Add 25-view finetuned checkpoints (pelvis/luna/abdomen/tooth)

README.md

@@ -0,0 +1,84 @@
+---
+license: other
+license_name: mixed-see-readme
+tags:
+  - sparse-view-ct
+  - cbct
+  - ct-reconstruction
+  - medical-imaging
+  - deepsparse
+---
+
+# DeepSparse — 25-view finetuned checkpoints
+
+Finetuned checkpoints of [DeepSparse](https://github.com/xmed-lab/DeepSparse) for **25-view (180°) sparse-view CT reconstruction** on four anatomies. All models start from the official pretrained weights (`pretrain/ep_700.pth` from [HajihajihaJimmy/DeepSparse](https://huggingface.co/HajihajihaJimmy/DeepSparse)) and use the official two-stage finetuning protocol.
+
+The folder layout matches the official repo, so the checkpoints drop into the codebase's `logs/` directory.
+
+## Results (official test splits, 256³, 3D PSNR / SSIM)
+
+| Checkpoint | Train cases | Test cases | PSNR (dB) | SSIM (×10⁻²) |
+|---|---|---|---|---|
+| `pelvis+25v+n250+s2` (PENGWIN) | 60 (all) | 30 | **30.96** ± 2.63 | **90.31** ± 3.20 |
+| `luna+25v+n250+s2` (LUNA16) | 250 / 738 | 100 | **32.47** ± 1.05 | **92.33** ± 1.83 |
+| `abdomen+25v+n250+s2` (PANORAMA) | 250 / 1244 | 600 | **29.79** ± 2.11 | **89.52** ± 3.21 |
+| `tooth+25v+n250+s2` (ToothFairy) | 250 / 343 | 75 | **34.03** ± 1.15 | **94.29** ± 1.53 |
+
+Mean ± std over test cases, from the official `code/evaluate.py`. Per-case results are in each folder's `results_1.0x.csv`.
+
+For reference, paper Table III (10 views, full training sets): pelvis 29.03 / 90.27, LUNA16 31.86 / 91.41, abdomen 29.42 / 88.36, tooth 31.79 / 92.50. On the full LUNA16 test set, the official 10-view checkpoint gives 31.79 / 91.46 on our preprocessed data.
+
+## Setup
+
+- Code: xmed-lab/DeepSparse @ `a055aba3bcb5732a68f89cc0bf3ee6fbfbb1b1e4`
+- Views: 25 input views, uniformly over 180°, taken from a 300-view projection cache
+- Stage 1: `--num_views 30 --vq_w 0.1`, resumed from `pretrain/ep_700.pth`, 400 epochs
+- Stage 2: `--num_views 30 --min_views 25 --random_views --vq_w 1.0 --safely_load --freeze_ft`, resumed from stage-1 `ep_400.pth`, 400 epochs
+- Batch size 2, lr 1e-4, weight decay 1e-3, no LR schedule, 1 GPU per job
+- Environment: PyTorch 2.7.1 + CUDA 12.8, TIGRE 3.1.3
+
+## Differences from the paper protocol
+
+1. **Training set size**: at most 250 training cases per dataset (the paper uses the full training sets). Test splits are the official ones. The exact case lists are in `splits/<DATASET>/meta_info.json`.
+2. **Dense views**: `num_views=30` during training, so stage 2 has a teacher/student view ratio of 1.2× (official: 24 dense views vs 6/8/10).
+3. **Environment**: newer PyTorch and TIGRE than the official (PyTorch 1.13, TIGRE 2.3), needed for Blackwell GPUs.
+4. **ToothFairy config**: the official `meta_info.json` references an unreleased `config+new.yaml` / `processed+new/`. We use the repo's public `config.yaml`. The official tooth 10-view checkpoint reproduces the paper numbers with this config (31.89 / 92.74).
+
+## Files
+
+```
+<dataset>+25v+n250+s2/
+  ep_400.pth          final stage-2 checkpoint (use this)
+  config.yaml         config saved by train.py (note: min_views stays 10 here)
+  results_1.0x.csv    per-case test PSNR/SSIM + average
+  train.log           stage-2 training log (args + val curve)
+  train_s1.log        stage-1 training log
+configs/
+  finetune_s1_n250.yaml, finetune_s2_n250.yaml   training configs (root_dir ./data_n250)
+  eval_25v_n250.yaml                             evaluation config (min_views: 25)
+splits/<DATASET>/meta_info.json                  train/eval/test case lists used
+```
+
+## Usage
+
+```bash
+# inside a DeepSparse checkout with data prepared as in the official README
+hf download Potestates/DeepSparse-25v --local-dir ./logs
+cp logs/configs/eval_25v_n250.yaml configs/generated/   # set root_dir to your data root
+
+python code/evaluate.py --name luna+25v+n250+s2 --epoch 400 --dst_name luna \
+  --split test --num_views 25 --cfg_path configs/generated/eval_25v_n250.yaml \
+  --out_res_scale 1.0
+```
+
+Evaluation must use `min_views: 25`. The `config.yaml` saved inside each checkpoint folder keeps `min_views: 10`, so use `configs/eval_25v_n250.yaml` instead.
+
+## License
+
+The DeepSparse code is MIT. These weights are derived from datasets with their own terms. Check each dataset's license before use:
+
+- PANORAMA (abdomen): CC BY-NC 4.0 (**non-commercial**)
+- PENGWIN (pelvis): CC BY 4.0
+- LUNA16 (lung), ToothFairy (tooth): see the original dataset terms
+
+Please cite the DeepSparse paper and the datasets if you use these checkpoints.

abdomen+25v+n250+s2/config.yaml

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+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

abdomen+25v+n250+s2/ep_400.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28d0e14f0c4b5698aedf62a01e277af4bfb93052bd1f7950d61262ba51f4298f
+size 28985755

abdomen+25v+n250+s2/results_1.0x.csv

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abdomen+25v+n250+s2/train.log

