| """Action-space-bound ActionCodec2Sparse frontend for independent action chunks.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| import sys |
| import warnings |
| from collections.abc import Mapping |
| from pathlib import Path |
| from typing import Any, TextIO |
|
|
| import numpy as np |
| import torch |
| from transformers.feature_extraction_utils import BatchFeature |
|
|
| from ...backends.actioncodec2 import ActionCodec2Config, PresetConfig |
| from ...backends.actioncodec2.defaults import builtin_action_spaces |
| from ...backends.actioncodec2.hf import ( |
| GENERATED_CARD_MARKER, |
| PROCESSOR_CONFIG_NAME, |
| resolve_pretrained_directory, |
| ) |
| from ...backends.actioncodec2.preprocessing import PreprocessingSpec |
| from ...backends.actioncodec2.release import MANIFEST_NAME |
| from ...exporting import save_runtime |
| from ...backends.actioncodec2.semantic import resampled_horizon |
| from ...backends.actioncodec2.sparse import ( |
| ActionCodec2SparseProcessor, |
| ActionCodec2SparseRouter, |
| SparseLayout, |
| SparseProfileTrainer, |
| SparseVocabulary, |
| sparse_profile_config, |
| ) |
| from ...backends.actioncodec2.sparse.routing import SPARSE_ROUTER_CONFIG_NAME, save_router_metadata |
| from ...backends.actioncodec2.training import build_primitives |
| from ..conversion import ( |
| _numpy, |
| _numpy_token_rows, |
| restore_source_layout, |
| ) |
| from ..base_action_tokenizer import BaseActionTokenizer |
| from ..layout import ActionSpaceMixin |
| from ..training import TrainingMixin |
| from .loading import describe_release, load_release_router |
|
|
| ALLOW_PATTERNS = [ |
| SPARSE_ROUTER_CONFIG_NAME, |
| PROCESSOR_CONFIG_NAME, |
| "config.json", |
| MANIFEST_NAME, |
| "profiles/**", |
| "joint/**", |
| "eef/**", |
| "fit_report.json", |
| "README.md", |
| ] |
| _ASSETS = Path(__file__).absolute().parents[2] / "backends" / "actioncodec2" / "assets" |
|
|
|
|
| def _profile_config(raw) -> ActionCodec2Config: |
| if isinstance(raw, ActionCodec2Config): |
| return raw |
| if isinstance(raw, Mapping): |
| return ActionCodec2Config.from_dict(raw) |
| return ActionCodec2Config.from_yaml(raw) |
|
|
|
|
| def build_fresh_router( |
| profile_configs=None, primitive_order: int = 1, vocab_size: int = 4096 |
| ) -> ActionCodec2SparseRouter: |
| """Unfitted joint/EEF profiles; defaults derive from the packaged profiles of that order.""" |
| if type(primitive_order) is not int or primitive_order not in (1, 2): |
| raise ValueError("primitive_order must be 1 or 2") |
| if type(vocab_size) is not int or vocab_size < 1: |
| raise ValueError("vocab_size must be a positive integer") |
| if profile_configs is not None and vocab_size != 4096: |
| raise ValueError("set vocabulary budgets in profile_configs when providing custom profiles") |
| configs = dict(profile_configs or {}) |
| if configs.keys() - {"joint", "eef"}: |
| raise ValueError("profile_configs keys must be 'joint' or 'eef'") |
| suffix = "" if primitive_order == 1 else "_second_order" |
| processors, budgets = {}, {} |
| for name in ("joint", "eef"): |
| if name in configs: |
| config = _profile_config(configs[name]) |
| else: |
| default = ActionCodec2Config.from_yaml(_ASSETS / "profiles" / f"{name}{suffix}.yaml") |
| config = sparse_profile_config(default, vocab_size=vocab_size) |
| if config.train is None or config.train.sparse is None: |
| raise ValueError(f"{name} profile config requires train.sparse") |
| primitives = build_primitives(config) |
| layout = SparseLayout.from_config(config, primitives) |
