from_struct_array#

tensordict.from_struct_array(struct_array, *, auto_batch_size: bool = False, batch_dims: int | None = None, device: device | None = None, batch_size: Size | None = None) → TensorDictBase#

Converts a NumPy structured array to a tensordict, with one entry per field.

The tensors share memory with the array. See TensorDictBase.from_struct_array.

Parameters:

struct_array (np.ndarray) – the structured array to convert.

Keyword Arguments:

auto_batch_size (bool, optional) – if True, sets the batch size to the largest leading shape that all the entries share. Exclusive with batch_size. Defaults to False.
batch_dims (int, optional) – with auto_batch_size, the maximum number of batch dimensions. Ignored without auto_batch_size, with a FutureWarning when it would change the result. Starting with TensorDict 0.17, the default will change to turning on auto_batch_size when batch_dims is given, as from_dict does. Defaults to None.
device (device, str or int, optional) – the device of the result. Leaves on another device are moved to it. None leaves each leaf where it is, and the result has no device. Defaults to None.
batch_size (Size or list of int, optional) – the batch size of the result: the leading dimensions that all its entries share. Exclusive with auto_batch_size. Defaults to None, which uses the shape of the array.

Returns:

A TensorDict.

Examples

>>> import numpy as np
>>> from tensordict import from_struct_array
>>> x = np.array(
...     [("Rex", 9, 81.0), ("Fido", 3, 27.0)],
...     dtype=[("name", "U10"), ("age", "i4"), ("weight", "f4")],
... )
>>> td = from_struct_array(x)
>>> td.batch_size, td["age"]
(torch.Size([2]), tensor([9, 3], dtype=torch.int32))
>>> x["age"] += 1  # the tensordict shares memory with the array
>>> td["age"]
tensor([10,  4], dtype=torch.int32)
>>> bool((td.to_struct_array() == x).all())
True

from_struct_array#

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