@@ -0,0 +1,60 @@
+Namespace(name='abdomen+25v+n250+s2', dst_name='abdomen', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s2_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/abdomen+25v+n250+s1/ep_400.pth', vq_w=1.0, safely_load=True, freeze_ft=True, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'finetune': True, 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: abdomen, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: abdomen, split: eval, len: 50.
+min_views: 25, max_views: 30
+resume model from logs/abdomen+25v+n250+s1/ep_400.pth
+--- freeze encoder
+--- freeze codebook
+epoch: 0, loss: 0.4888, loss_task: 0.00216, loss_vq: 0.4866
+ --- epoch 0, abdomen, psnr: 29.37, ssim: 0.9015
+epoch: 10, loss: 0.1769, loss_task: 0.0008547, loss_vq: 0.1761
+epoch: 20, loss: 0.1501, loss_task: 0.0007699, loss_vq: 0.1494
+epoch: 30, loss: 0.1469, loss_task: 0.0007473, loss_vq: 0.1461
+epoch: 40, loss: 0.1449, loss_task: 0.0007407, loss_vq: 0.1442
+epoch: 50, loss: 0.1431, loss_task: 0.0007612, loss_vq: 0.1424
+ --- epoch 50, abdomen, psnr: 30.61, ssim: 0.9284
+epoch: 60, loss: 0.1417, loss_task: 0.0007866, loss_vq: 0.1409
+epoch: 70, loss: 0.1408, loss_task: 0.000725, loss_vq: 0.1401
+epoch: 80, loss: 0.14, loss_task: 0.0007304, loss_vq: 0.1392
+epoch: 90, loss: 0.1398, loss_task: 0.000723, loss_vq: 0.1391
+epoch: 100, loss: 0.1395, loss_task: 0.0007039, loss_vq: 0.1388
+ --- epoch 100, abdomen, psnr: 30.17, ssim: 0.9213
+epoch: 110, loss: 0.139, loss_task: 0.000685, loss_vq: 0.1383
+epoch: 120, loss: 0.1387, loss_task: 0.0007344, loss_vq: 0.138
+epoch: 130, loss: 0.1384, loss_task: 0.000698, loss_vq: 0.1377
+epoch: 140, loss: 0.1381, loss_task: 0.00068, loss_vq: 0.1375
+epoch: 150, loss: 0.138, loss_task: 0.0006814, loss_vq: 0.1373
+ --- epoch 150, abdomen, psnr: 29.99, ssim: 0.9276
+epoch: 160, loss: 0.1378, loss_task: 0.0007044, loss_vq: 0.1371
+epoch: 170, loss: 0.1374, loss_task: 0.0006788, loss_vq: 0.1367
+epoch: 180, loss: 0.1371, loss_task: 0.0006756, loss_vq: 0.1364
+epoch: 190, loss: 0.1369, loss_task: 0.0006731, loss_vq: 0.1362
+epoch: 200, loss: 0.1367, loss_task: 0.0007693, loss_vq: 0.136
+ --- epoch 200, abdomen, psnr: 30.53, ssim: 0.9238
+epoch: 210, loss: 0.1365, loss_task: 0.0006767, loss_vq: 0.1358
+epoch: 220, loss: 0.1361, loss_task: 0.0006632, loss_vq: 0.1355
+epoch: 230, loss: 0.1359, loss_task: 0.0006621, loss_vq: 0.1352
+epoch: 240, loss: 0.1356, loss_task: 0.0006773, loss_vq: 0.1349
+epoch: 250, loss: 0.1355, loss_task: 0.0006623, loss_vq: 0.1348
+ --- epoch 250, abdomen, psnr: 30.57, ssim: 0.9288
+epoch: 260, loss: 0.1352, loss_task: 0.0006479, loss_vq: 0.1345
+epoch: 270, loss: 0.135, loss_task: 0.0006941, loss_vq: 0.1343
+epoch: 280, loss: 0.1347, loss_task: 0.0006482, loss_vq: 0.134
+epoch: 290, loss: 0.1345, loss_task: 0.0006659, loss_vq: 0.1338
+epoch: 300, loss: 0.1342, loss_task: 0.0006717, loss_vq: 0.1335
+ --- epoch 300, abdomen, psnr: 30.03, ssim: 0.9211
+epoch: 310, loss: 0.134, loss_task: 0.0006485, loss_vq: 0.1333
+epoch: 320, loss: 0.1337, loss_task: 0.0006474, loss_vq: 0.1331
+epoch: 330, loss: 0.1334, loss_task: 0.0006311, loss_vq: 0.1328
+epoch: 340, loss: 0.1332, loss_task: 0.0006224, loss_vq: 0.1326
+epoch: 350, loss: 0.1328, loss_task: 0.0006499, loss_vq: 0.1321
+ --- epoch 350, abdomen, psnr: 30.47, ssim: 0.9274
+epoch: 360, loss: 0.1326, loss_task: 0.0005924, loss_vq: 0.132
+epoch: 370, loss: 0.1324, loss_task: 0.0006489, loss_vq: 0.1318
+epoch: 380, loss: 0.1323, loss_task: 0.0006438, loss_vq: 0.1317
+epoch: 390, loss: 0.132, loss_task: 0.0006261, loss_vq: 0.1314
+epoch: 400, loss: 0.1317, loss_task: 0.0006095, loss_vq: 0.1311
+ --- epoch 400, abdomen, psnr: 30.5, ssim: 0.9301
+[rank0]:[W913 14:35:23.963307382 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