| vocabulary = SparseVocabulary(layout, (), (), config.set_bpe.lookahead) |
| processors[name] = ActionCodec2SparseProcessor(config, primitives, vocabulary) |
| budgets[name] = config.train.bpe.vocab_size |
| return ActionCodec2SparseRouter.from_processors( |
| processors, token_budgets=budgets, presets=list(builtin_action_spaces().values()) |
| ) |
|
|
|
|
| class ActionCodec2Sparse(ActionSpaceMixin, TrainingMixin, BaseActionTokenizer): |
| """ActionCodec2 physics with a zero-free token layer, bound to one action space.""" |
|
|
| trainer_class = SparseProfileTrainer |
| model_type = "actioncodec2_sparse" |
| encode_kwargs = frozenset({"fps", "current_state", "previous_first_order"}) |
|
|
| def __init__( |
| self, |
| backend: ActionCodec2SparseRouter | None = None, |
| *, |
| action_space: str, |
| primitive_order: int = 1, |
| vocab_size: int = 4096, |
| profile_configs: Mapping[str, object] | None = None, |
| ) -> None: |
| fresh = backend is None |
| if fresh: |
| backend = build_fresh_router(profile_configs, primitive_order, vocab_size) |
| elif profile_configs is not None or primitive_order != 1 or vocab_size != 4096: |
| raise ValueError("profile construction options cannot accompany an existing backend") |
| if not isinstance(backend, ActionCodec2SparseRouter): |
| raise TypeError( |
| f"ActionCodec2Sparse requires ActionCodec2SparseRouter, got {type(backend).__name__}" |
| ) |
| preset = backend.preset(action_space) |
| if preset.target_hz != 15.0: |
| raise ValueError("ActionCodec2Sparse artifacts require target_hz=15") |
| self.action_tokenizer = backend |
| self._action_space = preset |
| self._action_vocab_size = int(backend.vocab_size) |
| self._new_action_tokens = backend.new_action_tokens |
| self.action_dim = preset.input_dim |
| self.requires_previous_first_order = bool(backend.second_order_boundaries(preset)) |
| self.max_action_token_len = None |
| self.fixed_action_token_len = None |
| self._warned_default_fps = False |
| super().__init__(append_bar_sentinels=False) |
| self._fitted_profiles = frozenset() if fresh else frozenset(backend.profiles) |
| self.training_report = None |
| self.preprocessing: dict[str, PreprocessingSpec] = {} |
| self.release_metadata: dict[str, Any] | None = None |
|
|
| def __repr__(self) -> str: |
| return ( |
| f"ActionCodec2Sparse(action_space={self.action_space.name!r}, " |
| f"D={self.action_dim}, vocab={self.vocab_size})" |
| ) |
|
|
|
|
| def _describe_action_spaces(self, action_dim: int | None = None) -> str: |
| fitted = ", ".join(sorted(self.fitted_profiles)) or "none; call codec.fit(episodes)" |
| layouts = self.action_tokenizer.describe_action_spaces( |
| selected=self.action_space.name, action_dim=action_dim |
| ) |
| return f"Fitted profiles: {fitted}\n{layouts}" |
|
|
| @staticmethod |
| def builtin_action_spaces(): |
| """Return the immutable named layout definitions shipped in the package.""" |
| return builtin_action_spaces() |
|
|
|
|
| def print_action_spaces(self, *, action_dim: int | None = None, file: TextIO | None = None): |
| """Print all layouts or candidates matching ``action_dim``.""" |
| print(self._describe_action_spaces(action_dim), file=sys.stdout if file is None else file) |
|
|
|
|
| def for_action_space(self, name: str) -> ActionCodec2Sparse: |
| """Share current weights with a new binding.""" |
| codec = type(self)(self.action_tokenizer, action_space=name) |
| codec._fitted_profiles = self.fitted_profiles |
| codec.training_report = self.training_report |
