0e6064181a
- 新增BaseFeatureExtractionSAR和DetailFeatureExtractionSAR类，专门用于SAR图像的特征提取 - 在Restormer_Encoder中加入SAR图像处理的支持，通过新增的SAR特征提取模块提高模型对SAR图像的处理能力 - 更新test_IVF.py，增加对SAR图像的测试，验证模型在不同数据集上的性能 - 通过这些修改，模型在TNO和RoadScene数据集上的表现得到显著提升，详细指标见日志文件
44 lines
20 KiB
Plaintext
44 lines
20 KiB
Plaintext
2.4.1+cu121
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True
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Model: PFCFuse
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Number of epochs: 60
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Epoch gap: 40
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Learning rate: 0.0001
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Weight decay: 0
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Batch size: 1
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GPU number: 0
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Coefficient of MSE loss VF: 1.0
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Coefficient of MSE loss IF: 1.0
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Coefficient of RMI loss VF: 1.0
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Coefficient of RMI loss IF: 1.0
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Coefficient of Cosine loss VF: 1.0
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Coefficient of Cosine loss IF: 1.0
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Coefficient of Decomposition loss: 2.0
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Coefficient of Total Variation loss: 5.0
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Clip gradient norm value: 0.01
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Optimization step: 20
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Optimization gamma: 0.5
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[Epoch 0/60] [Batch 0/6487] [loss: 6.843450] ETA: 10 days, 1
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[Epoch 0/60] [Batch 1/6487] [loss: 17.473789] ETA: 10:18:13.0
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[Epoch 0/60] [Batch 2/6487] [loss: 6.973145] ETA: 9:26:17.17
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[Epoch 0/60] [Batch 3/6487] [loss: 7.598927] ETA: 9:24:09.86
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[Epoch 0/60] [Batch 4/6487] [loss: 7.397294] ETA: 9:21:32.02
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[Epoch 0/60] [Batch 5/6487] [loss: 17.675234] ETA: 9:24:20.36
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[Epoch 0/60] [Batch 6/6487] [loss: 11.842889] ETA: 9:43:36.42
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[Epoch 0/60] [Batch 7/6487] [loss: 8.561872] ETA: 9:32:16.41
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[Epoch 0/60] [Batch 8/6487] [loss: 8.628882] ETA: 9:40:58.48
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[Epoch 0/60] [Batch 9/6487] [loss: 3.025908] ETA: 9:26:52.01
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[Epoch 0/60] [Batch 10/6487] [loss: 12.309198] ETA: 9:37:37.31
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[Epoch 0/60] [Batch 11/6487] [loss: 10.065054] ETA: 9:23:22.58
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[Epoch 0/60] [Batch 12/6487] [loss: 5.186013] ETA: 9:38:36.34
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[Epoch 0/60] [Batch 13/6487] [loss: 5.387490] ETA: 9:49:38.61
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[Epoch 0/60] [Batch 14/6487] [loss: 5.509142] ETA: 9:21:29.86
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[Epoch 0/60] [Batch 15/6487] [loss: 6.785795] ETA: 9:27:37.98
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[Epoch 0/60] [Batch 16/6487] [loss: 7.973134] ETA: 9:29:15.88
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[Epoch 0/60] [Batch 17/6487] [loss: 21.794090] ETA: 9:29:22.66
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[Epoch 0/60] [Batch 18/6487] [loss: 4.961427] ETA: 9:33:11.30
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[Epoch 0/60] [Batch 19/6487] [loss: 14.073445] ETA: 9:27:57.20
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[Epoch 0/60] [Batch 20/6487] [loss: 6.013936] ETA: 9:26:16.99
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[Epoch 0/60] [Batch 21/6487] [loss: 13.236930] ETA: 9:24:25.93
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[Epoch 0/60] [Batch 22/6487] [loss: 8.306091] ETA: 9:36:14.12
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[Epoch 0/60] [Batch 23/6487] [loss: 3.355170] ETA: 9:59:40.66
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[Epoch 0/60] [Batch 24/6487] [loss: 2.904986] ETA: 9:07:01.95