abdomen+25v+n250+s2/train_s1.log

@@ -0,0 +1,58 @@
+Namespace(name='abdomen+25v+n250+s1', dst_name='abdomen', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s1_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/pretrain/ep_700.pth', vq_w=0.1, safely_load=False, freeze_ft=False, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: abdomen, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: abdomen, split: eval, len: 50.
+min_views: 25, max_views: 30
+resume model from logs/pretrain/ep_700.pth
+epoch: 0, loss: 0.004298, loss_task: 0.003601, loss_vq: 0.006965
+ --- epoch 0, abdomen, psnr: 26.54, ssim: 0.543
+epoch: 10, loss: 0.001573, loss_task: 0.001523, loss_vq: 0.0004987
+epoch: 20, loss: 0.001398, loss_task: 0.001357, loss_vq: 0.0004056
+epoch: 30, loss: 0.001362, loss_task: 0.001326, loss_vq: 0.0003628
+epoch: 40, loss: 0.00127, loss_task: 0.001235, loss_vq: 0.0003487
+epoch: 50, loss: 0.001224, loss_task: 0.001194, loss_vq: 0.0002992
+ --- epoch 50, abdomen, psnr: 29.98, ssim: 0.8857
+epoch: 60, loss: 0.001178, loss_task: 0.00115, loss_vq: 0.0002795
+epoch: 70, loss: 0.001125, loss_task: 0.001098, loss_vq: 0.0002687
+epoch: 80, loss: 0.001077, loss_task: 0.001051, loss_vq: 0.0002536
+epoch: 90, loss: 0.001064, loss_task: 0.00104, loss_vq: 0.0002352
+epoch: 100, loss: 0.001098, loss_task: 0.001075, loss_vq: 0.0002222
+ --- epoch 100, abdomen, psnr: 30.27, ssim: 0.9123
+epoch: 110, loss: 0.001023, loss_task: 0.001002, loss_vq: 0.0002109
+epoch: 120, loss: 0.001051, loss_task: 0.001029, loss_vq: 0.0002172
+epoch: 130, loss: 0.001017, loss_task: 0.0009975, loss_vq: 0.000198
+epoch: 140, loss: 0.0009919, loss_task: 0.0009714, loss_vq: 0.0002054
+epoch: 150, loss: 0.000984, loss_task: 0.0009652, loss_vq: 0.0001878
+ --- epoch 150, abdomen, psnr: 30.4, ssim: 0.9244
+epoch: 160, loss: 0.0009417, loss_task: 0.0009222, loss_vq: 0.0001948
+epoch: 170, loss: 0.0009386, loss_task: 0.0009211, loss_vq: 0.0001753
+epoch: 180, loss: 0.0009206, loss_task: 0.0009025, loss_vq: 0.0001808
+epoch: 190, loss: 0.0008865, loss_task: 0.0008689, loss_vq: 0.0001758
+epoch: 200, loss: 0.0009242, loss_task: 0.000907, loss_vq: 0.0001729
+ --- epoch 200, abdomen, psnr: 30.54, ssim: 0.9235
+epoch: 210, loss: 0.0009007, loss_task: 0.0008844, loss_vq: 0.0001625
+epoch: 220, loss: 0.0008722, loss_task: 0.0008559, loss_vq: 0.0001636
+epoch: 230, loss: 0.0009252, loss_task: 0.0009086, loss_vq: 0.0001659
+epoch: 240, loss: 0.0008959, loss_task: 0.0008803, loss_vq: 0.0001565
+epoch: 250, loss: 0.0008645, loss_task: 0.0008481, loss_vq: 0.0001646
+ --- epoch 250, abdomen, psnr: 30.31, ssim: 0.9261
+epoch: 260, loss: 0.0008412, loss_task: 0.0008268, loss_vq: 0.0001442
+epoch: 270, loss: 0.0008401, loss_task: 0.0008259, loss_vq: 0.0001416
+epoch: 280, loss: 0.0008309, loss_task: 0.0008158, loss_vq: 0.0001509
+epoch: 290, loss: 0.0008228, loss_task: 0.000809, loss_vq: 0.0001386
+epoch: 300, loss: 0.0008686, loss_task: 0.0008544, loss_vq: 0.0001413
+ --- epoch 300, abdomen, psnr: 30.16, ssim: 0.9232
+epoch: 310, loss: 0.0008029, loss_task: 0.0007894, loss_vq: 0.0001347
+epoch: 320, loss: 0.0008298, loss_task: 0.0008152, loss_vq: 0.000146
+epoch: 330, loss: 0.000836, loss_task: 0.0008216, loss_vq: 0.000144
+epoch: 340, loss: 0.000848, loss_task: 0.0008336, loss_vq: 0.0001443
+epoch: 350, loss: 0.0008357, loss_task: 0.0008217, loss_vq: 0.0001407
+ --- epoch 350, abdomen, psnr: 30.32, ssim: 0.9156
+epoch: 360, loss: 0.0008411, loss_task: 0.0008262, loss_vq: 0.0001488
+epoch: 370, loss: 0.0007951, loss_task: 0.0007826, loss_vq: 0.0001256
+epoch: 380, loss: 0.0008327, loss_task: 0.000818, loss_vq: 0.0001478
+epoch: 390, loss: 0.0007824, loss_task: 0.0007694, loss_vq: 0.0001304
+epoch: 400, loss: 0.0007915, loss_task: 0.0007781, loss_vq: 0.0001334
+ --- epoch 400, abdomen, psnr: 30.54, ssim: 0.9289
+[rank0]:[W913 09:40:22.797893007 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

configs/eval_25v_n250.yaml

@@ -0,0 +1,23 @@
+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 25

configs/finetune_s1_n250.yaml

@@ -0,0 +1,22 @@
+model:
+  name: model_v7.Model_mv
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

configs/finetune_s2_n250.yaml

@@ -0,0 +1,23 @@
+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

luna+25v+n250+s2/config.yaml

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+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
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+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

luna+25v+n250+s2/ep_400.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bbb1e995c30b8ce254c85410ffc88e5247001ebe999a851a5f6cd04528439e6
+size 28985755

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+luna,subset9_1.3.6.1.4.1.14519.5.2.1.6279.6001.946129570505893110165820050204,32.44612276144522,0.9310447556287976
+luna,average,32.470964585057125,0.9232633265310448

luna+25v+n250+s2/train.log

@@ -0,0 +1,60 @@
+Namespace(name='luna+25v+n250+s2', dst_name='luna', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s2_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/luna+25v+n250+s1/ep_400.pth', vq_w=1.0, safely_load=True, freeze_ft=True, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'finetune': True, 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: luna, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: luna, split: eval, len: 50.
+min_views: 25, max_views: 30
+resume model from logs/luna+25v+n250+s1/ep_400.pth
+--- freeze encoder
+--- freeze codebook
+epoch: 0, loss: 0.488, loss_task: 0.00151, loss_vq: 0.4865
+ --- epoch 0, luna, psnr: 30.53, ssim: 0.8782
+epoch: 10, loss: 0.2025, loss_task: 0.0008665, loss_vq: 0.2016
+epoch: 20, loss: 0.1607, loss_task: 0.000817, loss_vq: 0.1599
+epoch: 30, loss: 0.1539, loss_task: 0.0008007, loss_vq: 0.1531
+epoch: 40, loss: 0.1508, loss_task: 0.0008284, loss_vq: 0.15
+epoch: 50, loss: 0.1489, loss_task: 0.0007681, loss_vq: 0.1481
+ --- epoch 50, luna, psnr: 32.31, ssim: 0.9383
+epoch: 60, loss: 0.1478, loss_task: 0.0007631, loss_vq: 0.1471
+epoch: 70, loss: 0.1468, loss_task: 0.0007804, loss_vq: 0.1461
+epoch: 80, loss: 0.1457, loss_task: 0.000769, loss_vq: 0.1449
+epoch: 90, loss: 0.1451, loss_task: 0.000753, loss_vq: 0.1443
+epoch: 100, loss: 0.1447, loss_task: 0.0007372, loss_vq: 0.1439
+ --- epoch 100, luna, psnr: 32.22, ssim: 0.938
+epoch: 110, loss: 0.1442, loss_task: 0.0007473, loss_vq: 0.1435
+epoch: 120, loss: 0.1438, loss_task: 0.0007555, loss_vq: 0.1431
+epoch: 130, loss: 0.1435, loss_task: 0.0007816, loss_vq: 0.1428
+epoch: 140, loss: 0.1432, loss_task: 0.0007255, loss_vq: 0.1425
+epoch: 150, loss: 0.1429, loss_task: 0.0007203, loss_vq: 0.1422
+ --- epoch 150, luna, psnr: 32.3, ssim: 0.9372
+epoch: 160, loss: 0.1425, loss_task: 0.0007413, loss_vq: 0.1417
+epoch: 170, loss: 0.1421, loss_task: 0.0007385, loss_vq: 0.1414
+epoch: 180, loss: 0.1419, loss_task: 0.000716, loss_vq: 0.1412
+epoch: 190, loss: 0.1416, loss_task: 0.0007687, loss_vq: 0.1408
+epoch: 200, loss: 0.1413, loss_task: 0.0007138, loss_vq: 0.1406
+ --- epoch 200, luna, psnr: 32.18, ssim: 0.9286
+epoch: 210, loss: 0.1411, loss_task: 0.0007287, loss_vq: 0.1404
+epoch: 220, loss: 0.1409, loss_task: 0.0007044, loss_vq: 0.1402
+epoch: 230, loss: 0.1407, loss_task: 0.0007113, loss_vq: 0.1399
+epoch: 240, loss: 0.1404, loss_task: 0.000701, loss_vq: 0.1397
+epoch: 250, loss: 0.1403, loss_task: 0.0007386, loss_vq: 0.1395
+ --- epoch 250, luna, psnr: 32.24, ssim: 0.9338
+epoch: 260, loss: 0.1399, loss_task: 0.0007016, loss_vq: 0.1392
+epoch: 270, loss: 0.1398, loss_task: 0.000708, loss_vq: 0.1391
+epoch: 280, loss: 0.1398, loss_task: 0.0007151, loss_vq: 0.1391
+epoch: 290, loss: 0.1395, loss_task: 0.0007163, loss_vq: 0.1388
+epoch: 300, loss: 0.1392, loss_task: 0.0007012, loss_vq: 0.1385
+ --- epoch 300, luna, psnr: 31.59, ssim: 0.9301
+epoch: 310, loss: 0.1389, loss_task: 0.0007022, loss_vq: 0.1382
+epoch: 320, loss: 0.1386, loss_task: 0.0007009, loss_vq: 0.1379
+epoch: 330, loss: 0.1386, loss_task: 0.0006943, loss_vq: 0.1379
+epoch: 340, loss: 0.1381, loss_task: 0.0007066, loss_vq: 0.1374
+epoch: 350, loss: 0.138, loss_task: 0.000706, loss_vq: 0.1373
+ --- epoch 350, luna, psnr: 32.37, ssim: 0.9387
+epoch: 360, loss: 0.138, loss_task: 0.0007081, loss_vq: 0.1373
+epoch: 370, loss: 0.1377, loss_task: 0.0007075, loss_vq: 0.137
+epoch: 380, loss: 0.1373, loss_task: 0.0006848, loss_vq: 0.1367
+epoch: 390, loss: 0.1373, loss_task: 0.0006946, loss_vq: 0.1366
+epoch: 400, loss: 0.1369, loss_task: 0.0007673, loss_vq: 0.1362
+ --- epoch 400, luna, psnr: 32.24, ssim: 0.9387
+[rank0]:[W913 12:07:11.565673854 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

