+        'score_bytes': tf.identity(score_bytes, name='score_bytes'),
+    }
+
+    return outputs

community-content/vertex_vision_model_garden/model_oss/tfvision/train_hpt_oss.py

@@ -0,0 +1,385 @@
+"""TensorFlow Model Garden Vision training driver.
+
+This is the main function to start OSS vision training dockers, and will run in
+external environment.
+"""
+
+import json
+import os
+import time
+from typing import Any
+
+from absl import app
+from absl import flags
+from absl import logging
+import gin
+import hypertune
+import tensorflow as tf
+
+from util import constants
+from util import hypertune_utils
+from official.common import distribute_utils
+from official.common import flags as tfm_flags
+from official.core import task_factory
+from official.core import train_lib
+from official.core import train_utils
+from official.modeling import performance
+# pylint: disable=unused-import
+from tfvision import registry_imports as vision_registry_imports
+from official.projects.yolo.common import registry_imports as yolo_imports
+from official.vision import registry_imports
+
+if os.environ.get('ENABLE_MAX_VIT', ''):
+  # pylint: disable=g-import-not-at-top
+  # pylint: disable=import-error
+  # pylint: disable=no-name-in-module
+  from official.projects.maxvit import registry_imports as maxvit_imports
+# pylint: enable=unused-import
+
+# File type tfrecord.
+_FILE_TYPE_TFRECORD = 'tfrecord'
+
+
+_OBJECTIVE = flags.DEFINE_enum(
+    'objective',
+    constants.OBJECTIVE_IMAGE_CLASSIFICATION,
+    [
+        constants.OBJECTIVE_IMAGE_CLASSIFICATION,
+        constants.OBJECTIVE_IMAGE_OBJECT_DETECTION,
+        constants.OBJECTIVE_IMAGE_SEGMENTATION,
+    ],
+    'The objective of this training job.',
+)
+
+_MODEL_NAME = flags.DEFINE_string(
+    'model_name',
+    None,
+    (
+        'The model name for backbones. e.g.: the model names can be `vit-ti16`,'
+        '`vit-b16`, `vit-s16`, `vit-l16`, for `deit_imagenet_pretrain`.'
+    ),
+)
+
+_INIT_CHECKPOINT = flags.DEFINE_string(
+    'init_checkpoint', None, 'The initial checkpoint of this training job.'
+)
+
+_BACKBONE_TRAINABLE = flags.DEFINE_bool(
+    'backbone_trainable', None, 'Whether to train the backbone.'
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate', None, 'The learning rate of this training job.'
+)
+
+_WEIGHT_DECAY = flags.DEFINE_float(
+    'weight_decay', None, 'The weight decay of this training job.'
+)
+
+_NUM_CLASSES = flags.DEFINE_integer(
+    'num_classes', None, 'The number of classes.'
+)
+
+_INPUT_SIZE = flags.DEFINE_list(
+    'input_size', None, 'Expected width and height of the input image.'
+)
+
+_INPUT_TRAIN_DATA_PATH = flags.DEFINE_string(
+    'input_train_data_path', None, 'Input train data path.'
+)
+
+_INPUT_VALIDATION_DATA_PATH = flags.DEFINE_string(
+    'input_validation_data_path', None, 'Input validation data path.'
+)
+
+_GLOBAL_BATCH_SIZE = flags.DEFINE_integer(
+    'global_batch_size', None, 'Global batch size.'
+)
+
+_PREFETCH_BUFFER_SIZE = flags.DEFINE_integer(
+    'prefetch_buffer_size', None, 'Prefetch buffer size.'
+)
+
+_TRAIN_STEPS = flags.DEFINE_integer('train_steps', None, 'Train steps.')
+
+
+_ANCHOR_SIZE = flags.DEFINE_integer(
+    'anchor_size', None, 'IOD model anchor size.'
