pretrain recon seed=20260716 device=cuda train=33584 val=3615 trainable=1,613,588 recon step 01000/36000 train=0.37529 val=0.32340 best=0.32340 recon step 02000/36000 train=0.27660 val=0.22187 best=0.22187 recon step 03000/36000 train=0.21662 val=0.20076 best=0.20076 recon step 04000/36000 train=0.20445 val=0.19302 best=0.19302 recon step 05000/36000 train=0.19664 val=0.18667 best=0.18667 recon step 06000/36000 train=0.19273 val=0.18407 best=0.18407 recon step 07000/36000 train=0.18978 val=0.18219 best=0.18219 recon step 08000/36000 train=0.18768 val=0.18208 best=0.18208 recon step 09000/36000 train=0.18667 val=0.17896 best=0.17896 recon step 10000/36000 train=0.18440 val=0.17729 best=0.17729 recon step 11000/36000 train=0.18331 val=0.17656 best=0.17656 recon step 12000/36000 train=0.18226 val=0.17520 best=0.17520 recon step 13000/36000 train=0.18172 val=0.17486 best=0.17486 recon step 14000/36000 train=0.18056 val=0.17497 best=0.17486 recon step 15000/36000 train=0.17927 val=0.17371 best=0.17371 recon step 16000/36000 train=0.17874 val=0.17248 best=0.17248 recon step 17000/36000 train=0.17773 val=0.17210 best=0.17210 recon step 18000/36000 train=0.17754 val=0.17131 best=0.17131 recon step 19000/36000 train=0.17669 val=0.17089 best=0.17089 recon step 20000/36000 train=0.17550 val=0.17070 best=0.17070 recon step 21000/36000 train=0.17515 val=0.16954 best=0.16954 recon step 22000/36000 train=0.17493 val=0.16905 best=0.16905 recon step 23000/36000 train=0.17390 val=0.16856 best=0.16856 recon step 24000/36000 train=0.17359 val=0.16838 best=0.16838 recon step 25000/36000 train=0.17291 val=0.16762 best=0.16762 recon step 26000/36000 train=0.17272 val=0.16744 best=0.16744 recon step 27000/36000 train=0.17205 val=0.16758 best=0.16744 recon step 28000/36000 train=0.17177 val=0.16649 best=0.16649 recon step 29000/36000 train=0.17108 val=0.16632 best=0.16632 recon step 30000/36000 train=0.17020 val=0.16588 best=0.16588 recon step 31000/36000 train=0.17143 val=0.16572 best=0.16572 recon step 32000/36000 train=0.17055 val=0.16568 best=0.16568 recon step 33000/36000 train=0.16986 val=0.16552 best=0.16552 recon step 34000/36000 train=0.17001 val=0.16543 best=0.16543 recon step 35000/36000 train=0.17004 val=0.16539 best=0.16539 recon step 36000/36000 train=0.16999 val=0.16536 best=0.16536 Best checkpoint: /workspace/xchannel/outputs/pretrain/recon/20260716/best.pt pretrain latent seed=20260716 device=cuda train=33584 val=3615 trainable=1,646,612 latent step 01000/36000 train=0.70735 val=0.59347 best=0.59347 latent step 02000/36000 train=0.49685 val=0.38240 best=0.38240 latent step 03000/36000 train=0.35598 val=0.33155 best=0.33155 latent step 04000/36000 train=0.34749 val=0.33754 best=0.33155 latent step 05000/36000 train=0.35080 val=0.34320 best=0.33155 latent step 06000/36000 train=0.35674 val=0.35306 best=0.33155 latent step 07000/36000 train=0.36037 val=0.35426 best=0.33155 latent step 08000/36000 train=0.36074 val=0.35311 best=0.33155 latent step 09000/36000 train=0.35968 val=0.34989 best=0.33155 latent step 10000/36000 train=0.35530 val=0.34783 best=0.33155 latent step 11000/36000 train=0.35096 val=0.34263 best=0.33155 latent step 12000/36000 train=0.34593 val=0.33611 best=0.33155 latent step 13000/36000 train=0.33894 val=0.33087 best=0.33087 latent step 14000/36000 train=0.33290 val=0.32501 best=0.32501 