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  1. 20-08-28 10:08:46.412 - INFO: Model [SRRaGANModel] is created.
  2. 20-08-28 10:08:46.412 - INFO: Start training from epoch: 0, iter: 0
  3. I_features_i
  4. shape, torch.Size([1, 256, 32, 32])
  5. number of elements =  262144
  6. data type =  torch.float32
  7. requires gradient = True
  8. mean = -0.025, min = -0.196, max = 0.709, median = -0.029, std=0.057
  9. T_features_i
  10. shape, torch.Size([1024, 256, 1, 1])
  11. number of elements =  262144
  12. data type =  torch.float32
  13. requires gradient = False
  14. mean = -0.028, min = -0.196, max = 0.671, median = -0.031, std=0.056
  15. I_features_i
  16. shape, torch.Size([1, 256, 32, 32])
  17. number of elements =  262144
  18. data type =  torch.float32
  19. requires gradient = True
  20. mean = 0.004, min = -0.195, max = 0.649, median = -0.012, std=0.062
  21. T_features_i
  22. shape, torch.Size([1024, 256, 1, 1])
  23. number of elements =  262144
  24. data type =  torch.float32
  25. requires gradient = False
  26. mean = 0.005, min = -0.194, max = 0.665, median = -0.012, std=0.062
  27. I_features_i
  28. shape, torch.Size([1, 256, 32, 32])
  29. number of elements =  262144
  30. data type =  torch.float32
  31. requires gradient = True
  32. mean = 0.003, min = -0.199, max = 0.687, median = -0.015, std=0.062
  33. T_features_i
  34. shape, torch.Size([1024, 256, 1, 1])
  35. number of elements =  262144
  36. data type =  torch.float32
  37. requires gradient = False
  38. mean = 0.004, min = -0.198, max = 0.721, median = -0.015, std=0.062
  39. I_features_i
  40. shape, torch.Size([1, 256, 32, 32])
  41. number of elements =  262144
  42. data type =  torch.float32
  43. requires gradient = True
  44. mean = -0.012, min = -0.199, max = 0.623, median = -0.026, std=0.061
  45. T_features_i
  46. shape, torch.Size([1024, 256, 1, 1])
  47. number of elements =  262144
  48. data type =  torch.float32
  49. requires gradient = False
  50. mean = -0.012, min = -0.198, max = 0.604, median = -0.026, std=0.061
  51. I_features_i
  52. shape, torch.Size([1, 256, 32, 32])
  53. number of elements =  262144
  54. data type =  torch.float32
  55. requires gradient = True
  56. mean = -0.010, min = -0.155, max = 0.630, median = -0.025, std=0.062
  57. T_features_i
  58. shape, torch.Size([1024, 256, 1, 1])
  59. number of elements =  262144
  60. data type =  torch.float32
  61. requires gradient = False
  62. mean = -0.010, min = -0.159, max = 0.596, median = -0.025, std=0.062
  63. I_features_i
  64. shape, torch.Size([1, 256, 32, 32])
  65. number of elements =  262144
  66. data type =  torch.float32
  67. requires gradient = True
  68. mean = -0.005, min = -0.194, max = 0.646, median = -0.018, std=0.062
  69. T_features_i
  70. shape, torch.Size([1024, 256, 1, 1])
  71. number of elements =  262144
  72. data type =  torch.float32
  73. requires gradient = False
  74. mean = -0.004, min = -0.196, max = 0.625, median = -0.018, std=0.062
  75. I_features_i
  76. shape, torch.Size([1, 256, 32, 32])
  77. number of elements =  262144
  78. data type =  torch.float32
  79. requires gradient = True
  80. mean = -0.026, min = -0.208, max = 0.573, median = -0.034, std=0.057
  81. T_features_i
  82. shape, torch.Size([1024, 256, 1, 1])
  83. number of elements =  262144
  84. data type =  torch.float32
  85. requires gradient = False
  86. mean = -0.026, min = -0.207, max = 0.594, median = -0.034, std=0.057
  87. I_features_i
  88. shape, torch.Size([1, 256, 32, 32])
  89. number of elements =  262144
  90. data type =  torch.float32
  91. requires gradient = True
  92. mean = -0.014, min = -0.196, max = 0.626, median = -0.020, std=0.061
  93. T_features_i
  94. shape, torch.Size([1024, 256, 1, 1])
  95. number of elements =  262144
  96. data type =  torch.float32
  97. requires gradient = False
  98. mean = -0.013, min = -0.196, max = 0.621, median = -0.020, std=0.061
