ConsistentDropoutModule¶
- class torchrl.modules.ConsistentDropoutModule(*args, **kwargs)[source]¶
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 withmake_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)[source]¶
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()[source]¶
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 givenmodule.
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()