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haofeixu
can you share your training log
Thank you for your great work. I have tried use the code to train with custom dataset, but the trained network alway output 0, the disp_loss is quickly converged around 0.1. could you share us the log you trained? thank you very much. @haofeixu @all
below is my training log when I use a small dataset to train, is there any abnormal things happen?
[2022-02-08 08:45:08,330] => 100 training samples found in the training set /opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py:475: UserWarning: This DataLoader will create 32 worker processes in total. Our suggested max number of worker in current system is 12, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. warnings.warn(_create_warning_msg( [2022-02-08 08:45:10,523] => Loading pretrained AANet: pretrained/aanet+_sceneflow-d3e13ef0.pth [2022-02-08 08:45:10,754] => Number of trainable parameters: 8514130 [2022-02-08 08:45:10,770] => Start training... /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:3499: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. warnings.warn( /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:3825: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. warnings.warn( [2022-02-08 08:45:15,963] cur_loss: 84.850 [2022-02-08 08:45:18,179] cur_loss: 59.475 [2022-02-08 08:45:20,229] cur_loss: 37.604 [2022-02-08 08:45:22,276] cur_loss: 5.961 [2022-02-08 08:45:24,318] cur_loss: 0.305 [2022-02-08 08:45:26,360] cur_loss: 0.130 [2022-02-08 08:45:28,412] cur_loss: 0.098 [2022-02-08 08:45:30,464] cur_loss: 0.120 [2022-02-08 08:45:32,511] cur_loss: 0.117 [2022-02-08 08:45:34,553] cur_loss: 0.133 [2022-02-08 08:45:36,181] Epoch: [ 1/200] [ 10/ 25] time: 25.41s disp_loss: 0.133 [2022-02-08 08:45:36,598] cur_loss: 0.095 [2022-02-08 08:45:38,647] cur_loss: 0.124 [2022-02-08 08:45:40,705] cur_loss: 0.094 [2022-02-08 08:45:42,752] cur_loss: 0.115 [2022-02-08 08:45:44,795] cur_loss: 0.101 [2022-02-08 08:45:46,841] cur_loss: 0.093 [2022-02-08 08:45:48,884] cur_loss: 0.099 [2022-02-08 08:45:50,932] cur_loss: 0.110 [2022-02-08 08:45:52,974] cur_loss: 0.095 [2022-02-08 08:45:55,018] cur_loss: 0.095 [2022-02-08 08:45:56,643] Epoch: [ 1/200] [ 20/ 25] time: 20.46s disp_loss: 0.095 [2022-02-08 08:45:57,060] cur_loss: 0.102 [2022-02-08 08:45:59,103] cur_loss: 0.115 [2022-02-08 08:46:01,143] cur_loss: 0.094 [2022-02-08 08:46:03,180] cur_loss: 0.116 [2022-02-08 08:46:05,216] cur_loss: 0.143 [2022-02-08 08:46:10,908] cur_loss: 0.108 [2022-02-08 08:46:12,967] cur_loss: 0.102 [2022-02-08 08:46:15,004] cur_loss: 0.093 [2022-02-08 08:46:17,046] cur_loss: 0.100 [2022-02-08 08:46:19,091] cur_loss: 0.089 [2022-02-08 08:46:20,719] Epoch: [ 2/200] [ 5/ 25] time: 13.18s disp_loss: 0.089 [2022-02-08 08:46:21,135] cur_loss: 0.101 [2022-02-08 08:46:23,172] cur_loss: 0.114 [2022-02-08 08:46:25,208] cur_loss: 0.116 [2022-02-08 08:46:27,253] cur_loss: 0.117 [2022-02-08 08:46:29,297] cur_loss: 0.107 [2022-02-08 08:46:31,338] cur_loss: 