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
21 lines
8.0 KiB
Plaintext
21 lines
8.0 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: 18.997314] ETA: 9 days, 21
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[Epoch 0/60] [Batch 1000/6487] [loss: 0.168454] ETA: 9:49:11.56
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[Epoch 0/60] [Batch 2000/6487] [loss: 0.350209] ETA: 9:50:02.48
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[Epoch 0/60] [Batch 3000/6487] [loss: 0.069812] ETA: 9:50:01.39
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[Epoch 0/60] [Batch 4000/6487] [loss: 0.065885] ETA: 10:07:51.9
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[Epoch 0/60] [Batch 5000/6487] [loss: 0.245787] ETA: 9:54:20.43
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[Epoch 0/60] [Batch 6000/6487] [loss: 0.042268] ETA: 10:03:01.5
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[Epoch 1/60] [Batch 0/6487] [loss: 0.066391] ETA: 9:49:42.62
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[Epoch 1/60] [Batch 1000/6487] [loss: 0.261340] ETA: 9:53:01.14
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[Epoch 1/60] [Batch 2000/6487] [loss: 0.095140] ETA: 10:00:23.2
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[Epoch 1/60] [Batch 3000/6487] [loss: 0.028199] ETA: 10:17:34.6
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[Epoch 1/60] [Batch 4000/6487] [loss: 0.089190] ETA: 9:03:05.02
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[Epoch 1/60] [Batch 5000/6487] [loss: 0.134494] ETA: 9:40:49.06
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[Epoch 1/60] [Batch 6000/6487] [loss: 0.134911] ETA: 10:01:31.0
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[Epoch 2/60] [Batch 0/6487] [loss: 0.105676] ETA: 9:39:54.85
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[Epoch 2/60] [Batch 1000/6487] [loss: 0.048246] ETA: 9:53:22.48
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[Epoch 2/60] [Batch 2000/6487] [loss: 0.028375] ETA: 9:40:39.29
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[Epoch 2/60] [Batch 3000/6487] [loss: 0.034128] ETA: 9:56:36.89
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[Epoch 2/60] [Batch 4000/6487] [loss: 0.022569] ETA: 9:42:22.52
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[Epoch 2/60] [Batch 5000/6487] [loss: 0.055698] ETA: 9:37:40.30
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[Epoch 2/60] [Batch 6000/6487] [loss: 0.056826] ETA: 9:44:44.57
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[Epoch 3/60] [Batch 0/6487] [loss: 0.020840] ETA: 9:25:04.54
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[Epoch 3/60] [Batch 1000/6487] [loss: 0.033522] ETA: 9:21:26.95
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[Epoch 3/60] [Batch 2000/6487] [loss: 0.087376] ETA: 9:33:31.90
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[Epoch 3/60] [Batch 3000/6487] [loss: 0.054073] ETA: 9:33:48.42
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[Epoch 3/60] [Batch 4000/6487] [loss: 0.015202] ETA: 9:35:51.08
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[Epoch 3/60] [Batch 5000/6487] [loss: 0.023986] ETA: 9:13:47.44
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[Epoch 3/60] [Batch 6000/6487] [loss: 0.027252] ETA: 9:12:56.42
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[Epoch 4/60] [Batch 0/6487] [loss: 0.028229] ETA: 9:07:31.99
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[Epoch 4/60] [Batch 1000/6487] [loss: 0.009431] ETA: 9:17:32.62
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[Epoch 4/60] [Batch 2000/6487] [loss: 0.019674] ETA: 9:11:41.71
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[Epoch 4/60] [Batch 3000/6487] [loss: 0.036560] ETA: 9:13:51.69
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[Epoch 4/60] [Batch 4000/6487] [loss: 0.023415] ETA: 9:04:01.70
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[Epoch 4/60] [Batch 5000/6487] [loss: 0.040478] ETA: 8:51:47.13
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[Epoch 4/60] [Batch 6000/6487] [loss: 0.082253] ETA: 9:05:46.11
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[Epoch 5/60] [Batch 0/6487] [loss: 0.071554] ETA: 9:08:54.46
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[Epoch 5/60] [Batch 1000/6487] [loss: 0.049842] ETA: 8:58:09.51
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[Epoch 5/60] [Batch 2000/6487] [loss: 0.010863] ETA: 8:56:36.05
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[Epoch 5/60] [Batch 3000/6487] [loss: 0.043838] ETA: 9:04:35.75
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[Epoch 5/60] [Batch 4000/6487] [loss: 0.108259] ETA: 8:57:50.84
