pfcfuse/logs/log_20241006_165643.log

2.4.1+cu121
True
Model: PFCFuse
Number of epochs: 60
Epoch gap: 40
Learning rate: 0.0001
Weight decay: 0
Batch size: 1
GPU number: 0
Coefficient of MSE loss VF: 1.0
Coefficient of MSE loss IF: 1.0
Coefficient of RMI loss VF: 1.0
Coefficient of RMI loss IF: 1.0
Coefficient of Cosine loss VF: 1.0
Coefficient of Cosine loss IF: 1.0
Coefficient of Decomposition loss: 2.0
Coefficient of Total Variation loss: 5.0
Clip gradient norm value: 0.01
Optimization step: 20
Optimization gamma: 0.5

[Epoch 0/60] [Batch 0/6487] [loss: 7.545208] ETA: 10 days, 9
[Epoch 0/60] [Batch 1/6487] [loss: 12.812655] ETA: 10:26:47.2
[Epoch 0/60] [Batch 2/6487] [loss: 10.012913] ETA: 9:22:39.01
[Epoch 0/60] [Batch 3/6487] [loss: 9.317457] ETA: 9:29:37.80
[Epoch 0/60] [Batch 4/6487] [loss: 5.509483] ETA: 9:22:39.67
[Epoch 0/60] [Batch 5/6487] [loss: 4.066029] ETA: 9:02:15.70
[Epoch 0/60] [Batch 6/6487] [loss: 7.261652] ETA: 9:30:38.69
[Epoch 0/60] [Batch 7/6487] [loss: 32.691277] ETA: 9:27:08.42
[Epoch 0/60] [Batch 8/6487] [loss: 17.467869] ETA: 9:28:55.98
[Epoch 0/60] [Batch 9/6487] [loss: 2.090473] ETA: 9:11:53.85
[Epoch 0/60] [Batch 10/6487] [loss: 6.664465] ETA: 9:26:22.97
[Epoch 0/60] [Batch 11/6487] [loss: 19.230927] ETA: 9:28:32.33
[Epoch 0/60] [Batch 12/6487] [loss: 43.584057] ETA: 9:16:59.16
[Epoch 0/60] [Batch 13/6487] [loss: 11.860549] ETA: 9:21:56.20
[Epoch 0/60] [Batch 14/6487] [loss: 17.524237] ETA: 9:27:49.11
[Epoch 0/60] [Batch 15/6487] [loss: 15.270466] ETA: 9:43:48.50
[Epoch 0/60] [Batch 16/6487] [loss: 12.399554] ETA: 9:08:12.87
[Epoch 0/60] [Batch 17/6487] [loss: 3.254870] ETA: 9:14:04.10
[Epoch 0/60] [Batch 18/6487] [loss: 22.740582] ETA: 9:34:07.72
[Epoch 0/60] [Batch 19/6487] [loss: 24.545734] ETA: 9:33:31.44
[Epoch 0/60] [Batch 20/6487] [loss: 3.285551] ETA: 9:28:28.39
[Epoch 0/60] [Batch 21/6487] [loss: 8.354909] ETA: 9:36:55.41
[Epoch 0/60] [Batch 22/6487] [loss: 13.838474] ETA: 9:23:32.48
[Epoch 0/60] [Batch 23/6487] [loss: 26.450424] ETA: 9:46:10.59
[Epoch 0/60] [Batch 24/6487] [loss: 7.527445] ETA: 9:28:00.66
[Epoch 0/60] [Batch 25/6487] [loss: 10.322123] ETA: 9:28:59.87
[Epoch 0/60] [Batch 26/6487] [loss: 4.332734] ETA: 9:36:32.32
[Epoch 0/60] [Batch 27/6487] [loss: 29.715450] ETA: 10:03:48.1
[Epoch 0/60] [Batch 28/6487] [loss: 12.476555] ETA: 9:53:16.51
