Add scale option to ToDtype. Remove ConvertDtype. #7759

NicolasHug · 2023-07-25T16:54:44Z

Closes #7756

this PR:

adds a scale parameter to ToDtype, which only affects images or videos
removes ConvetDtype to keep ConvertImageDtype. ConvertImageDtype now only supports images, not videos.
removes all dispatchers / kernels associated with convert_.* and replace with to_dtype* dispatchers / kernels.
When passing ToDtype(torch.float32) i.e. the dtype parameter is just a dtype, not a dict, we only convert images and videos - this is for BC with ConvertImageDtype and reduces adoption friction

(I may have removed too much stuff on the existing tests, we'll see with the CI)

cc @vfdev-5

pytorch-bot · 2023-07-25T16:54:47Z

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torchvision/transforms/v2/_misc.py

test/test_transforms_v2_refactored.py

pmeier

I have a bunch of comments, but overall looks solid.

test/test_transforms_v2_refactored.py

pmeier · 2023-07-26T07:00:49Z

torchvision/transforms/v2/functional/_meta.py

@@ -296,7 +296,10 @@ def _num_value_bits(dtype: torch.dtype) -> int:
        raise TypeError(f"Number of value bits is only defined for integer dtypes, but got {dtype}.")


-def convert_dtype_image_tensor(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
+def to_dtype_image_tensor(image: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
+    if not scale:


Should this come after the dtype check? Functionally, this is a no-op here in case image.dtype == dtype, but it is still an unnecessary call.

if image.dtype == dtype: return image elif not scale: return image.to(dtype)

makes the behavior a little more clear and should be minimally more performant.

I'll do it but TBH I don't really follow the reasoning behind the change

The elif part actually adds confusion to me because it suggests these 2 blocks are related when in reality they're really not

Re-reading the original implementation: I was wrong. Since we already return'ed in both branches, this should have no effect on the performance. Thus, feel free to revert to what you had.

Still, I would put the dtype check at the top, because that is the "more important" one. Again, just style / personal preference. Ignore if you feel different.

The elif part actually adds confusion to me because it suggests these 2 blocks are related when in reality they're really not

Fair enough. Happy with a second if as well.

torchvision/transforms/v2/_misc.py

torchvision/transforms/v2/functional/_meta.py

test/test_transforms_v2_refactored.py

pmeier · 2023-07-26T07:50:01Z

test/test_transforms_v2_refactored.py

@@ -125,6 +128,7 @@ def check_kernel(
    check_cuda_vs_cpu=True,
    check_scripted_vs_eager=True,
    check_batched_vs_unbatched=True,
+    expect_same_dtype=True,


Not against it, but are we expecting more kernels to set this to False?

I don't think so (more precisely: I don't know). We still want the rest of the checks to be done for ToDtype though. Is there a better way than to add a parameter?

Not really. Since this will be the only the kernel that ever needs this, we could implement a custom check_kernel inside the test class. Basically copy-pasting the current function, but add the new parameter as well as stripping everything that is more generic, but not needed in this specific case. This would keep the check_kernel signature clean since it already has quite a few parameters.

Kinda torn on this. Up to you. I'm ok with both.

I have a slight preference for adding the parameter, because otherwise we'd have to change both implementation of check_kernel if we ever needed to update it.
(looks like I'm advocating for this single entry point after all :p )

test/test_transforms_v2_refactored.py

torchvision/transforms/v2/functional/_meta.py

vfdev-5 · 2023-07-26T14:26:17Z

torchvision/transforms/v2/_misc.py

        super().__init__()
-        if not isinstance(dtype, dict):
-            dtype = _get_defaultdict(dtype)


Removing this behaviour we would like to go away from defaultdict(lambda: dtype) ?

Yes, this is one of the goals #7756 (comment). It basically just gets replaced by {"others": dtype}:

no need to know what a defaultdict is

no need to import defaultdict

no need to know what a lambda is

no need to understand how defaultdict and lambda interact

But should not this be done in a separate PR and everywhere it is used ?

If this gets accepted here I was going to submit a follow-up PR to do the same changes for the fill parameters (which is probably much more work). Are there other places where this pattern is used?

OK, sounds good to me.