0.127 [2022-02-08 08:46:33,379] cur_loss: 0.085 [2022-02-08 08:46:35,417] cur_loss: 0.091 [2022-02-08 08:46:37,459] cur_loss: 0.103 [2022-02-08 08:46:39,500] cur_loss: 0.092 [2022-02-08 08:46:41,127] Epoch: [ 2/200] [ 15/ 25] time: 20.41s disp_loss: 0.092 [2022-02-08 08:46:41,541] cur_loss: 0.166 [2022-02-08 08:46:43,579] cur_loss: 0.097 [2022-02-08 08:46:45,625] cur_loss: 0.142 [2022-02-08 08:46:47,671] cur_loss: 0.105 [2022-02-08 08:46:49,709] cur_loss: 0.103 [2022-02-08 08:46:51,747] cur_loss: 0.128 [2022-02-08 08:46:53,784] cur_loss: 0.104 [2022-02-08 08:46:55,824] cur_loss: 0.135 [2022-02-08 08:46:57,861] cur_loss: 0.094 [2022-02-08 08:46:59,900] cur_loss: 0.108 [2022-02-08 08:47:01,525] Epoch: [ 2/200] [ 25/ 25] time: 20.40s disp_loss: 0.108 [2022-02-08 08:47:05,687] cur_loss: 0.099 [2022-02-08 08:47:07,731] cur_loss: 0.110 [2022-02-08 08:47:09,768] cur_loss: 0.138 [2022-02-08 08:47:11,809] cur_loss: 0.157 [2022-02-08 08:47:13,840] cur_loss: 0.115 [2022-02-08 08:47:15,875] cur_loss: 0.100 [2022-02-08 08:47:17,914] cur_loss: 0.105 [2022-02-08 08:47:19,957] cur_loss: 0.118 [2022-02-08 08:47:22,000] cur_loss: 0.098 [2022-02-08 08:47:24,035] cur_loss: 0.096 [2022-02-08 08:47:25,654] Epoch: [ 3/200] [ 10/ 25] time: 23.38s disp_loss: 0.096 [2022-02-08 08:47:26,068] cur_loss: 0.106 [2022-02-08 08:47:28,103] cur_loss: 0.081 [2022-02-08 08:47:30,138] cur_loss: 0.106 [2022-02-08 08:47:32,179] cur_loss: 0.098 [2022-02-08 08:47:34,215] cur_loss: 0.105 [2022-02-08 08:47:36,248] cur_loss: 0.100 [2022-02-08 08:47:38,293] cur_loss: 0.090 [2022-02-08 08:47:40,332] cur_loss: 0.098 [2022-02-08 08:47:42,371] cur_loss: 0.099 [2022-02-08 08:47:44,404] cur_loss: 0.173 [2022-02-08 08:47:46,028] Epoch: [ 3/200] [ 20/ 25] time: 20.37s disp_loss: 0.173 [2022-02-08 08:47:46,440] cur_loss: 0.102 [2022-02-08 08:47:48,475] cur_loss: 0.102 [2022-02-08 08:47:50,510] cur_loss: 0.116 [2022-02-08 08:47:52,545] cur_loss: 0.100 [2022-02-08 08:47:54,581] cur_loss: 0.126 [2022-02-08 08:48:00,155] cur_loss: 0.106 [2022-02-08 08:48:02,237] cur_loss: 0.111 [2022-02-08 08:48:04,284] cur_loss: 0.086 [2022-02-08 08:48:06,334] cur_loss: 0.081 [2022-02-08 08:48:08,378] cur_loss: 0.137 [2022-02-08 08:48:10,010] Epoch: [ 4/200] [ 5/ 25] time: 12.94s disp_loss: 0.137 [2022-02-08 08:48:10,424] cur_loss: 0.117 [2022-02-08 08:48:12,468] cur_loss: 0.090 [2022-02-08 08:48:14,507] cur_loss: 0.102 [2022-02-08 08:48:16,549] cur_loss: 0.125 [2022-02-08 08:48:18,592] cur_loss: 0.100 [2022-02-08 08:48:20,630] cur_loss: 0.100 [2022-02-08 08:48:22,669] cur_loss: 0.126 [2022-02-08 08:48:24,706] cur_loss: 0.101 [2022-02-08 08:48:26,753] cur_loss: 0.098 [2022-02-08 08:48:28,792] cur_loss: 0.099 [2022-02-08 08:48:30,418] Epoch: [ 4/200] [ 15/ 25] time: 20.41s disp_loss: 0.099 [2022-02-08 08:48:30,830] cur_loss: 0.114 [2022-02-08 08:48:32,869] cur_loss: 0.082 [2022-02-08 08:48:34,906] cur_loss: 0.103 [2022-02-08 08:48:36,942] cur_loss: 0.098 [2022-02-08 08:48:38,984] cur_loss: 0.104 [2022-02-08 08:48:41,025] cur_loss: 0.103 [2022-02-08 