LazyStackedCompositeSpec¶
- class torchrl.data.LazyStackedCompositeSpec(*specs: tuple[T, ...], dim: int)[source]¶
A lazy representation of a stack of composite specs.
Stacks composite specs together along one dimension. When random samples are drawn, a LazyStackedTensorDict is returned.
Indexing is allowed but only along the stack dimension.
This class is aimed to be used in multi-task and multi-agent settings, where heterogeneous specs may occur (same semantic but different shape).
- assert_is_in(value: Tensor) None ¶
Asserts whether a tensor belongs to the box, and raises an exception otherwise.
- Parameters:
value (torch.Tensor) – value to be checked.
- clear_device_()¶
Clears the device of the CompositeSpec.
- contains(item)¶
Returns whether a sample is contained within the space defined by the TensorSpec.
See
is_in()
for more information.
- encode(vals: Dict[str, Any], ignore_device: bool = False) Dict[str, Tensor] [source]¶
Encodes a value given the specified spec, and return the corresponding tensor.
- Parameters:
val (np.ndarray or torch.Tensor) – value to be encoded as tensor.
- Keyword Arguments:
ignore_device (bool, optional) – if
True
, the spec device will be ignored. This is used to group tensor casting within a call toTensorDict(..., device="cuda")
which is faster.- Returns:
torch.Tensor matching the required tensor specs.
- expand(*shape)[source]¶
Returns a new Spec with the extended shape.
- Parameters:
*shape (tuple or iterable of int) – the new shape of the Spec. Must comply with the current shape: its length must be at least as long as the current shape length, and its last values must be complient too; ie they can only differ from it if the current dimension is a singleton.
- flatten(start_dim, end_dim)¶
Flattens a tensorspec.
Check
flatten()
for more information on this method.
- get(item, default=<object object>)¶
Gets an item from the CompositeSpec.
If the item is absent, a default value can be passed.
- classmethod implements_for_spec(torch_function: Callable) Callable ¶
Register a torch function override for TensorSpec.
- abstract index(index: Union[int, Tensor, ndarray, slice, List], tensor_to_index: Tensor) Tensor ¶
Indexes the input tensor.
- Parameters:
index (int, torch.Tensor, slice or list) – index of the tensor
tensor_to_index – tensor to be indexed
- Returns:
indexed tensor
- is_empty()¶
Whether the composite spec contains specs or not.
- is_in(value) bool [source]¶
If the value
val
is in the box defined by the TensorSpec, returns True, otherwise False.- Parameters:
val (torch.Tensor) – value to be checked
- Returns:
boolean indicating if values belongs to the TensorSpec box
- items(include_nested: bool = False, leaves_only: bool = False, *, is_leaf: Callable[[type], bool] | None = None) _CompositeSpecItemsView [source]¶
Items of the CompositeSpec.
- Parameters:
include_nested (bool, optional) – if
False
, the returned keys will not be nested. They will represent only the immediate children of the root, and not the whole nested sequence, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next"]. Default is ``False`
, i.e. nested keys will not be returned.leaves_only (bool, optional) – if
False
, the values returned will contain every level of nesting, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next", ("next", "obs")]
. Default isFalse
.
- Keyword Arguments:
is_leaf (callable, optional) – reads a type and returns a boolean indicating if that type should be seen as a leaf. By default, all non-CompositeSpec nodes are considered as leaves.
- keys(include_nested: bool = False, leaves_only: bool = False, *, is_leaf: Callable[[type], bool] | None = None) _CompositeSpecKeysView [source]¶
Keys of the CompositeSpec.
The keys argument reflect those of
tensordict.TensorDict
.- Parameters:
include_nested (bool, optional) – if
False
, the returned keys will not be nested. They will represent only the immediate children of the root, and not the whole nested sequence, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next"]. Default is ``False`
, i.e. nested keys will not be returned.leaves_only (bool, optional) – if
False
, the values returned will contain every level of nesting, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next", ("next", "obs")]
. Default isFalse
.
- Keyword Arguments:
is_leaf (callable, optional) – reads a type and returns a boolean indicating if that type should be seen as a leaf. By default, all non-CompositeSpec nodes are considered as leaves.
