Proposal to add Gaussian Blur to data augmentation

fastai fastai dev
2

It would definitely be convenient if Gaussian Blur were incorporated as a default fastai transform.

For those interested in this, the relevant code is:

def gaussian_kernel(size, sigma=2., dim=2, channels=3):
    # The gaussian kernel is the product of the gaussian function of each dimension.
    # kernel_size should be an odd number.
    
    kernel_size = 2*size + 1

    kernel_size = [kernel_size] * dim
    sigma = [sigma] * dim
    kernel = 1
    meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])
    
    for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
        mean = (size - 1) / 2
        kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-((mgrid - mean) / (2 * std)) ** 2)

    # Make sure sum of values in gaussian kernel equals 1.
    kernel = kernel / torch.sum(kernel)

    # Reshape to depthwise convolutional weight
    kernel = kernel.view(1, 1, *kernel.size())
    kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))

    return kernel

def _gaussian_blur(x, size:uniform_int):
    kernel = gaussian_kernel(size=size)
    kernel_size = 2*size + 1

    x = x[None,...]
    padding = int((kernel_size - 1) / 2)
    x = F.pad(x, (padding, padding, padding, padding), mode='reflect')
    x = torch.squeeze(F.conv2d(x, kernel, groups=3))

    return x

gaussian_blur = TfmPixel(_gaussian_blur)

The default values for sigma were a little high for my purposes but when I played around with them I got satisfactory results.

I think you would need to expose sigma in _gaussian_blur.

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