latent step 15000/36000 train=0.32660 val=0.31882 best=0.31882 latent step 16000/36000 train=0.32100 val=0.31297 best=0.31297 latent step 17000/36000 train=0.31548 val=0.30951 best=0.30951 latent step 18000/36000 train=0.31099 val=0.30368 best=0.30368 latent step 19000/36000 train=0.30557 val=0.29753 best=0.29753 latent step 20000/36000 train=0.30014 val=0.29329 best=0.29329 latent step 21000/36000 train=0.29642 val=0.28978 best=0.28978 latent step 22000/36000 train=0.29259 val=0.28656 best=0.28656 latent step 23000/36000 train=0.28854 val=0.28268 best=0.28268 latent step 24000/36000 train=0.28531 val=0.27942 best=0.27942 latent step 25000/36000 train=0.28174 val=0.27733 best=0.27733 latent step 26000/36000 train=0.27907 val=0.27426 best=0.27426 latent step 27000/36000 train=0.27615 val=0.27149 best=0.27149 latent step 28000/36000 train=0.27375 val=0.26986 best=0.26986 latent step 29000/36000 train=0.27147 val=0.26759 best=0.26759 latent step 30000/36000 train=0.26951 val=0.26613 best=0.26613 latent step 31000/36000 train=0.26920 val=0.26481 best=0.26481 latent step 32000/36000 train=0.26740 val=0.26389 best=0.26389 latent step 33000/36000 train=0.26613 val=0.26291 best=0.26291 latent step 34000/36000 train=0.26551 val=0.26239 best=0.26239 latent step 35000/36000 train=0.26492 val=0.26197 best=0.26197 latent step 36000/36000 train=0.26466 val=0.26176 best=0.26176 Best checkpoint: /workspace/xchannel/outputs/pretrain/latent/20260716/best.pt pretrain xchannel seed=20260716 device=cuda train=33584 val=3615 trainable=1,696,408 xchannel step 01000/36000 train=0.70385 val=0.57829 best=0.57829 xchannel step 02000/36000 train=0.50322 val=0.44120 best=0.44120 xchannel step 03000/36000 train=0.37475 val=0.32561 best=0.32561 xchannel step 04000/36000 train=0.32731 val=0.31651 best=0.31651 xchannel step 05000/36000 train=0.31819 val=0.30726 best=0.30726 xchannel step 06000/36000 train=0.31241 val=0.30431 best=0.30431 xchannel step 07000/36000 train=0.30721 val=0.29832 best=0.29832 xchannel step 08000/36000 train=0.30191 val=0.29880 best=0.29832 xchannel step 09000/36000 train=0.29680 val=0.29060 best=0.29060 xchannel step 10000/36000 train=0.29118 val=0.28369 best=0.28369 xchannel step 11000/36000 train=0.28487 val=0.27720 best=0.27720 xchannel step 12000/36000 train=0.27770 val=0.27267 best=0.27267 xchannel step 13000/36000 train=0.27170 val=0.26784 best=0.26784 xchannel step 14000/36000 train=0.26542 val=0.26207 best=0.26207 xchannel step 15000/36000 train=0.25881 val=0.25262 best=0.25262 xchannel step 16000/36000 train=0.25111 val=0.24774 best=0.24774 xchannel step 17000/36000 train=0.24468 val=0.24166 best=0.24166 xchannel step 18000/36000 train=0.23840 val=0.23706 best=0.23706 xchannel step 19000/36000 train=0.23400 val=0.23461 best=0.23461 xchannel step 20000/36000 train=0.22964 val=0.22950 best=0.22950 xchannel step 21000/36000 train=0.22609 val=0.22627 best=0.22627 xchannel step 22000/36000 train=0.22284 val=0.22415 best=0.22415 xchannel step 23000/36000 train=0.21995 val=0.22124 best=0.22124 xchannel step 24000/36000 train=0.21764 val=0.21850 best=0.21850 xchannel step 25000/36000 train=0.21510 val=0.21692 best=0.21692 xchannel step 26000/36000 train=0.21318 val=0.21464 best=0.21464 xchannel step 27000/36000 train=0.21137 val=0.21313 best=0.21313 xchannel step 28000/36000 train=0.20937 val=0.21210 best=0.21210 