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Dear gdlg, Wishing you all the best lately.
Thank you very much for sharing the CBP codes. The time I was trying to test them, I found an interesting result. My test codes are shown as follows:
import torch
from compact_bilinear_pooling import CountSketch, CompactBilinearPooling
input_size = 3
output_size = 3
mcb = CompactBilinearPooling(input_size, input_size, output_size).cuda()
x = torch.rand(4,32,32,3).cuda()
y = torch.rand(4,32,32,3).cuda()
z = mcb(x,y)
As you had answered in another issues named "Merge",we should make sure the channel dimension be put in the last dimension of tensor, I had follow this advise in the code. With the codes above, I expected to get the output z with dimension (4, 32, 32, 3), however, the actual output dimension of z is (4, 3, 32, 2). I have been confused about this problem for a while.
It would be very kind of you to take some time out of your busy schedule to discuss this issue with me.
The text was updated successfully, but these errors were encountered:
Dear gdlg, Wishing you all the best lately.
Thank you very much for sharing the CBP codes. The time I was trying to test them, I found an interesting result. My test codes are shown as follows:
import torch
from compact_bilinear_pooling import CountSketch, CompactBilinearPooling
input_size = 3
output_size = 3
mcb = CompactBilinearPooling(input_size, input_size, output_size).cuda()
x = torch.rand(4,32,32,3).cuda()
y = torch.rand(4,32,32,3).cuda()
z = mcb(x,y)
As you had answered in another issues named "Merge",we should make sure the channel dimension be put in the last dimension of tensor, I had follow this advise in the code. With the codes above, I expected to get the output z with dimension (4, 32, 32, 3), however, the actual output dimension of z is (4, 3, 32, 2). I have been confused about this problem for a while.
It would be very kind of you to take some time out of your busy schedule to discuss this issue with me.
The text was updated successfully, but these errors were encountered: