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Merge pull request #615 from roboflow/add-inference-ids-detection-blocks
Add inference_ids to model blocks
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189
tests/workflows/integration_tests/execution/test_workflow_inference_id_response.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,189 @@ | ||
| import numpy as np | ||
| import pytest | ||
|
|
||
| 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 | ||
|
|
||
| DETECTION_PLUS_CLASSIFICATION_WORKFLOW = { | ||
| "version": "1.0", | ||
| "inputs": [{"type": "WorkflowImage", "name": "image"}], | ||
| "steps": [ | ||
| { | ||
| "type": "ObjectDetectionModel", | ||
| "name": "general_detection", | ||
| "image": "$inputs.image", | ||
| "model_id": "yolov8n-640", | ||
| "class_filter": ["dog"], | ||
| }, | ||
| { | ||
| "type": "Crop", | ||
| "name": "cropping", | ||
| "image": "$inputs.image", | ||
| "predictions": "$steps.general_detection.predictions", | ||
| }, | ||
| { | ||
| "type": "ClassificationModel", | ||
| "name": "breds_classification", | ||
| "image": "$steps.cropping.crops", | ||
| "model_id": "dog-breed-xpaq6/1", | ||
| }, | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "type": "JsonField", | ||
| "name": "predictions", | ||
| "selector": "$steps.breds_classification.predictions", | ||
| }, | ||
| ], | ||
| } | ||
|
|
||
| OBJECT_DETECTION_WORKFLOW = { | ||
| "version": "1.0", | ||
| "inputs": [{"type": "WorkflowImage", "name": "image"}], | ||
| "steps": [ | ||
| { | ||
| "type": "ObjectDetectionModel", | ||
| "name": "general_detection", | ||
| "image": "$inputs.image", | ||
| "model_id": "yolov8n-640", | ||
| "class_filter": ["dog"], | ||
| }, | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "type": "JsonField", | ||
| "name": "predictions", | ||
| "coordinates_system": "own", | ||
| "selector": "$steps.general_detection.predictions", | ||
| } | ||
| ], | ||
| } | ||
|
|
||
| INSTANCE_SEGMENTATION_WORKFLOW = { | ||
| "version": "1.0", | ||
| "inputs": [{"type": "WorkflowImage", "name": "image"}], | ||
| "steps": [ | ||
| { | ||
| "type": "InstanceSegmentationModel", | ||
| "name": "instance_segmentation", | ||
| "image": "$inputs.image", | ||
| "model_id": "yolov8n-640", | ||
| }, | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "type": "JsonField", | ||
| "name": "predictions", | ||
| "selector": "$steps.instance_segmentation.*", | ||
| } | ||
| ], | ||
| } | ||
|
|
||
|
|
||
| @pytest.mark.workflows | ||
| def test_detection_plus_classification_workflow_with_inference_id( | ||
| 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=DETECTION_PLUS_CLASSIFICATION_WORKFLOW, | ||
| init_parameters=workflow_init_parameters, | ||
| max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS, | ||
| ) | ||
|
|
||
| # when | ||
| result = execution_engine.run( | ||
| runtime_parameters={ | ||
| "image": dogs_image, | ||
| } | ||
| ) | ||
|
|
||
| # then | ||
| assert ( | ||
| len(result[0]["predictions"]) == 2 | ||
| ), "Expected 2 dogs crops on input image, hence 2 nested classification results" | ||
|
|
||
| for prediction in result[0]["predictions"]: | ||
| assert "inference_id" in prediction, "Expected inference_id in each prediction" | ||
| assert prediction["inference_id"] is not None, "Expected non-null inference_id" | ||
|
|
||
| assert [result[0]["predictions"][0]["top"], result[0]["predictions"][1]["top"]] == [ | ||
| "116.Parson_russell_terrier", | ||
| "131.Wirehaired_pointing_griffon", | ||
| ], "Expected predictions to be as measured in reference run" | ||
|
|
||
|
|
||
| @pytest.mark.workflows | ||
| def test_object_detection_workflow_with_inference_id( | ||
| 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=OBJECT_DETECTION_WORKFLOW, | ||
| init_parameters=workflow_init_parameters, | ||
| max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS, | ||
| ) | ||
|
|
||
| # when | ||
| result = execution_engine.run( | ||
| runtime_parameters={ | ||
| "image": dogs_image, | ||
| } | ||
| ) | ||
|
|
||
| # then | ||
| assert len(result[0]["predictions"]) == 2, "Expected 2 predictions" | ||
| assert ( | ||
| result[0]["predictions"][0]["inference_id"] is not None | ||
| ), "Expected non-null inference_id" | ||
| assert ( | ||
| result[0]["predictions"][1]["inference_id"] is not None | ||
| ), "Expected non-null inference_id" | ||
|
|
||
|
|
||
| @pytest.mark.workflows | ||
| def test_instance_segmentation_workflow_with_inference_id( | ||
| 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=INSTANCE_SEGMENTATION_WORKFLOW, | ||
| init_parameters=workflow_init_parameters, | ||
| max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS, | ||
| ) | ||
|
|
||
| # when | ||
| result = execution_engine.run( | ||
| runtime_parameters={ | ||
| "image": dogs_image, | ||
| } | ||
| ) | ||
|
|
||
| # then | ||
| assert len(result[0]["predictions"]) == 2, "Expected 2 predictions" | ||
| assert ( | ||
| result[0]["predictions"].get("inference_id") is not None | ||
| ), "Expected non-null inference_id" |