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[Epoch 5/60] [Batch 5000/6487] [loss: 0.024255] ETA: 8:58:37.17
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[Epoch 5/60] [Batch 6000/6487] [loss: 0.040892] ETA: 8:55:42.00
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[Epoch 6/60] [Batch 0/6487] [loss: 0.023312] ETA: 8:54:04.34
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[Epoch 6/60] [Batch 1000/6487] [loss: 0.035162] ETA: 8:54:12.47
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[Epoch 6/60] [Batch 2000/6487] [loss: 0.015531] ETA: 8:53:49.38
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[Epoch 6/60] [Batch 3000/6487] [loss: 0.077984] ETA: 8:48:18.87
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[Epoch 6/60] [Batch 4000/6487] [loss: 0.020643] ETA: 9:11:35.39
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[Epoch 6/60] [Batch 5000/6487] [loss: 0.018760] ETA: 9:03:14.95
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[Epoch 6/60] [Batch 6000/6487] [loss: 0.064921] ETA: 8:45:53.60
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[Epoch 7/60] [Batch 0/6487] [loss: 0.020156] ETA: 8:57:09.24
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[Epoch 7/60] [Batch 1000/6487] [loss: 0.023395] ETA: 8:24:13.53
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[Epoch 7/60] [Batch 2000/6487] [loss: 0.029281] ETA: 8:43:02.96
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[Epoch 7/60] [Batch 3000/6487] [loss: 0.034845] ETA: 8:42:22.83
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[Epoch 7/60] [Batch 4000/6487] [loss: 0.003461] ETA: 8:38:22.20
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[Epoch 7/60] [Batch 5000/6487] [loss: 0.017755] ETA: 8:34:32.13
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[Epoch 7/60] [Batch 6000/6487] [loss: 0.029525] ETA: 8:56:35.38
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[Epoch 8/60] [Batch 0/6487] [loss: 0.013173] ETA: 8:39:37.20
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[Epoch 8/60] [Batch 1000/6487] [loss: 0.006751] ETA: 9:16:29.80
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[Epoch 8/60] [Batch 2000/6487] [loss: 0.017919] ETA: 8:39:39.59
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[Epoch 8/60] [Batch 3000/6487] [loss: 0.194062] ETA: 8:26:52.51
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[Epoch 8/60] [Batch 4000/6487] [loss: 0.043372] ETA: 8:28:11.61
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[Epoch 8/60] [Batch 5000/6487] [loss: 0.033920] ETA: 8:30:32.83
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[Epoch 8/60] [Batch 6000/6487] [loss: 0.008135] ETA: 8:38:24.67
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[Epoch 9/60] [Batch 0/6487] [loss: 0.008534] ETA: 8:22:37.70
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[Epoch 9/60] [Batch 1000/6487] [loss: 0.019043] ETA: 8:18:04.41
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[Epoch 9/60] [Batch 2000/6487] [loss: 0.034581] ETA: 8:28:55.32
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[Epoch 9/60] [Batch 3000/6487] [loss: 0.044300] ETA: 8:14:17.40
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[Epoch 9/60] [Batch 4000/6487] [loss: 0.019384] ETA: 8:13:10.31
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[Epoch 9/60] [Batch 5000/6487] [loss: 0.049925] ETA: 8:14:50.03
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[Epoch 9/60] [Batch 6000/6487] [loss: 0.017746] ETA: 8:15:26.70
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[Epoch 10/60] [Batch 0/6487] [loss: 0.028521] ETA: 8:19:38.16
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[Epoch 10/60] [Batch 1000/6487] [loss: 0.014648] ETA: 8:17:37.82
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[Epoch 10/60] [Batch 2000/6487] [loss: 0.045394] ETA: 8:16:26.00
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[Epoch 10/60] [Batch 3000/6487] [loss: 0.024051] ETA: 8:10:38.39
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[Epoch 10/60] [Batch 4000/6487] [loss: 0.004026] ETA: 8:13:11.58
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[Epoch 10/60] [Batch 5000/6487] [loss: 0.016114] ETA: 8:10:52.15
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[Epoch 10/60] [Batch 6000/6487] [loss: 0.055893] ETA: 8:07:49.83
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[Epoch 11/60] [Batch 0/6487] [loss: 0.066364] ETA: 8:09:19.87
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[Epoch 11/60] [Batch 1000/6487] [loss: 0.006531] ETA: 8:15:30.68
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[Epoch 11/60] [Batch 2000/6487] [loss: 0.012910] ETA: 8:38:10.06
