Tensorflow, we can use
tf.image.resize_images(img, img_h, img_w) to convert a feature map into another size.
How can we do the same thing in
Resizing? Any simple direct way?
you can look at these Upsampling* modules to resize up:
To resize down, you can use AvgPool2d
It works for me.
Thank you very much.
Is there a way for fractional resize, e.g., 128x128 to 96x96?
Well, 128x128 -> 96x96 can be done by nn.UpsamplingNearest2d(scale_factor=3) followed by nn.AvgPool2d(4).
I hope I can use another direct way (with interpolation).
Has this been implemented yet for float scale factors? Greatly appreciated!
Jwin, I had the same problem and I solved using a sampling grid:
def downsampling(x, size=None, scale_factor=None, mode='nearest'): # define size if user has specified scale_factor if size is None: size = (int(scale_factor*x.size(2)), int(scale_factor*x.size(3))) # create coordinates h = torch.arange(0,size) / (size-1) * 2 - 1 w = torch.arange(0,size) / (size-1) * 2 - 1 # create grid grid = torch.zeros(size,size,2) grid[:,:,0] = w.unsqueeze(0).repeat(size,1) grid[:,:,1] = h.unsqueeze(0).repeat(size,1).transpose(0,1) # expand to match batch size grid = grid.unsqueeze(0).repeat(x.size(0),1,1,1) if x.is_cuda: grid = grid.cuda() # do sampling return F.grid_sample(x, grid, mode=mode)
You can either use nearest or bilinear interpolation, depending on your needs. In my case I was downsizing a semantic layout, so I couldn’t use any type of pooling (non-integer values wheren’t allowed and a cross integer value between two classes didn’t have sense).
I didn’t try this in upsampling. Notice that it only works with 4D tensors as input, so if you need to use it with a single image, before you have to .unsqueeze(0) it.
My $0.02 about
tf.image.resize_images and the state of related functionality in PyTorch.
Does anyone have a solution for autogradable general nearest neighbours upsampling?