+)
+
+_OUTPUT_SIZE = flags.DEFINE_list(
+    'output_size',
+    None,
+    'Expected width and height of the output image for ISG models.',
+)
+
+_MAX_EVAL_WAIT_TIME = flags.DEFINE_integer(
+    'max_eval_wait_time',
+    0,
+    (
+        'Maximum duration to wait for evaluation result file after finishing'
+        ' the training job in seconds. Defaults to 0, immediately looking for'
+        ' the evaluation file.'
+    ),
+)
+
+_LOG_LEVEL = flags.DEFINE_string('log_level', 'INFO', 'Log level.')
+
+FLAGS = flags.FLAGS
+
+
+def get_best_eval_metric(objective: str, params: Any) -> str:
+  """Gets best eval metric.
+
+  Args:
+    objective: The objective of this training job.
+    params: Experiment config.
+
+  Returns:
+    Eval metric to use.
+
+  Raises:
+    ValueError: If params does not have best_checkpoint_eval_metric set and the
+      objective is not valid.
+  """
+  try:
+    eval_metric_name = params.trainer.best_checkpoint_eval_metric
+  except AttributeError:
+    eval_metric_name = None
+
+  if not eval_metric_name:
+    # If eval metric is not given in params, use the default value.
+    if objective == constants.OBJECTIVE_IMAGE_CLASSIFICATION:
+      try:
+        is_multilabel = params.task.train_data.is_multilabel
+      except AttributeError:
+        # Set default.
+        is_multilabel = False
+      if is_multilabel:
+        eval_metric_name = (
+            constants.IMAGE_CLASSIFICATION_MULTI_LABEL_BEST_EVAL_METRIC
+        )
+      else:
+        eval_metric_name = (
+            constants.IMAGE_CLASSIFICATION_SINGLE_LABEL_BEST_EVAL_METRIC
+        )
+    elif objective == constants.OBJECTIVE_IMAGE_OBJECT_DETECTION:
+      eval_metric_name = constants.IMAGE_OBJECT_DETECTION_BEST_EVAL_METRIC
+    elif objective == constants.OBJECTIVE_IMAGE_SEGMENTATION:
+      eval_metric_name = constants.IMAGE_SEGMENTATION_BEST_EVAL_METRIC
+    else:
+      raise ValueError(
+          'The objective must be {}, {}, or {}.'.format(
+              constants.OBJECTIVE_IMAGE_CLASSIFICATION,
+              constants.OBJECTIVE_IMAGE_OBJECT_DETECTION,
+              constants.OBJECTIVE_IMAGE_SEGMENTATION,
+          )
+      )
+  return eval_metric_name
+
+
+def parse_params() -> Any:
+  """Parses parameters."""
+  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
+  params = train_utils.parse_configuration(FLAGS, lock_return=False)
+  if _INIT_CHECKPOINT.value:
+    params.task.init_checkpoint = _INIT_CHECKPOINT.value
+    if 'yolov7' in FLAGS.experiment:
+      params.task.init_checkpoint_modules = ['backbone', 'decoder']
+    else:
+      params.task.init_checkpoint_modules = 'backbone'
+  if _MODEL_NAME.value:
+    if FLAGS.experiment in [
+        'deit_imagenet_pretrain',
+        'vit_imagenet_pretrain',
+        'vit_imagenet_finetune',
+    ]:
+      params.task.model.backbone.vit.model_name = _MODEL_NAME.value
+  if _NUM_CLASSES.value:
+    params.task.model.num_classes = _NUM_CLASSES.value
+  if _INPUT_SIZE.value:
+    input_size = [int(elem) for elem in _INPUT_SIZE.value]
+    if len(input_size) != 2:
+      raise ValueError('The input size must contain 2 integers.')
+    if input_size[0] < 0 or input_size[1] < 0:
+      raise ValueError('The input size must be positive.')
+    params.task.model.input_size = [input_size[0], input_size[1], 3]
+  # If users set input train/validation data path, we assume the data are
+  # converted from data converter as tfrecord. Users can use tfds by writing
+  # their own config directly, and no need to override this parameter.
