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View Code? Open in Web Editor NEW[ICCV 2023] SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
License: MIT License
[ICCV 2023] SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
License: MIT License
Thanks for your amazing work!
If I only change the some basic setting in the config (like dataset path), and run main_pcn directly, the test result doesn't go as expected. It seems that the training process doesn't work.
Here is the log output
2023-10-16 00:57:28,897 - INFO - 'epoch: ', 1, 'optimizer: ', 0.0
2023-10-16 01:37:58,150 - INFO - [Epoch 1/400] EpochTime = 2429.254 (s) Losses = ['47.3596', '22.5916', '18.0766']
2023-10-16 01:38:52,107 - INFO - ============================ TEST RESULTS ============================
2023-10-16 01:38:52,107 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 01:38:52,108 - INFO - 02691156 13 11.3990 0.7054 0.6294
2023-10-16 01:38:52,108 - INFO - 02933112 12 20.8394 0.7446 0.3774
2023-10-16 01:38:52,108 - INFO - 02958343 13 14.9069 0.7059 0.4695
2023-10-16 01:38:52,109 - INFO - 03001627 12 18.8508 0.7260 0.4516
2023-10-16 01:38:52,109 - INFO - 03636649 13 17.8343 0.7393 0.4878
2023-10-16 01:38:52,109 - INFO - 04256520 12 21.6226 0.7700 0.3663
2023-10-16 01:38:52,119 - INFO - 04379243 13 15.1328 0.6857 0.5245
2023-10-16 01:38:52,119 - INFO - 04530566 12 13.5874 0.7076 0.5510
2023-10-16 01:38:52,120 - INFO - Overall 16.6935 0.7225 0.4840
2023-10-16 01:38:52,120 - INFO - Epoch 1 16.6935 0.7225 0.4840
2023-10-16 01:38:53,844 - INFO - Saved checkpoint to SVDFormer-main/experiment/ckpt\ckpt-best.pth ...
2023-10-16 01:38:53,844 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 01:38:53,845 - INFO - 'epoch: ', 2, 'optimizer: ', 0.001
2023-10-16 02:18:53,104 - INFO - [Epoch 2/400] EpochTime = 2399.259 (s) Losses = ['37.4754', '19.4601', '102.9291']
2023-10-16 02:19:45,864 - INFO - ============================ TEST RESULTS ============================
2023-10-16 02:19:45,864 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 02:19:45,864 - INFO - 02691156 13 11.5185 0.9148 0.6104
2023-10-16 02:19:45,864 - INFO - 02933112 12 22.4493 0.9322 0.2161
2023-10-16 02:19:45,864 - INFO - 02958343 13 18.0634 0.9226 0.2638
2023-10-16 02:19:45,864 - INFO - 03001627 12 20.2612 0.9288 0.3075
2023-10-16 02:19:45,864 - INFO - 03636649 13 18.2606 0.9323 0.4070
2023-10-16 02:19:45,864 - INFO - 04256520 12 22.6796 0.9337 0.2306
2023-10-16 02:19:45,864 - INFO - 04379243 13 16.2068 0.9182 0.3807
2023-10-16 02:19:45,864 - INFO - 04530566 12 14.9223 0.9204 0.4352
2023-10-16 02:19:45,864 - INFO - Overall 17.9639 0.9252 0.3588
2023-10-16 02:19:45,864 - INFO - Epoch 2 17.9639 0.9252 0.3588
2023-10-16 02:19:45,864 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 02:19:45,864 - INFO - 'epoch: ', 3, 'optimizer: ', 0.001
2023-10-16 02:59:26,815 - INFO - [Epoch 3/400] EpochTime = 2380.950 (s) Losses = ['61.0343', '130.7800', '128.8345']
2023-10-16 03:00:19,536 - INFO - ============================ TEST RESULTS ============================
2023-10-16 03:00:19,536 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 03:00:19,536 - INFO - 02691156 13 18.2846 0.9746 0.4420
2023-10-16 03:00:19,536 - INFO - 02933112 12 31.3285 0.9778 0.1221
2023-10-16 03:00:19,536 - INFO - 02958343 13 27.4990 0.9778 0.1485
2023-10-16 03:00:19,536 - INFO - 03001627 12 25.8402 0.9779 0.2059
2023-10-16 03:00:19,536 - INFO - 03636649 13 21.2641 0.9762 0.4088
2023-10-16 03:00:19,536 - INFO - 04256520 12 32.3640 0.9780 0.1478
2023-10-16 03:00:19,536 - INFO - 04379243 13 22.2649 0.9776 0.2051
2023-10-16 03:00:19,536 - INFO - 04530566 12 19.3846 0.9752 0.3185