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[Epoch 0/60] [Batch 25/6487] [loss: 2.231014] ETA: 9:44:41.24
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[Epoch 0/60] [Batch 26/6487] [loss: 6.787667] ETA: 9:43:30.81
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[Epoch 0/60] [Batch 27/6487] [loss: 7.387001] ETA: 9:34:29.57
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[Epoch 0/60] [Batch 28/6487] [loss: 4.501630] ETA: 9:39:13.60
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[Epoch 0/60] [Batch 29/6487] [loss: 2.489206] ETA: 9:28:18.69
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[Epoch 0/60] [Batch 30/6487] [loss: 3.574013] ETA: 9:29:23.56
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[Epoch 0/60] [Batch 31/6487] [loss: 6.969161] ETA: 9:43:31.66
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[Epoch 0/60] [Batch 32/6487] [loss: 3.075920] ETA: 9:34:19.75
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[Epoch 0/60] [Batch 33/6487] [loss: 2.088318] ETA: 9:28:40.24
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[Epoch 0/60] [Batch 34/6487] [loss: 3.432371] ETA: 9:32:47.99
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[Epoch 0/60] [Batch 35/6487] [loss: 4.036960] ETA: 9:39:26.43
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[Epoch 0/60] [Batch 36/6487] [loss: 2.675624] ETA: 9:32:24.90
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[Epoch 0/60] [Batch 37/6487] [loss: 2.401388] ETA: 9:36:22.25
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[Epoch 0/60] [Batch 38/6487] [loss: 2.432465] ETA: 9:29:30.37
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[Epoch 0/60] [Batch 39/6487] [loss: 3.220938] ETA: 9:31:25.53
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[Epoch 0/60] [Batch 40/6487] [loss: 2.949226] ETA: 9:56:07.63
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[Epoch 0/60] [Batch 41/6487] [loss: 2.188518] ETA: 9:39:39.16
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[Epoch 0/60] [Batch 42/6487] [loss: 2.371767] ETA: 9:21:48.31
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[Epoch 0/60] [Batch 43/6487] [loss: 2.663700] ETA: 9:31:15.52
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[Epoch 0/60] [Batch 44/6487] [loss: 1.953101] ETA: 9:36:49.47
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[Epoch 0/60] [Batch 45/6487] [loss: 1.967318] ETA: 9:28:22.67
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[Epoch 0/60] [Batch 46/6487] [loss: 1.681611] ETA: 9:40:13.14
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[Epoch 0/60] [Batch 47/6487] [loss: 1.203847] ETA: 11:38:35.6
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[Epoch 0/60] [Batch 48/6487] [loss: 1.616149] ETA: 10:20:08.6
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[Epoch 0/60] [Batch 49/6487] [loss: 2.641722] ETA: 10:36:38.7
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[Epoch 0/60] [Batch 50/6487] [loss: 2.627393] ETA: 10:16:08.0
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[Epoch 0/60] [Batch 51/6487] [loss: 2.047213] ETA: 10:27:03.4
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[Epoch 0/60] [Batch 52/6487] [loss: 1.524367] ETA: 10:14:40.0
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[Epoch 0/60] [Batch 53/6487] [loss: 1.499193] ETA: 10:31:07.6
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[Epoch 0/60] [Batch 54/6487] [loss: 1.482741] ETA: 10:04:11.4
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[Epoch 0/60] [Batch 55/6487] [loss: 0.953166] ETA: 10:25:37.7
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[Epoch 0/60] [Batch 56/6487] [loss: 1.346713] ETA: 10:16:22.0
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[Epoch 0/60] [Batch 57/6487] [loss: 1.526123] ETA: 10:15:25.8
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[Epoch 0/60] [Batch 58/6487] [loss: 1.643487] ETA: 10:17:12.1
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[Epoch 0/60] [Batch 59/6487] [loss: 0.794820] ETA: 10:02:32.9
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[Epoch 0/60] [Batch 60/6487] [loss: 1.490152] ETA: 10:10:54.4
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[Epoch 0/60] [Batch 61/6487] [loss: 1.175192] ETA: 10:23:03.9
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[Epoch 0/60] [Batch 62/6487] [loss: 1.577219] ETA: 10:32:47.5
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[Epoch 0/60] [Batch 63/6487] [loss: 1.808056] ETA: 10:17:46.9
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[Epoch 0/60] [Batch 64/6487] [loss: 1.146155] ETA: 10:13:44.6
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[Epoch 0/60] [Batch 65/6487] [loss: 0.982462] ETA: 10:10:09.3
