def make_config(self, **options: Any) -> DatasetConfig:
if not self._valid_options and options:
raise ValueError(
f"Dataset {self.name} does not take any options, "
f"but got {sequence_to_str(list(options), separate_last=' and')}."
)
for name, arg in options.items():
if name not in self._valid_options:
raise ValueError(
add_suggestion(
f"Unknown option '{name}' of dataset {self.name}.",
word=name,
possibilities=sorted(self._valid_options.keys()),
))
valid_args = self._valid_options[name]
if arg not in valid_args:
raise ValueError(
add_suggestion(
f"Invalid argument '{arg}' for option '{name}' of dataset {self.name}.",
word=arg,
possibilities=valid_args,
))
return DatasetConfig(self.default_config, **options)
def __new__(cls, data, *, dtype=None, device=None, like=None, **kwargs):
unknown_meta_attrs = kwargs.keys() - cls._META_ATTRS
if unknown_meta_attrs:
unknown_meta_attr = sorted(unknown_meta_attrs)[0]
raise TypeError(
add_suggestion(
f"{cls.__name__}() got unexpected keyword '{unknown_meta_attr}'.",
word=unknown_meta_attr,
possibilities=sorted(cls._META_ATTRS),
)
)
if like is not None:
dtype = dtype or like.dtype
device = device or like.device
data = cls._to_tensor(data, dtype=dtype, device=device)
requires_grad = False
self = torch.Tensor._make_subclass(cast(_TensorBase, cls), data, requires_grad)
meta_data = dict()
for attr, (explicit, fallback) in cls._parse_meta_data(**kwargs).items():
if explicit is not DEFAULT:
value = explicit
elif like is not None:
value = getattr(like, attr)
else:
value = fallback(data) if callable(fallback) else fallback
meta_data[attr] = value
self._meta_data = meta_data
return self
def make_config(self, **options: Any) -> DatasetConfig:
for name, arg in options.items():
if name not in self._valid_options:
raise ValueError(
add_suggestion(
f"Unknown option '{name}' of dataset {self.name}.",
word=name,
possibilities=sorted(self._valid_options.keys()),
))
valid_args = self._valid_options[name]
if arg not in valid_args:
raise ValueError(
add_suggestion(
f"Invalid argument '{arg}' for option '{name}' of dataset {self.name}.",
word=arg,
possibilities=valid_args,
))
return DatasetConfig(self.default_config, **options)
def find(dct: Dict[str, T], name: str) -> T:
name = name.lower()
try:
return dct[name]
except KeyError as error:
raise ValueError(
add_suggestion(
f"Unknown dataset '{name}'.",
word=name,
possibilities=dct.keys(),
alternative_hint=lambda _:
("You can use torchvision.datasets.list_datasets() to get a list of all available datasets."
),
)) from error