ToTensorImage

class torchrl.envs.transforms.ToTensorImage(from_int: Optional[bool] = None, unsqueeze: bool = False, dtype: Optional[torch.device] = None, *, in_keys: Sequence[NestedKey] | None = None, out_keys: Sequence[NestedKey] | None = None, shape_tolerant: bool = False)

Transforms a numpy-like image (W x H x C) to a pytorch image (C x W x H).

Transforms an observation image from a (… x W x H x C) tensor to a (… x C x W x H) tensor. Optionally, scales the input tensor from the range [0, 255] to the range [0.0, 1.0] (see from_int for more details).

In the other cases, tensors are returned without scaling.

Parameters:

• from_int (bool, optional) – if True, the tensor will be scaled from the range [0, 255] to the range [0.0, 1.0]. if False`, the tensor will not be scaled. if None, the tensor will be scaled if it’s not a floating-point tensor. default=None.

• unsqueeze (bool) – if True, the observation tensor is unsqueezed along the first dimension. default=False.

• dtype (torch.dtype, optional) – dtype to use for the resulting observations.

Keyword Arguments:

• in_keys (list of NestedKeys) – keys to process.

• out_keys (list of NestedKeys) – keys to write.

• shape_tolerant (bool, optional) – if True, the shape of the input images will be check. If the last channel is not 3, the permuation will be ignored. Defaults to False.

Examples

>>> transform = ToTensorImage(in_keys=["pixels"])
>>> ri = torch.randint(0, 255, (1 , 1, 10, 11, 3), dtype=torch.uint8)
>>> td = TensorDict(
...     {"pixels": ri},
...     [1, 1])
>>> _ = transform(td)
>>> obs = td.get("pixels")
>>> print(obs.shape, obs.dtype)
torch.Size([1, 1, 3, 10, 11]) torch.float32

transform_observation_spec(observation_spec: TensorSpec) → TensorSpec

Transforms the observation spec such that the resulting spec matches transform mapping.

Parameters:

observation_spec (TensorSpec) – spec before the transform

Returns:

expected spec after the transform