[Epoch 0/60] [Batch 29/6487] [loss: 18.366808] ETA: 9:31:49.97
[Epoch 0/60] [Batch 30/6487] [loss: 3.876387] ETA: 9:12:55.71
[Epoch 0/60] [Batch 31/6487] [loss: 17.348400] ETA: 9:36:43.20
[Epoch 0/60] [Batch 32/6487] [loss: 9.509404] ETA: 9:24:34.53
[Epoch 0/60] [Batch 33/6487] [loss: 3.085104] ETA: 9:55:22.80
[Epoch 0/60] [Batch 34/6487] [loss: 3.542013] ETA: 9:13:45.94
[Epoch 0/60] [Batch 35/6487] [loss: 3.223781] ETA: 9:40:16.54
[Epoch 0/60] [Batch 36/6487] [loss: 7.462015] ETA: 9:16:28.80
[Epoch 0/60] [Batch 37/6487] [loss: 14.630236] ETA: 9:48:22.38
[Epoch 0/60] [Batch 38/6487] [loss: 3.471924] ETA: 9:14:36.63
[Epoch 0/60] [Batch 39/6487] [loss: 7.701048] ETA: 9:25:34.14
[Epoch 0/60] [Batch 40/6487] [loss: 2.392703] ETA: 9:31:07.44
[Epoch 0/60] [Batch 41/6487] [loss: 2.766702] ETA: 9:27:27.91
[Epoch 0/60] [Batch 42/6487] [loss: 2.663168] ETA: 9:55:27.36
[Epoch 0/60] [Batch 43/6487] [loss: 5.584369] ETA: 8:55:57.84
[Epoch 0/60] [Batch 44/6487] [loss: 2.279996] ETA: 9:17:25.55
[Epoch 0/60] [Batch 45/6487] [loss: 2.126114] ETA: 9:42:06.24
[Epoch 0/60] [Batch 46/6487] [loss: 4.639381] ETA: 9:16:48.82
[Epoch 0/60] [Batch 47/6487] [loss: 2.298779] ETA: 9:23:29.85
[Epoch 0/60] [Batch 48/6487] [loss: 2.247969] ETA: 9:23:51.84
[Epoch 0/60] [Batch 49/6487] [loss: 1.340102] ETA: 9:45:32.80
[Epoch 0/60] [Batch 50/6487] [loss: 1.482023] ETA: 9:31:44.04
[Epoch 0/60] [Batch 51/6487] [loss: 1.175750] ETA: 9:23:34.33
[Epoch 0/60] [Batch 52/6487] [loss: 1.477088] ETA: 9:40:15.20
[Epoch 0/60] [Batch 53/6487] [loss: 1.893422] ETA: 9:52:19.30
[Epoch 0/60] [Batch 54/6487] [loss: 1.346209] ETA: 9:27:52.01
[Epoch 0/60] [Batch 55/6487] [loss: 4.965276] ETA: 9:22:15.02
[Epoch 0/60] [Batch 56/6487] [loss: 1.183521] ETA: 9:25:23.65
[Epoch 0/60] [Batch 57/6487] [loss: 1.382612] ETA: 9:25:21.25
[Epoch 0/60] [Batch 58/6487] [loss: 3.456125] ETA: 9:22:32.11
[Epoch 0/60] [Batch 59/6487] [loss: 2.503100] ETA: 9:37:14.67
[Epoch 0/60] [Batch 60/6487] [loss: 1.369404] ETA: 9:28:09.57
[Epoch 0/60] [Batch 61/6487] [loss: 1.679654] ETA: 9:25:50.59
[Epoch 0/60] [Batch 62/6487] [loss: 2.253558] ETA: 9:15:18.65
[Epoch 0/60] [Batch 63/6487] [loss: 1.851084] ETA: 9:29:23.91
[Epoch 0/60] [Batch 64/6487] [loss: 3.680471] ETA: 9:22:44.76
[Epoch 0/60] [Batch 65/6487] [loss: 1.162899] ETA: 9:5
修改代码结构，提高可读性和可维护性；调整训练输出频率。改进 self.enhancement_module 为 self.enhancement_module = WTConv2d(32, 32) 2024-10-09 11:35:06 +08:00			`2.4.1+cu121`