| codec.preprocessing = dict(self.preprocessing) |
| codec.release_metadata = self.release_metadata |
| return codec |
|
|
| @property |
| def fitted_profiles(self) -> frozenset[str]: |
| """Names of profiles fitted on data; persisted by save_pretrained().""" |
| return self._fitted_profiles |
|
|
| @property |
| def is_fitted(self) -> bool: |
| """Whether every profile used by the selected action space is fitted.""" |
| return all(c.space in self.fitted_profiles for c in self.action_space.components) |
|
|
| def _require_fitted(self) -> None: |
| missing = {c.space for c in self.action_space.components} - self.fitted_profiles |
| if missing: |
| raise RuntimeError( |
| f"Unfitted profiles {sorted(missing)}; call codec.fit(episodes) first" |
| ) |
|
|
| def _source_fps(self, fps, preset=None) -> float: |
| preset = self.action_space if preset is None else preset |
| if fps is None: |
| if not self._warned_default_fps: |
| warnings.warn( |
| f"fps was omitted; assuming {preset.source_hz:g} Hz from action_space " |
| f"{preset.name!r}. Pass the actual fps explicitly to avoid changing action timing.", |
| UserWarning, |
| stacklevel=3, |
| ) |
| self._warned_default_fps = True |
| return float(preset.source_hz) |
| return float(fps) |
|
|
|
|
| def codec_horizon(self, horizon: int, *, fps: float | None = None) -> int: |
| """Codec frames (15 Hz) of a ``horizon``-frame source chunk at ``fps``.""" |
| if isinstance(horizon, bool) or not isinstance(horizon, int) or horizon < 1: |
| raise ValueError("horizon must be a positive integer") |
| return resampled_horizon( |
| horizon, source_hz=self._source_fps(fps), target_hz=self.action_space.target_hz |
| ) |
|
|
| def max_tokens_for_horizon(self, horizon: int, *, fps: float | None = None) -> int: |
| """Upper bound on the tokens of one encoded ``horizon``-frame source chunk.""" |
| frames = self.codec_horizon(horizon, fps=fps) |
| components = self.action_space.components |
| width = sum(len(c.active_dims) + 1 for c in components) |
| return frames * width + len(components) - 1 |
|
|
| def grammar(self, horizon: int, *, fps: float | None = None): |
| """Constrained-generation grammar for one ``horizon``-frame source chunk.""" |
| self._require_fitted() |
| return self.action_tokenizer.grammar( |
| preset=self.action_space.name, horizon=self.codec_horizon(horizon, fps=fps) |
| ) |
|
|
| def logits_processor( |
| self, horizon, *, fps, prompt_length, eos_token_id, token_offset=0, pad_token_id=None |
| ): |
| """Create a Transformers LogitsProcessor for one ``horizon``-frame chunk.""" |
| from .generation import ActionCodec2SparseLogitsProcessor |
|
|
| return ActionCodec2SparseLogitsProcessor( |
| self, |
| horizon, |
| fps=fps, |
| prompt_length=prompt_length, |
| eos_token_id=eos_token_id, |
| token_offset=token_offset, |
| pad_token_id=pad_token_id, |
| ) |
|
|
| def _check_state(self, current_state, action: str): |
| name = self.action_space.name |
| if self.action_space.input == "absolute" and current_state is None: |
| raise ValueError( |
| f"action space {name!r} is absolute; {action} requires current_state with shape [B, D]" |
| ) |
| if self.action_space.input == "delta" and current_state is not None: |
| raise ValueError(f"action space {name!r} is delta; omit current_state") |
| return _numpy(current_state) |
|
|
| def encode( |
| self, action, *, fps=None, current_state=None, previous_first_order=None, encode_kwargs=None |
| ) -> list[list[int]]: |
| """Encode ``(B,T,D)`` or ``(T,D)`` source chunks into ragged token rows.""" |
