Prototype transforms cleanup #5504
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💊 CI failures summary and remediationsAs of commit 448bec2 (more details on the Dr. CI page): 💚 💚 Looks good so far! There are no failures yet. 💚 💚 This comment was automatically generated by Dr. CI (expand for details).Please report bugs/suggestions to the (internal) Dr. CI Users group. |
datumbox
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Mar 1, 2022
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Mar 5, 2022
Summary: * fix grayscale to RGB for batches * make unsupported types in auto augment a parameter * make auto augment kwargs explicit * add missing error message * add support for specifying probabilites on RandomChoice * remove TODO for deprecating p on random transforms * streamline sample type checking * address comments * split image_size into height and width in auto augment Reviewed By: datumbox Differential Revision: D34579511 fbshipit-source-id: 757663a5a77f229cd1592b4c23dc17c7e8fe4807 Co-authored-by: Vasilis Vryniotis <[email protected]>
vfdev-5
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Jul 11, 2022
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| def _extract_types(sample: Any) -> Iterator[Type]: | ||
| return query_recursively(lambda id, input: type(input), sample) | ||
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| def has_any(sample: Any, *types: Type) -> bool: | ||
| return any(issubclass(type, types) for type in _extract_types(sample)) | ||
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| def has_all(sample: Any, *types: Type) -> bool: | ||
| return not bool(set(types) - set(_extract_types(sample))) |
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@pmeier fyi maybe we could code has_all and has_any using torch tree_flatten and simplify the code:
from typing import Any, Type
from torch.utils._pytree import tree_flatten
def has_any(sample: Any, *types: Type) -> bool:
flat_sample, _ = tree_flatten(sample)
return any(issubclass(type(obj), types) for obj in flat_sample)
def has_all(sample: Any, *types: Type) -> bool:
flat_sample, _ = tree_flatten(sample)
return not bool(set(types) - set([type(obj) for obj in flat_sample]))
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Addresses minor comments from #5500.