			`True`
			`Model: PFCFuse`
			`Number of epochs: 60`
			`Epoch gap: 40`
			`Learning rate: 0.0001`
			`Weight decay: 0`
			`Batch size: 1`
			`GPU number: 0`
			`Coefficient of MSE loss VF: 1.0`
			`Coefficient of MSE loss IF: 1.0`
			`Coefficient of RMI loss VF: 1.0`
			`Coefficient of RMI loss IF: 1.0`
			`Coefficient of Cosine loss VF: 1.0`
			`Coefficient of Cosine loss IF: 1.0`
			`Coefficient of Decomposition loss: 2.0`
			`Coefficient of Total Variation loss: 5.0`
			`Clip gradient norm value: 0.01`
			`Optimization step: 20`
			`Optimization gamma: 0.5`
			[Epoch 0/60] [Batch 0/6487] [loss: 7.545208] ETA: 10 days, 9 [Epoch 0/60] [Batch 1/6487] [loss: 12.812655] ETA: 10:26:47.2 [Epoch 0/60] [Batch 2/6487] [loss: 10.012913] ETA: 9:22:39.01 [Epoch 0/60] [Batch 3/6487] [loss: 9.317457] ETA: 9:29:37.80 [Epoch 0/60] [Batch 4/6487] [loss: 5.509483] ETA: 9:22:39.67 [Epoch 0/60] [Batch 5/6487] [loss: 4.066029] ETA: 9:02:15.70 [Epoch 0/60] [Batch 6/6487] [loss: 7.261652] ETA: 9:30:38.69 [Epoch 0/60] [Batch 7/6487] [loss: 32.691277] ETA: 9:27:08.42 [Epoch 0/60] [Batch 8/6487] [loss: 17.467869] ETA: 9:28:55.98 [Epoch 0/60] [Batch 9/6487] [loss: 2.090473] ETA: 9:11:53.85 [Epoch 0/60] [Batch 10/6487] [loss: 6.664465] ETA: 9:26:22.97 [Epoch 0/60] [Batch 11/6487] [loss: 19.230927] ETA: 9:28:32.33 [Epoch 0/60] [Batch 12/6487] [loss: 43.584057] ETA: 9:16:59.16 [Epoch 0/60] [Batch 13/6487] [loss: 11.860549] ETA: 9:21:56.20 [Epoch 0/60] [Batch 14/6487] [loss: 17.524237] ETA: 9:27:49.11 [Epoch 0/60] [Batch 15/6487] [loss: 15.270466] ETA: 9:43:48.50 [Epoch 0/60] [Batch 16/6487] [loss: 12.399554] ETA: 9:08:12.87 [Epoch 0/60] [Batch 17/6487] [loss: 3.254870] ETA: 9:14:04.10 [Epoch 0/60] [Batch 18/6487] [loss: 22.740582] ETA: 9:34:07.72 [Epoch 0/60] [Batch 19/6487] [loss: 24.545734] ETA: 9:33:31.44 [Epoch 0/60] [Batch 20/6487] [loss: 3.285551] ETA: 9:28:28.39 [Epoch 0/60] [Batch 21/6487] [loss: 8.354909] ETA: 9:36:55.41 [Epoch 0/60] [Batch 22/6487] [loss: 13.838474] ETA: 9:23:32.48 [Epoch 0/60] [Batch 23/6487] [loss: 26.450424] ETA: 9:46:10.59 [Epoch 0/60] [Batch 24/6487] [loss: 7.527445] ETA: 9:28:00.66 [Epoch 0/60] [Batch 25/6487] [loss: 10.322123] ETA: 9:28:59.87 [Epoch 0/60] [Batch 26/6487] [loss: 4.332734] ETA: 9:36:32.32 [Epoch 0/60] [Batch 27/6487] [loss: 29.715450] ETA: 