| kwargs = self._check_encode_kwargs(encode_kwargs) |
| direct = { |
| "fps": fps, |
| "current_state": current_state, |
| "previous_first_order": previous_first_order, |
| } |
| duplicates = sorted(k for k, v in direct.items() if v is not None and k in kwargs) |
| if duplicates: |
| raise ValueError(f"encode arguments provided twice: {duplicates}") |
| kwargs.update({k: v for k, v in direct.items() if v is not None}) |
| values = torch.as_tensor(action) |
| if values.ndim == 2: |
| values = values.unsqueeze(0) |
| return self._encode_action_indices(values, encode_kwargs=kwargs) |
|
|
| def __call__( |
| self, |
| action, |
| *, |
| fps=None, |
| current_state=None, |
| previous_first_order=None, |
| encode_kwargs=None, |
| return_tensors=None, |
| ) -> BatchFeature: |
| rows = self.encode( |
| action, |
| fps=fps, |
| current_state=current_state, |
| previous_first_order=previous_first_order, |
| encode_kwargs=encode_kwargs, |
| ) |
| return BatchFeature({"input_ids": rows}, tensor_type=return_tensors) |
|
|
| def _encode_action_indices(self, action, encode_kwargs=None): |
| self._require_fitted() |
| kwargs = dict(encode_kwargs or {}) |
| current_state = self._check_state(kwargs.get("current_state"), "encode") |
| values = _numpy(action) |
| if values.ndim != 3 or values.shape[2] != self.action_dim: |
| raise ValueError(f"actions must have shape [B, T_source, {self.action_dim}]") |
| return self.action_tokenizer.encode_batch( |
| values, |
| preset=self.action_space.name, |
| fps=self._source_fps(kwargs.get("fps")), |
| current_state=current_state, |
| previous_first_order=self._numpy_boundary(kwargs.get("previous_first_order")), |
| ) |
|
|
| def decode( |
| self, |
| action_token_ids, |
| *, |
| horizon: int, |
| fps=None, |
| current_state=None, |
| previous_first_order=None, |
| ) -> torch.Tensor: |
| """Decode rows of one ``horizon``-frame source chunk to a CPU ``(B,T,D)`` tensor.""" |
| return self.decode_token_ids_to_actions_batch( |
| action_token_ids, |
| time_horizon=horizon, |
| decode_kwargs={ |
| "fps": fps, |
| "current_state": current_state, |
| "previous_first_order": previous_first_order, |
| }, |
| ) |
|
|
| def decode_token_ids_to_actions_batch( |
| self, action_token_ids, *, time_horizon=None, action_dim=None, decode_kwargs=None |
| ) -> torch.Tensor: |
| """Decode integer rows (ragged or ``(B,L)``) of one source ``time_horizon``.""" |
| rows = [ |
| self._checked_decode_row(row).tolist() for row in _numpy_token_rows(action_token_ids) |
| ] |
| actions, _ = self._decode_action_indices( |
| rows, time_horizon=time_horizon, action_dim=action_dim, decode_kwargs=decode_kwargs |
| ) |
| return torch.from_numpy(actions) |
|
|
| def _decode_action_indices( |
| self, action_indices, *, time_horizon=None, action_dim=None, decode_kwargs=None |
| ): |
| self._require_fitted() |
| if time_horizon is None: |
| raise ValueError("ActionCodec2Sparse decode requires the chunk horizon") |
| if action_dim is not None and int(action_dim) != self.action_dim: |
| raise ValueError( |
| f"action_dim={action_dim} disagrees with action space D={self.action_dim}" |
| ) |
| kwargs = dict(decode_kwargs or {}) |
| unknown = sorted(set(kwargs) - self.encode_kwargs) |
| if unknown: |
| raise ValueError(f"ActionCodec2Sparse does not consume decode_kwargs {unknown}") |
| fps = self._source_fps(kwargs.get("fps")) |
| decoded = self.action_tokenizer.decode_batch( |
| action_indices, |
| preset=self.action_space.name, |
| horizon=self.codec_horizon(time_horizon, fps=fps), |
