Swin3D - missing keys - Load pretrained model dict from Microsoft repo possible?
RauchLukas opened this issue · comments
Hi to authors,
Thank you very much for your additional work in integrating the Swin3D backbone into this awesome repo.
Can you tell me if this implementation is supposed to be compatible with the original Swin3D pytorch implementation from the paper in the Microsoft repo? They offer the pre-trained .pth state_dict models for Structured-3D in 4 configurations small/large | normals/no_normals.
I followed your READMe to fine-tune and tried to load one of these models, but at the point where the model gets loaded, I got the warning: Missing keys: followed by a long list of which keys (console LOG, line 5).
The Training starts after that warning, but the validation results indicate that the model has not loaded a pre-trained model.
My general question:
Is your PointCept implementation of the Swin3D backbone even intended to load the original model dict and then fine-tune it, or should it not do that, and what I'm doing here is complete bullsh*t?
If this is not the case, it is certainly not a big problem for me. I can of course pre-train the model again. But it would still save me time if I could use the author's weights directly.
[2024-05-27 16:07:34,965 INFO train.py line 147 10497] => Building hooks ...
[2024-05-27 16:07:34,965 INFO misc.py line 225 10497] => Loading checkpoint & weight ...
[2024-05-27 16:07:34,965 INFO misc.py line 227 10497] Loading weight at: Pointcept/exp/swin3dmodels/swin3d-s-RGBN/Swin3D_RGBN_S.pth
[2024-05-27 16:07:35,854 INFO misc.py line 232 10497] Loading layer weights with keyword: , replace keyword with:
[2024-05-27 16:07:35,868 INFO misc.py line 250 10497] Missing keys: ['module.backbone.stem_layer.conv_layers.0.kernel', 'module.backbone.stem_layer.conv_layers.1.bn.weight', 'module.backbone.stem_layer.conv_layers.1.bn.bias', 'module.backbone.stem_layer.conv_layers.1.bn.running_mean', 'module.backbone.stem_layer.conv_layers.1.bn.running_var', 'module.backbone.layers.0.blocks.0.norm1.weight', 'module.backbone.layers.0.blocks.0.norm1.bias', 'module.backbone.layers.0.blocks.0.attn.query_xyz_table', 'module.backbone.layers.0.blocks.0.attn.key_xyz_table', 'module.backbone.layers.0.blocks.0.attn.value_xyz_table', 'module.backbone.layers.0.blocks.0.attn.query_rgb_table', 'module.backbone.layers.0.blocks.0.attn.key_rgb_table', 'module.backbone.layers.0.blocks.0.attn.value_rgb_table', 'module.backbone.layers.0.blocks.0.attn.query_norm_table', 'module.backbone.layers.0.blocks.0.attn.key_norm_table', 'module.backbone.layers.0.blocks.0.attn.value_norm_table', 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[2024-05-27 16:07:35,870 INFO train.py line 154 10497] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>
/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
[2024-05-27 16:08:14,400 INFO misc.py line 120 10497] Train: [1/100][1/17] Data 20.327 (20.327) Batch 38.395 (38.395) Remain 18:07:13 loss: 2.5279 Lr: 0.00100
[2024-05-27 16:08:19,046 INFO misc.py line 120 10497] Train: [1/100][2/17] Data 0.003 (0.003) Batch 4.642 (4.642) Remain 02:11:22 loss: 2.4452 Lr: 0.00100