xchannel step 29000/36000 train=0.20797 val=0.21052 best=0.21052 xchannel step 30000/36000 train=0.20641 val=0.20949 best=0.20949 xchannel step 31000/36000 train=0.20583 val=0.20849 best=0.20849 xchannel step 32000/36000 train=0.20456 val=0.20750 best=0.20750 xchannel step 33000/36000 train=0.20393 val=0.20699 best=0.20699 xchannel step 34000/36000 train=0.20341 val=0.20674 best=0.20674 xchannel step 35000/36000 train=0.20318 val=0.20647 best=0.20647 xchannel step 36000/36000 train=0.20328 val=0.20637 best=0.20637 Best checkpoint: /workspace/xchannel/outputs/pretrain/xchannel/20260716/best.pt screen seed=20260716 sub1 recon_finetune fold 1/5 /usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_INTERNAL_ERROR when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 160 k 20 mat1_ld 20 mat2_ld 20 result_ld 128 abType 0 cType 0 computeType 77 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.) return F.linear(input, self.weight, self.bias) Traceback (most recent call last): File "/workspace/xchannel/run_xchannel_ssl.py", line 152, in main() File "/workspace/xchannel/run_xchannel_ssl.py", line 139, in main run_screen(args) File "/workspace/xchannel/run_xchannel_ssl.py", line 92, in run_screen run_finetune_modes( File "/workspace/xchannel/xchannel_ssl/training.py", line 646, in run_finetune_modes metrics = train_classifier_fold(cache_root, subject_id, split, mode, checkpoint, config, fold_seed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/xchannel_ssl/training.py", line 603, in train_classifier_fold validation = evaluate_classifier(model, loaders["val"], device) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/xchannel_ssl/training.py", line 548, in evaluate_classifier logits = model(x) ^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1789, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/model/CrossChannelEEG.py", line 321, in forward tokens = self.encoder.forward_tokens(x) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/model/CrossChannelEEG.py", line 222, in forward_tokens tokens = self.patch_embedding(patches) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1789, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py", line 134, in forward return F.linear(input, self.weight, self.bias) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)` SCREEN_RESTART_AMP_EVAL screen seed=20260716 sub1 recon_finetune fold 1/5 screen seed=20260716 sub1 recon_linear fold 1/5 screen seed=20260716 sub1 latent_finetune fold 1/5 screen seed=20260716 sub1 latent_linear fold 1/5 screen seed=20260716 sub1 xchannel_finetune fold 1/5 screen seed=20260716 sub1 xchannel_linear fold 1/5 screen seed=20260716 sub1 scratch fold 1/5 screen seed=20260716 sub1 recon_finetune fold 2/5 screen seed=20260716 sub1 recon_linear fold 2/5 screen seed=20260716 sub1 latent_finetune fold 2/5 screen seed=20260716 sub1 latent_linear fold 2/5 screen seed=20260716 sub1 xchannel_finetune fold 2/5 screen seed=20260716 sub1 xchannel_linear fold 2/5 screen seed=20260716 sub1 scratch fold 2/5 screen seed=20260716 sub1 recon_finetune fold 3/5 screen seed=20260716 sub1 recon_linear fold 3/5 screen seed=20260716 sub1 latent_finetune fold 3/5 screen seed=20260716 sub1 latent_linear fold 3/5 screen seed=20260716 sub1 xchannel_finetune fold 3/5 screen seed=20260716 sub1 xchannel_linear fold 3/5 screen seed=20260716 