luna+25v+n250+s2/train_s1.log

@@ -0,0 +1,58 @@
+Namespace(name='luna+25v+n250+s1', dst_name='luna', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s1_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/pretrain/ep_700.pth', vq_w=0.1, safely_load=False, freeze_ft=False, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: luna, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: luna, split: eval, len: 50.
+min_views: 25, max_views: 30
+resume model from logs/pretrain/ep_700.pth
+epoch: 0, loss: 0.006718, loss_task: 0.005936, loss_vq: 0.007823
+ --- epoch 0, luna, psnr: 24.04, ssim: 0.4495
+epoch: 10, loss: 0.001827, loss_task: 0.00177, loss_vq: 0.00057
+epoch: 20, loss: 0.001605, loss_task: 0.001565, loss_vq: 0.0004047
+epoch: 30, loss: 0.001443, loss_task: 0.00141, loss_vq: 0.0003339
+epoch: 40, loss: 0.001456, loss_task: 0.001425, loss_vq: 0.0003159
+epoch: 50, loss: 0.001313, loss_task: 0.001285, loss_vq: 0.0002781
+ --- epoch 50, luna, psnr: 31.56, ssim: 0.9263
+epoch: 60, loss: 0.001304, loss_task: 0.001278, loss_vq: 0.0002557
+epoch: 70, loss: 0.001219, loss_task: 0.001194, loss_vq: 0.0002467
+epoch: 80, loss: 0.001161, loss_task: 0.001139, loss_vq: 0.0002194
+epoch: 90, loss: 0.001154, loss_task: 0.001133, loss_vq: 0.0002081
+epoch: 100, loss: 0.00106, loss_task: 0.001041, loss_vq: 0.0001935
+ --- epoch 100, luna, psnr: 31.47, ssim: 0.9204
+epoch: 110, loss: 0.001076, loss_task: 0.001058, loss_vq: 0.0001802
+epoch: 120, loss: 0.001099, loss_task: 0.00108, loss_vq: 0.0001869
+epoch: 130, loss: 0.001123, loss_task: 0.001105, loss_vq: 0.0001786
+epoch: 140, loss: 0.0009779, loss_task: 0.000962, loss_vq: 0.000159
+epoch: 150, loss: 0.0009677, loss_task: 0.000952, loss_vq: 0.0001566
+ --- epoch 150, luna, psnr: 31.88, ssim: 0.9299
+epoch: 160, loss: 0.0009459, loss_task: 0.0009309, loss_vq: 0.0001503
+epoch: 170, loss: 0.0009603, loss_task: 0.0009461, loss_vq: 0.0001418
+epoch: 180, loss: 0.0009517, loss_task: 0.0009381, loss_vq: 0.0001364
+epoch: 190, loss: 0.0009849, loss_task: 0.0009706, loss_vq: 0.0001436
+epoch: 200, loss: 0.0009097, loss_task: 0.0008974, loss_vq: 0.0001235
+ --- epoch 200, luna, psnr: 31.92, ssim: 0.9278
+epoch: 210, loss: 0.0009304, loss_task: 0.0009172, loss_vq: 0.0001315
+epoch: 220, loss: 0.0009728, loss_task: 0.0009597, loss_vq: 0.000131
+epoch: 230, loss: 0.0009355, loss_task: 0.0009225, loss_vq: 0.0001296
+epoch: 240, loss: 0.0009062, loss_task: 0.0008945, loss_vq: 0.0001163
+epoch: 250, loss: 0.0009132, loss_task: 0.0009019, loss_vq: 0.0001132
+ --- epoch 250, luna, psnr: 31.95, ssim: 0.9287
+epoch: 260, loss: 0.0008784, loss_task: 0.0008657, loss_vq: 0.0001265
+epoch: 270, loss: 0.000933, loss_task: 0.000921, loss_vq: 0.0001202
+epoch: 280, loss: 0.0009314, loss_task: 0.0009187, loss_vq: 0.0001262
+epoch: 290, loss: 0.00087, loss_task: 0.0008587, loss_vq: 0.0001138
+epoch: 300, loss: 0.0008842, loss_task: 0.0008701, loss_vq: 0.0001408
+ --- epoch 300, luna, psnr: 31.62, ssim: 0.9331
+epoch: 310, loss: 0.0008549, loss_task: 0.0008435, loss_vq: 0.0001144
+epoch: 320, loss: 0.0008552, loss_task: 0.0008444, loss_vq: 0.0001075
+epoch: 330, loss: 0.0008399, loss_task: 0.0008286, loss_vq: 0.0001131
+epoch: 340, loss: 0.0008178, loss_task: 0.0008075, loss_vq: 0.0001025
+epoch: 350, loss: 0.0008665, loss_task: 0.0008562, loss_vq: 0.0001036
+ --- epoch 350, luna, psnr: 31.83, ssim: 0.9288
+epoch: 360, loss: 0.0009235, loss_task: 0.0009132, loss_vq: 0.0001033
+epoch: 370, loss: 0.0008086, loss_task: 0.0007991, loss_vq: 9.512e-05
+epoch: 380, loss: 0.000831, loss_task: 0.0008207, loss_vq: 0.0001033
+epoch: 390, loss: 0.000875, loss_task: 0.0008647, loss_vq: 0.0001035
+epoch: 400, loss: 0.0007951, loss_task: 0.0007865, loss_vq: 8.585e-05
+ --- epoch 400, luna, psnr: 31.94, ssim: 0.9327
+[rank0]:[W913 07:16:34.699923779 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