08:48:43,068] cur_loss: 0.119 [2022-02-08 08:48:45,107] cur_loss: 0.161 [2022-02-08 08:48:47,159] cur_loss: 0.101 [2022-02-08 08:48:49,201] cur_loss: 0.167 [2022-02-08 08:48:50,829] Epoch: [ 4/200] [ 25/ 25] time: 20.41s disp_loss: 0.167 [2022-02-08 08:48:56,882] cur_loss: 0.111 [2022-02-08 08:48:58,936] cur_loss: 0.082 [2022-02-08 08:49:00,979] cur_loss: 0.096 [2022-02-08 08:49:03,021] cur_loss: 0.185 [2022-02-08 08:49:05,058] cur_loss: 0.142 [2022-02-08 08:49:07,103] cur_loss: 0.088 [2022-02-08 08:49:09,147] cur_loss: 0.099 [2022-02-08 08:49:11,194] cur_loss: 0.109 [2022-02-08 08:49:13,241] cur_loss: 0.101 [2022-02-08 08:49:15,281] cur_loss: 0.130 [2022-02-08 08:49:16,915] Epoch: [ 5/200] [ 10/ 25] time: 23.52s disp_loss: 0.130 [2022-02-08 08:49:17,333] cur_loss: 0.112 [2022-02-08 08:49:19,379] cur_loss: 0.093 [2022-02-08 08:49:21,421] cur_loss: 0.111 [2022-02-08 08:49:23,462] cur_loss: 0.134 [2022-02-08 08:49:25,505] cur_loss: 0.108 [2022-02-08 08:49:27,544] cur_loss: 0.109 [2022-02-08 08:49:29,586] cur_loss: 0.102 [2022-02-08 08:49:31,633] cur_loss: 0.101 [2022-02-08 08:49:33,674] cur_loss: 0.092 [2022-02-08 08:49:35,714] cur_loss: 0.108 [2022-02-08 08:49:37,348] Epoch: [ 5/200] [ 20/ 25] time: 20.43s disp_loss: 0.108 [2022-02-08 08:49:37,763] cur_loss: 0.104 [2022-02-08 08:49:39,804] cur_loss: 0.096 [2022-02-08 08:49:41,847] cur_loss: 0.099 [2022-02-08 08:49:43,883] cur_loss: 0.112 [2022-02-08 08:49:45,921] cur_loss: 0.121 [2022-02-08 08:49:51,606] cur_loss: 0.101 [2022-02-08 08:49:53,676] cur_loss: 0.192 [2022-02-08 08:49:55,715] cur_loss: 0.114 [2022-02-08 08:49:57,758] cur_loss: 0.173 [2022-02-08 08:49:59,806] cur_loss: 0.111 [2022-02-08 08:50:01,439] Epoch: [ 6/200] [ 5/ 25] time: 12.84s disp_loss: 0.111 [2022-02-08 08:50:01,873] cur_loss: 0.104 [2022-02-08 08:50:03,921] cur_loss: 0.086 [2022-02-08 08:50:05,970] cur_loss: 0.106 [2022-02-08 08:50:08,020] cur_loss: 0.098 [2022-02-08 08:50:10,081] cur_loss: 0.096 [2022-02-08 08:50:12,130] cur_loss: 0.096 [2022-02-08 08:50:14,167] cur_loss: 0.100 [2022-02-08 08:50:16,211] cur_loss: 0.104 [2022-02-08 08:50:18,253] cur_loss: 0.091 [2022-02-08 08:50:20,295] cur_loss: 0.103 [2022-02-08 08:50:21,922] Epoch: [ 6/200] [ 15/ 25] time: 20.48s disp_loss: 0.103 [2022-02-08 08:50:22,339] cur_loss: 0.102 [2022-02-08 08:50:24,379] cur_loss: 0.114 [2022-02-08 08:50:26,417] cur_loss: 0.084 [2022-02-08 08:50:28,459] cur_loss: 0.128 [2022-02-08 08:50:30,500] cur_loss: 0.089 [2022-02-08 08:50:32,546] cur_loss: 0.105 [2022-02-08 08:50:34,592] cur_loss: 0.101 [2022-02-08 08:50:36,636] cur_loss: 0.098 [2022-02-08 08:50:38,688] cur_loss: 0.102 [2022-02-08 08:50:40,733] cur_loss: 0.125 [2022-02-08 08:50:42,361] Epoch: [ 6/200] [ 25/ 25] time: 20.44s disp_loss: 0.125 [2022-02-08 08:50:46,862] cur_loss: 0.103 [2022-02-08 08:50:48,899] cur_loss: 0.189 [2022-02-08 08:50:50,941] cur_loss: 0.110 [2022-02-08 08:50:52,983] cur_loss: 0.117 [2022-02-08 08:50:55,020] cur_loss: 0.085 [2022-02-08 08:50:57,056] cur_loss: 0.094 [2022-02-08 08:50:59,092] cur_loss: 