def test_random_crop_with_bbox_op_coco_c(plot_vis=False): """ Prints images and bboxes side by side with and without RandomCropWithBBox Op applied, Testing with Coco dataset """ logger.info("test_random_crop_with_bbox_op_coco_c") # load dataset dataCoco1 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection", decode=True, shuffle=False) dataCoco2 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection", decode=True, shuffle=False) test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200]) dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"], output_columns=["image", "bbox"], columns_order=["image", "bbox"], operations=[test_op]) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
def test_bounding_box_augment_op_coco_c(plot_vis=False): """ Prints images and bboxes side by side with and without BoundingBoxAugment Op applied, Testing with COCO dataset """ logger.info("test_bounding_box_augment_op_coco_c") dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1) dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"], output_columns=["image", "bbox"], columns_order=["image", "bbox"], operations=[test_op]) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
def test_resize_with_bbox_op_coco_c(plot_vis=False): """ Prints images and bboxes side by side with and without ResizeWithBBox Op applied, tests with MD5 check, expected to pass Testing with COCO dataset """ logger.info("test_resize_with_bbox_op_coco_c") # Load dataset dataCOCO1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) dataCOCO2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) test_op = c_vision.ResizeWithBBox(200) # map to apply ops dataCOCO2 = dataCOCO2.map(operations=[test_op], input_columns=["image", "bbox"], output_columns=["image", "bbox"], column_order=["image", "bbox"]) filename = "resize_with_bbox_op_01_c_coco_result.npz" save_and_check_md5(dataCOCO2, filename, generate_golden=GENERATE_GOLDEN) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataCOCO1.create_dict_iterator(num_epochs=1, output_numpy=True), dataCOCO2.create_dict_iterator(num_epochs=1, output_numpy=True)): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp, annot_name="bbox")
def test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False): """ Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied, tests with MD5 check, expected to pass testing with COCO dataset """ logger.info("test_random_resize_with_bbox_op_rand_coco_c") original_seed = config_get_set_seed(231) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # Load dataset dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) test_op = c_vision.RandomResizeWithBBox(200) # map to apply ops dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"], output_columns=["image", "bbox"], columns_order=["image", "bbox"], operations=[test_op]) filename = "random_resize_with_bbox_op_01_c_coco_result.npz" save_and_check_md5(dataCoco2, filename, generate_golden=GENERATE_GOLDEN) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp, annot_name="bbox") # Restore config setting ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_coco_detection(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True, shuffle=False) num_iter = 0 image_shape = [] bbox = [] category_id = [] for data in data1.create_dict_iterator(): image_shape.append(data["image"].shape) bbox.append(data["bbox"]) category_id.append(data["category_id"]) num_iter += 1 assert num_iter == 6 assert image_shape[0] == (2268, 4032, 3) assert image_shape[1] == (561, 595, 3) assert image_shape[2] == (607, 585, 3) assert image_shape[3] == (642, 675, 3) assert image_shape[4] == (2268, 4032, 3) assert image_shape[5] == (2268, 4032, 3) assert np.array_equal(np.array([[10., 10., 10., 10.], [70., 70., 70., 70.]]), bbox[0]) assert np.array_equal(np.array([[20., 20., 20., 20.], [80., 80., 80.0, 80.]]), bbox[1]) assert np.array_equal(np.array([[30.0, 30.0, 30.0, 30.]]), bbox[2]) assert np.array_equal(np.array([[40., 40., 40., 40.]]), bbox[3]) assert np.array_equal(np.array([[50., 50., 50., 50.]]), bbox[4]) assert np.array_equal(np.array([[60., 60., 60., 60.]]), bbox[5]) assert np.array_equal(np.array([[1], [7]]), category_id[0]) assert np.array_equal(np.array([[2], [8]]), category_id[1]) assert np.array_equal(np.array([[3]]), category_id[2]) assert np.array_equal(np.array([[4]]), category_id[3]) assert np.array_equal(np.array([[5]]), category_id[4]) assert np.array_equal(np.array([[6]]), category_id[5])
def test_coco_panoptic(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic", decode=True, shuffle=False) num_iter = 0 image_shape = [] bbox = [] category_id = [] iscrowd = [] area = [] for data in data1.create_dict_iterator(): image_shape.append(data["image"].shape) bbox.append(data["bbox"]) category_id.append(data["category_id"]) iscrowd.append(data["iscrowd"]) area.append(data["area"]) num_iter += 1 assert num_iter == 2 assert image_shape[0] == (2268, 4032, 3) assert np.array_equal( np.array([[472, 173, 36, 48], [340, 22, 154, 301], [486, 183, 30, 35]]), bbox[0]) assert np.array_equal(np.array([[1], [1], [2]]), category_id[0]) assert np.array_equal(np.array([[0], [0], [0]]), iscrowd[0]) assert np.array_equal(np.array([[705], [14062], [626]]), area[0]) assert image_shape[1] == (642, 675, 3) assert np.array_equal( np.array([[103, 133, 229, 422], [243, 175, 93, 164]]), bbox[1]) assert np.array_equal(np.array([[1], [3]]), category_id[1]) assert np.array_equal(np.array([[0], [0]]), iscrowd[1]) assert np.array_equal(np.array([[43102], [6079]]), area[1])
