[prototype] Minor speed and nit optimizations on Transform Classes #6837

datumbox · 2022-10-25T17:01:01Z

Further speed optimizations, nits and code-quality changes:

Switching from torch.distributions.Uniform() to tensor.uniform_() speeds up CUDA by 20%. It also aligns with the implementation on V1.
Moves the input sanitisation from the runtime to construction time for as many params as possible.
Contains a few non performance refactorings to avoid unnecessary casting and moving methods around.

datumbox · 2022-10-25T17:02:48Z

torchvision/prototype/transforms/_color.py

@@ -51,7 +51,7 @@ def _check_input(

    @staticmethod
    def _generate_value(left: float, right: float) -> float:
-        return float(torch.distributions.Uniform(left, right).sample())
+        return torch.empty(1).uniform_(left, right).item()


Switching to this random generator we get a performance boost on GPU. Moreover this option is JIT-scriptable (if on the future we decide to add support) and doesn't require to constantly initialize a distribution object as before:

[--------- ColorJitter cpu torch.float32 ---------] | old random | new random 1 threads: ---------------------------------------- (3, 400, 400) | 17 | 17 6 threads: ---------------------------------------- (3, 400, 400) | 21 | 21 Times are in milliseconds (ms). [--------- ColorJitter cuda torch.float32 --------] | old random | new random 1 threads: ---------------------------------------- (3, 400, 400) | 1090 | 883 6 threads: ---------------------------------------- (3, 400, 400) | 1090 | 882 Times are in microseconds (us).

torchvision/prototype/transforms/_utils.py

torchvision/prototype/transforms/_auto_augment.py

torchvision/prototype/transforms/_geometry.py

torchvision/prototype/transforms/_type_conversion.py

datumbox · 2022-10-26T12:54:12Z

torchvision/prototype/transforms/_utils.py

@@ -80,7 +97,7 @@ def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect",


 def query_bounding_box(flat_inputs: List[Any]) -> features.BoundingBox:
-    bounding_boxes = {inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)}
+    bounding_boxes = [inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)]


I think we can use a list here instead of a set.

Is this a perf optimization? Otherwise I would prefer the set since

it makes it more clear we are looking for duplicates

it is aligned with the other query_* function below.

Functionally, the only difference is that with a set, passing the same bounding box twice in the same sample would not be caught. This indeed seems like a user error, but if we raise there, we should probably check for duplicates everywhere. Not sure if we want to single out bounding boxes here.

It's both perf and code correctness. What we want here is to ensure there is only 1 bbox in the input. If there were two, even if they were the same exact copy, our transforms that rely on query_bounding_box would end up working incorrectly. Effectively they would end up modifying the first copy but not the second. This is a different use-case from the rest of the query_* methods where we are able to handle multiple entries but just want to ensure same size across.

It's both perf and code correctness.

Are list's actually faster than set's? I'm honestly curious since I never benchmarked this 😇

If there were two, even if they were the same exact copy, our transforms that rely on query_bounding_box would end up working incorrectly. Effectively they would end up modifying the first copy but not the second.

Agreed.

This is a different use-case from the rest of the query_* methods where we are able to handle multiple entries but just want to ensure same size across.

You are right that, I was not clear enough. What I meant was: we are not checking for duplicate images, videos, masks, ... anywhere. Either we should do that (and that seems like the right thing to do, but that probably has perf implications) or we shouldn't single out bounding boxes here. No strong opinion though. A single mitigation is probably better than none. But we should still discuss if we need to check this for everything.

As discussed offline, query_chw and query_spatial_size must use sets because what we are interested in is enforcing that all images have a single size. Multiple images/videos are allowed in these cases, we just enforce the size is the same everywhere.

On the other hand the query_bounding_box is used to extract the single bbox. The transforms that use the specific util, handle only 1 bbox and this is why we need a list. Note that this is identical to the _flatten_and_extract_image_or_video from AA where we also use a list.

So this is primarily a bug fix to restore the right semantics on the function and also has secondary positive performance implications.

torchvision/prototype/transforms/_geometry.py

torchvision/prototype/transforms/_type_conversion.py

pmeier · 2022-10-26T13:02:20Z

torchvision/prototype/transforms/_type_conversion.py

@@ -13,7 +13,7 @@ class DecodeImage(Transform):
    _transformed_types = (features.EncodedImage,)

    def _transform(self, inpt: torch.Tensor, params: Dict[str, Any]) -> features.Image:
-        return cast(features.Image, F.decode_image_with_pil(inpt))
+        return F.decode_image_with_pil(inpt)  # type: ignore[no-any-return]


This has to be here, because it seems

vision/torchvision/prototype/transforms/functional/_type_conversion.py

Lines 13 to 14 in c84dbfa

@torch.jit.unused

def decode_image_with_pil(encoded_image: torch.Tensor) -> features.Image:

doesn't "forward" the type annotations 🙄

In all other places we took the decision to silence with ignore rather than cast, do we really need the cast here?

Nono, I was just explaining why we need the ignore for future me that is looking confused at the blame why we introduced it in the first place.

pmeier · 2022-10-26T13:06:09Z

torchvision/prototype/transforms/_utils.py

@@ -80,7 +97,7 @@ def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect",


 def query_bounding_box(flat_inputs: List[Any]) -> features.BoundingBox:
-    bounding_boxes = {inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)}
+    bounding_boxes = [inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)]


Is this a perf optimization? Otherwise I would prefer the set since

it makes it more clear we are looking for duplicates

it is aligned with the other query_* function below.

