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refactor: add missing explicit (non-dynamic) imports silence linter errors #719

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Merged
Borda merged 4 commits into from
Apr 8, 2026
Merged

refactor: add missing explicit (non-dynamic) imports silence linter errors #719

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fa3f3d2
refactor: add explicit (non-dynamic) imports for _develop.py and main…
anatoly-linqapp Feb 22, 2026
8ed14e7
fix(pre-commit): 🎨 auto format pre-commit hooks
pre-commit-ci[bot] Feb 22, 2026
a15b030
Merge branch 'develop' into explicit-imports
Borda Apr 8, 2026
80e35e5
refactor: move `numpy`, `torch`, and `PIL` imports to top-level
Borda Apr 8, 2026
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2 changes: 2 additions & 0 deletions src/rfdetr/datasets/_develop.py
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Expand Up @@ -21,6 +21,8 @@
from typing import TYPE_CHECKING, Any, Generator, Optional, Tuple
from urllib.request import urlretrieve

import torch
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Copilot AI Feb 22, 2026

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Adding a module-level torch import conflicts with the documented design intention of _SimpleDataset. The class docstring explicitly states that it "does not pull in torch at module load time" (lines 44-46), and torch was intentionally imported locally within __getitem__ (line 72) to achieve lazy loading.

While the module-level import is needed for linter compatibility with the torch.Tensor type hint on line 70, this breaks the lazy loading behavior. Consider one of these alternatives:

  1. Use a string literal for the type hint: def __getitem__(self, idx: int) -> Tuple["torch.Tensor", dict]: combined with from __future__ import annotations (already present)
  2. Import torch conditionally using TYPE_CHECKING: if TYPE_CHECKING: import torch
  3. Update the docstring to reflect that torch is now imported at module load time

Since this file already uses from __future__ import annotations (line 13), option 1 (string literal) would preserve the lazy loading behavior while satisfying linters.

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from rfdetr.util.logger import get_logger

logger = get_logger()
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