2023-10-16 03:00:19,536 - INFO - Overall 24.6807 0.9769 0.2519
2023-10-16 03:00:19,536 - INFO - Epoch 3 24.6807 0.9769 0.2519
2023-10-16 03:00:19,536 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 03:00:19,536 - INFO - 'epoch: ', 4, 'optimizer: ', 0.001
2023-10-16 03:39:52,293 - INFO - [Epoch 4/400] EpochTime = 2372.757 (s) Losses = ['52.4664', '25.0615', '23.0664']
2023-10-16 03:40:45,025 - INFO - ============================ TEST RESULTS ============================
2023-10-16 03:40:45,025 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 03:40:45,025 - INFO - 02691156 13 16.3487 0.9754 0.4308
2023-10-16 03:40:45,025 - INFO - 02933112 12 29.5060 0.9789 0.1103
2023-10-16 03:40:45,025 - INFO - 02958343 13 22.8731 0.9781 0.1376
2023-10-16 03:40:45,025 - INFO - 03001627 12 24.0258 0.9792 0.1787
2023-10-16 03:40:45,025 - INFO - 03636649 13 22.2710 0.9788 0.3626
2023-10-16 03:40:45,025 - INFO - 04256520 12 27.1255 0.9786 0.1333
2023-10-16 03:40:45,025 - INFO - 04379243 13 22.3254 0.9788 0.1864
2023-10-16 03:40:45,025 - INFO - 04530566 12 18.4356 0.9766 0.2924
2023-10-16 03:40:45,025 - INFO - Overall 22.7875 0.9781 0.2310
2023-10-16 03:40:45,025 - INFO - Epoch 4 22.7875 0.9781 0.2310
2023-10-16 03:40:45,025 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 03:40:45,025 - INFO - 'epoch: ', 5, 'optimizer: ', 0.001
2023-10-16 04:20:17,369 - INFO - [Epoch 5/400] EpochTime = 2372.343 (s) Losses = ['47.6701', '24.3645', '22.4079']
2023-10-16 04:21:10,066 - INFO - ============================ TEST RESULTS ============================
2023-10-16 04:21:10,066 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 04:21:10,066 - INFO - 02691156 13 15.6621 0.9750 0.4384
2023-10-16 04:21:10,066 - INFO - 02933112 12 29.6768 0.9786 0.1132
2023-10-16 04:21:10,066 - INFO - 02958343 13 22.7800 0.9779 0.1396
2023-10-16 04:21:10,066 - INFO - 03001627 12 23.3449 0.9788 0.1872
2023-10-16 04:21:10,066 - INFO - 03636649 13 21.1941 0.9780 0.3753
2023-10-16 04:21:10,066 - INFO - 04256520 12 27.7056 0.9784 0.1371
2023-10-16 04:21:10,066 - INFO - 04379243 13 20.6201 0.9777 0.1997
2023-10-16 04:21:10,066 - INFO - 04530566 12 17.4098 0.9762 0.2985
2023-10-16 04:21:10,066 - INFO - Overall 22.2098 0.9776 0.2382
2023-10-16 04:21:10,066 - INFO - Epoch 5 22.2098 0.9776 0.2382
2023-10-16 04:21:10,066 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 04:21:10,066 - INFO - 'epoch: ', 6, 'optimizer: ', 0.001
2023-10-16 05:00:43,427 - INFO - [Epoch 6/400] EpochTime = 2373.361 (s) Losses = ['48.0799', '24.5652', '22.6182']
2023-10-16 05:01:36,117 - INFO - ============================ TEST RESULTS ============================
2023-10-16 05:01:36,117 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 05:01:36,117 - INFO - 02691156 13 15.9628 0.9753 0.4295
2023-10-16 05:01:36,117 - INFO - 02933112 12 29.7972 0.9794 0.1073
2023-10-16 05:01:36,117 - INFO - 02958343 13 22.4433 0.9783 0.1341
2023-10-16 05:01:36,117 - INFO - 03001627 12 24.8605 0.9795 0.1770
2023-10-16 05:01:36,117 - INFO - 03636649 13 22.1154 0.9789 0.3588
2023-10-16 05:01:36,117 - INFO - 04256520 12 27.2313 0.9787 0.1317
2023-10-16 05:01:36,117 - INFO - 04379243 13 21.7234 0.9787 0.1848
2023-10-16 05:01:36,117 - INFO - 04530566 12 17.6590 0.9768 0.2859
2023-10-16 05:01:36,117 - INFO - Overall 22.6376 0.9782 0.2282
2023-10-16 05:01:36,117 - INFO - Epoch 6 22.6376 0.9782 0.2282
2023-10-16 05:01:36,117 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 05:01:36,117 - INFO - 'epoch: ', 7, 'optimizer: ', 0.001
2023-10-16 05:41:14,628 - INFO - [Epoch 7/400] EpochTime = 2378.511 (s) Losses = ['59.4867', '25.3906', '23.3751']
2023-10-16 05:42:07,531 - INFO - ============================ TEST RESULTS ============================
