实现一下多输入通道互相关运算
import torch
from d2l import torch as d2l
def corr2d_multi_in(X, K):
return sum(d2l.corr2d(x, k) for x, k in zip(X, K))
验证互相关运算的输出
X = torch.tensor([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])
K = torch.tensor([[[0.0, 1.0], [2.0, 3.0]], [[1.0, 2.0], [3.0, 4.0]]])
corr2d_multi_in(X, K)
tensor([[ 56., 72.], [104., 120.]])
计算多个通道的输出的互相关函数
def corr2d_multi_in_out(X, K):
return torch.stack([corr2d_multi_in(X, k) for k in K], 0)
K = torch.stack((K, K + 1, K + 2), 0)
K.shape
torch.Size([3, 2, 2, 2])
corr2d_multi_in_out(X, K)
tensor([[[ 56., 72.], [104., 120.]], [[ 76., 100.], [148., 172.]], [[ 96., 128.], [192., 224.]]])
1x1卷积
def corr2d_multi_in_out_1x1(X, K):
c_i, h, w = X.shape
c_o = K.shape[0]
X = X.reshape((c_i, h * w))
K = K.reshape((c_o, c_i))
Y = torch.matmul(K, X)
return Y.reshape((c_o, h, w))
X = torch.normal(0, 1, (3, 3, 3))
K = torch.normal(0, 1, (2, 3, 1, 1))
Y1 = corr2d_multi_in_out_1x1(X, K)
Y2 = corr2d_multi_in_out(X, K)
assert float(torch.abs(Y1 - Y2).sum()) < 1e-6