  99. I_features_i
  100. shape, torch.Size([1, 256, 32, 32])
  101. number of elements =  262144
  102. data type =  torch.float32
  103. requires gradient = True
  104. mean = 0.003, min = -0.205, max = 0.708, median = -0.013, std=0.062
  105. T_features_i
  106. shape, torch.Size([1024, 256, 1, 1])
  107. number of elements =  262144
  108. data type =  torch.float32
  109. requires gradient = False
  110. mean = 0.005, min = -0.203, max = 0.629, median = -0.012, std=0.062
  111. I_features_i
  112. shape, torch.Size([1, 256, 32, 32])
  113. number of elements =  262144
  114. data type =  torch.float32
  115. requires gradient = True
  116. mean = -0.001, min = -0.176, max = 0.547, median = -0.018, std=0.062
  117. T_features_i
  118. shape, torch.Size([1024, 256, 1, 1])
  119. number of elements =  262144
  120. data type =  torch.float32
  121. requires gradient = False
  122. mean = -0.001, min = -0.179, max = 0.561, median = -0.018, std=0.062
  123. I_features_i
  124. shape, torch.Size([1, 256, 32, 32])
  125. number of elements =  262144
  126. data type =  torch.float32
  127. requires gradient = True
  128. mean = 0.002, min = -0.190, max = 0.600, median = -0.016, std=0.062
  129. T_features_i
  130. shape, torch.Size([1024, 256, 1, 1])
  131. number of elements =  262144
  132. data type =  torch.float32
  133. requires gradient = False
  134. mean = 0.003, min = -0.185, max = 0.590, median = -0.015, std=0.062
  135. I_features_i
  136. shape, torch.Size([1, 256, 32, 32])
  137. number of elements =  262144
  138. data type =  torch.float32
  139. requires gradient = True
  140. mean = 0.001, min = -0.153, max = 0.685, median = -0.016, std=0.062
  141. T_features_i
  142. shape, torch.Size([1024, 256, 1, 1])
  143. number of elements =  262144
  144. data type =  torch.float32
  145. requires gradient = False
  146. mean = 0.002, min = -0.155, max = 0.667, median = -0.015, std=0.062
  147. I_features_i
  148. shape, torch.Size([1, 256, 32, 32])
  149. number of elements =  262144
  150. data type =  torch.float32
  151. requires gradient = True
  152. mean = -0.014, min = -0.195, max = 0.623, median = -0.021, std=0.061
  153. T_features_i
  154. shape, torch.Size([1024, 256, 1, 1])
  155. number of elements =  262144
  156. data type =  torch.float32
  157. requires gradient = False
  158. mean = -0.013, min = -0.194, max = 0.604, median = -0.020, std=0.061
  159. I_features_i
  160. shape, torch.Size([1, 256, 32, 32])
  161. number of elements =  262144
  162. data type =  torch.float32
  163. requires gradient = True
  164. mean = -0.006, min = -0.196, max = 0.612, median = -0.018, std=0.062
  165. T_features_i
  166. shape, torch.Size([1024, 256, 1, 1])
  167. number of elements =  262144
  168. data type =  torch.float32
  169. requires gradient = False
  170. mean = -0.005, min = -0.195, max = 0.625, median = -0.017, std=0.062
  171. I_features_i
  172. shape, torch.Size([1, 256, 32, 32])
  173. number of elements =  262144
  174. data type =  torch.float32
  175. requires gradient = True
  176. mean = -0.013, min = -0.196, max = 0.719, median = -0.020, std=0.061
  177. T_features_i
  178. shape, torch.Size([1024, 256, 1, 1])
  179. number of elements =  262144
  180. data type =  torch.float32
  181. requires gradient = False
  182. mean = -0.013, min = -0.196, max = 0.698, median = -0.020, std=0.061
  183. I_features_i
  184. shape, torch.Size([1, 256, 32, 32])
  185. number of elements =  262144
  186. data type =  torch.float32
  187. requires gradient = True
  188. mean = -0.010, min = -0.210, max = 0.628, median = -0.023, std=0.062
  189. T_features_i
  190. shape, torch.Size([1024, 256, 1, 1])
  191. number of elements =  262144
  192. data type =  torch.float32
  193. requires gradient = False
  194. mean = -0.009, min = -0.210, max = 0.647, median = -0.023, std=0.062
  195. I_features_i
  196. shape, torch.Size([1, 512, 16, 16])
  197. number of elements =  131072