PermuteDimensions, TransposeDimensions and _setup_fill_arg

Co-authored-by: vfdev <vfdev.5@gmail.com>

NicolasHug · 2023-07-27T08:44:59Z

test/test_transforms_v2_refactored.py

+    @pytest.mark.parametrize("output_dtype", [torch.float32, torch.float64, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("scale", (True, False))
+    @pytest.mark.parametrize("as_dict", (True, False))


I added this because when the dtype parameters isn't a dict and when the input is a single bbox or a single mask (as is the case here), the input just gets passed-through. I'm converting the dtype into a dict so that we also test the rest of the code-paths for those single bboxes and masks. Not incredibly critical, just for coverage.

pmeier

Some comments, but nothing blocking. LGTM if CI is green. Thanks Nicolas!

pmeier · 2023-07-27T08:47:51Z

test/test_transforms_v2_refactored.py

@@ -125,6 +128,7 @@ def check_kernel(
    check_cuda_vs_cpu=True,
    check_scripted_vs_eager=True,
    check_batched_vs_unbatched=True,
+    expect_same_dtype=True,


Not really. Since this will be the only the kernel that ever needs this, we could implement a custom check_kernel inside the test class. Basically copy-pasting the current function, but add the new parameter as well as stripping everything that is more generic, but not needed in this specific case. This would keep the check_kernel signature clean since it already has quite a few parameters.

Kinda torn on this. Up to you. I'm ok with both.

test/test_transforms_v2_refactored.py

pmeier · 2023-07-27T08:53:21Z

test/test_transforms_v2_refactored.py

+            return inpt.max() <= 1
+
+        H, W = 10, 10
+        sample = {


Should we also throw a video in there?

We do (from the parametrization), although I kept img as a name which I agree is confusing. I'll use inpt instead

Thanks, I missed that.

test/test_transforms_v2_refactored.py

pmeier · 2023-07-27T08:56:56Z

test/test_transforms_v2_refactored.py

+            make_video,
+        ),
+    )
+    def test_behaviour(self, make_input):


This test is huge. Should we maybe split it into multiple ones?

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

NicolasHug · 2023-07-27T12:44:41Z

Tests are green in 92f2588 except for the linter which I just fixed. Merging, thanks for the reviews!

github-actions · 2023-07-27T12:45:47Z

Hey @NicolasHug!

You merged this PR, but no labels were added. The list of valid labels is available at https://github.com/pytorch/vision/blob/main/.github/process_commit.py

Reviewed By: matteobettini Differential Revision: D48642282 fbshipit-source-id: 95a2eea16407f17e1ebeb386cd5e2618a105450f Co-authored-by: vfdev <vfdev.5@gmail.com> Co-authored-by: Philip Meier <github.pmeier@posteo.de>

Add scale option to ToDtype. Remove ConvertDtype.

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NicolasHug commented Jul 25, 2023

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pmeier reviewed Jul 26, 2023

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NicolasHug added 3 commits July 26, 2023 10:48

Address comments

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Add back comment

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Let non-dict dtype only care about images and videos

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vfdev-5 reviewed Jul 26, 2023

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NicolasHug and others added 3 commits July 27, 2023 09:28

Update torchvision/transforms/v2/functional/_meta.py

4afaee4

Co-authored-by: vfdev <vfdev.5@gmail.com>

Merge branch 'main' of github.com:pytorch/vision into dtypeeeeeeeeeeeee

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Add docs

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NicolasHug commented Jul 27, 2023

View reviewed changes

Minor stuff

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pmeier approved these changes Jul 27, 2023

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NicolasHug and others added 2 commits July 27, 2023 10:14

Update test/test_transforms_v2_refactored.py

760d014

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

img -> inpt, split tests

ea7c1e2

This was referenced Jul 27, 2023

ToDype / ConvertImageDtype file location #7764

Closed

Allow "others" key as a catch-all in dictionary parameters #7765

Closed

NicolasHug added 3 commits July 27, 2023 10:54

mypy

fb288d2

Remove some old tests

3d0d5d9

Hopefully fix some tests

92f2588

NicolasHug mentioned this pull request Jul 27, 2023

Test mode dtypes in the ToDtype tests #7767

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Fix linter

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NicolasHug merged commit 1402eb8 into pytorch:main Jul 27, 2023

NicolasHug added enhancement module: transforms labels Jul 27, 2023

lyuwenyu mentioned this pull request Aug 31, 2023

Issue with training on user data lyuwenyu/RT-DETR#52

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Add scale option to ToDtype. Remove ConvertDtype. #7759

Add scale option to ToDtype. Remove ConvertDtype. #7759

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Add scale option to ToDtype. Remove ConvertDtype. #7759

Add scale option to ToDtype. Remove ConvertDtype. #7759

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