61 lines
19 KiB
Plaintext
61 lines
19 KiB
Plaintext
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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: 9.264997] ETA: 9 days, 23
[Epoch 0/60] [Batch 1/6487] [loss: 34.969955] ETA: 10:10:55.4
[Epoch 0/60] [Batch 2/6487] [loss: 4.685072] ETA: 9:18:17.32
[Epoch 0/60] [Batch 3/6487] [loss: 6.396908] ETA: 9:07:01.12
[Epoch 0/60] [Batch 4/6487] [loss: 7.493115] ETA: 9:13:54.91
[Epoch 0/60] [Batch 5/6487] [loss: 10.458361] ETA: 9:13:42.39
[Epoch 0/60] [Batch 6/6487] [loss: 6.037536] ETA: 9:16:39.82
[Epoch 0/60] [Batch 7/6487] [loss: 8.048889] ETA: 9:16:37.41
[Epoch 0/60] [Batch 8/6487] [loss: 8.864985] ETA: 9:21:25.64
[Epoch 0/60] [Batch 9/6487] [loss: 5.235039] ETA: 9:21:18.97
[Epoch 0/60] [Batch 10/6487] [loss: 8.189146] ETA: 9:10:39.52
[Epoch 0/60] [Batch 11/6487] [loss: 28.157166] ETA: 9:12:02.58
[Epoch 0/60] [Batch 12/6487] [loss: 24.957096] ETA: 9:20:00.30
[Epoch 0/60] [Batch 13/6487] [loss: 6.462063] ETA: 9:14:48.89
[Epoch 0/60] [Batch 14/6487] [loss: 7.996860] ETA: 9:10:29.91
[Epoch 0/60] [Batch 15/6487] [loss: 31.331648] ETA: 9:09:35.26
[Epoch 0/60] [Batch 16/6487] [loss: 3.947951] ETA: 9:13:43.21
[Epoch 0/60] [Batch 17/6487] [loss: 3.096982] ETA: 9:13:46.84
[Epoch 0/60] [Batch 18/6487] [loss: 10.590578] ETA: 9:20:45.90
[Epoch 0/60] [Batch 19/6487] [loss: 24.223045] ETA: 9:16:02.33
[Epoch 0/60] [Batch 20/6487] [loss: 4.455335] ETA: 9:18:29.60
[Epoch 0/60] [Batch 21/6487] [loss: 6.397137] ETA: 9:17:36.25
[Epoch 0/60] [Batch 22/6487] [loss: 5.610237] ETA: 9:14:34.20
[Epoch 0/60] [Batch 23/6487] [loss: 14.335532] ETA: 9:16:27.60
[Epoch 0/60] [Batch 24/6487] [loss: 6.131247] ETA: 9:21:42.91
[Epoch 0/60] [Batch 25/6487] [loss: 25.698673] ETA: 9:48:32.66
[Epoch 0/60] [Batch 26/6487] [loss: 17.905416] ETA: 8:45:44.79
[Epoch 0/60] [Batch 27/6487] [loss: 12.629596] ETA: 9:29:06.19
[Epoch 0/60] [Batch 28/6487] [loss: 2.759912] ETA: 9:19:09.09
[Epoch 0/60] [Batch 29/6487] [loss: 3.437797] ETA: 9:16:02.49
[Epoch 0/60] [Batch 30/6487] [loss: 4.955575] ETA: 9:15:34.85
[Epoch 0/60] [Batch 31/6487] [loss: 12.267755] ETA: 9:25:52.84
[Epoch 0/60] [Batch 32/6487] [loss: 6.603993] ETA: 9:23:30.97
[Epoch 0/60] [Batch 33/6487] [loss: 4.700416] ETA: 9:19:23.87
[Epoch 0/60] [Batch 34/6487] [loss: 2.034435] ETA: 9:21:06.78
[Epoch 0/60] [Batch 35/6487] [loss: 2.524534] ETA: 9:20:06.57
[Epoch 0/60] [Batch 36/6487] [loss: 2.145031] ETA: 9:21:24.33
[Epoch 0/60] [Batch 37/6487] [loss: 5.916213] ETA: 9:10:36.86
[Epoch 0/60] [Batch 38/6487] [loss: 2.583171] ETA: 9:21:26.02
[Epoch 0/60] [Batch 39/6487] [loss: 3.443858] ETA: 9:21:26.21
[Epoch 0/60] [Batch 40/6487] [loss: 2.324850] ETA: 9:18:54.13
[Epoch 0/60] [Batch 41/6487] [loss: 1.963822] ETA: 9:18:47.65
[Epoch 0/60] [Batch 42/6487] [loss: 2.487358] ETA: 9:15:25.28
[Epoch 0/60] [Batch 43/6487] [loss: 4.819328] ETA: 9:22:20.42
[Epoch 0/60] [Batch 44/6487] [loss: 2.229982] ETA: 9:17:03.19
