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)
