Adding SQL Dataset related files to the build script

Summary: Now that we have SQLAlchemy 2.0, we can fully use them.

Reviewed By: bottler

Differential Revision: D66920096

fbshipit-source-id: 25c0ea1c4f7361e66348035519627dc961b9e6e6
This commit is contained in:
Roman Shapovalov 2024-12-23 16:05:26 -08:00 committed by Facebook GitHub Bot
parent 055ab3a2e3
commit 64a5bfadc8
2 changed files with 56 additions and 34 deletions

View File

@ -8,7 +8,8 @@ import hashlib
import json import json
import logging import logging
import os import os
from dataclasses import dataclass import urllib
from dataclasses import dataclass, Field, field
from typing import ( from typing import (
Any, Any,
ClassVar, ClassVar,
@ -29,9 +30,9 @@ import sqlalchemy as sa
import torch import torch
from pytorch3d.implicitron.dataset.dataset_base import DatasetBase from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
from pytorch3d.implicitron.dataset.frame_data import ( # noqa from pytorch3d.implicitron.dataset.frame_data import (
FrameData, FrameData,
FrameDataBuilder, FrameDataBuilder, # noqa
FrameDataBuilderBase, FrameDataBuilderBase,
) )
from pytorch3d.implicitron.tools.config import ( from pytorch3d.implicitron.tools.config import (
@ -51,7 +52,7 @@ _SET_LISTS_TABLE: str = "set_lists"
@registry.register @registry.register
class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore class SqlIndexDataset(DatasetBase, ReplaceableBase):
""" """
A dataset with annotations stored as SQLite tables. This is an index-based dataset. A dataset with annotations stored as SQLite tables. This is an index-based dataset.
The length is returned after all sequence and frame filters are applied (see param The length is returned after all sequence and frame filters are applied (see param
@ -125,9 +126,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
seed: int = 0 seed: int = 0
remove_empty_masks_poll_whole_table_threshold: int = 300_000 remove_empty_masks_poll_whole_table_threshold: int = 300_000
# we set it manually in the constructor # we set it manually in the constructor
# _index: pd.DataFrame = field(init=False) _index: pd.DataFrame = field(init=False, metadata={"omegaconf_ignore": True})
_sql_engine: sa.engine.Engine = field(
init=False, metadata={"omegaconf_ignore": True}
)
eval_batches: Optional[List[Any]] = field(
init=False, metadata={"omegaconf_ignore": True}
)
frame_data_builder: FrameDataBuilderBase frame_data_builder: FrameDataBuilderBase # pyre-ignore[13]
frame_data_builder_class_type: str = "FrameDataBuilder" frame_data_builder_class_type: str = "FrameDataBuilder"
def __post_init__(self) -> None: def __post_init__(self) -> None:
@ -138,17 +145,23 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
raise ValueError("sqlite_metadata_file must be set") raise ValueError("sqlite_metadata_file must be set")
if self.dataset_root: if self.dataset_root:
frame_builder_type = self.frame_data_builder_class_type frame_args = f"frame_data_builder_{self.frame_data_builder_class_type}_args"
getattr(self, f"frame_data_builder_{frame_builder_type}_args")[ getattr(self, frame_args)["dataset_root"] = self.dataset_root
"dataset_root" getattr(self, frame_args)["path_manager"] = self.path_manager
] = self.dataset_root
run_auto_creation(self) run_auto_creation(self)
self.frame_data_builder.path_manager = self.path_manager
# pyre-ignore # NOTE: sqlite-specific args (read-only mode). if self.path_manager is not None:
self.sqlite_metadata_file = self.path_manager.get_local_path(
self.sqlite_metadata_file
)
self.subset_lists_file = self.path_manager.get_local_path(
self.subset_lists_file
)
# NOTE: sqlite-specific args (read-only mode).
self._sql_engine = sa.create_engine( self._sql_engine = sa.create_engine(
f"sqlite:///file:{self.sqlite_metadata_file}?mode=ro&uri=true" f"sqlite:///file:{urllib.parse.quote(self.sqlite_metadata_file)}?mode=ro&uri=true"
) )
sequences = self._get_filtered_sequences_if_any() sequences = self._get_filtered_sequences_if_any()
@ -166,16 +179,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
if len(index) == 0: if len(index) == 0:
raise ValueError(f"There are no frames in the subsets: {self.subsets}!") raise ValueError(f"There are no frames in the subsets: {self.subsets}!")
self._index = index.set_index(["sequence_name", "frame_number"]) # pyre-ignore self._index = index.set_index(["sequence_name", "frame_number"])
self.eval_batches = None # pyre-ignore self.eval_batches = None
if self.eval_batches_file: if self.eval_batches_file:
self.eval_batches = self._load_filter_eval_batches() self.eval_batches = self._load_filter_eval_batches()
logger.info(str(self)) logger.info(str(self))
def __len__(self) -> int: def __len__(self) -> int:
# pyre-ignore[16]
return len(self._index) return len(self._index)
def __getitem__(self, frame_idx: Union[int, Tuple[str, int]]) -> FrameData: def __getitem__(self, frame_idx: Union[int, Tuple[str, int]]) -> FrameData:
@ -250,7 +262,6 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
return frame_data return frame_data
def __str__(self) -> str: def __str__(self) -> str:
# pyre-ignore[16]
return f"SqlIndexDataset #frames={len(self._index)}" return f"SqlIndexDataset #frames={len(self._index)}"
def sequence_names(self) -> Iterable[str]: def sequence_names(self) -> Iterable[str]:
