python如何对二维图像进行卷积

def my_conv2d(inputs: np.ndarray, kernel: np.ndarray):
    # 计算需要填充的行列数目，这里假定mode为“same”
    # 一般卷积核的hw都是奇数，这里实现方式也是基于奇数尺寸的卷积核
    h, w = inputs.shape
    kernel = kernel[::-1, ...][..., ::-1]  # 卷积的定义，必须旋转180度
    h1, w1 = kernel.shape
    h_pad = (h1 - 1) // 2
    w_pad = (w1 - 1) // 2
    inputs = np.pad(inputs, pad_width=[(h_pad, h_pad), (w_pad, w_pad)], mode="constant", constant_values=0)
    outputs = np.zeros(shape=(h, w))
    for i in range(h):  # 行号
        for j in range(w):  # 列号
            outputs[i, j] = np.sum(np.multiply(inputs[i: i + h1, j: j + w1], kernel))
return outputs