  198. data type =  torch.float32
  199. requires gradient = True
  200. mean = -0.014, min = -0.189, max = 0.473, median = -0.014, std=0.042
  201. T_features_i
  202. shape, torch.Size([256, 512, 1, 1])
  203. number of elements =  131072
  204. data type =  torch.float32
  205. requires gradient = False
  206. mean = -0.015, min = -0.189, max = 0.402, median = -0.015, std=0.041
  207. I_features_i
  208. shape, torch.Size([1, 512, 16, 16])
  209. number of elements =  131072
  210. data type =  torch.float32
  211. requires gradient = True
  212. mean = 0.005, min = -0.101, max = 0.520, median = -0.006, std=0.044
  213. T_features_i
  214. shape, torch.Size([256, 512, 1, 1])
  215. number of elements =  131072
  216. data type =  torch.float32
  217. requires gradient = False
  218. mean = 0.005, min = -0.109, max = 0.526, median = -0.006, std=0.044
  219. I_features_i
  220. shape, torch.Size([1, 512, 16, 16])
  221. number of elements =  131072
  222. data type =  torch.float32
  223. requires gradient = True
  224. mean = 0.002, min = -0.101, max = 0.564, median = -0.008, std=0.044
  225. T_features_i
  226. shape, torch.Size([256, 512, 1, 1])
  227. number of elements =  131072
  228. data type =  torch.float32
  229. requires gradient = False
  230. mean = 0.002, min = -0.108, max = 0.509, median = -0.007, std=0.044
  231. I_features_i
  232. shape, torch.Size([1, 512, 16, 16])
  233. number of elements =  131072
  234. data type =  torch.float32
  235. requires gradient = True
  236. mean = -0.005, min = -0.148, max = 0.570, median = -0.011, std=0.044
  237. T_features_i
  238. shape, torch.Size([256, 512, 1, 1])
  239. number of elements =  131072
  240. data type =  torch.float32
  241. requires gradient = False
  242. mean = -0.004, min = -0.141, max = 0.532, median = -0.011, std=0.044
  243. I_features_i
  244. shape, torch.Size([1, 512, 16, 16])
  245. number of elements =  131072
  246. data type =  torch.float32
  247. requires gradient = True
  248. mean = -0.005, min = -0.147, max = 0.582, median = -0.011, std=0.044
  249. T_features_i
  250. shape, torch.Size([256, 512, 1, 1])
  251. number of elements =  131072
  252. data type =  torch.float32
  253. requires gradient = False
  254. mean = -0.005, min = -0.148, max = 0.580, median = -0.011, std=0.044
  255. I_features_i
  256. shape, torch.Size([1, 512, 16, 16])
  257. number of elements =  131072
  258. data type =  torch.float32
  259. requires gradient = True
  260. mean = -0.003, min = -0.154, max = 0.515, median = -0.010, std=0.044
  261. T_features_i
  262. shape, torch.Size([256, 512, 1, 1])
  263. number of elements =  131072
  264. data type =  torch.float32
  265. requires gradient = False
  266. mean = -0.003, min = -0.158, max = 0.508, median = -0.010, std=0.044
  267. I_features_i
  268. shape, torch.Size([1, 512, 16, 16])
  269. number of elements =  131072
  270. data type =  torch.float32
  271. requires gradient = True
  272. mean = -0.011, min = -0.188, max = 0.547, median = -0.015, std=0.043
  273. T_features_i
  274. shape, torch.Size([256, 512, 1, 1])
  275. number of elements =  131072
  276. data type =  torch.float32
  277. requires gradient = False
  278. mean = -0.012, min = -0.188, max = 0.551, median = -0.015, std=0.043
  279. I_features_i
  280. shape, torch.Size([1, 512, 16, 16])
  281. number of elements =  131072
  282. data type =  torch.float32
  283. requires gradient = True
  284. mean = -0.006, min = -0.172, max = 0.611, median = -0.011, std=0.044
  285. T_features_i
  286. shape, torch.Size([256, 512, 1, 1])
  287. number of elements =  131072
  288. data type =  torch.float32
  289. requires gradient = False
  290. mean = -0.005, min = -0.172, max = 0.631, median = -0.011, std=0.044
  291. I_features_i
  292. shape, torch.Size([1, 512, 16, 16])
  293. number of elements =  131072
  294. data type =  torch.float32
  295. requires gradient = True
