equidiff/equi_diffpo/policy/base_lowdim_policy.py

from typing import Dict
import torch
import torch.nn as nn
from equi_diffpo.model.common.module_attr_mixin import ModuleAttrMixin
from equi_diffpo.model.common.normalizer import LinearNormalizer

class BaseLowdimPolicy(ModuleAttrMixin):  
    # ========= inference  ============
    # also as self.device and self.dtype for inference device transfer
    def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        obs_dict:
            obs: B,To,Do
        return: 
            action: B,Ta,Da
        To = 3
        Ta = 4
        T = 6
        |o|o|o|
        | | |a|a|a|a|
        |o|o|
        | |a|a|a|a|a|
        | | | | |a|a|
        """
        raise NotImplementedError()

    # reset state for stateful policies
    def reset(self):
        pass

    # ========== training ===========
    # no standard training interface except setting normalizer
    def set_normalizer(self, normalizer: LinearNormalizer):
        raise NotImplementedError()