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subcortical/schaefer400_lr3e-4_3/eval_v2/aabc_sex__patch__logistic/log.txt

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+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:25:18
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/aabc_sex__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: aabc_sex (schaefer400)
+train (n=471):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 471
+}),
+    labels=[0 1],
+    counts=[269 202]
+)
+
+validation (n=58):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 58
+}),
+    labels=[0 1],
+    counts=[36 22]
+)
+
+test (n=55):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 55
+}),
+    labels=[0 1],
+    counts=[33 22]
+)
+
+extracting features for all splits
+extract (train)  [  0/236]  eta: 0:04:36    time: 1.1701  data: 0.8256  max mem: 1814
+extract (train)  [ 20/236]  eta: 0:00:23    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [ 40/236]  eta: 0:00:16    time: 0.0566  data: 0.0020  max mem: 2020
+extract (train)  [ 60/236]  eta: 0:00:13    time: 0.0571  data: 0.0025  max mem: 2020
+extract (train)  [ 80/236]  eta: 0:00:11    time: 0.0570  data: 0.0025  max mem: 2020
+extract (train)  [100/236]  eta: 0:00:09    time: 0.0574  data: 0.0026  max mem: 2020
+extract (train)  [120/236]  eta: 0:00:07    time: 0.0574  data: 0.0026  max mem: 2020
+extract (train)  [140/236]  eta: 0:00:06    time: 0.0574  data: 0.0027  max mem: 2020
+extract (train)  [160/236]  eta: 0:00:04    time: 0.0571  data: 0.0024  max mem: 2020
+extract (train)  [180/236]  eta: 0:00:03    time: 0.0574  data: 0.0026  max mem: 2020
+extract (train)  [200/236]  eta: 0:00:02    time: 0.0574  data: 0.0025  max mem: 2020
+extract (train)  [220/236]  eta: 0:00:00    time: 0.0575  data: 0.0025  max mem: 2020
+extract (train)  [235/236]  eta: 0:00:00    time: 0.0564  data: 0.0025  max mem: 2020
+extract (train) Total time: 0:00:14 (0.0624 s / it)
+extract (validation)  [ 0/29]  eta: 0:00:21    time: 0.7376  data: 0.6821  max mem: 2020
+extract (validation)  [20/29]  eta: 0:00:00    time: 0.0575  data: 0.0028  max mem: 2020
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.0563  data: 0.0012  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0838 s / it)
+extract (test)  [ 0/28]  eta: 0:00:19    time: 0.7057  data: 0.6500  max mem: 2020
+extract (test)  [20/28]  eta: 0:00:00    time: 0.0574  data: 0.0029  max mem: 2020
+extract (test)  [27/28]  eta: 0:00:00    time: 0.0555  data: 0.0019  max mem: 2020
+extract (test) Total time: 0:00:02 (0.0826 s / it)
+feature extraction time: 0:00:19
+train features: (471, 768)
+validation features: (58, 768)
+test features: (55, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |      C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 21.544 | train   | 1       |  0        | 1       |  0       | 1       |   0        |
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 21.544 | test    | 0.76364 |  0.058826 | 0.75853 |  0.05976 | 0.76515 |   0.060196 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.059690385731374966, "f1": 0.6212121212121212, "f1_std": 0.0622787901047053, "bacc": 0.6202445652173914, "bacc_std": 0.061337157614024244}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.053332687323911235, "f1": 0.7417840375586855, "f1_std": 0.06272624405514958, "bacc": 0.7357336956521738, "bacc_std": 0.05887039332352182}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05944620458590626, "f1": 0.7348484848484849, "f1_std": 0.0631562490975009, "bacc": 0.7323369565217391, "bacc_std": 0.06275795603196098}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05674008013930607, "f1": 0.7518222839291913, "f1_std": 0.060920475454328424, "bacc": 0.7479619565217391, "bacc_std": 0.06006754814778257}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05518149168691788, "f1": 0.7555555555555555, "f1_std": 0.057937463097488456, "bacc": 0.7540760869565217, "bacc_std": 0.05782239669991449}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05207948153250196, "f1": 0.7931623931623932, "f1_std": 0.0544671284436109, "bacc": 0.7914402173913043, "bacc_std": 0.05455281752931679}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 2.782559402207126, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.0634790679367278, "f1": 0.6699999999999999, "f1_std": 0.06369706631391539, "bacc": 0.6759510869565217, "bacc_std": 0.06360035481056511}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06345923400235219, "f1": 0.6637228260869565, "f1_std": 0.06551251466070464, "bacc": 0.6637228260869565, "bacc_std": 0.06513123377952452}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.05911455750566785, "f1": 0.638157894736842, "f1_std": 0.06785675220183075, "bacc": 0.639266304347826, "bacc_std": 0.06195412721524082}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 2.782559402207126, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.0578761401901145, "f1": 0.6623329721921272, "f1_std": 0.0674290747907821, "bacc": 0.6610054347826086, "bacc_std": 0.061871104517228624}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05004824119068511, "f1": 0.8074229691876751, "f1_std": 0.05491487401809608, "bacc": 0.8009510869565217, "bacc_std": 0.054216887290066684}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06069188952854786, "f1": 0.6754599097535579, "f1_std": 0.06493781299696523, "bacc": 0.6732336956521738, "bacc_std": 0.06317569021378451}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06439525674231833, "f1": 0.6427350427350427, "f1_std": 0.06714352671002054, "bacc": 0.6419836956521738, "bacc_std": 0.0667068737548363}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05802610982555701, "f1": 0.7623795280824195, "f1_std": 0.05788467622329295, "bacc": 0.7724184782608696, "bacc_std": 0.056535796877447954}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05611412220416355, "f1": 0.7727272727272727, "f1_std": 0.05890968596234436, "bacc": 0.7697010869565217, "bacc_std": 0.05826466179647354}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05981670072772666, "f1": 0.7136410968413746, "f1_std": 0.06368599974836799, "bacc": 0.7105978260869565, "bacc_std": 0.06223078093197983}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.050407345653860486, "f1": 0.7642857142857142, "f1_std": 0.057820477047713155, "bacc": 0.7574728260869565, "bacc_std": 0.055423591205008546}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05507921868162721, "f1": 0.7472605160834218, "f1_std": 0.06097604898743436, "bacc": 0.7418478260869565, "bacc_std": 0.05869117799473157}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06420932709467071, "f1": 0.69049983449189, "f1_std": 0.06436818084959092, "bacc": 0.7038043478260869, "bacc_std": 0.06368637728499366}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05874431500540749, "f1": 0.7758152173913043, "f1_std": 0.06096125478620749, "bacc": 0.7758152173913043, "bacc_std": 0.06089389876277097}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 2.782559402207126, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.0633136335898289, "f1": 0.6427350427350427, "f1_std": 0.06662129216460397, "bacc": 0.6419836956521738, "bacc_std": 0.06596504655281393}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 2.782559402207126, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06379330258377801, "f1": 0.637278722665741, "f1_std": 0.06861515855884968, "bacc": 0.6358695652173914, "bacc_std": 0.0665795404494704}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06200399880466256, "f1": 0.7043010752688172, "f1_std": 0.062487659346173244, "bacc": 0.7072010869565217, "bacc_std": 0.06240797892521633}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05669942206768883, "f1": 0.6918767507002801, "f1_std": 0.06204375177149115, "bacc": 0.6888586956521738, "bacc_std": 0.05975426197701821}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05822129342658461, "f1": 0.7066666666666667, "f1_std": 0.0585140769096229, "bacc": 0.7133152173913043, "bacc_std": 0.05879135482098159}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 1291.5496650148827, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06026378378730991, "f1": 0.7348484848484849, "f1_std": 0.06401948769419798, "bacc": 0.7323369565217391, "bacc_std": 0.06315458109409393}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06032787549701606, "f1": 0.7303921568627451, "f1_std": 0.0650230011426589, "bacc": 0.7262228260869565, "bacc_std": 0.06352234865444534}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05259499394837641, "f1": 0.76890756302521, "f1_std": 0.05747016277311382, "bacc": 0.7635869565217391, "bacc_std": 0.05603331597727608}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.056974333789381, "f1": 0.7518222839291913, "f1_std": 0.06101869607608169, "bacc": 0.7479619565217391, "bacc_std": 0.06001406003728747}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05824191214734036, "f1": 0.741263440860215, "f1_std": 0.05941662870718195, "bacc": 0.7445652173913043, "bacc_std": 0.059914418303742244}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04208859604875614, "f1": 0.8639095086603039, "f1_std": 0.047478106114810174, "bacc": 0.8539402173913043, "bacc_std": 0.04804695933126906}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 166.81005372000556, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.0616010196330768, "f1": 0.6754599097535579, "f1_std": 0.06585023854566921, "bacc": 0.6732336956521738, "bacc_std": 0.06440277487508547}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 0.046415888336127774, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.057132480521840764, "f1": 0.6283783783783784, "f1_std": 0.07005831592880042, "bacc": 0.6331521739130435, "bacc_std": 0.061233859218194485}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 0.3593813663804626, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06005277018266161, "f1": 0.717948717948718, "f1_std": 0.0625528380791051, "bacc": 0.7167119565217391, "bacc_std": 0.06210747811375566}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 35, "C": 0.3593813663804626, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.06251039186334616, "f1": 0.6148459383753502, "f1_std": 0.06798454100226876, "bacc": 0.6141304347826086, "bacc_std": 0.0655794467876448}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 89, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05716589169735497, "f1": 0.7727272727272727, "f1_std": 0.059902028627188686, "bacc": 0.7697010869565217, "bacc_std": 0.059382844721051056}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 90, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05940803295990611, "f1": 0.6892655367231638, "f1_std": 0.05986551625849581, "bacc": 0.6976902173913043, "bacc_std": 0.05986660508463497}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 91, "C": 1291.5496650148827, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.0640154939922702, "f1": 0.6470786896318811, "f1_std": 0.06594087620372677, "bacc": 0.6480978260869565, "bacc_std": 0.06609261252076827}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 94, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05526099157708487, "f1": 0.7348484848484849, "f1_std": 0.058326175830697144, "bacc": 0.7323369565217391, "bacc_std": 0.05757696303537663}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05812746324648165, "f1": 0.6857142857142857, "f1_std": 0.06560639808510087, "bacc": 0.6827445652173914, "bacc_std": 0.06184109790903994}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 96, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05109415712742488, "f1": 0.76890756302521, "f1_std": 0.05666305710655174, "bacc": 0.7635869565217391, "bacc_std": 0.05540902099813711}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.057870199584933196, "f1": 0.6464285714285715, "f1_std": 0.06499823249074309, "bacc": 0.6453804347826086, "bacc_std": 0.0608593670337675}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 2.782559402207126, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.06536125675273942, "f1": 0.6148459383753502, "f1_std": 0.07050888572563051, "bacc": 0.6141304347826086, "bacc_std": 0.06793142832751724}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 1291.5496650148827, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.06453983074091625, "f1": 0.6303763440860215, "f1_std": 0.0653865255187098, "bacc": 0.6324728260869565, "bacc_std": 0.06557451496757345}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.052379732374615684, "f1": 0.76890756302521, "f1_std": 0.05702815057377004, "bacc": 0.7635869565217391, "bacc_std": 0.056061380749207045}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_sex  | train   |        100 | 180.47 |  1038.6 | 0.90849 |  0.067379 | 0.90508 | 0.070267 | 0.90293 |   0.071676 |
+| schaefer400_mae | patch  | logistic | aabc_sex  | test    |        100 | 180.47 |  1038.6 | 0.72382 |  0.058056 | 0.71184 | 0.06094  | 0.7113  |   0.059598 |
+
+
+done! total time: 0:04:31

subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic/eval_table.csv

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subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:24:22
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/abide_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: abide_dx (schaefer400)
+train (n=578):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 578
+}),
+    labels=['Autism' 'Control'],
+    counts=[260 318]
+)
+
+validation (n=124):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 124
+}),
+    labels=['Autism' 'Control'],
+    counts=[54 70]
+)
+
+test (n=124):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 124
+}),
+    labels=['Autism' 'Control'],
+    counts=[57 67]
+)
+
+extracting features for all splits
+extract (train)  [  0/289]  eta: 0:06:08    time: 1.2743  data: 0.9320  max mem: 1546
+extract (train)  [ 20/289]  eta: 0:00:28    time: 0.0489  data: 0.0039  max mem: 1715
+extract (train)  [ 40/289]  eta: 0:00:19    time: 0.0469  data: 0.0014  max mem: 1715
+extract (train)  [ 60/289]  eta: 0:00:15    time: 0.0474  data: 0.0022  max mem: 1715
+extract (train)  [ 80/289]  eta: 0:00:13    time: 0.0475  data: 0.0022  max mem: 1715
+extract (train)  [100/289]  eta: 0:00:11    time: 0.0473  data: 0.0021  max mem: 1715
+extract (train)  [120/289]  eta: 0:00:09    time: 0.0475  data: 0.0022  max mem: 1715
+extract (train)  [140/289]  eta: 0:00:08    time: 0.0476  data: 0.0023  max mem: 1715
+extract (train)  [160/289]  eta: 0:00:07    time: 0.0474  data: 0.0020  max mem: 1715
+extract (train)  [180/289]  eta: 0:00:05    time: 0.0473  data: 0.0020  max mem: 1715
+extract (train)  [200/289]  eta: 0:00:04    time: 0.0477  data: 0.0022  max mem: 1715
+extract (train)  [220/289]  eta: 0:00:03    time: 0.0477  data: 0.0022  max mem: 1715
+extract (train)  [240/289]  eta: 0:00:02    time: 0.0475  data: 0.0022  max mem: 1715
+extract (train)  [260/289]  eta: 0:00:01    time: 0.0476  data: 0.0023  max mem: 1715
+extract (train)  [280/289]  eta: 0:00:00    time: 0.0476  data: 0.0023  max mem: 1715
+extract (train)  [288/289]  eta: 0:00:00    time: 0.0475  data: 0.0022  max mem: 1715
+extract (train) Total time: 0:00:15 (0.0524 s / it)
+extract (validation)  [ 0/62]  eta: 0:00:54    time: 0.8774  data: 0.8313  max mem: 1715
+extract (validation)  [20/62]  eta: 0:00:03    time: 0.0477  data: 0.0026  max mem: 1715
+extract (validation)  [40/62]  eta: 0:00:01    time: 0.0469  data: 0.0012  max mem: 1715
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.0475  data: 0.0020  max mem: 1715
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.0475  data: 0.0020  max mem: 1715
+extract (validation) Total time: 0:00:03 (0.0625 s / it)
+extract (test)  [ 0/62]  eta: 0:01:01    time: 0.9841  data: 0.9367  max mem: 1715
+extract (test)  [20/62]  eta: 0:00:03    time: 0.0481  data: 0.0028  max mem: 1715
+extract (test)  [40/62]  eta: 0:00:01    time: 0.0469  data: 0.0012  max mem: 1715
+extract (test)  [60/62]  eta: 0:00:00    time: 0.0477  data: 0.0023  max mem: 1715
+extract (test)  [61/62]  eta: 0:00:00    time: 0.0477  data: 0.0023  max mem: 1715
+extract (test) Total time: 0:00:03 (0.0644 s / it)
+feature extraction time: 0:00:23
+train features: (578, 768)
+validation features: (124, 768)
+test features: (124, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |       C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|--------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.35938 | train   | 0.86752 |  0.013067 | 0.86564 | 0.013307 | 0.86467 |   0.013434 |
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.35938 | test    | 0.57258 |  0.043461 | 0.57032 | 0.043754 | 0.57044 |   0.043738 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04211243992282923, "f1": 0.6217205613178767, "f1_std": 0.04441201775443483, "bacc": 0.6234243697478992, "bacc_std": 0.042660460945746874}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.042857773643839164, "f1": 0.6227513227513227, "f1_std": 0.04413364054626836, "bacc": 0.6223739495798319, "bacc_std": 0.04360945901334667}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 2.782559402207126, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.045957302153497734, "f1": 0.650475254015077, "f1_std": 0.04639272111078835, "bacc": 0.6507352941176471, "bacc_std": 0.04642531400176056}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 2.782559402207126, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.042918078922979844, "f1": 0.6852996681199974, "f1_std": 0.04300204416943409, "bacc": 0.6896008403361344, "bacc_std": 0.042991088672500545}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04012021477652981, "f1": 0.6753076721654884, "f1_std": 0.04040702204584405, "bacc": 0.6759453781512605, "bacc_std": 0.04039404549275042}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04067235393494924, "f1": 0.6375232527238905, "f1_std": 0.041613078701866396, "bacc": 0.6370798319327731, "bacc_std": 0.04106668254725189}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 21.54434690031882, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.046041477364189905, "f1": 0.5989703649924097, "f1_std": 0.04665522991463319, "bacc": 0.5987394957983193, "bacc_std": 0.04629582976785813}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.04071504488924196, "f1": 0.6829891838741396, "f1_std": 0.04100895446693964, "bacc": 0.6832983193277311, "bacc_std": 0.04090637303160308}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.044826672206433264, "f1": 0.6227513227513227, "f1_std": 0.045825713521983655, "bacc": 0.6223739495798319, "bacc_std": 0.045320060504257124}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.6935483870967742, "acc_std": 0.03963484143486787, "f1": 0.6835885038947085, "f1_std": 0.04194249245854244, "bacc": 0.6827731092436975, "bacc_std": 0.04077360879639521}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.046719656748984956, "f1": 0.6035753898349319, "f1_std": 0.04683021440391855, "bacc": 0.6050420168067226, "bacc_std": 0.04707268575606125}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 21.54434690031882, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04320052221521185, "f1": 0.6342182890855457, "f1_std": 0.043706854722724675, "bacc": 0.634453781512605, "bacc_std": 0.04373903965527134}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 21.54434690031882, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04170368629476679, "f1": 0.6227513227513227, "f1_std": 0.042732300376330606, "bacc": 0.6223739495798319, "bacc_std": 0.04225365367041529}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.041822860554155815, "f1": 0.6753076721654884, "f1_std": 0.042172017335992185, "bacc": 0.6759453781512605, "bacc_std": 0.042210897249237586}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.04081345088513059, "f1": 0.6721578198088265, "f1_std": 0.043811742572229494, "bacc": 0.6722689075630253, "bacc_std": 0.04200847683973647}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.7258064516129032, "acc_std": 0.04104228676389235, "f1": 0.724011521340665, "f1_std": 0.04123790136604574, "bacc": 0.7247899159663866, "bacc_std": 0.04123780849630671}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | abide_dx  | train   |        100 | 122.53 |  1006.5 | 0.8992  |  0.076084 | 0.89738 | 0.078443 | 0.89656 |   0.078495 |
+| schaefer400_mae | patch  | logistic | abide_dx  | test    |        100 | 122.53 |  1006.5 | 0.63629 |  0.04311  | 0.63081 | 0.043222 | 0.63121 |   0.043025 |
+
+
+done! total time: 0:04:59

subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,test,0.5384615384615384,0.05275253380156451,0.4846723044397463,0.05916027528092928,0.5033783783783784,0.053067334339330835
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+schaefer400_mae,patch,logistic,adhd200_dx,93,0.005994842503189409,test,0.676923076923077,0.05099844759210553,0.6351242983159583,0.06349424546424157,0.6423745173745173,0.05452824703318599
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subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:24:22
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: adhd200_dx (schaefer400)
+train (n=301):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 301
+}),
+    labels=['ADHD' 'Control'],
+    counts=[131 170]
+)
+
+validation (n=64):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 64
+}),
+    labels=['ADHD' 'Control'],
+    counts=[28 36]
+)
+
+test (n=65):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 65
+}),
+    labels=['ADHD' 'Control'],
+    counts=[28 37]
+)
+
+extracting features for all splits
+extract (train)  [  0/151]  eta: 0:03:44    time: 1.4898  data: 1.0706  max mem: 1546
+extract (train)  [ 20/151]  eta: 0:00:15    time: 0.0487  data: 0.0039  max mem: 1715
+extract (train)  [ 40/151]  eta: 0:00:09    time: 0.0468  data: 0.0016  max mem: 1715
+extract (train)  [ 60/151]  eta: 0:00:06    time: 0.0476  data: 0.0027  max mem: 1715
+extract (train)  [ 80/151]  eta: 0:00:04    time: 0.0474  data: 0.0026  max mem: 1715
+extract (train)  [100/151]  eta: 0:00:03    time: 0.0475  data: 0.0026  max mem: 1715
+extract (train)  [120/151]  eta: 0:00:01    time: 0.0474  data: 0.0024  max mem: 1715
+extract (train)  [140/151]  eta: 0:00:00    time: 0.0475  data: 0.0026  max mem: 1715
+extract (train)  [150/151]  eta: 0:00:00    time: 0.0465  data: 0.0025  max mem: 1715
+extract (train) Total time: 0:00:08 (0.0579 s / it)
+extract (validation)  [ 0/32]  eta: 0:00:31    time: 0.9815  data: 0.9347  max mem: 1715
+extract (validation)  [20/32]  eta: 0:00:01    time: 0.0476  data: 0.0029  max mem: 1715
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.0468  data: 0.0019  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.0796 s / it)
+extract (test)  [ 0/33]  eta: 0:00:36    time: 1.1153  data: 1.0682  max mem: 1715
+extract (test)  [20/33]  eta: 0:00:01    time: 0.0480  data: 0.0032  max mem: 1715
+extract (test)  [32/33]  eta: 0:00:00    time: 0.0458  data: 0.0018  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0825 s / it)
+feature extraction time: 0:00:14
+train features: (301, 768)
+validation features: (64, 768)
+test features: (65, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset    | trial   |          C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:-----------|:--------|-----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | train   | 0.64932 |  0.021546 | 0.61498 | 0.025695 | 0.62116 |   0.022801 |
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | test    | 0.55385 |  0.044393 | 0.46886 | 0.055956 | 0.5082  |   0.045087 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.0609170470986483, "f1": 0.543030303030303, "f1_std": 0.06238693171169796, "bacc": 0.542953667953668, "bacc_std": 0.0617993250418841}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.000774263682681127, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.04977486830512864, "f1": 0.6091503267973856, "f1_std": 0.059280208330442535, "bacc": 0.6153474903474904, "bacc_std": 0.05243623870956999}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.060848096827871, "f1": 0.545, "f1_std": 0.06471801940172638, "bacc": 0.5477799227799228, "bacc_std": 0.06182548013561877}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.057679793514554614, "f1": 0.5966741126830479, "f1_std": 0.061502968885112974, "bacc": 0.597007722007722, "bacc_std": 0.05923914156313084}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.058290307942229974, "f1": 0.5192307692307693, "f1_std": 0.061561192502326105, "bacc": 0.5207528957528957, "bacc_std": 0.059582148303568644}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.05420224236065402, "f1": 0.675, "f1_std": 0.05958420358263566, "bacc": 0.6732625482625483, "bacc_std": 0.05683428348229921}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05980857036209272, "f1": 0.5644080416976918, "f1_std": 0.06352647872163493, "bacc": 0.5656370656370656, "bacc_std": 0.06123344203838421}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.06080909306030281, "f1": 0.5833333333333333, "f1_std": 0.063879291143112, "bacc": 0.5834942084942085, "bacc_std": 0.062060413532088916}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05848753268252936, "f1": 0.5833333333333333, "f1_std": 0.061282048983597126, "bacc": 0.5834942084942085, "bacc_std": 0.059635677322739895}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.0587830511337385, "f1": 0.5250692869740489, "f1_std": 0.06294984044225697, "bacc": 0.5299227799227799, "bacc_std": 0.05977640013041927}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset    | split   |   n_trials |       C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:-----------|:--------|-----------:|--------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adhd200_dx | train   |        100 | 0.62221 |  3.0758 | 0.7309  |  0.093172 | 0.71274 | 0.10196  | 0.71278 |   0.10057  |
+| schaefer400_mae | patch  | logistic | adhd200_dx | test    |        100 | 0.62221 |  3.0758 | 0.58723 |  0.058799 | 0.5579  | 0.062055 | 0.56411 |   0.058599 |
+
+
+done! total time: 0:04:16

subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,,2.782559402207126,test,0.7804878048780488,0.05365345873977954,0.6328358208955224,0.09092790515553806,0.6197916666666666,0.08267885471494694
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,2,1291.5496650148827,test,0.8536585365853658,0.05585608685742666,0.8016129032258064,0.07692971255374345,0.8016129032258064,0.08076077255038143
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,5,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,5,1291.5496650148827,test,0.7560975609756098,0.054863976515522245,0.6117424242424243,0.0910433299117926,0.6016129032258064,0.07903004730285151
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,166.81005372000556,test,0.6585365853658537,0.05983715756402587,0.5017361111111112,0.07997143183563733,0.5032258064516129,0.07412023474565702
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,0.3593813663804626,train,0.9349593495934959,0.011993638195841944,0.9005748158792886,0.020129762803947085,0.8685594543512203,0.02439747094801633
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,0.3593813663804626,test,0.7560975609756098,0.06243330766512638,0.6693548387096775,0.08384843221535256,0.6693548387096775,0.08496073917305841

subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt

@@ -0,0 +1,240 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:24:53
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: adni_ad_vs_cn (schaefer400)
+train (n=328):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 525
+}),
+    labels=[0 1],
+    counts=[251  77]
+)
+
+validation (n=41):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 66
+}),
+    labels=[0 1],
+    counts=[31 10]
+)
+
+test (n=41):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 66
+}),
+    labels=[0 1],
+    counts=[32  9]
+)
+
+extracting features for all splits
+extract (train)  [  0/164]  eta: 0:03:29    time: 1.2764  data: 0.9717  max mem: 1546
+extract (train)  [ 20/164]  eta: 0:00:15    time: 0.0469  data: 0.0020  max mem: 1715
+extract (train)  [ 40/164]  eta: 0:00:09    time: 0.0466  data: 0.0015  max mem: 1715
+extract (train)  [ 60/164]  eta: 0:00:06    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train)  [ 80/164]  eta: 0:00:05    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [100/164]  eta: 0:00:03    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [120/164]  eta: 0:00:02    time: 0.0473  data: 0.0025  max mem: 1715
+extract (train)  [140/164]  eta: 0:00:01    time: 0.0474  data: 0.0024  max mem: 1715
+extract (train)  [160/164]  eta: 0:00:00    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [163/164]  eta: 0:00:00    time: 0.0475  data: 0.0026  max mem: 1715
+extract (train) Total time: 0:00:09 (0.0557 s / it)
+extract (validation)  [ 0/21]  eta: 0:00:20    time: 0.9914  data: 0.9457  max mem: 1715
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.0451  data: 0.0011  max mem: 1715
+extract (validation) Total time: 0:00:01 (0.0950 s / it)
+extract (test)  [ 0/21]  eta: 0:00:20    time: 0.9934  data: 0.9475  max mem: 1715
+extract (test)  [20/21]  eta: 0:00:00    time: 0.0448  data: 0.0010  max mem: 1715
+extract (test) Total time: 0:00:01 (0.0948 s / it)
+feature extraction time: 0:00:13
+train features: (328, 768)
+validation features: (41, 768)
+test features: (41, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset       | trial   |      C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:--------------|:--------|-------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | train   | 1       |  0        | 1       | 0        | 1       |   0        |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | test    | 0.78049 |  0.053653 | 0.63284 | 0.090928 | 0.61979 |   0.082679 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.8780487804878049, "acc_std": 0.04158258341310164, "f1": 0.8144796380090498, "f1_std": 0.07252153037282717, "bacc": 0.7838709677419355, "bacc_std": 0.07463863822670634}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 1291.5496650148827, "split": "test", "acc": 0.8536585365853658, "acc_std": 0.05585608685742666, "f1": 0.8016129032258064, "f1_std": 0.07692971255374345, "bacc": 0.8016129032258064, "bacc_std": 0.08076077255038143}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.054015751112367165, "f1": 0.5340909090909092, "f1_std": 0.08125952838262694, "bacc": 0.535483870967742, "bacc_std": 0.0687290455058838}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.9024390243902439, "acc_std": 0.04281293093592416, "f1": 0.8576388888888888, "f1_std": 0.06838057645222723, "bacc": 0.8338709677419355, "bacc_std": 0.07605767951984736}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 1291.5496650148827, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.054863976515522245, "f1": 0.6117424242424243, "f1_std": 0.0910433299117926, "bacc": 0.6016129032258064, "bacc_std": 0.07903004730285151}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 2.782559402207126, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.05550119913384275, "f1": 0.5918552036199095, "f1_std": 0.08486607045876306, "bacc": 0.5854838709677419, "bacc_std": 0.07707446026121592}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06407184768426141, "f1": 0.6232247284878863, "f1_std": 0.08916510631351453, "bacc": 0.6193548387096774, "bacc_std": 0.08808653505893432}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 21.54434690031882, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06274763671363995, "f1": 0.6893939393939394, "f1_std": 0.07763198647753655, "bacc": 0.7032258064516128, "bacc_std": 0.08372099221187411}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 1291.5496650148827, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.061533274274579476, "f1": 0.6917293233082706, "f1_std": 0.08753627676594254, "bacc": 0.685483870967742, "bacc_std": 0.08671610263252859}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 21.54434690031882, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06322493201700127, "f1": 0.7119437939110069, "f1_std": 0.08185129713969748, "bacc": 0.7193548387096774, "bacc_std": 0.08645876860130561}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05220822721274113, "f1": 0.7402714932126697, "f1_std": 0.08398733707003288, "bacc": 0.717741935483871, "bacc_std": 0.08053864003707693}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.8536585365853658, "acc_std": 0.05152983807250614, "f1": 0.7864583333333333, "f1_std": 0.08535631733864563, "bacc": 0.7677419354838709, "bacc_std": 0.08641799418548583}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.05482915979504932, "f1": 0.5340909090909092, "f1_std": 0.08229643698514978, "bacc": 0.535483870967742, "bacc_std": 0.0698941833620366}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 85, "C": 1291.5496650148827, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.07159638008921843, "f1": 0.5876436781609196, "f1_std": 0.08154195262620177, "bacc": 0.6048387096774194, "bacc_std": 0.09110176417613526}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 86, "C": 2.782559402207126, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.0546586182126272, "f1": 0.6660633484162897, "f1_std": 0.08986485790425117, "bacc": 0.6516129032258065, "bacc_std": 0.08228462569271502}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 87, "C": 10000.0, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0636658999572818, "f1": 0.603225806451613, "f1_std": 0.08253200980275384, "bacc": 0.603225806451613, "bacc_std": 0.08370613927509327}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 88, "C": 2.782559402207126, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06341333957402806, "f1": 0.7119437939110069, "f1_std": 0.0789896939772267, "bacc": 0.7193548387096774, "bacc_std": 0.08301910966903543}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 89, "C": 0.3593813663804626, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05735164499662721, "f1": 0.7602339181286549, "f1_std": 0.08338389016106572, "bacc": 0.7516129032258064, "bacc_std": 0.08524713775439839}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 90, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.05751405694249479, "f1": 0.6917293233082706, "f1_std": 0.08559748839964339, "bacc": 0.685483870967742, "bacc_std": 0.08593501670898884}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 91, "C": 166.81005372000556, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.07285094346888693, "f1": 0.5199063231850116, "f1_std": 0.08467830102196579, "bacc": 0.5209677419354839, "bacc_std": 0.08828244634686146}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 92, "C": 166.81005372000556, "split": "test", "acc": 0.5853658536585366, "acc_std": 0.07565743579265563, "f1": 0.4863669859985261, "f1_std": 0.08242307377740882, "bacc": 0.4887096774193548, "bacc_std": 0.09101403140500397}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 93, "C": 166.81005372000556, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.07312875218539383, "f1": 0.646551724137931, "f1_std": 0.08336783195087395, "bacc": 0.6709677419354838, "bacc_std": 0.09261872494370514}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 94, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06012976251635829, "f1": 0.5918552036199095, "f1_std": 0.08916912462209876, "bacc": 0.5854838709677419, "bacc_std": 0.07938847824884954}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 95, "C": 2.782559402207126, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06760778514003213, "f1": 0.7054597701149425, "f1_std": 0.07624615428138976, "bacc": 0.7370967741935484, "bacc_std": 0.08357991035645174}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 96, "C": 2.782559402207126, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06633176040950814, "f1": 0.603225806451613, "f1_std": 0.08487428245843184, "bacc": 0.603225806451613, "bacc_std": 0.08448292742332363}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.05955704028069088, "f1": 0.5176470588235295, "f1_std": 0.0797719640650401, "bacc": 0.5193548387096775, "bacc_std": 0.07118399683065486}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.062027147632520804, "f1": 0.6232247284878863, "f1_std": 0.08486759189516242, "bacc": 0.6193548387096774, "bacc_std": 0.08436612365149407}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 166.81005372000556, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.05983715756402587, "f1": 0.5017361111111112, "f1_std": 0.07997143183563733, "bacc": 0.5032258064516129, "bacc_std": 0.07412023474565702}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06243330766512638, "f1": 0.6693548387096775, "f1_std": 0.08384843221535256, "bacc": 0.6693548387096775, "bacc_std": 0.08496073917305841}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset       | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:--------------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | train   |        100 | 631.72 |  1973.8 | 0.97436 |  0.043484 | 0.95852 | 0.07352  | 0.94848 |   0.087341 |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 631.72 |  1973.8 | 0.74732 |  0.065089 | 0.63955 | 0.090697 | 0.63848 |   0.08667  |
+
+
+done! total time: 0:04:12

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_probe
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: true
+  norm: true
+lr_scale_grid:
+- 0.02
+- 0.023
+- 0.028
+- 0.033
+- 0.038
+- 0.045
+- 0.053
+- 0.062
+- 0.074
+- 0.087
+- 0.1
+- 0.12
+- 0.14
+- 0.17
+- 0.2
+- 0.23
+- 0.27
+- 0.32
+- 0.38
+- 0.44
+- 0.52
+- 0.61
+- 0.72
+- 0.85
+- 1
+- 1.2
+- 1.4
+- 1.6
+- 1.9
+- 2.3
+- 2.7
+- 3.1
+- 3.7
+- 4.3
+- 5.1
+- 6
+- 7.1
+- 8.3
+- 9.8
+- 12
+- 14
+- 16
+- 19
+- 22
+- 26
+- 31
+- 36
+- 43
+- 50
+wd_scale_grid:
+- 1.0
+num_workers: 8
+prefetch_factor: null
+balanced_sampling: false
+epochs: 20
+steps_per_epoch: 200
+batch_size: 64
+accum_iter: 2
+lr: 0.0003
+warmup_epochs: 5
+no_decay: false
+weight_decay: 0.05
+clip_grad: 1.0
+metrics:
+- acc
+- f1
+cv_metric: acc
+early_stopping: true
+amp: true
+device: cuda
+seed: 4466
+debug: false
+wandb: false
+wandb_entity: null
+wandb_project: fMRI-fm-eval
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 13, "eval/id_best": 36, "eval/lr_best": 0.0021299999999999995, "eval/wd_best": 0.05, "eval/train/loss": 9.498229337623343e-05, "eval/train/acc": 1.0, "eval/train/acc_std": 0.0, "eval/train/f1": 1.0, "eval/train/f1_std": 0.0, "eval/validation/loss": 0.15430089831352234, "eval/validation/acc": 0.9776785714285714, "eval/validation/acc_std": 0.0022329876597979932, "eval/validation/f1": 0.9732729617516623, "eval/validation/f1_std": 0.0029378996580647593, "eval/test/loss": 0.2003052532672882, "eval/test/acc": 0.9714285714285714, "eval/test/acc_std": 0.0023715181808253855, "eval/test/f1": 0.9647678512804001, "eval/test/f1_std": 0.0032151136203305585}

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 13, "eval/best/id_best": 36, "eval/best/lr_best": 0.0021299999999999995, "eval/best/wd_best": 0.05, "eval/best/train/loss": 9.498229337623343e-05, "eval/best/train/acc": 1.0, "eval/best/train/acc_std": 0.0, "eval/best/train/f1": 1.0, "eval/best/train/f1_std": 0.0, "eval/best/validation/loss": 0.15430089831352234, "eval/best/validation/acc": 0.9776785714285714, "eval/best/validation/acc_std": 0.0022329876597979932, "eval/best/validation/f1": 0.9732729617516623, "eval/best/validation/f1_std": 0.0029378996580647593, "eval/best/test/loss": 0.2003052532672882, "eval/best/test/acc": 0.9714285714285714, "eval/best/test/acc_std": 0.0023715181808253855, "eval/best/test/f1": 0.9647678512804001, "eval/best/test/f1_std": 0.0032151136203305585}

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 36, "eval/last/lr_best": 0.0021299999999999995, "eval/last/wd_best": 0.05, "eval/last/train/loss": 7.540707156294957e-05, "eval/last/train/acc": 1.0, "eval/last/train/acc_std": 0.0, "eval/last/train/f1": 1.0, "eval/last/train/f1_std": 0.0, "eval/last/validation/loss": 0.14834365248680115, "eval/last/validation/acc": 0.9771825396825397, "eval/last/validation/acc_std": 0.0022310898640639015, "eval/last/validation/f1": 0.9725073846016076, "eval/last/validation/f1_std": 0.0029789466516055114, "eval/last/test/loss": 0.19190773367881775, "eval/last/test/acc": 0.9716269841269841, "eval/last/test/acc_std": 0.0024160420583601476, "eval/last/test/f1": 0.96544940446876, "eval/last/test/f1_std": 0.003217601171640498}

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_table.csv

@@ -0,0 +1,4 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",train,9.498229337623343e-05,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",validation,0.15430089831352234,0.9776785714285714,0.0022329876597979932,0.9732729617516623,0.0029378996580647593
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",test,0.2003052532672882,0.9714285714285714,0.0023715181808253855,0.9647678512804001,0.0032151136203305585

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv

@@ -0,0 +1,4 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",train,9.498229337623343e-05,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",validation,0.15430089831352234,0.9776785714285714,0.0022329876597979932,0.9732729617516623,0.0029378996580647593
+schaefer400_mae,patch,attn,hcpya_task21,best,13,0.0021299999999999995,0.05,36,"[7.1, 1.0]",test,0.2003052532672882,0.9714285714285714,0.0023715181808253855,0.9647678512804001,0.0032151136203305585

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/eval_table_last.csv

@@ -0,0 +1,4 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,hcpya_task21,last,19,0.0021299999999999995,0.05,36,"[7.1, 1.0]",train,7.540707156294957e-05,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,last,19,0.0021299999999999995,0.05,36,"[7.1, 1.0]",validation,0.14834365248680115,0.9771825396825397,0.0022310898640639015,0.9725073846016076,0.0029789466516055114
+schaefer400_mae,patch,attn,hcpya_task21,last,19,0.0021299999999999995,0.05,36,"[7.1, 1.0]",test,0.19190773367881775,0.9716269841269841,0.0024160420583601476,0.96544940446876,0.003217601171640498

subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/log.txt

@@ -0,0 +1,890 @@
+fMRI foundation model probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 14:49:48
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_probe
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: true
+  norm: true
+lr_scale_grid:
+- 0.02
+- 0.023
+- 0.028
+- 0.033
+- 0.038
+- 0.045
+- 0.053
+- 0.062
+- 0.074
+- 0.087
+- 0.1
+- 0.12
+- 0.14
+- 0.17
+- 0.2
+- 0.23
+- 0.27
+- 0.32
+- 0.38
+- 0.44
+- 0.52
+- 0.61
+- 0.72
+- 0.85
+- 1
+- 1.2
+- 1.4
+- 1.6
+- 1.9
+- 2.3
+- 2.7
+- 3.1
+- 3.7
+- 4.3
+- 5.1
+- 6
+- 7.1
+- 8.3
+- 9.8
+- 12
+- 14
+- 16
+- 19
+- 22
+- 26
+- 31
+- 36
+- 43
+- 50
+wd_scale_grid:
+- 1.0
+num_workers: 8
+prefetch_factor: null
+balanced_sampling: false
+epochs: 20
+steps_per_epoch: 200
+batch_size: 64
+accum_iter: 2
+lr: 0.0003
+warmup_epochs: 5
+no_decay: false
+weight_decay: 0.05
+clip_grad: 1.0
+metrics:
+- acc
+- f1
+cv_metric: acc
+early_stopping: true
+amp: true
+device: cuda
+seed: 4466
+debug: false
+wandb: false
+wandb_entity: null
+wandb_project: fMRI-fm-eval
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: hcpya_task21 (schaefer400)
+train (n=18999):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 18999
+}),
+    labels=[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20],
+    counts=[ 832 1248 3201 1660  832  832  832  832  832 1248 1247 1243  832  416
+  416  416  416  416  416  416  416]
+)
+
+validation (n=4032):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 4032
+}),
+    labels=[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20],
+    counts=[176 264 688 352 176 176 176 176 176 264 264 264 176  88  88  88  88  88
+  88  88  88]
+)
+
+test (n=5040):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5040
+}),
+    labels=[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20],
+    counts=[220 330 860 440 220 220 220 220 220 330 330 330 220 110 110 110 110 110
+ 110 110 110]
+)
+
+running backbone on example batch to get embedding dim
+embedding feature dim (patch): 768
+initializing sweep of classifier heads
+classifiers:
+ModuleList(
+  (0-48): 49 x AttnPoolClassifier(
+    (kv): Linear(in_features=768, out_features=1536, bias=True)
+    (linear): Linear(in_features=768, out_features=21, bias=True)
+  )
+)
+classifier params (train): 58.7M (58.7M)
+setting up optimizer
+total batch size: 128 = 64 bs per gpu x 2 accum
+lr: 3.00e-04
+full schedule: epochs = 20 (steps = 4000) (decay = True)
+warmup: epochs = 5 (steps = 1000)
+start training for 20 epochs
+train: [0]  [  0/400]  eta: 0:07:22  lr: nan  time: 1.1055  data: 0.6115  max mem: 23058
+train: [0]  [ 20/400]  eta: 0:01:53  lr: 0.000003  loss: 3.0270 (3.0336)  grad: 0.3381 (0.3465)  time: 0.2571  data: 0.0026  max mem: 23762
+train: [0]  [ 40/400]  eta: 0:01:38  lr: 0.000006  loss: 2.9620 (2.9571)  grad: 0.3600 (0.3512)  time: 0.2478  data: 0.0025  max mem: 23762
+train: [0]  [ 60/400]  eta: 0:01:30  lr: 0.000009  loss: 2.7953 (2.8829)  grad: 0.3403 (0.3409)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [0]  [ 80/400]  eta: 0:01:23  lr: 0.000012  loss: 2.6093 (2.7922)  grad: 0.3160 (0.3351)  time: 0.2469  data: 0.0023  max mem: 23762
+train: [0]  [100/400]  eta: 0:01:17  lr: 0.000015  loss: 2.4406 (2.7132)  grad: 0.3026 (0.3261)  time: 0.2476  data: 0.0027  max mem: 23762
+train: [0]  [120/400]  eta: 0:01:11  lr: 0.000018  loss: 2.3183 (2.6397)  grad: 0.2922 (0.3226)  time: 0.2473  data: 0.0024  max mem: 23762
+train: [0]  [140/400]  eta: 0:01:06  lr: 0.000021  loss: 2.2334 (2.5732)  grad: 0.2922 (0.3186)  time: 0.2484  data: 0.0029  max mem: 23762
+train: [0]  [160/400]  eta: 0:01:01  lr: 0.000024  loss: 2.1277 (2.5106)  grad: 0.2806 (0.3137)  time: 0.2485  data: 0.0025  max mem: 23762
+train: [0]  [180/400]  eta: 0:00:55  lr: 0.000027  loss: 2.0185 (2.4508)  grad: 0.2715 (0.3084)  time: 0.2484  data: 0.0029  max mem: 23762
+train: [0]  [200/400]  eta: 0:00:50  lr: 0.000030  loss: 1.9194 (2.3943)  grad: 0.2628 (0.3045)  time: 0.2486  data: 0.0025  max mem: 23762
+train: [0]  [220/400]  eta: 0:00:45  lr: 0.000033  loss: 1.8313 (2.3421)  grad: 0.2604 (0.3007)  time: 0.2477  data: 0.0027  max mem: 23762
+train: [0]  [240/400]  eta: 0:00:40  lr: 0.000036  loss: 1.7858 (2.2922)  grad: 0.2604 (0.2976)  time: 0.2477  data: 0.0024  max mem: 23762
+train: [0]  [260/400]  eta: 0:00:35  lr: 0.000039  loss: 1.7143 (2.2470)  grad: 0.2582 (0.2942)  time: 0.2474  data: 0.0027  max mem: 23762
+train: [0]  [280/400]  eta: 0:00:30  lr: 0.000042  loss: 1.7045 (2.2058)  grad: 0.2421 (0.2907)  time: 0.2477  data: 0.0024  max mem: 23762
+train: [0]  [300/400]  eta: 0:00:25  lr: 0.000045  loss: 1.6250 (2.1642)  grad: 0.2421 (0.2876)  time: 0.2794  data: 0.0336  max mem: 23762
+train: [0]  [320/400]  eta: 0:00:20  lr: 0.000048  loss: 1.5433 (2.1240)  grad: 0.2432 (0.2848)  time: 0.2494  data: 0.0026  max mem: 23762
+train: [0]  [340/400]  eta: 0:00:15  lr: 0.000051  loss: 1.5360 (2.0887)  grad: 0.2339 (0.2814)  time: 0.2486  data: 0.0027  max mem: 23762
+train: [0]  [360/400]  eta: 0:00:10  lr: 0.000054  loss: 1.4985 (2.0548)  grad: 0.2236 (0.2785)  time: 0.2488  data: 0.0029  max mem: 23762
+train: [0]  [380/400]  eta: 0:00:05  lr: 0.000057  loss: 1.4260 (2.0201)  grad: 0.2335 (0.2763)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 1.3768 (1.9869)  grad: 0.2246 (0.2735)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [0] Total time: 0:01:40 (0.2524 s / it)
+train: [0] Summary: lr: 0.000060  loss: 1.3768 (1.9869)  grad: 0.2246 (0.2735)
+eval (validation): [0]  [ 0/63]  eta: 0:00:43    time: 0.6892  data: 0.5677  max mem: 23762
+eval (validation): [0]  [20/63]  eta: 0:00:06    time: 0.1236  data: 0.0029  max mem: 23762
+eval (validation): [0]  [40/63]  eta: 0:00:03    time: 0.1233  data: 0.0021  max mem: 23762
+eval (validation): [0]  [60/63]  eta: 0:00:00    time: 0.1241  data: 0.0027  max mem: 23762
+eval (validation): [0]  [62/63]  eta: 0:00:00    time: 0.1238  data: 0.0025  max mem: 23762
+eval (validation): [0] Total time: 0:00:08 (0.1343 s / it)
+cv: [0]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.141  acc: 0.957  f1: 0.944
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:05:40  lr: nan  time: 0.8506  data: 0.6102  max mem: 23762
+train: [1]  [ 20/400]  eta: 0:01:45  lr: 0.000063  loss: 1.2868 (1.3078)  grad: 0.2135 (0.2114)  time: 0.2485  data: 0.0025  max mem: 23762
+train: [1]  [ 40/400]  eta: 0:01:34  lr: 0.000066  loss: 1.2868 (1.2960)  grad: 0.2135 (0.2127)  time: 0.2483  data: 0.0023  max mem: 23762
+train: [1]  [ 60/400]  eta: 0:01:27  lr: 0.000069  loss: 1.2720 (1.2863)  grad: 0.2082 (0.2116)  time: 0.2480  data: 0.0026  max mem: 23762
+train: [1]  [ 80/400]  eta: 0:01:21  lr: 0.000072  loss: 1.2163 (1.2679)  grad: 0.2058 (0.2108)  time: 0.2483  data: 0.0023  max mem: 23762
+train: [1]  [100/400]  eta: 0:01:16  lr: 0.000075  loss: 1.2003 (1.2596)  grad: 0.2005 (0.2085)  time: 0.2486  data: 0.0026  max mem: 23762
+train: [1]  [120/400]  eta: 0:01:10  lr: 0.000078  loss: 1.1804 (1.2453)  grad: 0.1974 (0.2070)  time: 0.2487  data: 0.0023  max mem: 23762
+train: [1]  [140/400]  eta: 0:01:05  lr: 0.000081  loss: 1.1714 (1.2362)  grad: 0.1991 (0.2052)  time: 0.2486  data: 0.0027  max mem: 23762
+train: [1]  [160/400]  eta: 0:01:00  lr: 0.000084  loss: 1.1644 (1.2220)  grad: 0.2014 (0.2048)  time: 0.2479  data: 0.0023  max mem: 23762
+train: [1]  [180/400]  eta: 0:00:55  lr: 0.000087  loss: 1.1151 (1.2129)  grad: 0.2044 (0.2045)  time: 0.2495  data: 0.0027  max mem: 23762
+train: [1]  [200/400]  eta: 0:00:50  lr: 0.000090  loss: 1.0933 (1.2004)  grad: 0.1981 (0.2037)  time: 0.2492  data: 0.0024  max mem: 23762
+train: [1]  [220/400]  eta: 0:00:45  lr: 0.000093  loss: 1.0684 (1.1878)  grad: 0.1933 (0.2030)  time: 0.2489  data: 0.0027  max mem: 23762
+train: [1]  [240/400]  eta: 0:00:40  lr: 0.000096  loss: 1.0299 (1.1746)  grad: 0.1906 (0.2018)  time: 0.2485  data: 0.0023  max mem: 23762
+train: [1]  [260/400]  eta: 0:00:35  lr: 0.000099  loss: 1.0249 (1.1650)  grad: 0.1849 (0.2007)  time: 0.2492  data: 0.0026  max mem: 23762
+train: [1]  [280/400]  eta: 0:00:30  lr: 0.000102  loss: 1.0018 (1.1523)  grad: 0.1850 (0.1999)  time: 0.2480  data: 0.0023  max mem: 23762
+train: [1]  [300/400]  eta: 0:00:25  lr: 0.000105  loss: 0.9831 (1.1418)  grad: 0.1850 (0.1989)  time: 0.2813  data: 0.0355  max mem: 23762
+train: [1]  [320/400]  eta: 0:00:20  lr: 0.000108  loss: 0.9562 (1.1294)  grad: 0.1801 (0.1979)  time: 0.2481  data: 0.0023  max mem: 23762
+train: [1]  [340/400]  eta: 0:00:15  lr: 0.000111  loss: 0.9511 (1.1186)  grad: 0.1810 (0.1975)  time: 0.2496  data: 0.0030  max mem: 23762
+train: [1]  [360/400]  eta: 0:00:10  lr: 0.000114  loss: 0.9005 (1.1068)  grad: 0.1810 (0.1964)  time: 0.2489  data: 0.0023  max mem: 23762
+train: [1]  [380/400]  eta: 0:00:05  lr: 0.000117  loss: 0.8982 (1.0964)  grad: 0.1774 (0.1961)  time: 0.2495  data: 0.0030  max mem: 23762
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 0.9162 (1.0873)  grad: 0.1856 (0.1959)  time: 0.2489  data: 0.0022  max mem: 23762
+train: [1] Total time: 0:01:40 (0.2521 s / it)
+train: [1] Summary: lr: 0.000120  loss: 0.9162 (1.0873)  grad: 0.1856 (0.1959)
+eval (validation): [1]  [ 0/63]  eta: 0:00:40    time: 0.6496  data: 0.5302  max mem: 23762
+eval (validation): [1]  [20/63]  eta: 0:00:06    time: 0.1243  data: 0.0022  max mem: 23762
+eval (validation): [1]  [40/63]  eta: 0:00:03    time: 0.1252  data: 0.0028  max mem: 23762
+eval (validation): [1]  [60/63]  eta: 0:00:00    time: 0.1237  data: 0.0017  max mem: 23762
+eval (validation): [1]  [62/63]  eta: 0:00:00    time: 0.1237  data: 0.0017  max mem: 23762
+eval (validation): [1] Total time: 0:00:08 (0.1343 s / it)
+cv: [1]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.107  acc: 0.967  f1: 0.957
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:05:25  lr: nan  time: 0.8145  data: 0.5764  max mem: 23762
+train: [2]  [ 20/400]  eta: 0:01:44  lr: 0.000123  loss: 0.8408 (0.8636)  grad: 0.1779 (0.1825)  time: 0.2479  data: 0.0026  max mem: 23762
+train: [2]  [ 40/400]  eta: 0:01:34  lr: 0.000126  loss: 0.8440 (0.8569)  grad: 0.1834 (0.1878)  time: 0.2486  data: 0.0029  max mem: 23762
+train: [2]  [ 60/400]  eta: 0:01:27  lr: 0.000129  loss: 0.8609 (0.8599)  grad: 0.1948 (0.1939)  time: 0.2475  data: 0.0022  max mem: 23762
+train: [2]  [ 80/400]  eta: 0:01:21  lr: 0.000132  loss: 0.8668 (0.8637)  grad: 0.2045 (0.1970)  time: 0.2484  data: 0.0028  max mem: 23762
+train: [2]  [100/400]  eta: 0:01:16  lr: 0.000135  loss: 0.8615 (0.8586)  grad: 0.2041 (0.2025)  time: 0.2489  data: 0.0023  max mem: 23762
+train: [2]  [120/400]  eta: 0:01:10  lr: 0.000138  loss: 0.8306 (0.8544)  grad: 0.2148 (0.2051)  time: 0.2488  data: 0.0030  max mem: 23762
+train: [2]  [140/400]  eta: 0:01:05  lr: 0.000141  loss: 0.8308 (0.8542)  grad: 0.2296 (0.2170)  time: 0.2484  data: 0.0022  max mem: 23762
+train: [2]  [160/400]  eta: 0:01:00  lr: 0.000144  loss: 0.8308 (0.8450)  grad: 0.2432 (0.2191)  time: 0.2489  data: 0.0029  max mem: 23762
+train: [2]  [180/400]  eta: 0:00:55  lr: 0.000147  loss: 0.7914 (0.8498)  grad: 0.2493 (0.2257)  time: 0.2482  data: 0.0022  max mem: 23762
+train: [2]  [200/400]  eta: 0:00:50  lr: 0.000150  loss: 0.8644 (0.8552)  grad: 0.2835 (0.2336)  time: 0.2486  data: 0.0029  max mem: 23762
+train: [2]  [220/400]  eta: 0:00:45  lr: 0.000153  loss: 0.8911 (0.8582)  grad: 0.2879 (0.2392)  time: 0.2485  data: 0.0023  max mem: 23762
+train: [2]  [240/400]  eta: 0:00:40  lr: 0.000156  loss: 0.8911 (0.8623)  grad: 0.2840 (0.2440)  time: 0.2490  data: 0.0029  max mem: 23762
+train: [2]  [260/400]  eta: 0:00:35  lr: 0.000159  loss: 0.8568 (0.8623)  grad: 0.2751 (0.2468)  time: 0.2493  data: 0.0023  max mem: 23762
+train: [2]  [280/400]  eta: 0:00:30  lr: 0.000162  loss: 0.8805 (0.8681)  grad: 0.2919 (0.2553)  time: 0.2497  data: 0.0031  max mem: 23762
+train: [2]  [300/400]  eta: 0:00:25  lr: 0.000165  loss: 0.8805 (0.8670)  grad: 0.2937 (0.2566)  time: 0.2774  data: 0.0310  max mem: 23762
+train: [2]  [320/400]  eta: 0:00:20  lr: 0.000168  loss: 0.7968 (0.8638)  grad: 0.2785 (0.2604)  time: 0.2494  data: 0.0032  max mem: 23762
+train: [2]  [340/400]  eta: 0:00:15  lr: 0.000171  loss: 0.7858 (0.8607)  grad: 0.3019 (0.2631)  time: 0.2482  data: 0.0020  max mem: 23762
+train: [2]  [360/400]  eta: 0:00:10  lr: 0.000174  loss: 0.7868 (0.8571)  grad: 0.3221 (0.2690)  time: 0.2486  data: 0.0030  max mem: 23762
+train: [2]  [380/400]  eta: 0:00:05  lr: 0.000177  loss: 0.8037 (0.8577)  grad: 0.3173 (0.2713)  time: 0.2476  data: 0.0021  max mem: 23762
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 0.9288 (0.8619)  grad: 0.3328 (0.2759)  time: 0.2484  data: 0.0024  max mem: 23762
+train: [2] Total time: 0:01:40 (0.2517 s / it)
+train: [2] Summary: lr: 0.000180  loss: 0.9288 (0.8619)  grad: 0.3328 (0.2759)
+eval (validation): [2]  [ 0/63]  eta: 0:00:42    time: 0.6824  data: 0.5613  max mem: 23762
+eval (validation): [2]  [20/63]  eta: 0:00:06    time: 0.1240  data: 0.0022  max mem: 23762
+eval (validation): [2]  [40/63]  eta: 0:00:03    time: 0.1241  data: 0.0020  max mem: 23762
+eval (validation): [2]  [60/63]  eta: 0:00:00    time: 0.1243  data: 0.0025  max mem: 23762
+eval (validation): [2]  [62/63]  eta: 0:00:00    time: 0.1237  data: 0.0018  max mem: 23762
+eval (validation): [2] Total time: 0:00:08 (0.1346 s / it)
+cv: [2]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.100  acc: 0.967  f1: 0.960
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:05:54  lr: nan  time: 0.8868  data: 0.6516  max mem: 23762
+train: [3]  [ 20/400]  eta: 0:01:45  lr: 0.000183  loss: 0.7830 (0.8401)  grad: 0.3202 (0.3258)  time: 0.2482  data: 0.0025  max mem: 23762
+train: [3]  [ 40/400]  eta: 0:01:34  lr: 0.000186  loss: 0.7830 (0.8114)  grad: 0.3202 (0.3203)  time: 0.2481  data: 0.0023  max mem: 23762
+train: [3]  [ 60/400]  eta: 0:01:28  lr: 0.000189  loss: 0.7680 (0.7988)  grad: 0.3127 (0.3133)  time: 0.2493  data: 0.0029  max mem: 23762
+train: [3]  [ 80/400]  eta: 0:01:21  lr: 0.000192  loss: 0.7680 (0.8094)  grad: 0.3293 (0.3291)  time: 0.2474  data: 0.0022  max mem: 23762
+train: [3]  [100/400]  eta: 0:01:16  lr: 0.000195  loss: 0.7777 (0.8089)  grad: 0.3319 (0.3321)  time: 0.2478  data: 0.0025  max mem: 23762
+train: [3]  [120/400]  eta: 0:01:10  lr: 0.000198  loss: 0.7736 (0.8191)  grad: 0.3214 (0.3333)  time: 0.2479  data: 0.0022  max mem: 23762
+train: [3]  [140/400]  eta: 0:01:05  lr: 0.000201  loss: 0.7647 (0.8100)  grad: 0.3297 (0.3350)  time: 0.2477  data: 0.0024  max mem: 23762
+train: [3]  [160/400]  eta: 0:01:00  lr: 0.000204  loss: 0.8042 (0.8205)  grad: 0.3630 (0.3418)  time: 0.2487  data: 0.0023  max mem: 23762
+train: [3]  [180/400]  eta: 0:00:55  lr: 0.000207  loss: 0.8795 (0.8283)  grad: 0.3988 (0.3548)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [3]  [200/400]  eta: 0:00:50  lr: 0.000210  loss: 0.8738 (0.8465)  grad: 0.4011 (0.3660)  time: 0.2479  data: 0.0022  max mem: 23762
+train: [3]  [220/400]  eta: 0:00:45  lr: 0.000213  loss: 0.8665 (0.8470)  grad: 0.3942 (0.3719)  time: 0.2480  data: 0.0027  max mem: 23762
+train: [3]  [240/400]  eta: 0:00:40  lr: 0.000216  loss: 0.8444 (0.8546)  grad: 0.3942 (0.3733)  time: 0.2474  data: 0.0022  max mem: 23762
+train: [3]  [260/400]  eta: 0:00:35  lr: 0.000219  loss: 0.8135 (0.8504)  grad: 0.3657 (0.3771)  time: 0.2477  data: 0.0025  max mem: 23762
+train: [3]  [280/400]  eta: 0:00:30  lr: 0.000222  loss: 0.7777 (0.8475)  grad: 0.3862 (0.3789)  time: 0.2484  data: 0.0023  max mem: 23762
+train: [3]  [300/400]  eta: 0:00:25  lr: 0.000225  loss: 0.7960 (0.8493)  grad: 0.4137 (0.3834)  time: 0.2828  data: 0.0368  max mem: 23762
+train: [3]  [320/400]  eta: 0:00:20  lr: 0.000228  loss: 0.8270 (0.8464)  grad: 0.4223 (0.3848)  time: 0.2480  data: 0.0027  max mem: 23762
+train: [3]  [340/400]  eta: 0:00:15  lr: 0.000231  loss: 0.8270 (0.8476)  grad: 0.4116 (0.3854)  time: 0.2465  data: 0.0017  max mem: 23762
+train: [3]  [360/400]  eta: 0:00:10  lr: 0.000234  loss: 0.7525 (0.8430)  grad: 0.3721 (0.3859)  time: 0.2471  data: 0.0025  max mem: 23762
+train: [3]  [380/400]  eta: 0:00:05  lr: 0.000237  loss: 0.7007 (0.8400)  grad: 0.3827 (0.3866)  time: 0.2469  data: 0.0018  max mem: 23762
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 0.7243 (0.8369)  grad: 0.3922 (0.3898)  time: 0.2463  data: 0.0019  max mem: 23762
+train: [3] Total time: 0:01:40 (0.2514 s / it)
+train: [3] Summary: lr: 0.000240  loss: 0.7243 (0.8369)  grad: 0.3922 (0.3898)
+eval (validation): [3]  [ 0/63]  eta: 0:00:33    time: 0.5257  data: 0.4078  max mem: 23762
+eval (validation): [3]  [20/63]  eta: 0:00:06    time: 0.1233  data: 0.0027  max mem: 23762
+eval (validation): [3]  [40/63]  eta: 0:00:03    time: 0.1235  data: 0.0023  max mem: 23762
+eval (validation): [3]  [60/63]  eta: 0:00:00    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [3]  [62/63]  eta: 0:00:00    time: 0.1235  data: 0.0020  max mem: 23762
+eval (validation): [3] Total time: 0:00:08 (0.1315 s / it)
+cv: [3]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 0.097  acc: 0.968  f1: 0.960
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:04:59  lr: nan  time: 0.7495  data: 0.5144  max mem: 23762
+train: [4]  [ 20/400]  eta: 0:01:43  lr: 0.000243  loss: 0.7557 (0.7834)  grad: 0.3801 (0.4013)  time: 0.2472  data: 0.0025  max mem: 23762
+train: [4]  [ 40/400]  eta: 0:01:33  lr: 0.000246  loss: 0.7558 (0.7936)  grad: 0.4209 (0.4547)  time: 0.2474  data: 0.0026  max mem: 23762
+train: [4]  [ 60/400]  eta: 0:01:26  lr: 0.000249  loss: 0.7953 (0.7826)  grad: 0.4395 (0.4514)  time: 0.2463  data: 0.0017  max mem: 23762
+train: [4]  [ 80/400]  eta: 0:01:21  lr: 0.000252  loss: 0.7471 (0.7834)  grad: 0.4221 (0.4548)  time: 0.2477  data: 0.0026  max mem: 23762
+train: [4]  [100/400]  eta: 0:01:15  lr: 0.000255  loss: 0.7837 (0.8330)  grad: 0.4679 (0.4645)  time: 0.2463  data: 0.0017  max mem: 23762
+train: [4]  [120/400]  eta: 0:01:10  lr: 0.000258  loss: 1.0930 (0.8795)  grad: 0.4721 (0.4682)  time: 0.2477  data: 0.0025  max mem: 23762
+train: [4]  [140/400]  eta: 0:01:05  lr: 0.000261  loss: 1.0365 (0.9019)  grad: 0.4830 (0.4860)  time: 0.2465  data: 0.0017  max mem: 23762
+train: [4]  [160/400]  eta: 0:01:00  lr: 0.000264  loss: 0.9646 (0.9252)  grad: 0.5092 (0.5062)  time: 0.2477  data: 0.0025  max mem: 23762
+train: [4]  [180/400]  eta: 0:00:54  lr: 0.000267  loss: 0.9140 (0.9357)  grad: 0.5092 (0.5089)  time: 0.2462  data: 0.0017  max mem: 23762
+train: [4]  [200/400]  eta: 0:00:49  lr: 0.000270  loss: 0.9190 (0.9368)  grad: 0.4909 (0.5098)  time: 0.2473  data: 0.0025  max mem: 23762
+train: [4]  [220/400]  eta: 0:00:44  lr: 0.000273  loss: 0.7774 (0.9234)  grad: 0.4909 (0.5113)  time: 0.2468  data: 0.0017  max mem: 23762
+train: [4]  [240/400]  eta: 0:00:39  lr: 0.000276  loss: 0.7487 (0.9263)  grad: 0.4895 (0.5118)  time: 0.2472  data: 0.0025  max mem: 23762
+train: [4]  [260/400]  eta: 0:00:34  lr: 0.000279  loss: 0.9313 (0.9353)  grad: 0.5133 (0.5157)  time: 0.2468  data: 0.0018  max mem: 23762
+train: [4]  [280/400]  eta: 0:00:29  lr: 0.000282  loss: 1.0964 (0.9538)  grad: 0.5810 (0.5226)  time: 0.2473  data: 0.0025  max mem: 23762
+train: [4]  [300/400]  eta: 0:00:25  lr: 0.000285  loss: 1.1876 (0.9649)  grad: 0.5810 (0.5290)  time: 0.2765  data: 0.0314  max mem: 23762
+train: [4]  [320/400]  eta: 0:00:20  lr: 0.000288  loss: 1.0481 (0.9696)  grad: 0.5550 (0.5332)  time: 0.2482  data: 0.0028  max mem: 23762
+train: [4]  [340/400]  eta: 0:00:15  lr: 0.000291  loss: 1.0000 (0.9703)  grad: 0.5550 (0.5417)  time: 0.2472  data: 0.0021  max mem: 23762
+train: [4]  [360/400]  eta: 0:00:10  lr: 0.000294  loss: 1.0341 (0.9795)  grad: 0.5773 (0.5446)  time: 0.2478  data: 0.0027  max mem: 23762
+train: [4]  [380/400]  eta: 0:00:05  lr: 0.000297  loss: 0.9120 (0.9733)  grad: 0.5903 (0.5476)  time: 0.2471  data: 0.0020  max mem: 23762
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 0.9120 (0.9747)  grad: 0.5903 (0.5502)  time: 0.2468  data: 0.0021  max mem: 23762
+train: [4] Total time: 0:01:40 (0.2502 s / it)
+train: [4] Summary: lr: 0.000300  loss: 0.9120 (0.9747)  grad: 0.5903 (0.5502)
+eval (validation): [4]  [ 0/63]  eta: 0:00:36    time: 0.5727  data: 0.4518  max mem: 23762
+eval (validation): [4]  [20/63]  eta: 0:00:06    time: 0.1236  data: 0.0020  max mem: 23762
+eval (validation): [4]  [40/63]  eta: 0:00:03    time: 0.1246  data: 0.0024  max mem: 23762
+eval (validation): [4]  [60/63]  eta: 0:00:00    time: 0.1235  data: 0.0016  max mem: 23762
+eval (validation): [4]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0016  max mem: 23762
+eval (validation): [4] Total time: 0:00:08 (0.1326 s / it)
+cv: [4]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.094  acc: 0.972  f1: 0.967
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:05:07  lr: nan  time: 0.7682  data: 0.5311  max mem: 23762
+train: [5]  [ 20/400]  eta: 0:01:43  lr: 0.000300  loss: 0.7902 (0.8875)  grad: 0.5516 (0.5784)  time: 0.2463  data: 0.0018  max mem: 23762
+train: [5]  [ 40/400]  eta: 0:01:33  lr: 0.000300  loss: 1.0172 (1.0419)  grad: 0.6008 (0.6115)  time: 0.2465  data: 0.0019  max mem: 23762
+train: [5]  [ 60/400]  eta: 0:01:26  lr: 0.000300  loss: 1.1946 (1.1102)  grad: 0.6119 (0.6089)  time: 0.2471  data: 0.0024  max mem: 23762
+train: [5]  [ 80/400]  eta: 0:01:20  lr: 0.000300  loss: 1.0349 (1.1062)  grad: 0.6105 (0.6149)  time: 0.2467  data: 0.0019  max mem: 23762
+train: [5]  [100/400]  eta: 0:01:15  lr: 0.000300  loss: 1.0698 (1.1354)  grad: 0.6122 (0.6366)  time: 0.2474  data: 0.0025  max mem: 23762
+train: [5]  [120/400]  eta: 0:01:10  lr: 0.000300  loss: 1.0698 (1.1349)  grad: 0.5982 (0.6354)  time: 0.2473  data: 0.0019  max mem: 23762
+train: [5]  [140/400]  eta: 0:01:05  lr: 0.000300  loss: 1.1295 (1.1530)  grad: 0.5982 (0.6344)  time: 0.2476  data: 0.0025  max mem: 23762
+train: [5]  [160/400]  eta: 0:01:00  lr: 0.000299  loss: 1.0998 (1.1404)  grad: 0.6154 (0.6382)  time: 0.2468  data: 0.0019  max mem: 23762
+train: [5]  [180/400]  eta: 0:00:55  lr: 0.000299  loss: 0.9746 (1.1473)  grad: 0.6853 (0.6439)  time: 0.2488  data: 0.0025  max mem: 23762
+train: [5]  [200/400]  eta: 0:00:49  lr: 0.000299  loss: 1.0518 (1.1443)  grad: 0.6858 (0.6454)  time: 0.2476  data: 0.0020  max mem: 23762
+train: [5]  [220/400]  eta: 0:00:44  lr: 0.000299  loss: 0.9966 (1.1376)  grad: 0.6306 (0.6407)  time: 0.2481  data: 0.0025  max mem: 23762
+train: [5]  [240/400]  eta: 0:00:39  lr: 0.000299  loss: 0.9792 (1.1427)  grad: 0.6423 (0.6424)  time: 0.2468  data: 0.0020  max mem: 23762
+train: [5]  [260/400]  eta: 0:00:34  lr: 0.000299  loss: 0.9860 (1.1593)  grad: 0.6233 (0.6419)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [5]  [280/400]  eta: 0:00:29  lr: 0.000298  loss: 1.1821 (1.1573)  grad: 0.6233 (0.6436)  time: 0.2467  data: 0.0019  max mem: 23762
+train: [5]  [300/400]  eta: 0:00:25  lr: 0.000298  loss: 1.1206 (1.1467)  grad: 0.6497 (0.6431)  time: 0.2745  data: 0.0291  max mem: 23762
+train: [5]  [320/400]  eta: 0:00:20  lr: 0.000298  loss: 0.9474 (1.1385)  grad: 0.5894 (0.6385)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [5]  [340/400]  eta: 0:00:15  lr: 0.000298  loss: 0.9255 (1.1248)  grad: 0.6008 (0.6372)  time: 0.2468  data: 0.0018  max mem: 23762
+train: [5]  [360/400]  eta: 0:00:10  lr: 0.000297  loss: 0.8473 (1.1202)  grad: 0.6191 (0.6364)  time: 0.2482  data: 0.0027  max mem: 23762
+train: [5]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 1.0821 (1.1199)  grad: 0.6100 (0.6388)  time: 0.2468  data: 0.0018  max mem: 23762
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 1.0068 (1.1219)  grad: 0.6253 (0.6399)  time: 0.2480  data: 0.0019  max mem: 23762
+train: [5] Total time: 0:01:40 (0.2502 s / it)
+train: [5] Summary: lr: 0.000297  loss: 1.0068 (1.1219)  grad: 0.6253 (0.6399)
+eval (validation): [5]  [ 0/63]  eta: 0:00:42    time: 0.6781  data: 0.5573  max mem: 23762
+eval (validation): [5]  [20/63]  eta: 0:00:06    time: 0.1232  data: 0.0019  max mem: 23762
+eval (validation): [5]  [40/63]  eta: 0:00:03    time: 0.1243  data: 0.0027  max mem: 23762
+eval (validation): [5]  [60/63]  eta: 0:00:00    time: 0.1234  data: 0.0017  max mem: 23762
+eval (validation): [5]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0017  max mem: 23762
+eval (validation): [5] Total time: 0:00:08 (0.1339 s / it)
+cv: [5]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 0.101  acc: 0.972  f1: 0.967
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:05:34  lr: nan  time: 0.8374  data: 0.6007  max mem: 23762
+train: [6]  [ 20/400]  eta: 0:01:44  lr: 0.000296  loss: 0.9020 (1.0152)  grad: 0.5907 (0.5957)  time: 0.2470  data: 0.0023  max mem: 23762
+train: [6]  [ 40/400]  eta: 0:01:34  lr: 0.000296  loss: 0.8177 (0.8857)  grad: 0.5766 (0.5768)  time: 0.2469  data: 0.0025  max mem: 23762
+train: [6]  [ 60/400]  eta: 0:01:27  lr: 0.000296  loss: 0.6494 (0.8569)  grad: 0.5617 (0.5695)  time: 0.2465  data: 0.0020  max mem: 23762
+train: [6]  [ 80/400]  eta: 0:01:21  lr: 0.000295  loss: 0.6917 (0.8563)  grad: 0.5464 (0.5602)  time: 0.2475  data: 0.0025  max mem: 23762
+train: [6]  [100/400]  eta: 0:01:15  lr: 0.000295  loss: 0.8931 (0.8841)  grad: 0.5383 (0.5647)  time: 0.2464  data: 0.0019  max mem: 23762
+train: [6]  [120/400]  eta: 0:01:10  lr: 0.000295  loss: 0.9049 (0.8891)  grad: 0.5322 (0.5562)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [6]  [140/400]  eta: 0:01:05  lr: 0.000294  loss: 0.8387 (0.8758)  grad: 0.5220 (0.5536)  time: 0.2477  data: 0.0020  max mem: 23762
+train: [6]  [160/400]  eta: 0:01:00  lr: 0.000294  loss: 0.8401 (0.8941)  grad: 0.5452 (0.5554)  time: 0.2478  data: 0.0026  max mem: 23762
+train: [6]  [180/400]  eta: 0:00:55  lr: 0.000293  loss: 0.9591 (0.9082)  grad: 0.5200 (0.5504)  time: 0.2474  data: 0.0024  max mem: 23762
+train: [6]  [200/400]  eta: 0:00:50  lr: 0.000293  loss: 0.7467 (0.8954)  grad: 0.4812 (0.5449)  time: 0.2480  data: 0.0026  max mem: 23762
+train: [6]  [220/400]  eta: 0:00:45  lr: 0.000292  loss: 0.6858 (0.8905)  grad: 0.5244 (0.5432)  time: 0.2474  data: 0.0020  max mem: 23762
+train: [6]  [240/400]  eta: 0:00:39  lr: 0.000292  loss: 0.8802 (0.8884)  grad: 0.5458 (0.5451)  time: 0.2474  data: 0.0026  max mem: 23762
+train: [6]  [260/400]  eta: 0:00:34  lr: 0.000291  loss: 0.9597 (0.8995)  grad: 0.5589 (0.5483)  time: 0.2474  data: 0.0019  max mem: 23762
+train: [6]  [280/400]  eta: 0:00:29  lr: 0.000291  loss: 0.8618 (0.8910)  grad: 0.5196 (0.5470)  time: 0.2480  data: 0.0027  max mem: 23762
+train: [6]  [300/400]  eta: 0:00:25  lr: 0.000290  loss: 0.7392 (0.8767)  grad: 0.4898 (0.5445)  time: 0.2822  data: 0.0360  max mem: 23762
+train: [6]  [320/400]  eta: 0:00:20  lr: 0.000290  loss: 0.6144 (0.8630)  grad: 0.4898 (0.5439)  time: 0.2484  data: 0.0025  max mem: 23762
+train: [6]  [340/400]  eta: 0:00:15  lr: 0.000289  loss: 0.5341 (0.8516)  grad: 0.4962 (0.5414)  time: 0.2482  data: 0.0026  max mem: 23762
+train: [6]  [360/400]  eta: 0:00:10  lr: 0.000288  loss: 0.5434 (0.8415)  grad: 0.4875 (0.5400)  time: 0.2480  data: 0.0024  max mem: 23762
+train: [6]  [380/400]  eta: 0:00:05  lr: 0.000288  loss: 0.5865 (0.8317)  grad: 0.4626 (0.5355)  time: 0.2492  data: 0.0027  max mem: 23762
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 0.6176 (0.8217)  grad: 0.4458 (0.5310)  time: 0.2487  data: 0.0024  max mem: 23762
+train: [6] Total time: 0:01:40 (0.2511 s / it)
+train: [6] Summary: lr: 0.000287  loss: 0.6176 (0.8217)  grad: 0.4458 (0.5310)
+eval (validation): [6]  [ 0/63]  eta: 0:00:38    time: 0.6114  data: 0.4921  max mem: 23762
+eval (validation): [6]  [20/63]  eta: 0:00:06    time: 0.1235  data: 0.0023  max mem: 23762
+eval (validation): [6]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [6]  [60/63]  eta: 0:00:00    time: 0.1246  data: 0.0025  max mem: 23762
+eval (validation): [6]  [62/63]  eta: 0:00:00    time: 0.1245  data: 0.0022  max mem: 23762
+eval (validation): [6] Total time: 0:00:08 (0.1333 s / it)
+cv: [6]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 0.081  acc: 0.975  f1: 0.971
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:06:28  lr: nan  time: 0.9704  data: 0.7338  max mem: 23762
+train: [7]  [ 20/400]  eta: 0:01:47  lr: 0.000286  loss: 0.4915 (0.5284)  grad: 0.3674 (0.3936)  time: 0.2472  data: 0.0022  max mem: 23762
+train: [7]  [ 40/400]  eta: 0:01:35  lr: 0.000286  loss: 0.4915 (0.5505)  grad: 0.3953 (0.4076)  time: 0.2477  data: 0.0027  max mem: 23762
+train: [7]  [ 60/400]  eta: 0:01:28  lr: 0.000285  loss: 0.4858 (0.5578)  grad: 0.3970 (0.4107)  time: 0.2464  data: 0.0017  max mem: 23762
+train: [7]  [ 80/400]  eta: 0:01:21  lr: 0.000284  loss: 0.3779 (0.5205)  grad: 0.3903 (0.4108)  time: 0.2479  data: 0.0025  max mem: 23762
+train: [7]  [100/400]  eta: 0:01:16  lr: 0.000284  loss: 0.3868 (0.5173)  grad: 0.3876 (0.4124)  time: 0.2469  data: 0.0017  max mem: 23762
+train: [7]  [120/400]  eta: 0:01:10  lr: 0.000283  loss: 0.4980 (0.5426)  grad: 0.4086 (0.4157)  time: 0.2484  data: 0.0026  max mem: 23762
+train: [7]  [140/400]  eta: 0:01:05  lr: 0.000282  loss: 0.5853 (0.5461)  grad: 0.4387 (0.4179)  time: 0.2476  data: 0.0018  max mem: 23762
+train: [7]  [160/400]  eta: 0:01:00  lr: 0.000282  loss: 0.5157 (0.5568)  grad: 0.4084 (0.4204)  time: 0.2484  data: 0.0026  max mem: 23762
+train: [7]  [180/400]  eta: 0:00:55  lr: 0.000281  loss: 0.5133 (0.5577)  grad: 0.4300 (0.4257)  time: 0.2480  data: 0.0018  max mem: 23762
+train: [7]  [200/400]  eta: 0:00:50  lr: 0.000280  loss: 0.6455 (0.5733)  grad: 0.4427 (0.4302)  time: 0.2488  data: 0.0026  max mem: 23762
+train: [7]  [220/400]  eta: 0:00:45  lr: 0.000279  loss: 0.6614 (0.5755)  grad: 0.4182 (0.4290)  time: 0.2479  data: 0.0018  max mem: 23762
+train: [7]  [240/400]  eta: 0:00:40  lr: 0.000278  loss: 0.6534 (0.5808)  grad: 0.3960 (0.4282)  time: 0.2487  data: 0.0026  max mem: 23762
+train: [7]  [260/400]  eta: 0:00:35  lr: 0.000278  loss: 0.6554 (0.5888)  grad: 0.4113 (0.4299)  time: 0.2476  data: 0.0017  max mem: 23762
+train: [7]  [280/400]  eta: 0:00:30  lr: 0.000277  loss: 0.6554 (0.5932)  grad: 0.4177 (0.4296)  time: 0.2490  data: 0.0026  max mem: 23762
+train: [7]  [300/400]  eta: 0:00:25  lr: 0.000276  loss: 0.5903 (0.5914)  grad: 0.4655 (0.4360)  time: 0.2798  data: 0.0339  max mem: 23762
+train: [7]  [320/400]  eta: 0:00:20  lr: 0.000275  loss: 0.5461 (0.5866)  grad: 0.4578 (0.4354)  time: 0.2496  data: 0.0027  max mem: 23762
+train: [7]  [340/400]  eta: 0:00:15  lr: 0.000274  loss: 0.4252 (0.5790)  grad: 0.4145 (0.4331)  time: 0.2489  data: 0.0025  max mem: 23762
+train: [7]  [360/400]  eta: 0:00:10  lr: 0.000273  loss: 0.3892 (0.5702)  grad: 0.3315 (0.4264)  time: 0.2488  data: 0.0027  max mem: 23762
+train: [7]  [380/400]  eta: 0:00:05  lr: 0.000272  loss: 0.3681 (0.5641)  grad: 0.3446 (0.4247)  time: 0.2489  data: 0.0025  max mem: 23762
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 0.4344 (0.5598)  grad: 0.3514 (0.4204)  time: 0.2487  data: 0.0024  max mem: 23762
+train: [7] Total time: 0:01:40 (0.2518 s / it)
+train: [7] Summary: lr: 0.000271  loss: 0.4344 (0.5598)  grad: 0.3514 (0.4204)
+eval (validation): [7]  [ 0/63]  eta: 0:00:43    time: 0.6880  data: 0.5669  max mem: 23762
+eval (validation): [7]  [20/63]  eta: 0:00:06    time: 0.1235  data: 0.0019  max mem: 23762
+eval (validation): [7]  [40/63]  eta: 0:00:03    time: 0.1250  data: 0.0027  max mem: 23762
+eval (validation): [7]  [60/63]  eta: 0:00:00    time: 0.1238  data: 0.0017  max mem: 23762
+eval (validation): [7]  [62/63]  eta: 0:00:00    time: 0.1243  data: 0.0018  max mem: 23762
+eval (validation): [7] Total time: 0:00:08 (0.1347 s / it)
+cv: [7]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 0.082  acc: 0.975  f1: 0.970
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:05:52  lr: nan  time: 0.8816  data: 0.6480  max mem: 23762
+train: [8]  [ 20/400]  eta: 0:01:45  lr: 0.000270  loss: 0.2747 (0.3542)  grad: 0.3242 (0.3636)  time: 0.2471  data: 0.0017  max mem: 23762
+train: [8]  [ 40/400]  eta: 0:01:34  lr: 0.000270  loss: 0.3534 (0.3761)  grad: 0.3407 (0.3511)  time: 0.2482  data: 0.0027  max mem: 23762
+train: [8]  [ 60/400]  eta: 0:01:27  lr: 0.000269  loss: 0.4431 (0.3977)  grad: 0.3344 (0.3474)  time: 0.2480  data: 0.0019  max mem: 23762
+train: [8]  [ 80/400]  eta: 0:01:21  lr: 0.000268  loss: 0.3999 (0.4096)  grad: 0.3394 (0.3478)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [8]  [100/400]  eta: 0:01:16  lr: 0.000267  loss: 0.3311 (0.3890)  grad: 0.2955 (0.3383)  time: 0.2481  data: 0.0018  max mem: 23762
+train: [8]  [120/400]  eta: 0:01:10  lr: 0.000266  loss: 0.3486 (0.3888)  grad: 0.2863 (0.3333)  time: 0.2484  data: 0.0029  max mem: 23762
+train: [8]  [140/400]  eta: 0:01:05  lr: 0.000265  loss: 0.3833 (0.3975)  grad: 0.3039 (0.3362)  time: 0.2476  data: 0.0019  max mem: 23762
+train: [8]  [160/400]  eta: 0:01:00  lr: 0.000264  loss: 0.4108 (0.4070)  grad: 0.3643 (0.3464)  time: 0.2484  data: 0.0028  max mem: 23762
+train: [8]  [180/400]  eta: 0:00:55  lr: 0.000263  loss: 0.4639 (0.4203)  grad: 0.3969 (0.3550)  time: 0.2476  data: 0.0018  max mem: 23762
+train: [8]  [200/400]  eta: 0:00:50  lr: 0.000262  loss: 0.4639 (0.4330)  grad: 0.3969 (0.3587)  time: 0.2487  data: 0.0029  max mem: 23762
+train: [8]  [220/400]  eta: 0:00:45  lr: 0.000260  loss: 0.3447 (0.4245)  grad: 0.3041 (0.3522)  time: 0.2482  data: 0.0020  max mem: 23762
+train: [8]  [240/400]  eta: 0:00:40  lr: 0.000259  loss: 0.3883 (0.4238)  grad: 0.2935 (0.3496)  time: 0.2495  data: 0.0029  max mem: 23762
+train: [8]  [260/400]  eta: 0:00:35  lr: 0.000258  loss: 0.4210 (0.4267)  grad: 0.3600 (0.3516)  time: 0.2488  data: 0.0021  max mem: 23762
+train: [8]  [280/400]  eta: 0:00:30  lr: 0.000257  loss: 0.4708 (0.4314)  grad: 0.3789 (0.3530)  time: 0.2494  data: 0.0029  max mem: 23762
+train: [8]  [300/400]  eta: 0:00:25  lr: 0.000256  loss: 0.3609 (0.4250)  grad: 0.3477 (0.3505)  time: 0.2820  data: 0.0359  max mem: 23762
+train: [8]  [320/400]  eta: 0:00:20  lr: 0.000255  loss: 0.2889 (0.4148)  grad: 0.2914 (0.3449)  time: 0.2483  data: 0.0023  max mem: 23762
+train: [8]  [340/400]  eta: 0:00:15  lr: 0.000254  loss: 0.2391 (0.4057)  grad: 0.2568 (0.3409)  time: 0.2483  data: 0.0027  max mem: 23762
+train: [8]  [360/400]  eta: 0:00:10  lr: 0.000253  loss: 0.2570 (0.3992)  grad: 0.2674 (0.3378)  time: 0.2490  data: 0.0023  max mem: 23762
+train: [8]  [380/400]  eta: 0:00:05  lr: 0.000252  loss: 0.2833 (0.3951)  grad: 0.2811 (0.3347)  time: 0.2491  data: 0.0026  max mem: 23762
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 0.2737 (0.3893)  grad: 0.2640 (0.3315)  time: 0.2486  data: 0.0022  max mem: 23762
+train: [8] Total time: 0:01:40 (0.2519 s / it)
+train: [8] Summary: lr: 0.000250  loss: 0.2737 (0.3893)  grad: 0.2640 (0.3315)
+eval (validation): [8]  [ 0/63]  eta: 0:00:49    time: 0.7866  data: 0.6678  max mem: 23762
+eval (validation): [8]  [20/63]  eta: 0:00:06    time: 0.1249  data: 0.0027  max mem: 23762
+eval (validation): [8]  [40/63]  eta: 0:00:03    time: 0.1246  data: 0.0022  max mem: 23762
+eval (validation): [8]  [60/63]  eta: 0:00:00    time: 0.1241  data: 0.0027  max mem: 23762
+eval (validation): [8]  [62/63]  eta: 0:00:00    time: 0.1240  data: 0.0024  max mem: 23762
+eval (validation): [8] Total time: 0:00:08 (0.1366 s / it)
+cv: [8]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 0.082  acc: 0.976  f1: 0.972
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:05:45  lr: nan  time: 0.8634  data: 0.6251  max mem: 23762
+train: [9]  [ 20/400]  eta: 0:01:45  lr: 0.000249  loss: 0.2991 (0.3345)  grad: 0.2319 (0.2588)  time: 0.2485  data: 0.0027  max mem: 23762
+train: [9]  [ 40/400]  eta: 0:01:34  lr: 0.000248  loss: 0.2877 (0.3012)  grad: 0.2319 (0.2391)  time: 0.2476  data: 0.0023  max mem: 23762
+train: [9]  [ 60/400]  eta: 0:01:27  lr: 0.000247  loss: 0.2838 (0.3038)  grad: 0.2579 (0.2562)  time: 0.2485  data: 0.0030  max mem: 23762
+train: [9]  [ 80/400]  eta: 0:01:21  lr: 0.000246  loss: 0.2461 (0.2887)  grad: 0.2449 (0.2552)  time: 0.2477  data: 0.0022  max mem: 23762
+train: [9]  [100/400]  eta: 0:01:16  lr: 0.000244  loss: 0.2461 (0.2908)  grad: 0.2276 (0.2530)  time: 0.2489  data: 0.0029  max mem: 23762
+train: [9]  [120/400]  eta: 0:01:10  lr: 0.000243  loss: 0.2637 (0.2854)  grad: 0.2676 (0.2553)  time: 0.2482  data: 0.0022  max mem: 23762
+train: [9]  [140/400]  eta: 0:01:05  lr: 0.000242  loss: 0.2353 (0.2777)  grad: 0.2676 (0.2567)  time: 0.2489  data: 0.0029  max mem: 23762
+train: [9]  [160/400]  eta: 0:01:00  lr: 0.000241  loss: 0.2558 (0.2842)  grad: 0.2589 (0.2566)  time: 0.2490  data: 0.0025  max mem: 23762
+train: [9]  [180/400]  eta: 0:00:55  lr: 0.000240  loss: 0.2788 (0.2846)  grad: 0.2435 (0.2554)  time: 0.2488  data: 0.0029  max mem: 23762
+train: [9]  [200/400]  eta: 0:00:50  lr: 0.000238  loss: 0.2788 (0.2880)  grad: 0.2587 (0.2568)  time: 0.2489  data: 0.0023  max mem: 23762
+train: [9]  [220/400]  eta: 0:00:45  lr: 0.000237  loss: 0.2832 (0.2867)  grad: 0.2645 (0.2568)  time: 0.2493  data: 0.0030  max mem: 23762
+train: [9]  [240/400]  eta: 0:00:40  lr: 0.000236  loss: 0.2394 (0.2851)  grad: 0.2455 (0.2547)  time: 0.2490  data: 0.0023  max mem: 23762
+train: [9]  [260/400]  eta: 0:00:35  lr: 0.000234  loss: 0.2448 (0.2852)  grad: 0.2392 (0.2540)  time: 0.2491  data: 0.0029  max mem: 23762
+train: [9]  [280/400]  eta: 0:00:30  lr: 0.000233  loss: 0.2633 (0.2867)  grad: 0.2276 (0.2515)  time: 0.2491  data: 0.0024  max mem: 23762
+train: [9]  [300/400]  eta: 0:00:25  lr: 0.000232  loss: 0.2236 (0.2838)  grad: 0.2276 (0.2504)  time: 0.2830  data: 0.0374  max mem: 23762
+train: [9]  [320/400]  eta: 0:00:20  lr: 0.000230  loss: 0.2069 (0.2798)  grad: 0.2432 (0.2504)  time: 0.2488  data: 0.0026  max mem: 23762
+train: [9]  [340/400]  eta: 0:00:15  lr: 0.000229  loss: 0.2089 (0.2776)  grad: 0.2231 (0.2477)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [9]  [360/400]  eta: 0:00:10  lr: 0.000228  loss: 0.2037 (0.2760)  grad: 0.2012 (0.2470)  time: 0.2489  data: 0.0027  max mem: 23762
+train: [9]  [380/400]  eta: 0:00:05  lr: 0.000226  loss: 0.1818 (0.2719)  grad: 0.2156 (0.2463)  time: 0.2488  data: 0.0026  max mem: 23762
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 0.2015 (0.2698)  grad: 0.2156 (0.2441)  time: 0.2485  data: 0.0026  max mem: 23762
+train: [9] Total time: 0:01:40 (0.2522 s / it)
+train: [9] Summary: lr: 0.000225  loss: 0.2015 (0.2698)  grad: 0.2156 (0.2441)
+eval (validation): [9]  [ 0/63]  eta: 0:00:40    time: 0.6469  data: 0.5265  max mem: 23762
+eval (validation): [9]  [20/63]  eta: 0:00:06    time: 0.1247  data: 0.0030  max mem: 23762
+eval (validation): [9]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [9]  [60/63]  eta: 0:00:00    time: 0.1246  data: 0.0029  max mem: 23762
+eval (validation): [9]  [62/63]  eta: 0:00:00    time: 0.1244  data: 0.0025  max mem: 23762
+eval (validation): [9] Total time: 0:00:08 (0.1342 s / it)
+cv: [9]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 0.093  acc: 0.974  f1: 0.966
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:05:44  lr: nan  time: 0.8620  data: 0.6247  max mem: 23762
+train: [10]  [ 20/400]  eta: 0:01:45  lr: 0.000224  loss: 0.2619 (0.2414)  grad: 0.1949 (0.2005)  time: 0.2484  data: 0.0022  max mem: 23762
+train: [10]  [ 40/400]  eta: 0:01:34  lr: 0.000222  loss: 0.2337 (0.2455)  grad: 0.2168 (0.2222)  time: 0.2490  data: 0.0029  max mem: 23762
+train: [10]  [ 60/400]  eta: 0:01:27  lr: 0.000221  loss: 0.2103 (0.2264)  grad: 0.2168 (0.2200)  time: 0.2486  data: 0.0022  max mem: 23762
+train: [10]  [ 80/400]  eta: 0:01:22  lr: 0.000220  loss: 0.1720 (0.2246)  grad: 0.1975 (0.2166)  time: 0.2492  data: 0.0028  max mem: 23762
+train: [10]  [100/400]  eta: 0:01:16  lr: 0.000218  loss: 0.1720 (0.2185)  grad: 0.2061 (0.2161)  time: 0.2488  data: 0.0022  max mem: 23762
+train: [10]  [120/400]  eta: 0:01:11  lr: 0.000217  loss: 0.1939 (0.2200)  grad: 0.2058 (0.2173)  time: 0.2493  data: 0.0029  max mem: 23762
+train: [10]  [140/400]  eta: 0:01:05  lr: 0.000215  loss: 0.1963 (0.2199)  grad: 0.1941 (0.2157)  time: 0.2480  data: 0.0021  max mem: 23762
+train: [10]  [160/400]  eta: 0:01:00  lr: 0.000214  loss: 0.2054 (0.2185)  grad: 0.1911 (0.2136)  time: 0.2493  data: 0.0028  max mem: 23762
+train: [10]  [180/400]  eta: 0:00:55  lr: 0.000213  loss: 0.2222 (0.2204)  grad: 0.1911 (0.2140)  time: 0.2487  data: 0.0023  max mem: 23762
+train: [10]  [200/400]  eta: 0:00:50  lr: 0.000211  loss: 0.2138 (0.2186)  grad: 0.2009 (0.2124)  time: 0.2489  data: 0.0029  max mem: 23762
+train: [10]  [220/400]  eta: 0:00:45  lr: 0.000210  loss: 0.2092 (0.2193)  grad: 0.1939 (0.2099)  time: 0.2482  data: 0.0022  max mem: 23762
