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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| """ | |
| Backward compatibility of configs. | |
| Instructions to bump version: | |
| + It's not needed to bump version if new keys are added. | |
| It's only needed when backward-incompatible changes happen | |
| (i.e., some existing keys disappear, or the meaning of a key changes) | |
| + To bump version, do the following: | |
| 1. Increment _C.VERSION in defaults.py | |
| 2. Add a converter in this file. | |
| Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X, | |
| and a function "downgrade" which in-place downgrades config from X to X-1 | |
| In each function, VERSION is left unchanged. | |
| Each converter assumes that its input has the relevant keys | |
| (i.e., the input is not a partial config). | |
| 3. Run the tests (test_config.py) to make sure the upgrade & downgrade | |
| functions are consistent. | |
| """ | |
| import logging | |
| from typing import List, Optional, Tuple | |
| from .config import CfgNode as CN | |
| from .defaults import _C | |
| __all__ = ["upgrade_config", "downgrade_config"] | |
| def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN: | |
| """ | |
| Upgrade a config from its current version to a newer version. | |
| Args: | |
| cfg (CfgNode): | |
| to_version (int): defaults to the latest version. | |
| """ | |
| cfg = cfg.clone() | |
| if to_version is None: | |
| to_version = _C.VERSION | |
| assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format( | |
| cfg.VERSION, to_version | |
| ) | |
| for k in range(cfg.VERSION, to_version): | |
| converter = globals()["ConverterV" + str(k + 1)] | |
| converter.upgrade(cfg) | |
| cfg.VERSION = k + 1 | |
| return cfg | |
| def downgrade_config(cfg: CN, to_version: int) -> CN: | |
| """ | |
| Downgrade a config from its current version to an older version. | |
| Args: | |
| cfg (CfgNode): | |
| to_version (int): | |
| Note: | |
| A general downgrade of arbitrary configs is not always possible due to the | |
| different functionalities in different versions. | |
| The purpose of downgrade is only to recover the defaults in old versions, | |
| allowing it to load an old partial yaml config. | |
| Therefore, the implementation only needs to fill in the default values | |
| in the old version when a general downgrade is not possible. | |
| """ | |
| cfg = cfg.clone() | |
| assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format( | |
| cfg.VERSION, to_version | |
| ) | |
| for k in range(cfg.VERSION, to_version, -1): | |
| converter = globals()["ConverterV" + str(k)] | |
| converter.downgrade(cfg) | |
| cfg.VERSION = k - 1 | |
| return cfg | |
| def guess_version(cfg: CN, filename: str) -> int: | |
| """ | |
| Guess the version of a partial config where the VERSION field is not specified. | |
| Returns the version, or the latest if cannot make a guess. | |
| This makes it easier for users to migrate. | |
| """ | |
| logger = logging.getLogger(__name__) | |
| def _has(name: str) -> bool: | |
| cur = cfg | |
| for n in name.split("."): | |
| if n not in cur: | |
| return False | |
| cur = cur[n] | |
| return True | |
| # Most users' partial configs have "MODEL.WEIGHT", so guess on it | |
| ret = None | |
| if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"): | |
| ret = 1 | |
| if ret is not None: | |
| logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret)) | |
| else: | |
| ret = _C.VERSION | |
| logger.warning( | |
| "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format( | |
| filename, ret | |
| ) | |
| ) | |
| return ret | |
| def _rename(cfg: CN, old: str, new: str) -> None: | |
| old_keys = old.split(".") | |
| new_keys = new.split(".") | |
| def _set(key_seq: List[str], val: str) -> None: | |
| cur = cfg | |
| for k in key_seq[:-1]: | |
| if k not in cur: | |
| cur[k] = CN() | |
| cur = cur[k] | |
| cur[key_seq[-1]] = val | |
| def _get(key_seq: List[str]) -> CN: | |
| cur = cfg | |
| for k in key_seq: | |
| cur = cur[k] | |
| return cur | |
| def _del(key_seq: List[str]) -> None: | |
| cur = cfg | |
| for k in key_seq[:-1]: | |
| cur = cur[k] | |
| del cur[key_seq[-1]] | |
| if len(cur) == 0 and len(key_seq) > 1: | |
| _del(key_seq[:-1]) | |
| _set(new_keys, _get(old_keys)) | |
| _del(old_keys) | |
| class _RenameConverter: | |
| """ | |
| A converter that handles simple rename. | |
| """ | |
| RENAME: List[Tuple[str, str]] = [] # list of tuples of (old name, new name) | |
| def upgrade(cls, cfg: CN) -> None: | |
| for old, new in cls.RENAME: | |
| _rename(cfg, old, new) | |
| def downgrade(cls, cfg: CN) -> None: | |
| for old, new in cls.RENAME[::-1]: | |
| _rename(cfg, new, old) | |
| class ConverterV1(_RenameConverter): | |
| RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")] | |
| class ConverterV2(_RenameConverter): | |
| """ | |
| A large bulk of rename, before public release. | |
| """ | |
| RENAME = [ | |
| ("MODEL.WEIGHT", "MODEL.WEIGHTS"), | |
| ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"), | |
| ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"), | |
| ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"), | |
| ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"), | |
| ( | |
| "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD", | |
| "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH", | |
| ), | |
| ( | |
| "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT", | |
| "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT", | |
| ), | |
| ( | |
| "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD", | |
| "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH", | |
| ), | |
| ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"), | |
| ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"), | |
| ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"), | |
| ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"), | |
| ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"), | |
| ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"), | |
| ("TEST.AUG_ON", "TEST.AUG.ENABLED"), | |
| ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"), | |
| ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"), | |
| ("TEST.AUG_FLIP", "TEST.AUG.FLIP"), | |
| ] | |
| def upgrade(cls, cfg: CN) -> None: | |
| super().upgrade(cfg) | |
| if cfg.MODEL.META_ARCHITECTURE == "RetinaNet": | |
| _rename( | |
| cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS" | |
| ) | |
| _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") | |
| del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"] | |
| del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"] | |
| else: | |
| _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS") | |
| _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") | |
| del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"] | |
| del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"] | |
| del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"] | |
| def downgrade(cls, cfg: CN) -> None: | |
| super().downgrade(cfg) | |
| _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS") | |
| _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES") | |
| cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS | |
| cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES | |
| cfg.MODEL.RETINANET.ANCHOR_STRIDES = [] # this is not used anywhere in any version | |