def test_coco_sampler_chain(): """ Test Coco sampler chain """ logger.info("test_coco_sampler_chain") sampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=False, num_samples=5) child_sampler = ds.RandomSampler(replacement=True, num_samples=2) sampler.add_child(child_sampler) data1 = ds.CocoDataset(COCO_DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True, sampler=sampler) # Verify dataset size data1_size = data1.get_dataset_size() logger.info("dataset size is: {}".format(data1_size)) assert data1_size == 1 # Verify number of rows assert sum([1 for _ in data1]) == 1 # Verify dataset contents res = [] for item in data1.create_tuple_iterator(num_epochs=1, output_numpy=True): logger.info("item: {}".format(item)) res.append(item) logger.info("dataset: {}".format(res))
def test_coco_keypoint(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint", decode=True, shuffle=False) num_iter = 0 image_shape = [] keypoints = [] num_keypoints = [] for data in data1.create_dict_iterator(): image_shape.append(data["image"].shape) keypoints.append(data["keypoints"]) num_keypoints.append(data["num_keypoints"]) num_iter += 1 assert num_iter == 2 assert image_shape[0] == (2268, 4032, 3) assert image_shape[1] == (561, 595, 3) assert np.array_equal( np.array([[ 368., 61., 1., 369., 52., 2., 0., 0., 0., 382., 48., 2., 0., 0., 0., 368., 84., 2., 435., 81., 2., 362., 125., 2., 446., 125., 2., 360., 153., 2., 0., 0., 0., 397., 167., 1., 439., 166., 1., 369., 193., 2., 461., 234., 2., 361., 246., 2., 474., 287., 2. ]]), keypoints[0]) assert np.array_equal(np.array([[14]]), num_keypoints[0]) assert np.array_equal( np.array([[ 244., 139., 2., 0., 0., 0., 226., 118., 2., 0., 0., 0., 154., 159., 2., 143., 261., 2., 135., 312., 2., 271., 423., 2., 184., 530., 2., 261., 280., 2., 347., 592., 2., 0., 0., 0., 123., 596., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ]]), keypoints[1]) assert np.array_equal(np.array([[10]]), num_keypoints[1])
def test_coco_dataset_size(): dataset = ds.CocoDataset(COCO_DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True, shuffle=False) assert dataset.get_dataset_size() == 6 dataset_shard_2_0 = ds.CocoDataset(COCO_DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True, shuffle=False, num_shards=2, shard_id=0) assert dataset_shard_2_0.get_dataset_size() == 3
def test_get_column_name_coco(): data = ds.CocoDataset(COCO_DIR, annotation_file=COCO_ANNOTATION, task="Detection", decode=True, shuffle=False) assert data.get_col_names() == ["image", "bbox", "category_id", "iscrowd"]
def test_coco_panootic_classindex(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic", decode=True) class_index = data1.get_class_indexing() assert class_index == {'person': [1, 1], 'bicycle': [2, 1], 'car': [3, 1]} num_iter = 0 for _ in data1.__iter__(): num_iter += 1 assert num_iter == 2
def test_coco_case_0(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True) data1 = data1.shuffle(10) data1 = data1.batch(3, pad_info={}) num_iter = 0 for _ in data1.create_dict_iterator(): num_iter += 1 assert num_iter == 2
def test_coco_detection_classindex(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True) class_index = data1.get_class_indexing() assert class_index == {'person': [1], 'bicycle': [2], 'car': [3], 'cat': [4], 'dog': [5], 'monkey': [6], 'bag': [7], 'orange': [8]} num_iter = 0 for _ in data1.__iter__(): num_iter += 1 assert num_iter == 6
def test_coco_case_2(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True) resize_op = vision.Resize((224, 224)) data1 = data1.map(input_columns=["image"], operations=resize_op) data1 = data1.repeat(4) num_iter = 0 for _ in data1.__iter__(): num_iter += 1 assert num_iter == 24
def test_coco_case_exception(): try: data1 = ds.CocoDataset("path_not_exist/", annotation_file=ANNOTATION_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "does not exist or permission denied" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file="./file_not_exist", task="Detection") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "does not exist or permission denied" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Invalid task") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "Invalid task type" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=LACKOFIMAGE_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "Invalid node found in json" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_CATEGORY_ID_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "category_id can't find in categories" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "json.exception.parse_error" in str(e) try: sampler = ds.PKSampler(3) data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection", sampler=sampler) for _ in data1.__iter__(): pass assert False except ValueError as e: assert "CocoDataset doesn't support PKSampler" in str(e)