+train: [10]  [240/400]  eta: 0:00:40  lr: 0.000208  loss: 0.2182 (0.2200)  grad: 0.1961 (0.2093)  time: 0.2497  data: 0.0029  max mem: 23762
+train: [10]  [260/400]  eta: 0:00:35  lr: 0.000207  loss: 0.2032 (0.2180)  grad: 0.1986 (0.2082)  time: 0.2486  data: 0.0021  max mem: 23762
+train: [10]  [280/400]  eta: 0:00:30  lr: 0.000205  loss: 0.1981 (0.2179)  grad: 0.1791 (0.2071)  time: 0.2488  data: 0.0028  max mem: 23762
+train: [10]  [300/400]  eta: 0:00:25  lr: 0.000204  loss: 0.1934 (0.2195)  grad: 0.2046 (0.2087)  time: 0.2821  data: 0.0360  max mem: 23762
+train: [10]  [320/400]  eta: 0:00:20  lr: 0.000202  loss: 0.1759 (0.2173)  grad: 0.2063 (0.2071)  time: 0.2491  data: 0.0029  max mem: 23762
+train: [10]  [340/400]  eta: 0:00:15  lr: 0.000201  loss: 0.1759 (0.2151)  grad: 0.1625 (0.2048)  time: 0.2477  data: 0.0022  max mem: 23762
+train: [10]  [360/400]  eta: 0:00:10  lr: 0.000199  loss: 0.1641 (0.2121)  grad: 0.1474 (0.2022)  time: 0.2488  data: 0.0029  max mem: 23762
+train: [10]  [380/400]  eta: 0:00:05  lr: 0.000198  loss: 0.1844 (0.2111)  grad: 0.1698 (0.2021)  time: 0.2476  data: 0.0021  max mem: 23762
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 0.1970 (0.2104)  grad: 0.1747 (0.2003)  time: 0.2497  data: 0.0026  max mem: 23762
+train: [10] Total time: 0:01:40 (0.2522 s / it)
+train: [10] Summary: lr: 0.000196  loss: 0.1970 (0.2104)  grad: 0.1747 (0.2003)
+eval (validation): [10]  [ 0/63]  eta: 0:00:43    time: 0.6934  data: 0.5723  max mem: 23762
+eval (validation): [10]  [20/63]  eta: 0:00:06    time: 0.1234  data: 0.0024  max mem: 23762
+eval (validation): [10]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0021  max mem: 23762
+eval (validation): [10]  [60/63]  eta: 0:00:00    time: 0.1240  data: 0.0024  max mem: 23762
+eval (validation): [10]  [62/63]  eta: 0:00:00    time: 0.1236  data: 0.0018  max mem: 23762
+eval (validation): [10] Total time: 0:00:08 (0.1344 s / it)
+cv: [10]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 0.079  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:05:13  lr: nan  time: 0.7826  data: 0.5464  max mem: 23762
+train: [11]  [ 20/400]  eta: 0:01:44  lr: 0.000195  loss: 0.1746 (0.1878)  grad: 0.1548 (0.1718)  time: 0.2485  data: 0.0031  max mem: 23762
+train: [11]  [ 40/400]  eta: 0:01:33  lr: 0.000193  loss: 0.1819 (0.1814)  grad: 0.1566 (0.1662)  time: 0.2473  data: 0.0025  max mem: 23762
+train: [11]  [ 60/400]  eta: 0:01:27  lr: 0.000192  loss: 0.1700 (0.1746)  grad: 0.1520 (0.1655)  time: 0.2473  data: 0.0028  max mem: 23762
+train: [11]  [ 80/400]  eta: 0:01:21  lr: 0.000190  loss: 0.1703 (0.1809)  grad: 0.1498 (0.1646)  time: 0.2477  data: 0.0025  max mem: 23762
+train: [11]  [100/400]  eta: 0:01:15  lr: 0.000189  loss: 0.1659 (0.1767)  grad: 0.1488 (0.1592)  time: 0.2479  data: 0.0029  max mem: 23762
+train: [11]  [120/400]  eta: 0:01:10  lr: 0.000187  loss: 0.1499 (0.1767)  grad: 0.1335 (0.1555)  time: 0.2480  data: 0.0025  max mem: 23762
+train: [11]  [140/400]  eta: 0:01:05  lr: 0.000186  loss: 0.1533 (0.1740)  grad: 0.1319 (0.1534)  time: 0.2478  data: 0.0029  max mem: 23762
+train: [11]  [160/400]  eta: 0:01:00  lr: 0.000184  loss: 0.1538 (0.1756)  grad: 0.1516 (0.1565)  time: 0.2475  data: 0.0024  max mem: 23762
+train: [11]  [180/400]  eta: 0:00:55  lr: 0.000183  loss: 0.1735 (0.1752)  grad: 0.1670 (0.1565)  time: 0.2490  data: 0.0029  max mem: 23762
+train: [11]  [200/400]  eta: 0:00:50  lr: 0.000181  loss: 0.1666 (0.1738)  grad: 0.1504 (0.1566)  time: 0.2485  data: 0.0024  max mem: 23762
+train: [11]  [220/400]  eta: 0:00:45  lr: 0.000180  loss: 0.1666 (0.1752)  grad: 0.1551 (0.1555)  time: 0.2500  data: 0.0031  max mem: 23762
+train: [11]  [240/400]  eta: 0:00:40  lr: 0.000178  loss: 0.1727 (0.1744)  grad: 0.1551 (0.1549)  time: 0.2485  data: 0.0025  max mem: 23762
+train: [11]  [260/400]  eta: 0:00:35  lr: 0.000177  loss: 0.1425 (0.1720)  grad: 0.1365 (0.1540)  time: 0.2488  data: 0.0030  max mem: 23762
+train: [11]  [280/400]  eta: 0:00:30  lr: 0.000175  loss: 0.1425 (0.1712)  grad: 0.1475 (0.1538)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [11]  [300/400]  eta: 0:00:25  lr: 0.000174  loss: 0.1500 (0.1708)  grad: 0.1472 (0.1536)  time: 0.2798  data: 0.0342  max mem: 23762
+train: [11]  [320/400]  eta: 0:00:20  lr: 0.000172  loss: 0.1500 (0.1711)  grad: 0.1347 (0.1524)  time: 0.2486  data: 0.0031  max mem: 23762
+train: [11]  [340/400]  eta: 0:00:15  lr: 0.000170  loss: 0.1709 (0.1710)  grad: 0.1309 (0.1519)  time: 0.2480  data: 0.0021  max mem: 23762
+train: [11]  [360/400]  eta: 0:00:10  lr: 0.000169  loss: 0.1575 (0.1700)  grad: 0.1247 (0.1502)  time: 0.2485  data: 0.0028  max mem: 23762
+train: [11]  [380/400]  eta: 0:00:05  lr: 0.000167  loss: 0.1304 (0.1689)  grad: 0.1220 (0.1488)  time: 0.2480  data: 0.0022  max mem: 23762
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 0.1332 (0.1676)  grad: 0.1175 (0.1474)  time: 0.2476  data: 0.0023  max mem: 23762
+train: [11] Total time: 0:01:40 (0.2514 s / it)
+train: [11] Summary: lr: 0.000166  loss: 0.1332 (0.1676)  grad: 0.1175 (0.1474)
+eval (validation): [11]  [ 0/63]  eta: 0:00:39    time: 0.6339  data: 0.5140  max mem: 23762
+eval (validation): [11]  [20/63]  eta: 0:00:06    time: 0.1237  data: 0.0024  max mem: 23762
+eval (validation): [11]  [40/63]  eta: 0:00:03    time: 0.1237  data: 0.0022  max mem: 23762
+eval (validation): [11]  [60/63]  eta: 0:00:00    time: 0.1231  data: 0.0024  max mem: 23762
+eval (validation): [11]  [62/63]  eta: 0:00:00    time: 0.1231  data: 0.0022  max mem: 23762
+eval (validation): [11] Total time: 0:00:08 (0.1331 s / it)
+cv: [11]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 0.085  acc: 0.976  f1: 0.972
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:05:22  lr: nan  time: 0.8071  data: 0.5730  max mem: 23762
+train: [12]  [ 20/400]  eta: 0:01:44  lr: 0.000164  loss: 0.1251 (0.1244)  grad: 0.1005 (0.1035)  time: 0.2473  data: 0.0026  max mem: 23762
+train: [12]  [ 40/400]  eta: 0:01:34  lr: 0.000163  loss: 0.1287 (0.1305)  grad: 0.1013 (0.1076)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [12]  [ 60/400]  eta: 0:01:27  lr: 0.000161  loss: 0.1442 (0.1378)  grad: 0.1104 (0.1100)  time: 0.2484  data: 0.0027  max mem: 23762
+train: [12]  [ 80/400]  eta: 0:01:21  lr: 0.000160  loss: 0.1482 (0.1391)  grad: 0.1132 (0.1131)  time: 0.2478  data: 0.0026  max mem: 23762
+train: [12]  [100/400]  eta: 0:01:16  lr: 0.000158  loss: 0.1352 (0.1386)  grad: 0.1146 (0.1138)  time: 0.2489  data: 0.0028  max mem: 23762
+train: [12]  [120/400]  eta: 0:01:10  lr: 0.000156  loss: 0.1352 (0.1380)  grad: 0.1149 (0.1155)  time: 0.2486  data: 0.0026  max mem: 23762
+train: [12]  [140/400]  eta: 0:01:05  lr: 0.000155  loss: 0.1403 (0.1391)  grad: 0.1190 (0.1160)  time: 0.2482  data: 0.0027  max mem: 23762
+train: [12]  [160/400]  eta: 0:01:00  lr: 0.000153  loss: 0.1437 (0.1396)  grad: 0.1136 (0.1165)  time: 0.2485  data: 0.0026  max mem: 23762
+train: [12]  [180/400]  eta: 0:00:55  lr: 0.000152  loss: 0.1309 (0.1398)  grad: 0.1006 (0.1157)  time: 0.2481  data: 0.0027  max mem: 23762
+train: [12]  [200/400]  eta: 0:00:50  lr: 0.000150  loss: 0.1247 (0.1394)  grad: 0.1003 (0.1148)  time: 0.2479  data: 0.0025  max mem: 23762
+train: [12]  [220/400]  eta: 0:00:45  lr: 0.000149  loss: 0.1330 (0.1398)  grad: 0.1045 (0.1149)  time: 0.2480  data: 0.0026  max mem: 23762
+train: [12]  [240/400]  eta: 0:00:40  lr: 0.000147  loss: 0.1328 (0.1394)  grad: 0.1128 (0.1154)  time: 0.2491  data: 0.0026  max mem: 23762
+train: [12]  [260/400]  eta: 0:00:35  lr: 0.000145  loss: 0.1231 (0.1396)  grad: 0.1265 (0.1163)  time: 0.2488  data: 0.0028  max mem: 23762
+train: [12]  [280/400]  eta: 0:00:30  lr: 0.000144  loss: 0.1330 (0.1396)  grad: 0.1153 (0.1164)  time: 0.2494  data: 0.0028  max mem: 23762
+train: [12]  [300/400]  eta: 0:00:25  lr: 0.000142  loss: 0.1330 (0.1404)  grad: 0.1121 (0.1173)  time: 0.2774  data: 0.0313  max mem: 23762
+train: [12]  [320/400]  eta: 0:00:20  lr: 0.000141  loss: 0.1228 (0.1391)  grad: 0.1120 (0.1165)  time: 0.2482  data: 0.0026  max mem: 23762
+train: [12]  [340/400]  eta: 0:00:15  lr: 0.000139  loss: 0.1172 (0.1384)  grad: 0.0978 (0.1159)  time: 0.2484  data: 0.0024  max mem: 23762
+train: [12]  [360/400]  eta: 0:00:10  lr: 0.000138  loss: 0.1261 (0.1381)  grad: 0.1005 (0.1152)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [12]  [380/400]  eta: 0:00:05  lr: 0.000136  loss: 0.1298 (0.1376)  grad: 0.0992 (0.1140)  time: 0.2486  data: 0.0024  max mem: 23762
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 0.1251 (0.1368)  grad: 0.0921 (0.1136)  time: 0.2495  data: 0.0025  max mem: 23762
+train: [12] Total time: 0:01:40 (0.2515 s / it)
+train: [12] Summary: lr: 0.000134  loss: 0.1251 (0.1368)  grad: 0.0921 (0.1136)
+eval (validation): [12]  [ 0/63]  eta: 0:00:44    time: 0.7074  data: 0.5863  max mem: 23762
+eval (validation): [12]  [20/63]  eta: 0:00:06    time: 0.1241  data: 0.0021  max mem: 23762
+eval (validation): [12]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0025  max mem: 23762
+eval (validation): [12]  [60/63]  eta: 0:00:00    time: 0.1240  data: 0.0026  max mem: 23762
+eval (validation): [12]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0022  max mem: 23762
+eval (validation): [12] Total time: 0:00:08 (0.1348 s / it)
+cv: [12]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.160  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:05:28  lr: nan  time: 0.8217  data: 0.5875  max mem: 23762
+train: [13]  [ 20/400]  eta: 0:01:44  lr: 0.000133  loss: 0.1297 (0.1274)  grad: 0.0851 (0.1062)  time: 0.2480  data: 0.0025  max mem: 23762
+train: [13]  [ 40/400]  eta: 0:01:34  lr: 0.000131  loss: 0.1297 (0.1300)  grad: 0.0886 (0.1007)  time: 0.2467  data: 0.0021  max mem: 23762
+train: [13]  [ 60/400]  eta: 0:01:27  lr: 0.000130  loss: 0.1238 (0.1301)  grad: 0.0913 (0.0977)  time: 0.2484  data: 0.0027  max mem: 23762
+train: [13]  [ 80/400]  eta: 0:01:21  lr: 0.000128  loss: 0.1235 (0.1296)  grad: 0.0932 (0.0958)  time: 0.2474  data: 0.0021  max mem: 23762
+train: [13]  [100/400]  eta: 0:01:15  lr: 0.000127  loss: 0.1193 (0.1292)  grad: 0.0891 (0.0962)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [13]  [120/400]  eta: 0:01:10  lr: 0.000125  loss: 0.1309 (0.1314)  grad: 0.0890 (0.0955)  time: 0.2472  data: 0.0021  max mem: 23762
+train: [13]  [140/400]  eta: 0:01:05  lr: 0.000124  loss: 0.1163 (0.1299)  grad: 0.0816 (0.0932)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [13]  [160/400]  eta: 0:01:00  lr: 0.000122  loss: 0.1239 (0.1312)  grad: 0.0818 (0.0933)  time: 0.2474  data: 0.0020  max mem: 23762
+train: [13]  [180/400]  eta: 0:00:55  lr: 0.000120  loss: 0.1345 (0.1300)  grad: 0.0867 (0.0923)  time: 0.2487  data: 0.0026  max mem: 23762
+train: [13]  [200/400]  eta: 0:00:50  lr: 0.000119  loss: 0.1197 (0.1295)  grad: 0.0795 (0.0915)  time: 0.2480  data: 0.0021  max mem: 23762
+train: [13]  [220/400]  eta: 0:00:45  lr: 0.000117  loss: 0.1301 (0.1316)  grad: 0.0814 (0.0917)  time: 0.2477  data: 0.0026  max mem: 23762
+train: [13]  [240/400]  eta: 0:00:40  lr: 0.000116  loss: 0.1339 (0.1311)  grad: 0.0878 (0.0913)  time: 0.2471  data: 0.0021  max mem: 23762
+train: [13]  [260/400]  eta: 0:00:34  lr: 0.000114  loss: 0.1114 (0.1291)  grad: 0.0878 (0.0910)  time: 0.2478  data: 0.0026  max mem: 23762
+train: [13]  [280/400]  eta: 0:00:29  lr: 0.000113  loss: 0.1122 (0.1288)  grad: 0.0806 (0.0908)  time: 0.2469  data: 0.0020  max mem: 23762
+train: [13]  [300/400]  eta: 0:00:25  lr: 0.000111  loss: 0.1177 (0.1292)  grad: 0.0877 (0.0907)  time: 0.2756  data: 0.0302  max mem: 23762
+train: [13]  [320/400]  eta: 0:00:20  lr: 0.000110  loss: 0.1153 (0.1280)  grad: 0.0882 (0.0898)  time: 0.2484  data: 0.0023  max mem: 23762
+train: [13]  [340/400]  eta: 0:00:15  lr: 0.000108  loss: 0.1125 (0.1271)  grad: 0.0730 (0.0892)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [13]  [360/400]  eta: 0:00:10  lr: 0.000107  loss: 0.1158 (0.1267)  grad: 0.0741 (0.0887)  time: 0.2484  data: 0.0025  max mem: 23762
+train: [13]  [380/400]  eta: 0:00:05  lr: 0.000105  loss: 0.1222 (0.1262)  grad: 0.0765 (0.0881)  time: 0.2482  data: 0.0025  max mem: 23762
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 0.1137 (0.1256)  grad: 0.0703 (0.0872)  time: 0.2477  data: 0.0023  max mem: 23762
+train: [13] Total time: 0:01:40 (0.2508 s / it)
+train: [13] Summary: lr: 0.000104  loss: 0.1137 (0.1256)  grad: 0.0703 (0.0872)
+eval (validation): [13]  [ 0/63]  eta: 0:00:37    time: 0.6015  data: 0.4825  max mem: 23762
+eval (validation): [13]  [20/63]  eta: 0:00:06    time: 0.1235  data: 0.0023  max mem: 23762
+eval (validation): [13]  [40/63]  eta: 0:00:03    time: 0.1242  data: 0.0021  max mem: 23762
+eval (validation): [13]  [60/63]  eta: 0:00:00    time: 0.1235  data: 0.0024  max mem: 23762
+eval (validation): [13]  [62/63]  eta: 0:00:00    time: 0.1231  data: 0.0019  max mem: 23762
+eval (validation): [13] Total time: 0:00:08 (0.1329 s / it)
+cv: [13]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.154  acc: 0.978  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [14]  [  0/400]  eta: 0:05:29  lr: nan  time: 0.8233  data: 0.5873  max mem: 23762
+train: [14]  [ 20/400]  eta: 0:01:44  lr: 0.000102  loss: 0.1131 (0.1189)  grad: 0.0762 (0.0787)  time: 0.2482  data: 0.0028  max mem: 23762
+train: [14]  [ 40/400]  eta: 0:01:34  lr: 0.000101  loss: 0.1134 (0.1193)  grad: 0.0726 (0.0727)  time: 0.2488  data: 0.0030  max mem: 23762
+train: [14]  [ 60/400]  eta: 0:01:27  lr: 0.000099  loss: 0.1150 (0.1230)  grad: 0.0708 (0.0737)  time: 0.2476  data: 0.0021  max mem: 23762
+train: [14]  [ 80/400]  eta: 0:01:21  lr: 0.000098  loss: 0.1158 (0.1217)  grad: 0.0694 (0.0735)  time: 0.2476  data: 0.0028  max mem: 23762
+train: [14]  [100/400]  eta: 0:01:16  lr: 0.000096  loss: 0.1074 (0.1203)  grad: 0.0651 (0.0726)  time: 0.2469  data: 0.0021  max mem: 23762
+train: [14]  [120/400]  eta: 0:01:10  lr: 0.000095  loss: 0.1098 (0.1207)  grad: 0.0668 (0.0720)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [14]  [140/400]  eta: 0:01:05  lr: 0.000093  loss: 0.1179 (0.1192)  grad: 0.0669 (0.0717)  time: 0.2470  data: 0.0022  max mem: 23762
+train: [14]  [160/400]  eta: 0:01:00  lr: 0.000092  loss: 0.1028 (0.1178)  grad: 0.0649 (0.0708)  time: 0.2481  data: 0.0029  max mem: 23762
+train: [14]  [180/400]  eta: 0:00:55  lr: 0.000090  loss: 0.1022 (0.1170)  grad: 0.0723 (0.0719)  time: 0.2477  data: 0.0021  max mem: 23762
+train: [14]  [200/400]  eta: 0:00:50  lr: 0.000089  loss: 0.1113 (0.1173)  grad: 0.0787 (0.0719)  time: 0.2477  data: 0.0027  max mem: 23762
+train: [14]  [220/400]  eta: 0:00:45  lr: 0.000088  loss: 0.1195 (0.1179)  grad: 0.0740 (0.0719)  time: 0.2466  data: 0.0020  max mem: 23762
+train: [14]  [240/400]  eta: 0:00:39  lr: 0.000086  loss: 0.1125 (0.1170)  grad: 0.0709 (0.0723)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [14]  [260/400]  eta: 0:00:34  lr: 0.000085  loss: 0.1061 (0.1170)  grad: 0.0705 (0.0720)  time: 0.2470  data: 0.0021  max mem: 23762
+train: [14]  [280/400]  eta: 0:00:29  lr: 0.000083  loss: 0.1096 (0.1166)  grad: 0.0625 (0.0717)  time: 0.2481  data: 0.0028  max mem: 23762
+train: [14]  [300/400]  eta: 0:00:25  lr: 0.000082  loss: 0.1107 (0.1168)  grad: 0.0709 (0.0719)  time: 0.2736  data: 0.0286  max mem: 23762
+train: [14]  [320/400]  eta: 0:00:20  lr: 0.000081  loss: 0.1102 (0.1162)  grad: 0.0696 (0.0715)  time: 0.2470  data: 0.0024  max mem: 23762
+train: [14]  [340/400]  eta: 0:00:15  lr: 0.000079  loss: 0.1081 (0.1164)  grad: 0.0708 (0.0716)  time: 0.2475  data: 0.0025  max mem: 23762
+train: [14]  [360/400]  eta: 0:00:10  lr: 0.000078  loss: 0.1153 (0.1162)  grad: 0.0662 (0.0711)  time: 0.2474  data: 0.0022  max mem: 23762
+train: [14]  [380/400]  eta: 0:00:05  lr: 0.000076  loss: 0.1187 (0.1169)  grad: 0.0662 (0.0711)  time: 0.2477  data: 0.0026  max mem: 23762
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 0.1187 (0.1169)  grad: 0.0662 (0.0708)  time: 0.2469  data: 0.0021  max mem: 23762
+train: [14] Total time: 0:01:40 (0.2505 s / it)
+train: [14] Summary: lr: 0.000075  loss: 0.1187 (0.1169)  grad: 0.0662 (0.0708)
+eval (validation): [14]  [ 0/63]  eta: 0:00:37    time: 0.5924  data: 0.4725  max mem: 23762
+eval (validation): [14]  [20/63]  eta: 0:00:06    time: 0.1230  data: 0.0019  max mem: 23762
+eval (validation): [14]  [40/63]  eta: 0:00:03    time: 0.1237  data: 0.0025  max mem: 23762
+eval (validation): [14]  [60/63]  eta: 0:00:00    time: 0.1233  data: 0.0018  max mem: 23762
+eval (validation): [14]  [62/63]  eta: 0:00:00    time: 0.1235  data: 0.0020  max mem: 23762
+eval (validation): [14] Total time: 0:00:08 (0.1323 s / it)
+cv: [14]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.151  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:05:10  lr: nan  time: 0.7769  data: 0.5379  max mem: 23762
+train: [15]  [ 20/400]  eta: 0:01:43  lr: 0.000074  loss: 0.1147 (0.1185)  grad: 0.0593 (0.0654)  time: 0.2469  data: 0.0024  max mem: 23762
+train: [15]  [ 40/400]  eta: 0:01:33  lr: 0.000072  loss: 0.1191 (0.1224)  grad: 0.0618 (0.0667)  time: 0.2466  data: 0.0019  max mem: 23762
+train: [15]  [ 60/400]  eta: 0:01:26  lr: 0.000071  loss: 0.1243 (0.1212)  grad: 0.0662 (0.0671)  time: 0.2476  data: 0.0026  max mem: 23762
+train: [15]  [ 80/400]  eta: 0:01:21  lr: 0.000070  loss: 0.1262 (0.1219)  grad: 0.0629 (0.0668)  time: 0.2478  data: 0.0021  max mem: 23762
+train: [15]  [100/400]  eta: 0:01:15  lr: 0.000068  loss: 0.1141 (0.1202)  grad: 0.0666 (0.0663)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [15]  [120/400]  eta: 0:01:10  lr: 0.000067  loss: 0.1106 (0.1192)  grad: 0.0593 (0.0649)  time: 0.2483  data: 0.0020  max mem: 23762
+train: [15]  [140/400]  eta: 0:01:05  lr: 0.000066  loss: 0.1088 (0.1182)  grad: 0.0579 (0.0647)  time: 0.2490  data: 0.0028  max mem: 23762
+train: [15]  [160/400]  eta: 0:01:00  lr: 0.000064  loss: 0.1048 (0.1169)  grad: 0.0596 (0.0643)  time: 0.2479  data: 0.0019  max mem: 23762
+train: [15]  [180/400]  eta: 0:00:55  lr: 0.000063  loss: 0.1013 (0.1167)  grad: 0.0607 (0.0644)  time: 0.2492  data: 0.0027  max mem: 23762
+train: [15]  [200/400]  eta: 0:00:50  lr: 0.000062  loss: 0.1102 (0.1169)  grad: 0.0659 (0.0647)  time: 0.2476  data: 0.0019  max mem: 23762
+train: [15]  [220/400]  eta: 0:00:45  lr: 0.000061  loss: 0.1197 (0.1178)  grad: 0.0659 (0.0646)  time: 0.2479  data: 0.0026  max mem: 23762
+train: [15]  [240/400]  eta: 0:00:40  lr: 0.000059  loss: 0.1156 (0.1162)  grad: 0.0661 (0.0645)  time: 0.2477  data: 0.0019  max mem: 23762
+train: [15]  [260/400]  eta: 0:00:34  lr: 0.000058  loss: 0.0982 (0.1156)  grad: 0.0637 (0.0642)  time: 0.2486  data: 0.0027  max mem: 23762
+train: [15]  [280/400]  eta: 0:00:29  lr: 0.000057  loss: 0.1110 (0.1157)  grad: 0.0615 (0.0639)  time: 0.2478  data: 0.0020  max mem: 23762
+train: [15]  [300/400]  eta: 0:00:25  lr: 0.000056  loss: 0.1146 (0.1154)  grad: 0.0590 (0.0636)  time: 0.2803  data: 0.0347  max mem: 23762
+train: [15]  [320/400]  eta: 0:00:20  lr: 0.000054  loss: 0.1091 (0.1145)  grad: 0.0539 (0.0632)  time: 0.2482  data: 0.0028  max mem: 23762
+train: [15]  [340/400]  eta: 0:00:15  lr: 0.000053  loss: 0.1005 (0.1143)  grad: 0.0593 (0.0633)  time: 0.2476  data: 0.0022  max mem: 23762
+train: [15]  [360/400]  eta: 0:00:10  lr: 0.000052  loss: 0.1081 (0.1141)  grad: 0.0594 (0.0630)  time: 0.2481  data: 0.0028  max mem: 23762
+train: [15]  [380/400]  eta: 0:00:05  lr: 0.000051  loss: 0.0921 (0.1129)  grad: 0.0550 (0.0628)  time: 0.2477  data: 0.0021  max mem: 23762
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 0.0986 (0.1129)  grad: 0.0534 (0.0625)  time: 0.2478  data: 0.0022  max mem: 23762
+train: [15] Total time: 0:01:40 (0.2512 s / it)
+train: [15] Summary: lr: 0.000050  loss: 0.0986 (0.1129)  grad: 0.0534 (0.0625)
+eval (validation): [15]  [ 0/63]  eta: 0:00:36    time: 0.5773  data: 0.4581  max mem: 23762
+eval (validation): [15]  [20/63]  eta: 0:00:06    time: 0.1238  data: 0.0027  max mem: 23762
+eval (validation): [15]  [40/63]  eta: 0:00:03    time: 0.1232  data: 0.0020  max mem: 23762
+eval (validation): [15]  [60/63]  eta: 0:00:00    time: 0.1243  data: 0.0025  max mem: 23762
+eval (validation): [15]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0018  max mem: 23762
+eval (validation): [15] Total time: 0:00:08 (0.1324 s / it)
+cv: [15]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.149  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:05:59  lr: nan  time: 0.8991  data: 0.6620  max mem: 23762
+train: [16]  [ 20/400]  eta: 0:01:46  lr: 0.000048  loss: 0.1153 (0.1149)  grad: 0.0500 (0.0543)  time: 0.2481  data: 0.0023  max mem: 23762
+train: [16]  [ 40/400]  eta: 0:01:35  lr: 0.000047  loss: 0.1163 (0.1133)  grad: 0.0560 (0.0576)  time: 0.2479  data: 0.0023  max mem: 23762
+train: [16]  [ 60/400]  eta: 0:01:27  lr: 0.000046  loss: 0.1128 (0.1110)  grad: 0.0551 (0.0566)  time: 0.2481  data: 0.0026  max mem: 23762
+train: [16]  [ 80/400]  eta: 0:01:21  lr: 0.000045  loss: 0.1081 (0.1126)  grad: 0.0551 (0.0568)  time: 0.2482  data: 0.0023  max mem: 23762
+train: [16]  [100/400]  eta: 0:01:16  lr: 0.000044  loss: 0.1086 (0.1110)  grad: 0.0565 (0.0580)  time: 0.2488  data: 0.0027  max mem: 23762
+train: [16]  [120/400]  eta: 0:01:11  lr: 0.000043  loss: 0.1086 (0.1106)  grad: 0.0551 (0.0580)  time: 0.2479  data: 0.0022  max mem: 23762
+train: [16]  [140/400]  eta: 0:01:05  lr: 0.000042  loss: 0.1055 (0.1102)  grad: 0.0567 (0.0584)  time: 0.2483  data: 0.0027  max mem: 23762
+train: [16]  [160/400]  eta: 0:01:00  lr: 0.000041  loss: 0.1129 (0.1123)  grad: 0.0606 (0.0590)  time: 0.2475  data: 0.0022  max mem: 23762
+train: [16]  [180/400]  eta: 0:00:55  lr: 0.000040  loss: 0.1153 (0.1126)  grad: 0.0575 (0.0590)  time: 0.2475  data: 0.0027  max mem: 23762
+train: [16]  [200/400]  eta: 0:00:50  lr: 0.000039  loss: 0.1158 (0.1137)  grad: 0.0554 (0.0590)  time: 0.2478  data: 0.0023  max mem: 23762
+train: [16]  [220/400]  eta: 0:00:45  lr: 0.000038  loss: 0.1210 (0.1135)  grad: 0.0572 (0.0591)  time: 0.2476  data: 0.0027  max mem: 23762
+train: [16]  [240/400]  eta: 0:00:40  lr: 0.000036  loss: 0.1105 (0.1126)  grad: 0.0552 (0.0588)  time: 0.2473  data: 0.0022  max mem: 23762
+train: [16]  [260/400]  eta: 0:00:35  lr: 0.000035  loss: 0.1035 (0.1127)  grad: 0.0552 (0.0590)  time: 0.2480  data: 0.0027  max mem: 23762
+train: [16]  [280/400]  eta: 0:00:30  lr: 0.000034  loss: 0.1048 (0.1124)  grad: 0.0609 (0.0593)  time: 0.2476  data: 0.0024  max mem: 23762
+train: [16]  [300/400]  eta: 0:00:25  lr: 0.000033  loss: 0.1069 (0.1129)  grad: 0.0606 (0.0595)  time: 0.2811  data: 0.0356  max mem: 23762
+train: [16]  [320/400]  eta: 0:00:20  lr: 0.000032  loss: 0.1075 (0.1128)  grad: 0.0583 (0.0595)  time: 0.2479  data: 0.0023  max mem: 23762
+train: [16]  [340/400]  eta: 0:00:15  lr: 0.000031  loss: 0.1072 (0.1129)  grad: 0.0517 (0.0592)  time: 0.2485  data: 0.0028  max mem: 23762
+train: [16]  [360/400]  eta: 0:00:10  lr: 0.000031  loss: 0.1033 (0.1127)  grad: 0.0583 (0.0593)  time: 0.2478  data: 0.0021  max mem: 23762
+train: [16]  [380/400]  eta: 0:00:05  lr: 0.000030  loss: 0.1010 (0.1125)  grad: 0.0583 (0.0591)  time: 0.2487  data: 0.0028  max mem: 23762
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 0.0981 (0.1117)  grad: 0.0536 (0.0588)  time: 0.2473  data: 0.0020  max mem: 23762
+train: [16] Total time: 0:01:40 (0.2515 s / it)
+train: [16] Summary: lr: 0.000029  loss: 0.0981 (0.1117)  grad: 0.0536 (0.0588)
+eval (validation): [16]  [ 0/63]  eta: 0:00:44    time: 0.7009  data: 0.5810  max mem: 23762
+eval (validation): [16]  [20/63]  eta: 0:00:06    time: 0.1232  data: 0.0021  max mem: 23762
+eval (validation): [16]  [40/63]  eta: 0:00:03    time: 0.1245  data: 0.0027  max mem: 23762
+eval (validation): [16]  [60/63]  eta: 0:00:00    time: 0.1229  data: 0.0020  max mem: 23762
+eval (validation): [16]  [62/63]  eta: 0:00:00    time: 0.1228  data: 0.0019  max mem: 23762
+eval (validation): [16] Total time: 0:00:08 (0.1341 s / it)
+cv: [16]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.149  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:05:41  lr: nan  time: 0.8544  data: 0.6167  max mem: 23762
+train: [17]  [ 20/400]  eta: 0:01:45  lr: 0.000028  loss: 0.1241 (0.1237)  grad: 0.0554 (0.0584)  time: 0.2475  data: 0.0021  max mem: 23762
+train: [17]  [ 40/400]  eta: 0:01:34  lr: 0.000027  loss: 0.1113 (0.1123)  grad: 0.0580 (0.0585)  time: 0.2477  data: 0.0027  max mem: 23762
+train: [17]  [ 60/400]  eta: 0:01:27  lr: 0.000026  loss: 0.1051 (0.1124)  grad: 0.0587 (0.0600)  time: 0.2475  data: 0.0019  max mem: 23762
+train: [17]  [ 80/400]  eta: 0:01:21  lr: 0.000025  loss: 0.1129 (0.1117)  grad: 0.0577 (0.0595)  time: 0.2490  data: 0.0028  max mem: 23762
+train: [17]  [100/400]  eta: 0:01:16  lr: 0.000024  loss: 0.1129 (0.1115)  grad: 0.0580 (0.0598)  time: 0.2473  data: 0.0018  max mem: 23762
+train: [17]  [120/400]  eta: 0:01:10  lr: 0.000023  loss: 0.1109 (0.1108)  grad: 0.0580 (0.0596)  time: 0.2486  data: 0.0028  max mem: 23762
+train: [17]  [140/400]  eta: 0:01:05  lr: 0.000023  loss: 0.1167 (0.1123)  grad: 0.0555 (0.0592)  time: 0.2482  data: 0.0019  max mem: 23762
+train: [17]  [160/400]  eta: 0:01:00  lr: 0.000022  loss: 0.1167 (0.1123)  grad: 0.0584 (0.0601)  time: 0.2485  data: 0.0027  max mem: 23762
+train: [17]  [180/400]  eta: 0:00:55  lr: 0.000021  loss: 0.1116 (0.1125)  grad: 0.0614 (0.0600)  time: 0.2473  data: 0.0018  max mem: 23762
+train: [17]  [200/400]  eta: 0:00:50  lr: 0.000020  loss: 0.1116 (0.1121)  grad: 0.0598 (0.0597)  time: 0.2486  data: 0.0027  max mem: 23762
+train: [17]  [220/400]  eta: 0:00:45  lr: 0.000019  loss: 0.1047 (0.1116)  grad: 0.0544 (0.0594)  time: 0.2480  data: 0.0019  max mem: 23762
+train: [17]  [240/400]  eta: 0:00:40  lr: 0.000019  loss: 0.1047 (0.1111)  grad: 0.0541 (0.0592)  time: 0.2488  data: 0.0028  max mem: 23762
+train: [17]  [260/400]  eta: 0:00:35  lr: 0.000018  loss: 0.1061 (0.1102)  grad: 0.0570 (0.0594)  time: 0.2474  data: 0.0018  max mem: 23762
+train: [17]  [280/400]  eta: 0:00:30  lr: 0.000017  loss: 0.0996 (0.1096)  grad: 0.0574 (0.0591)  time: 0.2492  data: 0.0028  max mem: 23762
+train: [17]  [300/400]  eta: 0:00:25  lr: 0.000016  loss: 0.0994 (0.1094)  grad: 0.0550 (0.0589)  time: 0.2779  data: 0.0321  max mem: 23762
+train: [17]  [320/400]  eta: 0:00:20  lr: 0.000016  loss: 0.1086 (0.1098)  grad: 0.0562 (0.0589)  time: 0.2486  data: 0.0024  max mem: 23762
+train: [17]  [340/400]  eta: 0:00:15  lr: 0.000015  loss: 0.1135 (0.1099)  grad: 0.0546 (0.0586)  time: 0.2489  data: 0.0027  max mem: 23762
+train: [17]  [360/400]  eta: 0:00:10  lr: 0.000014  loss: 0.1049 (0.1098)  grad: 0.0546 (0.0586)  time: 0.2485  data: 0.0023  max mem: 23762
+train: [17]  [380/400]  eta: 0:00:05  lr: 0.000014  loss: 0.1022 (0.1098)  grad: 0.0591 (0.0587)  time: 0.2489  data: 0.0027  max mem: 23762
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 0.1114 (0.1104)  grad: 0.0585 (0.0587)  time: 0.2478  data: 0.0022  max mem: 23762
+train: [17] Total time: 0:01:40 (0.2515 s / it)
+train: [17] Summary: lr: 0.000013  loss: 0.1114 (0.1104)  grad: 0.0585 (0.0587)
+eval (validation): [17]  [ 0/63]  eta: 0:00:36    time: 0.5858  data: 0.4661  max mem: 23762
+eval (validation): [17]  [20/63]  eta: 0:00:06    time: 0.1240  data: 0.0025  max mem: 23762
+eval (validation): [17]  [40/63]  eta: 0:00:03    time: 0.1234  data: 0.0021  max mem: 23762
+eval (validation): [17]  [60/63]  eta: 0:00:00    time: 0.1246  data: 0.0025  max mem: 23762
+eval (validation): [17]  [62/63]  eta: 0:00:00    time: 0.1237  data: 0.0020  max mem: 23762
+eval (validation): [17] Total time: 0:00:08 (0.1329 s / it)
+cv: [17]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.149  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:06:01  lr: nan  time: 0.9042  data: 0.6699  max mem: 23762
+train: [18]  [ 20/400]  eta: 0:01:45  lr: 0.000012  loss: 0.1045 (0.1113)  grad: 0.0513 (0.0570)  time: 0.2476  data: 0.0023  max mem: 23762
+train: [18]  [ 40/400]  eta: 0:01:35  lr: 0.000012  loss: 0.1003 (0.1067)  grad: 0.0523 (0.0565)  time: 0.2482  data: 0.0025  max mem: 23762
+train: [18]  [ 60/400]  eta: 0:01:27  lr: 0.000011  loss: 0.1032 (0.1086)  grad: 0.0555 (0.0576)  time: 0.2482  data: 0.0028  max mem: 23762
+train: [18]  [ 80/400]  eta: 0:01:21  lr: 0.000011  loss: 0.1022 (0.1067)  grad: 0.0568 (0.0579)  time: 0.2479  data: 0.0026  max mem: 23762
+train: [18]  [100/400]  eta: 0:01:16  lr: 0.000010  loss: 0.0965 (0.1060)  grad: 0.0568 (0.0577)  time: 0.2489  data: 0.0028  max mem: 23762
+train: [18]  [120/400]  eta: 0:01:10  lr: 0.000009  loss: 0.1058 (0.1080)  grad: 0.0580 (0.0581)  time: 0.2480  data: 0.0025  max mem: 23762
+train: [18]  [140/400]  eta: 0:01:05  lr: 0.000009  loss: 0.1052 (0.1094)  grad: 0.0572 (0.0578)  time: 0.2483  data: 0.0027  max mem: 23762
+train: [18]  [160/400]  eta: 0:01:00  lr: 0.000008  loss: 0.1031 (0.1081)  grad: 0.0556 (0.0577)  time: 0.2478  data: 0.0025  max mem: 23762
+train: [18]  [180/400]  eta: 0:00:55  lr: 0.000008  loss: 0.1036 (0.1088)  grad: 0.0544 (0.0576)  time: 0.2480  data: 0.0027  max mem: 23762
+train: [18]  [200/400]  eta: 0:00:50  lr: 0.000007  loss: 0.1049 (0.1080)  grad: 0.0568 (0.0578)  time: 0.2479  data: 0.0025  max mem: 23762
+train: [18]  [220/400]  eta: 0:00:45  lr: 0.000007  loss: 0.1017 (0.1078)  grad: 0.0571 (0.0579)  time: 0.2484  data: 0.0028  max mem: 23762
+train: [18]  [240/400]  eta: 0:00:40  lr: 0.000006  loss: 0.1017 (0.1084)  grad: 0.0593 (0.0580)  time: 0.2484  data: 0.0026  max mem: 23762
+train: [18]  [260/400]  eta: 0:00:35  lr: 0.000006  loss: 0.1161 (0.1099)  grad: 0.0593 (0.0580)  time: 0.2484  data: 0.0027  max mem: 23762
+train: [18]  [280/400]  eta: 0:00:30  lr: 0.000006  loss: 0.1122 (0.1095)  grad: 0.0566 (0.0580)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [18]  [300/400]  eta: 0:00:25  lr: 0.000005  loss: 0.1047 (0.1096)  grad: 0.0566 (0.0580)  time: 0.2798  data: 0.0342  max mem: 23762
+train: [18]  [320/400]  eta: 0:00:20  lr: 0.000005  loss: 0.1047 (0.1090)  grad: 0.0561 (0.0579)  time: 0.2483  data: 0.0026  max mem: 23762
+train: [18]  [340/400]  eta: 0:00:15  lr: 0.000004  loss: 0.1014 (0.1089)  grad: 0.0562 (0.0578)  time: 0.2485  data: 0.0024  max mem: 23762
+train: [18]  [360/400]  eta: 0:00:10  lr: 0.000004  loss: 0.1030 (0.1092)  grad: 0.0569 (0.0578)  time: 0.2485  data: 0.0026  max mem: 23762
+train: [18]  [380/400]  eta: 0:00:05  lr: 0.000004  loss: 0.1030 (0.1092)  grad: 0.0603 (0.0582)  time: 0.2483  data: 0.0023  max mem: 23762
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 0.0968 (0.1088)  grad: 0.0614 (0.0581)  time: 0.2489  data: 0.0027  max mem: 23762
+train: [18] Total time: 0:01:40 (0.2517 s / it)
+train: [18] Summary: lr: 0.000003  loss: 0.0968 (0.1088)  grad: 0.0614 (0.0581)
+eval (validation): [18]  [ 0/63]  eta: 0:00:43    time: 0.6903  data: 0.5718  max mem: 23762
+eval (validation): [18]  [20/63]  eta: 0:00:06    time: 0.1230  data: 0.0023  max mem: 23762
+eval (validation): [18]  [40/63]  eta: 0:00:03    time: 0.1243  data: 0.0025  max mem: 23762
+eval (validation): [18]  [60/63]  eta: 0:00:00    time: 0.1251  data: 0.0024  max mem: 23762
+eval (validation): [18]  [62/63]  eta: 0:00:00    time: 0.1247  data: 0.0022  max mem: 23762
+eval (validation): [18] Total time: 0:00:08 (0.1347 s / it)
+cv: [18]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.149  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:06:04  lr: nan  time: 0.9101  data: 0.6736  max mem: 23762
+train: [19]  [ 20/400]  eta: 0:01:46  lr: 0.000003  loss: 0.1007 (0.1090)  grad: 0.0525 (0.0519)  time: 0.2482  data: 0.0024  max mem: 23762
+train: [19]  [ 40/400]  eta: 0:01:35  lr: 0.000003  loss: 0.1007 (0.1046)  grad: 0.0535 (0.0553)  time: 0.2482  data: 0.0025  max mem: 23762
+train: [19]  [ 60/400]  eta: 0:01:28  lr: 0.000002  loss: 0.1114 (0.1120)  grad: 0.0595 (0.0578)  time: 0.2492  data: 0.0029  max mem: 23762
+train: [19]  [ 80/400]  eta: 0:01:22  lr: 0.000002  loss: 0.1083 (0.1091)  grad: 0.0575 (0.0562)  time: 0.2491  data: 0.0025  max mem: 23762
+train: [19]  [100/400]  eta: 0:01:16  lr: 0.000002  loss: 0.1030 (0.1105)  grad: 0.0551 (0.0568)  time: 0.2486  data: 0.0029  max mem: 23762
+train: [19]  [120/400]  eta: 0:01:11  lr: 0.000002  loss: 0.1021 (0.1088)  grad: 0.0560 (0.0565)  time: 0.2480  data: 0.0025  max mem: 23762
+train: [19]  [140/400]  eta: 0:01:05  lr: 0.000001  loss: 0.0984 (0.1102)  grad: 0.0563 (0.0581)  time: 0.2484  data: 0.0030  max mem: 23762
+train: [19]  [160/400]  eta: 0:01:00  lr: 0.000001  loss: 0.1007 (0.1091)  grad: 0.0574 (0.0575)  time: 0.2485  data: 0.0025  max mem: 23762
+train: [19]  [180/400]  eta: 0:00:55  lr: 0.000001  loss: 0.1019 (0.1095)  grad: 0.0574 (0.0576)  time: 0.2489  data: 0.0029  max mem: 23762
+train: [19]  [200/400]  eta: 0:00:50  lr: 0.000001  loss: 0.1090 (0.1092)  grad: 0.0594 (0.0576)  time: 0.2481  data: 0.0024  max mem: 23762
+train: [19]  [220/400]  eta: 0:00:45  lr: 0.000001  loss: 0.1084 (0.1090)  grad: 0.0551 (0.0576)  time: 0.2491  data: 0.0030  max mem: 23762
+train: [19]  [240/400]  eta: 0:00:40  lr: 0.000001  loss: 0.1034 (0.1088)  grad: 0.0571 (0.0580)  time: 0.2492  data: 0.0025  max mem: 23762
+train: [19]  [260/400]  eta: 0:00:35  lr: 0.000000  loss: 0.1002 (0.1080)  grad: 0.0523 (0.0575)  time: 0.2488  data: 0.0029  max mem: 23762
+train: [19]  [280/400]  eta: 0:00:30  lr: 0.000000  loss: 0.1002 (0.1079)  grad: 0.0523 (0.0576)  time: 0.2485  data: 0.0025  max mem: 23762
+train: [19]  [300/400]  eta: 0:00:25  lr: 0.000000  loss: 0.1059 (0.1075)  grad: 0.0592 (0.0577)  time: 0.2801  data: 0.0345  max mem: 23762
+train: [19]  [320/400]  eta: 0:00:20  lr: 0.000000  loss: 0.1043 (0.1075)  grad: 0.0628 (0.0580)  time: 0.2493  data: 0.0027  max mem: 23762
+train: [19]  [340/400]  eta: 0:00:15  lr: 0.000000  loss: 0.1042 (0.1074)  grad: 0.0628 (0.0581)  time: 0.2488  data: 0.0019  max mem: 23762
+train: [19]  [360/400]  eta: 0:00:10  lr: 0.000000  loss: 0.1042 (0.1079)  grad: 0.0568 (0.0582)  time: 0.2489  data: 0.0026  max mem: 23762
+train: [19]  [380/400]  eta: 0:00:05  lr: 0.000000  loss: 0.1090 (0.1081)  grad: 0.0568 (0.0580)  time: 0.2479  data: 0.0018  max mem: 23762
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 0.1049 (0.1082)  grad: 0.0551 (0.0579)  time: 0.2478  data: 0.0020  max mem: 23762
+train: [19] Total time: 0:01:40 (0.2521 s / it)
+train: [19] Summary: lr: 0.000000  loss: 0.1049 (0.1082)  grad: 0.0551 (0.0579)
+eval (validation): [19]  [ 0/63]  eta: 0:00:39    time: 0.6211  data: 0.4997  max mem: 23762
+eval (validation): [19]  [20/63]  eta: 0:00:06    time: 0.1244  data: 0.0027  max mem: 23762
+eval (validation): [19]  [40/63]  eta: 0:00:03    time: 0.1236  data: 0.0022  max mem: 23762
+eval (validation): [19]  [60/63]  eta: 0:00:00    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [19]  [62/63]  eta: 0:00:00    time: 0.1233  data: 0.0019  max mem: 23762
+eval (validation): [19] Total time: 0:00:08 (0.1335 s / it)
+cv: [19]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.148  acc: 0.977  f1: 0.973
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.9771825396825397, "hparam": [7.1, 1.0], "hparam_id": 36, "epoch": 19, "is_best": false, "best_score": 0.9776785714285714}
+eval (train): [20]  [  0/297]  eta: 0:03:28    time: 0.7030  data: 0.5816  max mem: 23762
+eval (train): [20]  [ 20/297]  eta: 0:00:42    time: 0.1242  data: 0.0026  max mem: 23762
+eval (train): [20]  [ 40/297]  eta: 0:00:35    time: 0.1227  data: 0.0019  max mem: 23762
+eval (train): [20]  [ 60/297]  eta: 0:00:31    time: 0.1248  data: 0.0027  max mem: 23762
+eval (train): [20]  [ 80/297]  eta: 0:00:28    time: 0.1237  data: 0.0019  max mem: 23762
+eval (train): [20]  [100/297]  eta: 0:00:25    time: 0.1242  data: 0.0028  max mem: 23762
+eval (train): [20]  [120/297]  eta: 0:00:22    time: 0.1240  data: 0.0021  max mem: 23762
+eval (train): [20]  [140/297]  eta: 0:00:20    time: 0.1242  data: 0.0028  max mem: 23762
+eval (train): [20]  [160/297]  eta: 0:00:17    time: 0.1229  data: 0.0019  max mem: 23762
+eval (train): [20]  [180/297]  eta: 0:00:14    time: 0.1247  data: 0.0028  max mem: 23762
+eval (train): [20]  [200/297]  eta: 0:00:12    time: 0.1232  data: 0.0019  max mem: 23762
+eval (train): [20]  [220/297]  eta: 0:00:09    time: 0.1245  data: 0.0028  max mem: 23762
+eval (train): [20]  [240/297]  eta: 0:00:07    time: 0.1237  data: 0.0019  max mem: 23762
+eval (train): [20]  [260/297]  eta: 0:00:04    time: 0.1246  data: 0.0027  max mem: 23762
+eval (train): [20]  [280/297]  eta: 0:00:02    time: 0.1232  data: 0.0020  max mem: 23762
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.1231  data: 0.0020  max mem: 23762
+eval (train): [20] Total time: 0:00:37 (0.1263 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:00:45    time: 0.7283  data: 0.6086  max mem: 23762
+eval (validation): [20]  [20/63]  eta: 0:00:06    time: 0.1241  data: 0.0029  max mem: 23762
+eval (validation): [20]  [40/63]  eta: 0:00:03    time: 0.1235  data: 0.0021  max mem: 23762
+eval (validation): [20]  [60/63]  eta: 0:00:00    time: 0.1241  data: 0.0027  max mem: 23762
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0020  max mem: 23762
+eval (validation): [20] Total time: 0:00:08 (0.1351 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:01:01    time: 0.7814  data: 0.6605  max mem: 23762
+eval (test): [20]  [20/79]  eta: 0:00:09    time: 0.1234  data: 0.0022  max mem: 23762
+eval (test): [20]  [40/79]  eta: 0:00:05    time: 0.1240  data: 0.0028  max mem: 23762
+eval (test): [20]  [60/79]  eta: 0:00:02    time: 0.1238  data: 0.0020  max mem: 23762
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.1220  data: 0.0020  max mem: 23762
+eval (test): [20] Total time: 0:00:10 (0.1330 s / it)
+evaluating best checkpoint: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.9776785714285714, "hparam": [7.1, 1.0], "hparam_id": 36, "epoch": 13, "is_best": true, "best_score": 0.9776785714285714}
+eval (train): [20]  [  0/297]  eta: 0:03:48    time: 0.7701  data: 0.6484  max mem: 23762
+eval (train): [20]  [ 20/297]  eta: 0:00:42    time: 0.1242  data: 0.0027  max mem: 23762
+eval (train): [20]  [ 40/297]  eta: 0:00:35    time: 0.1241  data: 0.0023  max mem: 23762
+eval (train): [20]  [ 60/297]  eta: 0:00:31    time: 0.1240  data: 0.0026  max mem: 23762
+eval (train): [20]  [ 80/297]  eta: 0:00:28    time: 0.1238  data: 0.0022  max mem: 23762
+eval (train): [20]  [100/297]  eta: 0:00:25    time: 0.1253  data: 0.0026  max mem: 23762
+eval (train): [20]  [120/297]  eta: 0:00:22    time: 0.1234  data: 0.0022  max mem: 23762
+eval (train): [20]  [140/297]  eta: 0:00:20    time: 0.1240  data: 0.0025  max mem: 23762
+eval (train): [20]  [160/297]  eta: 0:00:17    time: 0.1234  data: 0.0022  max mem: 23762
+eval (train): [20]  [180/297]  eta: 0:00:14    time: 0.1245  data: 0.0026  max mem: 23762
+eval (train): [20]  [200/297]  eta: 0:00:12    time: 0.1246  data: 0.0022  max mem: 23762
+eval (train): [20]  [220/297]  eta: 0:00:09    time: 0.1240  data: 0.0026  max mem: 23762
+eval (train): [20]  [240/297]  eta: 0:00:07    time: 0.1240  data: 0.0021  max mem: 23762
+eval (train): [20]  [260/297]  eta: 0:00:04    time: 0.1245  data: 0.0025  max mem: 23762
+eval (train): [20]  [280/297]  eta: 0:00:02    time: 0.1242  data: 0.0022  max mem: 23762
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.1232  data: 0.0024  max mem: 23762
+eval (train): [20] Total time: 0:00:37 (0.1268 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:00:51    time: 0.8102  data: 0.6909  max mem: 23762
+eval (validation): [20]  [20/63]  eta: 0:00:06    time: 0.1232  data: 0.0019  max mem: 23762
+eval (validation): [20]  [40/63]  eta: 0:00:03    time: 0.1232  data: 0.0025  max mem: 23762
+eval (validation): [20]  [60/63]  eta: 0:00:00    time: 0.1233  data: 0.0017  max mem: 23762
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0017  max mem: 23762
+eval (validation): [20] Total time: 0:00:08 (0.1376 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:00:58    time: 0.7361  data: 0.6159  max mem: 23762
+eval (test): [20]  [20/79]  eta: 0:00:09    time: 0.1239  data: 0.0030  max mem: 23762
+eval (test): [20]  [40/79]  eta: 0:00:05    time: 0.1239  data: 0.0032  max mem: 23762
+eval (test): [20]  [60/79]  eta: 0:00:02    time: 0.1236  data: 0.0021  max mem: 23762
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.1233  data: 0.0023  max mem: 23762
+eval (test): [20] Total time: 0:00:10 (0.1328 s / it)
+eval results:
+
+| model           | repr   | clf   | dataset      | ckpt   |   epoch |      lr |   wd |   hparam_id | hparam     | split      |       loss |     acc |   acc_std |      f1 |    f1_std |
+|:----------------|:-------|:------|:-------------|:-------|--------:|--------:|-----:|------------:|:-----------|:-----------|-----------:|--------:|----------:|--------:|----------:|
+| schaefer400_mae | patch  | attn  | hcpya_task21 | best   |      13 | 0.00213 | 0.05 |          36 | [7.1, 1.0] | train      | 9.4982e-05 | 1       | 0         | 1       | 0         |
+| schaefer400_mae | patch  | attn  | hcpya_task21 | best   |      13 | 0.00213 | 0.05 |          36 | [7.1, 1.0] | validation | 0.1543     | 0.97768 | 0.002233  | 0.97327 | 0.0029379 |
+| schaefer400_mae | patch  | attn  | hcpya_task21 | best   |      13 | 0.00213 | 0.05 |          36 | [7.1, 1.0] | test       | 0.20031    | 0.97143 | 0.0023715 | 0.96477 | 0.0032151 |
+
+
+done! total time: 0:39:50

