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[CI/code quality] Add clear github runner caches job + mypy fixes #1783

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merged 9 commits into from
Nov 19, 2024
Merged

[CI/code quality] Add clear github runner caches job + mypy fixes #1783

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6cc029f
[CI] Add clear github caches job
felixdittrich92 Nov 18, 2024
6fc3c43
[CI] Add clear github caches job
felixdittrich92 Nov 18, 2024
ad59720
mypy fixes
felixdittrich92 Nov 18, 2024
7ad6057
mypy fixes
felixdittrich92 Nov 18, 2024
7b4ad9b
mypy fixes
felixdittrich92 Nov 19, 2024
6ada11b
mypy fixes
felixdittrich92 Nov 19, 2024
2dc3e86
mypy ci job
felixdittrich92 Nov 19, 2024
6ccac18
update mypy job
felixdittrich92 Nov 19, 2024
1350aaf
typo fix
felixdittrich92 Nov 19, 2024
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mypy fixes
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@felixdittrich92
felixdittrich92 committed Nov 19, 2024
commit 7b4ad9b53c1c8fbf1abe1fb9934582c97dde8888
4 changes: 2 additions & 2 deletions doctr/models/modules/transformer/pytorch.py
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Expand Up @@ -46,10 +46,10 @@ def scaled_dot_product_attention(
query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Scaled Dot-Product Attention"""
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query.size(-1)) # type: ignore[assignment]
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query.size(-1))
if mask is not None:
# NOTE: to ensure the ONNX compatibility, masked_fill works only with int equal condition
scores = scores.masked_fill(mask == 0, float("-inf"))
scores = scores.masked_fill(mask == 0, float("-inf")) # type: ignore[attr-defined]
p_attn = torch.softmax(scores, dim=-1)
return torch.matmul(p_attn, value), p_attn

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6 changes: 3 additions & 3 deletions doctr/transforms/functional/pytorch.py
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Expand Up @@ -30,12 +30,12 @@ def invert_colors(img: torch.Tensor, min_val: float = 0.6) -> torch.Tensor:
out = F.rgb_to_grayscale(img, num_output_channels=3)
# Random RGB shift
shift_shape = [img.shape[0], 3, 1, 1] if img.ndim == 4 else [3, 1, 1]
rgb_shift = min_val + (1 - min_val) * torch.rand(shift_shape) # type: ignore[assignment]
rgb_shift = min_val + (1 - min_val) * torch.rand(shift_shape)
# Inverse the color
if out.dtype == torch.uint8:
out = (out.to(dtype=rgb_shift.dtype) * rgb_shift).to(dtype=torch.uint8)
out = (out.to(dtype=rgb_shift.dtype) * rgb_shift).to(dtype=torch.uint8) # type: ignore[attr-defined]
else:
out = out * rgb_shift.to(dtype=out.dtype)
out = out * rgb_shift.to(dtype=out.dtype) # type: ignore[attr-defined]
# Inverse the color
out = 255 - out if out.dtype == torch.uint8 else 1 - out
return out
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