@ -335,12 +346,12 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
rows = self._index.index.get_loc(seq_name) rows = self._index.index.get_loc(seq_name)
if isinstance(rows, slice): if isinstance(rows, slice):
assert rows.stop is not None, "Unexpected result from pandas" assert rows.stop is not None, "Unexpected result from pandas"
rows = range(rows.start or 0, rows.stop, rows.step or 1) rows_seq = range(rows.start or 0, rows.stop, rows.step or 1)
else: else:
rows = np.where(rows)[0] rows_seq = list(np.where(rows)[0])
index_slice, idx = self._get_frame_no_coalesced_ts_by_row_indices( index_slice, idx = self._get_frame_no_coalesced_ts_by_row_indices(
rows, seq_name, subset_filter rows_seq, seq_name, subset_filter
) )
index_slice["idx"] = idx index_slice["idx"] = idx
@ -461,14 +472,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
return [SqlSequenceAnnotation.sequence_name.notin_(self.exclude_sequences)] return [SqlSequenceAnnotation.sequence_name.notin_(self.exclude_sequences)]
def _load_subsets_from_json(self, subset_lists_path: str) -> pd.DataFrame: def _load_subsets_from_json(self, subset_lists_path: str) -> pd.DataFrame:
assert self.subsets is not None subsets = self.subsets
assert subsets is not None
with open(subset_lists_path, "r") as f: with open(subset_lists_path, "r") as f:
subset_to_seq_frame = json.load(f) subset_to_seq_frame = json.load(f)
seq_frame_list = sum( seq_frame_list = sum(
( (
[(*row, subset) for row in subset_to_seq_frame[subset]] [(*row, subset) for row in subset_to_seq_frame[subset]]
for subset in self.subsets for subset in subsets
), ),
[], [],
) )
@ -522,7 +534,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
stmt = sa.select( stmt = sa.select(
self.frame_annotations_type.sequence_name, self.frame_annotations_type.sequence_name,
self.frame_annotations_type.frame_number, self.frame_annotations_type.frame_number,
).where(self.frame_annotations_type._mask_mass == 0) ).where(self.frame_annotations_type._mask_mass == 0) # pyre-ignore[16]
with Session(self._sql_engine) as session: with Session(self._sql_engine) as session:
to_remove = session.execute(stmt).all() to_remove = session.execute(stmt).all()
@ -586,7 +598,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
stmt = sa.select( stmt = sa.select(
self.frame_annotations_type.sequence_name, self.frame_annotations_type.sequence_name,
self.frame_annotations_type.frame_number, self.frame_annotations_type.frame_number,
self.frame_annotations_type._image_path, self.frame_annotations_type._image_path, # pyre-ignore[16]
sa.null().label("subset"), sa.null().label("subset"),
) )
where_conditions = [] where_conditions = []
@ -600,7 +612,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
logger.info(" excluding samples with empty masks") logger.info(" excluding samples with empty masks")
where_conditions.append( where_conditions.append(
sa.or_( sa.or_(
self.frame_annotations_type._mask_mass.is_(None), self.frame_annotations_type._mask_mass.is_(None), # pyre-ignore[16]
self.frame_annotations_type._mask_mass != 0, self.frame_annotations_type._mask_mass != 0,
) )
) )
@ -634,7 +646,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
assert self.eval_batches_file assert self.eval_batches_file
logger.info(f"Loading eval batches from {self.eval_batches_file}") logger.info(f"Loading eval batches from {self.eval_batches_file}")
if not os.path.isfile(self.eval_batches_file): if (
self.path_manager and not self.path_manager.isfile(self.eval_batches_file)
) or (not self.path_manager and not os.path.isfile(self.eval_batches_file)):
# The batch indices file does not exist. # The batch indices file does not exist.
# Most probably the user has not specified the root folder. # Most probably the user has not specified the root folder.
raise ValueError( raise ValueError(
@ -642,7 +656,8 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
+ "Please specify a correct dataset_root folder." + "Please specify a correct dataset_root folder."
) )
with open(self.eval_batches_file, "r") as f: eval_batches_file = self._local_path(self.eval_batches_file)
with open(eval_batches_file, "r") as f:
eval_batches = json.load(f) eval_batches = json.load(f)
# limit the dataset to sequences to allow multiple evaluations in one file # limit the dataset to sequences to allow multiple evaluations in one file
@ -758,11 +773,18 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase): # pyre-ignore
prefixes=["TEMP"], # NOTE SQLite specific! prefixes=["TEMP"], # NOTE SQLite specific!
) )
@classmethod
def pre_expand(cls) -> None:
# remove dataclass annotations that are not meant to be init params
# because they cause troubles for OmegaConf
for attr, attr_value in list(cls.__dict__.items()): # need to copy as we mutate
if isinstance(attr_value, Field) and attr_value.metadata.get(
"omegaconf_ignore", False
):
delattr(cls, attr)
del cls.__annotations__[attr]
def _seq_name_to_seed(seq_name) -> int: def _seq_name_to_seed(seq_name) -> int:
"""Generates numbers in [0, 2 ** 28)""" """Generates numbers in [0, 2 ** 28)"""
return int(hashlib.sha1(seq_name.encode("utf-8")).hexdigest()[:7], 16) return int(hashlib.sha1(seq_name.encode("utf-8")).hexdigest()[:7], 16)
def _safe_as_tensor(data, dtype):
return torch.tensor(data, dtype=dtype) if data is not None else None

