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关于在单位阵上做卷积,单位阵里有很多0啊,局部信息不会丢失嘛,(还是我理解错了) 比如这段代码里:
RepMLP/repmlp.py
Line 107 in 55c7677
在这个上面做卷积,I的形状是(9,1,3,3),每个(3,3)中只有一个值不为0,卷积后reshape回去,也只有对角元上不为0,这样做(9,9)x(9,1)的矩阵乘的话,相当与给(3,3)里的每一个元素乘了一个单独的值,也不是卷积吧。
The text was updated successfully, but these errors were encountered:
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关于在单位阵上做卷积,单位阵里有很多0啊,局部信息不会丢失嘛,(还是我理解错了)
比如这段代码里:
RepMLP/repmlp.py
Line 107 in 55c7677
假设输入就是(1,1,3,3), groups=1, c_in=c_out=1, 就是简单地在一张(3,3)的图上做一个3x3卷积。
I = torch.eye(9).repeat(1,1).reshape(9,1,3,3)
I = tensor([[[[1., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]],
[[[0., 1., 0.],
[0., 0., 0.],
[0., 0., 0.]]],
[[[0., 0., 1.],
[0., 0., 0.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[1., 0., 0.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 1., 0.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 0., 1.],
[0., 0., 0.]]],
[[[0., 0., 0.],
[0., 0., 0.],
[1., 0., 0.]]],
[[[0., 0., 0.],
[0., 0., 0.],
[0., 1., 0.]]],
[[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 1.]]]])
在这个上面做卷积,I的形状是(9,1,3,3),每个(3,3)中只有一个值不为0,卷积后reshape回去,也只有对角元上不为0,这样做(9,9)x(9,1)的矩阵乘的话,相当与给(3,3)里的每一个元素乘了一个单独的值,也不是卷积吧。
The text was updated successfully, but these errors were encountered: