make_composite_from_td¶
- torchrl.envs.utils.make_composite_from_td(data, unsqueeze_null_shapes: bool = True)[source]¶
Creates a Composite instance from a tensordict, assuming all values are unbounded.
- Parameters:
data (tensordict.TensorDict) – a tensordict to be mapped onto a Composite.
unsqueeze_null_shapes (bool, optional) – if
True
, every empty shape will be unsqueezed to (1,). Defaults toTrue
.
Examples
>>> from tensordict import TensorDict >>> data = TensorDict({ ... "obs": torch.randn(3), ... "action": torch.zeros(2, dtype=torch.int), ... "next": {"obs": torch.randn(3), "reward": torch.randn(1)} ... }, []) >>> spec = make_composite_from_td(data) >>> print(spec) Composite( obs: UnboundedContinuous( shape=torch.Size([3]), space=None, device=cpu, dtype=torch.float32, domain=continuous), action: UnboundedContinuous( shape=torch.Size([2]), space=None, device=cpu, dtype=torch.int32, domain=continuous), next: Composite( obs: UnboundedContinuous( shape=torch.Size([3]), space=None, device=cpu, dtype=torch.float32, domain=continuous), reward: UnboundedContinuous( shape=torch.Size([1]), space=ContinuousBox(low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) >>> assert (spec.zero() == data.zero_()).all()