[2024-05-27 16:08:24,346 INFO misc.py line 120 10497] Train: [1/100][3/17] Data 0.007 (0.007) Batch 5.300 (5.300) Remain 02:29:53 loss: 2.3683 Lr: 0.00100
[2024-05-27 16:08:29,248 INFO misc.py line 120 10497] Train: [1/100][4/17] Data 0.007 (0.007) Batch 4.903 (4.903) Remain 02:18:35 loss: 2.3319 Lr: 0.00100
[2024-05-27 16:08:34,097 INFO misc.py line 120 10497] Train: [1/100][5/17] Data 0.005 (0.006) Batch 4.849 (4.876) Remain 02:17:45 loss: 2.2988 Lr: 0.00100
[2024-05-27 16:08:39,086 INFO misc.py line 120 10497] Train: [1/100][6/17] Data 0.004 (0.005) Batch 4.988 (4.914) Remain 02:18:43 loss: 2.2331 Lr: 0.00100
[2024-05-27 16:08:43,884 INFO misc.py line 120 10497] Train: [1/100][7/17] Data 0.005 (0.005) Batch 4.798 (4.885) Remain 02:17:50 loss: 2.2315 Lr: 0.00100
[2024-05-27 16:08:49,086 INFO misc.py line 120 10497] Train: [1/100][8/17] Data 0.004 (0.005) Batch 5.202 (4.948) Remain 02:19:32 loss: 2.1370 Lr: 0.00100
[2024-05-27 16:08:53,994 INFO misc.py line 120 10497] Train: [1/100][9/17] Data 0.005 (0.005) Batch 4.907 (4.942) Remain 02:19:16 loss: 2.1766 Lr: 0.00100
[2024-05-27 16:08:59,061 INFO misc.py line 120 10497] Train: [1/100][10/17] Data 0.005 (0.005) Batch 5.067 (4.959) Remain 02:19:41 loss: 2.0289 Lr: 0.00100
[2024-05-27 16:09:03,647 INFO misc.py line 120 10497] Train: [1/100][11/17] Data 0.005 (0.005) Batch 4.586 (4.913) Remain 02:18:17 loss: 2.1034 Lr: 0.00100
[2024-05-27 16:09:08,912 INFO misc.py line 120 10497] Train: [1/100][12/17] Data 0.005 (0.005) Batch 5.264 (4.952) Remain 02:19:18 loss: 2.1174 Lr: 0.00100
[2024-05-27 16:09:12,949 INFO misc.py line 120 10497] Train: [1/100][13/17] Data 0.005 (0.005) Batch 4.038 (4.860) Remain 02:16:39 loss: 2.0264 Lr: 0.00100
[2024-05-27 16:09:17,340 INFO misc.py line 120 10497] Train: [1/100][14/17] Data 0.004 (0.005) Batch 4.391 (4.818) Remain 02:15:22 loss: 2.0877 Lr: 0.00100
[2024-05-27 16:09:21,598 INFO misc.py line 120 10497] Train: [1/100][15/17] Data 0.006 (0.005) Batch 4.258 (4.771) Remain 02:13:59 loss: 2.0202 Lr: 0.00100
[2024-05-27 16:09:26,288 INFO misc.py line 120 10497] Train: [1/100][16/17] Data 0.005 (0.005) Batch 4.691 (4.765) Remain 02:13:44 loss: 1.9965 Lr: 0.00100
[2024-05-27 16:09:31,624 INFO misc.py line 120 10497] Train: [1/100][17/17] Data 0.005 (0.005) Batch 5.336 (4.806) Remain 02:14:47 loss: 1.9193 Lr: 0.00100
[2024-05-27 16:09:31,625 INFO misc.py line 143 10497] Train result: loss: 2.1794
[2024-05-27 16:09:31,626 INFO evaluator.py line 114 10497] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-05-27 16:09:58,590 INFO evaluator.py line 161 10497] Test: [1/23] Loss 2.1853
[2024-05-27 16:10:01,517 INFO evaluator.py line 161 10497] Test: [2/23] Loss 2.3560
[2024-05-27 16:10:03,568 INFO evaluator.py line 161 10497] Test: [3/23] Loss 2.2103
[2024-05-27 16:10:05,766 INFO evaluator.py line 161 10497] Test: [4/23] Loss 2.2811
[2024-05-27 16:10:07,999 INFO evaluator.py line 161 10497] Test: [5/23] Loss 2.1352
[2024-05-27 16:10:11,470 INFO evaluator.py line 161 10497] Test: [6/23] Loss 2.3465
[2024-05-27 16:10:13,513 INFO evaluator.py line 161 10497] Test: [7/23] Loss 2.2605
[2024-05-27 16:10:15,508 INFO evaluator.py line 161 10497] Test: [8/23] Loss 2.1397