2023-10-16 05:42:07,531 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 05:42:07,531 - INFO - 02691156 13 19.1921 0.9720 0.4686
2023-10-16 05:42:07,531 - INFO - 02933112 12 45.4983 0.9759 0.1426
2023-10-16 05:42:07,531 - INFO - 02958343 13 39.2203 0.9760 0.1704
2023-10-16 05:42:07,531 - INFO - 03001627 12 27.5350 0.9748 0.2490
2023-10-16 05:42:07,531 - INFO - 03636649 13 21.0602 0.9727 0.4666
2023-10-16 05:42:07,531 - INFO - 04256520 12 44.8739 0.9760 0.1736
2023-10-16 05:42:07,531 - INFO - 04379243 13 21.7614 0.9744 0.2468
2023-10-16 05:42:07,531 - INFO - 04530566 12 23.6168 0.9733 0.3510
2023-10-16 05:42:07,531 - INFO - Overall 30.1433 0.9744 0.2858
2023-10-16 05:42:07,531 - INFO - Epoch 7 30.1433 0.9744 0.2858
2023-10-16 05:42:07,531 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 05:42:07,531 - INFO - 'epoch: ', 8, 'optimizer: ', 0.001
2023-10-16 06:21:44,065 - INFO - [Epoch 8/400] EpochTime = 2376.533 (s) Losses = ['62.6372', '26.2596', '24.1856']
2023-10-16 06:22:36,760 - INFO - ============================ TEST RESULTS ============================
2023-10-16 06:22:36,760 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 06:22:36,760 - INFO - 02691156 13 17.1301 0.9754 0.4338
2023-10-16 06:22:36,760 - INFO - 02933112 12 30.7366 0.9786 0.1168
2023-10-16 06:22:36,760 - INFO - 02958343 13 24.6502 0.9780 0.1437
2023-10-16 06:22:36,760 - INFO - 03001627 12 24.7679 0.9782 0.2011
2023-10-16 06:22:36,760 - INFO - 03636649 13 22.4678 0.9775 0.3907
2023-10-16 06:22:36,760 - INFO - 04256520 12 28.2933 0.9778 0.1457
2023-10-16 06:22:36,760 - INFO - 04379243 13 21.2204 0.9775 0.2030
2023-10-16 06:22:36,760 - INFO - 04530566 12 18.0671 0.9759 0.3071
2023-10-16 06:22:36,760 - INFO - Overall 23.3347 0.9773 0.2447
2023-10-16 06:22:36,760 - INFO - Epoch 8 23.3347 0.9773 0.2447
2023-10-16 06:22:36,760 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 06:22:36,760 - INFO - 'epoch: ', 9, 'optimizer: ', 0.001
2023-10-16 07:02:13,236 - INFO - [Epoch 9/400] EpochTime = 2376.476 (s) Losses = ['50.3192', '24.6044', '22.6177']
2023-10-16 07:03:05,981 - INFO - ============================ TEST RESULTS ============================
2023-10-16 07:03:05,981 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 07:03:05,981 - INFO - 02691156 13 17.2960 0.9755 0.4323
2023-10-16 07:03:05,981 - INFO - 02933112 12 30.2031 0.9782 0.1174
2023-10-16 07:03:05,981 - INFO - 02958343 13 24.6921 0.9783 0.1409
2023-10-16 07:03:05,981 - INFO - 03001627 12 24.2498 0.9785 0.1930
2023-10-16 07:03:05,981 - INFO - 03636649 13 20.9219 0.9775 0.3840
2023-10-16 07:03:05,981 - INFO - 04256520 12 28.4580 0.9782 0.1404
2023-10-16 07:03:05,981 - INFO - 04379243 13 21.0785 0.9778 0.1979
2023-10-16 07:03:05,981 - INFO - 04530566 12 18.1918 0.9763 0.2991
2023-10-16 07:03:05,981 - INFO - Overall 23.0508 0.9775 0.2402
2023-10-16 07:03:05,981 - INFO - Epoch 9 23.0508 0.9775 0.2402
2023-10-16 07:03:05,981 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 07:03:05,981 - INFO - 'epoch: ', 10, 'optimizer: ', 0.001
2023-10-16 07:42:39,156 - INFO - [Epoch 10/400] EpochTime = 2373.176 (s) Losses = ['49.9786', '24.7791', '22.8088']
2023-10-16 07:43:31,821 - INFO - ============================ TEST RESULTS ============================
2023-10-16 07:43:31,821 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 07:43:31,821 - INFO - 02691156 13 16.1790 0.9751 0.4319
2023-10-16 07:43:31,821 - INFO - 02933112 12 29.5857 0.9783 0.1150
2023-10-16 07:43:31,821 - INFO - 02958343 13 23.4589 0.9779 0.1413
2023-10-16 07:43:31,821 - INFO - 03001627 12 24.6318 0.9789 0.1869
2023-10-16 07:43:31,821 - INFO - 03636649 13 21.5646 0.9778 0.3769