sub1 scratch fold 3/5 screen seed=20260716 sub1 recon_finetune fold 4/5 screen seed=20260716 sub1 recon_linear fold 4/5 screen seed=20260716 sub1 latent_finetune fold 4/5 screen seed=20260716 sub1 latent_linear fold 4/5 screen seed=20260716 sub1 xchannel_finetune fold 4/5 screen seed=20260716 sub1 xchannel_linear fold 4/5 screen seed=20260716 sub1 scratch fold 4/5 screen seed=20260716 sub1 recon_finetune fold 5/5 screen seed=20260716 sub1 recon_linear fold 5/5 screen seed=20260716 sub1 latent_finetune fold 5/5 screen seed=20260716 sub1 latent_linear fold 5/5 screen seed=20260716 sub1 xchannel_finetune fold 5/5 screen seed=20260716 sub1 xchannel_linear fold 5/5 screen seed=20260716 sub1 scratch fold 5/5 screen seed=20260716 sub2 recon_finetune fold 1/5 screen seed=20260716 sub2 recon_linear fold 1/5 screen seed=20260716 sub2 latent_finetune fold 1/5 screen seed=20260716 sub2 latent_linear fold 1/5 screen seed=20260716 sub2 xchannel_finetune fold 1/5 screen seed=20260716 sub2 xchannel_linear fold 1/5 screen seed=20260716 sub2 scratch fold 1/5 screen seed=20260716 sub2 recon_finetune fold 2/5 screen seed=20260716 sub2 recon_linear fold 2/5 Traceback (most recent call last): File "/workspace/xchannel/run_xchannel_ssl.py", line 152, in main() File "/workspace/xchannel/run_xchannel_ssl.py", line 139, in main run_screen(args) File "/workspace/xchannel/run_xchannel_ssl.py", line 92, in run_screen run_finetune_modes( File "/workspace/xchannel/xchannel_ssl/training.py", line 648, in run_finetune_modes metrics = train_classifier_fold(cache_root, subject_id, split, mode, checkpoint, config, fold_seed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/xchannel_ssl/training.py", line 605, in train_classifier_fold validation = evaluate_classifier(model, loaders["val"], device) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/xchannel/xchannel_ssl/training.py", line 551, in evaluate_classifier losses += float(loss) * x.shape[0] ^^^^^^^^^^^ torch.AcceleratorError: CUDA error: an illegal memory access was encountered Search for `cudaErrorIllegalAddress' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 screen seed=20260716 sub2 recon_linear fold 2/5 screen seed=20260716 sub2 latent_finetune fold 2/5 screen seed=20260716 sub2 latent_linear fold 2/5 screen seed=20260716 sub2 xchannel_finetune fold 2/5 screen seed=20260716 sub2 xchannel_linear fold 2/5 screen seed=20260716 sub2 scratch fold 2/5 screen seed=20260716 sub2 recon_finetune fold 3/5 screen seed=20260716 sub2 recon_linear fold 3/5 screen seed=20260716 sub2 latent_finetune fold 3/5 screen seed=20260716 sub2 latent_linear fold 3/5 screen seed=20260716 sub2 xchannel_finetune fold 3/5 screen seed=20260716 sub2 xchannel_linear fold 3/5 screen seed=20260716 sub2 scratch fold 3/5 screen seed=20260716 sub2 recon_finetune fold 4/5 screen seed=20260716 sub2 recon_linear fold 4/5 screen seed=20260716 sub2 latent_finetune fold 4/5 screen seed=20260716 sub2 latent_linear fold 4/5 screen seed=20260716 sub2 xchannel_finetune fold 4/5 screen seed=20260716 sub2 xchannel_linear fold 4/5 screen seed=20260716 sub2 scratch fold 4/5 screen seed=20260716 sub2 recon_finetune fold 5/5 screen seed=20260716 sub2 recon_linear fold 5/5 screen seed=20260716 sub2 latent_finetune fold 5/5 screen seed=20260716 sub2 latent_linear fold 5/5 screen seed=20260716 sub2 xchannel_finetune