0.130 [2022-02-08 08:51:01,128] cur_loss: 0.097 [2022-02-08 08:51:03,167] cur_loss: 0.145 [2022-02-08 08:51:05,200] cur_loss: 0.094 [2022-02-08 08:51:06,824] Epoch: [ 7/200] [ 10/ 25] time: 23.69s disp_loss: 0.094 [2022-02-08 08:51:07,238] cur_loss: 0.119 [2022-02-08 08:51:09,279] cur_loss: 0.091 [2022-02-08 08:51:11,320] cur_loss: 0.091 [2022-02-08 08:51:13,358] cur_loss: 0.089 [2022-02-08 08:51:15,396] cur_loss: 0.097 [2022-02-08 08:51:17,436] cur_loss: 0.101 [2022-02-08 08:51:19,484] cur_loss: 0.112 [2022-02-08 08:51:21,525] cur_loss: 0.079 [2022-02-08 08:51:23,565] cur_loss: 0.120 [2022-02-08 08:51:25,603] cur_loss: 0.101 [2022-02-08 08:51:27,229] Epoch: [ 7/200] [ 20/ 25] time: 20.40s disp_loss: 0.101 [2022-02-08 08:51:27,641] cur_loss: 0.098 [2022-02-08 08:51:29,690] cur_loss: 0.099 [2022-02-08 08:51:31,730] cur_loss: 0.107 [2022-02-08 08:51:33,768] cur_loss: 0.137 [2022-02-08 08:51:35,805] cur_loss: 0.121 [2022-02-08 08:51:41,497] cur_loss: 0.104 [2022-02-08 08:51:43,559] cur_loss: 0.108 [2022-02-08 08:51:45,594] cur_loss: 0.088 [2022-02-08 08:51:47,641] cur_loss: 0.105 [2022-02-08 08:51:49,685] cur_loss: 0.108 [2022-02-08 08:51:51,310] Epoch: [ 8/200] [ 5/ 25] time: 12.95s disp_loss: 0.108 [2022-02-08 08:51:51,724] cur_loss: 0.103 [2022-02-08 08:51:53,760] cur_loss: 0.121 [2022-02-08 08:51:55,799] cur_loss: 0.110 [2022-02-08 08:51:57,839] cur_loss: 0.170 [2022-02-08 08:51:59,879] cur_loss: 0.136 [2022-02-08 08:52:01,925] cur_loss: 0.118 [2022-02-08 08:52:03,966] cur_loss: 0.108 [2022-02-08 08:52:06,004] cur_loss: 0.111 [2022-02-08 08:52:08,044] cur_loss: 0.098 [2022-02-08 08:52:10,090] cur_loss: 0.135 [2022-02-08 08:52:11,718] Epoch: [ 8/200] [ 15/ 25] time: 20.41s disp_loss: 0.135 [2022-02-08 08:52:12,134] cur_loss: 0.107 [2022-02-08 08:52:14,178] cur_loss: 0.101 [2022-02-08 08:52:16,223] cur_loss: 0.091 [2022-02-08 08:52:18,265] cur_loss: 0.111 [2022-02-08 08:52:20,308] cur_loss: 0.117 [2022-02-08 08:52:22,347] cur_loss: 0.095 [2022-02-08 08:52:24,386] cur_loss: 0.089 [2022-02-08 08:52:26,428] cur_loss: 0.098 [2022-02-08 08:52:28,469] cur_loss: 0.101 [2022-02-08 08:52:30,512] cur_loss: 0.095 [2022-02-08 08:52:32,145] Epoch: [ 8/200] [ 25/ 25] time: 20.43s disp_loss: 0.095 [2022-02-08 08:52:37,867] cur_loss: 0.093 [2022-02-08 08:52:39,931] cur_loss: 0.089 [2022-02-08 08:52:41,978] cur_loss: 0.118 [2022-02-08 08:52:44,025] cur_loss: 0.083 [2022-02-08 08:52:46,066] cur_loss: 0.097 [2022-02-08 08:52:48,111] cur_loss: 0.103 [2022-02-08 08:52:50,152] cur_loss: 0.106 [2022-02-08 08:52:52,198] cur_loss: 0.099 [2022-02-08 08:52:54,243] cur_loss: 0.091 [2022-02-08 08:52:56,287] cur_loss: 0.122
I print the netword prediction result, found that it is normal value(non-zero) in very begin, but it is quickly that all output is zero. what reasons can be conduct this phenomenon probably？
