Merge pull request #412 from roboflow/fixes

Pass Inputs to Outputs when Wired

inference/core/version.py

@@ -1,4 +1,4 @@
-__version__ = "0.11.0"
+__version__ = "0.11.1"
 
 
 if __name__ == "__main__":

inference/core/workflows/core_steps/flow_control/condition.py

@@ -83,7 +83,7 @@ class BlockManifest(WorkflowBlockManifest):
         set,
     ] = Field(
         description="Left operand of expression `left operator right` to evaluate boolean value of condition statement",
-        examples=["$steps.classification.top", 3, "some"],
+        examples=["$steps.classification.top", 3, "foo"],
     )
     operator: Operator = Field(
         description="Operator in expression `left operator right` to evaluate boolean value of condition statement",
@@ -117,7 +117,7 @@ class BlockManifest(WorkflowBlockManifest):
         set,
     ] = Field(
         description="Right operand of expression `left operator right` to evaluate boolean value of condition statement",
-        examples=["$steps.classification.top", 3, "some"],
+        examples=["$steps.classification.top", 3, "bar"],
     )
     step_if_true: StepSelector = Field(
         description="Reference to step which shall be executed if expression evaluates to true",

inference/core/workflows/core_steps/models/foundation/clip_comparison.py

@@ -57,7 +57,7 @@ class BlockManifest(WorkflowBlockManifest):
     type: Literal["ClipComparison"]
     name: str = Field(description="Unique name of step in workflows")
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )
@@ -112,13 +112,13 @@ async def run_locally(
                 prompt_type="text",
                 api_key=self._api_key,
             )
-            doctr_model_id = load_core_model(
+            clip_model_id = load_core_model(
                 model_manager=self._model_manager,
                 inference_request=inference_request,
                 core_model="clip",
             )
             prediction = await self._model_manager.infer_from_request(
-                doctr_model_id, inference_request
+                clip_model_id, inference_request
             )
             predictions.append(prediction.dict())
         return self._post_process_result(image=images, predictions=predictions)

inference/core/workflows/core_steps/models/foundation/lmm.py

@@ -85,7 +85,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["LMM"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/foundation/lmm_classifier.py

@@ -60,7 +60,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["LMMForClassification"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/foundation/ocr.py

@@ -58,7 +58,7 @@ class BlockManifest(WorkflowBlockManifest):
     type: Literal["OCRModel"]
     name: str = Field(description="Unique name of step in workflows")
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/foundation/yolo_world.py

@@ -64,7 +64,7 @@ class BlockManifest(WorkflowBlockManifest):
     type: Literal["YoloWorldModel", "YoloWorld"]
     name: str = Field(description="Unique name of step in workflows")
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/roboflow/instance_segmentation.py

@@ -68,7 +68,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["RoboflowInstanceSegmentationModel", "InstanceSegmentationModel"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/roboflow/keypoint_detection.py

@@ -67,7 +67,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["RoboflowKeypointDetectionModel", "KeypointsDetectionModel"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/roboflow/multi_class_classification.py

@@ -61,7 +61,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["RoboflowClassificationModel", "ClassificationModel"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/roboflow/multi_label_classification.py

@@ -63,7 +63,7 @@ class BlockManifest(WorkflowBlockManifest):
         "RoboflowMultiLabelClassificationModel", "MultiLabelClassificationModel"
     ]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/roboflow/object_detection.py

@@ -42,7 +42,7 @@
 from inference_sdk import InferenceConfiguration, InferenceHTTPClient
 
 LONG_DESCRIPTION = """
-Run inference on a multi-label classification model hosted on or uploaded to Roboflow.
+Run inference on a object-detection model hosted on or uploaded to Roboflow.
 
 You can query any model that is private to your account, or any public model available 
 on [Roboflow Universe](https://universe.roboflow.com).
@@ -65,7 +65,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["RoboflowObjectDetectionModel", "ObjectDetectionModel"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/third_party/barcode_detection.py

@@ -42,7 +42,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["BarcodeDetector", "BarcodeDetection"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/models/third_party/qr_code_detection.py

@@ -41,7 +41,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["QRCodeDetector", "QRCodeDetection"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/sinks/active_learning/data_collector.py

@@ -67,7 +67,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["ActiveLearningDataCollector"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/transformations/absolute_static_crop.py

@@ -52,7 +52,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["AbsoluteStaticCrop"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/transformations/crop.py

@@ -59,7 +59,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["Crop"]
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/core_steps/transformations/relative_static_crop.py

@@ -54,7 +54,7 @@ class BlockManifest(WorkflowBlockManifest):
     type: Literal["RelativeStaticCrop"]
     name: str = Field(description="Unique name of step in workflows")
     images: Union[WorkflowImageSelector, StepOutputImageSelector] = Field(
-        description="Reference at image to be used as input for step processing",
+        description="Reference an image to be used as input for step processing",
         examples=["$inputs.image", "$steps.cropping.crops"],
         validation_alias=AliasChoices("images", "image"),
     )

inference/core/workflows/execution_engine/executor/core.py

@@ -55,6 +55,7 @@ async def run_workflow(
     return construct_workflow_output(
         workflow_outputs=workflow.workflow_definition.outputs,
         execution_cache=execution_cache,
+        runtime_parameters=runtime_parameters,
     )
 
