代码：
def easy_conv1(img, kernal, step = 1):
N = img.shape[0] # 图片边长
F = kernal.shape[0] # filter长
L = int((N-F)/step) + 1 # 输出结果边长
res = torch.zeros(L, L, requires_grad = True)
for row in range(0, L):
for column in range(0, L):
tmp_row, tmp_col = row * step, column * step
res[row, column] = (img[tmp_row : tmp_row + F, tmp_col : tmp_col + F] * kernal).sum().item()
return res
X = torch.arange(1,17).view(4,4)
ker = torch.tensor([[1,1],[1,1]])
print("+-"*25)
print(easy_conv1(X, ker, 1))
print("=="*25)
print(easy_conv1(X, ker, 2))
结果：