` [2022-02-08 09:20:52,391] cur_loss: 84.850 [2022-02-08 09:20:54,613] cur_loss: 59.421 [2022-02-08 09:20:56,657] cur_loss: 37.729 [2022-02-08 09:20:58,700] cur_loss: 5.955 pred: tensor([[[ 60.3958, 57.9909, 55.7744, ..., 120.9461, 118.6578, 124.5530], [ 57.4691, 56.0375, 54.9852, ..., 120.7754, 118.1667, 121.8229], [ 57.2455, 54.5802, 54.8936, ..., 125.5730, 122.2032, 124.1629], ..., [136.7134, 136.7585, 135.2419, ..., 176.4765, 176.3001, 167.1669], [134.0021, 130.6874, 132.8948, ..., 173.2103, 170.2460, 163.4151], [142.1232, 130.4962, 136.2892, ..., 168.4543, 164.3932, 154.5437]],
[[ 35.1972, 36.5312, 38.4757, ..., 77.5482, 77.8897, 78.8743],
[ 35.8069, 38.0379, 39.2738, ..., 75.5471, 75.5091, 78.4552],
[ 38.3917, 39.2442, 40.5765, ..., 75.8006, 75.4843, 78.0489],
...,
[144.0496, 156.2293, 153.2752, ..., 135.3938, 128.4638, 131.3442],
[147.0923, 154.5704, 153.6310, ..., 126.2524, 122.4596, 131.9489],
[143.0517, 150.0596, 151.9824, ..., 127.7324, 128.8004, 139.6858]],
[[ 46.7300, 46.4000, 45.8144, ..., 49.4122, 46.3503, 43.4428],
[ 46.4703, 45.8650, 45.8478, ..., 50.3494, 48.6170, 46.1489],
[ 46.0063, 44.8311, 45.6043, ..., 51.4486, 50.0680, 48.6887],
...,
[166.9636, 169.2554, 158.5244, ..., 93.4978, 93.9326, 95.7670],
[165.7707, 166.1769, 159.4405, ..., 90.3437, 90.5251, 93.7652],
[161.5703, 163.0487, 163.8259, ..., 91.9817, 94.8161, 96.8855]],
[[ 38.9851, 37.0821, 35.8220, ..., 58.3030, 50.8931, 42.7645],
[ 37.5203, 35.6132, 35.4405, ..., 61.0706, 56.8674, 40.9830],
[ 37.2854, 34.9211, 35.0649, ..., 59.9734, 57.2902, 50.2336],
...,
[141.0944, 141.6223, 137.9460, ..., 54.5146, 49.8646, 57.1323],
[139.3730, 139.5795, 137.5088, ..., 51.2052, 51.0434, 65.5106],
[134.8223, 139.8197, 140.4545, ..., 55.1063, 62.1394, 76.7297]]],
device='cuda:0', grad_fn=<SqueezeBackward1>)
pred: tensor([[[ 40.2422, 40.3682, 40.5317, ..., 28.9843, 28.3127, 38.7624], [ 40.1813, 40.3274, 40.6232, ..., 28.8020, 26.8436, 30.5479], [ 40.0127, 39.8091, 40.5936, ..., 31.1058, 28.9942, 33.5787], ..., [153.3121, 156.9409, 152.2289, ..., 27.2218, 27.1960, 28.7927], [151.1767, 156.0058, 152.4844, ..., 28.0512, 29.0036, 29.6318], [150.5991, 149.9173, 155.7352, ..., 29.9039, 30.3969, 31.0580]],
[[ 40.6195, 40.2955, 39.5429, ..., 65.1209, 67.3683, 86.9687],
[ 39.8015, 38.8467, 38.6322, ..., 59.8054, 62.3455, 75.8580],
[ 39.0024, 37.6076, 38.0337, ..., 69.2202, 67.5764, 77.9588],
...,
[ 70.1766, 75.0723, 73.6379, ..., 92.3470, 92.3131, 88.9738],
[ 76.8734, 75.2559, 74.9278, ..., 90.8862, 90.8725, 87.8768],
[ 71.8072, 76.8179, 76.4584, ..., 90.5880, 89.3136, 90.2819]],
[[ 58.1412, 55.9844, 58.0036, ..., 39.2926, 30.7917, 36.1742],
[ 60.6683, 58.0760, 57.4861, ..., 36.8728, 39.1118, 35.9307],
[ 60.9955, 58.8031, 58.7510, ..., 39.8082, 39.2346, 44.6219],
...,
[153.8498, 151.0643, 149.7025, ..., 12.8411, 13.3434, 13.2162],
[147.4372, 150.5965, 149.0699, ..., 12.6299, 13.1242, 13.3502],
[154.4095, 144.5931, 154.6027, ..., 13.0644, 13.2323, 13.4366]],
[[ 39.8573, 37.9619, 36.9057, ..., 42.1044, 40.2077, 38.0919],
[ 38.7509, 36.6201, 36.5197, ..., 40.4639, 40.0419, 38.8953],
[ 38.3336, 36.4496, 36.7688, ..., 39.2797, 39.6609, 40.1514],
...,
[135.7857, 134.6154, 130.9226, ..., 47.5453, 38.8995, 47.3962],
