Merge pull request #815 from roboflow/fix/deserialization

Fix issue with serialisation of empty outputs

.github/workflows/test.nvidia_t4.yml

@@ -34,61 +34,46 @@ jobs:
         id: regression_tests
         run: |
           MINIMUM_FPS=25 FUNCTIONAL=true PORT=9101 SKIP_LMM_TEST=True API_KEY=${{ secrets.API_KEY }} asl_instance_segmentation_API_KEY=${{ secrets.ASL_INSTANCE_SEGMENTATION_API_KEY }} asl_poly_instance_seg_API_KEY=${{ secrets.ASL_POLY_INSTANCE_SEG_API_KEY }} bccd_favz3_API_KEY=${{ secrets.BCCD_FAVZ3_API_KEY }} bccd_i4nym_API_KEY=${{ secrets.BCCD_I4NYM_API_KEY }} cats_and_dogs_smnpl_API_KEY=${{ secrets.CATS_AND_DOGS_SMNPL_API_KEY }} coins_xaz9i_API_KEY=${{ secrets.COINS_XAZ9I_API_KEY }} melee_API_KEY=${{ secrets.MELEE_API_KEY }} yolonas_test_API_KEY=${{ secrets.YOLONAS_TEST_API_KEY }} python3 -m pytest tests/inference/integration_tests/
-      - name: 🚨 Show server logs on error
-        run: docker logs inference-test
-        if: ${{ steps.regression_tests.outcome != 'success' }}
       - name: 🧹 Cleanup Test Docker - GPU
         run: make stop_test_docker
-        if: success() || failure()
       - name: 🔋 Start Test Docker - GPU
         run: |
           PORT=9101 INFERENCE_SERVER_REPO=roboflow-inference-server-gpu make start_test_docker_gpu
       - name: 🧪 Regression CogVLM - GPU
         id: cog_vlm_tests
         run: |
           PORT=9101 API_KEY=${{ secrets.API_KEY }} python3 -m pytest tests/inference/integration_tests/test_cogvlm.py
-      - name: 🚨 Show server logs on error
-        run: docker logs inference-test
-        if: ${{ steps.cog_vlm_tests.outcome != 'success' }}
       - name: 🧹 Cleanup Test Docker - GPU
         run: make stop_test_docker
-        if: success() || failure()
       - name: 🔋 Start Test Docker - GPU
         run: |
           PORT=9101 INFERENCE_SERVER_REPO=roboflow-inference-server-gpu make start_test_docker_gpu
       - name: 🧪 Regression Paligemma - GPU
         id: paligemma_tests
         run: |
           PORT=9101 melee_API_KEY=${{ secrets.MELEE_API_KEY }} python3 -m pytest tests/inference/integration_tests/test_paligemma.py
-      - name: 🚨 Show server logs on error
-        run: docker logs inference-test
-        if: ${{ steps.paligemma_tests.outcome != 'success' }}
       - name: 🧹 Cleanup Test Docker - GPU
         run: make stop_test_docker
-        if: success() || failure()
       - name: 🔋 Start Test Docker - GPU
         run: |
           PORT=9101 INFERENCE_SERVER_REPO=roboflow-inference-server-gpu make start_test_docker_gpu
       - name: 🧪 Regression Florence - GPU
         id: florence_tests
         run: |
           PORT=9101 melee_API_KEY=${{ secrets.MELEE_API_KEY }} python3 -m pytest tests/inference/integration_tests/test_florence.py
-      - name: 🚨 Show server logs on error
-        run: docker logs inference-test
-        if: ${{ steps.florence_tests.outcome != 'success' }}
       - name: 🧹 Cleanup Test Docker - GPU
         run: make stop_test_docker
-        if: success() || failure()
       - name: � Start Test Docker - SAM2
         run: |
           PORT=9101 INFERENCE_SERVER_REPO=roboflow-inference-server-gpu make start_test_docker_gpu
       - name: 🧪 Regression Tests - SAM2
         id: sam2_tests
         run: |
            PORT=9101 API_KEY=${{ secrets.API_KEY }} SKIP_SAM2_TESTS=False python3 -m pytest tests/inference/integration_tests/test_sam2.py
-      - name: 🚨 Show server logs on error
-        run: docker logs inference-test
-        if: ${{ steps.sam2_tests.outcome != 'success' }}
       - name: 🧹 Cleanup Test Docker - SAM2
         run: make stop_test_docker
-        if: success() || failure()
+      - name: 🚨 Show server logs on error
+        run: |
+          docker logs inference-test
+          make stop_test_docker
+        if: failure()