pelvis+25v+n250+s2/config.yaml

@@ -0,0 +1,23 @@
+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

pelvis+25v+n250+s2/ep_400.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e0216e28a9ce1b6c979678f2db5ec3c74c5b5dffecd68c1b7ce4cb14a39b9a9
+size 28985755

pelvis+25v+n250+s2/results_1.0x.csv

@@ -0,0 +1,32 @@
+dataset,obj_id,psnr,ssim
+pelvis,001,25.045496081219714,0.7923504361775652
+pelvis,004,34.04714675492711,0.9482504134162111
+pelvis,005,28.351214146794284,0.90571736733937
+pelvis,012,29.509418421679626,0.8797000700397386
+pelvis,014,31.01112777712274,0.922852700803232
+pelvis,015,33.95990759785688,0.9252603130755788
+pelvis,018,28.25616364713462,0.8991891316555742
+pelvis,026,32.331043279585735,0.8961844800947578
+pelvis,028,33.463010755253684,0.9333687915765032
+pelvis,029,27.605534757368048,0.8534324394067988
+pelvis,030,31.444485149686106,0.9116868822787971
+pelvis,032,32.564549981231686,0.8988302641959818
+pelvis,036,25.54221372619631,0.8731492959823555
+pelvis,054,29.520482645542142,0.8906752719071785
+pelvis,055,33.67245308372772,0.9324273935016529
+pelvis,058,31.581515002731454,0.8976546129090807
+pelvis,065,27.906675293256242,0.8520991781259369
+pelvis,070,33.86611526924982,0.9160028355629152
+pelvis,072,33.75193655135488,0.9439194019935131
+pelvis,076,32.71828504742202,0.9115107836440333
+pelvis,078,32.29912481288045,0.904380269465015
+pelvis,082,33.00654336909834,0.9280420932813461
+pelvis,085,31.109331959801985,0.9272365873330805
+pelvis,087,32.60991145718799,0.906368403018748
+pelvis,089,28.406994623091876,0.8953903579493953
+pelvis,090,31.898634866959952,0.9318789375083276
+pelvis,094,33.45997261352992,0.9372927468321971
+pelvis,095,32.35384419615532,0.9069751812586142
+pelvis,096,27.006866492253884,0.8911093882332497
+pelvis,098,30.542507086317237,0.8802330017603939
+pelvis,average,30.961416881553923,0.903105634344238

pelvis+25v+n250+s2/train.log

@@ -0,0 +1,60 @@
+Namespace(name='pelvis+25v+n250+s2', dst_name='pelvis', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s2_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/pelvis+25v+n250+s1/ep_400.pth', vq_w=1.0, safely_load=True, freeze_ft=True, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'finetune': True, 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: pelvis, split: train, len: 60.
+min_views: 25, max_views: 30
+CBCT_dataset, name: pelvis, split: eval, len: 10.
+min_views: 25, max_views: 30
+resume model from logs/pelvis+25v+n250+s1/ep_400.pth
+--- freeze encoder
+--- freeze codebook
+epoch: 0, loss: 0.6358, loss_task: 0.001615, loss_vq: 0.6341
+ --- epoch 0, pelvis, psnr: 29.48, ssim: 0.8831
+epoch: 10, loss: 0.3144, loss_task: 0.0006543, loss_vq: 0.3137
+epoch: 20, loss: 0.2627, loss_task: 0.0006603, loss_vq: 0.262
+epoch: 30, loss: 0.2259, loss_task: 0.0005427, loss_vq: 0.2254
+epoch: 40, loss: 0.2009, loss_task: 0.0004956, loss_vq: 0.2004
+epoch: 50, loss: 0.1851, loss_task: 0.0004979, loss_vq: 0.1847
+ --- epoch 50, pelvis, psnr: 30.98, ssim: 0.8997
+epoch: 60, loss: 0.178, loss_task: 0.0004713, loss_vq: 0.1776
+epoch: 70, loss: 0.1731, loss_task: 0.0004709, loss_vq: 0.1726
+epoch: 80, loss: 0.1698, loss_task: 0.0004772, loss_vq: 0.1693
+epoch: 90, loss: 0.1677, loss_task: 0.0004767, loss_vq: 0.1672
+epoch: 100, loss: 0.1662, loss_task: 0.0006339, loss_vq: 0.1655
+ --- epoch 100, pelvis, psnr: 31.26, ssim: 0.9244
+epoch: 110, loss: 0.1653, loss_task: 0.000421, loss_vq: 0.1649
+epoch: 120, loss: 0.1641, loss_task: 0.0004905, loss_vq: 0.1636
+epoch: 130, loss: 0.1631, loss_task: 0.0004884, loss_vq: 0.1626
+epoch: 140, loss: 0.1619, loss_task: 0.000414, loss_vq: 0.1615
+epoch: 150, loss: 0.1616, loss_task: 0.0004293, loss_vq: 0.1612
+ --- epoch 150, pelvis, psnr: 31.3, ssim: 0.917
+epoch: 160, loss: 0.1608, loss_task: 0.0004391, loss_vq: 0.1603
+epoch: 170, loss: 0.1603, loss_task: 0.0004872, loss_vq: 0.1598
+epoch: 180, loss: 0.1597, loss_task: 0.0004506, loss_vq: 0.1592
+epoch: 190, loss: 0.16, loss_task: 0.0004225, loss_vq: 0.1596
+epoch: 200, loss: 0.1593, loss_task: 0.0004307, loss_vq: 0.1588
+ --- epoch 200, pelvis, psnr: 31.16, ssim: 0.9041
+epoch: 210, loss: 0.1589, loss_task: 0.0004281, loss_vq: 0.1585
+epoch: 220, loss: 0.1587, loss_task: 0.0004551, loss_vq: 0.1583
+epoch: 230, loss: 0.1585, loss_task: 0.0004966, loss_vq: 0.1581
+epoch: 240, loss: 0.1578, loss_task: 0.0004342, loss_vq: 0.1574
+epoch: 250, loss: 0.1575, loss_task: 0.000399, loss_vq: 0.1571
+ --- epoch 250, pelvis, psnr: 31.21, ssim: 0.905
+epoch: 260, loss: 0.1574, loss_task: 0.0003908, loss_vq: 0.157
+epoch: 270, loss: 0.1569, loss_task: 0.0004115, loss_vq: 0.1564
+epoch: 280, loss: 0.1563, loss_task: 0.0003848, loss_vq: 0.1559
+epoch: 290, loss: 0.1563, loss_task: 0.0004495, loss_vq: 0.1558
+epoch: 300, loss: 0.1562, loss_task: 0.0004843, loss_vq: 0.1557
+ --- epoch 300, pelvis, psnr: 30.17, ssim: 0.8894
+epoch: 310, loss: 0.1559, loss_task: 0.0003821, loss_vq: 0.1555
+epoch: 320, loss: 0.1562, loss_task: 0.000394, loss_vq: 0.1558
+epoch: 330, loss: 0.1557, loss_task: 0.0003824, loss_vq: 0.1554
+epoch: 340, loss: 0.1558, loss_task: 0.0003619, loss_vq: 0.1554
+epoch: 350, loss: 0.1556, loss_task: 0.0004434, loss_vq: 0.1552
+ --- epoch 350, pelvis, psnr: 31.32, ssim: 0.9238
+epoch: 360, loss: 0.1554, loss_task: 0.0004171, loss_vq: 0.155
+epoch: 370, loss: 0.1554, loss_task: 0.0004459, loss_vq: 0.155
+epoch: 380, loss: 0.155, loss_task: 0.0004062, loss_vq: 0.1546
+epoch: 390, loss: 0.1549, loss_task: 0.000387, loss_vq: 0.1545
+epoch: 400, loss: 0.155, loss_task: 0.0003705, loss_vq: 0.1547
+ --- epoch 400, pelvis, psnr: 31.16, ssim: 0.9154
+[rank0]:[W913 03:38:05.013576631 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