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_probe
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: true
+  norm: true
+lr_scale_grid:
+- 0.02
+- 0.023
+- 0.028
+- 0.033
+- 0.038
+- 0.045
+- 0.053
+- 0.062
+- 0.074
+- 0.087
+- 0.1
+- 0.12
+- 0.14
+- 0.17
+- 0.2
+- 0.23
+- 0.27
+- 0.32
+- 0.38
+- 0.44
+- 0.52
+- 0.61
+- 0.72
+- 0.85
+- 1
+- 1.2
+- 1.4
+- 1.6
+- 1.9
+- 2.3
+- 2.7
+- 3.1
+- 3.7
+- 4.3
+- 5.1
+- 6
+- 7.1
+- 8.3
+- 9.8
+- 12
+- 14
+- 16
+- 19
+- 22
+- 26
+- 31
+- 36
+- 43
+- 50
+wd_scale_grid:
+- 1.0
+num_workers: 8
+prefetch_factor: null
+balanced_sampling: false
+epochs: 20
+steps_per_epoch: 200
+batch_size: 64
+accum_iter: 2
+lr: 0.0003
+warmup_epochs: 5
+no_decay: false
+weight_decay: 0.05
+clip_grad: 1.0
+metrics:
+- acc
+- f1
+cv_metric: acc
+early_stopping: true
+amp: true
+device: cuda
+seed: 4466
+debug: false
+wandb: false
+wandb_entity: null
+wandb_project: fMRI-fm-eval
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 14, "eval/id_best": 37, "eval/lr_best": 0.00249, "eval/wd_best": 0.05, "eval/train/loss": 2.034559488296509, "eval/train/acc": 0.380712375918129, "eval/train/acc_std": 0.002470009517031906, "eval/train/f1": 0.330662275364035, "eval/train/f1_std": 0.0027080199731635787, "eval/validation/loss": 2.5135498046875, "eval/validation/acc": 0.2678110003691399, "eval/validation/acc_std": 0.005525262062644749, "eval/validation/f1": 0.20350143237935944, "eval/validation/f1_std": 0.005008638293271794, "eval/test/loss": 2.468745231628418, "eval/test/acc": 0.2753246753246753, "eval/test/acc_std": 0.005251403785086813, "eval/test/f1": 0.2143488152521974, "eval/test/f1_std": 0.005159215751085167, "eval/testid/loss": 2.4959957599639893, "eval/testid/acc": 0.25718141507615194, "eval/testid/acc_std": 0.005606547631141551, "eval/testid/f1": 0.20646256908401242, "eval/testid/f1_std": 0.005257456778690613}

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 14, "eval/best/id_best": 37, "eval/best/lr_best": 0.00249, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.034559488296509, "eval/best/train/acc": 0.380712375918129, "eval/best/train/acc_std": 0.002470009517031906, "eval/best/train/f1": 0.330662275364035, "eval/best/train/f1_std": 0.0027080199731635787, "eval/best/validation/loss": 2.5135498046875, "eval/best/validation/acc": 0.2678110003691399, "eval/best/validation/acc_std": 0.005525262062644749, "eval/best/validation/f1": 0.20350143237935944, "eval/best/validation/f1_std": 0.005008638293271794, "eval/best/test/loss": 2.468745231628418, "eval/best/test/acc": 0.2753246753246753, "eval/best/test/acc_std": 0.005251403785086813, "eval/best/test/f1": 0.2143488152521974, "eval/best/test/f1_std": 0.005159215751085167, "eval/best/testid/loss": 2.4959957599639893, "eval/best/testid/acc": 0.25718141507615194, "eval/best/testid/acc_std": 0.005606547631141551, "eval/best/testid/f1": 0.20646256908401242, "eval/best/testid/f1_std": 0.005257456778690613}

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 25, "eval/last/lr_best": 0.00035999999999999997, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.119687557220459, "eval/last/train/acc": 0.36442422938627494, "eval/last/train/acc_std": 0.0024952486356130134, "eval/last/train/f1": 0.3169676957488727, "eval/last/train/f1_std": 0.002580799588052676, "eval/last/validation/loss": 2.472348213195801, "eval/last/validation/acc": 0.2622739018087855, "eval/last/validation/acc_std": 0.005290895469313323, "eval/last/validation/f1": 0.20254516807612888, "eval/last/validation/f1_std": 0.004943366880113296, "eval/last/test/loss": 2.3941986560821533, "eval/last/test/acc": 0.2781076066790352, "eval/last/test/acc_std": 0.00508891007232383, "eval/last/test/f1": 0.21469190358449172, "eval/last/test/f1_std": 0.004961970587797507, "eval/last/testid/loss": 2.4633915424346924, "eval/last/testid/acc": 0.26122999807210334, "eval/last/testid/acc_std": 0.005344861381971109, "eval/last/testid/f1": 0.2142067000439046, "eval/last/testid/f1_std": 0.005122949906935294}

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_table.csv

@@ -0,0 +1,5 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",train,2.034559488296509,0.380712375918129,0.002470009517031906,0.330662275364035,0.0027080199731635787
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",validation,2.5135498046875,0.2678110003691399,0.005525262062644749,0.20350143237935944,0.005008638293271794
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",test,2.468745231628418,0.2753246753246753,0.005251403785086813,0.2143488152521974,0.005159215751085167
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",testid,2.4959957599639893,0.25718141507615194,0.005606547631141551,0.20646256908401242,0.005257456778690613

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_table_best.csv

@@ -0,0 +1,5 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",train,2.034559488296509,0.380712375918129,0.002470009517031906,0.330662275364035,0.0027080199731635787
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",validation,2.5135498046875,0.2678110003691399,0.005525262062644749,0.20350143237935944,0.005008638293271794
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",test,2.468745231628418,0.2753246753246753,0.005251403785086813,0.2143488152521974,0.005159215751085167
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.00249,0.05,37,"[8.3, 1.0]",testid,2.4959957599639893,0.25718141507615194,0.005606547631141551,0.20646256908401242,0.005257456778690613

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/eval_table_last.csv

@@ -0,0 +1,5 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00035999999999999997,0.05,25,"[1.2, 1.0]",train,2.119687557220459,0.36442422938627494,0.0024952486356130134,0.3169676957488727,0.002580799588052676
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00035999999999999997,0.05,25,"[1.2, 1.0]",validation,2.472348213195801,0.2622739018087855,0.005290895469313323,0.20254516807612888,0.004943366880113296
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00035999999999999997,0.05,25,"[1.2, 1.0]",test,2.3941986560821533,0.2781076066790352,0.00508891007232383,0.21469190358449172,0.004961970587797507
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00035999999999999997,0.05,25,"[1.2, 1.0]",testid,2.4633915424346924,0.26122999807210334,0.005344861381971109,0.2142067000439046,0.005122949906935294

subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/log.txt

@@ -0,0 +1,969 @@
+fMRI foundation model probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 14:49:48
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_probe
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: true
+  norm: true
+lr_scale_grid:
+- 0.02
+- 0.023
+- 0.028
+- 0.033
+- 0.038
+- 0.045
+- 0.053
+- 0.062
+- 0.074
+- 0.087
+- 0.1
+- 0.12
+- 0.14
+- 0.17
+- 0.2
+- 0.23
+- 0.27
+- 0.32
+- 0.38
+- 0.44
+- 0.52
+- 0.61
+- 0.72
+- 0.85
+- 1
+- 1.2
+- 1.4
+- 1.6
+- 1.9
+- 2.3
+- 2.7
+- 3.1
+- 3.7
+- 4.3
+- 5.1
+- 6
+- 7.1
+- 8.3
+- 9.8
+- 12
+- 14
+- 16
+- 19
+- 22
+- 26
+- 31
+- 36
+- 43
+- 50
+wd_scale_grid:
+- 1.0
+num_workers: 8
+prefetch_factor: null
+balanced_sampling: false
+epochs: 20
+steps_per_epoch: 200
+batch_size: 64
+accum_iter: 2
+lr: 0.0003
+warmup_epochs: 5
+no_decay: false
+weight_decay: 0.05
+clip_grad: 1.0
+metrics:
+- acc
+- f1
+cv_metric: acc
+early_stopping: true
+amp: true
+device: cuda
+seed: 4466
+debug: false
+wandb: false
+wandb_entity: null
+wandb_project: fMRI-fm-eval
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: nsd_cococlip (schaefer400)
+train (n=32539):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 32539
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[1286 1180 1639 1868  834  824 1026 1042  913 1853 1503 2092 1001 1410
+  794 1241 1904 1872 2267 1428  889  904 1447 1322]
+)
+
+validation (n=5418):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5418
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[197 161 276 345 126 142 143 185 112 295 285 387 169 250 159 193 316 334
+ 343 215 172 141 226 246]
+)
+
+test (n=5390):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5390
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[202 172 274 298 144 180 134 182 186 293 218 343 165 185 140 177 346 333
+ 345 271 165 140 251 246]
+)
+
+testid (n=5187):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5187
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[197 159 267 273 123 153 175 184 139 310 215 386 153 230 118 192 330 306
+ 349 223 143 127 249 186]
+)
+
+running backbone on example batch to get embedding dim
+embedding feature dim (patch): 768
+initializing sweep of classifier heads
+classifiers:
+ModuleList(
+  (0-48): 49 x AttnPoolClassifier(
+    (kv): Linear(in_features=768, out_features=1536, bias=True)
+    (linear): Linear(in_features=768, out_features=24, bias=True)
+  )
+)
+classifier params (train): 58.8M (58.8M)
+setting up optimizer
+total batch size: 128 = 64 bs per gpu x 2 accum
+lr: 3.00e-04
+full schedule: epochs = 20 (steps = 4000) (decay = True)
+warmup: epochs = 5 (steps = 1000)
+start training for 20 epochs
+train: [0]  [  0/400]  eta: 0:07:46  lr: nan  time: 1.1675  data: 0.7326  max mem: 23058
+train: [0]  [ 20/400]  eta: 0:01:52  lr: 0.000003  loss: 3.1788 (3.1795)  grad: 0.1900 (0.1928)  time: 0.2537  data: 0.0028  max mem: 23764
+train: [0]  [ 40/400]  eta: 0:01:37  lr: 0.000006  loss: 3.1725 (3.1713)  grad: 0.1780 (0.1846)  time: 0.2456  data: 0.0035  max mem: 23764
+train: [0]  [ 60/400]  eta: 0:01:29  lr: 0.000009  loss: 3.1618 (3.1688)  grad: 0.1684 (0.1803)  time: 0.2454  data: 0.0036  max mem: 23764
+train: [0]  [ 80/400]  eta: 0:01:22  lr: 0.000012  loss: 3.1664 (3.1697)  grad: 0.1601 (0.1754)  time: 0.2453  data: 0.0036  max mem: 23764
+train: [0]  [100/400]  eta: 0:01:16  lr: 0.000015  loss: 3.1652 (3.1665)  grad: 0.1699 (0.1760)  time: 0.2457  data: 0.0037  max mem: 23764
+train: [0]  [120/400]  eta: 0:01:11  lr: 0.000018  loss: 3.1532 (3.1660)  grad: 0.1748 (0.1745)  time: 0.2454  data: 0.0036  max mem: 23764
+train: [0]  [140/400]  eta: 0:01:05  lr: 0.000021  loss: 3.1556 (3.1642)  grad: 0.1711 (0.1729)  time: 0.2457  data: 0.0036  max mem: 23764
+train: [0]  [160/400]  eta: 0:01:00  lr: 0.000024  loss: 3.1503 (3.1642)  grad: 0.1730 (0.1732)  time: 0.2461  data: 0.0036  max mem: 23764
+train: [0]  [180/400]  eta: 0:00:55  lr: 0.000027  loss: 3.1652 (3.1650)  grad: 0.1684 (0.1723)  time: 0.2463  data: 0.0037  max mem: 23764
+train: [0]  [200/400]  eta: 0:00:50  lr: 0.000030  loss: 3.1652 (3.1627)  grad: 0.1652 (0.1724)  time: 0.2462  data: 0.0037  max mem: 23764
+train: [0]  [220/400]  eta: 0:00:45  lr: 0.000033  loss: 3.1642 (3.1643)  grad: 0.1774 (0.1738)  time: 0.2461  data: 0.0036  max mem: 23764
+train: [0]  [240/400]  eta: 0:00:40  lr: 0.000036  loss: 3.1559 (3.1618)  grad: 0.1799 (0.1738)  time: 0.2458  data: 0.0036  max mem: 23764
+train: [0]  [260/400]  eta: 0:00:34  lr: 0.000039  loss: 3.1335 (3.1603)  grad: 0.1771 (0.1737)  time: 0.2458  data: 0.0036  max mem: 23764
+train: [0]  [280/400]  eta: 0:00:29  lr: 0.000042  loss: 3.1465 (3.1602)  grad: 0.1722 (0.1736)  time: 0.2461  data: 0.0036  max mem: 23764
+train: [0]  [300/400]  eta: 0:00:24  lr: 0.000045  loss: 3.1270 (3.1573)  grad: 0.1682 (0.1728)  time: 0.2467  data: 0.0036  max mem: 23764
+train: [0]  [320/400]  eta: 0:00:19  lr: 0.000048  loss: 3.1140 (3.1549)  grad: 0.1679 (0.1727)  time: 0.2471  data: 0.0037  max mem: 23764
+train: [0]  [340/400]  eta: 0:00:14  lr: 0.000051  loss: 3.1045 (3.1527)  grad: 0.1701 (0.1727)  time: 0.2459  data: 0.0035  max mem: 23764
+train: [0]  [360/400]  eta: 0:00:09  lr: 0.000054  loss: 3.1080 (3.1506)  grad: 0.1758 (0.1734)  time: 0.2455  data: 0.0034  max mem: 23764
+train: [0]  [380/400]  eta: 0:00:04  lr: 0.000057  loss: 3.1078 (3.1484)  grad: 0.1827 (0.1741)  time: 0.2458  data: 0.0035  max mem: 23764
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.1021 (3.1469)  grad: 0.1826 (0.1746)  time: 0.2465  data: 0.0036  max mem: 23764
+train: [0] Total time: 0:01:39 (0.2489 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.1021 (3.1469)  grad: 0.1826 (0.1746)
+eval (validation): [0]  [ 0/85]  eta: 0:01:12    time: 0.8493  data: 0.7308  max mem: 23764
+eval (validation): [0]  [20/85]  eta: 0:00:10    time: 0.1218  data: 0.0023  max mem: 23764
+eval (validation): [0]  [40/85]  eta: 0:00:06    time: 0.1220  data: 0.0034  max mem: 23764
+eval (validation): [0]  [60/85]  eta: 0:00:03    time: 0.1228  data: 0.0036  max mem: 23764
+eval (validation): [0]  [80/85]  eta: 0:00:00    time: 0.1223  data: 0.0036  max mem: 23764
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.1206  data: 0.0036  max mem: 23764
+eval (validation): [0] Total time: 0:00:11 (0.1316 s / it)
+cv: [0]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 2.867  acc: 0.155  f1: 0.083
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:05:33  lr: nan  time: 0.8341  data: 0.5996  max mem: 23764
+train: [1]  [ 20/400]  eta: 0:01:43  lr: 0.000063  loss: 3.0599 (3.0672)  grad: 0.1653 (0.1729)  time: 0.2456  data: 0.0030  max mem: 23764
+train: [1]  [ 40/400]  eta: 0:01:33  lr: 0.000066  loss: 3.0686 (3.0724)  grad: 0.1657 (0.1757)  time: 0.2459  data: 0.0035  max mem: 23764
+train: [1]  [ 60/400]  eta: 0:01:26  lr: 0.000069  loss: 3.0736 (3.0757)  grad: 0.1851 (0.1804)  time: 0.2465  data: 0.0036  max mem: 23764
+train: [1]  [ 80/400]  eta: 0:01:21  lr: 0.000072  loss: 3.0540 (3.0651)  grad: 0.1857 (0.1820)  time: 0.2465  data: 0.0036  max mem: 23764
+train: [1]  [100/400]  eta: 0:01:15  lr: 0.000075  loss: 3.0481 (3.0630)  grad: 0.1857 (0.1823)  time: 0.2461  data: 0.0035  max mem: 23764
+train: [1]  [120/400]  eta: 0:01:10  lr: 0.000078  loss: 3.0710 (3.0673)  grad: 0.1851 (0.1835)  time: 0.2464  data: 0.0035  max mem: 23764
+train: [1]  [140/400]  eta: 0:01:05  lr: 0.000081  loss: 3.0773 (3.0672)  grad: 0.1851 (0.1838)  time: 0.2460  data: 0.0035  max mem: 23764
+train: [1]  [160/400]  eta: 0:00:59  lr: 0.000084  loss: 3.0453 (3.0634)  grad: 0.1834 (0.1841)  time: 0.2465  data: 0.0036  max mem: 23764
+train: [1]  [180/400]  eta: 0:00:54  lr: 0.000087  loss: 3.0301 (3.0610)  grad: 0.1865 (0.1855)  time: 0.2470  data: 0.0037  max mem: 23764
+train: [1]  [200/400]  eta: 0:00:49  lr: 0.000090  loss: 3.0346 (3.0586)  grad: 0.1930 (0.1858)  time: 0.2467  data: 0.0036  max mem: 23764
+train: [1]  [220/400]  eta: 0:00:44  lr: 0.000093  loss: 3.0346 (3.0562)  grad: 0.1906 (0.1859)  time: 0.2467  data: 0.0036  max mem: 23764
+train: [1]  [240/400]  eta: 0:00:39  lr: 0.000096  loss: 3.0210 (3.0546)  grad: 0.1906 (0.1859)  time: 0.2466  data: 0.0036  max mem: 23764
+train: [1]  [260/400]  eta: 0:00:34  lr: 0.000099  loss: 3.0168 (3.0527)  grad: 0.1782 (0.1853)  time: 0.2461  data: 0.0035  max mem: 23764
+train: [1]  [280/400]  eta: 0:00:29  lr: 0.000102  loss: 3.0327 (3.0514)  grad: 0.1826 (0.1866)  time: 0.2460  data: 0.0035  max mem: 23764
+train: [1]  [300/400]  eta: 0:00:24  lr: 0.000105  loss: 3.0329 (3.0504)  grad: 0.1959 (0.1870)  time: 0.2463  data: 0.0035  max mem: 23764
+train: [1]  [320/400]  eta: 0:00:19  lr: 0.000108  loss: 3.0056 (3.0470)  grad: 0.1940 (0.1885)  time: 0.2469  data: 0.0036  max mem: 23764
+train: [1]  [340/400]  eta: 0:00:14  lr: 0.000111  loss: 2.9931 (3.0454)  grad: 0.1951 (0.1890)  time: 0.2469  data: 0.0036  max mem: 23764
+train: [1]  [360/400]  eta: 0:00:09  lr: 0.000114  loss: 3.0128 (3.0428)  grad: 0.1949 (0.1894)  time: 0.2467  data: 0.0036  max mem: 23764
+train: [1]  [380/400]  eta: 0:00:04  lr: 0.000117  loss: 3.0128 (3.0409)  grad: 0.1996 (0.1914)  time: 0.2467  data: 0.0036  max mem: 23764
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 3.0209 (3.0398)  grad: 0.2293 (0.1931)  time: 0.2472  data: 0.0037  max mem: 23764
+train: [1] Total time: 0:01:39 (0.2482 s / it)
+train: [1] Summary: lr: 0.000120  loss: 3.0209 (3.0398)  grad: 0.2293 (0.1931)
+eval (validation): [1]  [ 0/85]  eta: 0:01:01    time: 0.7250  data: 0.6076  max mem: 23764
+eval (validation): [1]  [20/85]  eta: 0:00:09    time: 0.1221  data: 0.0032  max mem: 23764
+eval (validation): [1]  [40/85]  eta: 0:00:06    time: 0.1218  data: 0.0033  max mem: 23764
+eval (validation): [1]  [60/85]  eta: 0:00:03    time: 0.1222  data: 0.0034  max mem: 23764
+eval (validation): [1]  [80/85]  eta: 0:00:00    time: 0.1222  data: 0.0036  max mem: 23764
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.1202  data: 0.0037  max mem: 23764
+eval (validation): [1] Total time: 0:00:11 (0.1299 s / it)
+cv: [1]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 2.747  acc: 0.193  f1: 0.123
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:05:55  lr: nan  time: 0.8899  data: 0.6552  max mem: 23764
+train: [2]  [ 20/400]  eta: 0:01:44  lr: 0.000123  loss: 2.9256 (2.9534)  grad: 0.2226 (0.2271)  time: 0.2452  data: 0.0030  max mem: 23764
+train: [2]  [ 40/400]  eta: 0:01:34  lr: 0.000126  loss: 3.0001 (3.0196)  grad: 0.2628 (0.3628)  time: 0.2457  data: 0.0035  max mem: 23764
+train: [2]  [ 60/400]  eta: 0:01:27  lr: 0.000129  loss: 3.1554 (3.2189)  grad: 0.7075 (0.7067)  time: 0.2462  data: 0.0035  max mem: 23764
+WARNING: classifier 48 (50, 1.0) diverged (loss=69.74 > 63.56) at step 432. Freezing.
+train: [2]  [ 80/400]  eta: 0:01:21  lr: 0.000132  loss: 3.1470 (3.2284)  grad: 0.8882 (0.6696)  time: 0.2442  data: 0.0037  max mem: 23764
+train: [2]  [100/400]  eta: 0:01:15  lr: 0.000135  loss: 2.9943 (3.1778)  grad: 0.2145 (0.5789)  time: 0.2440  data: 0.0037  max mem: 23764
+train: [2]  [120/400]  eta: 0:01:10  lr: 0.000138  loss: 2.9820 (3.1452)  grad: 0.2230 (0.5223)  time: 0.2435  data: 0.0037  max mem: 23764
+train: [2]  [140/400]  eta: 0:01:04  lr: 0.000141  loss: 2.9820 (3.1220)  grad: 0.2381 (0.4890)  time: 0.2437  data: 0.0036  max mem: 23764
+train: [2]  [160/400]  eta: 0:00:59  lr: 0.000144  loss: 3.0718 (3.1579)  grad: 0.5131 (0.5696)  time: 0.2435  data: 0.0036  max mem: 23764
+WARNING: classifier 47 (43, 1.0) diverged (loss=74.71 > 63.56) at step 486. Freezing.
+train: [2]  [180/400]  eta: 0:00:54  lr: 0.000147  loss: 3.3610 (3.2008)  grad: 1.0989 (0.6296)  time: 0.2431  data: 0.0037  max mem: 23764
+train: [2]  [200/400]  eta: 0:00:49  lr: 0.000150  loss: 3.3610 (3.2221)  grad: 1.0989 (0.6843)  time: 0.2415  data: 0.0038  max mem: 23764
+WARNING: classifier 46 (36, 1.0) diverged (loss=76.95 > 63.56) at step 501. Freezing.
+train: [2]  [220/400]  eta: 0:00:44  lr: 0.000153  loss: 3.0588 (3.2125)  grad: 0.5028 (0.6610)  time: 0.2389  data: 0.0037  max mem: 23764
+train: [2]  [240/400]  eta: 0:00:39  lr: 0.000156  loss: 2.9722 (3.1932)  grad: 0.2412 (0.6267)  time: 0.2390  data: 0.0037  max mem: 23764
+train: [2]  [260/400]  eta: 0:00:34  lr: 0.000159  loss: 3.0291 (3.1824)  grad: 0.2678 (0.6172)  time: 0.2389  data: 0.0037  max mem: 23764
+train: [2]  [280/400]  eta: 0:00:29  lr: 0.000162  loss: 3.1740 (3.1965)  grad: 0.7267 (0.6448)  time: 0.2389  data: 0.0037  max mem: 23764
+train: [2]  [300/400]  eta: 0:00:24  lr: 0.000165  loss: 3.4816 (3.2312)  grad: 1.2316 (0.7072)  time: 0.2394  data: 0.0036  max mem: 23764
+train: [2]  [320/400]  eta: 0:00:19  lr: 0.000168  loss: 3.8593 (3.2747)  grad: 1.5986 (0.7644)  time: 0.2384  data: 0.0036  max mem: 23764
+train: [2]  [340/400]  eta: 0:00:14  lr: 0.000171  loss: 3.8954 (3.3093)  grad: 1.5986 (0.8096)  time: 0.2377  data: 0.0035  max mem: 23764
+WARNING: classifier 45 (31, 1.0) diverged (loss=63.94 > 63.56) at step 579. Freezing.
+train: [2]  [360/400]  eta: 0:00:09  lr: 0.000174  loss: 3.8162 (3.3351)  grad: 1.3636 (0.8423)  time: 0.2377  data: 0.0036  max mem: 23764
+train: [2]  [380/400]  eta: 0:00:04  lr: 0.000177  loss: 2.9925 (3.3134)  grad: 0.2266 (0.8087)  time: 0.2357  data: 0.0037  max mem: 23764
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 2.9426 (3.2952)  grad: 0.2026 (0.7782)  time: 0.2363  data: 0.0037  max mem: 23764
+train: [2] Total time: 0:01:37 (0.2429 s / it)
+train: [2] Summary: lr: 0.000180  loss: 2.9426 (3.2952)  grad: 0.2026 (0.7782)
+eval (validation): [2]  [ 0/85]  eta: 0:01:01    time: 0.7267  data: 0.6077  max mem: 23764
+eval (validation): [2]  [20/85]  eta: 0:00:09    time: 0.1201  data: 0.0024  max mem: 23764
+eval (validation): [2]  [40/85]  eta: 0:00:06    time: 0.1212  data: 0.0032  max mem: 23764
+eval (validation): [2]  [60/85]  eta: 0:00:03    time: 0.1214  data: 0.0036  max mem: 23764
+eval (validation): [2]  [80/85]  eta: 0:00:00    time: 0.1217  data: 0.0035  max mem: 23764
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.1195  data: 0.0036  max mem: 23764
+eval (validation): [2] Total time: 0:00:10 (0.1291 s / it)
+cv: [2]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 2.707  acc: 0.214  f1: 0.122
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:05:50  lr: nan  time: 0.8757  data: 0.6492  max mem: 23764
+train: [3]  [ 20/400]  eta: 0:01:40  lr: 0.000183  loss: 2.9406 (2.9383)  grad: 0.2115 (0.2118)  time: 0.2348  data: 0.0022  max mem: 23764
+train: [3]  [ 40/400]  eta: 0:01:30  lr: 0.000186  loss: 2.9484 (2.9448)  grad: 0.2096 (0.2101)  time: 0.2365  data: 0.0037  max mem: 23764
+train: [3]  [ 60/400]  eta: 0:01:23  lr: 0.000189  loss: 2.9498 (2.9482)  grad: 0.2113 (0.2158)  time: 0.2351  data: 0.0035  max mem: 23764
+train: [3]  [ 80/400]  eta: 0:01:17  lr: 0.000192  loss: 2.9140 (2.9392)  grad: 0.2208 (0.2161)  time: 0.2356  data: 0.0035  max mem: 23764
+train: [3]  [100/400]  eta: 0:01:12  lr: 0.000195  loss: 2.9107 (2.9366)  grad: 0.2174 (0.2174)  time: 0.2363  data: 0.0036  max mem: 23764
+train: [3]  [120/400]  eta: 0:01:07  lr: 0.000198  loss: 2.9129 (2.9321)  grad: 0.2222 (0.2183)  time: 0.2354  data: 0.0035  max mem: 23764
+train: [3]  [140/400]  eta: 0:01:02  lr: 0.000201  loss: 2.9286 (2.9389)  grad: 0.2354 (0.2340)  time: 0.2366  data: 0.0037  max mem: 23764
+train: [3]  [160/400]  eta: 0:00:57  lr: 0.000204  loss: 3.0357 (2.9905)  grad: 0.4306 (0.3481)  time: 0.2359  data: 0.0036  max mem: 23764
+WARNING: classifier 44 (26, 1.0) diverged (loss=78.42 > 63.56) at step 687. Freezing.
+train: [3]  [180/400]  eta: 0:00:52  lr: 0.000207  loss: 3.4904 (3.0712)  grad: 1.1985 (0.4796)  time: 0.2348  data: 0.0036  max mem: 23764
+train: [3]  [200/400]  eta: 0:00:47  lr: 0.000210  loss: 2.9689 (3.0574)  grad: 0.2108 (0.4514)  time: 0.2332  data: 0.0036  max mem: 23764
+train: [3]  [220/400]  eta: 0:00:42  lr: 0.000213  loss: 2.9015 (3.0430)  grad: 0.1962 (0.4289)  time: 0.2328  data: 0.0036  max mem: 23764
+train: [3]  [240/400]  eta: 0:00:38  lr: 0.000216  loss: 2.9015 (3.0321)  grad: 0.1916 (0.4088)  time: 0.2326  data: 0.0035  max mem: 23764
+train: [3]  [260/400]  eta: 0:00:33  lr: 0.000219  loss: 2.9281 (3.0253)  grad: 0.1913 (0.3925)  time: 0.2322  data: 0.0035  max mem: 23764
+train: [3]  [280/400]  eta: 0:00:28  lr: 0.000222  loss: 2.9325 (3.0194)  grad: 0.1927 (0.3790)  time: 0.2333  data: 0.0037  max mem: 23764
+train: [3]  [300/400]  eta: 0:00:23  lr: 0.000225  loss: 2.9506 (3.0138)  grad: 0.1963 (0.3672)  time: 0.2337  data: 0.0037  max mem: 23764
+train: [3]  [320/400]  eta: 0:00:18  lr: 0.000228  loss: 2.9376 (3.0075)  grad: 0.1931 (0.3561)  time: 0.2328  data: 0.0035  max mem: 23764
+train: [3]  [340/400]  eta: 0:00:14  lr: 0.000231  loss: 2.9052 (3.0016)  grad: 0.1946 (0.3470)  time: 0.2318  data: 0.0033  max mem: 23764
+train: [3]  [360/400]  eta: 0:00:09  lr: 0.000234  loss: 2.9282 (2.9984)  grad: 0.2017 (0.3396)  time: 0.2317  data: 0.0033  max mem: 23764
+train: [3]  [380/400]  eta: 0:00:04  lr: 0.000237  loss: 2.9175 (2.9930)  grad: 0.2110 (0.3332)  time: 0.2314  data: 0.0033  max mem: 23764
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.8842 (2.9884)  grad: 0.2187 (0.3273)  time: 0.2315  data: 0.0032  max mem: 23764
+train: [3] Total time: 0:01:34 (0.2357 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.8842 (2.9884)  grad: 0.2187 (0.3273)
+eval (validation): [3]  [ 0/85]  eta: 0:00:57    time: 0.6803  data: 0.5645  max mem: 23764
+eval (validation): [3]  [20/85]  eta: 0:00:09    time: 0.1202  data: 0.0023  max mem: 23764
+eval (validation): [3]  [40/85]  eta: 0:00:06    time: 0.1211  data: 0.0034  max mem: 23764
+eval (validation): [3]  [60/85]  eta: 0:00:03    time: 0.1209  data: 0.0034  max mem: 23764
+eval (validation): [3]  [80/85]  eta: 0:00:00    time: 0.1211  data: 0.0034  max mem: 23764
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.1191  data: 0.0034  max mem: 23764
+eval (validation): [3] Total time: 0:00:10 (0.1281 s / it)
+cv: [3]  best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00')  loss: 2.580  acc: 0.225  f1: 0.165
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:05:10  lr: nan  time: 0.7762  data: 0.5552  max mem: 23764
+train: [4]  [ 20/400]  eta: 0:01:37  lr: 0.000243  loss: 2.8661 (2.8690)  grad: 0.2103 (0.2140)  time: 0.2316  data: 0.0025  max mem: 23764
+train: [4]  [ 40/400]  eta: 0:01:28  lr: 0.000246  loss: 2.8565 (2.8650)  grad: 0.2103 (0.2127)  time: 0.2325  data: 0.0034  max mem: 23764
+train: [4]  [ 60/400]  eta: 0:01:21  lr: 0.000249  loss: 2.8759 (2.8735)  grad: 0.2184 (0.2176)  time: 0.2326  data: 0.0034  max mem: 23764
+train: [4]  [ 80/400]  eta: 0:01:16  lr: 0.000252  loss: 2.8818 (2.8683)  grad: 0.2184 (0.2160)  time: 0.2327  data: 0.0035  max mem: 23764
+train: [4]  [100/400]  eta: 0:01:11  lr: 0.000255  loss: 2.8719 (2.8718)  grad: 0.2109 (0.2165)  time: 0.2325  data: 0.0033  max mem: 23764
+train: [4]  [120/400]  eta: 0:01:06  lr: 0.000258  loss: 2.9121 (2.8858)  grad: 0.2255 (0.2190)  time: 0.2322  data: 0.0033  max mem: 23764
+train: [4]  [140/400]  eta: 0:01:01  lr: 0.000261  loss: 2.8916 (2.8823)  grad: 0.2290 (0.2194)  time: 0.2320  data: 0.0033  max mem: 23764
+train: [4]  [160/400]  eta: 0:00:56  lr: 0.000264  loss: 2.8764 (2.8913)  grad: 0.2355 (0.2449)  time: 0.2322  data: 0.0034  max mem: 23764
+WARNING: classifier 43 (22, 1.0) diverged (loss=75.08 > 63.56) at step 889. Freezing.
+train: [4]  [180/400]  eta: 0:00:51  lr: 0.000267  loss: 3.0865 (2.9669)  grad: 0.7473 (0.4040)  time: 0.2323  data: 0.0034  max mem: 23764
+train: [4]  [200/400]  eta: 0:00:46  lr: 0.000270  loss: 2.9266 (2.9561)  grad: 0.2358 (0.3851)  time: 0.2297  data: 0.0034  max mem: 23764
+train: [4]  [220/400]  eta: 0:00:42  lr: 0.000273  loss: 2.8537 (2.9511)  grad: 0.2200 (0.3716)  time: 0.2292  data: 0.0033  max mem: 23764
+train: [4]  [240/400]  eta: 0:00:37  lr: 0.000276  loss: 2.9179 (2.9493)  grad: 0.2234 (0.3585)  time: 0.2289  data: 0.0033  max mem: 23764
+train: [4]  [260/400]  eta: 0:00:32  lr: 0.000279  loss: 2.9083 (2.9443)  grad: 0.2085 (0.3466)  time: 0.2296  data: 0.0034  max mem: 23764
+train: [4]  [280/400]  eta: 0:00:27  lr: 0.000282  loss: 2.8668 (2.9393)  grad: 0.2262 (0.3404)  time: 0.2297  data: 0.0033  max mem: 23764
+train: [4]  [300/400]  eta: 0:00:23  lr: 0.000285  loss: 2.9682 (2.9581)  grad: 0.2920 (0.3815)  time: 0.2293  data: 0.0033  max mem: 23764
+WARNING: classifier 42 (19, 1.0) diverged (loss=75.82 > 63.56) at step 951. Freezing.
+train: [4]  [320/400]  eta: 0:00:18  lr: 0.000288  loss: 3.0047 (2.9614)  grad: 0.2768 (0.3825)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [4]  [340/400]  eta: 0:00:13  lr: 0.000291  loss: 2.8812 (2.9575)  grad: 0.2053 (0.3723)  time: 0.2273  data: 0.0035  max mem: 23764
+train: [4]  [360/400]  eta: 0:00:09  lr: 0.000294  loss: 2.8982 (2.9547)  grad: 0.1998 (0.3630)  time: 0.2272  data: 0.0034  max mem: 23764
+train: [4]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 2.8787 (2.9498)  grad: 0.1998 (0.3549)  time: 0.2271  data: 0.0034  max mem: 23764
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.8504 (2.9440)  grad: 0.2033 (0.3474)  time: 0.2272  data: 0.0034  max mem: 23764
+train: [4] Total time: 0:01:32 (0.2318 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.8504 (2.9440)  grad: 0.2033 (0.3474)
+eval (validation): [4]  [ 0/85]  eta: 0:00:46    time: 0.5524  data: 0.4354  max mem: 23764
+eval (validation): [4]  [20/85]  eta: 0:00:09    time: 0.1206  data: 0.0027  max mem: 23764
+eval (validation): [4]  [40/85]  eta: 0:00:05    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [4]  [60/85]  eta: 0:00:03    time: 0.1217  data: 0.0035  max mem: 23764
+eval (validation): [4]  [80/85]  eta: 0:00:00    time: 0.1211  data: 0.0033  max mem: 23764
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.1194  data: 0.0033  max mem: 23764
+eval (validation): [4] Total time: 0:00:10 (0.1270 s / it)
+cv: [4]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 2.553  acc: 0.235  f1: 0.168
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:05:40  lr: nan  time: 0.8515  data: 0.6347  max mem: 23764
+train: [5]  [ 20/400]  eta: 0:01:37  lr: 0.000300  loss: 2.8321 (2.8604)  grad: 0.2036 (0.2063)  time: 0.2266  data: 0.0029  max mem: 23764
+train: [5]  [ 40/400]  eta: 0:01:27  lr: 0.000300  loss: 2.8478 (2.8600)  grad: 0.2069 (0.2076)  time: 0.2272  data: 0.0032  max mem: 23764
+train: [5]  [ 60/400]  eta: 0:01:20  lr: 0.000300  loss: 2.8715 (2.8721)  grad: 0.2156 (0.2129)  time: 0.2271  data: 0.0033  max mem: 23764
+train: [5]  [ 80/400]  eta: 0:01:15  lr: 0.000300  loss: 2.8839 (2.8700)  grad: 0.2178 (0.2158)  time: 0.2271  data: 0.0033  max mem: 23764
+train: [5]  [100/400]  eta: 0:01:09  lr: 0.000300  loss: 2.8669 (2.8661)  grad: 0.2176 (0.2165)  time: 0.2273  data: 0.0032  max mem: 23764
+train: [5]  [120/400]  eta: 0:01:05  lr: 0.000300  loss: 2.8240 (2.8629)  grad: 0.2194 (0.2173)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [5]  [140/400]  eta: 0:01:00  lr: 0.000300  loss: 2.8678 (2.8676)  grad: 0.2145 (0.2166)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [5]  [160/400]  eta: 0:00:55  lr: 0.000299  loss: 2.8798 (2.8701)  grad: 0.2088 (0.2155)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [5]  [180/400]  eta: 0:00:50  lr: 0.000299  loss: 2.8573 (2.8676)  grad: 0.2046 (0.2145)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [5]  [200/400]  eta: 0:00:46  lr: 0.000299  loss: 2.8573 (2.8659)  grad: 0.2005 (0.2130)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [5]  [220/400]  eta: 0:00:41  lr: 0.000299  loss: 2.8771 (2.8644)  grad: 0.2005 (0.2121)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [5]  [240/400]  eta: 0:00:36  lr: 0.000299  loss: 2.8605 (2.8620)  grad: 0.2084 (0.2125)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [5]  [260/400]  eta: 0:00:32  lr: 0.000299  loss: 2.8332 (2.8619)  grad: 0.2200 (0.2131)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [5]  [280/400]  eta: 0:00:27  lr: 0.000298  loss: 2.8332 (2.8606)  grad: 0.2093 (0.2124)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [5]  [300/400]  eta: 0:00:22  lr: 0.000298  loss: 2.8311 (2.8590)  grad: 0.2051 (0.2122)  time: 0.2269  data: 0.0033  max mem: 23764
+train: [5]  [320/400]  eta: 0:00:18  lr: 0.000298  loss: 2.8311 (2.8566)  grad: 0.2038 (0.2118)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [5]  [340/400]  eta: 0:00:13  lr: 0.000298  loss: 2.8513 (2.8580)  grad: 0.2038 (0.2113)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [5]  [360/400]  eta: 0:00:09  lr: 0.000297  loss: 2.8441 (2.8565)  grad: 0.1980 (0.2108)  time: 0.2269  data: 0.0033  max mem: 23764
+train: [5]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 2.8382 (2.8556)  grad: 0.2022 (0.2106)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.8321 (2.8537)  grad: 0.2022 (0.2100)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [5] Total time: 0:01:31 (0.2293 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.8321 (2.8537)  grad: 0.2022 (0.2100)
+eval (validation): [5]  [ 0/85]  eta: 0:00:50    time: 0.5890  data: 0.4722  max mem: 23764
+eval (validation): [5]  [20/85]  eta: 0:00:09    time: 0.1203  data: 0.0028  max mem: 23764
+eval (validation): [5]  [40/85]  eta: 0:00:05    time: 0.1206  data: 0.0030  max mem: 23764
+eval (validation): [5]  [60/85]  eta: 0:00:03    time: 0.1210  data: 0.0034  max mem: 23764
+eval (validation): [5]  [80/85]  eta: 0:00:00    time: 0.1210  data: 0.0033  max mem: 23764
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.1189  data: 0.0032  max mem: 23764
+eval (validation): [5] Total time: 0:00:10 (0.1270 s / it)
+cv: [5]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 2.547  acc: 0.238  f1: 0.162
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:05:57  lr: nan  time: 0.8931  data: 0.6760  max mem: 23764
+train: [6]  [ 20/400]  eta: 0:01:38  lr: 0.000296  loss: 2.8153 (2.8492)  grad: 0.1917 (0.1904)  time: 0.2269  data: 0.0028  max mem: 23764
+train: [6]  [ 40/400]  eta: 0:01:27  lr: 0.000296  loss: 2.7935 (2.8195)  grad: 0.1883 (0.1877)  time: 0.2275  data: 0.0035  max mem: 23764
+train: [6]  [ 60/400]  eta: 0:01:20  lr: 0.000296  loss: 2.7709 (2.8051)  grad: 0.1932 (0.1927)  time: 0.2271  data: 0.0033  max mem: 23764
+train: [6]  [ 80/400]  eta: 0:01:15  lr: 0.000295  loss: 2.7833 (2.8043)  grad: 0.1973 (0.1977)  time: 0.2269  data: 0.0032  max mem: 23764
+train: [6]  [100/400]  eta: 0:01:10  lr: 0.000295  loss: 2.8028 (2.8099)  grad: 0.2204 (0.2011)  time: 0.2273  data: 0.0034  max mem: 23764
+train: [6]  [120/400]  eta: 0:01:05  lr: 0.000295  loss: 2.8214 (2.8110)  grad: 0.2073 (0.2021)  time: 0.2270  data: 0.0033  max mem: 23764
+train: [6]  [140/400]  eta: 0:01:00  lr: 0.000294  loss: 2.8214 (2.8114)  grad: 0.1996 (0.2029)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [6]  [160/400]  eta: 0:00:55  lr: 0.000294  loss: 2.8079 (2.8089)  grad: 0.2113 (0.2042)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [6]  [180/400]  eta: 0:00:50  lr: 0.000293  loss: 2.7961 (2.8074)  grad: 0.2072 (0.2042)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [6]  [200/400]  eta: 0:00:46  lr: 0.000293  loss: 2.8123 (2.8082)  grad: 0.2006 (0.2043)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [6]  [220/400]  eta: 0:00:41  lr: 0.000292  loss: 2.8409 (2.8099)  grad: 0.2146 (0.2058)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [6]  [240/400]  eta: 0:00:36  lr: 0.000292  loss: 2.8539 (2.8159)  grad: 0.2199 (0.2065)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [6]  [260/400]  eta: 0:00:32  lr: 0.000291  loss: 2.8394 (2.8157)  grad: 0.2149 (0.2073)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [6]  [280/400]  eta: 0:00:27  lr: 0.000291  loss: 2.8085 (2.8156)  grad: 0.2136 (0.2078)  time: 0.2275  data: 0.0035  max mem: 23764
+train: [6]  [300/400]  eta: 0:00:22  lr: 0.000290  loss: 2.8162 (2.8158)  grad: 0.2114 (0.2082)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [6]  [320/400]  eta: 0:00:18  lr: 0.000290  loss: 2.8122 (2.8145)  grad: 0.2022 (0.2081)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [6]  [340/400]  eta: 0:00:13  lr: 0.000289  loss: 2.8029 (2.8153)  grad: 0.2075 (0.2089)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [6]  [360/400]  eta: 0:00:09  lr: 0.000288  loss: 2.8122 (2.8148)  grad: 0.2129 (0.2087)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [6]  [380/400]  eta: 0:00:04  lr: 0.000288  loss: 2.7984 (2.8123)  grad: 0.1931 (0.2080)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.7757 (2.8112)  grad: 0.1946 (0.2079)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [6] Total time: 0:01:31 (0.2296 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.7757 (2.8112)  grad: 0.1946 (0.2079)
+eval (validation): [6]  [ 0/85]  eta: 0:01:00    time: 0.7115  data: 0.5947  max mem: 23764
+eval (validation): [6]  [20/85]  eta: 0:00:09    time: 0.1210  data: 0.0023  max mem: 23764
+eval (validation): [6]  [40/85]  eta: 0:00:06    time: 0.1211  data: 0.0033  max mem: 23764
+eval (validation): [6]  [60/85]  eta: 0:00:03    time: 0.1212  data: 0.0035  max mem: 23764
+eval (validation): [6]  [80/85]  eta: 0:00:00    time: 0.1212  data: 0.0036  max mem: 23764
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.1191  data: 0.0036  max mem: 23764
+eval (validation): [6] Total time: 0:00:10 (0.1288 s / it)
+cv: [6]  best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00')  loss: 2.485  acc: 0.255  f1: 0.184
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:05:44  lr: nan  time: 0.8600  data: 0.6435  max mem: 23764
+train: [7]  [ 20/400]  eta: 0:01:37  lr: 0.000286  loss: 2.7327 (2.7609)  grad: 0.2071 (0.2103)  time: 0.2271  data: 0.0022  max mem: 23764
+train: [7]  [ 40/400]  eta: 0:01:27  lr: 0.000286  loss: 2.7489 (2.7538)  grad: 0.2071 (0.2084)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [7]  [ 60/400]  eta: 0:01:21  lr: 0.000285  loss: 2.7594 (2.7598)  grad: 0.2065 (0.2099)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [7]  [ 80/400]  eta: 0:01:15  lr: 0.000284  loss: 2.7818 (2.7742)  grad: 0.2094 (0.2113)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [7]  [100/400]  eta: 0:01:10  lr: 0.000284  loss: 2.7818 (2.7695)  grad: 0.2124 (0.2110)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [7]  [120/400]  eta: 0:01:05  lr: 0.000283  loss: 2.7694 (2.7728)  grad: 0.2119 (0.2121)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [7]  [140/400]  eta: 0:01:00  lr: 0.000282  loss: 2.7694 (2.7704)  grad: 0.2084 (0.2108)  time: 0.2272  data: 0.0034  max mem: 23764
+train: [7]  [160/400]  eta: 0:00:55  lr: 0.000282  loss: 2.7622 (2.7707)  grad: 0.2110 (0.2127)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [7]  [180/400]  eta: 0:00:50  lr: 0.000281  loss: 2.7743 (2.7728)  grad: 0.2192 (0.2130)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [7]  [200/400]  eta: 0:00:46  lr: 0.000280  loss: 2.7835 (2.7742)  grad: 0.2192 (0.2137)  time: 0.2283  data: 0.0036  max mem: 23764
+train: [7]  [220/400]  eta: 0:00:41  lr: 0.000279  loss: 2.7835 (2.7759)  grad: 0.2152 (0.2136)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [7]  [240/400]  eta: 0:00:36  lr: 0.000278  loss: 2.7703 (2.7757)  grad: 0.2132 (0.2134)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [7]  [260/400]  eta: 0:00:32  lr: 0.000278  loss: 2.7703 (2.7763)  grad: 0.2103 (0.2130)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [7]  [280/400]  eta: 0:00:27  lr: 0.000277  loss: 2.7640 (2.7766)  grad: 0.2036 (0.2126)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [7]  [300/400]  eta: 0:00:23  lr: 0.000276  loss: 2.7725 (2.7772)  grad: 0.2125 (0.2128)  time: 0.2286  data: 0.0037  max mem: 23764
+train: [7]  [320/400]  eta: 0:00:18  lr: 0.000275  loss: 2.7973 (2.7779)  grad: 0.2166 (0.2133)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [7]  [340/400]  eta: 0:00:13  lr: 0.000274  loss: 2.8037 (2.7799)  grad: 0.2186 (0.2136)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [7]  [360/400]  eta: 0:00:09  lr: 0.000273  loss: 2.7647 (2.7770)  grad: 0.2102 (0.2133)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [7]  [380/400]  eta: 0:00:04  lr: 0.000272  loss: 2.7228 (2.7747)  grad: 0.2079 (0.2131)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.7482 (2.7741)  grad: 0.2116 (0.2136)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [7] Total time: 0:01:31 (0.2299 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.7482 (2.7741)  grad: 0.2116 (0.2136)
+eval (validation): [7]  [ 0/85]  eta: 0:00:56    time: 0.6660  data: 0.5487  max mem: 23764
+eval (validation): [7]  [20/85]  eta: 0:00:09    time: 0.1214  data: 0.0034  max mem: 23764
+eval (validation): [7]  [40/85]  eta: 0:00:06    time: 0.1212  data: 0.0034  max mem: 23764
+eval (validation): [7]  [60/85]  eta: 0:00:03    time: 0.1211  data: 0.0034  max mem: 23764
+eval (validation): [7]  [80/85]  eta: 0:00:00    time: 0.1210  data: 0.0032  max mem: 23764
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.1191  data: 0.0032  max mem: 23764
+eval (validation): [7] Total time: 0:00:10 (0.1283 s / it)
+cv: [7]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.483  acc: 0.253  f1: 0.192
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:05:49  lr: nan  time: 0.8725  data: 0.6564  max mem: 23764
+train: [8]  [ 20/400]  eta: 0:01:38  lr: 0.000270  loss: 2.7305 (2.7371)  grad: 0.2015 (0.2051)  time: 0.2282  data: 0.0026  max mem: 23764
+train: [8]  [ 40/400]  eta: 0:01:27  lr: 0.000270  loss: 2.7402 (2.7383)  grad: 0.2073 (0.2056)  time: 0.2292  data: 0.0035  max mem: 23764
+train: [8]  [ 60/400]  eta: 0:01:21  lr: 0.000269  loss: 2.7265 (2.7247)  grad: 0.2065 (0.2072)  time: 0.2289  data: 0.0036  max mem: 23764
+train: [8]  [ 80/400]  eta: 0:01:15  lr: 0.000268  loss: 2.7580 (2.7409)  grad: 0.2065 (0.2075)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [8]  [100/400]  eta: 0:01:10  lr: 0.000267  loss: 2.7607 (2.7389)  grad: 0.2106 (0.2089)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [8]  [120/400]  eta: 0:01:05  lr: 0.000266  loss: 2.7338 (2.7393)  grad: 0.2111 (0.2108)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [8]  [140/400]  eta: 0:01:00  lr: 0.000265  loss: 2.7407 (2.7430)  grad: 0.2147 (0.2114)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [8]  [160/400]  eta: 0:00:55  lr: 0.000264  loss: 2.7349 (2.7394)  grad: 0.2059 (0.2105)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [8]  [180/400]  eta: 0:00:51  lr: 0.000263  loss: 2.7356 (2.7431)  grad: 0.2010 (0.2099)  time: 0.2283  data: 0.0036  max mem: 23764
+train: [8]  [200/400]  eta: 0:00:46  lr: 0.000262  loss: 2.7586 (2.7441)  grad: 0.2029 (0.2091)  time: 0.2283  data: 0.0036  max mem: 23764
+train: [8]  [220/400]  eta: 0:00:41  lr: 0.000260  loss: 2.6927 (2.7374)  grad: 0.2024 (0.2091)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [8]  [240/400]  eta: 0:00:36  lr: 0.000259  loss: 2.6856 (2.7367)  grad: 0.2043 (0.2090)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [8]  [260/400]  eta: 0:00:32  lr: 0.000258  loss: 2.7406 (2.7354)  grad: 0.2033 (0.2089)  time: 0.2285  data: 0.0035  max mem: 23764
+train: [8]  [280/400]  eta: 0:00:27  lr: 0.000257  loss: 2.7423 (2.7378)  grad: 0.2056 (0.2088)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [8]  [300/400]  eta: 0:00:23  lr: 0.000256  loss: 2.7375 (2.7379)  grad: 0.2102 (0.2091)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [8]  [320/400]  eta: 0:00:18  lr: 0.000255  loss: 2.7215 (2.7378)  grad: 0.2125 (0.2094)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [8]  [340/400]  eta: 0:00:13  lr: 0.000254  loss: 2.7067 (2.7357)  grad: 0.2146 (0.2096)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [8]  [360/400]  eta: 0:00:09  lr: 0.000253  loss: 2.6726 (2.7342)  grad: 0.2053 (0.2096)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [8]  [380/400]  eta: 0:00:04  lr: 0.000252  loss: 2.7301 (2.7338)  grad: 0.2053 (0.2092)  time: 0.2289  data: 0.0037  max mem: 23764
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.7373 (2.7347)  grad: 0.2012 (0.2089)  time: 0.2288  data: 0.0037  max mem: 23764
+train: [8] Total time: 0:01:32 (0.2303 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.7373 (2.7347)  grad: 0.2012 (0.2089)
+eval (validation): [8]  [ 0/85]  eta: 0:01:01    time: 0.7213  data: 0.6022  max mem: 23764
+eval (validation): [8]  [20/85]  eta: 0:00:09    time: 0.1205  data: 0.0023  max mem: 23764
+eval (validation): [8]  [40/85]  eta: 0:00:06    time: 0.1217  data: 0.0035  max mem: 23764
+eval (validation): [8]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0036  max mem: 23764
+eval (validation): [8]  [80/85]  eta: 0:00:00    time: 0.1214  data: 0.0034  max mem: 23764
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.1193  data: 0.0033  max mem: 23764
+eval (validation): [8] Total time: 0:00:10 (0.1291 s / it)
+cv: [8]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 2.483  acc: 0.256  f1: 0.198
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:05:37  lr: nan  time: 0.8431  data: 0.6227  max mem: 23764
+train: [9]  [ 20/400]  eta: 0:01:37  lr: 0.000249  loss: 2.6483 (2.6706)  grad: 0.1993 (0.2039)  time: 0.2280  data: 0.0032  max mem: 23764
+train: [9]  [ 40/400]  eta: 0:01:27  lr: 0.000248  loss: 2.6796 (2.6809)  grad: 0.2122 (0.2133)  time: 0.2291  data: 0.0037  max mem: 23764
+train: [9]  [ 60/400]  eta: 0:01:21  lr: 0.000247  loss: 2.6845 (2.6850)  grad: 0.2213 (0.2165)  time: 0.2291  data: 0.0037  max mem: 23764
+train: [9]  [ 80/400]  eta: 0:01:15  lr: 0.000246  loss: 2.7003 (2.6915)  grad: 0.2141 (0.2143)  time: 0.2297  data: 0.0037  max mem: 23764
+train: [9]  [100/400]  eta: 0:01:10  lr: 0.000244  loss: 2.7003 (2.6958)  grad: 0.2115 (0.2137)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [9]  [120/400]  eta: 0:01:05  lr: 0.000243  loss: 2.6921 (2.6918)  grad: 0.2103 (0.2130)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [9]  [140/400]  eta: 0:01:00  lr: 0.000242  loss: 2.7116 (2.7039)  grad: 0.2114 (0.2131)  time: 0.2291  data: 0.0036  max mem: 23764
+train: [9]  [160/400]  eta: 0:00:55  lr: 0.000241  loss: 2.7465 (2.7060)  grad: 0.2114 (0.2128)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [9]  [180/400]  eta: 0:00:51  lr: 0.000240  loss: 2.6849 (2.7000)  grad: 0.2097 (0.2124)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [9]  [200/400]  eta: 0:00:46  lr: 0.000238  loss: 2.6874 (2.7038)  grad: 0.2139 (0.2129)  time: 0.2287  data: 0.0035  max mem: 23764
+train: [9]  [220/400]  eta: 0:00:41  lr: 0.000237  loss: 2.6824 (2.7006)  grad: 0.2079 (0.2124)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [9]  [240/400]  eta: 0:00:37  lr: 0.000236  loss: 2.6707 (2.7043)  grad: 0.2055 (0.2129)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [9]  [260/400]  eta: 0:00:32  lr: 0.000234  loss: 2.7223 (2.7034)  grad: 0.2153 (0.2130)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [9]  [280/400]  eta: 0:00:27  lr: 0.000233  loss: 2.6920 (2.7034)  grad: 0.2078 (0.2122)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [9]  [300/400]  eta: 0:00:23  lr: 0.000232  loss: 2.7504 (2.7061)  grad: 0.2078 (0.2130)  time: 0.2283  data: 0.0037  max mem: 23764
+train: [9]  [320/400]  eta: 0:00:18  lr: 0.000230  loss: 2.7308 (2.7066)  grad: 0.2169 (0.2129)  time: 0.2284  data: 0.0037  max mem: 23764
+train: [9]  [340/400]  eta: 0:00:13  lr: 0.000229  loss: 2.7150 (2.7075)  grad: 0.2025 (0.2122)  time: 0.2292  data: 0.0037  max mem: 23764
+train: [9]  [360/400]  eta: 0:00:09  lr: 0.000228  loss: 2.7200 (2.7089)  grad: 0.1964 (0.2113)  time: 0.2291  data: 0.0037  max mem: 23764
+train: [9]  [380/400]  eta: 0:00:04  lr: 0.000226  loss: 2.7150 (2.7077)  grad: 0.1940 (0.2108)  time: 0.2293  data: 0.0037  max mem: 23764
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.6638 (2.7065)  grad: 0.1961 (0.2101)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [9] Total time: 0:01:32 (0.2306 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.6638 (2.7065)  grad: 0.1961 (0.2101)
+eval (validation): [9]  [ 0/85]  eta: 0:01:01    time: 0.7214  data: 0.6032  max mem: 23764
+eval (validation): [9]  [20/85]  eta: 0:00:09    time: 0.1222  data: 0.0038  max mem: 23764
+eval (validation): [9]  [40/85]  eta: 0:00:06    time: 0.1214  data: 0.0030  max mem: 23764
+eval (validation): [9]  [60/85]  eta: 0:00:03    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [9]  [80/85]  eta: 0:00:00    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.1196  data: 0.0036  max mem: 23764
+eval (validation): [9] Total time: 0:00:11 (0.1295 s / it)
+cv: [9]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.532  acc: 0.253  f1: 0.189
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:06:09  lr: nan  time: 0.9246  data: 0.7082  max mem: 23764
+train: [10]  [ 20/400]  eta: 0:01:39  lr: 0.000224  loss: 2.6865 (2.6887)  grad: 0.1977 (0.2032)  time: 0.2278  data: 0.0022  max mem: 23764
+train: [10]  [ 40/400]  eta: 0:01:28  lr: 0.000222  loss: 2.6805 (2.6867)  grad: 0.1985 (0.2061)  time: 0.2292  data: 0.0037  max mem: 23764
+train: [10]  [ 60/400]  eta: 0:01:21  lr: 0.000221  loss: 2.6805 (2.6911)  grad: 0.2089 (0.2055)  time: 0.2293  data: 0.0037  max mem: 23764
+train: [10]  [ 80/400]  eta: 0:01:15  lr: 0.000220  loss: 2.6896 (2.6986)  grad: 0.2050 (0.2062)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [10]  [100/400]  eta: 0:01:10  lr: 0.000218  loss: 2.6799 (2.6877)  grad: 0.2035 (0.2063)  time: 0.2293  data: 0.0037  max mem: 23764
+train: [10]  [120/400]  eta: 0:01:05  lr: 0.000217  loss: 2.6800 (2.6974)  grad: 0.2077 (0.2075)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [10]  [140/400]  eta: 0:01:00  lr: 0.000215  loss: 2.6957 (2.6907)  grad: 0.2092 (0.2082)  time: 0.2294  data: 0.0037  max mem: 23764
+train: [10]  [160/400]  eta: 0:00:56  lr: 0.000214  loss: 2.6592 (2.6900)  grad: 0.2092 (0.2087)  time: 0.2301  data: 0.0038  max mem: 23764
+train: [10]  [180/400]  eta: 0:00:51  lr: 0.000213  loss: 2.6787 (2.6910)  grad: 0.2079 (0.2096)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [10]  [200/400]  eta: 0:00:46  lr: 0.000211  loss: 2.6469 (2.6865)  grad: 0.2079 (0.2095)  time: 0.2293  data: 0.0037  max mem: 23764
+train: [10]  [220/400]  eta: 0:00:41  lr: 0.000210  loss: 2.6344 (2.6846)  grad: 0.2131 (0.2107)  time: 0.2288  data: 0.0037  max mem: 23764
+train: [10]  [240/400]  eta: 0:00:37  lr: 0.000208  loss: 2.7030 (2.6878)  grad: 0.2142 (0.2111)  time: 0.2291  data: 0.0038  max mem: 23764
+train: [10]  [260/400]  eta: 0:00:32  lr: 0.000207  loss: 2.7318 (2.6918)  grad: 0.2107 (0.2120)  time: 0.2295  data: 0.0038  max mem: 23764
+train: [10]  [280/400]  eta: 0:00:27  lr: 0.000205  loss: 2.7114 (2.6920)  grad: 0.2059 (0.2118)  time: 0.2290  data: 0.0038  max mem: 23764
+train: [10]  [300/400]  eta: 0:00:23  lr: 0.000204  loss: 2.7037 (2.6923)  grad: 0.2059 (0.2120)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [10]  [320/400]  eta: 0:00:18  lr: 0.000202  loss: 2.6904 (2.6916)  grad: 0.2077 (0.2113)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [10]  [340/400]  eta: 0:00:13  lr: 0.000201  loss: 2.6904 (2.6902)  grad: 0.2056 (0.2116)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [10]  [360/400]  eta: 0:00:09  lr: 0.000199  loss: 2.6683 (2.6899)  grad: 0.2046 (0.2113)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [10]  [380/400]  eta: 0:00:04  lr: 0.000198  loss: 2.6629 (2.6881)  grad: 0.2077 (0.2112)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.6572 (2.6879)  grad: 0.2202 (0.2119)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [10] Total time: 0:01:32 (0.2308 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.6572 (2.6879)  grad: 0.2202 (0.2119)
+eval (validation): [10]  [ 0/85]  eta: 0:01:00    time: 0.7123  data: 0.5940  max mem: 23764
+eval (validation): [10]  [20/85]  eta: 0:00:09    time: 0.1219  data: 0.0033  max mem: 23764
+eval (validation): [10]  [40/85]  eta: 0:00:06    time: 0.1217  data: 0.0036  max mem: 23764
+eval (validation): [10]  [60/85]  eta: 0:00:03    time: 0.1213  data: 0.0035  max mem: 23764
