Research Associate at Department of Engineering, University of Cambridge
๐ท 3D Computer Vision
๐ฎ Reinforcement Learning
๐ Adversarial Machine Learning
๐ Demand Side Management
๐ alwynm.github.io
Pytorch implimentation of STN bilinear sampler
Research Associate at Department of Engineering, University of Cambridge
๐ท 3D Computer Vision
๐ฎ Reinforcement Learning
๐ Adversarial Machine Learning
๐ Demand Side Management
๐ alwynm.github.io
hi๏ผdid you try the new function grid_sample in pytorch to implement the same effect?
Hi!
Thank you for the code. There is one small correction at the end of the '_interpolate' function - transformed image should be of the same shape as the input image (with the same number of channels), therefore the last lines in the function should be
im_flat = im.contiguous().view(-1, _num_channels)
pix_l = torch.gather(im_flat, 0, idx_l.repeat(_num_channels).view(-1, _num_channels))
pix_r = torch.gather(im_flat, 0, idx_r.repeat(_num_channels).view(-1, _num_channels))
to get the correct transformed image and the rest is the same.
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