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Merge pull request #823 from roboflow/fix/aligning_models_v2_into_bas…
…e64_payloads Fix issue with Workflows blocks for Roboflow models v2 not using base64
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..._platform_tests/workflows_examples/roboflow_models/v1/test_workflow_for_classification.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,174 @@ | ||
| import numpy as np | ||
| import pytest | ||
|
|
||
| from inference_sdk import InferenceHTTPClient | ||
| from tests.inference.hosted_platform_tests.conftest import ( | ||
| ROBOFLOW_API_KEY, | ||
| PlatformEnvironment, | ||
| ) | ||
|
|
||
| MULTI_CLASS_CLASSIFICATION_WORKFLOW = { | ||
| "version": "1.0", | ||
| "inputs": [ | ||
| {"type": "WorkflowImage", "name": "image"}, | ||
| {"type": "WorkflowParameter", "name": "model_id"}, | ||
| ], | ||
| "steps": [ | ||
| { | ||
| "type": "roboflow_core/roboflow_classification_model@v1", | ||
| "name": "classifier", | ||
| "image": "$inputs.image", | ||
| "model_id": "$inputs.model_id", | ||
| } | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "type": "JsonField", | ||
| "name": "predictions", | ||
| "selector": "$steps.classifier.predictions", | ||
| }, | ||
| { | ||
| "type": "JsonField", | ||
| "name": "inference_id", | ||
| "selector": "$steps.classifier.inference_id", | ||
| }, | ||
| ], | ||
| } | ||
|
|
||
| MULTI_CLASS_CLASSIFICATION_RESULTS_FOR_ENVIRONMENT = { | ||
| PlatformEnvironment.ROBOFLOW_STAGING: [ | ||
| 0.3673, | ||
| 0.593, | ||
| ], | ||
| PlatformEnvironment.ROBOFLOW_PLATFORM: [0.8252, 0.9962], | ||
| } | ||
|
|
||
|
|
||
| @pytest.mark.flaky(retries=4, delay=1) | ||
| def test_multi_class_classification_workflow( | ||
| platform_environment: PlatformEnvironment, | ||
| classification_service_url: str, | ||
| multi_class_classification_model_id: str, | ||
| dogs_image: np.ndarray, | ||
| license_plate_image: np.ndarray, | ||
| ) -> None: | ||
| # given | ||
| client = InferenceHTTPClient( | ||
| api_url=classification_service_url, | ||
| api_key=ROBOFLOW_API_KEY, | ||
| ) | ||
|
|
||
| # when | ||
| result = client.run_workflow( | ||
| specification=MULTI_CLASS_CLASSIFICATION_WORKFLOW, | ||
| images={ | ||
| "image": [dogs_image, license_plate_image], | ||
| }, | ||
| parameters={ | ||
| "model_id": multi_class_classification_model_id, | ||
| }, | ||
| ) | ||
|
|
||
| # then | ||
| assert len(result) == 2, "2 images submitted, expected two outputs" | ||
| assert set(result[0].keys()) == { | ||
| "predictions", | ||
| "inference_id", | ||
| }, "Expected all outputs to be registered" | ||
| assert set(result[1].keys()) == { | ||
| "predictions", | ||
| "inference_id", | ||
| }, "Expected all outputs to be registered" | ||
| unique_inference_ids = {r["inference_id"] for r in result} | ||
| assert len(unique_inference_ids) == 2, "Expected unique inference ids granted" | ||
| predicted_confidences = [r["predictions"]["confidence"] for r in result] | ||
| assert np.allclose( | ||
| predicted_confidences, | ||
| MULTI_CLASS_CLASSIFICATION_RESULTS_FOR_ENVIRONMENT[platform_environment], | ||
| atol=1e-3, | ||
| ), "Expected classification predictions to match expectations" | ||
|
|
||
|
|
||
| MULTI_LABEL_CLASSIFICATION_WORKFLOW = { | ||
| "version": "1.0", | ||
| "inputs": [ | ||
| {"type": "WorkflowImage", "name": "image"}, | ||
| {"type": "WorkflowParameter", "name": "model_id"}, | ||
| ], | ||
| "steps": [ | ||
| { | ||
| "type": "roboflow_core/roboflow_multi_label_classification_model@v1", | ||
| "name": "classifier", | ||
| "image": "$inputs.image", | ||
| "model_id": "$inputs.model_id", | ||
| } | ||
| ], | ||
| "outputs": [ | ||
| { | ||
| "type": "JsonField", | ||
| "name": "predictions", | ||
| "selector": "$steps.classifier.predictions", | ||
| }, | ||
| { | ||
| "type": "JsonField", | ||
| "name": "inference_id", | ||
| "selector": "$steps.classifier.inference_id", | ||
| }, | ||
| ], | ||
| } | ||
|
|
||
|
|
||
| MULTI_LABEL_CLASSIFICATION_RESULTS_FOR_ENVIRONMENT = { | ||
| PlatformEnvironment.ROBOFLOW_STAGING: [ | ||
| {"dog"}, | ||
| {"cat", "dog"}, | ||
| ], | ||
| PlatformEnvironment.ROBOFLOW_PLATFORM: [ | ||
| {"dog"}, | ||
| set(), | ||
| ], | ||
| } | ||
|
|
||
|
|
||
| @pytest.mark.flaky(retries=4, delay=1) | ||
| def test_multi_label_classification_workflow( | ||
| platform_environment: PlatformEnvironment, | ||
| classification_service_url: str, | ||
| classification_model_id: str, | ||
| dogs_image: np.ndarray, | ||
| license_plate_image: np.ndarray, | ||
| ) -> None: | ||
| # given | ||
| client = InferenceHTTPClient( | ||
| api_url=classification_service_url, | ||
| api_key=ROBOFLOW_API_KEY, | ||
| ) | ||
|
|
||
| # when | ||
| result = client.run_workflow( | ||
| specification=MULTI_LABEL_CLASSIFICATION_WORKFLOW, | ||
| images={ | ||
| "image": [dogs_image, license_plate_image], | ||
| }, | ||
| parameters={ | ||
| "model_id": classification_model_id, | ||
| }, | ||
| ) | ||
|
|
||
| # then | ||
| assert len(result) == 2, "2 images submitted, expected two outputs" | ||
| assert set(result[0].keys()) == { | ||
| "predictions", | ||
| "inference_id", | ||
| }, "Expected all outputs to be registered" | ||
| assert set(result[1].keys()) == { | ||
| "predictions", | ||
| "inference_id", | ||
| }, "Expected all outputs to be registered" | ||
| unique_inference_ids = {r["inference_id"] for r in result} | ||
| assert len(unique_inference_ids) == 2, "Expected unique inference ids granted" | ||
| predicted_classes = [set(r["predictions"]["predicted_classes"]) for r in result] | ||
| assert ( | ||
| predicted_classes | ||
| == MULTI_LABEL_CLASSIFICATION_RESULTS_FOR_ENVIRONMENT[platform_environment] | ||
| ) |
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