Functionally, the only difference is that with a set, passing the same bounding box twice in the same sample would not be caught. This indeed seems like a user error, but if we raise there, we should probably check for duplicates everywhere. Not sure if we want to single out bounding boxes here.

torchvision/prototype/transforms/_utils.py

datumbox · 2022-10-26T15:46:25Z

torchvision/prototype/transforms/_utils.py

+        if isinstance(fill, defaultdict) and callable(fill.default_factory):
+            default_value = fill.default_factory()
+            _check_fill_arg(default_value)


@pmeier Removing the support of defaultdicts would mean an extremely verbose API for users. They would need to define not only types that make sense but also those that dont (such as Labels, HotLabels and BBoxes) even for padding calls. Here I propose an alternative mitigation. This is part 1 to ensure we check things properly during the constructor. This is an attempt to "bug fix"/extend the validation of the existing method for default dicts.

As discussed offline, let's move forward with the current implementation and refactor later if needed.

datumbox · 2022-10-26T15:48:18Z

torchvision/prototype/transforms/_utils.py

+        for k, v in fill.items():
+            fill[k] = _convert_fill_arg(v)
+        if isinstance(fill, defaultdict) and callable(fill.default_factory):
+            default_value = fill.default_factory()
+            sanitized_default = _convert_fill_arg(default_value)
+            fill.default_factory = functools.partial(_default_arg, sanitized_default)
+        return fill  # type: ignore[return-value]


@pmeier Second mitigation. This sanitizes the input of all existing keys and patches the default_factory to have a sanitized value. If we don't like this mitigation, then I think we will have to revert the change of doing the verification on constructor time instead on runtime. Let me know your preference.

pmeier

Thanks Vasilis! LGTM if CI is green.

pmeier · 2022-10-27T09:50:35Z

torchvision/prototype/transforms/_utils.py

+        if isinstance(fill, defaultdict) and callable(fill.default_factory):
+            default_value = fill.default_factory()
+            _check_fill_arg(default_value)


As discussed offline, let's move forward with the current implementation and refactor later if needed.

datumbox · 2022-10-27T09:56:14Z

@pmeier Thanks. As mentioned offline, I've tested extensively this approach and know it works but I'm not terribly happy we need to check explicitly for defaultdict. I would be happy if we revert this change and move back to runtime input validation on a follow up PR. I'll merge to unblock further work but happy to discuss any changes.

…lasses (#6837) Summary: * Change random generator for ColorJitter. * Move `_convert_fill_arg` from runtime to constructor. * Remove unnecessary TypeVars. * Remove unnecessary casts * Update comments. * Minor code-quality changes on Geometical Transforms. * Fixing linter and other minor fixes. * Change mitigation for mypy.` * Fixing the tests. * Fixing the tests. * Fix linter * Restore dict copy. * Handling of defaultdicts * restore int idiom * Update todo Reviewed By: YosuaMichael Differential Revision: D40755989 fbshipit-source-id: d5b475ea9a603c7a137e85db08dcd0db30195e3c

Change random generator for ColorJitter.

49f7e5a

datumbox marked this pull request as draft October 25, 2022 17:01

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datumbox commented Oct 25, 2022

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datumbox added module: transforms Perf For performance improvements prototype labels Oct 25, 2022

datumbox and others added 2 commits October 26, 2022 10:27

Merge branch 'main' into prototype/class_speedup2

5ea065c

Move _convert_fill_arg from runtime to constructor.

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datumbox commented Oct 26, 2022

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datumbox added 4 commits October 26, 2022 11:34

Remove unnecessary TypeVars.

17d8184

Remove unnecessary casts

5e0be6e

Update comments.

08ae56f

Minor code-quality changes on Geometical Transforms.

7b8be17

datumbox force-pushed the prototype/class_speedup2 branch from 414a1ee to 7b8be17 Compare October 26, 2022 11:47

datumbox commented Oct 26, 2022

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Fixing linter and other minor fixes.

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datumbox requested review from pmeier and vfdev-5 October 26, 2022 12:54

datumbox changed the title ~~[WIP] [prototype] Speed up more Transform Classes~~ [prototype] Speed up more Transform Classes Oct 26, 2022

datumbox marked this pull request as ready for review October 26, 2022 12:54

pmeier reviewed Oct 26, 2022

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datumbox added 2 commits October 26, 2022 14:48

Change mitigation for mypy.`

b0b9b55

Fixing the tests.

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datumbox added 4 commits October 26, 2022 15:15

Fixing the tests.

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Fix linter

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Restore dict copy.

53f12bb

Handling of defaultdicts

843bcc9

restore int idiom

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datumbox force-pushed the prototype/class_speedup2 branch from 537e650 to 8e6af8d Compare October 26, 2022 15:42

datumbox commented Oct 26, 2022

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datumbox requested a review from pmeier October 26, 2022 15:48

Update todo

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datumbox mentioned this pull request Oct 26, 2022

Performance improvements for transforms v2 vs. v1 #6818

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datumbox changed the title ~~[prototype] Speed up more Transform Classes~~ [prototype] Minor speed and nit optimizations on Transform Classes Oct 26, 2022

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Merge branch 'main' into prototype/class_speedup2

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datumbox merged commit e1f464b into pytorch:main Oct 27, 2022

datumbox deleted the prototype/class_speedup2 branch October 27, 2022 09:56

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[prototype] Minor speed and nit optimizations on Transform Classes #6837

[prototype] Minor speed and nit optimizations on Transform Classes #6837

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datumbox commented Oct 27, 2022

	@torch.jit.unused
	def decode_image_with_pil(encoded_image: torch.Tensor) -> features.Image:

[prototype] Minor speed and nit optimizations on Transform Classes #6837

[prototype] Minor speed and nit optimizations on Transform Classes #6837

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