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[Epoch 0/60] [Batch 66/6487] [loss: 1.292355] ETA: 10:14:40.8
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[Epoch 0/60] [Batch 67/6487] [loss: 1.146832] ETA: 10:34:33.5
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[Epoch 0/60] [Batch 68/6487] [loss: 1.015941] ETA: 10:50:58.1
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[Epoch 0/60] [Batch 69/6487] [loss: 1.593252] ETA: 10:11:41.2
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[Epoch 0/60] [Batch 70/6487] [loss: 1.348927] ETA: 10:40:00.1
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[Epoch 0/60] [Batch 71/6487] [loss: 1.122736] ETA: 10:38:51.2
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[Epoch 0/60] [Batch 72/6487] [loss: 0.911590] ETA: 10:03:13.2
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[Epoch 0/60] [Batch 73/6487] [loss: 0.980177] ETA: 10:22:37.1
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[Epoch 0/60] [Batch 74/6487] [loss: 1.600290] ETA: 10:14:33.7
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[Epoch 0/60] [Batch 75/6487] [loss: 1.117924] ETA: 10:10:11.4
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[Epoch 0/60] [Batch 76/6487] [loss: 0.995830] ETA: 10:43:06.7
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[Epoch 0/60] [Batch 77/6487] [loss: 0.944761] ETA: 10:43:12.9
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[Epoch 0/60] [Batch 78/6487] [loss: 1.392014] ETA: 10:56:09.9
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[Epoch 0/60] [Batch 79/6487] [loss: 1.009044] ETA: 10:56:55.3
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[Epoch 0/60] [Batch 80/6487] [loss: 0.989809] ETA: 10:57:33.8
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[Epoch 0/60] [Batch 81/6487] [loss: 1.063357] ETA: 10:45:27.4
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[Epoch 0/60] [Batch 82/6487] [loss: 1.110687] ETA: 10:43:00.7
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[Epoch 0/60] [Batch 83/6487] [loss: 1.301277] ETA: 10:57:17.5
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[Epoch 0/60] [Batch 84/6487] [loss: 1.338078] ETA: 10:33:27.6
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[Epoch 0/60] [Batch 85/6487] [loss: 1.021930] ETA: 10:54:38.4
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[Epoch 0/60] [Batch 86/6487] [loss: 1.189236] ETA: 9:46:54.90
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[Epoch 0/60] [Batch 87/6487] [loss: 1.053584] ETA: 10:27:17.6
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[Epoch 0/60] [Batch 88/6487] [loss: 1.019790] ETA: 10:23:49.7
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[Epoch 0/60] [Batch 89/6487] [loss: 1.113650] ETA: 10:45:45.6
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[Epoch 0/60] [Batch 90/6487] [loss: 1.115821] ETA: 10:03:18.8
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[Epoch 0/60] [Batch 91/6487] [loss: 1.224019] ETA: 10:28:36.3
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[Epoch 0/60] [Batch 92/6487] [loss: 0.975078] ETA: 10:05:19.0
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[Epoch 0/60] [Batch 93/6487] [loss: 0.996540] ETA: 10:19:27.7
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[Epoch 0/60] [Batch 94/6487] [loss: 1.735214] ETA: 10:09:47.4
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[Epoch 0/60] [Batch 95/6487] [loss: 1.912559] ETA: 10:49:38.3
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[Epoch 0/60] [Batch 96/6487] [loss: 1.418693] ETA: 9:40:47.08
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[Epoch 0/60] [Batch 97/6487] [loss: 0.832918] ETA: 10:20:58.6
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[Epoch 0/60] [Batch 98/6487] [loss: 1.055999] ETA: 10:36:40.3
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[Epoch 0/60] [Batch 99/6487] [loss: 0.892974] ETA: 10:27:10.2
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[Epoch 0/60] [Batch 100/6487] [loss: 1.045057] ETA: 10:32:06.6
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[Epoch 0/60] [Batch 101/6487] [loss: 0.918750] ETA: 10:06:59.5
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[Epoch 0/60] [Batch 102/6487] [loss: 0.983213] ETA: 10:28:52.6
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[Epoch 0/60] [Batch 103/6487] [loss: 0.953088] ETA: 10:37:21.2
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[Epoch 0/60] [Batch 104/6487] [loss: 0.895300] ETA: 10:30:34.5
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[Epoch 0/60] [Batch 105/6487] [loss: 0.985907] ETA: 10:24:06.3
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[Epoch 0/60] [Batch 106/6487] [loss: 0.908739] ETA: 10:44:23.4
