projects.pointnav_baselines.models.point_nav_models
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ResnetTensorPointNavActorCritic
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class ResnetTensorPointNavActorCritic(ActorCriticModel[CategoricalDistr])
ResnetTensorPointNavActorCritic.recurrent_hidden_state_size
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| @property
| recurrent_hidden_state_size() -> int
The recurrent hidden state size of the model.
ResnetTensorPointNavActorCritic.is_blind
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| @property
| is_blind() -> bool
True if the model is blind (e.g. neither 'depth' or 'rgb' is an input observation type).
ResnetTensorPointNavActorCritic.num_recurrent_layers
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| @property
| num_recurrent_layers() -> int
Number of recurrent hidden layers.
ResnetTensorGoalEncoder
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class ResnetTensorGoalEncoder(nn.Module)
ResnetTensorGoalEncoder.get_object_type_encoding
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| get_object_type_encoding(observations: Dict[str, torch.FloatTensor]) -> torch.FloatTensor
Get the object type encoding from input batched observations.
ResnetDualTensorGoalEncoder
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class ResnetDualTensorGoalEncoder(nn.Module)
ResnetDualTensorGoalEncoder.get_object_type_encoding
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| get_object_type_encoding(observations: Dict[str, torch.FloatTensor]) -> torch.FloatTensor
Get the object type encoding from input batched observations.