[Epoch 0/60] [Batch 45/6487] [loss: 2.486492] ETA: 9:20:22.41
[Epoch 0/60] [Batch 46/6487] [loss: 1.955426] ETA: 9:20:36.80
[Epoch 0/60] [Batch 47/6487] [loss: 2.649206] ETA: 9:26:42.38
[Epoch 0/60] [Batch 48/6487] [loss: 2.574780] ETA: 9:20:39.78
[Epoch 0/60] [Batch 49/6487] [loss: 2.521971] ETA: 9:21:56.24
[Epoch 0/60] [Batch 50/6487] [loss: 1.926707] ETA: 9:48:40.60
[Epoch 0/60] [Batch 51/6487] [loss: 1.874200] ETA: 8:55:21.46
[Epoch 0/60] [Batch 52/6487] [loss: 1.727591] ETA: 9:21:21.56
[Epoch 0/60] [Batch 53/6487] [loss: 1.662356] ETA: 9:21:41.05
[Epoch 0/60] [Batch 54/6487] [loss: 1.870807] ETA: 9:23:10.96
[Epoch 0/60] [Batch 55/6487] [loss: 2.336739] ETA: 9:31:00.64
[Epoch 0/60] [Batch 56/6487] [loss: 2.303160] ETA: 9:16:50.56
[Epoch 0/60] [Batch 57/6487] [loss: 1.943992] ETA: 9:22:21.99
[Epoch 0/60] [Batch 58/6487] [loss: 2.118214] ETA: 9:20:51.35
[Epoch 0/60] [Batch 59/6487] [loss: 1.870196] ETA: 9:27:12.69
[Epoch 0/60] [Batch 60/6487] [loss: 2.090588] ETA: 9:13:20.16
[Epoch 0/60] [Batch 61/6487] [loss: 1.935504] ETA: 9:29:00.42
[Epoch 0/60] [Batch 62/6487] [loss: 1.839970] ETA: 10:03:28.4
[Epoch 0/60] [Batch 63/6487] [loss: 1.744011] ETA: 9:11:34.13
[Epoch 0/60] [Batch 64/6487] [loss: 2.003899] ETA: 10:14:38.9
[Epoch 0/60] [Batch 65/6487] [loss: 1.508669] ETA: 9:44:13.88
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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 184, in forward
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return self.module(*inputs[0], **module_kwargs[0])
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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/whaiDir/PFCFuse/net.py", line 455, in forward
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out_enc_level1 = self.encoder_level1(inp_enc_level1)
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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/modules/container.py", line 219, in forward
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input = module(input)
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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/whaiDir/PFCFuse/net.py", line 412, in forward
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x = x + self.ffn(self.norm2(x))
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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/whaiDir/PFCFuse/net.py", line 331, in forward
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return to_4d(self.body(to_3d(x)), h, w)
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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/whaiDir/PFCFuse/net.py", line 317, in forward
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sigma = x.var(-1, keepdim=True, unbiased=False)
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KeyboardInterrupt
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