Merge branch 'main' into feature/store-hostname-in-usage

inference/core/workflows/execution_engine/v1/executor/execution_data_manager/step_input_assembler.py

@@ -419,11 +419,11 @@ def get_compound_parameter_value(
             result[nested_element.parameter_specification.nested_element_key] = (
                 non_compound_parameter_value
             )
-            if non_compound_indices:
+            if non_compound_indices is not None:
                 batch_indices.append(non_compound_indices)
     ensure_compound_input_indices_match(indices=batch_indices)
     result_indices = None
-    if batch_indices:
+    if len(batch_indices) > 0:
         result_indices = batch_indices[0]
     return result, result_indices
 

tests/workflows/integration_tests/execution/test_workflow_with_two_stage_models_and_flow_control.py

@@ -0,0 +1,144 @@
+import numpy as np
+
+from inference.core.env import WORKFLOWS_MAX_CONCURRENT_STEPS
+from inference.core.managers.base import ModelManager
+from inference.core.workflows.core_steps.common.entities import StepExecutionMode
+from inference.core.workflows.execution_engine.core import ExecutionEngine
+
+TWO_STAGE_WORKFLOW_WITH_FLOW_CONTROL = {
+    "version": "1.0",
+    "inputs": [{"type": "WorkflowImage", "name": "image"}],
+    "steps": [
+        {
+            "type": "roboflow_core/roboflow_object_detection_model@v1",
+            "name": "general_detection",
+            "image": "$inputs.image",
+            "model_id": "yolov8n-640",
+            "class_filter": ["dog"],
+        },
+        {
+            "type": "roboflow_core/dynamic_crop@v1",
+            "name": "cropping",
+            "image": "$inputs.image",
+            "predictions": "$steps.general_detection.predictions",
+        },
+        {
+            "type": "roboflow_core/roboflow_classification_model@v1",
+            "name": "breds_classification",
+            "image": "$steps.cropping.crops",
+            "model_id": "dog-breed-xpaq6/1",
+        },
+        {
+            "type": "roboflow_core/continue_if@v1",
+            "name": "continue_if",
+            "condition_statement": {
+                "type": "StatementGroup",
+                "statements": [
+                    {
+                        "type": "BinaryStatement",
+                        "left_operand": {
+                            "type": "DynamicOperand",
+                            "operand_name": "predictions",
+                            "operations": [
+                                {
+                                    "type": "ClassificationPropertyExtract",
+                                    "property_name": "top_class_confidence",
+                                }
+                            ],
+                        },
+                        "comparator": {"type": "(Number) >="},
+                        "right_operand": {"type": "StaticOperand", "value": 0.35},
+                    }
+                ],
+            },
+            "evaluation_parameters": {
+                "predictions": "$steps.breds_classification.predictions"
+            },
+            "next_steps": ["$steps.property_definition"],
+        },
+        {
+            "type": "roboflow_core/property_definition@v1",
+            "name": "property_definition",
+            "operations": [
+                {"type": "ClassificationPropertyExtract", "property_name": "top_class"}
+            ],
+            "data": "$steps.breds_classification.predictions",
+        },
+    ],
+    "outputs": [
+        {
+            "type": "JsonField",
+            "name": "class_name",
+            "selector": "$steps.property_definition.output",
+        },
+    ],
+}
+
+
+def test_two_stage_workflow_with_flow_control_when_there_is_nothing_predicted_from_first_stage_model(
+    model_manager: ModelManager,
+    crowd_image: np.ndarray,
+    roboflow_api_key: str,
+) -> None:
+    # given
+    workflow_init_parameters = {
+        "workflows_core.model_manager": model_manager,
+        "workflows_core.api_key": roboflow_api_key,
+        "workflows_core.step_execution_mode": StepExecutionMode.LOCAL,
+    }
+    execution_engine = ExecutionEngine.init(
+        workflow_definition=TWO_STAGE_WORKFLOW_WITH_FLOW_CONTROL,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": crowd_image,
+        }
+    )
+
+    assert isinstance(result, list), "Expected list to be delivered"
+    assert len(result) == 1, "Expected 1 element in the output for one input image"
+    assert set(result[0].keys()) == {
+        "class_name",
+    }, "Expected all declared outputs to be delivered"
+    assert (
+        len(result[0]["class_name"]) == 0
+    ), "Expected no prediction from 2nd model, as no dogs detected"
+
+
+def test_two_stage_workflow_with_flow_control_when_there_is_something_predicted_from_first_stage_model(
+    model_manager: ModelManager,
+    dogs_image: np.ndarray,
+    roboflow_api_key: str,
+) -> None:
+    # given
+    workflow_init_parameters = {
+        "workflows_core.model_manager": model_manager,
+        "workflows_core.api_key": roboflow_api_key,
+        "workflows_core.step_execution_mode": StepExecutionMode.LOCAL,
+    }
+    execution_engine = ExecutionEngine.init(
+        workflow_definition=TWO_STAGE_WORKFLOW_WITH_FLOW_CONTROL,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": dogs_image,
+        }
+    )
+
+    assert isinstance(result, list), "Expected list to be delivered"
+    assert len(result) == 1, "Expected 1 element in the output for one input image"
+    assert set(result[0].keys()) == {
+        "class_name",
+    }, "Expected all declared outputs to be delivered"
+    assert result[0]["class_name"] == [
+        "116.Parson_russell_terrier",
+        None,
+    ], "Expected one crop to be passed by continue_if block and the other failed"

tests/workflows/unit_tests/core_steps/visualizations/test_keypoints.py

@@ -359,6 +359,7 @@ def test_keypoint_visualization_block_nocopy() -> None:
         == start_image.__array_interface__["data"][0]
     )
 
+
 def test_keypoint_visualization_block_no_predictions() -> None:
     # given
     block = KeypointVisualizationBlockV1()
@@ -374,7 +375,7 @@ def test_keypoint_visualization_block_no_predictions() -> None:
         predictions=empty_predictions,
         copy_image=True,
         annotator_type="edge",
-        color="#A351FB", 
+        color="#A351FB",
         text_color="black",
         text_scale=0.5,
         text_thickness=1,
@@ -389,7 +390,7 @@ def test_keypoint_visualization_block_no_predictions() -> None:
 
     # dimensions of output match input
     assert output.get("image").numpy_image.shape == (1000, 1000, 3)
-    
+
     # check if the image is unchanged since there were no predictions
     assert np.array_equal(
         output.get("image").numpy_image, np.zeros((1000, 1000, 3), dtype=np.uint8)