Merge branch 'main' into dependabot/npm_and_yarn/inference/landing/np…

…m_and_yarn-af93afb32e

inference/core/workflows/core_steps/common/query_language/entities/enums.py

@@ -68,3 +68,5 @@ class DetectionsSelectionMode(Enum):
     LEFT_MOST = "left_most"
     RIGHT_MOST = "right_most"
     TOP_CONFIDENCE = "top_confidence"
+    FIRST = "first"
+    LAST = "last"

inference/core/workflows/core_steps/common/query_language/operations/detections/base.py

@@ -131,7 +131,21 @@ def select_rightmost_detection(detections: sv.Detections) -> sv.Detections:
     return detections[index]
 
 
+def select_first_detection(detections: sv.Detections) -> sv.Detections:
+    if len(detections) == 0:
+        return deepcopy(detections)
+    return detections[0]
+
+
+def select_last_detection(detections: sv.Detections) -> sv.Detections:
+    if len(detections) == 0:
+        return deepcopy(detections)
+    return detections[-1]
+
+
 DETECTIONS_SELECTORS = {
+    DetectionsSelectionMode.FIRST: select_first_detection,
+    DetectionsSelectionMode.LAST: select_last_detection,
     DetectionsSelectionMode.LEFT_MOST: select_leftmost_detection,
     DetectionsSelectionMode.RIGHT_MOST: select_rightmost_detection,
     DetectionsSelectionMode.TOP_CONFIDENCE: select_top_confidence_detection,

inference/core/workflows/core_steps/sinks/roboflow/model_monitoring_inference_aggregator/v1.py

@@ -214,7 +214,6 @@ def __init__(
         api_key: Optional[str],
         background_tasks: Optional[BackgroundTasks],
         thread_pool_executor: Optional[ThreadPoolExecutor],
-        model_id: str,
     ):
         if api_key is None:
             raise ValueError(
@@ -228,7 +227,6 @@ def __init__(
         self._background_tasks = background_tasks
         self._thread_pool_executor = thread_pool_executor
         self._predictions_aggregator = PredictionsAggregator()
-        self._model_id = model_id
 
     @classmethod
     def get_init_parameters(cls) -> List[str]:
@@ -244,10 +242,11 @@ def run(
         predictions: Union[sv.Detections, dict],
         frequency: int,
         unique_aggregator_key: str,
+        model_id: str,
     ) -> BlockResult:
         self._last_report_time_cache_key = f"workflows:steps_cache:roboflow_core/model_monitoring_inference_aggregator@v1:{unique_aggregator_key}:last_report_time"
         if predictions:
-            self._predictions_aggregator.collect(predictions, self._model_id)
+            self._predictions_aggregator.collect(predictions, model_id)
         if not self._is_in_reporting_range(frequency):
             return {
                 "error_status": False,

tests/workflows/integration_tests/execution/test_workflow_top_prediction.py

@@ -166,3 +166,197 @@ def test_filtering_workflow_by_top_prediction_with_no_detections(
 