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[Epoch 0/60] [Batch 107/6487] [loss: 0.883114] ETA: 10:00:31.9
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[Epoch 0/60] [Batch 108/6487] [loss: 1.048095] ETA: 10:28:56.2
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[Epoch 0/60] [Batch 109/6487] [loss: 0.860237] ETA: 10:01:09.2
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[Epoch 0/60] [Batch 110/6487] [loss: 0.784510] ETA: 10:20:20.8
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[Epoch 0/60] [Batch 111/6487] [loss: 0.883551] ETA: 10:03:05.7
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[Epoch 0/60] [Batch 112/6487] [loss: 0.847589] ETA: 10:38:30.7
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[Epoch 0/60] [Batch 113/6487] [loss: 1.026890] ETA: 10:04:41.7
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[Epoch 0/60] [Batch 114/6487] [loss: 0.865966] ETA: 10:20:48.9
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[Epoch 0/60] [Batch 115/6487] [loss: 0.886507] ETA: 10:54:19.2
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[Epoch 0/60] [Batch 116/6487] [loss: 1.015066] ETA: 10:24:06.4
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[Epoch 0/60] [Batch 117/6487] [loss: 0.627025] ETA: 10:42:59.7
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[Epoch 0/60] [Batch 118/6487] [loss: 0.531863] ETA: 10:09:26.0
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[Epoch 0/60] [Batch 119/6487] [loss: 0.824727] ETA: 10:22:43.4
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[Epoch 0/60] [Batch 120/6487] [loss: 0.969884] ETA: 10:13:28.4
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[Epoch 0/60] [Batch 121/6487] [loss: 1.197793] ETA: 10:38:26.5
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[Epoch 0/60] [Batch 122/6487] [loss: 0.861838] ETA: 10:33:11.2
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[Epoch 0/60] [Batch 123/6487] [loss: 1.012118] ETA: 10:18:00.4
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[Epoch 0/60] [Batch 124/6487] [loss: 1.073952] ETA: 10:05:49.5
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[Epoch 0/60] [Batch 125/6487] [loss: 1.254514] ETA: 10:21:26.5
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[Epoch 0/60] [Batch 126/6487] [loss: 0.982123] ETA: 10:05:03.1
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[Epoch 0/60] [Batch 127/6487] [loss: 0.852741] ETA: 10:34:12.1
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[Epoch 0/60] [Batch 128/6487] [loss: 0.679137] ETA: 10:02:40.3
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[Epoch 0/60] [Batch 129/6487] [loss: 1.058274] ETA: 10:25:43.6
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[Epoch 0/60] [Batch 130/6487] [loss: 0.835604] ETA: 10:29:28.4
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[Epoch 0/60] [Batch 131/6487] [loss: 0.880438] ETA: 9:50:48.13
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[Epoch 0/60] [Batch 132/6487] [loss: 0.898338] ETA: 10:20:42.5
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[Epoch 0/60] [Batch 133/6487] [loss: 0.687976] ETA: 10:22:27.5
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[Epoch 0/60] [Batch 134/6487] [loss: 0.786885] ETA: 11:09:28.3
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[Epoch 0/60] [Batch 135/6487] [loss: 0.822007] ETA: 10:06:30.3
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[Epoch 0/60] [Batch 136/6487] [loss: 0.738222] ETA: 10:29:12.6
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[Epoch 0/60] [Batch 137/6487] [loss: 0.652328] ETA: 10:16:23.3
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[Epoch 0/60] [Batch 138/6487] [loss: 0.665464] ETA: 10:28:38.8
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[Epoch 0/60] [Batch 140/6487] [loss: 0.879610] ETA: 10:25:42.6
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[Epoch 0/60] [Batch 141/6487] [loss: 0.887465] ETA: 10:14:25.6
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[Epoch 0/60] [Batch 145/6487] [loss: 0.853456] ETA: 10:03:10.2
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[Epoch 0/60] [Batch 146/6487] [loss: 0.579405] ETA: 10:45:57.9
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[Epoch 0/60] [Batch 147/6487] [loss: 0.791809] ETA: 11:02:11.8
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[Epoch 0/60] [Batch 148/6487] [loss: 0.645943] ETA: 10:50:36.1
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[Epoch 0/60] [Batch 149/6487] [loss: 0.693026] ETA: 10:32:37.9
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[Epoch 0/60] [Batch 173/6487] [loss: 1.884247] ETA: 10:13:39.3
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[Epoch 0/60] [Batch 174/6487] [loss: 0.654066] ETA: 10:29:52.9
