def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
N, C, H, W = input_data.shape
out_h = (H + 2 * pad - filter_h) // stride + 1
out_w = (W + 2 * pad - filter_w) // stride + 1
img = np.pad(input_data, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))
for y in range(filter_h):
y_max = y + stride * out_h
for x in range(filter_w):
x_max = x + stride * out_w
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N * out_h * out_w, -1)
return col
array = np.array([[1,2,0,7,1,0],[0,9,2,3,2,3],[3,0,1,2,1,2],[2,4,0,1,0,1],[6,0,1,2,1,2],[2,4,0,1,8,1]]).reshape(1,1,6,6)
result = np.max(im2col(array,3,3,3),axis=1).reshape(2,2)
result