双线性插值扩大四维张量宽高至256，256python代码

以下是双线性插值扩大四维张量宽高至256，256的Python代码：

import numpy as np

def bilinear_interpolation_4d(x, y, z, w, img):
    x1, y1, z1, w1 = int(np.floor(x)), int(np.floor(y)), int(np.floor(z)), int(np.floor(w))
    x2, y2, z2, w2 = x1+1, y1+1, z1+1, w1+1
    
    if x2 >= img.shape[0]:
        x2 = x1
    if y2 >= img.shape[1]:
        y2 = y1
    if z2 >= img.shape[2]:
        z2 = z1
    if w2 >= img.shape[3]:
        w2 = w1
    
    Q11 = img[x1, y1, z1, w1]
    Q21 = img[x2, y1, z1, w1]
    Q12 = img[x1, y2, z1, w1]
    Q22 = img[x2, y2, z1, w1]
    Q13 = img[x1, y1, z2, w1]
    Q23 = img[x2, y1, z2, w1]
    Q14 = img[x1, y1, z1, w2]
    Q24 = img[x2, y1, z1, w2]
    Q15 = img[x1, y2, z2, w1]
    Q25 = img[x2, y2, z2, w1]
    Q16 = img[x1, y2, z1, w2]
    Q26 = img[x2, y2, z1, w2]
    Q17 = img[x1, y1, z2, w2]
    Q27 = img[x2, y1, z2, w2]
    Q18 = img[x1, y2, z2, w2]
    Q28 = img[x2, y2, z2, w2]
    
    x_ratio, y_ratio, z_ratio, w_ratio = x-x1, y-y1, z-z1, w-w1
    x_opposite, y_opposite, z_opposite, w_opposite = 1-x_ratio, 1-y_ratio, 1-z_ratio, 1-w_ratio
    
    pixel_value = (
        Q11*x_opposite*y_opposite*z_opposite*w_opposite + 
        Q21*x_ratio*y_opposite*z_opposite*w_opposite + 
        Q12*x_opposite*y_ratio*z_opposite*w_opposite + 
        Q22*x_ratio*y_ratio*z_opposite*w_opposite +
        Q13*x_opposite*y_opposite*z_ratio*w_opposite +
        Q23*x_ratio*y_opposite*z_ratio*w_opposite +
        Q14*x_opposite*y_opposite*z_opposite*w_ratio +
        Q24*x_ratio*y_opposite*z_opposite*w_ratio +
        Q15*x_opposite*y_ratio*z_ratio*w_opposite +
        Q25*x_ratio*y_ratio*z_ratio*w_opposite +
        Q16*x_opposite*y_ratio*z_opposite*w_ratio +
        Q26*x_ratio*y_ratio*z_opposite*w_ratio +
        Q17*x_opposite*y_opposite*z_ratio*w_ratio +
        Q27*x_ratio*y_opposite*z_ratio*w_ratio +
        Q18*x_opposite*y_ratio*z_ratio*w_ratio +
        Q28*x_ratio*y_ratio*z_ratio*w_ratio
    )
    
    return pixel_value

def resize_4d(img, new_size):
    new_img = np.zeros(new_size)
    for i in range(new_size[0]):
        for j in range(new_size[1]):
            for k in range(new_size[2]):
                for l in range(new_size[3]):
                    x = i * (img.shape[0] - 1) / (new_size[0] - 1)
                    y = j * (img.shape[1] - 1) / (new_size[1] - 1)
                    z = k * (img.shape[2] - 1) / (new_size[2] - 1)
                    w = l * (img.shape[3] - 1) / (new_size[3] - 1)
                    new_img[i, j, k, l] = bilinear_interpolation_4d(x, y, z, w, img)
    return new_img

其中，`img`是原始四维张量，`new_size`是目标四维张量的大小。`resize_4d`函数将原始四维张量扩大到目标大小，并返回新的四维张量。`bilinear_interpolation_4d`函数是双线性插值的具体实现函数，用于计算插值后的像素值。