@yegane

I think you set value for argument `size`

by mistake. Currently `interpolate`

function supports temporal, spatial and volumetric sampling, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width.

For your case, if you want to resize on 4D input, then `interpolate`

does spatial sampling and interpolates for the last 2 dimension(height * width), so *the list length of argument *`size`

must be less than 2. Can you let me know why would you like to interpolate in the second dimension(channels)? Otherwise, reshape your input first and interpolate later.

For example:

```
>>> input = torch.randn(1, 1, 2, 2)
>>> input
tensor([[[[-0.7523, -1.5534],
[-0.2479, 0.6668]]]])
>>> F.interpolate(input, 4, mode='bilinear')
tensor([[[[-0.7523, -0.9526, -1.3531, -1.5534],
[-0.6262, -0.7192, -0.9053, -0.9983],
[-0.3740, -0.2525, -0.0097, 0.1118],
[-0.2479, -0.0192, 0.4382, 0.6668]]]])
>>> F.interpolate(input, [4, 2], mode='bilinear')
tensor([[[[-0.7523, -1.5534],
[-0.6262, -0.9983],
[-0.3740, 0.1118],
[-0.2479, 0.6668]]]])
```