  296. mean = 0.003, min = -0.112, max = 0.488, median = -0.007, std=0.044
  297. T_features_i
  298. shape, torch.Size([256, 512, 1, 1])
  299. number of elements =  131072
  300. data type =  torch.float32
  301. requires gradient = False
  302. mean = 0.005, min = -0.092, max = 0.537, median = -0.006, std=0.044
  303. I_features_i
  304. shape, torch.Size([1, 512, 16, 16])
  305. number of elements =  131072
  306. data type =  torch.float32
  307. requires gradient = True
  308. mean = 0.002, min = -0.130, max = 0.671, median = -0.007, std=0.044
  309. T_features_i
  310. shape, torch.Size([256, 512, 1, 1])
  311. number of elements =  131072
  312. data type =  torch.float32
  313. requires gradient = False
  314. mean = 0.002, min = -0.132, max = 0.648, median = -0.007, std=0.044
  315. I_features_i
  316. shape, torch.Size([1, 512, 16, 16])
  317. number of elements =  131072
  318. data type =  torch.float32
  319. requires gradient = True
  320. mean = -0.000, min = -0.125, max = 0.549, median = -0.009, std=0.044
  321. T_features_i
  322. shape, torch.Size([256, 512, 1, 1])
  323. number of elements =  131072
  324. data type =  torch.float32
  325. requires gradient = False
  326. mean = -0.000, min = -0.123, max = 0.585, median = -0.009, std=0.044
  327. I_features_i
  328. shape, torch.Size([1, 512, 16, 16])
  329. number of elements =  131072
  330. data type =  torch.float32
  331. requires gradient = True
  332. mean = 0.001, min = -0.131, max = 0.697, median = -0.008, std=0.044
  333. T_features_i
  334. shape, torch.Size([256, 512, 1, 1])
  335. number of elements =  131072
  336. data type =  torch.float32
  337. requires gradient = False
  338. mean = 0.001, min = -0.140, max = 0.640, median = -0.008, std=0.044
  339. I_features_i
  340. shape, torch.Size([1, 512, 16, 16])
  341. number of elements =  131072
  342. data type =  torch.float32
  343. requires gradient = True
  344. mean = -0.005, min = -0.185, max = 0.570, median = -0.010, std=0.044
  345. T_features_i
  346. shape, torch.Size([256, 512, 1, 1])
  347. number of elements =  131072
  348. data type =  torch.float32
  349. requires gradient = False
  350. mean = -0.004, min = -0.185, max = 0.570, median = -0.010, std=0.044
  351. I_features_i
  352. shape, torch.Size([1, 512, 16, 16])
  353. number of elements =  131072
  354. data type =  torch.float32
  355. requires gradient = True
  356. mean = -0.002, min = -0.168, max = 0.587, median = -0.009, std=0.044
  357. T_features_i
  358. shape, torch.Size([256, 512, 1, 1])
  359. number of elements =  131072
  360. data type =  torch.float32
  361. requires gradient = False
  362. mean = -0.002, min = -0.168, max = 0.635, median = -0.010, std=0.044
  363. I_features_i
  364. shape, torch.Size([1, 512, 16, 16])
  365. number of elements =  131072
  366. data type =  torch.float32
  367. requires gradient = True
  368. mean = -0.006, min = -0.163, max = 0.611, median = -0.012, std=0.044
  369. T_features_i
  370. shape, torch.Size([256, 512, 1, 1])
  371. number of elements =  131072
  372. data type =  torch.float32
  373. requires gradient = False
  374. mean = -0.006, min = -0.163, max = 0.582, median = -0.012, std=0.044
  375. I_features_i
  376. shape, torch.Size([1, 512, 16, 16])
  377. number of elements =  131072
  378. data type =  torch.float32
  379. requires gradient = True
  380. mean = -0.003, min = -0.141, max = 0.532, median = -0.010, std=0.044
  381. T_features_i
  382. shape, torch.Size([256, 512, 1, 1])
  383. number of elements =  131072
  384. data type =  torch.float32
  385. requires gradient = False
  386. mean = -0.003, min = -0.147, max = 0.504, median = -0.010, std=0.044
  387. AMP Scaler state dict:  {'scale': 32768.0, 'growth_factor': 2.0, 'backoff_factor': 0.5, 'growth_interval': 2000, '_growth_tracker': 0}
  388. /usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  389.   "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