2023-10-16 07:43:31,821 - INFO - 04256520 12 28.1176 0.9785 0.1371
2023-10-16 07:43:31,821 - INFO - 04379243 13 21.8681 0.9781 0.1885
2023-10-16 07:43:31,821 - INFO - 04530566 12 17.9020 0.9763 0.2955
2023-10-16 07:43:31,821 - INFO - Overall 22.8276 0.9776 0.2361
2023-10-16 07:43:31,821 - INFO - Epoch 10 22.8276 0.9776 0.2361
2023-10-16 07:43:31,821 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 07:43:31,821 - INFO - 'epoch: ', 11, 'optimizer: ', 0.001
2023-10-16 08:23:06,061 - INFO - [Epoch 11/400] EpochTime = 2374.239 (s) Losses = ['48.8284', '24.2318', '22.2491']
2023-10-16 08:23:58,873 - INFO - ============================ TEST RESULTS ============================
2023-10-16 08:23:58,873 - INFO - Taxonomy #Sample CD DCD F1
2023-10-16 08:23:58,873 - INFO - 02691156 13 16.9094 0.9752 0.4345
2023-10-16 08:23:58,873 - INFO - 02933112 12 29.8556 0.9785 0.1145
2023-10-16 08:23:58,873 - INFO - 02958343 13 24.1074 0.9783 0.1389
2023-10-16 08:23:58,873 - INFO - 03001627 12 24.4389 0.9792 0.1858
2023-10-16 08:23:58,873 - INFO - 03636649 13 22.1557 0.9785 0.3676
2023-10-16 08:23:58,873 - INFO - 04256520 12 28.3786 0.9787 0.1378
2023-10-16 08:23:58,873 - INFO - 04379243 13 24.9475 0.9799 0.1859
2023-10-16 08:23:58,873 - INFO - 04530566 12 18.6872 0.9765 0.2950
2023-10-16 08:23:58,873 - INFO - Overall 23.6188 0.9781 0.2345
2023-10-16 08:23:58,873 - INFO - Epoch 11 23.6188 0.9781 0.2345
2023-10-16 08:23:58,873 - INFO - Best Performance: Epoch 1 -- CD 16.6935
2023-10-16 08:23:58,873 - INFO - 'epoch: ', 12, 'optimizer: ', 0.001
Hi,
I am trying to run training using PCN dataset, I have got this following error.
from torchvision.models.utils import load_state_dict_from_url
ModuleNotFoundError: No module named 'torchvision.models.utils'
Hello,
I followed the instructions for creating a conda environment with CUFDA 11.6 python version 3.9 and pytorch version 1.13 but when I execute this command at the beginning : cd pointnet2_ops_lib
python setup.py install
It gives me the following error : File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/setuptools/command/build_ext.py", line 84, in run
_build_ext.run(self)
File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/setuptools/_distutils/command/build_ext.py", line 345, in run
self.build_extensions()
File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 499, in build_extensions
_check_cuda_version(compiler_name, compiler_version)
File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/torch/utils/cpp_extension.py", line 382, in _check_cuda_version
torch_cuda_version = packaging.version.parse(torch.version.cuda)
File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/pkg_resources/_vendor/packaging/version.py", line 52, in parse
return Version(version)
File "/home/safebot/anaconda3/envs/svdformer/lib/python3.9/site-packages/pkg_resources/_vendor/packaging/version.py", line 196, in init
match = self._regex.search(version)
TypeError: expected string or bytes-like object
could you please help me and tell me where do you think the issue is? I think it is version.py but that is just one string variable when I checked inside .
kind regards
Nima
Hi authors, thanks for your great work,
I try to reproduce your work and when I tried to do train it, I found it is very slow to run one epoch, so, I want to know what devices do you use and how long time for you to train one epoch and the whole experiment. Thanks very much
May I ask how we should use our own dataset and perform complementary experiments with this amazing pre-trained weights of yours? We would like to get the result of inputting only the mutilated point cloud and outputting the complete point cloud
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