ConsistentDropoutModule

class torchrl.modules.ConsistentDropoutModule(*args, **kwargs)

A TensorDictModule wrapper for ConsistentDropout.

Parameters:

• p (float, optional) – Dropout probability. Default: 0.5.

• in_keys (NestedKey or list of NestedKeys) – keys to be read from input tensordict and passed to this module.

• out_keys (NestedKey or iterable of NestedKeys) – keys to be written to the input tensordict. Defaults to in_keys values.

Keyword Arguments:

• input_shape (tuple, optional) – the shape of the input (non-batchted), used to generate the tensordict primers with make_tensordict_primer().

• input_dtype (torch.dtype, optional) – the dtype of the input for the primer. If none is pased, torch.get_default_dtype is assumed.

Note

To use this class within a policy, one needs the mask to be reset at reset time. This can be achieved through a TensorDictPrimer transform that can be obtained with make_tensordict_primer(). See this method for more information.

Examples

>>> from tensordict import TensorDict
>>> module = ConsistentDropoutModule(p = 0.1)
>>> td = TensorDict({"x": torch.randn(3, 4)}, [3])
>>> module(td)
TensorDict(
    fields={
        mask_6127171760: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.bool, is_shared=False),
        x: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

forward(tensordict)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

make_tensordict_primer()

Makes a tensordict primer for the environment to generate random masks during reset calls.

See also

torchrl.modules.utils.get_primers_from_module() for a method to generate all primers for a given

module.

Examples

>>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
>>> from torchrl.envs import GymEnv, StepCounter, SerialEnv
>>> m = Seq(
...     Mod(torch.nn.Linear(7, 4), in_keys=["observation"], out_keys=["intermediate"]),
...     ConsistentDropoutModule(
...         p=0.5,
...         input_shape=(2, 4),
...         in_keys="intermediate",
...     ),
...     Mod(torch.nn.Linear(4, 7), in_keys=["intermediate"], out_keys=["action"]),
... )
>>> primer = get_primers_from_module(m)
>>> env0 = GymEnv("Pendulum-v1").append_transform(StepCounter(5))
>>> env1 = GymEnv("Pendulum-v1").append_transform(StepCounter(6))
>>> env = SerialEnv(2, [lambda env=env0: env, lambda env=env1: env])
>>> env = env.append_transform(primer)
>>> r = env.rollout(10, m, break_when_any_done=False)
>>> mask = [k for k in r.keys() if k.startswith("mask")][0]
>>> assert (r[mask][0, :5] != r[mask][0, 5:6]).any()
>>> assert (r[mask][0, :4] == r[mask][0, 4:5]).all()