fold 5/5 screen seed=20260716 sub2 xchannel_linear fold 5/5 screen seed=20260716 sub2 scratch fold 5/5 screen seed=20260716 sub3 recon_finetune fold 1/5 screen seed=20260716 sub3 recon_linear fold 1/5 screen seed=20260716 sub3 latent_finetune fold 1/5 screen seed=20260716 sub3 latent_linear fold 1/5 screen seed=20260716 sub3 xchannel_finetune fold 1/5 screen seed=20260716 sub3 xchannel_linear fold 1/5 screen seed=20260716 sub3 scratch fold 1/5 screen seed=20260716 sub3 recon_finetune fold 2/5 screen seed=20260716 sub3 recon_linear fold 2/5 screen seed=20260716 sub3 latent_finetune fold 2/5 screen seed=20260716 sub3 latent_linear fold 2/5 screen seed=20260716 sub3 xchannel_finetune fold 2/5 screen seed=20260716 sub3 xchannel_linear fold 2/5 screen seed=20260716 sub3 scratch fold 2/5 screen seed=20260716 sub3 recon_finetune fold 3/5 screen seed=20260716 sub3 recon_linear fold 3/5 screen seed=20260716 sub3 latent_finetune fold 3/5 screen seed=20260716 sub3 latent_linear fold 3/5 screen seed=20260716 sub3 xchannel_finetune fold 3/5 screen seed=20260716 sub3 xchannel_linear fold 3/5 screen seed=20260716 sub3 scratch fold 3/5 screen seed=20260716 sub3 recon_finetune fold 4/5 screen seed=20260716 sub3 recon_linear fold 4/5 screen seed=20260716 sub3 latent_finetune fold 4/5 screen seed=20260716 sub3 latent_linear fold 4/5 screen seed=20260716 sub3 xchannel_finetune fold 4/5 screen seed=20260716 sub3 xchannel_linear fold 4/5 screen seed=20260716 sub3 scratch fold 4/5 screen seed=20260716 sub3 recon_finetune fold 5/5 screen seed=20260716 sub3 recon_linear fold 5/5 screen seed=20260716 sub3 latent_finetune fold 5/5 screen seed=20260716 sub3 latent_linear fold 5/5 screen seed=20260716 sub3 xchannel_finetune fold 5/5 screen seed=20260716 sub3 xchannel_linear fold 5/5 screen seed=20260716 sub3 scratch fold 5/5 screen seed=20260716 sub9 recon_finetune fold 1/5 screen seed=20260716 sub9 recon_linear fold 1/5 screen seed=20260716 sub9 latent_finetune fold 1/5 screen seed=20260716 sub9 latent_linear fold 1/5 screen seed=20260716 sub9 xchannel_finetune fold 1/5 screen seed=20260716 sub9 xchannel_linear fold 1/5 screen seed=20260716 sub9 scratch fold 1/5 screen seed=20260716 sub9 recon_finetune fold 2/5 screen seed=20260716 sub9 recon_linear fold 2/5 screen seed=20260716 sub9 latent_finetune fold 2/5 screen seed=20260716 sub9 latent_linear fold 2/5 screen seed=20260716 sub9 xchannel_finetune fold 2/5 screen seed=20260716 sub9 xchannel_linear fold 2/5 screen seed=20260716 sub9 scratch fold 2/5 screen seed=20260716 sub9 recon_finetune fold 3/5 screen seed=20260716 sub9 recon_linear fold 3/5 screen seed=20260716 sub9 latent_finetune fold 3/5 screen seed=20260716 sub9 latent_linear fold 3/5 screen seed=20260716 sub9 xchannel_finetune fold 3/5 screen seed=20260716 sub9 xchannel_linear fold 3/5 screen seed=20260716 sub9 scratch fold 3/5 screen seed=20260716 sub9 recon_finetune fold 4/5 screen seed=20260716 sub9 recon_linear fold 4/5 screen seed=20260716 sub9 latent_finetune fold 4/5 screen seed=20260716 sub9 latent_linear fold 4/5 screen seed=20260716 sub9 xchannel_finetune fold 4/5 screen seed=20260716 sub9 xchannel_linear fold 4/5 screen seed=20260716 sub9 scratch fold 4/5 screen seed=20260716 sub9 recon_finetune fold 5/5 screen seed=20260716 sub9 recon_linear fold 5/5 screen seed=20260716 sub9 latent_finetune fold 5/5 