+eval (validation): [10]  [80/85]  eta: 0:00:00    time: 0.1214  data: 0.0035  max mem: 23764
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.1191  data: 0.0034  max mem: 23764
+eval (validation): [10] Total time: 0:00:10 (0.1292 s / it)
+cv: [10]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 2.486  acc: 0.258  f1: 0.192
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:05:41  lr: nan  time: 0.8528  data: 0.6360  max mem: 23764
+train: [11]  [ 20/400]  eta: 0:01:37  lr: 0.000195  loss: 2.6317 (2.6437)  grad: 0.2306 (0.2265)  time: 0.2279  data: 0.0031  max mem: 23764
+train: [11]  [ 40/400]  eta: 0:01:27  lr: 0.000193  loss: 2.6655 (2.6617)  grad: 0.2112 (0.2145)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [11]  [ 60/400]  eta: 0:01:21  lr: 0.000192  loss: 2.6701 (2.6692)  grad: 0.1986 (0.2103)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [11]  [ 80/400]  eta: 0:01:15  lr: 0.000190  loss: 2.6579 (2.6643)  grad: 0.2058 (0.2112)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [11]  [100/400]  eta: 0:01:10  lr: 0.000189  loss: 2.6355 (2.6615)  grad: 0.2089 (0.2113)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [11]  [120/400]  eta: 0:01:05  lr: 0.000187  loss: 2.6489 (2.6613)  grad: 0.2089 (0.2122)  time: 0.2295  data: 0.0038  max mem: 23764
+train: [11]  [140/400]  eta: 0:01:00  lr: 0.000186  loss: 2.6647 (2.6686)  grad: 0.2246 (0.2147)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [11]  [160/400]  eta: 0:00:55  lr: 0.000184  loss: 2.6896 (2.6696)  grad: 0.2240 (0.2141)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [11]  [180/400]  eta: 0:00:51  lr: 0.000183  loss: 2.6759 (2.6709)  grad: 0.2111 (0.2149)  time: 0.2286  data: 0.0036  max mem: 23764
+train: [11]  [200/400]  eta: 0:00:46  lr: 0.000181  loss: 2.6570 (2.6662)  grad: 0.2132 (0.2142)  time: 0.2280  data: 0.0036  max mem: 23764
+train: [11]  [220/400]  eta: 0:00:41  lr: 0.000180  loss: 2.6656 (2.6667)  grad: 0.2044 (0.2133)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [11]  [240/400]  eta: 0:00:36  lr: 0.000178  loss: 2.6656 (2.6673)  grad: 0.2044 (0.2128)  time: 0.2279  data: 0.0036  max mem: 23764
+train: [11]  [260/400]  eta: 0:00:32  lr: 0.000177  loss: 2.6650 (2.6690)  grad: 0.2089 (0.2136)  time: 0.2280  data: 0.0038  max mem: 23764
+train: [11]  [280/400]  eta: 0:00:27  lr: 0.000175  loss: 2.6475 (2.6643)  grad: 0.2127 (0.2132)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [11]  [300/400]  eta: 0:00:23  lr: 0.000174  loss: 2.6376 (2.6653)  grad: 0.2045 (0.2130)  time: 0.2278  data: 0.0036  max mem: 23764
+train: [11]  [320/400]  eta: 0:00:18  lr: 0.000172  loss: 2.6582 (2.6643)  grad: 0.2084 (0.2133)  time: 0.2280  data: 0.0037  max mem: 23764
+train: [11]  [340/400]  eta: 0:00:13  lr: 0.000170  loss: 2.6120 (2.6613)  grad: 0.2102 (0.2133)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [11]  [360/400]  eta: 0:00:09  lr: 0.000169  loss: 2.6334 (2.6611)  grad: 0.2061 (0.2128)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [11]  [380/400]  eta: 0:00:04  lr: 0.000167  loss: 2.6334 (2.6610)  grad: 0.2090 (0.2127)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.6326 (2.6614)  grad: 0.2114 (0.2128)  time: 0.2274  data: 0.0035  max mem: 23764
+train: [11] Total time: 0:01:31 (0.2299 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.6326 (2.6614)  grad: 0.2114 (0.2128)
+eval (validation): [11]  [ 0/85]  eta: 0:00:50    time: 0.5996  data: 0.4816  max mem: 23764
+eval (validation): [11]  [20/85]  eta: 0:00:09    time: 0.1207  data: 0.0030  max mem: 23764
+eval (validation): [11]  [40/85]  eta: 0:00:05    time: 0.1215  data: 0.0035  max mem: 23764
+eval (validation): [11]  [60/85]  eta: 0:00:03    time: 0.1211  data: 0.0035  max mem: 23764
+eval (validation): [11]  [80/85]  eta: 0:00:00    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.1194  data: 0.0035  max mem: 23764
+eval (validation): [11] Total time: 0:00:10 (0.1276 s / it)
+cv: [11]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.500  acc: 0.261  f1: 0.188
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [12]  [  0/400]  eta: 0:05:40  lr: nan  time: 0.8522  data: 0.6351  max mem: 23764
+train: [12]  [ 20/400]  eta: 0:01:37  lr: 0.000164  loss: 2.6622 (2.6654)  grad: 0.2097 (0.2145)  time: 0.2278  data: 0.0030  max mem: 23764
+train: [12]  [ 40/400]  eta: 0:01:27  lr: 0.000163  loss: 2.6622 (2.6490)  grad: 0.2107 (0.2136)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [12]  [ 60/400]  eta: 0:01:21  lr: 0.000161  loss: 2.6474 (2.6486)  grad: 0.2120 (0.2150)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [12]  [ 80/400]  eta: 0:01:15  lr: 0.000160  loss: 2.6109 (2.6370)  grad: 0.2066 (0.2122)  time: 0.2289  data: 0.0036  max mem: 23764
+train: [12]  [100/400]  eta: 0:01:10  lr: 0.000158  loss: 2.6031 (2.6343)  grad: 0.1980 (0.2111)  time: 0.2288  data: 0.0037  max mem: 23764
+train: [12]  [120/400]  eta: 0:01:05  lr: 0.000156  loss: 2.6081 (2.6340)  grad: 0.1990 (0.2093)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [12]  [140/400]  eta: 0:01:00  lr: 0.000155  loss: 2.6081 (2.6310)  grad: 0.2073 (0.2100)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [12]  [160/400]  eta: 0:00:55  lr: 0.000153  loss: 2.6314 (2.6321)  grad: 0.2135 (0.2110)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [12]  [180/400]  eta: 0:00:51  lr: 0.000152  loss: 2.6495 (2.6310)  grad: 0.2129 (0.2110)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [12]  [200/400]  eta: 0:00:46  lr: 0.000150  loss: 2.6395 (2.6294)  grad: 0.2114 (0.2114)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [12]  [220/400]  eta: 0:00:41  lr: 0.000149  loss: 2.6679 (2.6317)  grad: 0.2114 (0.2122)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [12]  [240/400]  eta: 0:00:36  lr: 0.000147  loss: 2.6873 (2.6350)  grad: 0.2182 (0.2124)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [12]  [260/400]  eta: 0:00:32  lr: 0.000145  loss: 2.6607 (2.6326)  grad: 0.2133 (0.2124)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [12]  [280/400]  eta: 0:00:27  lr: 0.000144  loss: 2.5769 (2.6286)  grad: 0.2082 (0.2119)  time: 0.2278  data: 0.0036  max mem: 23764
+train: [12]  [300/400]  eta: 0:00:23  lr: 0.000142  loss: 2.6107 (2.6298)  grad: 0.2082 (0.2119)  time: 0.2284  data: 0.0038  max mem: 23764
+train: [12]  [320/400]  eta: 0:00:18  lr: 0.000141  loss: 2.6115 (2.6277)  grad: 0.2058 (0.2114)  time: 0.2292  data: 0.0037  max mem: 23764
+train: [12]  [340/400]  eta: 0:00:13  lr: 0.000139  loss: 2.6271 (2.6307)  grad: 0.2027 (0.2113)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [12]  [360/400]  eta: 0:00:09  lr: 0.000138  loss: 2.6671 (2.6325)  grad: 0.2046 (0.2115)  time: 0.2280  data: 0.0036  max mem: 23764
+train: [12]  [380/400]  eta: 0:00:04  lr: 0.000136  loss: 2.6500 (2.6338)  grad: 0.2072 (0.2114)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.6277 (2.6343)  grad: 0.2072 (0.2116)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [12] Total time: 0:01:32 (0.2301 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.6277 (2.6343)  grad: 0.2072 (0.2116)
+eval (validation): [12]  [ 0/85]  eta: 0:00:59    time: 0.7028  data: 0.5866  max mem: 23764
+eval (validation): [12]  [20/85]  eta: 0:00:09    time: 0.1204  data: 0.0027  max mem: 23764
+eval (validation): [12]  [40/85]  eta: 0:00:06    time: 0.1210  data: 0.0034  max mem: 23764
+eval (validation): [12]  [60/85]  eta: 0:00:03    time: 0.1205  data: 0.0034  max mem: 23764
+eval (validation): [12]  [80/85]  eta: 0:00:00    time: 0.1210  data: 0.0035  max mem: 23764
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.1189  data: 0.0035  max mem: 23764
+eval (validation): [12] Total time: 0:00:10 (0.1284 s / it)
+cv: [12]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 2.506  acc: 0.260  f1: 0.198
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:06:10  lr: nan  time: 0.9272  data: 0.7101  max mem: 23764
+train: [13]  [ 20/400]  eta: 0:01:39  lr: 0.000133  loss: 2.6186 (2.6199)  grad: 0.2158 (0.2189)  time: 0.2288  data: 0.0026  max mem: 23764
+train: [13]  [ 40/400]  eta: 0:01:28  lr: 0.000131  loss: 2.6176 (2.6182)  grad: 0.2158 (0.2197)  time: 0.2289  data: 0.0036  max mem: 23764
+train: [13]  [ 60/400]  eta: 0:01:21  lr: 0.000130  loss: 2.5882 (2.5966)  grad: 0.2150 (0.2182)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [13]  [ 80/400]  eta: 0:01:15  lr: 0.000128  loss: 2.5823 (2.6048)  grad: 0.2150 (0.2192)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [13]  [100/400]  eta: 0:01:10  lr: 0.000127  loss: 2.6114 (2.5998)  grad: 0.2143 (0.2185)  time: 0.2287  data: 0.0035  max mem: 23764
+train: [13]  [120/400]  eta: 0:01:05  lr: 0.000125  loss: 2.5932 (2.6040)  grad: 0.2104 (0.2177)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [13]  [140/400]  eta: 0:01:00  lr: 0.000124  loss: 2.6245 (2.6033)  grad: 0.2087 (0.2170)  time: 0.2286  data: 0.0035  max mem: 23764
+train: [13]  [160/400]  eta: 0:00:55  lr: 0.000122  loss: 2.6599 (2.6133)  grad: 0.2163 (0.2172)  time: 0.2290  data: 0.0037  max mem: 23764
+train: [13]  [180/400]  eta: 0:00:51  lr: 0.000120  loss: 2.6480 (2.6142)  grad: 0.2175 (0.2174)  time: 0.2292  data: 0.0037  max mem: 23764
+train: [13]  [200/400]  eta: 0:00:46  lr: 0.000119  loss: 2.6233 (2.6132)  grad: 0.2144 (0.2179)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [13]  [220/400]  eta: 0:00:41  lr: 0.000117  loss: 2.6205 (2.6145)  grad: 0.2151 (0.2179)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [13]  [240/400]  eta: 0:00:37  lr: 0.000116  loss: 2.6232 (2.6161)  grad: 0.2141 (0.2178)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [13]  [260/400]  eta: 0:00:32  lr: 0.000114  loss: 2.6232 (2.6148)  grad: 0.2166 (0.2179)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [13]  [280/400]  eta: 0:00:27  lr: 0.000113  loss: 2.6006 (2.6155)  grad: 0.2274 (0.2189)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [13]  [300/400]  eta: 0:00:23  lr: 0.000111  loss: 2.6086 (2.6163)  grad: 0.2219 (0.2189)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [13]  [320/400]  eta: 0:00:18  lr: 0.000110  loss: 2.6367 (2.6176)  grad: 0.2136 (0.2185)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [13]  [340/400]  eta: 0:00:13  lr: 0.000108  loss: 2.6321 (2.6163)  grad: 0.2102 (0.2182)  time: 0.2270  data: 0.0034  max mem: 23764
+train: [13]  [360/400]  eta: 0:00:09  lr: 0.000107  loss: 2.5910 (2.6148)  grad: 0.2126 (0.2179)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [13]  [380/400]  eta: 0:00:04  lr: 0.000105  loss: 2.6018 (2.6154)  grad: 0.2022 (0.2170)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.6507 (2.6179)  grad: 0.2082 (0.2170)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [13] Total time: 0:01:32 (0.2302 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.6507 (2.6179)  grad: 0.2082 (0.2170)
+eval (validation): [13]  [ 0/85]  eta: 0:00:57    time: 0.6787  data: 0.5591  max mem: 23764
+eval (validation): [13]  [20/85]  eta: 0:00:09    time: 0.1216  data: 0.0032  max mem: 23764
+eval (validation): [13]  [40/85]  eta: 0:00:06    time: 0.1214  data: 0.0035  max mem: 23764
+eval (validation): [13]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0036  max mem: 23764
+eval (validation): [13]  [80/85]  eta: 0:00:00    time: 0.1210  data: 0.0033  max mem: 23764
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.1191  data: 0.0033  max mem: 23764
+eval (validation): [13] Total time: 0:00:10 (0.1287 s / it)
+cv: [13]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.480  acc: 0.257  f1: 0.196
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:05:35  lr: nan  time: 0.8395  data: 0.6236  max mem: 23764
+train: [14]  [ 20/400]  eta: 0:01:37  lr: 0.000102  loss: 2.5976 (2.5946)  grad: 0.2091 (0.2139)  time: 0.2275  data: 0.0029  max mem: 23764
+train: [14]  [ 40/400]  eta: 0:01:27  lr: 0.000101  loss: 2.6142 (2.6154)  grad: 0.2130 (0.2176)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [14]  [ 60/400]  eta: 0:01:20  lr: 0.000099  loss: 2.6257 (2.6210)  grad: 0.2151 (0.2159)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [14]  [ 80/400]  eta: 0:01:15  lr: 0.000098  loss: 2.6163 (2.6087)  grad: 0.2056 (0.2139)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [14]  [100/400]  eta: 0:01:10  lr: 0.000096  loss: 2.5480 (2.5992)  grad: 0.2085 (0.2143)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [14]  [120/400]  eta: 0:01:05  lr: 0.000095  loss: 2.5535 (2.6051)  grad: 0.2132 (0.2156)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [14]  [140/400]  eta: 0:01:00  lr: 0.000093  loss: 2.6175 (2.6032)  grad: 0.2179 (0.2162)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [14]  [160/400]  eta: 0:00:55  lr: 0.000092  loss: 2.5880 (2.5983)  grad: 0.2181 (0.2174)  time: 0.2285  data: 0.0035  max mem: 23764
+train: [14]  [180/400]  eta: 0:00:50  lr: 0.000090  loss: 2.5516 (2.5943)  grad: 0.2163 (0.2168)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [14]  [200/400]  eta: 0:00:46  lr: 0.000089  loss: 2.5828 (2.5942)  grad: 0.2095 (0.2166)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [14]  [220/400]  eta: 0:00:41  lr: 0.000088  loss: 2.5845 (2.5953)  grad: 0.2097 (0.2163)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [14]  [240/400]  eta: 0:00:36  lr: 0.000086  loss: 2.5744 (2.5917)  grad: 0.2031 (0.2154)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [14]  [260/400]  eta: 0:00:32  lr: 0.000085  loss: 2.5873 (2.5922)  grad: 0.2007 (0.2147)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [14]  [280/400]  eta: 0:00:27  lr: 0.000083  loss: 2.5883 (2.5947)  grad: 0.2048 (0.2144)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [14]  [300/400]  eta: 0:00:23  lr: 0.000082  loss: 2.5866 (2.5952)  grad: 0.2116 (0.2146)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [14]  [320/400]  eta: 0:00:18  lr: 0.000081  loss: 2.5826 (2.5955)  grad: 0.2173 (0.2149)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [14]  [340/400]  eta: 0:00:13  lr: 0.000079  loss: 2.5681 (2.5942)  grad: 0.2092 (0.2149)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [14]  [360/400]  eta: 0:00:09  lr: 0.000078  loss: 2.5681 (2.5940)  grad: 0.2109 (0.2149)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [14]  [380/400]  eta: 0:00:04  lr: 0.000076  loss: 2.5921 (2.5963)  grad: 0.2147 (0.2151)  time: 0.2271  data: 0.0032  max mem: 23764
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.5815 (2.5946)  grad: 0.2124 (0.2149)  time: 0.2269  data: 0.0032  max mem: 23764
+train: [14] Total time: 0:01:31 (0.2297 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.5815 (2.5946)  grad: 0.2124 (0.2149)
+eval (validation): [14]  [ 0/85]  eta: 0:00:48    time: 0.5662  data: 0.4489  max mem: 23764
+eval (validation): [14]  [20/85]  eta: 0:00:09    time: 0.1204  data: 0.0024  max mem: 23764
+eval (validation): [14]  [40/85]  eta: 0:00:05    time: 0.1203  data: 0.0032  max mem: 23764
+eval (validation): [14]  [60/85]  eta: 0:00:03    time: 0.1206  data: 0.0033  max mem: 23764
+eval (validation): [14]  [80/85]  eta: 0:00:00    time: 0.1208  data: 0.0030  max mem: 23764
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.1188  data: 0.0030  max mem: 23764
+eval (validation): [14] Total time: 0:00:10 (0.1265 s / it)
+cv: [14]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 2.514  acc: 0.268  f1: 0.204
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:04:48  lr: nan  time: 0.7224  data: 0.5059  max mem: 23764
+train: [15]  [ 20/400]  eta: 0:01:35  lr: 0.000074  loss: 2.5236 (2.5212)  grad: 0.2040 (0.2046)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [15]  [ 40/400]  eta: 0:01:26  lr: 0.000072  loss: 2.5287 (2.5442)  grad: 0.2103 (0.2088)  time: 0.2273  data: 0.0031  max mem: 23764
+train: [15]  [ 60/400]  eta: 0:01:20  lr: 0.000071  loss: 2.5747 (2.5563)  grad: 0.2103 (0.2077)  time: 0.2268  data: 0.0032  max mem: 23764
+train: [15]  [ 80/400]  eta: 0:01:14  lr: 0.000070  loss: 2.5706 (2.5659)  grad: 0.2114 (0.2085)  time: 0.2272  data: 0.0032  max mem: 23764
+train: [15]  [100/400]  eta: 0:01:09  lr: 0.000068  loss: 2.5705 (2.5626)  grad: 0.2120 (0.2094)  time: 0.2273  data: 0.0034  max mem: 23764
+train: [15]  [120/400]  eta: 0:01:04  lr: 0.000067  loss: 2.5705 (2.5673)  grad: 0.2117 (0.2101)  time: 0.2270  data: 0.0033  max mem: 23764
+train: [15]  [140/400]  eta: 0:00:59  lr: 0.000066  loss: 2.6132 (2.5713)  grad: 0.2095 (0.2104)  time: 0.2269  data: 0.0033  max mem: 23764
+train: [15]  [160/400]  eta: 0:00:55  lr: 0.000064  loss: 2.6112 (2.5732)  grad: 0.2193 (0.2116)  time: 0.2273  data: 0.0034  max mem: 23764
+train: [15]  [180/400]  eta: 0:00:50  lr: 0.000063  loss: 2.6112 (2.5761)  grad: 0.2198 (0.2122)  time: 0.2269  data: 0.0033  max mem: 23764
+train: [15]  [200/400]  eta: 0:00:45  lr: 0.000062  loss: 2.5759 (2.5729)  grad: 0.2156 (0.2133)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [15]  [220/400]  eta: 0:00:41  lr: 0.000061  loss: 2.5637 (2.5737)  grad: 0.2156 (0.2138)  time: 0.2273  data: 0.0034  max mem: 23764
+train: [15]  [240/400]  eta: 0:00:36  lr: 0.000059  loss: 2.5642 (2.5741)  grad: 0.2108 (0.2133)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [15]  [260/400]  eta: 0:00:32  lr: 0.000058  loss: 2.5633 (2.5752)  grad: 0.2103 (0.2138)  time: 0.2267  data: 0.0033  max mem: 23764
+train: [15]  [280/400]  eta: 0:00:27  lr: 0.000057  loss: 2.6215 (2.5801)  grad: 0.2126 (0.2140)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [15]  [300/400]  eta: 0:00:22  lr: 0.000056  loss: 2.5922 (2.5775)  grad: 0.2079 (0.2134)  time: 0.2270  data: 0.0033  max mem: 23764
+train: [15]  [320/400]  eta: 0:00:18  lr: 0.000054  loss: 2.5545 (2.5782)  grad: 0.2068 (0.2137)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [15]  [340/400]  eta: 0:00:13  lr: 0.000053  loss: 2.5786 (2.5793)  grad: 0.2169 (0.2140)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [15]  [360/400]  eta: 0:00:09  lr: 0.000052  loss: 2.5601 (2.5795)  grad: 0.2169 (0.2143)  time: 0.2279  data: 0.0036  max mem: 23764
+train: [15]  [380/400]  eta: 0:00:04  lr: 0.000051  loss: 2.5420 (2.5785)  grad: 0.2160 (0.2145)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.5612 (2.5789)  grad: 0.2117 (0.2143)  time: 0.2282  data: 0.0037  max mem: 23764
+train: [15] Total time: 0:01:31 (0.2288 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.5612 (2.5789)  grad: 0.2117 (0.2143)
+eval (validation): [15]  [ 0/85]  eta: 0:00:58    time: 0.6919  data: 0.5746  max mem: 23764
+eval (validation): [15]  [20/85]  eta: 0:00:09    time: 0.1217  data: 0.0034  max mem: 23764
+eval (validation): [15]  [40/85]  eta: 0:00:06    time: 0.1217  data: 0.0035  max mem: 23764
+eval (validation): [15]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0036  max mem: 23764
+eval (validation): [15]  [80/85]  eta: 0:00:00    time: 0.1210  data: 0.0038  max mem: 23764
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.1190  data: 0.0038  max mem: 23764
+eval (validation): [15] Total time: 0:00:10 (0.1289 s / it)
+cv: [15]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.475  acc: 0.262  f1: 0.207
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:05:26  lr: nan  time: 0.8160  data: 0.5992  max mem: 23764
+train: [16]  [ 20/400]  eta: 0:01:37  lr: 0.000048  loss: 2.5642 (2.5680)  grad: 0.2109 (0.2112)  time: 0.2281  data: 0.0033  max mem: 23764
+train: [16]  [ 40/400]  eta: 0:01:27  lr: 0.000047  loss: 2.5642 (2.5665)  grad: 0.2108 (0.2112)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [16]  [ 60/400]  eta: 0:01:20  lr: 0.000046  loss: 2.5624 (2.5690)  grad: 0.2108 (0.2144)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [16]  [ 80/400]  eta: 0:01:15  lr: 0.000045  loss: 2.5703 (2.5627)  grad: 0.2144 (0.2136)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [16]  [100/400]  eta: 0:01:10  lr: 0.000044  loss: 2.5893 (2.5681)  grad: 0.2133 (0.2137)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [16]  [120/400]  eta: 0:01:05  lr: 0.000043  loss: 2.5885 (2.5737)  grad: 0.2127 (0.2139)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [16]  [140/400]  eta: 0:01:00  lr: 0.000042  loss: 2.5800 (2.5722)  grad: 0.2093 (0.2135)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [16]  [160/400]  eta: 0:00:55  lr: 0.000041  loss: 2.5800 (2.5732)  grad: 0.2093 (0.2137)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [16]  [180/400]  eta: 0:00:50  lr: 0.000040  loss: 2.5595 (2.5683)  grad: 0.2147 (0.2142)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [16]  [200/400]  eta: 0:00:46  lr: 0.000039  loss: 2.5465 (2.5655)  grad: 0.2163 (0.2144)  time: 0.2284  data: 0.0037  max mem: 23764
+train: [16]  [220/400]  eta: 0:00:41  lr: 0.000038  loss: 2.5536 (2.5681)  grad: 0.2162 (0.2149)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [16]  [240/400]  eta: 0:00:36  lr: 0.000036  loss: 2.5406 (2.5645)  grad: 0.2129 (0.2149)  time: 0.2288  data: 0.0036  max mem: 23764
+train: [16]  [260/400]  eta: 0:00:32  lr: 0.000035  loss: 2.5406 (2.5652)  grad: 0.2129 (0.2152)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [16]  [280/400]  eta: 0:00:27  lr: 0.000034  loss: 2.5478 (2.5662)  grad: 0.2164 (0.2154)  time: 0.2272  data: 0.0034  max mem: 23764
+train: [16]  [300/400]  eta: 0:00:23  lr: 0.000033  loss: 2.5238 (2.5630)  grad: 0.2141 (0.2151)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [16]  [320/400]  eta: 0:00:18  lr: 0.000032  loss: 2.5501 (2.5656)  grad: 0.2073 (0.2150)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [16]  [340/400]  eta: 0:00:13  lr: 0.000031  loss: 2.5980 (2.5659)  grad: 0.2087 (0.2148)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [16]  [360/400]  eta: 0:00:09  lr: 0.000031  loss: 2.5487 (2.5646)  grad: 0.2059 (0.2142)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [16]  [380/400]  eta: 0:00:04  lr: 0.000030  loss: 2.5552 (2.5644)  grad: 0.2037 (0.2136)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.5653 (2.5650)  grad: 0.2092 (0.2134)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [16] Total time: 0:01:31 (0.2298 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.5653 (2.5650)  grad: 0.2092 (0.2134)
+eval (validation): [16]  [ 0/85]  eta: 0:01:06    time: 0.7852  data: 0.6665  max mem: 23764
+eval (validation): [16]  [20/85]  eta: 0:00:09    time: 0.1207  data: 0.0023  max mem: 23764
+eval (validation): [16]  [40/85]  eta: 0:00:06    time: 0.1213  data: 0.0033  max mem: 23764
+eval (validation): [16]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0033  max mem: 23764
+eval (validation): [16]  [80/85]  eta: 0:00:00    time: 0.1218  data: 0.0035  max mem: 23764
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.1197  data: 0.0036  max mem: 23764
+eval (validation): [16] Total time: 0:00:11 (0.1299 s / it)
+cv: [16]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.475  acc: 0.260  f1: 0.201
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:05:56  lr: nan  time: 0.8906  data: 0.6738  max mem: 23764
+train: [17]  [ 20/400]  eta: 0:01:38  lr: 0.000028  loss: 2.5396 (2.5450)  grad: 0.2110 (0.2137)  time: 0.2263  data: 0.0023  max mem: 23764
+train: [17]  [ 40/400]  eta: 0:01:27  lr: 0.000027  loss: 2.5396 (2.5446)  grad: 0.2132 (0.2145)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [17]  [ 60/400]  eta: 0:01:20  lr: 0.000026  loss: 2.5321 (2.5429)  grad: 0.2099 (0.2130)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [17]  [ 80/400]  eta: 0:01:15  lr: 0.000025  loss: 2.5032 (2.5378)  grad: 0.2069 (0.2147)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [17]  [100/400]  eta: 0:01:10  lr: 0.000024  loss: 2.5078 (2.5333)  grad: 0.2151 (0.2135)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [17]  [120/400]  eta: 0:01:05  lr: 0.000023  loss: 2.5482 (2.5372)  grad: 0.2095 (0.2130)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [17]  [140/400]  eta: 0:01:00  lr: 0.000023  loss: 2.5691 (2.5414)  grad: 0.2104 (0.2139)  time: 0.2275  data: 0.0035  max mem: 23764
+train: [17]  [160/400]  eta: 0:00:55  lr: 0.000022  loss: 2.5275 (2.5371)  grad: 0.2124 (0.2136)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [17]  [180/400]  eta: 0:00:50  lr: 0.000021  loss: 2.4716 (2.5321)  grad: 0.2057 (0.2131)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [17]  [200/400]  eta: 0:00:46  lr: 0.000020  loss: 2.5266 (2.5345)  grad: 0.2152 (0.2136)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [17]  [220/400]  eta: 0:00:41  lr: 0.000019  loss: 2.5776 (2.5386)  grad: 0.2153 (0.2133)  time: 0.2280  data: 0.0036  max mem: 23764
+train: [17]  [240/400]  eta: 0:00:36  lr: 0.000019  loss: 2.5824 (2.5408)  grad: 0.2137 (0.2136)  time: 0.2273  data: 0.0035  max mem: 23764
+train: [17]  [260/400]  eta: 0:00:32  lr: 0.000018  loss: 2.5488 (2.5416)  grad: 0.2146 (0.2137)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [17]  [280/400]  eta: 0:00:27  lr: 0.000017  loss: 2.5445 (2.5428)  grad: 0.2102 (0.2136)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [17]  [300/400]  eta: 0:00:22  lr: 0.000016  loss: 2.5402 (2.5424)  grad: 0.2124 (0.2135)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [17]  [320/400]  eta: 0:00:18  lr: 0.000016  loss: 2.5030 (2.5389)  grad: 0.2131 (0.2131)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [17]  [340/400]  eta: 0:00:13  lr: 0.000015  loss: 2.4801 (2.5383)  grad: 0.2090 (0.2128)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [17]  [360/400]  eta: 0:00:09  lr: 0.000014  loss: 2.5481 (2.5391)  grad: 0.2136 (0.2131)  time: 0.2274  data: 0.0035  max mem: 23764
+train: [17]  [380/400]  eta: 0:00:04  lr: 0.000014  loss: 2.5518 (2.5403)  grad: 0.2066 (0.2127)  time: 0.2272  data: 0.0034  max mem: 23764
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.5278 (2.5384)  grad: 0.2013 (0.2122)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [17] Total time: 0:01:31 (0.2296 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.5278 (2.5384)  grad: 0.2013 (0.2122)
+eval (validation): [17]  [ 0/85]  eta: 0:01:06    time: 0.7796  data: 0.6616  max mem: 23764
+eval (validation): [17]  [20/85]  eta: 0:00:09    time: 0.1197  data: 0.0021  max mem: 23764
+eval (validation): [17]  [40/85]  eta: 0:00:06    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [17]  [60/85]  eta: 0:00:03    time: 0.1211  data: 0.0035  max mem: 23764
+eval (validation): [17]  [80/85]  eta: 0:00:00    time: 0.1213  data: 0.0035  max mem: 23764
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.1194  data: 0.0035  max mem: 23764
+eval (validation): [17] Total time: 0:00:11 (0.1294 s / it)
+cv: [17]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.474  acc: 0.266  f1: 0.204
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:05:52  lr: nan  time: 0.8814  data: 0.6649  max mem: 23764
+train: [18]  [ 20/400]  eta: 0:01:38  lr: 0.000012  loss: 2.4861 (2.5060)  grad: 0.2135 (0.2076)  time: 0.2277  data: 0.0030  max mem: 23764
+train: [18]  [ 40/400]  eta: 0:01:27  lr: 0.000012  loss: 2.5033 (2.5252)  grad: 0.2136 (0.2102)  time: 0.2280  data: 0.0033  max mem: 23764
+train: [18]  [ 60/400]  eta: 0:01:21  lr: 0.000011  loss: 2.4814 (2.5248)  grad: 0.2093 (0.2080)  time: 0.2281  data: 0.0036  max mem: 23764
+train: [18]  [ 80/400]  eta: 0:01:15  lr: 0.000011  loss: 2.5458 (2.5368)  grad: 0.2007 (0.2072)  time: 0.2280  data: 0.0036  max mem: 23764
+train: [18]  [100/400]  eta: 0:01:10  lr: 0.000010  loss: 2.5439 (2.5302)  grad: 0.2067 (0.2079)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [18]  [120/400]  eta: 0:01:05  lr: 0.000009  loss: 2.5403 (2.5327)  grad: 0.2089 (0.2071)  time: 0.2282  data: 0.0036  max mem: 23764
+train: [18]  [140/400]  eta: 0:01:00  lr: 0.000009  loss: 2.5050 (2.5254)  grad: 0.2054 (0.2073)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [18]  [160/400]  eta: 0:00:55  lr: 0.000008  loss: 2.5187 (2.5292)  grad: 0.2076 (0.2078)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [18]  [180/400]  eta: 0:00:51  lr: 0.000008  loss: 2.5385 (2.5347)  grad: 0.2072 (0.2075)  time: 0.2286  data: 0.0035  max mem: 23764
+train: [18]  [200/400]  eta: 0:00:46  lr: 0.000007  loss: 2.5541 (2.5363)  grad: 0.2076 (0.2086)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [18]  [220/400]  eta: 0:00:41  lr: 0.000007  loss: 2.5165 (2.5329)  grad: 0.2135 (0.2092)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [18]  [240/400]  eta: 0:00:36  lr: 0.000006  loss: 2.5137 (2.5302)  grad: 0.2077 (0.2093)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [18]  [260/400]  eta: 0:00:32  lr: 0.000006  loss: 2.5159 (2.5287)  grad: 0.2110 (0.2100)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [18]  [280/400]  eta: 0:00:27  lr: 0.000006  loss: 2.5062 (2.5266)  grad: 0.2110 (0.2096)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [18]  [300/400]  eta: 0:00:23  lr: 0.000005  loss: 2.5204 (2.5274)  grad: 0.2046 (0.2096)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [18]  [320/400]  eta: 0:00:18  lr: 0.000005  loss: 2.5204 (2.5271)  grad: 0.2046 (0.2096)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [18]  [340/400]  eta: 0:00:13  lr: 0.000004  loss: 2.5184 (2.5279)  grad: 0.2083 (0.2097)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [18]  [360/400]  eta: 0:00:09  lr: 0.000004  loss: 2.5355 (2.5289)  grad: 0.2083 (0.2099)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [18]  [380/400]  eta: 0:00:04  lr: 0.000004  loss: 2.5154 (2.5275)  grad: 0.2156 (0.2099)  time: 0.2271  data: 0.0034  max mem: 23764
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.5154 (2.5286)  grad: 0.2098 (0.2100)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [18] Total time: 0:01:31 (0.2298 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.5154 (2.5286)  grad: 0.2098 (0.2100)
+eval (validation): [18]  [ 0/85]  eta: 0:01:06    time: 0.7839  data: 0.6667  max mem: 23764
+eval (validation): [18]  [20/85]  eta: 0:00:09    time: 0.1208  data: 0.0025  max mem: 23764
+eval (validation): [18]  [40/85]  eta: 0:00:06    time: 0.1211  data: 0.0034  max mem: 23764
+eval (validation): [18]  [60/85]  eta: 0:00:03    time: 0.1213  data: 0.0035  max mem: 23764
+eval (validation): [18]  [80/85]  eta: 0:00:00    time: 0.1216  data: 0.0035  max mem: 23764
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.1194  data: 0.0035  max mem: 23764
+eval (validation): [18] Total time: 0:00:11 (0.1298 s / it)
+cv: [18]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.475  acc: 0.262  f1: 0.203
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:06:06  lr: nan  time: 0.9170  data: 0.7004  max mem: 23764
+train: [19]  [ 20/400]  eta: 0:01:38  lr: 0.000003  loss: 2.5271 (2.5392)  grad: 0.2117 (0.2137)  time: 0.2269  data: 0.0022  max mem: 23764
+train: [19]  [ 40/400]  eta: 0:01:27  lr: 0.000003  loss: 2.5271 (2.5341)  grad: 0.2133 (0.2146)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [19]  [ 60/400]  eta: 0:01:21  lr: 0.000002  loss: 2.5209 (2.5370)  grad: 0.2104 (0.2145)  time: 0.2283  data: 0.0036  max mem: 23764
+train: [19]  [ 80/400]  eta: 0:01:15  lr: 0.000002  loss: 2.5209 (2.5351)  grad: 0.2100 (0.2137)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [19]  [100/400]  eta: 0:01:10  lr: 0.000002  loss: 2.5398 (2.5353)  grad: 0.2095 (0.2120)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [19]  [120/400]  eta: 0:01:05  lr: 0.000002  loss: 2.5341 (2.5291)  grad: 0.2017 (0.2110)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [19]  [140/400]  eta: 0:01:00  lr: 0.000001  loss: 2.5269 (2.5313)  grad: 0.2013 (0.2106)  time: 0.2285  data: 0.0035  max mem: 23764
+train: [19]  [160/400]  eta: 0:00:55  lr: 0.000001  loss: 2.5598 (2.5328)  grad: 0.2054 (0.2099)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [19]  [180/400]  eta: 0:00:51  lr: 0.000001  loss: 2.5574 (2.5306)  grad: 0.2054 (0.2091)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [19]  [200/400]  eta: 0:00:46  lr: 0.000001  loss: 2.5184 (2.5314)  grad: 0.2042 (0.2090)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [19]  [220/400]  eta: 0:00:41  lr: 0.000001  loss: 2.5000 (2.5276)  grad: 0.2023 (0.2089)  time: 0.2275  data: 0.0035  max mem: 23764
+train: [19]  [240/400]  eta: 0:00:36  lr: 0.000001  loss: 2.5153 (2.5248)  grad: 0.2111 (0.2091)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [19]  [260/400]  eta: 0:00:32  lr: 0.000000  loss: 2.5192 (2.5276)  grad: 0.2111 (0.2088)  time: 0.2283  data: 0.0036  max mem: 23764
+train: [19]  [280/400]  eta: 0:00:27  lr: 0.000000  loss: 2.5450 (2.5277)  grad: 0.2056 (0.2088)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [19]  [300/400]  eta: 0:00:23  lr: 0.000000  loss: 2.5184 (2.5282)  grad: 0.2060 (0.2091)  time: 0.2274  data: 0.0035  max mem: 23764
+train: [19]  [320/400]  eta: 0:00:18  lr: 0.000000  loss: 2.5731 (2.5325)  grad: 0.2075 (0.2093)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [19]  [340/400]  eta: 0:00:13  lr: 0.000000  loss: 2.5418 (2.5311)  grad: 0.2075 (0.2093)  time: 0.2285  data: 0.0036  max mem: 23764
+train: [19]  [360/400]  eta: 0:00:09  lr: 0.000000  loss: 2.4903 (2.5295)  grad: 0.2083 (0.2094)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [19]  [380/400]  eta: 0:00:04  lr: 0.000000  loss: 2.5274 (2.5293)  grad: 0.2097 (0.2096)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.5230 (2.5280)  grad: 0.2097 (0.2095)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [19] Total time: 0:01:31 (0.2299 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.5230 (2.5280)  grad: 0.2097 (0.2095)
+eval (validation): [19]  [ 0/85]  eta: 0:01:02    time: 0.7354  data: 0.6190  max mem: 23764
+eval (validation): [19]  [20/85]  eta: 0:00:09    time: 0.1207  data: 0.0032  max mem: 23764
+eval (validation): [19]  [40/85]  eta: 0:00:06    time: 0.1211  data: 0.0033  max mem: 23764
+eval (validation): [19]  [60/85]  eta: 0:00:03    time: 0.1209  data: 0.0034  max mem: 23764
+eval (validation): [19]  [80/85]  eta: 0:00:00    time: 0.1215  data: 0.0035  max mem: 23764
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.1193  data: 0.0034  max mem: 23764
+eval (validation): [19] Total time: 0:00:10 (0.1290 s / it)
+cv: [19]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.472  acc: 0.262  f1: 0.203
+saving checkpoint experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.2622739018087855, "hparam": [1.2, 1.0], "hparam_id": 25, "epoch": 19, "is_best": false, "best_score": 0.2678110003691399}
+eval (train): [20]  [  0/509]  eta: 0:06:20    time: 0.7484  data: 0.6295  max mem: 23764
+eval (train): [20]  [ 20/509]  eta: 0:01:14    time: 0.1216  data: 0.0031  max mem: 23764
+eval (train): [20]  [ 40/509]  eta: 0:01:04    time: 0.1217  data: 0.0035  max mem: 23764
+eval (train): [20]  [ 60/509]  eta: 0:00:59    time: 0.1214  data: 0.0034  max mem: 23764
+eval (train): [20]  [ 80/509]  eta: 0:00:55    time: 0.1213  data: 0.0033  max mem: 23764
+eval (train): [20]  [100/509]  eta: 0:00:52    time: 0.1218  data: 0.0034  max mem: 23764
+eval (train): [20]  [120/509]  eta: 0:00:49    time: 0.1219  data: 0.0036  max mem: 23764
+eval (train): [20]  [140/509]  eta: 0:00:46    time: 0.1218  data: 0.0035  max mem: 23764
+eval (train): [20]  [160/509]  eta: 0:00:43    time: 0.1214  data: 0.0035  max mem: 23764
+eval (train): [20]  [180/509]  eta: 0:00:41    time: 0.1214  data: 0.0034  max mem: 23764
+eval (train): [20]  [200/509]  eta: 0:00:38    time: 0.1216  data: 0.0034  max mem: 23764
+eval (train): [20]  [220/509]  eta: 0:00:35    time: 0.1212  data: 0.0034  max mem: 23764
+eval (train): [20]  [240/509]  eta: 0:00:33    time: 0.1211  data: 0.0035  max mem: 23764
+eval (train): [20]  [260/509]  eta: 0:00:30    time: 0.1211  data: 0.0035  max mem: 23764
+eval (train): [20]  [280/509]  eta: 0:00:28    time: 0.1210  data: 0.0035  max mem: 23764
+eval (train): [20]  [300/509]  eta: 0:00:25    time: 0.1207  data: 0.0032  max mem: 23764
+eval (train): [20]  [320/509]  eta: 0:00:23    time: 0.1210  data: 0.0033  max mem: 23764
+eval (train): [20]  [340/509]  eta: 0:00:20    time: 0.1214  data: 0.0035  max mem: 23764
+eval (train): [20]  [360/509]  eta: 0:00:18    time: 0.1204  data: 0.0032  max mem: 23764
+eval (train): [20]  [380/509]  eta: 0:00:15    time: 0.1213  data: 0.0036  max mem: 23764
+eval (train): [20]  [400/509]  eta: 0:00:13    time: 0.1210  data: 0.0035  max mem: 23764
+eval (train): [20]  [420/509]  eta: 0:00:10    time: 0.1210  data: 0.0033  max mem: 23764
+eval (train): [20]  [440/509]  eta: 0:00:08    time: 0.1208  data: 0.0034  max mem: 23764
+eval (train): [20]  [460/509]  eta: 0:00:06    time: 0.1210  data: 0.0034  max mem: 23764
+eval (train): [20]  [480/509]  eta: 0:00:03    time: 0.1214  data: 0.0035  max mem: 23764
+eval (train): [20]  [500/509]  eta: 0:00:01    time: 0.1211  data: 0.0034  max mem: 23764
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.1186  data: 0.0035  max mem: 23764
+eval (train): [20] Total time: 0:01:02 (0.1227 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:01:11    time: 0.8387  data: 0.7209  max mem: 23764
+eval (validation): [20]  [20/85]  eta: 0:00:10    time: 0.1203  data: 0.0026  max mem: 23764
+eval (validation): [20]  [40/85]  eta: 0:00:06    time: 0.1212  data: 0.0035  max mem: 23764
+eval (validation): [20]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0036  max mem: 23764
+eval (validation): [20]  [80/85]  eta: 0:00:00    time: 0.1220  data: 0.0037  max mem: 23764
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.1200  data: 0.0037  max mem: 23764
+eval (validation): [20] Total time: 0:00:11 (0.1305 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:01:03    time: 0.7487  data: 0.6278  max mem: 23764
+eval (test): [20]  [20/85]  eta: 0:00:09    time: 0.1214  data: 0.0034  max mem: 23764
+eval (test): [20]  [40/85]  eta: 0:00:06    time: 0.1215  data: 0.0034  max mem: 23764
+eval (test): [20]  [60/85]  eta: 0:00:03    time: 0.1215  data: 0.0036  max mem: 23764
+eval (test): [20]  [80/85]  eta: 0:00:00    time: 0.1217  data: 0.0036  max mem: 23764
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.1181  data: 0.0037  max mem: 23764
+eval (test): [20] Total time: 0:00:10 (0.1293 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:01:03    time: 0.7696  data: 0.6523  max mem: 23764
+eval (testid): [20]  [20/82]  eta: 0:00:09    time: 0.1210  data: 0.0030  max mem: 23764
+eval (testid): [20]  [40/82]  eta: 0:00:05    time: 0.1210  data: 0.0035  max mem: 23764
+eval (testid): [20]  [60/82]  eta: 0:00:02    time: 0.1209  data: 0.0035  max mem: 23764
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.1208  data: 0.0035  max mem: 23764
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.1164  data: 0.0035  max mem: 23764
+eval (testid): [20] Total time: 0:00:10 (0.1290 s / it)
+evaluating best checkpoint: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.2678110003691399, "hparam": [8.3, 1.0], "hparam_id": 37, "epoch": 14, "is_best": true, "best_score": 0.2678110003691399}
+eval (train): [20]  [  0/509]  eta: 0:07:02    time: 0.8303  data: 0.7109  max mem: 23764
+eval (train): [20]  [ 20/509]  eta: 0:01:15    time: 0.1209  data: 0.0029  max mem: 23764
+eval (train): [20]  [ 40/509]  eta: 0:01:04    time: 0.1212  data: 0.0034  max mem: 23764
+eval (train): [20]  [ 60/509]  eta: 0:00:59    time: 0.1214  data: 0.0034  max mem: 23764
+eval (train): [20]  [ 80/509]  eta: 0:00:55    time: 0.1211  data: 0.0034  max mem: 23764
+eval (train): [20]  [100/509]  eta: 0:00:52    time: 0.1216  data: 0.0035  max mem: 23764
+eval (train): [20]  [120/509]  eta: 0:00:49    time: 0.1217  data: 0.0034  max mem: 23764
+eval (train): [20]  [140/509]  eta: 0:00:46    time: 0.1212  data: 0.0034  max mem: 23764
+eval (train): [20]  [160/509]  eta: 0:00:43    time: 0.1216  data: 0.0033  max mem: 23764
+eval (train): [20]  [180/509]  eta: 0:00:41    time: 0.1219  data: 0.0034  max mem: 23764
+eval (train): [20]  [200/509]  eta: 0:00:38    time: 0.1216  data: 0.0033  max mem: 23764
+eval (train): [20]  [220/509]  eta: 0:00:36    time: 0.1218  data: 0.0035  max mem: 23764
+eval (train): [20]  [240/509]  eta: 0:00:33    time: 0.1212  data: 0.0033  max mem: 23764
+eval (train): [20]  [260/509]  eta: 0:00:30    time: 0.1218  data: 0.0033  max mem: 23764
+eval (train): [20]  [280/509]  eta: 0:00:28    time: 0.1218  data: 0.0034  max mem: 23764
+eval (train): [20]  [300/509]  eta: 0:00:25    time: 0.1219  data: 0.0035  max mem: 23764
+eval (train): [20]  [320/509]  eta: 0:00:23    time: 0.1214  data: 0.0033  max mem: 23764
+eval (train): [20]  [340/509]  eta: 0:00:20    time: 0.1219  data: 0.0036  max mem: 23764
+eval (train): [20]  [360/509]  eta: 0:00:18    time: 0.1217  data: 0.0035  max mem: 23764
+eval (train): [20]  [380/509]  eta: 0:00:15    time: 0.1217  data: 0.0036  max mem: 23764
+eval (train): [20]  [400/509]  eta: 0:00:13    time: 0.1220  data: 0.0035  max mem: 23764
+eval (train): [20]  [420/509]  eta: 0:00:10    time: 0.1220  data: 0.0035  max mem: 23764
+eval (train): [20]  [440/509]  eta: 0:00:08    time: 0.1218  data: 0.0034  max mem: 23764
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+eval (train): [20]  [480/509]  eta: 0:00:03    time: 0.1218  data: 0.0035  max mem: 23764
+eval (train): [20]  [500/509]  eta: 0:00:01    time: 0.1216  data: 0.0035  max mem: 23764
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.1185  data: 0.0036  max mem: 23764
+eval (train): [20] Total time: 0:01:02 (0.1232 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:01:19    time: 0.9310  data: 0.8147  max mem: 23764
+eval (validation): [20]  [20/85]  eta: 0:00:10    time: 0.1203  data: 0.0025  max mem: 23764
+eval (validation): [20]  [40/85]  eta: 0:00:06    time: 0.1210  data: 0.0035  max mem: 23764
+eval (validation): [20]  [60/85]  eta: 0:00:03    time: 0.1211  data: 0.0035  max mem: 23764
+eval (validation): [20]  [80/85]  eta: 0:00:00    time: 0.1207  data: 0.0033  max mem: 23764
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.1188  data: 0.0032  max mem: 23764
+eval (validation): [20] Total time: 0:00:11 (0.1312 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:01:08    time: 0.8052  data: 0.6889  max mem: 23764
+eval (test): [20]  [20/85]  eta: 0:00:09    time: 0.1207  data: 0.0028  max mem: 23764
+eval (test): [20]  [40/85]  eta: 0:00:06    time: 0.1208  data: 0.0033  max mem: 23764
+eval (test): [20]  [60/85]  eta: 0:00:03    time: 0.1211  data: 0.0035  max mem: 23764
+eval (test): [20]  [80/85]  eta: 0:00:00    time: 0.1215  data: 0.0035  max mem: 23764
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.1170  data: 0.0034  max mem: 23764
+eval (test): [20] Total time: 0:00:10 (0.1294 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:01:16    time: 0.9381  data: 0.8221  max mem: 23764
+eval (testid): [20]  [20/82]  eta: 0:00:09    time: 0.1203  data: 0.0021  max mem: 23764
+eval (testid): [20]  [40/82]  eta: 0:00:05    time: 0.1209  data: 0.0032  max mem: 23764
+eval (testid): [20]  [60/82]  eta: 0:00:02    time: 0.1217  data: 0.0036  max mem: 23764
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.1217  data: 0.0035  max mem: 23764
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.1164  data: 0.0034  max mem: 23764
+eval (testid): [20] Total time: 0:00:10 (0.1311 s / it)
+eval results:
+
+| model           | repr   | clf   | dataset      | ckpt   |   epoch |      lr |   wd |   hparam_id | hparam     | split      |   loss |     acc |   acc_std |      f1 |    f1_std |
+|:----------------|:-------|:------|:-------------|:-------|--------:|--------:|-----:|------------:|:-----------|:-----------|-------:|--------:|----------:|--------:|----------:|
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00249 | 0.05 |          37 | [8.3, 1.0] | train      | 2.0346 | 0.38071 | 0.00247   | 0.33066 | 0.002708  |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00249 | 0.05 |          37 | [8.3, 1.0] | validation | 2.5135 | 0.26781 | 0.0055253 | 0.2035  | 0.0050086 |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00249 | 0.05 |          37 | [8.3, 1.0] | test       | 2.4687 | 0.27532 | 0.0052514 | 0.21435 | 0.0051592 |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00249 | 0.05 |          37 | [8.3, 1.0] | testid     | 2.496  | 0.25718 | 0.0056065 | 0.20646 | 0.0052575 |
+
+
+done! total time: 0:39:41

subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
+schaefer400_mae,patch,logistic,ppmi_dx,,0.3593813663804626,train,0.8701067615658363,0.013764193442105062,0.8602861297562006,0.015078696586879423,0.8540239097041341,0.015533599761098773
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+schaefer400_mae,patch,logistic,ppmi_dx,1,0.046415888336127774,train,0.7544483985765125,0.01711202665267626,0.7270692567567567,0.019859222969074768,0.7196933204881182,0.019214380194258605
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+schaefer400_mae,patch,logistic,ppmi_dx,2,0.3593813663804626,train,0.8683274021352313,0.013430687490670602,0.8573349616477077,0.014992654555958397,0.8495771783344037,0.015648218954831993
+schaefer400_mae,patch,logistic,ppmi_dx,2,0.3593813663804626,test,0.65,0.04471055356400768,0.6224786970121885,0.04772139535382211,0.6209677419354839,0.04703175954742757
+schaefer400_mae,patch,logistic,ppmi_dx,3,0.3593813663804626,train,0.8647686832740213,0.014235221970871147,0.8543962037744082,0.01554687884969501,0.8484264611432242,0.015910072305544122
+schaefer400_mae,patch,logistic,ppmi_dx,3,0.3593813663804626,test,0.68,0.048430482136770016,0.6637242538881883,0.05025505802512628,0.6655348047538201,0.05060007608400258
+schaefer400_mae,patch,logistic,ppmi_dx,4,0.046415888336127774,train,0.7740213523131673,0.01741628430172273,0.7485086486048421,0.02022412841446392,0.7399379147934061,0.019643060065468006
+schaefer400_mae,patch,logistic,ppmi_dx,4,0.046415888336127774,test,0.65,0.0480188962805269,0.6266666666666667,0.050607711959457145,0.6260611205432938,0.05021806661599282
+schaefer400_mae,patch,logistic,ppmi_dx,5,0.3593813663804626,train,0.8754448398576512,0.014245592051455537,0.8653330047102641,0.015749375600863758,0.8579667094840505,0.01634147397390649
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+schaefer400_mae,patch,logistic,ppmi_dx,92,0.046415888336127774,test,0.61,0.04387259281145805,0.5623386825272135,0.04890568704690175,0.5632427843803056,0.04578107128581131
+schaefer400_mae,patch,logistic,ppmi_dx,93,0.005994842503189409,train,0.7117437722419929,0.01620645844167355,0.6649640838436175,0.020086221046536717,0.6606588524941126,0.018145865214388573
+schaefer400_mae,patch,logistic,ppmi_dx,93,0.005994842503189409,test,0.59,0.04428261961537505,0.5464100011063171,0.04959184799010053,0.5471137521222411,0.04676051078761457
+schaefer400_mae,patch,logistic,ppmi_dx,94,0.046415888336127774,train,0.7580071174377224,0.016104316038674762,0.7310247747747748,0.018769591939952177,0.7234532220081353,0.01821534620897557
+schaefer400_mae,patch,logistic,ppmi_dx,94,0.046415888336127774,test,0.68,0.043738728833837864,0.64349376114082,0.05019084912741246,0.6400679117147707,0.04773480786700581
+schaefer400_mae,patch,logistic,ppmi_dx,95,0.005994842503189409,train,0.7224199288256228,0.017291349868029642,0.6783623156504512,0.021268734961462612,0.6728082851637764,0.019348892590966976
+schaefer400_mae,patch,logistic,ppmi_dx,95,0.005994842503189409,test,0.62,0.044336328219643985,0.5634191176470589,0.052053203666201475,0.566213921901528,0.04723835868333039
+schaefer400_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,train,0.7277580071174378,0.016917063999660586,0.681083306443537,0.021561664859204462,0.6754040890601585,0.019319084764591102
+schaefer400_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,test,0.61,0.04188076408089996,0.5481404240528328,0.04932754905816071,0.5530560271646858,0.04425183070626947
+schaefer400_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,train,0.7669039145907474,0.01659304293511538,0.7405876611593253,0.019460584444931168,0.7324181117533719,0.01896901643608559
+schaefer400_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,test,0.74,0.03742090859399329,0.6904761904761905,0.049116650668036686,0.6833616298811545,0.043733799263752586
+schaefer400_mae,patch,logistic,ppmi_dx,98,0.005994842503189409,train,0.7224199288256228,0.016101830813927333,0.6732558139534883,0.02099630707997287,0.6684596446157139,0.01864964657234063
+schaefer400_mae,patch,logistic,ppmi_dx,98,0.005994842503189409,test,0.61,0.03359154060176461,0.48677457560205295,0.047950825377347975,0.5224957555178268,0.03604295576946989
+schaefer400_mae,patch,logistic,ppmi_dx,99,0.3593813663804626,train,0.8665480427046264,0.014046358015634336,0.8567471578840042,0.015262049582949695,0.8516110040676514,0.015569204682913328
+schaefer400_mae,patch,logistic,ppmi_dx,99,0.3593813663804626,test,0.63,0.04651390759762074,0.6053333333333333,0.05021708096166018,0.6048387096774194,0.050044168440618174
+schaefer400_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,train,0.7846975088967971,0.015956139779935513,0.7621600825420143,0.017995783919276555,0.753826803682295,0.017583406836321928
+schaefer400_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,test,0.58,0.042889975518761954,0.525101763907734,0.049638223333801655,0.5288624787775891,0.045893048588367555

subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic/log.txt

@@ -0,0 +1,247 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:25:11
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_3; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: ppmi_dx (schaefer400)
+train (n=463):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 463
+}),
+    labels=['PD' 'Prodromal'],
+    counts=[178 285]
+)
+
+validation (n=99):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 99
+}),
+    labels=['PD' 'Prodromal'],
+    counts=[39 60]
+)
+
+test (n=100):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 100
+}),
+    labels=['PD' 'Prodromal'],
+    counts=[37 63]
+)
+
+extracting features for all splits
+extract (train)  [  0/232]  eta: 0:04:20    time: 1.1247  data: 0.7935  max mem: 1546
+extract (train)  [ 20/232]  eta: 0:00:21    time: 0.0482  data: 0.0034  max mem: 1715
+extract (train)  [ 40/232]  eta: 0:00:14    time: 0.0461  data: 0.0013  max mem: 1715
+extract (train)  [ 60/232]  eta: 0:00:11    time: 0.0469  data: 0.0023  max mem: 1715
+extract (train)  [ 80/232]  eta: 0:00:09    time: 0.0470  data: 0.0024  max mem: 1715
+extract (train)  [100/232]  eta: 0:00:07    time: 0.0468  data: 0.0022  max mem: 1715
+extract (train)  [120/232]  eta: 0:00:06    time: 0.0467  data: 0.0021  max mem: 1715
+extract (train)  [140/232]  eta: 0:00:05    time: 0.0472  data: 0.0026  max mem: 1715
+extract (train)  [160/232]  eta: 0:00:03    time: 0.0470  data: 0.0024  max mem: 1715
+extract (train)  [180/232]  eta: 0:00:02    time: 0.0469  data: 0.0023  max mem: 1715
+extract (train)  [200/232]  eta: 0:00:01    time: 0.0470  data: 0.0023  max mem: 1715
+extract (train)  [220/232]  eta: 0:00:00    time: 0.0468  data: 0.0021  max mem: 1715
+extract (train)  [231/232]  eta: 0:00:00    time: 0.0463  data: 0.0023  max mem: 1715
+extract (train) Total time: 0:00:12 (0.0521 s / it)
+extract (validation)  [ 0/50]  eta: 0:00:44    time: 0.8992  data: 0.8530  max mem: 1715
+extract (validation)  [20/50]  eta: 0:00:02    time: 0.0475  data: 0.0029  max mem: 1715
+extract (validation)  [40/50]  eta: 0:00:00    time: 0.0465  data: 0.0014  max mem: 1715
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.0461  data: 0.0022  max mem: 1715
+extract (validation) Total time: 0:00:03 (0.0658 s / it)
+extract (test)  [ 0/50]  eta: 0:00:48    time: 0.9740  data: 0.9271  max mem: 1715
+extract (test)  [20/50]  eta: 0:00:02    time: 0.0476  data: 0.0027  max mem: 1715
+extract (test)  [40/50]  eta: 0:00:00    time: 0.0465  data: 0.0014  max mem: 1715
+extract (test)  [49/50]  eta: 0:00:00    time: 0.0472  data: 0.0022  max mem: 1715
+extract (test) Total time: 0:00:03 (0.0680 s / it)
+feature extraction time: 0:00:18
+train features: (463, 768)
+validation features: (99, 768)
+test features: (100, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |       C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|--------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | ppmi_dx   |         | 0.35938 | train   | 0.87011 |  0.013764 | 0.86029 | 0.015079 | 0.85402 |   0.015534 |
+| schaefer400_mae | patch  | logistic | ppmi_dx   |         | 0.35938 | test    | 0.63    |  0.044538 | 0.59066 | 0.049389 | 0.58923 |   0.047852 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.68, "acc_std": 0.041215429149773504, "f1": 0.6259934548854604, "f1_std": 0.052384510504862626, "bacc": 0.6247877758913413, "bacc_std": 0.04664730176629208}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.65, "acc_std": 0.04471055356400768, "f1": 0.6224786970121885, "f1_std": 0.04772139535382211, "bacc": 0.6209677419354839, "bacc_std": 0.04703175954742757}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.68, "acc_std": 0.048430482136770016, "f1": 0.6637242538881883, "f1_std": 0.05025505802512628, "bacc": 0.6655348047538201, "bacc_std": 0.05060007608400258}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.0480188962805269, "f1": 0.6266666666666667, "f1_std": 0.050607711959457145, "bacc": 0.6260611205432938, "bacc_std": 0.05021806661599282}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.67, "acc_std": 0.04464202056359008, "f1": 0.6349153667441089, "f1_std": 0.049748802740681296, "bacc": 0.6320033955857385, "bacc_std": 0.04783145971307935}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.56, "acc_std": 0.050492391506047715, "f1": 0.5331069609507639, "f1_std": 0.05288426123053668, "bacc": 0.5331069609507639, "bacc_std": 0.05257669107943259}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 21.54434690031882, "split": "test", "acc": 0.51, "acc_std": 0.05163909759087585, "f1": 0.5039983804028747, "f1_std": 0.051223652531976624, "bacc": 0.513157894736842, "bacc_std": 0.052773625413661464}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.57, "acc_std": 0.04582095153966141, "f1": 0.5361881134721174, "f1_std": 0.04919330762091671, "bacc": 0.5360780984719864, "bacc_std": 0.048029962912204044}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.66, "acc_std": 0.04363000802200247, "f1": 0.6212121212121212, "f1_std": 0.049677916125200026, "bacc": 0.6188455008488964, "bacc_std": 0.047335171588730074}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.62, "acc_std": 0.04002124935581098, "f1": 0.5476190476190476, "f1_std": 0.047618690946881564, "bacc": 0.5560271646859083, "bacc_std": 0.04185947356016022}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.66, "acc_std": 0.04520201765408265, "f1": 0.6353496353496353, "f1_std": 0.04904693515603877, "bacc": 0.634125636672326, "bacc_std": 0.04842191681948725}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.03760201590340603, "f1": 0.5706048337627285, "f1_std": 0.04937214131524405, "bacc": 0.580220713073005, "bacc_std": 0.04123267517753258}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.62, "acc_std": 0.0449967287699895, "f1": 0.5824175824175825, "f1_std": 0.04923452800318844, "bacc": 0.5814940577249575, "bacc_std": 0.04743458417258606}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04143206005015922, "f1": 0.6108031607500884, "f1_std": 0.051961966740015304, "bacc": 0.6116298811544991, "bacc_std": 0.045817066897867464}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04349252809391516, "f1": 0.6396986570586308, "f1_std": 0.04729307564537745, "bacc": 0.6370967741935484, "bacc_std": 0.0459309156798954}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.61, "acc_std": 0.0475474499842, "f1": 0.5983935742971888, "f1_std": 0.04783012699981768, "bacc": 0.6039898132427843, "bacc_std": 0.04883490324697994}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.62, "acc_std": 0.0448837431594113, "f1": 0.5824175824175825, "f1_std": 0.0491985563856134, "bacc": 0.5814940577249575, "bacc_std": 0.047302362065272996}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.62, "acc_std": 0.046782902859912406, "f1": 0.5876736111111112, "f1_std": 0.049903774133341565, "bacc": 0.5865874363327674, "bacc_std": 0.048739443315229815}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 19, "C": 166.81005372000556, "split": "test", "acc": 0.6, "acc_std": 0.05051708225937044, "f1": 0.5833333333333333, "f1_std": 0.052625883850652824, "bacc": 0.5857385398981324, "bacc_std": 0.053741417860783354}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.57, "acc_std": 0.04683746790764846, "f1": 0.5242836596968692, "f1_std": 0.051505121046066, "bacc": 0.5258913412563667, "bacc_std": 0.04879223785132542}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.7, "acc_std": 0.04341039506846257, "f1": 0.66078697421981, "f1_std": 0.05189452931823935, "bacc": 0.6561969439728353, "bacc_std": 0.048418806545962395}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.6, "acc_std": 0.05033749695803318, "f1": 0.5659722222222222, "f1_std": 0.053414647909561616, "bacc": 0.565365025466893, "bacc_std": 0.052300856218583366}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 23, "C": 0.046415888336127774, "split": "test", "acc": 0.7, "acc_std": 0.04478953449188772, "f1": 0.6703296703296704, "f1_std": 0.05072536424497573, "bacc": 0.666383701188455, "bacc_std": 0.04953004080052253}
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+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | ppmi_dx   | train   |        100 | 4.1684 |   23.64 | 0.80649 |  0.073365 | 0.78328 | 0.086454 | 0.77664 |   0.087277 |
+| schaefer400_mae | patch  | logistic | ppmi_dx   | test    |        100 | 4.1684 |   23.64 | 0.6379  |  0.045909 | 0.59643 | 0.0506   | 0.59736 |   0.047218 |
+
+
+done! total time: 0:04:44

subcortical/schaefer400_lr3e-4_3/pretrain/config.yaml

@@ -0,0 +1,102 @@
+name: subcortical/schaefer400_lr3e-4_3/pretrain
+notes: subcortical ablation schaefer400_lr3e-4_3 (input_space=schaefer400 base_lr=3e-4
+  seed=5403)
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_3/pretrain
+input_space: schaefer400
+patch_size: 1
+num_frames: 16
+t_patch_size: 4
+mask_ratio: 0.9
+pred_mask_ratio: null
+masking: tube
+masking_kwargs: {}
+mask_patch_size: null
+model: mae_vit_base
+model_kwargs:
+  decoding: attn
+  pos_embed: sep
+  target_norm: null
+  pca_norm_nc: 2
+  t_pred_stride: 2
+  no_decode_pos: true
+  mask_drop_scale: false
+  pred_edge_pad: 0
+  gauss_sigma: null
+  class_token: true
+  reg_tokens: 0
+  no_embed_class: true
+  head_init_scale: 0.0
+  decoder_depth: 4
+  drop_path_rate: 0.0
+datasets:
+  hcp-train:
+    type: wds
+    url: /data/fmri-datasets/pretrain/hcpya-all.${input_space}.wds/hcpya-all-${input_space}-{00000..01799}.tar
+    clipping: random
+    clipping_kwargs:
+      oversample: 4.0
+    shuffle: true
+    buffer_size: 2000
+    samples_per_epoch: 200000
+  hcp-train-subset:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/validation
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+  hcp-val:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/test
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+train_dataset: hcp-train
+eval_datasets:
+- hcp-train-subset
+- hcp-val
+val_dataset: null
+clip_vmax: 3.0
+normalize: frame
+tr_scale: null
+crop_scale: null
+crop_aspect: null
+gray_jitter: null
+num_workers: 16
+epochs: 100
+batch_size: 32
+accum_iter: 1
+base_lr: 0.0003
+min_lr: 0.0
+warmup_epochs: 5
+weight_decay: 0.05
+betas:
+- 0.9
+- 0.95
+clip_grad: 1.0
+amp: true
+amp_dtype: float16
+ckpt: null
+resume: true
+auto_resume: true
+start_epoch: 0
+max_checkpoints: 0
+checkpoint_period: null
+plot_period: 1
+device: cuda
+presend_cuda: false
+seed: 5403
+debug: false
+wandb: true
+wandb_entity: null
+wandb_project: fMRI-foundation-model
+rank: 0
+world_size: 1
+gpu: 0
+distributed: true
+dist_backend: nccl
+in_chans: 1
+img_size:
+- 400
+- 1

subcortical/schaefer400_lr3e-4_3/pretrain/log.json

@@ -0,0 +1,100 @@
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subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,aabc_age,88,9.999999999999999e-05,test,0.4423076923076923,0.057580969489383196,0.3918067226890757,0.04802166631802991,0.4340659340659341,0.05604474863423603
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+schaefer400_mae,patch,logistic,aabc_age,90,9.999999999999999e-05,train,0.4507874015748031,0.02059867012965697,0.4082827095449556,0.0202111048841251,0.44746640703965757,0.020349813233455874
+schaefer400_mae,patch,logistic,aabc_age,90,9.999999999999999e-05,test,0.5192307692307693,0.052826541540444034,0.44722062211981567,0.04603311222635258,0.510989010989011,0.05117824219664885
+schaefer400_mae,patch,logistic,aabc_age,91,0.046415888336127774,train,0.6948818897637795,0.020253099290628622,0.6936813186813188,0.020476578883293205,0.6949088156827244,0.020199349904063277
+schaefer400_mae,patch,logistic,aabc_age,91,0.046415888336127774,test,0.4807692307692308,0.06609102562859086,0.4757802916723456,0.06813163739263588,0.48054029304029305,0.06605117680452999
+schaefer400_mae,patch,logistic,aabc_age,92,0.005994842503189409,train,0.5964566929133859,0.020314769882520234,0.5899070459148106,0.0211772618168193,0.5958810264505005,0.02033588628715639
+schaefer400_mae,patch,logistic,aabc_age,92,0.005994842503189409,test,0.5192307692307693,0.049593898150153616,0.4649122807017544,0.05482719433203764,0.5242673992673993,0.050632115735138906
+schaefer400_mae,patch,logistic,aabc_age,93,0.3593813663804626,train,0.8759842519685039,0.01424918133010934,0.8765570132610806,0.014266529910914786,0.8767706309492528,0.014202901736051616
+schaefer400_mae,patch,logistic,aabc_age,93,0.3593813663804626,test,0.3076923076923077,0.0666109431811267,0.3086444805194805,0.07016764808150185,0.30883699633699635,0.06697973022484016
+schaefer400_mae,patch,logistic,aabc_age,94,0.005994842503189409,train,0.6003937007874016,0.020140406693310933,0.5937743482169613,0.020735845164544464,0.6001308523961955,0.0200707808060429
+schaefer400_mae,patch,logistic,aabc_age,94,0.005994842503189409,test,0.40384615384615385,0.0661770419977651,0.38887189469781236,0.06512370142402901,0.39880952380952384,0.0654332657012333
+schaefer400_mae,patch,logistic,aabc_age,95,0.3593813663804626,train,0.8779527559055118,0.01519567281554009,0.8789461216870312,0.015084292324046395,0.8788367466517322,0.01506226163441231
+schaefer400_mae,patch,logistic,aabc_age,95,0.3593813663804626,test,0.34615384615384615,0.06130561718329612,0.33929254476239046,0.0585771312142304,0.33951465201465203,0.06034748773466793
+schaefer400_mae,patch,logistic,aabc_age,96,0.3593813663804626,train,0.8523622047244095,0.01612685073567154,0.8532681166727076,0.016023973940507346,0.8528622583138501,0.016073874538263092
+schaefer400_mae,patch,logistic,aabc_age,96,0.3593813663804626,test,0.5192307692307693,0.06459894479508975,0.5204124579124578,0.06675912023779566,0.5176282051282052,0.06469115623164427
+schaefer400_mae,patch,logistic,aabc_age,97,0.000774263682681127,train,0.5177165354330708,0.020823115273759105,0.5044140502212253,0.021661938143758035,0.5165617705782055,0.020715669066272636
+schaefer400_mae,patch,logistic,aabc_age,97,0.000774263682681127,test,0.38461538461538464,0.06077014361534442,0.36737182395644286,0.05889891934741709,0.37934981684981683,0.06001674038533721
+schaefer400_mae,patch,logistic,aabc_age,98,9.999999999999999e-05,train,0.46062992125984253,0.018749615078612444,0.4096213446354452,0.01751894431744467,0.45739709219028407,0.018507572611410127
+schaefer400_mae,patch,logistic,aabc_age,98,9.999999999999999e-05,test,0.46153846153846156,0.0567969496639059,0.39166666666666666,0.05075602038825889,0.4519230769230769,0.05513989738858487
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+schaefer400_mae,patch,logistic,aabc_age,100,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_age,100,166.81005372000556,test,0.36538461538461536,0.0696459346481742,0.366031746031746,0.06861989384171104,0.36469780219780223,0.06978851874658464

subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:29:44
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_age__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: aabc_age (schaefer400)
+train (n=455):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 471
+}),
+    labels=[0 1 2 3],
+    counts=[110 127 109 109]
+)
+
+validation (n=53):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 58
+}),
+    labels=[0 1 2 3],
+    counts=[14 13 12 14]
+)
+
+test (n=52):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 55
+}),
+    labels=[0 1 2 3],
+    counts=[13 13 12 14]
+)
+
+extracting features for all splits
+extract (train)  [  0/228]  eta: 0:05:32    time: 1.4598  data: 1.1298  max mem: 1814
+extract (train)  [ 20/228]  eta: 0:00:26    time: 0.0597  data: 0.0050  max mem: 2020
+extract (train)  [ 40/228]  eta: 0:00:17    time: 0.0570  data: 0.0018  max mem: 2020
+extract (train)  [ 60/228]  eta: 0:00:13    time: 0.0578  data: 0.0029  max mem: 2020
+extract (train)  [ 80/228]  eta: 0:00:11    time: 0.0581  data: 0.0031  max mem: 2020
+extract (train)  [100/228]  eta: 0:00:09    time: 0.0579  data: 0.0030  max mem: 2020
+extract (train)  [120/228]  eta: 0:00:07    time: 0.0579  data: 0.0030  max mem: 2020
+extract (train)  [140/228]  eta: 0:00:05    time: 0.0580  data: 0.0030  max mem: 2020
+extract (train)  [160/228]  eta: 0:00:04    time: 0.0582  data: 0.0032  max mem: 2020
+extract (train)  [180/228]  eta: 0:00:03    time: 0.0581  data: 0.0031  max mem: 2020
+extract (train)  [200/228]  eta: 0:00:01    time: 0.0580  data: 0.0029  max mem: 2020
+extract (train)  [220/228]  eta: 0:00:00    time: 0.0580  data: 0.0030  max mem: 2020
+extract (train)  [227/228]  eta: 0:00:00    time: 0.0570  data: 0.0031  max mem: 2020
+extract (train) Total time: 0:00:14 (0.0649 s / it)
+extract (validation)  [ 0/27]  eta: 0:00:28    time: 1.0649  data: 1.0091  max mem: 2020
+extract (validation)  [20/27]  eta: 0:00:00    time: 0.0580  data: 0.0032  max mem: 2020
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.0562  data: 0.0025  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0984 s / it)
+extract (test)  [ 0/26]  eta: 0:00:27    time: 1.0622  data: 1.0066  max mem: 2020
+extract (test)  [20/26]  eta: 0:00:00    time: 0.0578  data: 0.0029  max mem: 2020
+extract (test)  [25/26]  eta: 0:00:00    time: 0.0571  data: 0.0021  max mem: 2020
+extract (test) Total time: 0:00:02 (0.1010 s / it)
+feature extraction time: 0:00:20
+train features: (455, 768)
+validation features: (53, 768)
+test features: (52, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |         C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_age  |         | 0.0059948 | train   | 0.59449 |  0.022145 | 0.59131 | 0.022294 | 0.59372 |   0.022157 |
+| schaefer400_mae | patch  | logistic | aabc_age  |         | 0.0059948 | test    | 0.26923 |  0.055929 | 0.26077 | 0.051999 | 0.25984 |   0.054706 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.06172417889575388, "f1": 0.4861247947454844, "f1_std": 0.06267252468132858, "bacc": 0.49793956043956045, "bacc_std": 0.06164534680993828}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06509124228715571, "f1": 0.4408818982321705, "f1_std": 0.0657482359023267, "bacc": 0.4578754578754579, "bacc_std": 0.06470132319180702}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.05674268453748651, "f1": 0.3571603128054741, "f1_std": 0.04943759127554413, "bacc": 0.4162087912087912, "bacc_std": 0.05541765459897285}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.05818368943248899, "f1": 0.45264423076923077, "f1_std": 0.06027244969371192, "bacc": 0.47847985347985345, "bacc_std": 0.05803848073400085}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06472518143988501, "f1": 0.4659595959595959, "f1_std": 0.0659879183246271, "bacc": 0.4862637362637363, "bacc_std": 0.06554498749348475}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.000774263682681127, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06256239489052406, "f1": 0.3658722109533469, "f1_std": 0.05991776130407981, "bacc": 0.37934981684981683, "bacc_std": 0.061718908539624806}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.000774263682681127, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.05694036899513188, "f1": 0.39207306854365676, "f1_std": 0.0497886794569048, "bacc": 0.4340659340659341, "bacc_std": 0.055510685814396055}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 9.999999999999999e-05, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.052600650208594044, "f1": 0.27598039215686276, "f1_std": 0.04261429875602183, "bacc": 0.3241758241758242, "bacc_std": 0.05187619802187867}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.061425376865566914, "f1": 0.37112135176651306, "f1_std": 0.05679858778671558, "bacc": 0.37934981684981683, "bacc_std": 0.060585798031071836}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 0.000774263682681127, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.0667936303344446, "f1": 0.4147950695704779, "f1_std": 0.06821576171419835, "bacc": 0.41964285714285715, "bacc_std": 0.0664821590817821}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 21.54434690031882, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06592145002929242, "f1": 0.44194664031620556, "f1_std": 0.06429060432669077, "bacc": 0.42880036630036633, "bacc_std": 0.06652958246615914}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.0609191310882253, "f1": 0.3769998117824205, "f1_std": 0.06025262367356975, "bacc": 0.38095238095238093, "bacc_std": 0.06061578415350016}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 0.000774263682681127, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06068488547771832, "f1": 0.3954932694080767, "f1_std": 0.058331322139573205, "bacc": 0.4164377289377289, "bacc_std": 0.05983310206681681}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.07012474092601576, "f1": 0.5389190550828482, "f1_std": 0.07244824403962809, "bacc": 0.5398351648351648, "bacc_std": 0.07034776131379232}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.0687015132947891, "f1": 0.517869142351901, "f1_std": 0.06882596168996617, "bacc": 0.516025641025641, "bacc_std": 0.06870039551148964}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.061947248029767546, "f1": 0.35843124435128737, "f1_std": 0.058358158306751534, "bacc": 0.36149267399267404, "bacc_std": 0.06136261487304094}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 0.000774263682681127, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.052725971840345214, "f1": 0.4136710239651416, "f1_std": 0.05550991592852183, "bacc": 0.4562728937728938, "bacc_std": 0.0518008331019928}
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+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_age  | train   |        100 | 16.822 |   130.9 | 0.6227  |  0.15269  | 0.6105  | 0.16307  | 0.62213 |   0.15342  |
+| schaefer400_mae | patch  | logistic | aabc_age  | test    |        100 | 16.822 |   130.9 | 0.43481 |  0.062443 | 0.41365 | 0.064021 | 0.43231 |   0.061959 |
+
+
+done! total time: 0:05:51

subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+schaefer400_mae,patch,logistic,aabc_sex,99,2.782559402207126,train,0.9848771266540642,0.005065536174349675,0.9844954424221108,0.0051924973472758105,0.9844954424221108,0.005214724006157187
+schaefer400_mae,patch,logistic,aabc_sex,99,2.782559402207126,test,0.7090909090909091,0.062157400841242105,0.7066666666666667,0.0624127991248105,0.7133152173913043,0.06210209204975724
+schaefer400_mae,patch,logistic,aabc_sex,100,0.3593813663804626,train,0.8941398865784499,0.013119153097162384,0.8906120760959471,0.01363738770547777,0.8878191037251971,0.013839911868752035
+schaefer400_mae,patch,logistic,aabc_sex,100,0.3593813663804626,test,0.7454545454545455,0.05567086274414729,0.7348484848484849,0.059264376134990526,0.7323369565217391,0.05870393364169782

subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:29:54
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: aabc_sex (schaefer400)
+train (n=471):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 471
+}),
+    labels=[0 1],
+    counts=[269 202]
+)
+
+validation (n=58):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 58
+}),
+    labels=[0 1],
+    counts=[36 22]
+)
+
+test (n=55):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
+    num_rows: 55
+}),
+    labels=[0 1],
+    counts=[33 22]
+)
+
+extracting features for all splits
+extract (train)  [  0/236]  eta: 0:05:06    time: 1.2993  data: 0.9384  max mem: 1814
+extract (train)  [ 20/236]  eta: 0:00:25    time: 0.0589  data: 0.0040  max mem: 2020
+extract (train)  [ 40/236]  eta: 0:00:17    time: 0.0574  data: 0.0021  max mem: 2020
+extract (train)  [ 60/236]  eta: 0:00:13    time: 0.0578  data: 0.0026  max mem: 2020
+extract (train)  [ 80/236]  eta: 0:00:11    time: 0.0580  data: 0.0027  max mem: 2020
+extract (train)  [100/236]  eta: 0:00:09    time: 0.0579  data: 0.0027  max mem: 2020
+extract (train)  [120/236]  eta: 0:00:07    time: 0.0579  data: 0.0028  max mem: 2020
+extract (train)  [140/236]  eta: 0:00:06    time: 0.0578  data: 0.0026  max mem: 2020
+extract (train)  [160/236]  eta: 0:00:04    time: 0.0579  data: 0.0026  max mem: 2020
+extract (train)  [180/236]  eta: 0:00:03    time: 0.0579  data: 0.0027  max mem: 2020
+extract (train)  [200/236]  eta: 0:00:02    time: 0.0582  data: 0.0028  max mem: 2020
+extract (train)  [220/236]  eta: 0:00:01    time: 0.0582  data: 0.0027  max mem: 2020
+extract (train)  [235/236]  eta: 0:00:00    time: 0.0570  data: 0.0026  max mem: 2020
+extract (train) Total time: 0:00:15 (0.0638 s / it)
+extract (validation)  [ 0/29]  eta: 0:00:26    time: 0.9139  data: 0.8575  max mem: 2020
+extract (validation)  [20/29]  eta: 0:00:00    time: 0.0580  data: 0.0028  max mem: 2020
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.0572  data: 0.0019  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0908 s / it)
+extract (test)  [ 0/28]  eta: 0:00:23    time: 0.8435  data: 0.7875  max mem: 2020
+extract (test)  [20/28]  eta: 0:00:00    time: 0.0574  data: 0.0022  max mem: 2020
+extract (test)  [27/28]  eta: 0:00:00    time: 0.0561  data: 0.0021  max mem: 2020
+extract (test) Total time: 0:00:02 (0.0882 s / it)
+feature extraction time: 0:00:20
+train features: (471, 768)
+validation features: (58, 768)
+test features: (55, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |      C | split   |     acc |   acc_std |      f1 |    f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-------:|:--------|--------:|----------:|--------:|----------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 2.7826 | train   | 0.97921 |  0.006093 | 0.97864 | 0.0062766 | 0.97722 |  0.0066442 |
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 2.7826 | test    | 0.70909 |  0.063223 | 0.7043  | 0.063386  | 0.71212 |  0.063795  |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 21.54434690031882, "split": "test", "acc": 0.4909090909090909, "acc_std": 0.0662570900619026, "f1": 0.4696969696969697, "f1_std": 0.06792568686069679, "bacc": 0.47078804347826086, "bacc_std": 0.06683415945651842}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06248258997182159, "f1": 0.637278722665741, "f1_std": 0.06641658667905236, "bacc": 0.6358695652173914, "bacc_std": 0.06489012691470572}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06228507110572027, "f1": 0.687186349949816, "f1_std": 0.06297048621982641, "bacc": 0.6915760869565217, "bacc_std": 0.06331609353984206}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.053182125873235765, "f1": 0.76890756302521, "f1_std": 0.05830802306231203, "bacc": 0.7635869565217391, "bacc_std": 0.05726598112963302}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.058962444974367985, "f1": 0.717948717948718, "f1_std": 0.061471774202089303, "bacc": 0.7167119565217391, "bacc_std": 0.06081486252361301}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06067982904468802, "f1": 0.7043010752688172, "f1_std": 0.061580600341210344, "bacc": 0.7072010869565217, "bacc_std": 0.06182323358203413}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.0507803567149658, "f1": 0.7782258064516129, "f1_std": 0.05170358858548202, "bacc": 0.7819293478260869, "bacc_std": 0.0519944385895575}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 1291.5496650148827, "split": "test", "acc": 0.6181818181818182, "acc_std": 0.06133305904837461, "f1": 0.6099290780141844, "f1_std": 0.062143926579389226, "bacc": 0.6107336956521738, "bacc_std": 0.06209306767541424}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06025966950286936, "f1": 0.6694945210321668, "f1_std": 0.0654690606148225, "bacc": 0.6671195652173914, "bacc_std": 0.06254748462000448}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.060126632209250234, "f1": 0.6918767507002801, "f1_std": 0.06527426323020569, "bacc": 0.6888586956521738, "bacc_std": 0.06318218978823133}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05287441537882845, "f1": 0.790003471017008, "f1_std": 0.0566007374031445, "bacc": 0.7853260869565217, "bacc_std": 0.05595516650760276}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05906824740281085, "f1": 0.6918767507002801, "f1_std": 0.06427282878724246, "bacc": 0.6888586956521738, "bacc_std": 0.06221792851080155}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.8, "acc_std": 0.052536218124985275, "f1": 0.7975911676145868, "f1_std": 0.052976075044526914, "bacc": 0.8036684782608696, "bacc_std": 0.052436483415143294}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06234015990667721, "f1": 0.6842283012495778, "f1_std": 0.06374352055767188, "bacc": 0.6854619565217391, "bacc_std": 0.06380021726917587}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05411147355457929, "f1": 0.7250000000000001, "f1_std": 0.0612435778141741, "bacc": 0.7201086956521738, "bacc_std": 0.05820488638131665}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.058445979861607864, "f1": 0.7348484848484849, "f1_std": 0.0613937793640615, "bacc": 0.7323369565217391, "bacc_std": 0.060591574104153036}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.0500345137078744, "f1": 0.7352832284339134, "f1_std": 0.06231961171524081, "bacc": 0.7296195652173914, "bacc_std": 0.05625435842178031}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.052458638567261524, "f1": 0.7518222839291913, "f1_std": 0.05680582179803631, "bacc": 0.7479619565217391, "bacc_std": 0.05600915230038504}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.046415888336127774, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.0625735560548973, "f1": 0.717948717948718, "f1_std": 0.06471474663460677, "bacc": 0.7167119565217391, "bacc_std": 0.06438835757766698}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06510178680057946, "f1": 0.7010869565217391, "f1_std": 0.06751605135067962, "bacc": 0.7010869565217391, "bacc_std": 0.06752207442909273}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05628353252113669, "f1": 0.7518222839291913, "f1_std": 0.06037349724722002, "bacc": 0.7479619565217391, "bacc_std": 0.05908364046833817}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 1291.5496650148827, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06308153978112002, "f1": 0.6533613445378151, "f1_std": 0.06838504173167574, "bacc": 0.6514945652173914, "bacc_std": 0.0658570095944576}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.062157400841242105, "f1": 0.7066666666666667, "f1_std": 0.0624127991248105, "bacc": 0.7133152173913043, "bacc_std": 0.06210209204975724}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05567086274414729, "f1": 0.7348484848484849, "f1_std": 0.059264376134990526, "bacc": 0.7323369565217391, "bacc_std": 0.05870393364169782}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | aabc_sex  | train   |        100 | 99.016 |  330.44 | 0.92161 |  0.059485 | 0.91891 | 0.061987 | 0.91725 |   0.063087 |
+| schaefer400_mae | patch  | logistic | aabc_sex  | test    |        100 | 99.016 |  330.44 | 0.72091 |  0.060956 | 0.7094  | 0.063546 | 0.70892 |   0.063026 |
+
+
+done! total time: 0:04:37

subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:25:37
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: abide_dx (schaefer400)
+train (n=578):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 578
+}),
+    labels=['Autism' 'Control'],
+    counts=[260 318]
+)
+
+validation (n=124):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 124
+}),
+    labels=['Autism' 'Control'],
+    counts=[54 70]
+)
+
+test (n=124):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 124
+}),
+    labels=['Autism' 'Control'],
+    counts=[57 67]
+)
+
+extracting features for all splits
+extract (train)  [  0/289]  eta: 0:05:28    time: 1.1363  data: 0.8163  max mem: 1546
+extract (train)  [ 20/289]  eta: 0:00:26    time: 0.0465  data: 0.0020  max mem: 1715
+extract (train)  [ 40/289]  eta: 0:00:18    time: 0.0459  data: 0.0013  max mem: 1715
+extract (train)  [ 60/289]  eta: 0:00:14    time: 0.0467  data: 0.0021  max mem: 1715
+extract (train)  [ 80/289]  eta: 0:00:12    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train)  [100/289]  eta: 0:00:10    time: 0.0469  data: 0.0022  max mem: 1715
+extract (train)  [120/289]  eta: 0:00:09    time: 0.0471  data: 0.0023  max mem: 1715
+extract (train)  [140/289]  eta: 0:00:08    time: 0.0474  data: 0.0024  max mem: 1715
+extract (train)  [160/289]  eta: 0:00:06    time: 0.0471  data: 0.0023  max mem: 1715
+extract (train)  [180/289]  eta: 0:00:05    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [200/289]  eta: 0:00:04    time: 0.0474  data: 0.0024  max mem: 1715
+extract (train)  [220/289]  eta: 0:00:03    time: 0.0469  data: 0.0021  max mem: 1715
+extract (train)  [240/289]  eta: 0:00:02    time: 0.0470  data: 0.0022  max mem: 1715
+extract (train)  [260/289]  eta: 0:00:01    time: 0.0474  data: 0.0024  max mem: 1715
+extract (train)  [280/289]  eta: 0:00:00    time: 0.0471  data: 0.0022  max mem: 1715
+extract (train)  [288/289]  eta: 0:00:00    time: 0.0470  data: 0.0021  max mem: 1715
+extract (train) Total time: 0:00:14 (0.0513 s / it)
+extract (validation)  [ 0/62]  eta: 0:00:45    time: 0.7406  data: 0.6946  max mem: 1715
+extract (validation)  [20/62]  eta: 0:00:03    time: 0.0472  data: 0.0023  max mem: 1715
+extract (validation)  [40/62]  eta: 0:00:01    time: 0.0465  data: 0.0015  max mem: 1715
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.0470  data: 0.0022  max mem: 1715
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.0470  data: 0.0022  max mem: 1715
+extract (validation) Total time: 0:00:03 (0.0597 s / it)
+extract (test)  [ 0/62]  eta: 0:00:41    time: 0.6740  data: 0.6278  max mem: 1715
+extract (test)  [20/62]  eta: 0:00:03    time: 0.0476  data: 0.0027  max mem: 1715
+extract (test)  [40/62]  eta: 0:00:01    time: 0.0464  data: 0.0013  max mem: 1715
+extract (test)  [60/62]  eta: 0:00:00    time: 0.0474  data: 0.0025  max mem: 1715
+extract (test)  [61/62]  eta: 0:00:00    time: 0.0474  data: 0.0025  max mem: 1715
+extract (test) Total time: 0:00:03 (0.0589 s / it)
+feature extraction time: 0:00:22
+train features: (578, 768)
+validation features: (124, 768)
+test features: (124, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset   | trial   |       C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|--------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.35938 | train   | 0.85613 |  0.012988 | 0.85308 | 0.013462 | 0.85041 |   0.013641 |
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.35938 | test    | 0.54839 |  0.043286 | 0.5465  | 0.0434   | 0.54674 |   0.043429 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04369234271190463, "f1": 0.6301451580831179, "f1_std": 0.04512062214618017, "bacc": 0.6297268907563025, "bacc_std": 0.04434286097035731}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.042573924200762114, "f1": 0.6753076721654884, "f1_std": 0.04292616919031657, "bacc": 0.6759453781512605, "bacc_std": 0.04309913139828005}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04466337266082989, "f1": 0.5735449735449736, "f1_std": 0.04580915918152806, "bacc": 0.5735294117647058, "bacc_std": 0.045244244000485656}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 166.81005372000556, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.042771067369826235, "f1": 0.6630211440312852, "f1_std": 0.0439442471228052, "bacc": 0.6622899159663866, "bacc_std": 0.04349341091218225}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.042791705654726946, "f1": 0.6368842324461508, "f1_std": 0.04288399296447123, "bacc": 0.6407563025210083, "bacc_std": 0.04300710420871968}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 2.782559402207126, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04363051991978027, "f1": 0.6017043592264831, "f1_std": 0.04387259439529192, "bacc": 0.601890756302521, "bacc_std": 0.0437373441877297}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04228844943072448, "f1": 0.6167554415729598, "f1_std": 0.04277697092162983, "bacc": 0.6165966386554622, "bacc_std": 0.04258562941119509}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 2.782559402207126, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.041882258406140786, "f1": 0.6118548118548119, "f1_std": 0.0429717609089427, "bacc": 0.6118697478991597, "bacc_std": 0.042255236629289795}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.040332318179076086, "f1": 0.6595915634415801, "f1_std": 0.0424633368396286, "bacc": 0.6591386554621849, "bacc_std": 0.04137376325970832}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04253742613591554, "f1": 0.6598746081504703, "f1_std": 0.04282208526455115, "bacc": 0.6612394957983193, "bacc_std": 0.042903817512608536}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 2.782559402207126, "split": "test", "acc": 0.7258064516129032, "acc_std": 0.04210208531568526, "f1": 0.7232142857142857, "f1_std": 0.04270139360369478, "bacc": 0.7232142857142857, "bacc_std": 0.0427867931628649}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.043461978192290064, "f1": 0.5557292684515667, "f1_std": 0.04361208176023482, "bacc": 0.5577731092436975, "bacc_std": 0.04372741964727911}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.6935483870967742, "acc_std": 0.04001950616998529, "f1": 0.6883597883597883, "f1_std": 0.04074135301334422, "bacc": 0.6875, "bacc_std": 0.04046851333781396}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.7016129032258065, "acc_std": 0.04231445080747709, "f1": 0.6982968369829683, "f1_std": 0.043009778671272175, "bacc": 0.6980042016806722, "bacc_std": 0.04292672226833426}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 2.782559402207126, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.041746983291536255, "f1": 0.6330637206549615, "f1_std": 0.042332014637706764, "bacc": 0.6328781512605042, "bacc_std": 0.04227132763675298}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 2.782559402207126, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.042278116395015865, "f1": 0.5860042735042735, "f1_std": 0.043956173157739496, "bacc": 0.5866596638655462, "bacc_std": 0.042937398800269116}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04215049100282695, "f1": 0.6342182890855457, "f1_std": 0.042280084421249094, "bacc": 0.634453781512605, "bacc_std": 0.04216524110577689}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 18, "C": 166.81005372000556, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.042151737694130186, "f1": 0.6569169960474308, "f1_std": 0.043010173114373954, "bacc": 0.6565126050420168, "bacc_std": 0.04282940979440804}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.04049085854103638, "f1": 0.6575739206573719, "f1_std": 0.042940680057706175, "bacc": 0.657563025210084, "bacc_std": 0.04152412663477981}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04181379366035269, "f1": 0.6405797101449275, "f1_std": 0.042489879438955366, "bacc": 0.6402310924369747, "bacc_std": 0.04231004209105743}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 21, "C": 2.782559402207126, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.04275188867678061, "f1": 0.6808131476470201, "f1_std": 0.043886094135266944, "bacc": 0.6801470588235294, "bacc_std": 0.04360951294335315}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.7096774193548387, "acc_std": 0.04097509459038438, "f1": 0.7059288537549406, "f1_std": 0.04176943048134497, "bacc": 0.7053571428571428, "bacc_std": 0.04165271582675369}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04424356717323184, "f1": 0.6405797101449275, "f1_std": 0.04507602143812087, "bacc": 0.6402310924369747, "bacc_std": 0.04489696961317429}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 24, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04447454813533052, "f1": 0.5662332519305657, "f1_std": 0.04597000746158411, "bacc": 0.5661764705882353, "bacc_std": 0.04542412101207522}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04402036646703183, "f1": 0.551522325244953, "f1_std": 0.044637297081396965, "bacc": 0.5514705882352942, "bacc_std": 0.04441755767603972}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 26, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04279964150802434, "f1": 0.6210470369386127, "f1_std": 0.044072046166900344, "bacc": 0.6207983193277311, "bacc_std": 0.0434198265700588}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 27, "C": 0.3593813663804626, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.04226573126709326, "f1": 0.6742776318448171, "f1_std": 0.04455907265991729, "bacc": 0.6738445378151261, "bacc_std": 0.043089142033795484}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 29, "C": 0.3593813663804626, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04302298347604477, "f1": 0.6704756842944459, "f1_std": 0.0443993019677239, "bacc": 0.6696428571428572, "bacc_std": 0.04374866310869115}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 30, "C": 2.782559402207126, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.041555486770692734, "f1": 0.610369206598586, "f1_std": 0.04193721524569742, "bacc": 0.6108193277310925, "bacc_std": 0.042009023057919856}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04325134957865026, "f1": 0.6555555555555556, "f1_std": 0.0439013024772795, "bacc": 0.654936974789916, "bacc_std": 0.04351859778558636}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 38, "C": 2.782559402207126, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.04225534342384175, "f1": 0.6667322189446083, "f1_std": 0.0426517264042761, "bacc": 0.6670168067226891, "bacc_std": 0.04262396261365504}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 39, "C": 0.3593813663804626, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04406466962614364, "f1": 0.5880946053680574, "f1_std": 0.04519405475742415, "bacc": 0.5882352941176471, "bacc_std": 0.04447599865181835}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 40, "C": 0.005994842503189409, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04157060553488416, "f1": 0.5268817204301075, "f1_std": 0.04554303134217468, "bacc": 0.5372899159663866, "bacc_std": 0.042213440244090114}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 41, "C": 2.782559402207126, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.040487646013427796, "f1": 0.635936582501468, "f1_std": 0.04068775039567635, "bacc": 0.6376050420168067, "bacc_std": 0.040869194337912966}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 42, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.042891275924927454, "f1": 0.607462787095036, "f1_std": 0.04534398809927716, "bacc": 0.608718487394958, "bacc_std": 0.043620873533566105}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 43, "C": 21.54434690031882, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04439891944481087, "f1": 0.6137071651090342, "f1_std": 0.04562851636810226, "bacc": 0.6134453781512605, "bacc_std": 0.0449645597537073}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 44, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.041008016144863144, "f1": 0.6210470369386127, "f1_std": 0.04258303046716506, "bacc": 0.6207983193277311, "bacc_std": 0.04182132640241592}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 45, "C": 21.54434690031882, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04149950477996466, "f1": 0.6555555555555556, "f1_std": 0.042498444006946065, "bacc": 0.654936974789916, "bacc_std": 0.04213936006995214}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04126817387306952, "f1": 0.6097756946769334, "f1_std": 0.042588139281187404, "bacc": 0.6102941176470589, "bacc_std": 0.041611988125333364}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04236277042897826, "f1": 0.6502820306204673, "f1_std": 0.04487361217390431, "bacc": 0.6502100840336134, "bacc_std": 0.043586390425544685}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | abide_dx  | train   |        100 | 166.79 |  1025.5 | 0.89335 |  0.082609 | 0.89124 | 0.084601 | 0.88967 |   0.085005 |
+| schaefer400_mae | patch  | logistic | abide_dx  | test    |        100 | 166.79 |  1025.5 | 0.63726 |  0.042193 | 0.6313  | 0.043764 | 0.63181 |   0.043492 |
+
+
+done! total time: 0:05:14

subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,adhd200_dx,93,0.046415888336127774,test,0.6615384615384615,0.052522806542497254,0.622093023255814,0.06329273867893967,0.6288610038610039,0.05557509362779113
+schaefer400_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,train,0.7287671232876712,0.022125641335420294,0.7150798344175044,0.024045764253263505,0.7123557428100384,0.023278138042852857
+schaefer400_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,test,0.5384615384615384,0.047526620382375324,0.4583333333333333,0.057438159657277596,0.49469111969111973,0.04792854114175709
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+schaefer400_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,test,0.5846153846153846,0.06153276127445583,0.5699583435432491,0.06373477055052656,0.5699806949806949,0.06259275887462239
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+schaefer400_mae,patch,logistic,adhd200_dx,98,0.005994842503189409,test,0.6307692307692307,0.05734780423771754,0.61,0.06131756434399683,0.6105212355212355,0.05861036608227733
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+schaefer400_mae,patch,logistic,adhd200_dx,100,0.000774263682681127,test,0.5846153846153846,0.05459501245182919,0.5501153550371699,0.060090438126177835,0.556949806949807,0.055722527565281815

subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:26:00
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: adhd200_dx (schaefer400)
+train (n=301):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 301
+}),
+    labels=['ADHD' 'Control'],
+    counts=[131 170]
+)
+
+validation (n=64):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 64
+}),
+    labels=['ADHD' 'Control'],
+    counts=[28 36]
+)
+
+test (n=65):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 65
+}),
+    labels=['ADHD' 'Control'],
+    counts=[28 37]
+)
+
+extracting features for all splits
+extract (train)  [  0/151]  eta: 0:02:40    time: 1.0640  data: 0.7436  max mem: 1546
+extract (train)  [ 20/151]  eta: 0:00:12    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [ 40/151]  eta: 0:00:08    time: 0.0470  data: 0.0015  max mem: 1715
+extract (train)  [ 60/151]  eta: 0:00:05    time: 0.0476  data: 0.0024  max mem: 1715
+extract (train)  [ 80/151]  eta: 0:00:04    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [100/151]  eta: 0:00:02    time: 0.0479  data: 0.0026  max mem: 1715
+extract (train)  [120/151]  eta: 0:00:01    time: 0.0477  data: 0.0024  max mem: 1715
+extract (train)  [140/151]  eta: 0:00:00    time: 0.0478  data: 0.0025  max mem: 1715
+extract (train)  [150/151]  eta: 0:00:00    time: 0.0468  data: 0.0025  max mem: 1715
+extract (train) Total time: 0:00:08 (0.0550 s / it)
+extract (validation)  [ 0/32]  eta: 0:00:22    time: 0.7064  data: 0.6601  max mem: 1715
+extract (validation)  [20/32]  eta: 0:00:00    time: 0.0472  data: 0.0020  max mem: 1715
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.0472  data: 0.0018  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.0703 s / it)
+extract (test)  [ 0/33]  eta: 0:00:24    time: 0.7566  data: 0.7103  max mem: 1715
+extract (test)  [20/33]  eta: 0:00:01    time: 0.0479  data: 0.0027  max mem: 1715
+extract (test)  [32/33]  eta: 0:00:00    time: 0.0458  data: 0.0013  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0710 s / it)
+feature extraction time: 0:00:12
+train features: (301, 768)
+validation features: (64, 768)
+test features: (65, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset    | trial   |          C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:-----------|:--------|-----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | train   | 0.64384 |  0.021933 | 0.61302 | 0.025522 | 0.61774 |   0.023064 |
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | test    | 0.53846 |  0.046342 | 0.45833 | 0.055796 | 0.49469 |   0.046574 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06059207673709171, "f1": 0.5745454545454545, "f1_std": 0.06239820520819125, "bacc": 0.5743243243243243, "bacc_std": 0.06166623154088144}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.058118540361297884, "f1": 0.6264367816091954, "f1_std": 0.05910284433004435, "bacc": 0.627895752895753, "bacc_std": 0.059338868336435154}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.057449903054285004, "f1": 0.6474358974358974, "f1_std": 0.06052701135257987, "bacc": 0.6462355212355213, "bacc_std": 0.05895463863325104}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 92, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.052501676501831435, "f1": 0.587737843551797, "f1_std": 0.06291501292215435, "bacc": 0.5974903474903475, "bacc_std": 0.05503295194160211}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.047526620382375324, "f1": 0.4583333333333333, "f1_std": 0.057438159657277596, "bacc": 0.49469111969111973, "bacc_std": 0.04792854114175709}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.05202093342869675, "f1": 0.4715447154471545, "f1_std": 0.05651732016690338, "bacc": 0.4806949806949807, "bacc_std": 0.05262893220464781}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06153276127445583, "f1": 0.5699583435432491, "f1_std": 0.06373477055052656, "bacc": 0.5699806949806949, "bacc_std": 0.06259275887462239}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 166.81005372000556, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06257492550355252, "f1": 0.5330459770114943, "f1_std": 0.06331593567497325, "bacc": 0.5337837837837838, "bacc_std": 0.06312432320817982}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05734780423771754, "f1": 0.61, "f1_std": 0.06131756434399683, "bacc": 0.6105212355212355, "bacc_std": 0.05861036608227733}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.05738502783930964, "f1": 0.49317738791423005, "f1_std": 0.058853323693430305, "bacc": 0.49372586872586877, "bacc_std": 0.05804813362167722}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.000774263682681127, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05459501245182919, "f1": 0.5501153550371699, "f1_std": 0.060090438126177835, "bacc": 0.556949806949807, "bacc_std": 0.055722527565281815}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset    | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:-----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adhd200_dx | train   |        100 | 3.8595 |  23.593 | 0.74778 |  0.087161 | 0.73345 | 0.095038 | 0.73224 |   0.093662 |
+| schaefer400_mae | patch  | logistic | adhd200_dx | test    |        100 | 3.8595 |  23.593 | 0.59492 |  0.054538 | 0.57059 | 0.057691 | 0.57482 |   0.054974 |
+
+
+done! total time: 0:04:10

subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,2.782559402207126,test,0.7560975609756098,0.060035761700230214,0.6440972222222222,0.09053692778531588,0.635483870967742,0.08596239504040243
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,10000.0,test,0.8048780487804879,0.05901050028060297,0.7354838709677419,0.07874640236557663,0.7354838709677419,0.08219236459116118
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,96,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,96,21.54434690031882,test,0.6585365853658537,0.06712726864927336,0.5651515151515152,0.07867197744140503,0.5709677419354839,0.08199488854909787
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,train,0.9295392953929539,0.012159793452898379,0.893324141611813,0.01973683752339161,0.8650258854466266,0.023135061546897782
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,test,0.7804878048780488,0.049814790824512616,0.6328358208955224,0.09609386243324446,0.6177419354838709,0.07902014462796138
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.3593813663804626,train,0.9322493224932249,0.011242790351939234,0.8959238241366472,0.019094167198839142,0.8627455008628482,0.022996095076540556
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.3593813663804626,test,0.7317073170731707,0.05494718680059268,0.5918552036199095,0.08578377426600162,0.5854838709677419,0.07820527831746242
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,166.81005372000556,test,0.7073170731707317,0.059665915025447436,0.5729166666666666,0.08599290279269649,0.5693548387096774,0.07919525489259191
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,21.54434690031882,test,0.7073170731707317,0.07098866697428849,0.6272727272727273,0.08610056418164184,0.6370967741935484,0.09055935693229979

subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt

@@ -0,0 +1,240 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 13f84fbf41ace5ac3ddf250dd8df21907e28e6e3, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-25 17:29:14
+config:
+output_root: experiments/subcortical/output
+name_prefix: eval_logistic
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: schaefer400_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 400, 1), (4, 1, 1), in_chans=1)
+    (patch_embed): Linear(in_features=4, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 400, 1))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: adni_ad_vs_cn (schaefer400)
+train (n=328):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 525
+}),
+    labels=[0 1],
+    counts=[251  77]
+)
+
+validation (n=41):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 66
+}),
+    labels=[0 1],
+    counts=[31 10]
+)
+
+test (n=41):
+ADNIDataset(
+    dataset=Dataset({
+    features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 66
+}),
+    labels=[0 1],
+    counts=[32  9]
+)
+
+extracting features for all splits
+extract (train)  [  0/164]  eta: 0:04:07    time: 1.5071  data: 1.0920  max mem: 1546
+extract (train)  [ 20/164]  eta: 0:00:17    time: 0.0499  data: 0.0052  max mem: 1715
+extract (train)  [ 40/164]  eta: 0:00:10    time: 0.0465  data: 0.0015  max mem: 1715
+extract (train)  [ 60/164]  eta: 0:00:07    time: 0.0476  data: 0.0028  max mem: 1715
+extract (train)  [ 80/164]  eta: 0:00:05    time: 0.0471  data: 0.0024  max mem: 1715
+extract (train)  [100/164]  eta: 0:00:03    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train)  [120/164]  eta: 0:00:02    time: 0.0475  data: 0.0026  max mem: 1715
+extract (train)  [140/164]  eta: 0:00:01    time: 0.0475  data: 0.0027  max mem: 1715
+extract (train)  [160/164]  eta: 0:00:00    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train)  [163/164]  eta: 0:00:00    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train) Total time: 0:00:09 (0.0575 s / it)
+extract (validation)  [ 0/21]  eta: 0:00:24    time: 1.1716  data: 1.1249  max mem: 1715
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.0457  data: 0.0017  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.1045 s / it)
+extract (test)  [ 0/21]  eta: 0:00:20    time: 0.9653  data: 0.9188  max mem: 1715
+extract (test)  [20/21]  eta: 0:00:00    time: 0.0452  data: 0.0015  max mem: 1715
+extract (test) Total time: 0:00:01 (0.0941 s / it)
+feature extraction time: 0:00:13
+train features: (328, 768)
+validation features: (41, 768)
+test features: (41, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model           | repr   | clf      | dataset       | trial   |      C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:--------------|:--------|-------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | train   | 1       |  0        | 1       |  0       | 1       |   0        |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | test    | 0.68293 |  0.071747 | 0.58392 |  0.08076 | 0.59722 |   0.087183 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.051485950446668344, "f1": 0.5512437810945273, "f1_std": 0.08579046625065631, "bacc": 0.5516129032258065, "bacc_std": 0.06838276393760417}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 166.81005372000556, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06336724307983084, "f1": 0.7119437939110069, "f1_std": 0.08211494017172537, "bacc": 0.7193548387096774, "bacc_std": 0.0872940067291109}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.04186886414332246, "f1": 0.4972129319955407, "f1_std": 0.07313622360407675, "bacc": 0.5177419354838709, "bacc_std": 0.052664921971303603}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 2.782559402207126, "split": "test", "acc": 0.8536585365853658, "acc_std": 0.0481305729484887, "f1": 0.7864583333333333, "f1_std": 0.07833513172194406, "bacc": 0.7677419354838709, "bacc_std": 0.07888274230995794}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.04547063692241055, "f1": 0.569327731092437, "f1_std": 0.08829727613926883, "bacc": 0.567741935483871, "bacc_std": 0.06762816098467099}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 2.782559402207126, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.0568643249978912, "f1": 0.5176470588235295, "f1_std": 0.08026127227020309, "bacc": 0.5193548387096775, "bacc_std": 0.07044377031558373}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 2.782559402207126, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06329314817254636, "f1": 0.6693548387096775, "f1_std": 0.08505386694705479, "bacc": 0.6693548387096775, "bacc_std": 0.08562181657419526}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 1291.5496650148827, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.07726841586587865, "f1": 0.5876436781609196, "f1_std": 0.08402977121152697, "bacc": 0.6048387096774194, "bacc_std": 0.09179598579068739}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 166.81005372000556, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.07075861557134136, "f1": 0.6072218128224024, "f1_std": 0.08214654152924837, "bacc": 0.6209677419354839, "bacc_std": 0.08997042779275441}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 2.782559402207126, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06251616804557773, "f1": 0.6440972222222222, "f1_std": 0.09403576542093672, "bacc": 0.635483870967742, "bacc_std": 0.08861530763439462}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 11, "C": 1291.5496650148827, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.061205342835994506, "f1": 0.6232247284878863, "f1_std": 0.08648920562035595, "bacc": 0.6193548387096774, "bacc_std": 0.08503362963307164}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 1291.5496650148827, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06970661727959514, "f1": 0.6893939393939394, "f1_std": 0.0845545481897141, "bacc": 0.7032258064516128, "bacc_std": 0.08967266238253502}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 13, "C": 21.54434690031882, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.07220441601349664, "f1": 0.5199063231850116, "f1_std": 0.08369657266365461, "bacc": 0.5209677419354839, "bacc_std": 0.08655333802101828}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 14, "C": 21.54434690031882, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06422921475897422, "f1": 0.6693548387096775, "f1_std": 0.08491960929000979, "bacc": 0.6693548387096775, "bacc_std": 0.08721277215793213}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 15, "C": 1291.5496650148827, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05651995799182779, "f1": 0.7602339181286549, "f1_std": 0.0811576834814191, "bacc": 0.7516129032258064, "bacc_std": 0.08290801442176142}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 16, "C": 21.54434690031882, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.041528757694463354, "f1": 0.5886287625418061, "f1_std": 0.09268693206541247, "bacc": 0.5838709677419355, "bacc_std": 0.06614739406760846}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06407988767748483, "f1": 0.6232247284878863, "f1_std": 0.09120754445545406, "bacc": 0.6193548387096774, "bacc_std": 0.08728292287974092}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 18, "C": 0.3593813663804626, "split": "test", "acc": 0.926829268292683, "acc_std": 0.04186408989343481, "f1": 0.8972431077694235, "f1_std": 0.06291807077613629, "bacc": 0.8838709677419355, "bacc_std": 0.07014655803800043}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 94, "C": 2.782559402207126, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.060035761700230214, "f1": 0.6440972222222222, "f1_std": 0.09053692778531588, "bacc": 0.635483870967742, "bacc_std": 0.08596239504040243}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 95, "C": 10000.0, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05901050028060297, "f1": 0.7354838709677419, "f1_std": 0.07874640236557663, "bacc": 0.7354838709677419, "bacc_std": 0.08219236459116118}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 96, "C": 21.54434690031882, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.06712726864927336, "f1": 0.5651515151515152, "f1_std": 0.07867197744140503, "bacc": 0.5709677419354839, "bacc_std": 0.08199488854909787}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.049814790824512616, "f1": 0.6328358208955224, "f1_std": 0.09609386243324446, "bacc": 0.6177419354838709, "bacc_std": 0.07902014462796138}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.05494718680059268, "f1": 0.5918552036199095, "f1_std": 0.08578377426600162, "bacc": 0.5854838709677419, "bacc_std": 0.07820527831746242}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 166.81005372000556, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.059665915025447436, "f1": 0.5729166666666666, "f1_std": 0.08599290279269649, "bacc": 0.5693548387096774, "bacc_std": 0.07919525489259191}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 21.54434690031882, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.07098866697428849, "f1": 0.6272727272727273, "f1_std": 0.08610056418164184, "bacc": 0.6370967741935484, "bacc_std": 0.09055935693229979}
+eval results (random splits):
+
+| model           | repr   | clf      | dataset       | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:----------------|:-------|:---------|:--------------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | train   |        100 | 855.54 |  2550.2 | 0.97051 |  0.047999 | 0.95152 | 0.08161  | 0.94014 |   0.09696  |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 855.54 |  2550.2 | 0.74683 |  0.056729 | 0.63289 | 0.078035 | 0.63206 |   0.075258 |
+
+
+done! total time: 0:04:17

subcortical/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/subcortical/output
+name_prefix: eval_probe
+remote_root: null
+notes: subcortical ablation schaefer400_lr3e-4_4; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: true
+  norm: true
+lr_scale_grid:
+- 0.02
+- 0.023
+- 0.028
+- 0.033
+- 0.038
+- 0.045
+- 0.053
+- 0.062
+- 0.074
+- 0.087
+- 0.1
+- 0.12
+- 0.14
+- 0.17
+- 0.2
+- 0.23
+- 0.27
+- 0.32
+- 0.38
+- 0.44
+- 0.52
+- 0.61
+- 0.72
+- 0.85
+- 1
+- 1.2
+- 1.4
+- 1.6
+- 1.9
+- 2.3
+- 2.7
+- 3.1
+- 3.7
+- 4.3
+- 5.1
+- 6
+- 7.1
+- 8.3
+- 9.8
+- 12
+- 14
+- 16
+- 19
+- 22
+- 26
+- 31
+- 36
+- 43
+- 50
+wd_scale_grid:
+- 1.0
+num_workers: 8
+prefetch_factor: null
+balanced_sampling: false
+epochs: 20
+steps_per_epoch: 200
+batch_size: 64
+accum_iter: 2
+lr: 0.0003
+warmup_epochs: 5
+no_decay: false
+weight_decay: 0.05
+clip_grad: 1.0
+metrics:
+- acc
+- f1
+cv_metric: acc
+early_stopping: true
+amp: true
+device: cuda
+seed: 4466
+debug: false
+wandb: false
+wandb_entity: null
+wandb_project: fMRI-fm-eval
+name: subcortical/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/subcortical/output/subcortical/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn
+remote_dir: null