.github/workflows/test.nvidia_t4_parallel_server.yml

@@ -44,7 +44,7 @@ jobs:
           IS_PARALLEL_SERVER=true SKIP_VISUALISATION_TESTS=true FUNCTIONAL=true PORT=9101 API_KEY=${{ secrets.API_KEY }} asl_instance_segmentation_API_KEY=${{ secrets.ASL_INSTANCE_SEGMENTATION_API_KEY }} asl_poly_instance_seg_API_KEY=${{ secrets.ASL_POLY_INSTANCE_SEG_API_KEY }} bccd_favz3_API_KEY=${{ secrets.BCCD_FAVZ3_API_KEY }} bccd_i4nym_API_KEY=${{ secrets.BCCD_I4NYM_API_KEY }} cats_and_dogs_smnpl_API_KEY=${{ secrets.CATS_AND_DOGS_SMNPL_API_KEY }} coins_xaz9i_API_KEY=${{ secrets.COINS_XAZ9I_API_KEY }} melee_API_KEY=${{ secrets.MELEE_API_KEY }} yolonas_test_API_KEY=${{ secrets.YOLONAS_TEST_API_KEY }} python3 -m pytest tests/inference/integration_tests/regression_test.py tests/inference/integration_tests/batch_regression_test.py
       - name: 🚨 Show server logs on error
         run: docker logs inference-test
-        if: ${{ steps.regression_tests.outcome != 'success' }}
+        if: failure()
       - name: 🧹 Cleanup Test Docker - Parallel GPU
         run: make stop_test_docker
         if: success() || failure()

inference/core/workflows/execution_engine/v1/executor/output_constructor.py

@@ -177,6 +177,8 @@ def serialize_data_piece(
     kind: Union[List[Union[Kind, str]], Dict[str, List[Union[Kind, str]]]],
     kinds_serializers: Dict[str, Callable[[Any], Any]],
 ) -> Any:
+    if data_piece is None:
+        return None
     if isinstance(kind, dict):
         if not isinstance(data_piece, dict):
             raise AssumptionError(
@@ -210,6 +212,8 @@ def serialize_single_workflow_result_field(
     kind: List[Union[Kind, str]],
     kinds_serializers: Dict[str, Callable[[Any], Any]],
 ) -> Any:
+    if value is None:
+        return None
     kinds_without_serializer = set()
     for single_kind in kind:
         kind_name = single_kind.name if isinstance(single_kind, Kind) else kind

tests/workflows/integration_tests/execution/test_workflow_detection_plus_classification.py

@@ -173,6 +173,45 @@ def test_detection_plus_classification_workflow_when_minimal_valid_input_provide
     ], "Expected predictions to be as measured in reference run"
 
 
+def test_detection_plus_classification_workflow_when_minimal_valid_input_provided_and_serialization_requested(
+    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_V2_BLOCKS,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": dogs_image,
+        },
+        serialize_results=True,
+    )
+
+    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()) == {
+        "predictions",
+    }, "Expected all declared outputs to be delivered"
+    assert (
+        len(result[0]["predictions"]) == 2
+    ), "Expected 2 dogs crops on input image, hence 2 nested classification results"
+    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"
+
+
 def test_detection_plus_classification_workflow_when_nothing_gets_predicted(
     model_manager: ModelManager,
     crowd_image: np.ndarray,
@@ -281,3 +320,38 @@ def test_detection_plus_classification_workflow_when_nothing_gets_predicted_and_
     assert (
         len(result[0]["predictions"]) == 0
     ), "Expected no prediction from 2nd model, as no dogs detected"
+
+
+def test_detection_plus_classification_workflow_when_nothing_gets_predicted_and_serialization_requested(
+    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=DETECTION_PLUS_CLASSIFICATION_PLUS_CONSENSUS_WORKFLOW,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={
+            "image": crowd_image,
+        },
+        serialize_results=True,
+    )
+
+    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()) == {
+        "predictions",
+    }, "Expected all declared outputs to be delivered"
+    assert (
+        len(result[0]["predictions"]) == 0
+    ), "Expected no prediction from 2nd model, as no dogs detected"

tests/workflows/integration_tests/execution/test_workflow_top_prediction.py

@@ -1,18 +1,10 @@
 import numpy as np
-import pytest
 import supervision as sv
 
 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.core_steps.common.query_language.errors import (
-    EvaluationEngineError,
-)
-from inference.core.workflows.errors import RuntimeInputError, StepExecutionError
 from inference.core.workflows.execution_engine.core import ExecutionEngine
-from tests.workflows.integration_tests.execution.workflows_gallery_collector.decorators import (
-    add_to_workflows_gallery,
-)
 
 TOP_PREDICTION_WORKFLOW = {
     "version": "1.0",