 

inference/core/workflows/execution_engine/executor/output_constructor.py

@@ -5,6 +5,7 @@
 from inference.core.workflows.execution_engine.compiler.utils import (
     get_last_chunk_of_selector,
     get_step_selector_from_its_output,
+    is_input_selector,
 )
 from inference.core.workflows.execution_engine.executor.execution_cache import (
     ExecutionCache,
@@ -14,9 +15,17 @@
 def construct_workflow_output(
     workflow_outputs: List[JsonField],
     execution_cache: ExecutionCache,
+    runtime_parameters: Dict[str, Any],
 ) -> Dict[str, List[Any]]:
     result = {}
     for node in workflow_outputs:
+        if is_input_selector(selector_or_value=node.selector):
+            input_name = get_last_chunk_of_selector(selector=node.selector)
+            result[node.name] = runtime_parameters[input_name]
+            # above returns List[<image>]
+            # for image input and value of parameter for singular input, we do not
+            # check parameter existence, as that should be checked by EE at compilation
+            continue
         step_selector = get_step_selector_from_its_output(
             step_output_selector=node.selector
         )

requirements/_requirements.txt

@@ -16,7 +16,7 @@ pybase64<2.0.0
 scikit-image>=0.19.0
 requests-toolbelt>=1.0.0
 wheel>=0.38.1
-setuptools>=65.5.1
+setuptools>=65.5.1,<70.0.0
 pytest-asyncio<=0.21.1
 networkx>=3.1
 pydantic~=2.6

tests/workflows/unit_tests/execution_engine/executor/test_output_constructor.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from inference.core.workflows.entities.base import (
     CoordinatesSystem,
     JsonField,
@@ -56,7 +58,9 @@ def test_construct_response_when_field_needs_to_be_grabbed_from_nested_output_in
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -98,7 +102,9 @@ def test_construct_response_when_field_needs_to_be_grabbed_from_nested_output_in
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -121,7 +127,9 @@ def test_construct_response_when_step_output_is_missing_due_to_conditional_execu
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -142,7 +150,9 @@ def test_construct_response_when_expected_step_property_is_missing() -> None:
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -195,7 +205,9 @@ def test_construct_response_when_wildcard_selector_used_and_parent_coordinates_s
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -264,7 +276,9 @@ def test_construct_response_when_wildcard_selector_used_and_own_coordinates_syst
 
     # when
     result = construct_workflow_output(
-        workflow_outputs=workflow_outputs, execution_cache=execution_cache
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters={},
     )
 
     # then
@@ -275,3 +289,80 @@ def test_construct_response_when_wildcard_selector_used_and_own_coordinates_syst
         ],
         "other": [{"predictions": ["g", "h", "i"]}],
     }
+
+
+def test_construct_response_when_results_to_be_grabbed_from_inputs() -> None:
+    # given
+    execution_cache = ExecutionCache.init()
+    workflow_outputs = [
+        JsonField(type="JsonField", name="some", selector="$inputs.image"),
+        JsonField(type="JsonField", name="other", selector="$inputs.confidence"),
+    ]
+    runtime_parameters = {
+        "image": [np.zeros((192, 168, 3), dtype=np.uint8)],
+        "confidence": 0.3,
+    }
+
+    # when
+    result = construct_workflow_output(
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters=runtime_parameters,
+    )
+
+    # then
+    assert len(result) == 2, "Expected 2 elements to be registered in the output"
+    assert (
+        result["other"] == 0.3
+    ), "Confidence value (without list wrapping) to be fetched from inputs and named `other`"
+    assert np.allclose(
+        result["some"], [np.zeros((192, 168, 3), dtype=np.uint8)]
+    ), "`some` output expected to carry one-element list of input image"
+
+
+def test_construct_response_when_results_to_be_grabbed_from_inputs_and_step_outputs() -> (
+    None
+):
+    # given
+    execution_cache = ExecutionCache.init()
+    execution_cache.register_step(
+        step_name="a",
+        output_definitions=[OutputDefinition(name="predictions")],
+        compatible_with_batches=True,
+    )
+    execution_cache.register_step_outputs(
+        step_name="a",
+        outputs=[
+            {"predictions": ["a", "b", "c"]},
+            {"predictions": ["d", "e", "f"]},
+        ],
+    )
+    workflow_outputs = [
+        JsonField(type="JsonField", name="a", selector="$inputs.image"),
+        JsonField(type="JsonField", name="b", selector="$inputs.confidence"),
+        JsonField(type="JsonField", name="c", selector="$steps.a.predictions"),
+    ]
+    runtime_parameters = {
+        "image": [np.zeros((192, 168, 3), dtype=np.uint8)],
+        "confidence": 0.3,
+    }
+
+    # when
+    result = construct_workflow_output(
+        workflow_outputs=workflow_outputs,
+        execution_cache=execution_cache,
+        runtime_parameters=runtime_parameters,
+    )
+
+    # then
+    assert len(result) == 3, "Expected 3 elements to be registered in the output"
+    assert (
+        result["b"] == 0.3
+    ), "Confidence value (without list wrapping) to be fetched from inputs and named `b`"
+    assert np.allclose(
+        result["a"], [np.zeros((192, 168, 3), dtype=np.uint8)]
+    ), "`a` output expected to carry one-element list of input image"
+    assert result["c"] == [
+        ["a", "b", "c"],
+        ["d", "e", "f"],
+    ], "All predictions from step `c` expected to be registered under `c` output"