I am wanting to standardize 3d tensors to shape (32,512,512) which is the (depth, height, width) using
tricubic mode for interpolation.
I am aware that bicubic mode is present in torch.nn.functional.interpolate as per the documentation
The modes available for resizing are: nearest, linear (3D-only), bilinear, bicubic (4D-only), trilinear (5D-only), area, nearest-exact
But its only for 4D data. Is there a way to implement with bicubic or tricubic interpolation on 3d tensors
Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape.
So I assume volumetric refers to a shape of
[batch_size, channels, depth, height, width].
Hey @ptrblck ,
thanks for your reply but, I am unable to understand how it helps my problem.
I just want to understand if there is a way in which I can implement
Bicubic transformation on 3d data points.
The current blocker I am facing is that the available bicubic transformation is present for 4d data points.
Another option is if I could add another dimension to my 3d data to make it 4d and then implement the available bicubic interpolation. But, the problem with that is if i make my data 4d then the interpolate function will add 2 more dimensions to it for batch and Num_channels which will make it 6d, which is not acceptable
Please let me know your thoughts on this