python numpy手写卷积（包含输入到输出纬度）

最近在准备秋招，所以八股文好好熟悉一下
输入维度： c_in*h*w
输出纬度：c_out*h*w
卷积参数量：c_in*c_out*k_h*k_w

import numpy as np
img # in: c_in*h*w out:c_out*h*w

def conv_onemap(image,k):#单张图的卷积
    h,w = image.shape()
    k_h, k_w = k.shape()

    ans = []
    for i in range(h-k_h):
        line = []
        for j in range(w-k_w):
            line.append(np.sum(image[i:i+k_h][j:j+k_w]*k))
        ans.append(line)
    return np.array(ans)


def convolution(c_out, image, kernel_size):#更正规的应该写成类，这里只是简单的表示一下
    c_in, h, w = image.shape()
    kernels = np.zeros((c_out,c_in,kernel_size[0], kernel_size[1]))

    ans = []
    for i in range(c_out):
        maps = []
        for j in range(c_in):
            k = kernels[i,j]
            new_map = conv_onemap(image[j], k)
            maps.append(new_map.tolist())
        maps = np.array(maps)
        maps = np.sum(maps, dim=0).tolist()
        ans.append(maps)
    return ans #c_out,h,w