  390. I_features_i
  391. shape, torch.Size([1, 256, 32, 32])
  392. number of elements =  262144
  393. data type =  torch.float32
  394. requires gradient = True
  395. mean = -0.000, min = -0.188, max = 0.671, median = -0.015, std=0.063
  396. T_features_i
  397. shape, torch.Size([1024, 256, 1, 1])
  398. number of elements =  262144
  399. data type =  torch.float32
  400. requires gradient = False
  401. mean = 0.001, min = -0.188, max = 0.640, median = -0.014, std=0.062
  402. I_features_i
  403. shape, torch.Size([1, 256, 32, 32])
  404. number of elements =  262144
  405. data type =  torch.float32
  406. requires gradient = True
  407. mean = -0.001, min = -0.192, max = 0.596, median = -0.014, std=0.062
  408. T_features_i
  409. shape, torch.Size([1024, 256, 1, 1])
  410. number of elements =  262144
  411. data type =  torch.float32
  412. requires gradient = False
  413. mean = -0.001, min = -0.193, max = 0.600, median = -0.014, std=0.062
  414. I_features_i
  415. shape, torch.Size([1, 256, 32, 32])
  416. number of elements =  262144
  417. data type =  torch.float32
  418. requires gradient = True
  419. mean = -0.010, min = -0.190, max = 0.615, median = -0.020, std=0.062
  420. T_features_i
  421. shape, torch.Size([1024, 256, 1, 1])
  422. number of elements =  262144
  423. data type =  torch.float32
  424. requires gradient = False
  425. mean = -0.010, min = -0.189, max = 0.607, median = -0.020, std=0.062
  426. I_features_i
  427. shape, torch.Size([1, 256, 32, 32])
  428. number of elements =  262144
  429. data type =  torch.float32
  430. requires gradient = True
  431. mean = -0.022, min = -0.199, max = 0.672, median = -0.031, std=0.058
  432. T_features_i
  433. shape, torch.Size([1024, 256, 1, 1])
  434. number of elements =  262144
  435. data type =  torch.float32
  436. requires gradient = False
  437. mean = -0.022, min = -0.199, max = 0.686, median = -0.031, std=0.058
  438. I_features_i
  439. shape, torch.Size([1, 256, 32, 32])
  440. number of elements =  262144
  441. data type =  torch.float32
  442. requires gradient = True
  443. mean = 0.003, min = -0.195, max = 0.717, median = -0.015, std=0.062
  444. T_features_i
  445. shape, torch.Size([1024, 256, 1, 1])
  446. number of elements =  262144
  447. data type =  torch.float32
  448. requires gradient = False
  449. mean = 0.004, min = -0.193, max = 0.723, median = -0.015, std=0.062
  450. I_features_i
  451. shape, torch.Size([1, 256, 32, 32])
  452. number of elements =  262144
  453. data type =  torch.float32
  454. requires gradient = True
  455. mean = -0.001, min = -0.192, max = 0.689, median = -0.015, std=0.062
  456. T_features_i
  457. shape, torch.Size([1024, 256, 1, 1])
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  1814. data type =  torch.float32
  1815. requires gradient = True
  1816. mean = -0.000, min = -0.158, max = 0.554, median = -0.009, std=0.044
  1817. T_features_i
  1818. shape, torch.Size([256, 512, 1, 1])
  1819. number of elements =  131072
  1820. data type =  torch.float32
  1821. requires gradient = False
  1822. mean = -0.001, min = -0.156, max = 0.509, median = -0.009, std=0.044
  1823. I_features_i
  1824. shape, torch.Size([1, 512, 16, 16])
  1825. number of elements =  131072
  1826. data type =  torch.float32
  1827. requires gradient = True
  1828. mean = -0.009, min = -0.184, max = 0.462, median = -0.014, std=0.043
  1829. T_features_i
  1830. shape, torch.Size([256, 512, 1, 1])
  1831. number of elements =  131072
  1832. data type =  torch.float32
  1833. requires gradient = False
  1834. mean = -0.009, min = -0.182, max = 0.483, median = -0.014, std=0.043
  1835. I_features_i
  1836. shape, torch.Size([1, 512, 16, 16])
  1837. number of elements =  131072
  1838. data type =  torch.float32
  1839. requires gradient = True
  1840. mean = 0.000, min = -0.127, max = 0.690, median = -0.009, std=0.044
  1841. T_features_i
  1842. shape, torch.Size([256, 512, 1, 1])