[133.2933, 133.7791, 131.4800, ..., 40.8988, 40.8693, 63.3618],
[125.8759, 128.7854, 134.1934, ..., 49.0825, 59.6753, 80.7865]]],
device='cuda:0', grad_fn=<SqueezeBackward1>)
pred: tensor([[[ 4.4194, 3.2357, 0.7156, ..., 8.0940, 7.6042, 14.6319], [ 3.2366, 0.8199, 0.0000, ..., 7.5350, 8.9960, 7.4030], [ 0.0000, 0.0000, 0.0000, ..., 7.4874, 8.2638, 10.1045], ..., [153.2203, 151.7722, 149.0503, ..., 161.7336, 155.4834, 159.0959], [155.0462, 151.8277, 150.1341, ..., 152.6343, 156.0988, 158.0346], [138.7147, 151.4463, 153.9102, ..., 164.6467, 159.8742, 163.0121]],
[[ 59.7358, 59.2537, 58.3708, ..., 0.0000, 0.0000, 7.6454],
[ 59.0069, 58.3980, 58.2597, ..., 0.0000, 0.0000, 0.6457],
[ 58.4347, 56.7561, 58.0880, ..., 0.0000, 0.6698, 1.9930],
...,
[102.2050, 103.8153, 103.2228, ..., 10.0267, 9.6739, 5.5513],
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device='cuda:0', grad_fn=<SqueezeBackward1>)
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device='cuda:0', grad_fn=<SqueezeBackward1>)
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device='cuda:0', grad_fn=<SqueezeBackward1>)[2022-02-08 09:21:00,747] cur_loss: 0.313
[2022-02-08 09:21:02,795] cur_loss: 0.126 [2022-02-08 09:21:04,843] cur_loss: 0.097 [2022-02-08 09:21:06,884] cur_loss: 0.120 [2022-02-08 09:21:08,931] cur_loss: 0.117 [2022-02-08 09:21:10,978] cur_loss: 0.133 [2022-02-08 09:21:12,607] Epoch: [ 1/200] [ 10/ 25] time: 25.51s disp_loss: 0.133
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device='cuda:0', grad_fn=<SqueezeBackward1>)
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device='cuda:0', grad_fn=<SqueezeBackward1>)
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device='cuda:0', grad_fn=<SqueezeBackward1>)
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device='cuda:0', grad_fn=<SqueezeBackward1>)
pred: tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 35.2818], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 5.8052], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], ..., [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
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device='cuda:0', grad_fn=<SqueezeBackward1>)[2022-02-08 09:21:13,025] cur_loss: 0.095
[2022-02-08 09:21:15,073] cur_loss: 0.124 [2022-02-08 09:21:17,117] cur_loss: 0.094 [2022-02-08 09:21:19,168] cur_loss: 0.115 [2022-02-08 09:21:21,215] cur_loss: 0.101 [2022-02-08 09:21:23,256] cur_loss: 0.093
pred: tensor([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]],
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grad_fn=<SqueezeBackward1>)
pred: tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], ..., [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
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device='cuda:0', grad_fn=<SqueezeBackward1>)`
Sorry that the training log is no longer available due to the long time of this project. I would suggest you to first try our model on our dataset, and then on you dataset to see whether this is some problem regrading the dataset.
