def test_exceptions():
HubException()
AuthenticationException()
AuthorizationException(Response())
AuthorizationException(Response(noerror=True))
NotFoundException()
BadRequestException(Response())
BadRequestException(Response(noerror=True))
OverLimitException()
ServerException()
BadGatewayException()
GatewayTimeoutException()
WaitTimeoutException()
LockedException()
HubDatasetNotFoundException("Hello")
PermissionException("Hello")
ShapeLengthException()
ShapeArgumentNotFoundException()
SchemaArgumentNotFoundException()
ValueShapeError("Shape 1", "Shape 2")
NoneValueException("Yahoo!")
ModuleNotInstalledException("my_module")
WrongUsernameException("usernameX")
NotHubDatasetToOverwriteException()
NotHubDatasetToAppendException()
DynamicTensorNotFoundException()
DynamicTensorShapeException("none")
DynamicTensorShapeException("length")
DynamicTensorShapeException("not_equal")
DynamicTensorShapeException("another_cause")
def _check_and_prepare_dir(self):
"""
Checks if input data is ok.
Creates or overwrites dataset folder.
Returns True dataset needs to be created opposed to read.
"""
fs, path, mode = self._fs, self._path, self._mode
if path.startswith("s3://"):
with open(posixpath.expanduser("~/.activeloop/store"), "rb") as f:
stored_username = json.load(f)["_id"]
current_username = path.split("/")[-2]
if stored_username != current_username:
try:
fs.listdir(path)
except:
raise WrongUsernameException(stored_username)
exist_meta = fs.exists(posixpath.join(path, "meta.json"))
if exist_meta:
if "w" in mode:
fs.rm(path, recursive=True)
fs.makedirs(path)
return True
return False
else:
if "r" in mode:
raise HubDatasetNotFoundException(path)
exist_dir = fs.exists(path)
if not exist_dir:
fs.makedirs(path)
elif get_file_count(fs, path) > 0:
if "w" in mode:
raise NotHubDatasetToOverwriteException()
else:
raise NotHubDatasetToAppendException()
return True
def load(tag, creds=None, session_creds=True) -> Dataset:
"""Load a dataset from repository using given url and credentials (optional)"""
fs, path = _load_fs_and_path(tag, creds, session_creds=session_creds)
fs: fsspec.AbstractFileSystem = fs
path_2 = f"{path}/meta.json"
if not fs.exists(path_2):
raise HubDatasetNotFoundException(tag)
with fs.open(path_2, "r") as f:
ds_meta = json.loads(f.read())
for name in ds_meta["tensors"]:
assert fs.exists(
f"{path}/{name}"
), f"Tensor {name} of {tag} dataset does not exist"
if "dask" not in sys.modules:
raise ModuleNotInstalledException("dask")
else:
import dask
import dask.array
global dask
if ds_meta["len"] == 0:
logger.warning("The dataset is empty (has 0 samples)")
return Dataset(
{
name: Tensor(
tmeta,
dask.array.from_array(
np.empty(shape=(0,) + tuple(tmeta["shape"][1:]), dtype="uint8"),
),
)
for name, tmeta in ds_meta["tensors"].items()
},
metainfo=ds_meta.get("metainfo"),
)
len_ = ds_meta["len"]
# added reverse compatibility for previous versions
for name, tmeta in ds_meta["tensors"].items():
if "chunksize" not in tmeta:
tmeta["chunksize"] = 1
return Dataset(
{
name: Tensor(
tmeta,
_dask_concat(
[
dask.array.from_delayed(
dask.delayed(_numpy_load)(
fs,
f"{path}/{name}/{i}.npy",
codec_from_name(tmeta.get("dcompress")),
),
shape=(min(tmeta["chunksize"], len_ - i),)
+ tuple(tmeta["shape"][1:]),
dtype=tmeta["dtype"],
)
for i in range(0, len_, tmeta["chunksize"])
]
),
)
for name, tmeta in ds_meta["tensors"].items()
},
metainfo=ds_meta.get("metainfo"),
)