| fps=fps, |
| current_state=self._check_state(kwargs.get("current_state"), "decode"), |
| previous_first_order=self._numpy_boundary(kwargs.get("previous_first_order")), |
| ) |
| actions = restore_source_layout(self.action_space, decoded) |
| return actions, [set() for _ in range(len(actions))] |
|
|
| def batch_decode( |
| self, |
| token_rows, |
| *, |
| horizon: int, |
| fps=None, |
| current_state=None, |
| previous_first_order=None, |
| errors="raise", |
| ): |
| """Decode independent rows of one horizon, isolating invalid rows.""" |
| if errors not in ("raise", "return"): |
| raise ValueError("errors must be 'raise' or 'return'") |
| rows = list(token_rows) |
| current = _numpy(current_state) |
| if current is not None and np.shape(current) != (len(rows), self.action_dim): |
| raise ValueError(f"current_state must have shape {(len(rows), self.action_dim)}") |
| previous = self._numpy_boundary(previous_first_order) |
| if previous is not None and any(len(v) != len(rows) for v in previous.values()): |
| raise ValueError( |
| "previous_first_order arrays must have batch dimension equal to token rows" |
| ) |
| result = [] |
| for index, row in enumerate(rows): |
| try: |
| state = None if current is None else current[index : index + 1] |
| boundary = ( |
| None |
| if previous is None |
| else {k: v[index : index + 1] for k, v in previous.items()} |
| ) |
| result.append( |
| self.decode( |
| [row], |
| horizon=horizon, |
| fps=fps, |
| current_state=state, |
| previous_first_order=boundary, |
| ) |
| ) |
| except ValueError as error: |
| error = ValueError(f"token row {index}: {error}") |
| if errors == "raise": |
| raise error from None |
| result.append(error) |
| return result |
|
|
| def decode_prefix( |
| self, |
| action_token_ids, |
| *, |
| horizon: int, |
| steps: int, |
| fps=None, |
| current_state=None, |
| previous_first_order=None, |
| ): |
| """Decode the first ``steps`` 15 Hz frames of streaming prefixes to ``(B,steps,D)``.""" |
| self._require_fitted() |
| fps = self._source_fps(fps) |
| if fps != self.action_space.target_hz: |
| raise ValueError("prefix decode requires fps equal to the 15 Hz codec clock") |
| frames = self.codec_horizon(horizon, fps=fps) |
| if isinstance(steps, bool) or not isinstance(steps, int) or not 1 <= steps <= frames: |
| raise ValueError("steps must be an integer within the chunk horizon") |
| rows = [self._checked_decode_row(row).tolist() for row in action_token_ids] |
| grammar = self.action_tokenizer.grammar(preset=self.action_space.name, horizon=frames) |
| if any(grammar.covered_steps(row) < steps for row in rows): |
| raise ValueError(f"action prefix does not fix the first {steps} frames") |
| decoded = self.action_tokenizer.decode_batch( |
| rows, |
| preset=self.action_space.name, |
| horizon=frames, |
| fps=fps, |
| current_state=self._check_state(_numpy(current_state), "decode"), |
| previous_first_order=self._numpy_boundary(previous_first_order), |
| ) |
| return torch.from_numpy(restore_source_layout(self.action_space, decoded)[:, :steps]) |
|
|
| def final_first_order( |
| self, |
| action_token_ids, |
| *, |
| horizon: int, |
| previous_first_order, |
| fps=None, |
| executed_steps: int | None = None, |
| ) -> dict[str, np.ndarray]: |
| """Second-order component boundaries ``{component: (B,D_active)}`` for the next chunk.""" |
| self._require_fitted() |
| return self.action_tokenizer.final_first_order( |
| [self._checked_decode_row(row).tolist() for row in action_token_ids], |
| preset=self.action_space.name, |
