[BC-breaking] ColorJitter gets its random params by calling get_params()

[datumbox]

As per @fmassa's comment, I'm rewriting ColorJitter so that it receives its random parameters from the get_params() method. The ColorJitter remains torch-scriptable.

Affects #2672 and #2669.

[codecov]

Codecov Report

Merging #3001 (66cdc72) into master (80f41f8) will increase coverage by 0.12%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master    #3001      +/-   ##
==========================================
+ Coverage   73.39%   73.51%   +0.12%     
==========================================
  Files          99       99              
  Lines        8825     8806      -19     
  Branches     1391     1383       -8     
==========================================
- Hits         6477     6474       -3     
+ Misses       1929     1913      -16     
  Partials      419      419              

Impacted Files	Coverage Δ
torchvision/transforms/transforms.py	82.57% <100.00%> (+2.06%)	⬆️

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[vfdev-5]

@datumbox looks awesome !

[fmassa]

Thanks a lot!

[zshn25]

This PR is not backward compatible. In previous versions, get_params method would return a callable transform. Not it just returns the params. As a result, this gives an error as follows

TypeError: 'tuple' object is not callable

when used like

color_jitter = transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
transform = transforms.ColorJitter.get_params(
    color_jitter.brightness, color_jitter.contrast, color_jitter.saturation,
    color_jitter.hue)

img_trans = transform(img)

[NicolasHug]

@zshn25 indeed this is a backward incompatible change, but it's probably for the best: the previous use of get_params was unconventional in the sense that get_params didn't behave as what you would expect from a "getter".

[EDIT: below is wrong]
I believe your code can be changed to simply

color_jitter = transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
img_trans = color_jitter(img)

to get the same results as before

[zshn25]

@NicolasHug, Thanks for clarification. Yes, indeed the following works

color_jitter = transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
img_trans = color_jitter(img)

but I'm interested in having the same ColorJitter transform on many images. I used to use get_params and use that transform on all images. How would I do this now?

[fmassa]

Hi @zshn25

Thanks for bringing this up. Indeed this is a BC-breaking change, sorry for breaking your code.
We wanted to uniformize how the transforms handled get_params, and in some sense the previous implementation of ColorJitter as "wrong".

I would propose to use the following helper function for what you want to achieve:

def get_random_color_jitter(
        brightness: Optional[List[float]],
        contrast: Optional[List[float]],
        saturation: Optional[List[float]],
        hue: Optional[List[float]]
    ) -> Tuple[Tensor, Optional[float], Optional[float], Optional[float], Optional[float]]:

    transforms = []

    if brightness is not None:
        brightness_factor = random.uniform(brightness[0], brightness[1])
        transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

    if contrast is not None:
        contrast_factor = random.uniform(contrast[0], contrast[1])
        transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

    if saturation is not None:
        saturation_factor = random.uniform(saturation[0], saturation[1])
        transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

    if hue is not None:
        hue_factor = random.uniform(hue[0], hue[1])
        transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

    random.shuffle(transforms)
    transform = Compose(transforms)

    return transform

This is basically a re-implementation of the previous method, and would also easily allow you to add more color augmentations if you wanted (like solalize, equalize, etc, which have been recently introduced).

Please let us know if this solution doesn't fit your needs.