def test_coco_stuff(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff", decode=True, shuffle=False) num_iter = 0 image_shape = [] segmentation = [] iscrowd = [] for data in data1.create_dict_iterator(): image_shape.append(data["image"].shape) segmentation.append(data["segmentation"]) iscrowd.append(data["iscrowd"]) num_iter += 1 assert num_iter == 6 assert image_shape[0] == (2268, 4032, 3) assert image_shape[1] == (561, 595, 3) assert image_shape[2] == (607, 585, 3) assert image_shape[3] == (642, 675, 3) assert image_shape[4] == (2268, 4032, 3) assert image_shape[5] == (2268, 4032, 3) assert np.array_equal( np.array([[10., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [70., 72., 73., 74., 75., -1., -1., -1., -1., -1.]]), segmentation[0]) assert np.array_equal(np.array([[0], [0]]), iscrowd[0]) assert np.array_equal( np.array([[ 20.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0 ], [10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, -1.0]]), segmentation[1]) assert np.array_equal(np.array([[0], [1]]), iscrowd[1]) assert np.array_equal( np.array([[40., 42., 43., 44., 45., 46., 47., 48., 49., 40., 41., 42.]]), segmentation[2]) assert np.array_equal(np.array([[0]]), iscrowd[2]) assert np.array_equal( np.array( [[50., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63.]]), segmentation[3]) assert np.array_equal(np.array([[0]]), iscrowd[3]) assert np.array_equal( np.array([[ 60., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74. ]]), segmentation[4]) assert np.array_equal(np.array([[0]]), iscrowd[4]) assert np.array_equal( np.array([[60., 62., 63., 64., 65., 66., 67.], [68., 69., 70., 71., 72., 73., 74.]]), segmentation[5]) assert np.array_equal(np.array([[0]]), iscrowd[5])
def test_coco_case_1(): data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True) sizes = [0.5, 0.5] randomize = False dataset1, dataset2 = data1.split(sizes=sizes, randomize=randomize) num_iter = 0 for _ in dataset1.create_dict_iterator(): num_iter += 1 assert num_iter == 3 num_iter = 0 for _ in dataset2.create_dict_iterator(): num_iter += 1 assert num_iter == 3
def test_coco_case_exception(): try: data1 = ds.CocoDataset("path_not_exist/", annotation_file=ANNOTATION_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "does not exist or permission denied" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file="./file_not_exist", task="Detection") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "does not exist or permission denied" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Invalid task") for _ in data1.__iter__(): pass assert False except ValueError as e: assert "Invalid task type" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=LACKOFIMAGE_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "invalid node found in json" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_CATEGORY_ID_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "category_id can't find in categories" in str(e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection") for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "json.exception.parse_error" in str(e) try: sampler = ds.PKSampler(3) data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection", sampler=sampler) for _ in data1.__iter__(): pass assert False except ValueError as e: assert "CocoDataset doesn't support PKSampler" in str(e) def exception_func(item): raise Exception("Error occur!") try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection") data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection") data1 = data1.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1) data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection") data1 = data1.map(operations=exception_func, input_columns=["bbox"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection") data1 = data1.map(operations=exception_func, input_columns=["category_id"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff") data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff") data1 = data1.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1) data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff") data1 = data1.map(operations=exception_func, input_columns=["segmentation"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff") data1 = data1.map(operations=exception_func, input_columns=["iscrowd"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint") data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint") data1 = data1.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1) data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint") data1 = data1.map(operations=exception_func, input_columns=["keypoints"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint") data1 = data1.map(operations=exception_func, input_columns=["num_keypoints"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic") data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic") data1 = data1.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1) data1 = data1.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic") data1 = data1.map(operations=exception_func, input_columns=["bbox"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic") data1 = data1.map(operations=exception_func, input_columns=["category_id"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e) try: data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic") data1 = data1.map(operations=exception_func, input_columns=["area"], num_parallel_workers=1) for _ in data1.__iter__(): pass assert False except RuntimeError as e: assert "map operation: [PyFunc] failed. The corresponding data files" in str( e)