VC1Transform

class torchrl.envs.transforms.VC1Transform(in_keys, out_keys, model_name, del_keys: bool = True)

VC1 Transform class.

VC1 provides pre-trained ResNet weights aimed at facilitating visual embedding for robotic tasks. The models are trained using Ego4d.

See the paper:

VC1: A Universal Visual Representation for Robot Manipulation (Suraj Nair,

Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta) https://arxiv.org/abs/2203.12601

The VC1Transform is created in a lazy manner: the object will be initialized only when an attribute (a spec or the forward method) will be queried. The reason for this is that the _init() method requires some attributes of the parent environment (if any) to be accessed: by making the class lazy we can ensure that the following code snippet works as expected:

Examples

>>> transform = VC1Transform("default", in_keys=["pixels"])
>>> env.append_transform(transform)
>>> # the forward method will first call _init which will look at env.observation_spec
>>> env.reset()

Parameters:

• in_keys (list of NestedKeys) – list of input keys. If left empty, the “pixels” key is assumed.

• out_keys (list of NestedKeys, optional) – list of output keys. If left empty, “VC1_vec” is assumed.

• model_name (str) – One of "large", "base" or any other compatible model name (see the github repo for more info). Defaults to "default" which provides a small, untrained model for testing.

• del_keys (bool, optional) – If True (default), the input key will be discarded from the returned tensordict.

forward(next_tensordict)

Reads the input tensordict, and for the selected keys, applies the transform.

By default, this method:

• calls directly _apply_transform().

• does not call _step() or _call().

This method is not called within env.step at any point. However, is is called within sample().

Note

forward also works with regular keyword arguments using dispatch to cast the args names to the keys.

Examples

>>> class TransformThatMeasuresBytes(Transform):
...     '''Measures the number of bytes in the tensordict, and writes it under `"bytes"`.'''
...     def __init__(self):
...         super().__init__(in_keys=[], out_keys=["bytes"])
...
...     def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
...         bytes_in_td = tensordict.bytes()
...         tensordict["bytes"] = bytes
...         return tensordict
>>> t = TransformThatMeasuresBytes()
>>> env = env.append_transform(t) # works within envs
>>> t(TensorDict(a=0))  # Works offline too.

classmethod make_noload_model()

Creates an naive model at a custom destination.

to(dest: DEVICE_TYPING | torch.dtype)

Move and/or cast the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point or complex dtypes. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:

• device (torch.device) – the desired device of the parameters and buffers in this module

• dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module

• tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module

• memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns:

self

Return type:

Module

Examples:

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

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