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improve performance of {invert, solarize}_image_tensor #6819

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Merged
merged 6 commits into from
Oct 24, 2022
Merged

improve performance of {invert, solarize}_image_tensor #6819

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f1a248f
improve performance of invert_image_tensor
pmeier Oct 24, 2022
0cc6cdd
cleanup
pmeier Oct 24, 2022
34c075c
lint
pmeier Oct 24, 2022
7adfeb5
more cleanup
pmeier Oct 24, 2022
d8af69b
Merge branch 'main' into perf/invert
pmeier Oct 24, 2022
f8f68d7
use new invert in solarize
pmeier Oct 24, 2022
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10 changes: 9 additions & 1 deletion torchvision/prototype/transforms/functional/_color.py
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Original file line number Diff line number Diff line change
Expand Up @@ -435,7 +435,15 @@ def equalize(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
return equalize_image_pil(inpt)


invert_image_tensor = _FT.invert
def invert_image_tensor(image: torch.Tensor):
_FT._assert_image_tensor(image)

if image.dtype == torch.uint8:
return image.bitwise_not()
else:
return _FT._max_value(image.dtype) - image
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I couldn't believe it, but this seems to be fastest way:

def scalar_sub(image):
    return _max_value(image.dtype) - image


def new_full(image):
    return image.new_full(image.shape, _max_value(image.dtype)).sub_(image)


def full_like(image):
    return torch.full_like(image, _max_value(image.dtype)).sub_(image)
[-------------------- invert float or signed integer tensors -------------------]
                                         |  scalar_sub  |  new_full  |  full_like
1 threads: ----------------------------------------------------------------------
      (3, 256, 256)    / int32   / cpu   |      20      |     28     |      26   
      (3, 256, 256)    / int32   / cuda  |       5      |      8     |       7   
      (3, 256, 256)    / int64   / cpu   |      88      |    109     |     107   
      (3, 256, 256)    / int64   / cuda  |      13      |     22     |      22   
      (3, 256, 256)    / float64 / cpu   |      35      |     49     |      49   
      (3, 256, 256)    / float64 / cuda  |      12      |     21     |      21   
      (5, 3, 256, 256) / int32   / cpu   |      76      |    112     |     110   
      (5, 3, 256, 256) / int32   / cuda  |      34      |     67     |      67   
      (5, 3, 256, 256) / int64   / cpu   |     439      |    510     |     524   
      (5, 3, 256, 256) / int64   / cuda  |      68      |    133     |     133   
      (5, 3, 256, 256) / float64 / cpu   |     140      |    215     |     220   
      (5, 3, 256, 256) / float64 / cuda  |      68      |    133     |     133   

Times are in microseconds (us).



invert_image_pil = _FP.invert


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