No dataaugmentation as in original flownet?

[onlytailei]

Have you tried to implement the dataAugmentation part of the original flownet?

[ClementPinard]

Data Augmentation is partly done within the data loader instead of a dedicated module. As far as I remember, the main data augmentation that is not applied here is chromatic data augmentation. i.e., warping on color values and contrast. And that augmentation is to me pointless as we apply a normalization on color values during loading, which will set mean intensity to 0 and contrast (std) to 1

Every other augmentation, like translation, corpping and such can be found here : https://github.com/ClementPinard/FlowNetPytorch/blob/master/flow_transforms.py

However, if you still feel the need to test intensity/contrast data augmentation, it would be very easy to do so with a function like this :

class RandomNormalize(object):
    def __init__(self, mean, std):
        """Args:
        mean (tuple of 2): min and max mean to apply to input
        std (tuple of 2): min and max std to apply to input
        """
        self.mean = mean
        self.std = std

    def __call__(self, tensor):
        for t in tensor:
            m = np.random.uniform(*self.mean)
            s = np.random.uniform(*self.std)
            t.mul_(s).add_(m)
        return tensor

And to apply it to your dataset :

input_transform = transforms.Compose([
                flow_transforms.ArrayToTensor(),
                transforms.Normalize(mean=[0,0,0], std=[255,255,255]),
                normalize,
                RandomNormalize((-1,1),(0.5,1.5))
        ])