Augmentation with keyword arguments

I was trying to implement an augmentation with keyword arguments that are fetched from a dataframe. I can do this inside my torch dataset class easily but it would be better to implement this in the augmentation pipeline. However, I couldn’t find any examples of what I’m trying to do.

I created a custom augmentation which its apply method takes arg1 and arg2.

class Aug(ImageOnlyTransform):

    def apply(self, image, arg1, arg2):

        image = do_something_based_on_args(image, arg1, arg2)

        return image

arg1 and arg2 can be accessed in the dataset’s __getitem__ method but I don’t how should I pass them to the augmentation pipeline. Is it supposed to be done like this?

transformed = self.transforms(image=image, mask=mask, arg1=arg1, arg2=arg2)

[Dipet]

I want to pad images to be at least 224 x 224, and then also have dimensions to be divisible by 32

Yes, looks like we need to add flag to support this kind of transform. You are right - you can use 2 PadIfNeeded:

t = A.Compose([
    A.PadIfNeeded(min_width=256, min_height=256),
    A.PadIfNeeded(pad_height_divisor=32, pad_width_divisor=32, min_width=None, min_height=None),
])

[Dipet]

• targets_as_params - if you want to use some targets (arguments that you pass when call the augmentation pipeline) to produce some augmentation parameters on aug call, you need to list all of them here. When the transform is called, they will be provided in get_params_dependent_on_targets. For example: image, mask, bboxes, keypoints - are standard names for our targets.
• get_params_dependent_on_targets - used to generate parameters based on some targets. If your transform doesn’t depend on any target, only it’s own arguments, you can use get_params. These functions are used to produce params once per call, this is useful when you are producing some random or heavy params.
• get_transform_init_args_names - used for serialization purposes. If params names in __init__ are equal to the params names stored inside the transform, you can just enumerate them inside this function. Otherwise, if you have some custom serialization logic, you will have to override the _to_dict method. We may remove remove this function in the future when someone implements automatic parsing of the __init__ call.