- lock_(recurse=False)¶
Locks the CompositeSpec and prevents modification of its content.
This is only a first-level lock, unless specified otherwise through the
recurse
arg.Leaf specs can always be modified in place, but they cannot be replaced in their CompositeSpec parent.
Examples
>>> shape = [3, 4, 5] >>> spec = CompositeSpec( ... a=CompositeSpec( ... b=CompositeSpec(shape=shape[:3], device="cpu"), shape=shape[:2] ... ), ... shape=shape[:1], ... ) >>> spec["a"] = spec["a"].clone() >>> recurse = False >>> spec.lock_(recurse=recurse) >>> try: ... spec["a"] = spec["a"].clone() ... except RuntimeError: ... print("failed!") failed! >>> try: ... spec["a", "b"] = spec["a", "b"].clone() ... print("succeeded!") ... except RuntimeError: ... print("failed!") succeeded! >>> recurse = True >>> spec.lock_(recurse=recurse) >>> try: ... spec["a", "b"] = spec["a", "b"].clone() ... print("succeeded!") ... except RuntimeError: ... print("failed!") failed!
- project(val: TensorDictBase) TensorDictBase [source]¶
If the input tensor is not in the TensorSpec box, it maps it back to it given some heuristic.
- Parameters:
val (torch.Tensor) – tensor to be mapped to the box.
- Returns:
a torch.Tensor belonging to the TensorSpec box.
- rand(shape=None) TensorDictBase [source]¶
Returns a random tensor in the space defined by the spec. The sampling will be uniform unless the box is unbounded.
- Parameters:
shape (torch.Size) – shape of the random tensor
- Returns:
a random tensor sampled in the TensorSpec box.
- squeeze(dim: Optional[int] = None)¶
Returns a new Spec with all the dimensions of size
1
removed.When
dim
is given, a squeeze operation is done only in that dimension.- Parameters:
dim (int or None) – the dimension to apply the squeeze operation to
- to_numpy(val: TensorDict, safe: Optional[bool] = None) dict [source]¶
Returns the np.ndarray correspondent of an input tensor.
- Parameters:
val (torch.Tensor) – tensor to be transformed_in to numpy.
safe (bool) – boolean value indicating whether a check should be performed on the value against the domain of the spec. Defaults to the value of the
CHECK_SPEC_ENCODE
environment variable.
- Returns:
a np.ndarray
- type_check(value: Union[Tensor, TensorDictBase], selected_keys: Optional[Union[NestedKey, Sequence[NestedKey]]] = None)[source]¶
Checks the input value dtype against the TensorSpec dtype and raises an exception if they don’t match.
- Parameters:
value (torch.Tensor) – tensor whose dtype has to be checked
key (str, optional) – if the TensorSpec has keys, the value dtype will be checked against the spec pointed by the indicated key.
- unflatten(dim, sizes)¶
Unflattens a tensorspec.
Check
unflatten()
for more information on this method.
- unlock_(recurse=False)¶
Unlocks the CompositeSpec and allows modification of its content.
This is only a first-level lock modification, unless specified otherwise through the
recurse
arg.
- values(include_nested: bool = False, leaves_only: bool = False, *, is_leaf: Callable[[type], bool] | None = None) _CompositeSpecValuesView [source]¶
Values of the CompositeSpec.
- Parameters:
include_nested (bool, optional) – if
False
, the returned keys will not be nested. They will represent only the immediate children of the root, and not the whole nested sequence, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next"]. Default is ``False`
, i.e. nested keys will not be returned.leaves_only (bool, optional) – if
False
, the values returned will contain every level of nesting, i.e. aCompositeSpec(next=CompositeSpec(obs=None))
will lead to the keys["next", ("next", "obs")]
. Default isFalse
.
- Keyword Arguments:
is_leaf (callable, optional) – reads a type and returns a boolean indicating if that type should be seen as a leaf. By default, all non-CompositeSpec nodes are considered as leaves.
- zero(shape=None) TensorDictBase [source]¶
Returns a zero-filled tensor in the box.
- Parameters:
shape (torch.Size) – shape of the zero-tensor
- Returns:
a zero-filled tensor sampled in the TensorSpec box.