fifa-tryon-demo / grid_sample.py
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# encoding: utf-8
import torch.nn.functional as F
from torch.autograd import Variable
def grid_sample(input, grid, canvas=None):
output = F.grid_sample(input, grid)
if canvas is None:
return output
else:
input_mask = Variable(input.data.new(input.size()).fill_(1))
output_mask = F.grid_sample(input_mask, grid)
padded_output = output * output_mask + canvas * (1 - output_mask)
return padded_output