def test_cifar100_exception():
"""
Test error cases for Cifar100Dataset
"""
logger.info("Test error cases for Cifar100Dataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, sampler=ds.PKSampler(3))
error_msg_2 = "sampler and sharding cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_2):
ds.Cifar100Dataset(DATA_DIR_100, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
with pytest.raises(RuntimeError, match=error_msg_3):
ds.Cifar100Dataset(DATA_DIR_100, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.Cifar100Dataset(DATA_DIR_100, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar100Dataset(DATA_DIR_100, num_shards=2, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar10Dataset(DATA_DIR_100, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=88)
def test_chained_sampler_06():
logger.info("Test Case Chained Sampler - WeightedRandom and PKSampler")
# Create chained sampler, WeightedRandom and PKSampler
weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
sampler = ds.WeightedRandomSampler(weights=weights, num_samples=12)
child_sampler = ds.PKSampler(num_val=3) # Number of elements per class is 3 (and there are 4 classes)
sampler.add_child(child_sampler)
# Create ImageFolderDataset with sampler
data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 12
# Verify number of iterations
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
# in this example, each dictionary has keys "image" and "label"
logger.info("image is {}".format(item["image"]))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
# Note: WeightedRandomSampler produces 12 samples
# Note: Child PKSampler produces 12 samples
assert num_iter == 12
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_cv_minddataset_pk_sample_error_class_column():
create_cv_mindrecord(1)
columns_list = ["data", "file_name", "label"]
num_readers = 4
sampler = ds.PKSampler(5, None, True, 'no_exsit_column')
with pytest.raises(Exception, match="MindRecordOp launch failed"):
data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, sampler=sampler)
num_iter = 0
for _ in data_set.create_dict_iterator():
num_iter += 1
os.remove(CV_FILE_NAME)
os.remove("{}.db".format(CV_FILE_NAME))
def test_cifar10_pk_sampler_get_dataset_size():
"""
Test Cifar10Dataset with PKSampler and get_dataset_size
"""
sampler = ds.PKSampler(3)
data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
num_iter = 0
ds_sz = data.get_dataset_size()
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
assert ds_sz == num_iter == 30
def test_cv_minddataset_pk_sample_exclusive_shuffle():
create_cv_mindrecord(1)
columns_list = ["data", "file_name", "label"]
num_readers = 4
sampler = ds.PKSampler(2)
with pytest.raises(Exception, match="sampler and shuffle cannot be specified at the same time."):
data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers,
sampler=sampler, shuffle=False)
num_iter = 0
for _ in data_set.create_dict_iterator():
num_iter += 1
os.remove(CV_FILE_NAME)
os.remove("{}.db".format(CV_FILE_NAME))
def test_cifar10_with_chained_sampler_get_dataset_size():
"""
Test Cifar10Dataset with PKSampler chained with a SequentialSampler and get_dataset_size
"""
sampler = ds.SequentialSampler(start_index=0, num_samples=5)
child_sampler = ds.PKSampler(4)
sampler.add_child(child_sampler)
data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
num_iter = 0
ds_sz = data.get_dataset_size()
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
assert ds_sz == num_iter == 5
def test_cifar100_pk_sampler():
"""
Test Cifar100Dataset with PKSampler
"""
logger.info("Test Cifar100Dataset with PKSampler")
golden = [i for i in range(20)]
sampler = ds.PKSampler(1)
data = ds.Cifar100Dataset(DATA_DIR_100, sampler=sampler)
num_iter = 0
label_list = []
for item in data.create_dict_iterator():
label_list.append(item["coarse_label"])
num_iter += 1
np.testing.assert_array_equal(golden, label_list)
assert num_iter == 20
def test_cv_minddataset_pk_sample_no_column(add_and_remove_cv_file):
"""tutorial for cv minderdataset."""
