diff --git a/tests/e2e/cli/test_cli.py b/tests/e2e/cli/test_cli.py index 18c6bda4d28..d97f2b2e9c5 100644 --- a/tests/e2e/cli/test_cli.py +++ b/tests/e2e/cli/test_cli.py @@ -327,38 +327,38 @@ def test_otx_explain_e2e_cli( reference_sal_vals = { # Classification "multi_label_cls_efficientnet_v2_light": ( - np.array([66, 97, 84, 33, 42, 79, 0], dtype=np.uint8), - "Slide6_class_0_saliency_map.png", + np.array([201, 209, 196, 158, 157, 119, 77], dtype=np.uint8), + "American_Crow_0031_25433_class_0_saliency_map.png", ), "h_label_cls_efficientnet_v2_light": ( - np.array([152, 193, 144, 132, 149, 204, 217], dtype=np.uint8), - "092_class_5_saliency_map.png", + np.array([102, 141, 134, 79, 66, 92, 84], dtype=np.uint8), + "108_class_4_saliency_map.png", ), # Detection "detection_yolox_tiny": ( - np.array([111, 163, 141, 141, 146, 147, 158, 169, 184, 193], dtype=np.uint8), - "Slide3_class_0_saliency_map.png", + np.array([182, 194, 187, 179, 188, 206, 215, 207, 177, 130], dtype=np.uint8), + "img_371_jpg_rf_a893e0bdc6fda0ba1b2a7f07d56cec23_class_0_saliency_map.png", ), "detection_ssd_mobilenetv2": ( - np.array([135, 80, 74, 34, 27, 32, 47, 42, 32, 34], dtype=np.uint8), - "Slide3_class_0_saliency_map.png", + np.array([118, 188, 241, 213, 160, 120, 86, 94, 111, 138], dtype=np.uint8), + "img_371_jpg_rf_a893e0bdc6fda0ba1b2a7f07d56cec23_class_0_saliency_map.png", ), "detection_atss_mobilenetv2": ( - np.array([22, 62, 64, 0, 27, 60, 59, 53, 37, 45], dtype=np.uint8), - "Slide3_class_0_saliency_map.png", + np.array([29, 39, 55, 69, 80, 88, 92, 86, 100, 88], dtype=np.uint8), + "img_371_jpg_rf_a893e0bdc6fda0ba1b2a7f07d56cec23_class_0_saliency_map.png", ), # Instance Segmentation "instance_segmentation_maskrcnn_efficientnetb2b": ( - np.array([54, 54, 54, 54, 0, 0, 0, 54, 0, 0], dtype=np.uint8), - "Slide3_class_0_saliency_map.png", + np.array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5], dtype=np.uint8), + "CDY_2018_class_0_saliency_map.png", ), } test_case_name = task + "_" + model_name if test_case_name in reference_sal_vals: actual_sal_vals = cv2.imread(str(latest_dir / "saliency_maps" / reference_sal_vals[test_case_name][1])) if test_case_name == "instance_segmentation_maskrcnn_efficientnetb2b": - # Take corner values due to map sparsity of InstSeg - actual_sal_vals = (actual_sal_vals[-10:, -1, -1]).astype(np.uint16) + # Take lower corner values due to map sparsity of InstSeg + actual_sal_vals = (actual_sal_vals[-10:, -1, 0]).astype(np.uint16) else: actual_sal_vals = (actual_sal_vals[:10, 0, 0]).astype(np.uint16) ref_sal_vals = reference_sal_vals[test_case_name][0] diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index ac61a5d3c7d..dd42c4fd41a 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -16,7 +16,7 @@ @pytest.fixture(scope="session") def fxt_ci_data_root() -> Path: - data_root = Path(os.environ.get("CI_DATA_ROOT", "/home/validation/data/v2")) + data_root = Path(os.environ.get("CI_DATA_ROOT", "/home/validation/data")) if not Path.is_dir(data_root): msg = f"cannot find {data_root}" raise FileNotFoundError(msg) @@ -87,22 +87,22 @@ def pytest_configure(config): @pytest.fixture() def fxt_target_dataset_per_task(fxt_ci_data_root) -> dict: return { - "multi_class_cls": Path(fxt_ci_data_root / "multiclass_classification/multiclass_CUB_small/1"), - "multi_label_cls": Path(fxt_ci_data_root / "multilabel_classification/multilabel_CUB_small/1"), - "h_label_cls": Path(fxt_ci_data_root / "hlabel_classification/hlabel_CUB_small/1"), - "detection": Path(fxt_ci_data_root / "detection/pothole_small/1"), - "rotated_detection": Path(fxt_ci_data_root / "detection/pothole_small/1"), - "instance_segmentation": Path(fxt_ci_data_root / "instance_seg/wgisd_small/1"), - "semantic_segmentation": Path(fxt_ci_data_root / "semantic_seg/kvasir_small/1"), - "action_classification": Path(fxt_ci_data_root / "action/action_classification/ucf_kinetics_5percent_small"), - "action_detection": Path(fxt_ci_data_root / "action/action_detection/UCF101_ava_5percent"), - "visual_prompting": Path(fxt_ci_data_root / "visual_prompting/wgisd_small/1"), + "multi_class_cls": Path(fxt_ci_data_root / "v2/multiclass_classification/multiclass_CUB_small/1"), + "multi_label_cls": Path(fxt_ci_data_root / "v2/multilabel_classification/multilabel_CUB_small/1"), + "h_label_cls": Path(fxt_ci_data_root / "v2/hlabel_classification/hlabel_CUB_small/1"), + "detection": Path(fxt_ci_data_root / "v2/detection/pothole_small/1"), + "rotated_detection": Path(fxt_ci_data_root / "v2/detection/pothole_small/1"), + "instance_segmentation": Path(fxt_ci_data_root / "v2/instance_seg/wgisd_small/1"), + "semantic_segmentation": Path(fxt_ci_data_root / "v2/semantic_seg/kvasir_small/1"), + "action_classification": Path(fxt_ci_data_root / "v2/action/action_classification/ucf_kinetics_5percent_small"), + "action_detection": Path(fxt_ci_data_root / "v2/action/action_detection/UCF101_ava_5percent"), + "visual_prompting": Path(fxt_ci_data_root / "v2/visual_prompting/wgisd_small/1"), "zero_shot_visual_prompting": Path( - fxt_ci_data_root / "zero_shot_visual_prompting/coco_car_person_medium_datumaro", + fxt_ci_data_root / "v2/zero_shot_visual_prompting/coco_car_person_medium", ), - "anomaly_classification": Path(fxt_ci_data_root / "anomaly/mvtec/bottle_small/1"), - "anomaly_detection": Path(fxt_ci_data_root / "anomaly/mvtec/hazelnut_large"), - "anomaly_segmentation": Path(fxt_ci_data_root / "anomaly/mvtec/hazelnut_large"), + "anomaly_classification": Path(fxt_ci_data_root / "v2/anomaly/mvtec/bottle_small/1"), + "anomaly_detection": Path(fxt_ci_data_root / "v2/anomaly/mvtec/hazelnut_large"), + "anomaly_segmentation": Path(fxt_ci_data_root / "v2/anomaly/mvtec/hazelnut_large"), } @@ -122,10 +122,7 @@ def fxt_cli_override_command_per_task() -> dict: "3", ], "visual_prompting": [], - "zero_shot_visual_prompting": [ - "--data.config.data_format", - "datumaro", - ], + "zero_shot_visual_prompting": [], "anomaly_classification": [], "anomaly_detection": [], "anomaly_segmentation": [],