pelvis+25v+n250+s2/train_s1.log

@@ -0,0 +1,58 @@
+Namespace(name='pelvis+25v+n250+s1', dst_name='pelvis', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s1_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/pretrain/ep_700.pth', vq_w=0.1, safely_load=False, freeze_ft=False, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: pelvis, split: train, len: 60.
+min_views: 25, max_views: 30
+CBCT_dataset, name: pelvis, split: eval, len: 10.
+min_views: 25, max_views: 30
+resume model from logs/pretrain/ep_700.pth
+epoch: 0, loss: 0.008564, loss_task: 0.007434, loss_vq: 0.0113
+ --- epoch 0, pelvis, psnr: 23.47, ssim: 0.395
+epoch: 10, loss: 0.004064, loss_task: 0.003863, loss_vq: 0.002001
+epoch: 20, loss: 0.002378, loss_task: 0.002268, loss_vq: 0.001105
+epoch: 30, loss: 0.001761, loss_task: 0.001698, loss_vq: 0.0006252
+epoch: 40, loss: 0.001378, loss_task: 0.001327, loss_vq: 0.0005026
+epoch: 50, loss: 0.00123, loss_task: 0.001185, loss_vq: 0.0004416
+ --- epoch 50, pelvis, psnr: 31.0, ssim: 0.9005
+epoch: 60, loss: 0.001127, loss_task: 0.001087, loss_vq: 0.0003961
+epoch: 70, loss: 0.00111, loss_task: 0.001073, loss_vq: 0.0003646
+epoch: 80, loss: 0.000926, loss_task: 0.000891, loss_vq: 0.0003504
+epoch: 90, loss: 0.0009585, loss_task: 0.0009241, loss_vq: 0.000344
+epoch: 100, loss: 0.0009265, loss_task: 0.0008947, loss_vq: 0.0003184
+ --- epoch 100, pelvis, psnr: 31.22, ssim: 0.9073
+epoch: 110, loss: 0.0008351, loss_task: 0.0008046, loss_vq: 0.0003044
+epoch: 120, loss: 0.0008354, loss_task: 0.0008066, loss_vq: 0.0002881
+epoch: 130, loss: 0.0009039, loss_task: 0.000875, loss_vq: 0.0002893
+epoch: 140, loss: 0.0008221, loss_task: 0.0007939, loss_vq: 0.0002813
+epoch: 150, loss: 0.0007547, loss_task: 0.0007303, loss_vq: 0.0002447
+ --- epoch 150, pelvis, psnr: 31.39, ssim: 0.9186
+epoch: 160, loss: 0.0008438, loss_task: 0.0008175, loss_vq: 0.0002631
+epoch: 170, loss: 0.0008005, loss_task: 0.0007758, loss_vq: 0.000247
+epoch: 180, loss: 0.0007178, loss_task: 0.0006948, loss_vq: 0.0002303
+epoch: 190, loss: 0.0008337, loss_task: 0.0008097, loss_vq: 0.0002396
+epoch: 200, loss: 0.0007516, loss_task: 0.0007301, loss_vq: 0.0002145
+ --- epoch 200, pelvis, psnr: 31.53, ssim: 0.9187
+epoch: 210, loss: 0.000693, loss_task: 0.0006719, loss_vq: 0.0002115
+epoch: 220, loss: 0.0006998, loss_task: 0.0006785, loss_vq: 0.0002131
+epoch: 230, loss: 0.000673, loss_task: 0.0006533, loss_vq: 0.0001968
+epoch: 240, loss: 0.0006598, loss_task: 0.0006402, loss_vq: 0.0001958
+epoch: 250, loss: 0.0007272, loss_task: 0.0007089, loss_vq: 0.0001834
+ --- epoch 250, pelvis, psnr: 30.6, ssim: 0.897
+epoch: 260, loss: 0.0006531, loss_task: 0.0006357, loss_vq: 0.0001739
+epoch: 270, loss: 0.0006214, loss_task: 0.0006043, loss_vq: 0.0001702
+epoch: 280, loss: 0.0006775, loss_task: 0.000659, loss_vq: 0.0001852
+epoch: 290, loss: 0.0005642, loss_task: 0.0005476, loss_vq: 0.0001657
+epoch: 300, loss: 0.0005917, loss_task: 0.0005725, loss_vq: 0.0001911
+ --- epoch 300, pelvis, psnr: 31.11, ssim: 0.9199
+epoch: 310, loss: 0.0006478, loss_task: 0.0006313, loss_vq: 0.000165
+epoch: 320, loss: 0.0005129, loss_task: 0.0004988, loss_vq: 0.0001416
+epoch: 330, loss: 0.0006188, loss_task: 0.0005998, loss_vq: 0.0001908
+epoch: 340, loss: 0.000554, loss_task: 0.00054, loss_vq: 0.0001403
+epoch: 350, loss: 0.0004833, loss_task: 0.0004704, loss_vq: 0.0001296
+ --- epoch 350, pelvis, psnr: 31.16, ssim: 0.9195
+epoch: 360, loss: 0.0005498, loss_task: 0.000536, loss_vq: 0.0001382
+epoch: 370, loss: 0.0005642, loss_task: 0.0005495, loss_vq: 0.0001465
+epoch: 380, loss: 0.0005496, loss_task: 0.0005362, loss_vq: 0.0001343
+epoch: 390, loss: 0.0005317, loss_task: 0.0005182, loss_vq: 0.0001343
+epoch: 400, loss: 0.000556, loss_task: 0.000543, loss_vq: 0.00013
+ --- epoch 400, pelvis, psnr: 30.75, ssim: 0.9061
+[rank0]:[W913 02:22:14.118774214 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

splits/LUNA16_v2/meta_info.json

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splits/PENGWIN/meta_info.json