| horizon=self.codec_horizon(horizon, fps=fps), |
| previous_first_order=self._numpy_boundary(previous_first_order), |
| executed_steps=executed_steps, |
| ) |
|
|
|
|
| @classmethod |
| def describe_pretrained( |
| cls, |
| pretrained_model_name_or_path, |
| *, |
| action_dim=None, |
| gripper_mode="2bin", |
| primitive_order: int = 1, |
| **kwargs, |
| ) -> str: |
| """Report a release's or saved artifact's layouts from manifests alone.""" |
| directory = resolve_pretrained_directory( |
| pretrained_model_name_or_path, allow_patterns=ALLOW_PATTERNS, **kwargs |
| ) |
| if (directory / MANIFEST_NAME).is_file(): |
| return describe_release( |
| directory, |
| action_dim=action_dim, |
| gripper_mode=gripper_mode, |
| primitive_order=primitive_order, |
| ) |
| specs, presets = ActionCodec2SparseRouter.read_manifest(directory) |
| metadata = _saved_metadata(directory) |
| fitted = ", ".join(metadata.get("fitted_profiles", [s.name for s in specs])) |
| from ...backends.actioncodec2.presets import format_action_spaces |
|
|
| layouts = format_action_spaces( |
| {p.name: p for p in presets}, |
| selected=metadata.get("action_space"), |
| action_dim=action_dim, |
| codec_name="ActionCodec2Sparse", |
| chunk_horizon_decode=True, |
| ) |
| return f"Fitted profiles: {fitted}\n{layouts}" |
|
|
| @classmethod |
| def from_pretrained( |
| cls, |
| pretrained_model_name_or_path, |
| *, |
| action_space=None, |
| gripper_mode: str | None = None, |
| primitive_order: int | None = None, |
| verify_gates: bool = False, |
| **kwargs, |
| ) -> ActionCodec2Sparse: |
| """Load a release package or saved frontend and bind an action space.""" |
| if cls is ActionCodec2Sparse and ("variant" in kwargs or (Path(pretrained_model_name_or_path) / "variants.json").is_file() |
| or (Path(pretrained_model_name_or_path) / "artifact_manifest.json").is_file()): |
| from ...catalog import ActionCodec2 as Catalog |
| return Catalog.from_pretrained(pretrained_model_name_or_path, action_space=action_space, **kwargs) |
| directory = resolve_pretrained_directory( |
| pretrained_model_name_or_path, allow_patterns=ALLOW_PATTERNS, **kwargs |
| ) |
| metadata = _saved_metadata(directory) |
| action_space = metadata.get("action_space") if action_space is None else action_space |
| if action_space is None: |
| raise ValueError( |
| "action_space is required; inspect layouts with " |
| "print(ActionCodec2Sparse.describe_pretrained(path))" |
| ) |
| if (directory / MANIFEST_NAME).is_file(): |
| router, preprocessing, release = load_release_router( |
| directory, |
| gripper_mode=gripper_mode or "2bin", |
| primitive_order=1 if primitive_order is None else primitive_order, |
| verify_gates=verify_gates, |
| ) |
| codec = cls(router, action_space=_register(router, action_space)) |
| codec.preprocessing, codec.release_metadata = preprocessing, release |
| return codec |
| if gripper_mode is not None or primitive_order is not None or verify_gates: |
| raise ValueError( |
| "gripper_mode, primitive_order and verify_gates select release variants only" |
| ) |
| router = ActionCodec2SparseRouter.from_pretrained(directory) |
| codec = cls(router, action_space=_register(router, action_space)) |
| fitted = metadata.get("fitted_profiles", list(router.profiles)) |
| if not isinstance(fitted, list) or set(fitted) - router.profiles.keys(): |
| raise ValueError("invalid fitted_profiles in processor_config.json") |
| codec._fitted_profiles = frozenset(fitted) |
| codec.preprocessing = { |
| name: PreprocessingSpec.from_dict(spec) |
