different requires_grad value between img1 and img2 in the example

[fengxiaohu]

whty do we need set the requires_grad = True in the img2 = Variable( img2, requires_grad = True) ,while the value in the img1 is False

import pytorch_ssim
import torch
from torch.autograd import Variable
from torch import optim
import cv2
import numpy as np

npImg1 = cv2.imread("einstein.png")

img1 = torch.from_numpy(np.rollaxis(npImg1, 2)).float().unsqueeze(0)/255.0
img2 = torch.rand(img1.size())

if torch.cuda.is_available():
    img1 = img1.cuda()
    img2 = img2.cuda()


img1 = Variable( img1,  requires_grad=False)
img2 = Variable( img2, requires_grad = True)


# Functional: pytorch_ssim.ssim(img1, img2, window_size = 11, size_average = True)
ssim_value = pytorch_ssim.ssim(img1, img2).data[0]
print("Initial ssim:", ssim_value)

# Module: pytorch_ssim.SSIM(window_size = 11, size_average = True)
ssim_loss = pytorch_ssim.SSIM()

optimizer = optim.Adam([img2], lr=0.01)

while ssim_value < 0.95:
    optimizer.zero_grad()
    ssim_out = -ssim_loss(img1, img2)
    ssim_value = - ssim_out.data[0]
    print(ssim_value)
    ssim_out.backward()
    optimizer.step()

[D876887913d]

The img1 is the target image, the img2 is the noise image. The goal of this code is using Adam optimizer to change noise image(img2) from the noise image to one similar to target image(img1). So the img2 should be grad each step, but the img1 is not necessary.