TensorSpec¶
- class torchrl.data.TensorSpec(shape: ~torch.Size, space: ~typing.Union[None, ~torchrl.data.tensor_specs.Box], device: torch.device | None = <property object>, dtype: ~torch.dtype = torch.float32, domain: str = '')[source]¶
Parent class of the tensor meta-data containers.
TorchRL’s TensorSpec are used to present what input/output is to be expected for a specific class, or sometimes to simulate simple behaviors by generating random data within a defined space.
TensorSpecs are primarily used in environments to specify their input/output structure without needing to execute the environment (or starting it). They can also be used to instantiate shared buffers to pass data from worker to worker.
TensorSpecs are dataclasses that always share the following fields: shape, space, `dtype and device.
As such, TensorSpecs possess some common behavior with
Tensor
andTensorDict
: they can be reshaped, indexed, squeezed, unsqueezed, moved to another device etc.- Parameters:
shape (torch.Size) – size of the tensor. The shape includes the batch dimensions as well as the feature dimension. A negative shape (
-1
) means that the dimension has a variable number of elements.space (Box) – Box instance describing what kind of values can be expected.
device (torch.device) – device of the tensor.
dtype (torch.dtype) – dtype of the tensor.
Note
A spec can be constructed from a
TensorDict
using themake_composite_from_td()
function. This function makes a low-assumption educated guess on the specs that may correspond to the input tensordict and can help to build specs automatically without an in-depth knowledge of the TensorSpec API.- assert_is_in(value: Tensor) None [source]¶
Asserts whether a tensor belongs to the box, and raises an exception otherwise.
- Parameters:
value (torch.Tensor) – value to be checked.
- clear_device_() T [source]¶
A no-op for all leaf specs (which must have a device).
For
Composite
specs, this method will erase the device.
- abstract clone() TensorSpec [source]¶
Creates a copy of the TensorSpec.
- contains(item: torch.Tensor | tensordict.base.TensorDictBase) bool [source]¶
If the value
val
could have been generated by theTensorSpec
, returnsTrue
, otherwiseFalse
.See
is_in()
for more information.
- property device: device¶
The device of the spec.
Only
Composite
specs can have aNone
device. All leaves must have a non-null device.
- encode(val: numpy.ndarray | torch.Tensor | tensordict.base.TensorDictBase, *, ignore_device: bool = False) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Encodes a value given the specified spec, and return the corresponding tensor.
This method is to be used in environments that return a value (eg, a numpy array) that can be easily mapped to the TorchRL required domain. If the value is already a tensor, the spec will not change its value and return it as-is.
- 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.
- abstract expand(shape: Size)[source]¶
Returns a new Spec with the expanded shape.
- Parameters:
*shape (tuple or iterable of int) – the new shape of the Spec. Must be broadcastable with the current shape: its length must be at least as long as the current shape length, and its last values must be compliant too; ie they can only differ from it if the current dimension is a singleton.
- flatten(start_dim: int, end_dim: int) T [source]¶
Flattens a
TensorSpec
.Check
flatten()
for more information on this method.
- classmethod implements_for_spec(torch_function: Callable) Callable [source]¶
Register a torch function override for TensorSpec.
- abstract index(index: Union[int, Tensor, ndarray, slice, List], tensor_to_index: torch.Tensor | tensordict.base.TensorDictBase) torch.Tensor | tensordict.base.TensorDictBase [source]¶
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
- abstract is_in(val: torch.Tensor | tensordict.base.TensorDictBase) bool [source]¶
If the value
val
could have been generated by theTensorSpec
, returnsTrue
, otherwiseFalse
.More precisely, the
is_in
methods checks that the valueval
is within the limits defined by thespace
attribute (the box), and that thedtype
,device
,shape
potentially other metadata match those of the spec. If any of these checks fails, theis_in
method will returnFalse
.- Parameters:
val (torch.Tensor) – value to be checked.
- Returns:
boolean indicating if values belongs to the TensorSpec box.
- property ndim: int¶
Number of dimensions of the spec shape.
Shortcut for
len(spec.shape)
.
- one(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Returns a one-filled tensor in the box.
Note
Even though there is no guarantee that
1
belongs to the spec domain, this method will not raise an exception when this condition is violated. The primary use case ofone
is to generate empty data buffers, not meaningful data.- Parameters:
shape (torch.Size) – shape of the one-tensor
- Returns:
a one-filled tensor sampled in the TensorSpec box.
- ones(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Proxy to
one()
.
- project(val: torch.Tensor | tensordict.base.TensorDictBase) torch.Tensor | tensordict.base.TensorDictBase [source]¶
If the input tensor is not in the TensorSpec box, it maps it back to it given some defined heuristic.
- Parameters:
val (torch.Tensor) – tensor to be mapped to the box.
- Returns:
a torch.Tensor belonging to the TensorSpec box.
- abstract rand(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Returns a random tensor in the space defined by the spec.
The sampling will be done uniformly over the space, unless the box is unbounded in which case normal values will be drawn.
- Parameters:
shape (torch.Size) – shape of the random tensor
- Returns:
a random tensor sampled in the TensorSpec box.
- reshape(shape) T [source]¶
Reshapes a
TensorSpec
.Check
reshape()
for more information on this method.
- sample(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Returns a random tensor in the space defined by the spec.
See
rand()
for details.
- squeeze(dim: Optional[int] = None) T [source]¶
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
- abstract to(dest: Union[dtype, device, str, int]) TensorSpec [source]¶
Casts a TensorSpec to a device or a dtype.
Returns the same spec if no change is made.
- to_numpy(val: torch.Tensor | tensordict.base.TensorDictBase, safe: Optional[bool] = None) numpy.ndarray | dict [source]¶
Returns the
np.ndarray
correspondent of an input tensor.This is intended to be the inverse operation of
encode()
.- 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: Tensor, key: Optional[NestedKey] = None) None [source]¶
Checks the input value
dtype
against theTensorSpec
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: int, sizes: Tuple[int]) T [source]¶
Unflattens a
TensorSpec
.Check
unflatten()
for more information on this method.
- unsqueeze(dim: int) T [source]¶
Returns a new Spec with one more singleton dimension (at the position indicated by
dim
).- Parameters:
dim (int or None) – the dimension to apply the unsqueeze operation to.
- zero(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Returns a zero-filled tensor in the box.
Note
Even though there is no guarantee that
0
belongs to the spec domain, this method will not raise an exception when this condition is violated. The primary use case ofzero
is to generate empty data buffers, not meaningful data.- Parameters:
shape (torch.Size) – shape of the zero-tensor
- Returns:
a zero-filled tensor sampled in the TensorSpec box.
- zeros(shape: Optional[Size] = None) torch.Tensor | tensordict.base.TensorDictBase [source]¶
Proxy to
zero()
.