| """Faithful minimal stand-ins for the openpi transforms used by g1_dex3_policy.""" |
| import dataclasses |
| from typing import Sequence |
| import numpy as np |
|
|
| class DataTransformFn: |
| pass |
|
|
| def pad_to_dim(x, target_dim, axis=-1): |
| current = x.shape[axis] |
| if current >= target_dim: return x |
| pad = [(0,0)]*x.ndim |
| pad[axis]=(0, target_dim-current) |
| return np.pad(x, pad) |
|
|
| def make_bool_mask(*dims): |
| out=[] |
| for d in dims: |
| out.extend([True]*d if d>0 else [False]*(-d)) |
| return tuple(out) |
|
|
| @dataclasses.dataclass(frozen=True) |
| class DeltaActions(DataTransformFn): |
| mask: Sequence[bool] | None |
| def __call__(self, data): |
| if "actions" not in data or self.mask is None: return data |
| state, actions = data["state"], np.array(data["actions"]) |
| mask = np.asarray(self.mask); dims = mask.shape[-1] |
| actions[..., :dims] = np.where(mask, actions[..., :dims]-state[..., :dims], actions[..., :dims]) |
| data["actions"]=actions; return data |
|
|
| @dataclasses.dataclass(frozen=True) |
| class AbsoluteActions(DataTransformFn): |
| mask: Sequence[bool] | None |
| def __call__(self, data): |
| if "actions" not in data or self.mask is None: return data |
| state, actions = data["state"], np.array(data["actions"]) |
| mask = np.asarray(self.mask); dims = mask.shape[-1] |
| actions[..., :dims] = np.where(mask, actions[..., :dims]+state[..., :dims], actions[..., :dims]) |
| data["actions"]=actions; return data |
|
|