num_readers = 4
sampler = ds.PKSampler(2)
data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers,
sampler=sampler)
assert data_set.get_dataset_size() == 6
num_iter = 0
for item in data_set.create_dict_iterator():
logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
logger.info("-------------- item[file_name]: \
{}------------------------".format("".join([chr(x) for x in item["file_name"]])))
logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
num_iter += 1
def test_cifar10_pk_sampler():
"""
Test Cifar10Dataset with PKSampler
"""
logger.info("Test Cifar10Dataset Op with PKSampler")
golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
sampler = ds.PKSampler(3)
data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
num_iter = 0
label_list = []
for item in data.create_dict_iterator():
label_list.append(item["label"])
num_iter += 1
np.testing.assert_array_equal(golden, label_list)
assert num_iter == 30
def test_mnist_pk_sampler():
"""
Test MnistDataset with PKSampler
"""
logger.info("Test MnistDataset Op with PKSampler")
golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
sampler = ds.PKSampler(3)
data = ds.MnistDataset(DATA_DIR, sampler=sampler)
num_iter = 0
label_list = []
for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
label_list.append(item["label"])
num_iter += 1
np.testing.assert_array_equal(golden, label_list)
assert num_iter == 30
def test_cv_minddataset_pk_sample_out_of_range(add_and_remove_cv_file):
"""tutorial for cv minderdataset."""
columns_list = ["data", "file_name", "label"]
num_readers = 4
sampler = ds.PKSampler(5, None, True)
data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
sampler=sampler)
assert data_set.get_dataset_size() == 15
num_iter = 0
for item in data_set.create_dict_iterator():
logger.info(
"-------------- cv reader basic: {} ------------------------".format(num_iter))
logger.info("-------------- item[file_name]: \
{}------------------------".format(to_str(item["file_name"])))
logger.info(
"-------------- item[label]: {} ----------------------------".format(item["label"]))
num_iter += 1
def test_pk_sampler():
logger.info("Test Case PKSampler")
# define parameters
repeat_count = 1
# apply dataset operations
sampler = ds.PKSampler(3)
data1 = ds.ImageFolderDatasetV2(DATA_DIR, sampler=sampler)
data1 = data1.repeat(repeat_count)
num_iter = 0
for item in data1.create_dict_iterator(): # each data is a dictionary
# in this example, each dictionary has keys "image" and "label"
logger.info("image is {}".format(item["image"]))
logger.info("label is {}".format(item["label"]))
num_iter += 1
logger.info("Number of data in data1: {}".format(num_iter))
assert num_iter == 12
def test_imagefolder_sampler_chain():
"""
Test ImageFolderDataset sampler chain
"""
logger.info("test_imagefolder_sampler_chain")
sampler = ds.SequentialSampler(start_index=1, num_samples=3)
child_sampler = ds.PKSampler(2)
sampler.add_child(child_sampler)
data1 = ds.ImageFolderDataset(IMAGENET_RAWDATA_DIR, sampler=sampler)
# Verify dataset size
data1_size = data1.get_dataset_size()
logger.info("dataset size is: {}".format(data1_size))
assert data1_size == 3
# Verify number of rows
assert sum([1 for _ in data1]) == 3
# 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_mnist_exception():
"""
Test error cases for MnistDataset
"""
logger.info("Test error cases for MnistDataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.MnistDataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
error_msg_2 = "sampler and sharding cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_2):
ds.MnistDataset(DATA_DIR,
sampler=ds.PKSampler(3),
num_shards=2,
shard_id=0)
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
with pytest.raises(RuntimeError, match=error_msg_3):
ds.MnistDataset(DATA_DIR, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.MnistDataset(DATA_DIR, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.MnistDataset(DATA_DIR, num_shards=5, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.MnistDataset(DATA_DIR, num_shards=5, shard_id=5)
with pytest.raises(ValueError, match=error_msg_5):
ds.MnistDataset(DATA_DIR, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
with pytest.raises(ValueError, match=error_msg_6):
ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
error_msg_7 = "Argument shard_id"
with pytest.raises(TypeError, match=error_msg_7):
ds.MnistDataset(DATA_DIR, num_shards=2, shard_id="0")
def exception_func(item):
raise Exception("Error occur!")
error_msg_8 = "The corresponding data files"
with pytest.raises(RuntimeError, match=error_msg_8):
data = ds.MnistDataset(DATA_DIR)
data = data.map(operations=exception_func,
input_columns=["image"],
num_parallel_workers=1)
for _ in data.__iter__():
pass
with pytest.raises(RuntimeError, match=error_msg_8):
data = ds.MnistDataset(DATA_DIR)
data = data.map(operations=vision.Decode(),
input_columns=["image"],
num_parallel_workers=1)
data = data.map(operations=exception_func,
input_columns=["image"],
num_parallel_workers=1)
for _ in data.__iter__():
pass
with pytest.raises(RuntimeError, match=error_msg_8):
data = ds.MnistDataset(DATA_DIR)
data = data.map(operations=exception_func,
input_columns=["label"],
num_parallel_workers=1)
for _ in data.__iter__():
pass
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)