screen seed=20260716 sub9 latent_linear fold 5/5 screen seed=20260716 sub9 xchannel_finetune fold 5/5 screen seed=20260716 sub9 xchannel_linear fold 5/5 screen seed=20260716 sub9 scratch fold 5/5 screen seed=20260716 sub10 recon_finetune fold 1/5 screen seed=20260716 sub10 recon_linear fold 1/5 screen seed=20260716 sub10 latent_finetune fold 1/5 screen seed=20260716 sub10 latent_linear fold 1/5 screen seed=20260716 sub10 xchannel_finetune fold 1/5 screen seed=20260716 sub10 xchannel_linear fold 1/5 screen seed=20260716 sub10 scratch fold 1/5 screen seed=20260716 sub10 recon_finetune fold 2/5 screen seed=20260716 sub10 recon_linear fold 2/5 screen seed=20260716 sub10 latent_finetune fold 2/5 screen seed=20260716 sub10 latent_linear fold 2/5 screen seed=20260716 sub10 xchannel_finetune fold 2/5 screen seed=20260716 sub10 xchannel_linear fold 2/5 screen seed=20260716 sub10 scratch fold 2/5 screen seed=20260716 sub10 recon_finetune fold 3/5 screen seed=20260716 sub10 recon_linear fold 3/5 screen seed=20260716 sub10 latent_finetune fold 3/5 screen seed=20260716 sub10 latent_linear fold 3/5 screen seed=20260716 sub10 xchannel_finetune fold 3/5 screen seed=20260716 sub10 xchannel_linear fold 3/5 screen seed=20260716 sub10 scratch fold 3/5 screen seed=20260716 sub10 recon_finetune fold 4/5 screen seed=20260716 sub10 recon_linear fold 4/5 screen seed=20260716 sub10 latent_finetune fold 4/5 screen seed=20260716 sub10 latent_linear fold 4/5 screen seed=20260716 sub10 xchannel_finetune fold 4/5 screen seed=20260716 sub10 xchannel_linear fold 4/5 screen seed=20260716 sub10 scratch fold 4/5 screen seed=20260716 sub10 recon_finetune fold 5/5 screen seed=20260716 sub10 recon_linear fold 5/5 screen seed=20260716 sub10 latent_finetune fold 5/5 screen seed=20260716 sub10 latent_linear fold 5/5 screen seed=20260716 sub10 xchannel_finetune fold 5/5 screen seed=20260716 sub10 xchannel_linear fold 5/5 screen seed=20260716 sub10 scratch fold 5/5 screen seed=20260716 sub11 recon_finetune fold 1/5 screen seed=20260716 sub11 recon_linear fold 1/5 screen seed=20260716 sub11 latent_finetune fold 1/5 screen seed=20260716 sub11 latent_linear fold 1/5 screen seed=20260716 sub11 xchannel_finetune fold 1/5 screen seed=20260716 sub11 xchannel_linear fold 1/5 screen seed=20260716 sub11 scratch fold 1/5 screen seed=20260716 sub11 recon_finetune fold 2/5 screen seed=20260716 sub11 recon_linear fold 2/5 screen seed=20260716 sub11 latent_finetune fold 2/5 screen seed=20260716 sub11 latent_linear fold 2/5 screen seed=20260716 sub11 xchannel_finetune fold 2/5 screen seed=20260716 sub11 xchannel_linear fold 2/5 screen seed=20260716 sub11 scratch fold 2/5 screen seed=20260716 sub11 recon_finetune fold 3/5 screen seed=20260716 sub11 recon_linear fold 3/5 screen seed=20260716 sub11 latent_finetune fold 3/5 screen seed=20260716 sub11 latent_linear fold 3/5 screen seed=20260716 sub11 xchannel_finetune fold 3/5 screen seed=20260716 sub11 xchannel_linear fold 3/5 screen seed=20260716 sub11 scratch fold 3/5 screen seed=20260716 sub11 recon_finetune fold 4/5 screen seed=20260716 sub11 recon_linear fold 4/5 screen seed=20260716 sub11 latent_finetune fold 4/5 screen seed=20260716 sub11 latent_linear fold 4/5 screen seed=20260716 sub11 xchannel_finetune fold 4/5 screen seed=20260716 sub11 xchannel_linear fold 4/5 screen seed=20260716 sub11 scratch fold 4/5