I read the unfold documentation but I fail to understand how to use it for my following case.
I have the following case where
tmp
is a 1D
tensor and step
is the overlapping parameter.
I have the following loop that concat the tmp
based on the step (ovedrlap)
as follow
N = 10
tmp = torch.arange(0,16*N).reshape(1, 16,N )
step=2 # this can change to 1,3,.ect
for i in range(N):
print(i*step,(i+1)*step+(step//2)*2)
tmp = torch.cat([tmp[:,:, i*step:(i+1)*step+(step//2)*2] for i in range(N) if (i+1)*step+(step//2)*2 < tmp.shape[2]], dim=0)
is there a way to do such a task via unfolding? or any other way?
the original tmp
looks like this:
tensor([[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
[ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[ 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
[ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
[ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
[ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],
[100, 101, 102, 103, 104, 105, 106, 107, 108, 109],
[110, 111, 112, 113, 114, 115, 116, 117, 118, 119],
[120, 121, 122, 123, 124, 125, 126, 127, 128, 129],
[130, 131, 132, 133, 134, 135, 136, 137, 138, 139],
[140, 141, 142, 143, 144, 145, 146, 147, 148, 149],
[150, 151, 152, 153, 154, 155, 156, 157, 158, 159]]])
and the final version looks like this (shape=torch.Size([3, 16, 4])
):
tensor([[[ 0, 1, 2, 3],
[ 10, 11, 12, 13],
[ 20, 21, 22, 23],
[ 30, 31, 32, 33],
[ 40, 41, 42, 43],
[ 50, 51, 52, 53],
[ 60, 61, 62, 63],
[ 70, 71, 72, 73],
[ 80, 81, 82, 83],
[ 90, 91, 92, 93],
[100, 101, 102, 103],
[110, 111, 112, 113],
[120, 121, 122, 123],
[130, 131, 132, 133],
[140, 141, 142, 143],
[150, 151, 152, 153]],
[[ 2, 3, 4, 5],
[ 12, 13, 14, 15],
[ 22, 23, 24, 25],
[ 32, 33, 34, 35],
[ 42, 43, 44, 45],
[ 52, 53, 54, 55],
[ 62, 63, 64, 65],
[ 72, 73, 74, 75],
[ 82, 83, 84, 85],
[ 92, 93, 94, 95],
[102, 103, 104, 105],
[112, 113, 114, 115],
[122, 123, 124, 125],
[132, 133, 134, 135],
[142, 143, 144, 145],
[152, 153, 154, 155]],
[[ 4, 5, 6, 7],
[ 14, 15, 16, 17],
[ 24, 25, 26, 27],
[ 34, 35, 36, 37],
[ 44, 45, 46, 47],
[ 54, 55, 56, 57],
[ 64, 65, 66, 67],
[ 74, 75, 76, 77],
[ 84, 85, 86, 87],
[ 94, 95, 96, 97],
[104, 105, 106, 107],
[114, 115, 116, 117],
[124, 125, 126, 127],
[134, 135, 136, 137],
[144, 145, 146, 147],
[154, 155, 156, 157]]])