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splits/ToothFairy/meta_info.json

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tooth+25v+n250+s2/config.yaml

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+model:
+  name: model_v7.Model_mv
+  finetune: True
+  encoder:
+    encoder_type: base
+    base_ch: 16
+    n_layer: 3
+    ch_up: 2
+  decoder:
+    out_ch: 128
+    n_conv3d: 2
+  point_decoder:
+    mlp_chs: [128, 64, 16, 1]
+  codebook:
+    n_embed: 512
+
+dataset:
+  name: dst_lr.CBCT_dataset_LR
+  root_dir: ./data_n250
+  lr_res: 32
+  sampling_method: v2
+  random_num_views: False
+  min_views: 10

tooth+25v+n250+s2/ep_400.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33f5927442d20a33ea4df023d7996bf6feee1dddd1b4dc65ab718b7a163a9ed0
+size 28985755

tooth+25v+n250+s2/results_1.0x.csv

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tooth+25v+n250+s2/train.log

@@ -0,0 +1,60 @@
+Namespace(name='tooth+25v+n250+s2', dst_name='tooth', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s2_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/tooth+25v+n250+s1/ep_400.pth', vq_w=1.0, safely_load=True, freeze_ft=True, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'finetune': True, 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: tooth, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: tooth, split: eval, len: 25.
+min_views: 25, max_views: 30
+resume model from logs/tooth+25v+n250+s1/ep_400.pth
+--- freeze encoder
+--- freeze codebook
+epoch: 0, loss: 0.4781, loss_task: 0.0006506, loss_vq: 0.4775
+ --- epoch 0, tooth, psnr: 33.35, ssim: 0.9465
+epoch: 10, loss: 0.1597, loss_task: 0.0003853, loss_vq: 0.1593
+epoch: 20, loss: 0.1277, loss_task: 0.0003572, loss_vq: 0.1274
+epoch: 30, loss: 0.1234, loss_task: 0.0003489, loss_vq: 0.123
+epoch: 40, loss: 0.1219, loss_task: 0.0003427, loss_vq: 0.1216
+epoch: 50, loss: 0.121, loss_task: 0.0003371, loss_vq: 0.1207
+ --- epoch 50, tooth, psnr: 34.61, ssim: 0.9573
+epoch: 60, loss: 0.1205, loss_task: 0.000344, loss_vq: 0.1201
+epoch: 70, loss: 0.1199, loss_task: 0.0003343, loss_vq: 0.1195
+epoch: 80, loss: 0.1193, loss_task: 0.0003327, loss_vq: 0.119
+epoch: 90, loss: 0.1188, loss_task: 0.000334, loss_vq: 0.1184
+epoch: 100, loss: 0.1184, loss_task: 0.0003265, loss_vq: 0.1181
+ --- epoch 100, tooth, psnr: 34.47, ssim: 0.9575
+epoch: 110, loss: 0.1179, loss_task: 0.0003218, loss_vq: 0.1176
+epoch: 120, loss: 0.1176, loss_task: 0.000321, loss_vq: 0.1173
+epoch: 130, loss: 0.1173, loss_task: 0.0003203, loss_vq: 0.117
+epoch: 140, loss: 0.117, loss_task: 0.0003205, loss_vq: 0.1167
+epoch: 150, loss: 0.1168, loss_task: 0.0003197, loss_vq: 0.1165
+ --- epoch 150, tooth, psnr: 34.68, ssim: 0.9576
+epoch: 160, loss: 0.1165, loss_task: 0.0003195, loss_vq: 0.1162
+epoch: 170, loss: 0.1162, loss_task: 0.0003245, loss_vq: 0.1159
+epoch: 180, loss: 0.1159, loss_task: 0.0003192, loss_vq: 0.1156
+epoch: 190, loss: 0.1157, loss_task: 0.000314, loss_vq: 0.1154
+epoch: 200, loss: 0.1154, loss_task: 0.0003113, loss_vq: 0.115
+ --- epoch 200, tooth, psnr: 34.61, ssim: 0.9582
+epoch: 210, loss: 0.1153, loss_task: 0.000313, loss_vq: 0.115
+epoch: 220, loss: 0.1149, loss_task: 0.000313, loss_vq: 0.1146
+epoch: 230, loss: 0.1148, loss_task: 0.0003158, loss_vq: 0.1145
+epoch: 240, loss: 0.1145, loss_task: 0.0003085, loss_vq: 0.1142
+epoch: 250, loss: 0.1143, loss_task: 0.0003115, loss_vq: 0.114
+ --- epoch 250, tooth, psnr: 34.68, ssim: 0.9581
+epoch: 260, loss: 0.1141, loss_task: 0.000309, loss_vq: 0.1138
+epoch: 270, loss: 0.1138, loss_task: 0.0003078, loss_vq: 0.1135
+epoch: 280, loss: 0.1136, loss_task: 0.0003029, loss_vq: 0.1133
+epoch: 290, loss: 0.1134, loss_task: 0.0003042, loss_vq: 0.1131
+epoch: 300, loss: 0.1131, loss_task: 0.0002991, loss_vq: 0.1128
+ --- epoch 300, tooth, psnr: 34.63, ssim: 0.9581
+epoch: 310, loss: 0.113, loss_task: 0.0003057, loss_vq: 0.1127
+epoch: 320, loss: 0.1128, loss_task: 0.000302, loss_vq: 0.1125
+epoch: 330, loss: 0.1125, loss_task: 0.0003032, loss_vq: 0.1122
+epoch: 340, loss: 0.1123, loss_task: 0.0002999, loss_vq: 0.112
+epoch: 350, loss: 0.1121, loss_task: 0.0003066, loss_vq: 0.1118
+ --- epoch 350, tooth, psnr: 34.47, ssim: 0.9572
+epoch: 360, loss: 0.112, loss_task: 0.0002934, loss_vq: 0.1117
+epoch: 370, loss: 0.1118, loss_task: 0.0002998, loss_vq: 0.1115
+epoch: 380, loss: 0.1114, loss_task: 0.0002947, loss_vq: 0.1111
+epoch: 390, loss: 0.1112, loss_task: 0.0002957, loss_vq: 0.1109
+epoch: 400, loss: 0.1111, loss_task: 0.0002961, loss_vq: 0.1108
+ --- epoch 400, tooth, psnr: 34.58, ssim: 0.9584
+[rank0]:[W914 00:36:14.524514258 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

