PearceMlp

CLASS cleandiffuser.nn_diffusion.PearceMlp(act_dim: int, To: int = 1, emb_dim: int = 128, hidden_dim: int = 512, timestep_emb_type: str = “positional”, timestep_emb_params: Optional[dict] = None) [SOURCE]

A carefully designed MLP neural network backbone for diffusion models. It is proposed in Diffusion Behavior Clone (DBC). It assumes the input tensor \(\bm x_t\) is the action tensor and the context tensor \(\bm c\) is the observation sequence tensor (contains the current observation and the previous observations).

Parameters:

• act_dim (int): The dimension of the action tensor \(\bm x_t\).
• To (int): The number of observations to consider. 1 means only the current observation. Default is 1.
• emb_dim (int): The dimension of the time embedding. Default is 128.
• hidden_dim (int): The dimension of the hidden layers of the MLP. Default is 512.
• timestep_emb_type (str): The type of the time embedding. It can be either “positional” or “fourier”. Default is “positional”.
• timestep_emb_params (Optional[dict]): The parameters for the time embedding. Default is None.

forward(x: torch.Tensor, t: torch.Tensor, c: torch.Tensor) -> torch.Tensor

Parameters:

• x (torch.Tensor): The input tensor \(\bm x_t\) in shape (..., act_dim)
• t (torch.Tensor): The time tensor \(t\) in shape (..., 1).
• c (torch.Tensor): The context tensor \(\bm c\) in shape (..., To, obs_dim) or (..., obs_dim).

Returns:

• torch.Tensor: The output tensor in shape (..., act_dim).