问在神经网络框架中有前向映射/翘曲的实现吗？

EN

import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt

# create artificial image
im = np.zeros((9,9)) 
im[4,4] = 1

# warping images
u = np.zeros((9,9))
v = np.zeros((9,9))

u[4,3] += 1


# shift to pytorch
h,w = np.shape(im)

im_t = torch.from_numpy(im).float().view((1,1,h,w)).cpu()
u_t = torch.from_numpy(u).float().view((1,h,w,1)).cpu()
v_t = torch.from_numpy(v).float().view((1,h,w,1)).cpu()


n, _, h, w = im_t.size()
batchSize, _, _, nmbSamples = im_t.size()

# warp source image
U = torch.arange(start=0, end=w).expand(h, w).float().view((n, h, w, 1)).cpu()
V = torch.arange(start=0, end=h).expand(w, h).t().float().view((n, h, w, 1)).cpu()

U_u = (U + u_t) / ((w-1) / 2) - 1
V_v = (V + v_t) / ((h-1) / 2) - 1

grid = torch.cat((U_u, V_v), dim=3).float().cpu()

warped_t = F.grid_sample(im_t, grid)

# plot routine
plt.figure()
plt.subplot(121)
plt.imshow(im_t.data.cpu().numpy()[0,0,:,:])
plt.grid()

plt.subplot(122)
plt.imshow(warped_t.data.cpu().numpy()[0,0,:,:])
plt.grid()

plt.show(block=False)
plt.draw()