Function as_numpy

python/paddle/base/executor.py:148–191

Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information. For higher dimensional sequence data, please use DenseTensor directly. Examples: .. code-block:: pycon >>> import paddle.base as base >>> import numpy >>>

(tensor, copy=False)

def as_numpy(tensor, copy=False):
    """
    Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information.
    For higher dimensional sequence data, please use DenseTensor directly.

    Examples:
        .. code-block:: pycon

            >>> import paddle.base as base
            >>> import numpy

            >>> new_scope = base.Scope()
            >>> with base.scope_guard(new_scope):
            ...     base.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), base.CPUPlace())
            >>> tensor = new_scope.find_var("data").get_tensor()
            >>> base.executor.as_numpy(tensor)  # or numpy.array(new_scope.find_var("data").get_tensor())

    Args:
       tensor(Variable): a instance of Tensor
       copy(bool, optional): Whether to use deep copy.

    Returns:
        numpy.ndarray
    """
    if isinstance(tensor, core.DenseTensorArray):
        return [as_numpy(t, copy) for t in tensor]
    if isinstance(tensor, list):
        return [as_numpy(t, copy) for t in tensor]
    assert isinstance(tensor, core.DenseTensor)
    lod = tensor.lod()
    if len(lod) > 0:
        raise RuntimeError(
            "Some of your fetched tensors hold LoD information. \
            They can not be completely cast to Python ndarray. \
            Please set the parameter 'return_numpy' as 'False' to \
            return DenseTensor itself directly."
        )
    if tensor._is_initialized():
        if copy:
            return np.array(tensor)
        else:
            return np.asarray(tensor)
    else:
        return None


def dtype_is_compatible_with(first, second):