Warning during spIsoNet anisotropy correction
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Dong-Hua Chen
Apr 12, 2024, 8:02:53 PM4/12/24
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Hi Yun-Tao,
I did the anisotropy correction for the test maps (emd_8731) according to your tutorial and got the warnings as follows.
When I compared the corrected half maps to the original half maps in Chimera, I could not see much improvement. Is the warning causing the issue (non-improvement)?
Best regards,
Dong-Hua
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spisonet.py reconstruct emd_8731_half_map_1.mrc emd_8731_half_map_2.mrc --aniso_file FSC3D.mrc --mask emd_8731_msk_1.mrc --limit_res 3.5 --epochs 30 --alpha 1 --beta 0.5 --output_dir isonet_maps --gpuID 0 --acc_batches 2
04-12 10:54:31, INFO voxel_size 1.309999942779541
04-12 10:54:31, INFO spIsoNet correction until resolution 3.5A!
Information beyond 3.5A remains unchanged
04-12 10:54:53, INFO Start preparing subvolumes!
04-12 10:55:29, INFO Done preparing subvolumes!
04-12 10:55:29, INFO Start training!
04-12 10:55:38, INFO Port number: 42105 learning rate 0.0003 ['isonet_maps/emd_8731_half_map_1_data', 'isonet_maps/emd_8731_half_map_2_data'] 0%| | 0/250 [00:00<?, ?batch/s][rank0]:[2024-04-12 10:55:50,625] [0/0] torch._dynamo.variables.torch: [WARNING] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored [rank0]:[2024-04-12 10:56:12,778] [0/1] torch._dynamo.variables.torch: [WARNING] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored 100%|██████████████████████████████████████████████| 250/250 [04:38<00:00, 1.12s/batch, Loss=0.452] Epoch [1/30], Train Loss: 0.5041 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.466] Epoch [2/30], Train Loss: 0.4434 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.442] Epoch [3/30], Train Loss: 0.4304 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.421] Epoch [4/30], Train Loss: 0.4219 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.388] Epoch [5/30], Train Loss: 0.4125 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.404] Epoch [6/30], Train Loss: 0.3997 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.432] Epoch [7/30], Train Loss: 0.3877 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.415] Epoch [8/30], Train Loss: 0.3798 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.391] Epoch [9/30], Train Loss: 0.3740 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.411] Epoch [10/30], Train Loss: 0.3705 100%|███████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.36] Epoch [11/30], Train Loss: 0.3674 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.398] Epoch [12/30], Train Loss: 0.3636 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.393] Epoch [13/30], Train Loss: 0.3621 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.317] Epoch [14/30], Train Loss: 0.3612 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.353] Epoch [15/30], Train Loss: 0.3586 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.338] Epoch [16/30], Train Loss: 0.3565 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.385] Epoch [17/30], Train Loss: 0.3558 100%|███████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.33] Epoch [18/30], Train Loss: 0.3551 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.359] Epoch [19/30], Train Loss: 0.3544 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.311] Epoch [20/30], Train Loss: 0.3538 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.358] Epoch [21/30], Train Loss: 0.3532 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.355] Epoch [22/30], Train Loss: 0.3515 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.358] Epoch [23/30], Train Loss: 0.3504 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.362] Epoch [24/30], Train Loss: 0.3503 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.352] Epoch [25/30], Train Loss: 0.3503 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.403] Epoch [26/30], Train Loss: 0.3491 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.387] Epoch [27/30], Train Loss: 0.3478 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.356] Epoch [28/30], Train Loss: 0.3480 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.354] Epoch [29/30], Train Loss: 0.3474 100%|██████████████████████████████████████████████| 250/250 [03:57<00:00, 1.05batch/s, Loss=0.269] Epoch [30/30], Train Loss: 0.3473 04-12 12:55:36, INFO Start predicting! data_shape torch.Size([125, 1, 80, 80, 80]) 100%|█████████████████████████████████████████████████████████████| 125/125 [00:06<00:00, 17.87it/s] size restored (334, 334, 334) data_shape torch.Size([125, 1, 80, 80, 80]) 100%|█████████████████████████████████████████████████████████████| 125/125 [00:06<00:00, 19.54it/s] size restored (334, 334, 334)
04-12 12:55:58, INFO Done predicting
04-12 12:55:58, INFO combining
04-12 12:55:58, INFO voxel_size 1.309999942779541
04-12 12:56:46, INFO voxel_size 1.309999942779541
04-12 12:57:34, INFO Finished
Yuntao Liu
Apr 14, 2024, 4:01:42 PM4/14/24
to Dong-Hua Chen, spis...@googlegroups.com
Hi Dong-Hua,
This error should not affect the final results.
To compare the before and after correction, I suggest you try visualizing maps after post processing, or use the XYZ view in 3dmod for halfmaps, see the attached image as example.
Yuntao Liu, Postdoc.
California NanoSystem Institute
University of California Los Angeles
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