+  if _INPUT_TRAIN_DATA_PATH.value:
+    params.task.train_data.input_path = _INPUT_TRAIN_DATA_PATH.value
+    params.task.train_data.file_type = _FILE_TYPE_TFRECORD
+    params.task.train_data.tfds_name = ''
+  if _INPUT_VALIDATION_DATA_PATH.value:
+    params.task.validation_data.input_path = _INPUT_VALIDATION_DATA_PATH.value
+    params.task.validation_data.file_type = _FILE_TYPE_TFRECORD
+    params.task.validation_data.tfds_name = ''
+  if _GLOBAL_BATCH_SIZE.value:
+    params.task.train_data.global_batch_size = _GLOBAL_BATCH_SIZE.value
+    params.task.validation_data.global_batch_size = _GLOBAL_BATCH_SIZE.value
+  if _PREFETCH_BUFFER_SIZE.value:
+    params.task.train_data.prefetch_buffer_size = _PREFETCH_BUFFER_SIZE.value
+    params.task.validation_data.prefetch_buffer_size = (
+        _PREFETCH_BUFFER_SIZE.value
+    )
+
+  # Use `get` method of train_utils.hyperparams.OneOfConfig to get learning
+  # rate config.
+  learning_rate = params.trainer.optimizer_config.learning_rate.get()
+
+  if _TRAIN_STEPS.value:
+    params.trainer.train_steps = _TRAIN_STEPS.value
+    if hasattr(learning_rate, 'decay_steps'):
+      learning_rate.decay_steps = _TRAIN_STEPS.value
+  if (
+      _BACKBONE_TRAINABLE.value is not None
+      and params.task.model.backbone.type == 'hub_model'
+  ):
+    params.task.model.backbone.hub_model.trainable = _BACKBONE_TRAINABLE.value
+  if _LEARNING_RATE.value:
+    logging.info('Updating learning_rate: %s', _LEARNING_RATE.value)
+    if hasattr(learning_rate, 'initial_learning_rate'):
+      learning_rate.initial_learning_rate = _LEARNING_RATE.value
+
+  if _WEIGHT_DECAY.value and 'yolo' in FLAGS.experiment:
+    if 'sgd_torch' == params.trainer.optimizer_config.optimizer.type:
+      params.trainer.optimizer_config.optimizer.sgd_torch.weight_decay = (
+          _WEIGHT_DECAY.value
+      )
+    elif 'adamw' == params.trainer.optimizer_config.optimizer.type:
+      params.trainer.optimizer_config.optimizer.adamw.weight_decay_rate = (
+          _WEIGHT_DECAY.value
+      )
+
+  # Yolo models does not support anchor size.
+  if _ANCHOR_SIZE.value and 'yolo' not in FLAGS.experiment:
+    params.task.model.anchor.anchor_size = _ANCHOR_SIZE.value
+
+  # Segmentation models will also set output size.
+  if _OUTPUT_SIZE.value:
+    output_size = [int(elem) for elem in _OUTPUT_SIZE.value]
+    if len(output_size) != 2:
+      raise ValueError('The output size must contain 2 integers.')
+    if output_size[0] < 0 or output_size[1] < 0:
+      raise ValueError('The output size must be positive.')
+    params.task.train_data.output_size = output_size
+    params.task.validation_data.output_size = output_size
+
+  # Set default params for best checkpoints.
+  params.trainer.best_checkpoint_export_subdir = constants.BEST_CKPT_DIRNAME
+  params.trainer.best_checkpoint_metric_comp = constants.BEST_CKPT_METRIC_COMP
+  params.trainer.best_checkpoint_eval_metric = get_best_eval_metric(
+      _OBJECTIVE.value, params
+  )
+  return params
+
+
+def wait_for_evaluation_file(
+    eval_filepath: str,
+    max_eval_wait_time: int,
+    eval_wait_interval: int = 30,
+) -> None:
+  """Waits for the evaluation file to be created.
