49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
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"""Lane mask + list txt adapter."""
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from __future__ import annotations
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from pathlib import Path
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from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
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def _line_count(path: Path) -> int:
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if not path.is_file():
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return 0
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try:
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return sum(1 for _ in path.open(encoding="utf-8", errors="ignore"))
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except OSError:
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return 0
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class LaneMaskAdapter(IngestAdapter):
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format_id = "lane_mask"
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projects = ("lane",)
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def can_handle(self, ctx: IngestContext) -> bool:
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root = ctx.source_path
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return (root / "list" / "train_gt.txt").is_file() or (root / "train_val_gt.txt").is_file()
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def inspect(self, ctx: IngestContext) -> NormalizedDataset:
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root = ctx.source_path
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train = _line_count(root / "list" / "train_gt.txt")
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val = _line_count(root / "list" / "val_gt.txt")
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test = _line_count(root / "list" / "test_gt.txt")
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if train == 0 and (root / "train_val_gt.txt").is_file():
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train = _line_count(root / "train_val_gt.txt")
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warnings: list[str] = []
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if train == 0:
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warnings.append("train split list is empty")
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return NormalizedDataset(
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format_id=self.format_id,
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project=ctx.project,
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task=ctx.task,
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source_path=str(root),
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split_counts={"train": train, "val": val, "test": test},
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sample_count=train + val + test,
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annotation_count=train + val + test,
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artifacts=["list/train_gt.txt", "list/val_gt.txt", "list/test_gt.txt"],
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warnings=warnings,
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)
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