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[Epoch 11/60] [Batch 3000/6487] [loss: 0.018403] ETA: 8:06:35.75
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[Epoch 11/60] [Batch 4000/6487] [loss: 0.033711] ETA: 8:31:08.71
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[Epoch 11/60] [Batch 5000/6487] [loss: 0.011338] ETA: 7:57:47.33
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[Epoch 11/60] [Batch 6000/6487] [loss: 0.100941] ETA: 7:53:55.39
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[Epoch 12/60] [Batch 0/6487] [loss: 0.015037] ETA: 8:01:30.09
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[Epoch 12/60] [Batch 1000/6487] [loss: 0.030051] ETA: 8:03:12.37
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[Epoch 12/60] [Batch 2000/6487] [loss: 0.118560] ETA: 7:55:56.12
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[Epoch 12/60] [Batch 3000/6487] [loss: 0.034878] ETA: 7:48:51.71
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[Epoch 12/60] [Batch 4000/6487] [loss: 0.070042] ETA: 7:59:20.80
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[Epoch 12/60] [Batch 5000/6487] [loss: 0.027831] ETA: 7:48:42.75
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[Epoch 12/60] [Batch 6000/6487] [loss: 0.069188] ETA: 7:48:37.23
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[Epoch 13/60] [Batch 0/6487] [loss: 0.012127] ETA: 7:50:06.80
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[Epoch 13/60] [Batch 1000/6487] [loss: 0.099335] ETA: 7:47:59.87
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[Epoch 13/60] [Batch 2000/6487] [loss: 0.053219] ETA: 7:44:02.39
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[Epoch 13/60] [Batch 3000/6487] [loss: 0.034852] ETA: 7:44:14.47
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[Epoch 13/60] [Batch 4000/6487] [loss: 0.037935] ETA: 7:43:17.21
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[Epoch 13/60] [Batch 5000/6487] [loss: 0.004800] ETA: 7:33:29.48
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[Epoch 13/60] [Batch 6000/6487] [loss: 0.011718] ETA: 7:39:18.29
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[Epoch 14/60] [Batch 0/6487] [loss: 0.066959] ETA: 7:46:41.01
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[Epoch 14/60] [Batch 1000/6487] [loss: 0.012596] ETA: 7:33:36.97
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[Epoch 14/60] [Batch 2000/6487] [loss: 0.044466] ETA: 7:36:43.39
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[Epoch 14/60] [Batch 3000/6487] [loss: 0.048720] ETA: 7:35:39.53
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[Epoch 14/60] [Batch 4000/6487] [loss: 0.009346] ETA: 7:24:57.95
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[Epoch 14/60] [Batch 5000/6487] [loss: 0.051516] ETA: 7:31:27.91
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[Epoch 14/60] [Batch 6000/6487] [loss: 0.016138] ETA: 7:32:52.03
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[Epoch 15/60] [Batch 0/6487] [loss: 0.005100] ETA: 7:26:40.78
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[Epoch 15/60] [Batch 1000/6487] [loss: 0.035449] ETA: 7:31:27.18
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[Epoch 15/60] [Batch 2000/6487] [loss: 0.082434] ETA: 7:27:34.45
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[Epoch 15/60] [Batch 3000/6487] [loss: 0.014699] ETA: 7:25:05.27
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[Epoch 15/60] [Batch 4000/6487] [loss: 0.058203] ETA: 7:22:03.05
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[Epoch 15/60] [Batch 5000/6487] [loss: 0.017237] ETA: 7:16:34.93
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[Epoch 15/60] [Batch 6000/6487] [loss: 0.027539] ETA: 7:17:09.26
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[Epoch 16/60] [Batch 0/6487] [loss: 0.061302] ETA: 7:20:07.38
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[Epoch 16/60] [Batch 1000/6487] [loss: 0.030785] ETA: 7:15:17.26
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[Epoch 16/60] [Batch 2000/6487] [loss: 0.029255] ETA: 7:19:16.69
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[Epoch 16/60] [Batch 3000/6487] [loss: 0.033562] ETA: 7:14:02.70
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[Epoch 16/60] [Batch 4000/6487] [loss: 0.029975] ETA: 7:11:28.15
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[Epoch 16/60] [Batch 5000/6487] [loss: 0.015033] ETA: 7:06:03.16
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[Epoch 16/60] [Batch 6000/6487] [loss: 0.014657] ETA: 7:05:38.42
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[Epoch 17/60] [Batch 0/6487] [loss: 0.018211] ETA: 7:07:44.70 |