[2024-05-27 16:10:17,439 INFO evaluator.py line 161 10497] Test: [9/23] Loss 2.3671
[2024-05-27 16:10:19,331 INFO evaluator.py line 161 10497] Test: [10/23] Loss 2.3735
[2024-05-27 16:10:21,253 INFO evaluator.py line 161 10497] Test: [11/23] Loss 2.3103
[2024-05-27 16:10:23,215 INFO evaluator.py line 161 10497] Test: [12/23] Loss 2.3664
[2024-05-27 16:10:25,358 INFO evaluator.py line 161 10497] Test: [13/23] Loss 2.3003
[2024-05-27 16:10:27,236 INFO evaluator.py line 161 10497] Test: [14/23] Loss 2.3089
[2024-05-27 16:10:29,042 INFO evaluator.py line 161 10497] Test: [15/23] Loss 2.3449
[2024-05-27 16:10:30,900 INFO evaluator.py line 161 10497] Test: [16/23] Loss 2.3640
[2024-05-27 16:10:32,857 INFO evaluator.py line 161 10497] Test: [17/23] Loss 2.2992
[2024-05-27 16:10:35,461 INFO evaluator.py line 161 10497] Test: [18/23] Loss 2.3856
[2024-05-27 16:10:37,682 INFO evaluator.py line 161 10497] Test: [19/23] Loss 2.3605
[2024-05-27 16:10:40,549 INFO evaluator.py line 161 10497] Test: [20/23] Loss 2.3650
[2024-05-27 16:10:42,488 INFO evaluator.py line 161 10497] Test: [21/23] Loss 2.3671
[2024-05-27 16:10:44,816 INFO evaluator.py line 161 10497] Test: [22/23] Loss 2.2625
[2024-05-27 16:10:47,868 INFO evaluator.py line 161 10497] Test: [23/23] Loss 2.3752
[2024-05-27 16:10:49,458 INFO evaluator.py line 176 10497] Val result: mIoU/mAcc/allAcc 0.1508/0.2305/0.6174.
[2024-05-27 16:10:49,458 INFO evaluator.py line 182 10497] Class_0-ceiling Result: iou/accuracy 0.7046/0.9848
[2024-05-27 16:10:49,458 INFO evaluator.py line 182 10497] Class_1-floor Result: iou/accuracy 0.6428/0.9908
[2024-05-27 16:10:49,458 INFO evaluator.py line 182 10497] Class_2-wall Result: iou/accuracy 0.5582/0.9546
[2024-05-27 16:10:49,458 INFO evaluator.py line 182 10497] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_4-column Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_5-window Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_6-door Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_7-table Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_8-chair Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_9-sofa Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_10-bookcase Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_11-board Result: iou/accuracy 0.0000/0.0000
[2024-05-27 16:10:49,459 INFO evaluator.py line 182 10497] Class_12-clutter Result: iou/accuracy 0.0545/0.0666
[2024-05-27 16:10:49,460 INFO evaluator.py line 204 10497] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2024-05-27 16:10:49,461 INFO misc.py line 172 10497] Best validation mIoU updated to: 0.1508
[2024-05-27 16:10:49,461 INFO misc.py line 177 10497] Currently Best mIoU: 0.1508
[2024-05-27 16:10:49,461 INFO misc.py line 186 10497] Saving checkpoint to: Pointcept/exp/s3dis/test_swin-small/model/model_last.pth
Thanks.
Great Work, Enormous contribution to the community!
Hi, original model weight from Swin3D is not support Pointcept as they didn't train with our codebase, the data pipeline is not aligned.
@Gofinge Thank you for the fast response. At least I now have the certainty and can retrain my model with a clear conscience, without the concern of wasting redundant computing capacity.