reference/generated/tensordict.TensorDictBase.get

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

TensorDictBase.get

TensorDictBase.get(key)

TensorDictBase.get(key, default)

Returns the value stored at key, or default if the key is missing.

td.get(key) is the same as td[key], except for a missing key: td[key] raises KeyError, and get returns default.

Parameters:

• key (NestedKey) – the key to read. A tuple reads a nested entry: td.get(("a", "b")) is td["a"]["b"]. See Keys, nested keys, and locking.

• default (optional) – the value returned if the key is missing. Defaults to None, as for dict.get(). A nested key that runs through a tensor is missing too: get returns the default passed to it, and without one raises a ValueError that names the tensor.

Changed in version 0.7: A missing key returns None instead of raising KeyError. set_get_defaults_to_none(False) and TD_GET_DEFAULTS_TO_NONE=0 restore the old behavior. They are deprecated and will be removed in 0.17.

Keyword Arguments:

• as_list (bool, optional) – if True, a lazy stack returns the entries as a list, one item per stacked tensordict. Exclusive with as_padded_tensor and as_nested_tensor. Defaults to False.

• as_padded_tensor (bool, optional) – if True, a lazy stack pads the entries to the largest shape and returns them as one tensor: each dim is padded to its largest size, also across the levels of a lazy stack of lazy stacks. padding_value sets the padding value and padding_side sets the side of every padded dim. Exclusive with as_list and as_nested_tensor. Defaults to False.

• as_nested_tensor (bool, optional) – if True, a lazy stack returns the entries as a nested tensor (torch.nested). layout sets its layout. A lazy stack of lazy stacks raises a ValueError, as a nested tensor has a single nested level. Exclusive with as_list and as_padded_tensor. Defaults to False.

• layout (layout, optional) – the layout of the nested tensor when as_nested_tensor=True: torch.jagged or torch.strided. Defaults to torch.jagged.

• padding_side (str) – The side of padding. Must be “left” or “right”. Defaults to “right”.

• padding_value (scalar or bool, optional) – The padding value. Defaults to 0.0.

A lazy stack reads the entry of every stacked tensordict and stacks the results into a new tensor. If the entry shapes differ, the plain call raises a RuntimeError: pass as_list, as_padded_tensor or as_nested_tensor to get a list, a padded tensor or a nested tensor instead.

Note

A dense tensordict ignores the keyword arguments today and warns for as_list=True and as_nested_tensor=True. Starting with TensorDict 0.17, the default will change to returning a list or a nested tensor, as a lazy stack does.

Examples

>>> from tensordict import TensorDict
>>> td = TensorDict({"x": 1, "nested": {"y": 2}}, batch_size=[])
>>> td.get("x")
tensor(1)
>>> td.get(("nested", "y"))
tensor(2)
>>> print(td.get("z"))
None
>>> td.get("z", 0)
0

>>> import torch
>>> from tensordict import lazy_stack
>>> td = lazy_stack([
...     TensorDict({"x": torch.ones(1,)}),
...     TensorDict({"x": torch.ones(2,) * 2}),
... ])
>>> nested = td.get("x", as_nested_tensor=True)
>>> nested.is_nested, nested.offsets()
(True, tensor([0, 1, 3]))
>>> td.get("x", as_padded_tensor=True)
tensor([[1., 0.],
        [2., 2.]])

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.

Tutorials that use get

Getting, setting, and nesting keys

Getting, setting, and nesting keys

Changing the shape of a TensorDict

Changing the shape of a TensorDict