Class Scalar

caffe2/python/schema.py:678–897

Represents a typed scalar or tensor of fixed shape. A Scalar is a leaf in a schema tree, translating to exactly one tensor in the dataset's underlying storage. Usually, the tensor storing the actual values of this field is a 1D tensor, representing a series of values in its domain.

class Scalar(Field):
    """Represents a typed scalar or tensor of fixed shape.

    A Scalar is a leaf in a schema tree, translating to exactly one tensor in
    the dataset's underlying storage.

    Usually, the tensor storing the actual values of this field is a 1D tensor,
    representing a series of values in its domain. It is possible however to
    have higher rank values stored as a Scalar, as long as all entries have
    the same shape.

    E.g.:

        Scalar(np.float64)

            Scalar field of type float64. Caffe2 will expect readers and
            datasets to expose it as a 1D tensor of doubles (vector), where
            the size of the vector is determined by this fields' domain.

        Scalar((np.int32, 5))

            Tensor field of type int32. Caffe2 will expect readers and
            datasets to implement it as a 2D tensor (matrix) of shape (L, 5),
            where L is determined by this fields' domain.

        Scalar((str, (10, 20)))

            Tensor field of type str. Caffe2 will expect readers and
            datasets to implement it as a 3D tensor of shape (L, 10, 20),
            where L is determined by this fields' domain.

    If the field type is unknown at construction time, call Scalar(), that will
    default to np.void as its dtype.

    It is an error to pass a structured dtype to Scalar, since it would contain
    more than one field. Instead, use from_dtype, which will construct
    a nested `Struct` field reflecting the given dtype's structure.

    A Scalar can also contain a blob, which represents the value of this
    Scalar. A blob can be either a numpy.ndarray, in which case it contain the
    actual contents of the Scalar, or a BlobReference, which represents a
    blob living in a caffe2 Workspace. If blob of different types are passed,
    a conversion to numpy.ndarray is attempted.
    """

    __slots__: Sequence[str] = ("_metadata", "dtype", "_original_dtype", "_blob")

    def __init__(self, dtype=None, blob=None, metadata=None):
        self._metadata = None
        self.set(dtype, blob, metadata, unsafe=True)
        super().__init__([])

    def field_names(self):
        return ['']

    def field_type(self):
        return self.dtype