     assert len(all_detections) == 0, "Expected 0 total predictions"
     assert len(top_detections) == 0, "Expected top prediction to be an empty array"
+
+
+SORT_DETECTIONS_WORKFLOW_LAST = {
+    "version": "1.0",
+    "inputs": [
+        {"type": "WorkflowImage", "name": "image"},
+        {"type": "WorkflowParameter", "name": "model_id"},
+        {"type": "WorkflowParameter", "name": "confidence", "default_value": 0.3},
+        {"type": "WorkflowParameter", "name": "classes"},
+    ],
+    "steps": [
+        {
+            "type": "ObjectDetectionModel",
+            "name": "model",
+            "image": "$inputs.image",
+            "model_id": "$inputs.model_id",
+            "confidence": "$inputs.confidence",
+        },
+        {
+            "type": "roboflow_core/property_definition@v1",
+            "name": "property_definition",
+            "data": "$steps.model.predictions",
+            "operations": [
+                {"type": "SortDetections", "mode": "confidence", "ascending": True},
+                {"type": "DetectionsSelection", "mode": "last"},
+            ],
+        },
+    ],
+    "outputs": [
+        {
+            "type": "JsonField",
+            "name": "all_predictions",
+            "selector": "$steps.model.predictions",
+        },
+        {
+            "type": "JsonField",
+            "name": "selected_box",
+            "selector": "$steps.property_definition.output",
+        },
+    ],
+}
+
+
+def test_extracting_largest_bbox_from_detections(
+    model_manager: ModelManager,
+    crowd_image: np.ndarray,
+) -> None:
+    # given
+    workflow_init_parameters = {
+        "workflows_core.model_manager": model_manager,
+        "workflows_core.api_key": None,
+        "workflows_core.step_execution_mode": StepExecutionMode.LOCAL,
+    }
+    execution_engine = ExecutionEngine.init(
+        workflow_definition=SORT_DETECTIONS_WORKFLOW_LAST,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": crowd_image,
+            "model_id": "yolov8n-640",
+            "classes": {"person"},
+        }
+    )
+
+    # then
+    assert isinstance(result, list), "Expected result to be list"
+    assert len(result) == 1, "Single image provided - single output expected"
+    all_detections: sv.Detections = result[0]["all_predictions"]
+    selected_box: sv.Detections = result[0]["selected_box"]
+
+    assert len(all_detections) == 12, "Expected 12 total predictions"
+    assert np.allclose(
+        all_detections.xyxy,
+        EXPECTED_OBJECT_DETECTION_BBOXES,
+        atol=1,
+    ), "Expected bboxes to match what was validated manually as workflow outcome"
+    assert np.allclose(
+        all_detections.confidence,
+        EXPECTED_OBJECT_DETECTION_CONFIDENCES,
+        atol=0.01,
+    ), "Expected confidences to match what was validated manually as workflow outcome"
+
+    assert len(selected_box) == 1, "Expected only one top prediction"
+    assert np.allclose(
+        selected_box.xyxy,
+        [EXPECTED_OBJECT_DETECTION_BBOXES[0]],
+        atol=1,
+    ), "Expected top bbox to match what was validated manually as workflow outcome"
+    assert np.allclose(
+        selected_box.confidence,
+        [EXPECTED_OBJECT_DETECTION_CONFIDENCES[0]],
+        atol=0.01,
+    ), "Expected top confidence to match what was validated manually as workflow outcome"
+
+
+SORT_DETECTIONS_WORKFLOW_FIRST = {
+    "version": "1.0",
+    "inputs": [
+        {"type": "WorkflowImage", "name": "image"},
+        {"type": "WorkflowParameter", "name": "model_id"},
+        {"type": "WorkflowParameter", "name": "confidence", "default_value": 0.3},
+        {"type": "WorkflowParameter", "name": "classes"},
+    ],
+    "steps": [
+        {
+            "type": "ObjectDetectionModel",
+            "name": "model",
+            "image": "$inputs.image",
+            "model_id": "$inputs.model_id",
+            "confidence": "$inputs.confidence",
+        },
+        {
+            "type": "roboflow_core/property_definition@v1",
+            "name": "property_definition",
+            "data": "$steps.model.predictions",
+            "operations": [
+                {"type": "SortDetections", "mode": "confidence", "ascending": True},
+                {"type": "DetectionsSelection", "mode": "first"},
+            ],
+        },
+    ],
+    "outputs": [
+        {
+            "type": "JsonField",
+            "name": "all_predictions",
+            "selector": "$steps.model.predictions",
+        },
+        {
+            "type": "JsonField",
+            "name": "first_box",
+            "selector": "$steps.property_definition.output",
+        },
+    ],
+}
+
+
+def test_extracting_smallest_bbox_from_detections(
+    model_manager: ModelManager,
+    crowd_image: np.ndarray,
+) -> None:
+    # given
+    workflow_init_parameters = {
+        "workflows_core.model_manager": model_manager,
+        "workflows_core.api_key": None,
+        "workflows_core.step_execution_mode": StepExecutionMode.LOCAL,
+    }
+    execution_engine = ExecutionEngine.init(
+        workflow_definition=SORT_DETECTIONS_WORKFLOW_FIRST,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": crowd_image,
+            "model_id": "yolov8n-640",
+            "classes": {"person"},
+        }
+    )
+
+    # then
+    assert isinstance(result, list), "Expected result to be list"
+    assert len(result) == 1, "Single image provided - single output expected"
+    all_detections: sv.Detections = result[0]["all_predictions"]
+    first_box: sv.Detections = result[0]["first_box"]
+
+    assert len(all_detections) == 12, "Expected 12 total predictions"
+    assert np.allclose(
+        all_detections.xyxy,
+        EXPECTED_OBJECT_DETECTION_BBOXES,
+        atol=1,
+    ), "Expected bboxes to match what was validated manually as workflow outcome"
+    assert np.allclose(
+        all_detections.confidence,
+        EXPECTED_OBJECT_DETECTION_CONFIDENCES,
+        atol=0.01,
+    ), "Expected confidences to match what was validated manually as workflow outcome"
+
+    assert len(first_box) == 1, "Expected only one prediction"
+    assert np.allclose(
+        first_box.xyxy,
+        [EXPECTED_OBJECT_DETECTION_BBOXES[-1]],
+        atol=1,
+    ), "Expected top bbox to match what was validated manually as workflow outcome"
+    assert np.allclose(
+        first_box.confidence,
+        [EXPECTED_OBJECT_DETECTION_CONFIDENCES[-1]],
+        atol=0.01,
+    ), "Expected top confidence to match what was validated manually as workflow outcome"

