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@Ayskin great effort in this PR, good that you are addressing this!
A suggestion: what do you think about centralizing all of this logic into a single place, such that it can be reused at different call sites? What I currently see repeated is: what format are we using? Does the file exist on disk? Should we overwrite existing? This also matters for correctness. With the implemented backends, a shelf is stored as side files (e.g. .db) next to _full.pickle. If a user ran once with use_shelve=True and later without, both a fresh pickle and an old shelf exist, and dbm.whichdb reports the shelf.
Ideas for the centralized code:
- the storage format could be persisted in
_meta.pickle(e.g."storage": "pickle" | "shelve"). - a centralized context manager could return the same mapping regardless whether the file is a pickle or a shelf (or maybe a zarr in the future). The if/else branches can be dropped in all call sites (i.e. assemblies, CTD, LoadFullMultiAnimalData, outlier frames).
- the writer owns the file: when an analysis actually (re)writes the output, it can remove leftovers of the other format (or refuse to open a shelf when a pickle already exist).
- it can convert both data layouts
predictions->full_dataand vice versa
I know this might be a bit of effort, but since you are refactoring it here anyway, this might pay off quickly: one place to test, and a natural spot to plug in a different storage backend later. Curious how this fits with your plans.
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deruyter92
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Great PR, looks good. Great that you managed to centralize helpers in a context manager. Call sites look cleaner now. Just a few minor comments.
| data = pickle.load(handle) | ||
| except (pickle.UnpicklingError, FileNotFoundError): | ||
| data = shelve.open(data_file, flag="r") | ||
| data = auxiliaryfunctions.load_full_data(data_file) |
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Before this change, if _full.pickle wasn't a pickle but a shelve, the except branch returned a lazy object. Now the whole shelf is turned into a dict.
| if not overwrite and existing_format is not None: | ||
| print(f"Video {video} already analyzed at {output_pkl}!") | ||
| else: | ||
| if existing_format is not None and bool(existing_format) != bool(use_shelve): |
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Small edge case: with overwrite=True, the existing full data is deleted before we know whether the video can be read. Before, this path left previous results untouched.
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Not sure if worth addressing in this PR. Only if you find an easy solution
| _DBM_SUFFIXES = (".db", ".pag", ".dir", ".dat", ".bak") | ||
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| @contextmanager |
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What do you think of splitting open_full_data into a plain opener + context manager? Then we can still use the shelf object if needed:
def read_full_data(path: str | Path) -> Mapping:
"""Returns a dict for pickles; shelves are opened read-only and lazily (caller closes)."""
path = Path(path)
kind = full_data_format(path)
if kind is None:
raise FileNotFoundError(f"No full data found at {path}")
if kind == "":
return read_pickle(path)
return shelve.open(str(path), flag="r")
@contextmanager
def open_full_data(path: str | Path) -> Generator[Mapping]:
data = read_full_data(path)
try:
yield data
finally:
if isinstance(data, shelve.Shelf):
data.close()
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3 participants
Bug description
When running
analyze_videosinference withuse_shelve=True, the predictions holding file_full.pickleis written as adbmdatabase. Depending on the Python version and the availabledbmbackend (dbm.gnu,dbm.sqlite3,dbm.ndbm, etc), it can end up as different file names"_full.pickle","_full.pickle.db","_full.pickle.dir", etc.Currently, the code only checks if the filename
"_full.pickle"exists before callingpickle.load. This fails if"_full.pickle"(with no additional extension) is a shelf.Fix
Centralized the predictions loading in
deeplabcut/utils/auxiliaryfunctions.py:open_full_data(path): context manager yielding a dict for a plain pickle, or a read-only shelf otherwise.load_full_data(path): same, but returns an in-memory dict copy.full_data_format(path): reads the type of the predictions file (None= no file,""= plain pickle, else the dbm module name).remove_full_data(path): removes existing files in case of overwrite.These helpers fix the loading error that may happen at several places in the code :
_generate_assemblies_file, CTD tracking,tracklets.py,outlier_frames.py,scoring.py,LoadFullMultiAnimalData.Additional fix :
overwrite=Trueis now safe to be used inanalyze_videosas the helpers look for previously existing files and remove/overwrite them accordingly depending on their format.This handles the issue where switching writing type (shelving or not) could leave coexisting files and mess up reading freshly created files.
Tests
Standard i/o tests for the helpers in
tests/test_auxiliaryfunctions.py.New tests to lock the fixes of
analyze_videosintests/pose_estimation_pytorch/apis/test_videos.py.Tests parametrize on available
dbmbackends with unavailable ones being skipped.I am not sure about best practice here so any comment on this is much appreciated.