10:03:48.1 [Epoch 0/60] [Batch 28/6487] [loss: 12.476555] ETA: 9:53:16.51 [Epoch 0/60] [Batch 29/6487] [loss: 18.366808] ETA: 9:31:49.97 [Epoch 0/60] [Batch 30/6487] [loss: 3.876387] ETA: 9:12:55.71 [Epoch 0/60] [Batch 31/6487] [loss: 17.348400] ETA: 9:36:43.20 [Epoch 0/60] [Batch 32/6487] [loss: 9.509404] ETA: 9:24:34.53 [Epoch 0/60] [Batch 33/6487] [loss: 3.085104] ETA: 9:55:22.80 [Epoch 0/60] [Batch 34/6487] [loss: 3.542013] ETA: 9:13:45.94 [Epoch 0/60] [Batch 35/6487] [loss: 3.223781] ETA: 9:40:16.54 [Epoch 0/60] [Batch 36/6487] [loss: 7.462015] ETA: 9:16:28.80 [Epoch 0/60] [Batch 37/6487] [loss: 14.630236] ETA: 9:48:22.38 [Epoch 0/60] [Batch 38/6487] [loss: 3.471924] ETA: 9:14:36.63 [Epoch 0/60] [Batch 39/6487] [loss: 7.701048] ETA: 9:25:34.14 [Epoch 0/60] [Batch 40/6487] [loss: 2.392703] ETA: 9:31:07.44 [Epoch 0/60] [Batch 41/6487] [loss: 2.766702] ETA: 9:27:27.91 [Epoch 0/60] [Batch 42/6487] [loss: 2.663168] ETA: 9:55:27.36 [Epoch 0/60] [Batch 43/6487] [loss: 5.584369] ETA: 8:55:57.84 [Epoch 0/60] [Batch 44/6487] [loss: 2.279996] ETA: 9:17:25.55 [Epoch 0/60] [Batch 45/6487] [loss: 2.126114] ETA: 9:42:06.24 [Epoch 0/60] [Batch 46/6487] [loss: 4.639381] ETA: 9:16:48.82 [Epoch 0/60] [Batch 47/6487] [loss: 2.298779] ETA: 9:23:29.85 [Epoch 0/60] [Batch 48/6487] [loss: 2.247969] ETA: 9:23:51.84 [Epoch 0/60] [Batch 49/6487] [loss: 1.340102] ETA: 9:45:32.80 [Epoch 0/60] [Batch 50/6487] [loss: 1.482023] ETA: 9:31:44.04 [Epoch 0/60] [Batch 51/6487] [loss: 1.175750] ETA: 9:23:34.33 [Epoch 0/60] [Batch 52/6487] [loss: 1.477088] ETA: 9:40:15.20 [Epoch 0/60] [Batch 53/6487] [loss: 1.893422] ETA: 9:52:19.30 [Epoch 0/60] [Batch 54/6487] [loss: 1.346209] ETA: 9:27:52.01 [Epoch 0/60] [Batch 55/6487] [loss: 4.965276] ETA: 9:22:15.02 [Epoch 0/60] [Batch 56/6487] [loss: 1.183521] ETA: 9:25:23.65 [Epoch 0/60] [Batch 57/6487] [loss: 1.382612] ETA: 9:25:21.25 [Epoch 0/60] [Batch 58/6487] [loss: 3.456125] ETA: 9:22:32.11 [Epoch 0/60] [Batch 59/6487] [loss: 2.503100] ETA: 9:37:14.67 [Epoch 0/60] [Batch 60/6487] [loss: 1.369404] ETA: 9:28:09.57 [Epoch 0/60] [Batch 61/6487] [loss: 1.679654] ETA: 9:25:50.59 [Epoch 0/60] [Batch 62/6487] [loss: 2.253558] ETA: 9:15:18.65 [Epoch 0/60] [Batch 63/6487] [loss: 1.851084] ETA: 9:29:23.91 [Epoch 0/60] [Batch 64/6487] [loss: 3.680471] ETA: 9:22:44.76 [Epoch 0/60] [Batch 65/6487] [loss: 1.162899] ETA: 9:5