tests/workflows/unit_tests/core_steps/sinks/roboflow/test_model_monitoring_inference_aggregator.py

@@ -54,13 +54,13 @@ def test_run_not_in_reporting_range_success(
         api_key="my_api_key",
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="my_model_id",
     )
     result = block.run(
         fire_and_forget=True,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="my_model_id",
     )
 
     # then
@@ -121,13 +121,13 @@ def test_run_in_reporting_range_success_with_object_detection(
         api_key=api_key,
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="construction-safety/10",
     )
     result = block.run(
         fire_and_forget=False,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="construction-safety/10",
     )
 
     # then
@@ -217,13 +217,13 @@ def test_run_in_reporting_range_success_with_single_label_classification(
         api_key=api_key,
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="pills-classification/1",
     )
     result = block.run(
         fire_and_forget=False,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="pills-classification/1",
     )
 
     # then
@@ -313,13 +313,13 @@ def test_run_in_reporting_range_success_with_multi_label_classification(
         api_key=api_key,
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="animals/32",
     )
     result = block.run(
         fire_and_forget=False,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="animals/32",
     )
 
     # then
@@ -415,13 +415,13 @@ def test_send_inference_results_to_model_monitoring_failure(
         api_key=api_key,
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="my_model_id",
     )
     result = block.run(
         fire_and_forget=False,
         frequency=1,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="my_model_id",
     )
 
     # then
@@ -479,13 +479,13 @@ def test_run_when_not_in_reporting_range(
         api_key=api_key,
         background_tasks=None,
         thread_pool_executor=None,
-        model_id="my_model_id",
     )
     result = block.run(
         fire_and_forget=False,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="my_model_id",
     )
 
     # then
@@ -545,13 +545,13 @@ def test_run_when_fire_and_forget_with_background_tasks(
         api_key=api_key,
         background_tasks=background_tasks,
         thread_pool_executor=None,
-        model_id="my_model_id",
     )
     result = block.run(
         fire_and_forget=True,
         frequency=10,
         predictions=predictions,
         unique_aggregator_key=unique_aggregator_key,
+        model_id="my_model_id",
     )
 
     # then
@@ -609,13 +609,13 @@ def test_run_when_fire_and_forget_with_thread_pool(
             api_key=api_key,
             background_tasks=None,
             thread_pool_executor=thread_pool_executor,
-            model_id="my_model_id",
         )
         result = block.run(
             fire_and_forget=True,
             frequency=10,
             predictions=predictions,
             unique_aggregator_key=unique_aggregator_key,
+            model_id="my_model_id",
         )
 
         # then