  1843. number of elements =  131072
  1844. data type =  torch.float32
  1845. requires gradient = False
  1846. mean = 0.000, min = -0.118, max = 0.665, median = -0.009, std=0.044
  1847. I_features_i
  1848. shape, torch.Size([1, 512, 16, 16])
  1849. number of elements =  131072
  1850. data type =  torch.float32
  1851. requires gradient = True
  1852. mean = -0.007, min = -0.154, max = 0.525, median = -0.011, std=0.044
  1853. T_features_i
  1854. shape, torch.Size([256, 512, 1, 1])
  1855. number of elements =  131072
  1856. data type =  torch.float32
  1857. requires gradient = False
  1858. mean = -0.008, min = -0.158, max = 0.531, median = -0.012, std=0.044
  1859. I_features_i
  1860. shape, torch.Size([1, 512, 16, 16])
  1861. number of elements =  131072
  1862. data type =  torch.float32
  1863. requires gradient = True
  1864. mean = -0.003, min = -0.125, max = 0.689, median = -0.010, std=0.044
  1865. T_features_i
  1866. shape, torch.Size([256, 512, 1, 1])
  1867. number of elements =  131072
  1868. data type =  torch.float32
  1869. requires gradient = False
  1870. mean = -0.002, min = -0.121, max = 0.726, median = -0.010, std=0.044
  1871. I_features_i
  1872. shape, torch.Size([1, 512, 16, 16])
  1873. number of elements =  131072
  1874. data type =  torch.float32
  1875. requires gradient = True
  1876. mean = -0.019, min = -0.199, max = 0.398, median = -0.020, std=0.040
  1877. T_features_i
  1878. shape, torch.Size([256, 512, 1, 1])
  1879. number of elements =  131072
  1880. data type =  torch.float32
  1881. requires gradient = False
  1882. mean = -0.019, min = -0.199, max = 0.399, median = -0.020, std=0.040
  1883. I_features_i
  1884. shape, torch.Size([1, 512, 16, 16])
  1885. number of elements =  131072
  1886. data type =  torch.float32
  1887. requires gradient = True
  1888. mean = 0.002, min = -0.120, max = 0.567, median = -0.008, std=0.044
  1889. T_features_i
  1890. shape, torch.Size([256, 512, 1, 1])
  1891. number of elements =  131072
  1892. data type =  torch.float32
  1893. requires gradient = False
  1894. mean = 0.002, min = -0.128, max = 0.608, median = -0.008, std=0.044
  1895. I_features_i
  1896. shape, torch.Size([1, 512, 16, 16])
  1897. number of elements =  131072
  1898. data type =  torch.float32
  1899. requires gradient = True
  1900. mean = -0.001, min = -0.158, max = 0.505, median = -0.010, std=0.044
  1901. T_features_i
  1902. shape, torch.Size([256, 512, 1, 1])
  1903. number of elements =  131072
  1904. data type =  torch.float32
  1905. requires gradient = False
  1906. mean = -0.001, min = -0.155, max = 0.564, median = -0.010, std=0.044
  1907. I_features_i
  1908. shape, torch.Size([1, 512, 16, 16])
  1909. number of elements =  131072
  1910. data type =  torch.float32
  1911. requires gradient = True
  1912. mean = 0.003, min = -0.133, max = 0.650, median = -0.007, std=0.044
  1913. T_features_i
  1914. shape, torch.Size([256, 512, 1, 1])
  1915. number of elements =  131072
  1916. data type =  torch.float32
  1917. requires gradient = False
  1918. mean = 0.003, min = -0.133, max = 0.660, median = -0.007, std=0.044
  1919. I_features_i
  1920. shape, torch.Size([1, 512, 16, 16])
  1921. number of elements =  131072
  1922. data type =  torch.float32
  1923. requires gradient = True
  1924. mean = 0.003, min = -0.136, max = 0.507, median = -0.007, std=0.044
  1925. T_features_i
  1926. shape, torch.Size([256, 512, 1, 1])
  1927. number of elements =  131072
  1928. data type =  torch.float32
  1929. requires gradient = False
  1930. mean = 0.004, min = -0.118, max = 0.540, median = -0.007, std=0.044
  1931. AMP Scaler state dict:  {'scale': 2048.0, 'growth_factor': 2.0, 'backoff_factor': 0.5, 'growth_interval': 2000, '_growth_tracker': 0}
  1932. 20-08-28 10:09:01.267 - INFO: Training interrupted. Latest models and training states saved.
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