+
+  Args:
+    eval_filepath: The path to the evaluation file.
+    max_eval_wait_time: The maximum amount of time to wait for the evaluation
+      file to be created, in seconds.
+    eval_wait_interval: The interval at which to check for the existence of the
+      evaluation file, in seconds. Defaults to 30 seconds.
+
+  Raises:
+    ValueError: If the evaluation file does not exist after the maximum amount
+      of time has passed.
+  """
+  eval_wait_start_time = time.time()
+  while not tf.io.gfile.exists(eval_filepath):
+    if time.time() - eval_wait_start_time >= max_eval_wait_time:
+      raise ValueError('The eval file {} does not exist.'.format(eval_filepath))
+    time.sleep(eval_wait_interval)
+  return
+
+
+def main(_):
+  log_level = _LOG_LEVEL.value
+  if log_level and log_level in ['FATAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']:
+    logging.set_verbosity(log_level)
+  params = parse_params()
+  logging.info('The actual training parameters are:\n%s', params.as_dict())
+  model_dir = os.path.join(
+      FLAGS.model_dir,
+      'trial_' + hypertune_utils.get_trial_id_from_environment(),
+  )
+  logging.info('model_dir in this trial is: %s', model_dir)
+  if 'train' in FLAGS.mode:
+    # Pure eval modes do not output yaml files. Otherwise continuous eval job
+    # may race against the train job for writing the same file.
+    train_utils.serialize_config(params, model_dir)
+
+  # Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
+  # can have significant impact on model speeds by utilizing float16 in case of
+  # GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
+  # dtype is float16
+  if params.runtime.mixed_precision_dtype:
+    performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype)
+  distribution_strategy = distribute_utils.get_distribution_strategy(
+      distribution_strategy=params.runtime.distribution_strategy,
+      all_reduce_alg=params.runtime.all_reduce_alg,
+      num_gpus=params.runtime.num_gpus,
+      tpu_address=params.runtime.tpu,
+  )
+  with distribution_strategy.scope():
+    task = task_factory.get_task(params.task, logging_dir=model_dir)
+
+  train_lib.run_experiment(
+      distribution_strategy=distribution_strategy,
+      task=task,
+      mode=FLAGS.mode,
+      params=params,
+      model_dir=model_dir,
+  )
+
+  train_utils.save_gin_config(FLAGS.mode, model_dir)
+
+  eval_metric_name = get_best_eval_metric(_OBJECTIVE.value, params)
+
+  eval_filepath = os.path.join(
+      model_dir, constants.BEST_CKPT_DIRNAME, constants.BEST_CKPT_EVAL_FILENAME
+  )
+  logging.info('Load eval metrics from: %s.', eval_filepath)
+  wait_for_evaluation_file(eval_filepath, _MAX_EVAL_WAIT_TIME.value)
+
+  with tf.io.gfile.GFile(eval_filepath, 'rb') as f:
+    eval_metric_results = json.load(f)
+    logging.info('eval metrics are: %s.', eval_metric_results)
+    if (
+        eval_metric_name in eval_metric_results
+        and constants.BEST_CKPT_STEP_NAME in eval_metric_results
+    ):
+      hp_metric = eval_metric_results[eval_metric_name]
+      hp_step = int(eval_metric_results[constants.BEST_CKPT_STEP_NAME])
+      hpt = hypertune.HyperTune()
+      hpt.report_hyperparameter_tuning_metric(
+          hyperparameter_metric_tag=constants.HP_METRIC_TAG,
+          metric_value=hp_metric,
+          global_step=hp_step,
+      )
+      logging.info(
+          'Send HP metric: %f and steps %d to hyperparameter tuning.',
+          hp_metric,
+          hp_step,
+      )
+    else:
+      logging.info(
+          'Either %s or %s is not included in the evaluation results: %s.',
+          eval_metric_name,
+          constants.BEST_CKPT_STEP_NAME,
+          eval_metric_results,
+      )
+
+
+if __name__ == '__main__':
+  tfm_flags.define_flags()
+  flags.mark_flags_as_required(['experiment', 'mode', 'model_dir'])
+  app.run(main)

community-content/vertex_vision_model_garden/model_oss/timm/dockerfile/serve.Dockerfile

@@ -0,0 +1,59 @@
+# Dockerfile for serving dockers with timm.