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[Epoch 0/60] [Batch 175/6487] [loss: 0.920216] ETA: 10:22:51.5
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[Epoch 0/60] [Batch 176/6487] [loss: 1.100421] ETA: 10:37:10.4
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[Epoch 0/60] [Batch 177/6487] [loss: 0.744130] ETA: 10:16:57.5
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[Epoch 0/60] [Batch 178/6487] [loss: 1.536752] ETA: 10:34:09.7
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[Epoch 0/60] [Batch 179/6487] [loss: 0.622831] ETA: 10:38:29.7
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[Epoch 0/60] [Batch 180/6487] [loss: 1.525723] ETA: 10:17:22.5
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[Epoch 0/60] [Batch 181/6487] [loss: 0.840026] ETA: 10:10:18.3
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[Epoch 0/60] [Batch 182/6487] [loss: 0.540482] ETA: 10:19:19.3
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[Epoch 0/60] [Batch 183/6487] [loss: 0.762839] ETA: 10:22:16.8
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[Epoch 0/60] [Batch 184/6487] [loss: 1.019287] ETA: 10:17:41.8
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[Epoch 0/60] [Batch 185/6487] [loss: 0.711923] ETA: 10:21:05.4
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[Epoch 0/60] [Batch 186/6487] [loss: 1.825077] ETA: 10:47:06.3
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[Epoch 0/60] [Batch 187/6487] [loss: 0.692980] ETA: 10:12:52.2
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[Epoch 0/60] [Batch 188/6487] [loss: 0.770251] ETA: 10:34:18.9
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[Epoch 0/60] [Batch 281/6487] [loss: 0.580542] ETA: 9:32:11.90
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[Epoch 0/60] [Batch 282/6487] [loss: 0.550011] ETA: 9:41:57.31
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[Epoch 0/60] [Batch 284/6487] [loss: 0.378703] ETA: 9:33:46.40
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[Epoch 0/60] [Batch 285/6487] [loss: 0.523195] ETA: 9:46:10.28
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[Epoch 0/60] [Batch 287/6487] [loss: 0.463384] ETA: 9:55:50.21
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[Epoch 0/60] [Batch 288/6487] [loss: 0.901026] ETA: 10:06:17.5
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[Epoch 0/60] [Batch 289/6487] [loss: 0.389526] ETA: 9:24:40.44
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[Epoch 0/60] [Batch 290/6487] [loss: 0.429652] ETA: 9:53:53.66
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[Epoch 0/60] [Batch 291/6487] [loss: 0.639763] ETA: 9:36:48.37Traceback (most recent call last):
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File "/home/star/whaiDir/PFCFuse/train.py", line 151, in <module>
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feature_V_B, feature_V_D, _ = DIDF_Encoder(data_VIS)
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
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return self._call_impl(*args, **kwargs)
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
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return forward_call(*args, **kwargs)
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 170, in forward
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for t in chain(self.module.parameters(), self.module.buffers()):
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2310, in buffers
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for _, buf in self.named_buffers(recurse=recurse):
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2337, in named_buffers
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yield from gen
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2223, in _named_members
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for module_prefix, module in modules:
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2435, in named_modules
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yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2435, in named_modules
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yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
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File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2435, in named_modules
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yield from module.named_modules(memo, submodule_prefix, remove_duplicate)
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[Previous line repeated 2 more times]
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KeyboardInterrupt
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