卷积的理解 python代码实现 pytorch 多输入多输出通道的理解

ef corr2d(X,K): 
    h,w = K.shape
    Y = torch.zeros(X.shape[0] - h + 1,X.shape[1] - w + 1)
    for i in range(Y.shape[0]):
        for j in range(Y.shape[1]):
            Y[i,j] = (X[i:i + h, j: j + w]*K).sum()
    return Y    

def corr2d_mutil_in(X,K):
    h,w = K.shape[1],K.shape[2]
    value = torch.zeros(X.shape[0] - h + 1,X.shape[1] - w + 1)
    for x,k in zip(X,K):
        value = value + corr2d(x,k)
    return value 

X = torch.tensor([[[1,2,3],[4,5,6],[7,8,9]],
                  [[1,1,1],[1,1,1],[1,1,1]],
                  [[2,2,2],[2,2,2],[2,2,2]]])
K = torch.tensor([[[1]],[[2]],[[3]]])
print(K.shape)
corr2d_mutil_in(X,K) 

Output:
tensor([[ 9., 10., 11.],
        [12., 13., 14.],
        [15., 16., 17.]])

X = torch.tensor([[[1,2,3],[4,5,6],[7,8,9]],
                  [[1,1,1],[1,1,1],[1,1,1]],
                  [[2,2,2],[2,2,2],[2,2,2]]]) 

K = torch.tensor([[[[1]],[[2]],[[3]]],
                  [[[4]],[[1]],[[1]]],
                  [[[5]],[[3]],[[3]]]])
print(K.shape)

输出：
torch.Size([3, 3, 1, 1])

def corr2d_multi_in_out(X,K):
    return torch.stack([corr2d_mutil_in(X,k) for k in K])

corr2d_multi_in_out(X,K)

输出：
tensor([[[ 9., 10., 11.],
         [12., 13., 14.],
         [15., 16., 17.]],

        [[ 7., 11., 15.],
         [19., 23., 27.],
         [31., 35., 39.]],

        [[14., 19., 24.],
         [29., 34., 39.],
         [44., 49., 54.]]])