+#
+# To build:
+# docker build -f model_oss/timm/dockerfile/serve.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM pytorch/torchserve:0.7.0-gpu
+
+USER root
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="timm_serving"
+
+# Install timm.
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install timm==0.6.12
+RUN python3 -m pip install google-cloud-storage==2.9.0
+
+# Copy model artifacts.
+COPY model_oss/timm/handler.py /home/model-server/handler.py
+
+# Create torchserve configuration file.
+RUN echo \
+    "default_response_timeout=1200\n" \
+    "service_envelope=json\n" \
+    "inference_address=http://0.0.0.0:${infer_port}\n"  \
+    "management_address=http://0.0.0.0:${mng_port}" >> /home/model-server/config.properties
+
+# Expose ports.
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
+
+# Archive eager mode model artifacts and dependencies.
+# Do not set --model-file and --serialized-file because model and checkpoint will be dynamically loaded in handler.py.
+RUN torch-model-archiver \
+--model-name=${model_name} \
+--version=1.0 \
+--handler=/home/model-server/handler.py \
+--runtime=python3 \
+--export-path=/home/model-server/model-store \
+--archive-format=default \
+--force
+
+# Run Torchserve HTTP serve to respond to prediction requests.
+CMD ["torchserve", "--start", \
+     "--ts-config", "/home/model-server/config.properties", \
+     "--models", "${model_name}=${model_name}.mar", \
+     "--model-store", "/home/model-server/model-store"]

community-content/vertex_vision_model_garden/model_oss/timm/dockerfile/train.Dockerfile

@@ -0,0 +1,47 @@
+# Dockerfile for basic training dockers with timm.
+#
+# To build:
+# docker build -f model_oss/timm/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+# Base on pytorch-cuda image.
+FROM pytorch/pytorch:1.13.0-cuda11.6-cudnn8-runtime
+
+# Install tools.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        curl \
+        wget \
+        vim
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Download timm source code with pinned version.
+RUN wget -q https://github.com/rwightman/pytorch-image-models/archive/refs/tags/v0.6.12.tar.gz
+RUN tar xzf v0.6.12.tar.gz
+
+# Install libraries.
+RUN pip install cloudml-hypertune==0.1.0.dev6
+
+# Switch to timm repo.
+WORKDIR /workspace/pytorch-image-models-0.6.12
+
+# NOTE: use 'sed' to modify the timm source code to
+#       make timm CheckpointSaver can work with gcsfuse.
+RUN sed -i "1 i\import shutil" timm/utils/checkpoint_saver.py
+RUN sed -i "s#os.link#shutil.copyfile#g" timm/utils/checkpoint_saver.py
+RUN sed -i "s#os.unlink#os.remove#g" timm/utils/checkpoint_saver.py
+
+# NOTE: use 'sed' to modify the timm source code to
+#       add hp training support to timm trainer.
+RUN sed -i "693 a\        if saver is not None: hpt = hypertune.HyperTune(); hpt.report_hyperparameter_tuning_metric(hyperparameter_metric_tag='top1_accuracy', metric_value=best_metric, global_step=best_epoch)" train.py
+RUN sed -i "1 i\import hypertune" train.py
+
+# Install timm from source code.
+RUN pip install -e .
+
+# https://pytorch.org/docs/stable/elastic/run.html
+ENTRYPOINT ["torchrun"]