Image-Super-Resolution-via-Iterative-Refinement
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Janspiry
val.log中的PSNR值时高时低？
从我的val.log情况来看，为什么会出现PSNR值时高时低呢？不应该是随着迭代次数的增多，PSNR值稳步提升吗？ 以下是我的val.log记录： 找了很久都没发现原因，恳请告知！
24-04-17 15:22:06.665 - INFO: <epoch: 7, iter: 10,000> psnr: 2.1367e+01 24-04-17 16:40:16.885 - INFO: <epoch: 14, iter: 20,000> psnr: 1.7173e+01 24-04-17 17:58:24.560 - INFO: <epoch: 20, iter: 30,000> psnr: 1.8179e+01 24-04-17 19:16:39.499 - INFO: <epoch: 27, iter: 40,000> psnr: 2.1871e+01 24-04-17 20:34:55.209 - INFO: <epoch: 34, iter: 50,000> psnr: 1.8104e+01 24-04-17 21:53:01.576 - INFO: <epoch: 40, iter: 60,000> psnr: 1.5948e+01 24-04-17 23:11:02.831 - INFO: <epoch: 47, iter: 70,000> psnr: 1.6152e+01 24-04-18 00:28:56.065 - INFO: <epoch: 54, iter: 80,000> psnr: 1.4302e+01 24-04-18 01:46:35.971 - INFO: <epoch: 60, iter: 90,000> psnr: 1.9275e+01 24-04-18 03:04:25.122 - INFO: <epoch: 67, iter: 100,000> psnr: 1.9152e+01 24-04-18 04:22:17.947 - INFO: <epoch: 74, iter: 110,000> psnr: 1.6359e+01 24-04-18 05:40:04.073 - INFO: <epoch: 80, iter: 120,000> psnr: 1.8555e+01 24-04-18 06:58:01.322 - INFO: <epoch: 87, iter: 130,000> psnr: 2.0622e+01 24-04-18 08:15:53.929 - INFO: <epoch: 94, iter: 140,000> psnr: 1.6522e+01 24-04-18 09:33:48.300 - INFO: <epoch:100, iter: 150,000> psnr: 1.9023e+01 24-04-18 10:51:47.212 - INFO: <epoch:107, iter: 160,000> psnr: 1.7184e+01 24-04-18 12:09:51.140 - INFO: <epoch:114, iter: 170,000> psnr: 1.9060e+01 24-04-18 13:27:48.133 - INFO: <epoch:120, iter: 180,000> psnr: 2.4105e+01 24-04-18 14:45:56.058 - INFO: <epoch:127, iter: 190,000> psnr: 1.6243e+01 24-04-18 16:03:52.822 - INFO: <epoch:134, iter: 200,000> psnr: 1.7937e+01 24-04-18 17:21:34.655 - INFO: <epoch:140, iter: 210,000> psnr: 2.1026e+01 24-04-18 18:39:27.664 - INFO: <epoch:147, iter: 220,000> psnr: 1.9718e+01 24-04-18 19:57:23.373 - INFO: <epoch:154, iter: 230,000> psnr: 1.7403e+01 24-04-18 21:15:04.094 - INFO: <epoch:160, iter: 240,000> psnr: 2.0071e+01 24-04-18 22:32:57.280 - INFO: <epoch:167, iter: 250,000> psnr: 2.0279e+01 24-04-18 23:50:52.820 - INFO: <epoch:174, iter: 260,000> psnr: 1.7413e+01 24-04-19 01:08:33.645 - INFO: <epoch:180, iter: 270,000> psnr: 1.8354e+01 24-04-19 02:26:23.701 - INFO: <epoch:187, iter: 280,000> psnr: 1.7029e+01 24-04-19 03:44:22.464 - INFO: <epoch:194, iter: 290,000> psnr: 1.7357e+01 24-04-19 05:02:08.237 - INFO: <epoch:200, iter: 300,000> psnr: 1.6554e+01 24-04-19 06:19:56.945 - INFO: <epoch:207, iter: 310,000> psnr: 1.8061e+01 24-04-19 07:37:48.817 - INFO: <epoch:214, iter: 320,000> psnr: 1.8480e+01 24-04-19 08:55:34.300 - INFO: <epoch:220, iter: 330,000> psnr: 1.9598e+01 24-04-19 10:16:14.327 - INFO: <epoch:227, iter: 340,000> psnr: 1.8503e+01 24-04-19 11:38:57.797 - INFO: <epoch:234, iter: 350,000> psnr: 1.7704e+01 24-04-19 13:01:18.061 - INFO: <epoch:240, iter: 360,000> psnr: 1.5860e+01 24-04-19 14:24:21.144 - INFO: <epoch:247, iter: 370,000> psnr: 1.7670e+01
Hi @One1209 @ranformer,
This phenomena is known as perception-distortion trade-off. The output psnr is computed by E[X0|Xt, y]. In the first step, it is given by E[X0|XT, y]. Technically speaking, the optimal PSNR is given by E[X0|y], which is an average of XT~N(0,I).
It is expected that the psnr get worse as the diffusion process proceed. And theoretically, the ending PSNR should be around 3dB worse than init PSNR. So the result printed is quite normal.
For more information, see [The Perception-Distortion Tradeoff]