tooth+25v+n250+s2/train_s1.log

@@ -0,0 +1,58 @@
+Namespace(name='tooth+25v+n250+s1', dst_name='tooth', epoch=400, num_views=30, cfg_path='configs/generated/finetune_s1_n250.yaml', out_res_scale=1.0, eval_npoint=100000, dst_subset=1.0, local_rank=0, dist=True, mixed_precision=False, batch_size=2, lr=0.0001, weight_decay=0.001, num_workers=4, num_points=10000, random_views=True, resume=None, resume_path='logs/pretrain/ep_700.pth', vq_w=0.1, safely_load=False, freeze_ft=False, min_views=25)
+{'model': {'name': 'model_v7.Model_mv', 'encoder': {'encoder_type': 'base', 'base_ch': 16, 'n_layer': 3, 'ch_up': 2}, 'decoder': {'out_ch': 128, 'n_conv3d': 2}, 'point_decoder': {'mlp_chs': [128, 64, 16, 1]}, 'codebook': {'n_embed': 512}}, 'dataset': {'name': 'dst_lr.CBCT_dataset_LR', 'root_dir': './data_n250', 'lr_res': 32, 'sampling_method': 'v2', 'random_num_views': False, 'min_views': 10}}
+CBCT_dataset, name: tooth, split: train, len: 250.
+min_views: 25, max_views: 30
+CBCT_dataset, name: tooth, split: eval, len: 25.
+min_views: 25, max_views: 30
+resume model from logs/pretrain/ep_700.pth
+epoch: 0, loss: 0.006833, loss_task: 0.005719, loss_vq: 0.01114
+ --- epoch 0, tooth, psnr: 24.8, ssim: 0.5624
+epoch: 10, loss: 0.001052, loss_task: 0.001014, loss_vq: 0.000374
+epoch: 20, loss: 0.0008405, loss_task: 0.000816, loss_vq: 0.000245
+epoch: 30, loss: 0.0007408, loss_task: 0.0007216, loss_vq: 0.000192
+epoch: 40, loss: 0.0006774, loss_task: 0.0006613, loss_vq: 0.0001604
+epoch: 50, loss: 0.0006315, loss_task: 0.000618, loss_vq: 0.0001357
+ --- epoch 50, tooth, psnr: 33.16, ssim: 0.9457
+epoch: 60, loss: 0.000603, loss_task: 0.0005912, loss_vq: 0.0001184
+epoch: 70, loss: 0.0005755, loss_task: 0.0005644, loss_vq: 0.0001108
+epoch: 80, loss: 0.0005532, loss_task: 0.0005433, loss_vq: 9.884e-05
+epoch: 90, loss: 0.0005313, loss_task: 0.0005223, loss_vq: 8.995e-05
+epoch: 100, loss: 0.0005288, loss_task: 0.0005201, loss_vq: 8.692e-05
+ --- epoch 100, tooth, psnr: 33.63, ssim: 0.9499
+epoch: 110, loss: 0.0005116, loss_task: 0.0005039, loss_vq: 7.722e-05
+epoch: 120, loss: 0.0005039, loss_task: 0.0004963, loss_vq: 7.556e-05
+epoch: 130, loss: 0.0004789, loss_task: 0.000472, loss_vq: 6.917e-05
+epoch: 140, loss: 0.0004723, loss_task: 0.0004657, loss_vq: 6.68e-05
+epoch: 150, loss: 0.0004736, loss_task: 0.0004671, loss_vq: 6.477e-05
+ --- epoch 150, tooth, psnr: 33.92, ssim: 0.9495
+epoch: 160, loss: 0.0004544, loss_task: 0.0004482, loss_vq: 6.158e-05
+epoch: 170, loss: 0.0004411, loss_task: 0.0004353, loss_vq: 5.821e-05
+epoch: 180, loss: 0.0004308, loss_task: 0.0004255, loss_vq: 5.321e-05
+epoch: 190, loss: 0.0004453, loss_task: 0.0004396, loss_vq: 5.728e-05
+epoch: 200, loss: 0.0004346, loss_task: 0.0004288, loss_vq: 5.76e-05
+ --- epoch 200, tooth, psnr: 34.22, ssim: 0.9531
+epoch: 210, loss: 0.0004233, loss_task: 0.000418, loss_vq: 5.25e-05
+epoch: 220, loss: 0.0004198, loss_task: 0.0004147, loss_vq: 5.152e-05
+epoch: 230, loss: 0.0004164, loss_task: 0.0004112, loss_vq: 5.246e-05
+epoch: 240, loss: 0.0004004, loss_task: 0.0003956, loss_vq: 4.825e-05
+epoch: 250, loss: 0.0004018, loss_task: 0.0003969, loss_vq: 4.938e-05
+ --- epoch 250, tooth, psnr: 34.09, ssim: 0.9522
+epoch: 260, loss: 0.0004299, loss_task: 0.0004249, loss_vq: 5.025e-05
+epoch: 270, loss: 0.0003896, loss_task: 0.000385, loss_vq: 4.605e-05
+epoch: 280, loss: 0.000391, loss_task: 0.0003861, loss_vq: 4.9e-05
+epoch: 290, loss: 0.0003924, loss_task: 0.0003875, loss_vq: 4.886e-05
+epoch: 300, loss: 0.0003859, loss_task: 0.0003813, loss_vq: 4.527e-05
+ --- epoch 300, tooth, psnr: 34.35, ssim: 0.9558
+epoch: 310, loss: 0.0004134, loss_task: 0.000408, loss_vq: 5.362e-05
+epoch: 320, loss: 0.0003716, loss_task: 0.0003673, loss_vq: 4.284e-05
+epoch: 330, loss: 0.0003731, loss_task: 0.0003687, loss_vq: 4.41e-05
+epoch: 340, loss: 0.0003767, loss_task: 0.0003722, loss_vq: 4.498e-05
+epoch: 350, loss: 0.0003641, loss_task: 0.0003598, loss_vq: 4.226e-05
+ --- epoch 350, tooth, psnr: 33.92, ssim: 0.9561
+epoch: 360, loss: 0.0003638, loss_task: 0.0003595, loss_vq: 4.258e-05
+epoch: 370, loss: 0.0003641, loss_task: 0.0003598, loss_vq: 4.289e-05
+epoch: 380, loss: 0.0003787, loss_task: 0.000374, loss_vq: 4.727e-05
+epoch: 390, loss: 0.0003568, loss_task: 0.0003527, loss_vq: 4.163e-05
+epoch: 400, loss: 0.000352, loss_task: 0.0003479, loss_vq: 4.134e-05
+ --- epoch 400, tooth, psnr: 34.38, ssim: 0.9565
+[rank0]:[W913 19:09:51.151413119 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())