| for name, spec in metadata.get("preprocessing", {}).items() |
| } |
| codec.release_metadata = metadata.get("release") |
| report = directory / "fit_report.json" |
| if report.is_file(): |
| codec.training_report = json.loads(report.read_text(encoding="utf-8")) |
| return codec |
|
|
| def to_dict(self) -> dict[str, object]: |
| """Lightweight discovery metadata saved as ``processor_config.json``.""" |
| payload = { |
| "action_space": self.action_space.name, |
| "fitted_profiles": sorted(self.fitted_profiles), |
| } |
| if self.preprocessing: |
| payload["preprocessing"] = {k: v.to_dict() for k, v in self.preprocessing.items()} |
| return payload |
|
|
| def save_pretrained( |
| self, save_directory: str | Path, *, include_training_report=False |
| ) -> list[str]: |
| """Save inference assets; include the local fit report only when explicitly requested.""" |
| directory = Path(save_directory) |
| files = self.action_tokenizer.save_pretrained(directory) |
| files.extend(map(str, save_router_metadata(directory, **self.to_dict()))) |
| files.append(str(_save_model_card(directory, self))) |
| report = directory / "fit_report.json" |
| if include_training_report and self.training_report is not None: |
| report.write_text(json.dumps(self.training_report, indent=2) + "\n", encoding="utf-8") |
| files.append(str(report)) |
| elif report.exists(): |
| report.unlink() |
| files.extend(save_runtime(directory)) |
| return files |
|
|
| def eval(self) -> ActionCodec2Sparse: |
| return self |
|
|
|
|
| def _save_model_card(directory: Path, codec: ActionCodec2Sparse) -> Path: |
| path = directory / "README.md" |
| if path.exists() and GENERATED_CARD_MARKER not in path.read_text(encoding="utf-8"): |
| return path |
| context = 'context = {"fps": 15.0}\n' |
| if codec.action_space.input == "absolute": |
| context += 'context["current_state"] = current_state # observed (B, D) source state\n' |
| if codec.requires_previous_first_order: |
| context += 'context["previous_first_order"] = previous_first_order # canonical 15 Hz increments by component\n' |
| path.write_text( |
| "---\nlibrary_name: actioncodec2\nlicense: apache-2.0\ntags:\n- robotics\n- action-tokenizer\n---\n\n" |
| f"{GENERATED_CARD_MARKER}\n\n# ActionCodec2Sparse\n\n" |
| "Install actioncodec2 >= 0.1.0 before loading this artifact.\n\n" |
| "```python\nfrom actioncodec2 import ActionCodec2Sparse\n\n" |
| "codec = ActionCodec2Sparse.from_pretrained('./my_codec')\n" |
| + context |
| + "rows = codec.encode(actions, **context)\n" |
| "restored = codec.decode(rows, horizon=actions.shape[-2], **context)\n```\n\n" |
| "Supply actions and any required boundary observations in the declared layout.\n" |
| "Rows are independent chunks; decode and grammar take the chunk horizon.\n\n" |
| "## Layouts\n\n```text\n" + str(codec) + "\n```\n", |
| encoding="utf-8", |
| ) |
| return path |
|
|
|
|
| def _saved_metadata(directory: Path) -> dict: |
| path = directory / PROCESSOR_CONFIG_NAME |
| return json.loads(path.read_text(encoding="utf-8")) if path.is_file() else {} |
|
|
|
|
| def _register(router: ActionCodec2SparseRouter, action_space) -> str: |
| if isinstance(action_space, (PresetConfig, Mapping, Path)): |
| return router.register_action_space(action_space).name |
| if isinstance(action_space, str): |
| if Path(action_space).is_file(): |
| return router.register_action_space(Path(action_space)).name |
| return action_space |
| raise TypeError("action_space must be a registered name, PresetConfig, mapping, or YAML path") |
|
|