View File

@ -43,7 +43,7 @@ logger = logging.getLogger(__name__)
@registry.register @registry.register
class SqlIndexDatasetMapProvider(DatasetMapProviderBase): # pyre-ignore [13] class SqlIndexDatasetMapProvider(DatasetMapProviderBase):
""" """
Generates the training, validation, and testing dataset objects for Generates the training, validation, and testing dataset objects for
a dataset laid out on disk like SQL-CO3D, with annotations in an SQLite data base. a dataset laid out on disk like SQL-CO3D, with annotations in an SQLite data base.
@ -193,9 +193,9 @@ class SqlIndexDatasetMapProvider(DatasetMapProviderBase): # pyre-ignore [13]
# this is a mould that is never constructed, used to build self._dataset_map values # this is a mould that is never constructed, used to build self._dataset_map values
dataset_class_type: str = "SqlIndexDataset" dataset_class_type: str = "SqlIndexDataset"
dataset: SqlIndexDataset dataset: SqlIndexDataset # pyre-ignore [13]
path_manager_factory: PathManagerFactory path_manager_factory: PathManagerFactory # pyre-ignore [13]
path_manager_factory_class_type: str = "PathManagerFactory" path_manager_factory_class_type: str = "PathManagerFactory"
def __post_init__(self): def __post_init__(self):