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input_space_v3/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic/log.txt

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+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:52:05
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/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: 0:03:59    time: 1.0146  data: 0.6546  max mem: 1814
+extract (train)  [ 20/236]  eta: 0:00:22    time: 0.0576  data: 0.0032  max mem: 2020
+extract (train)  [ 40/236]  eta: 0:00:15    time: 0.0567  data: 0.0017  max mem: 2020
+extract (train)  [ 60/236]  eta: 0:00:12    time: 0.0574  data: 0.0026  max mem: 2020
+extract (train)  [ 80/236]  eta: 0:00:10    time: 0.0574  data: 0.0027  max mem: 2020
+extract (train)  [100/236]  eta: 0:00:09    time: 0.0574  data: 0.0025  max mem: 2020
+extract (train)  [120/236]  eta: 0:00:07    time: 0.0573  data: 0.0025  max mem: 2020
+extract (train)  [140/236]  eta: 0:00:06    time: 0.0575  data: 0.0026  max mem: 2020
+extract (train)  [160/236]  eta: 0:00:04    time: 0.0574  data: 0.0025  max mem: 2020
+extract (train)  [180/236]  eta: 0:00:03    time: 0.0577  data: 0.0026  max mem: 2020
+extract (train)  [200/236]  eta: 0:00:02    time: 0.0576  data: 0.0026  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: 0.0570  data: 0.0026  max mem: 2020
+extract (train) Total time: 0:00:14 (0.0621 s / it)
+extract (validation)  [ 0/29]  eta: 0:00:20    time: 0.7101  data: 0.6541  max mem: 2020
+extract (validation)  [20/29]  eta: 0:00:00    time: 0.0572  data: 0.0024  max mem: 2020
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.0568  data: 0.0016  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0839 s / it)
+extract (test)  [ 0/28]  eta: 0:00:19    time: 0.7084  data: 0.6525  max mem: 2020
+extract (test)  [20/28]  eta: 0:00:00    time: 0.0575  data: 0.0027  max mem: 2020
+extract (test)  [27/28]  eta: 0:00:00    time: 0.0554  data: 0.0017  max mem: 2020
+extract (test) Total time: 0:00:02 (0.0828 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  |         | 0.35938 | train   | 0.8828  |  0.014165 | 0.87889 | 0.014726 | 0.87583 |   0.014949 |
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 0.35938 | test    | 0.72727 |  0.062054 | 0.72138 | 0.063001 | 0.72727 |   0.063141 |
+
+
+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": 0.06244850837567788, "f1": 0.6303763440860215, "f1_std": 0.06358873808773605, "bacc": 0.6324728260869565, "bacc_std": 0.0641400017044252}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06361299311117176, "f1": 0.637278722665741, "f1_std": 0.06815489276745053, "bacc": 0.6358695652173914, "bacc_std": 0.06627579931269235}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 2.782559402207126, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.0642203232423769, "f1": 0.6842283012495778, "f1_std": 0.06547880687973744, "bacc": 0.6854619565217391, "bacc_std": 0.0654860101384335}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.062146763111921265, "f1": 0.7043010752688172, "f1_std": 0.06309290490646235, "bacc": 0.7072010869565217, "bacc_std": 0.06327745531108894}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.058941349192465055, "f1": 0.7066666666666667, "f1_std": 0.05906387341083331, "bacc": 0.7133152173913043, "bacc_std": 0.05889881550225374}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 166.81005372000556, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06017191898810229, "f1": 0.7348484848484849, "f1_std": 0.06316551665263691, "bacc": 0.7323369565217391, "bacc_std": 0.06265940117247376}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.0629248535165768, "f1": 0.6427350427350427, "f1_std": 0.06571882701295431, "bacc": 0.6419836956521738, "bacc_std": 0.06481168959686427}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06442068350157362, "f1": 0.6470786896318811, "f1_std": 0.06562773402621483, "bacc": 0.6480978260869565, "bacc_std": 0.06543542972097596}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.059302836747334386, "f1": 0.6857142857142857, "f1_std": 0.06604076481188625, "bacc": 0.6827445652173914, "bacc_std": 0.06246929722005659}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.059926169451450566, "f1": 0.7384510869565217, "f1_std": 0.06187901454954507, "bacc": 0.7384510869565217, "bacc_std": 0.061602345840881735}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 21.54434690031882, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.055116717627081806, "f1": 0.7518222839291913, "f1_std": 0.059714805781264736, "bacc": 0.7479619565217391, "bacc_std": 0.05890068511769623}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 2.782559402207126, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.061728823116156906, "f1": 0.6533613445378151, "f1_std": 0.06713896539286879, "bacc": 0.6514945652173914, "bacc_std": 0.06466918289698945}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06020241339441798, "f1": 0.7213779128672746, "f1_std": 0.061264007542553726, "bacc": 0.7228260869565217, "bacc_std": 0.06130641227396209}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05903325859068522, "f1": 0.7555555555555555, "f1_std": 0.06108047126927827, "bacc": 0.7540760869565217, "bacc_std": 0.06096065552981887}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05957951836213905, "f1": 0.6918767507002801, "f1_std": 0.06460975720189044, "bacc": 0.6888586956521738, "bacc_std": 0.06247710621578733}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05979031127460411, "f1": 0.6694945210321668, "f1_std": 0.06545229702954697, "bacc": 0.6671195652173914, "bacc_std": 0.06251696855842984}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.051281030920130755, "f1": 0.6696696696696697, "f1_std": 0.06484627116362088, "bacc": 0.670516304347826, "bacc_std": 0.05632382514955627}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 21.54434690031882, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06834657427315605, "f1": 0.6470786896318811, "f1_std": 0.06937301630630646, "bacc": 0.6480978260869565, "bacc_std": 0.0693052285966515}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05379226648770523, "f1": 0.7931623931623932, "f1_std": 0.05674779722032561, "bacc": 0.7914402173913043, "bacc_std": 0.05687537084869956}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05704229545761968, "f1": 0.7758152173913043, "f1_std": 0.059345493946603566, "bacc": 0.7758152173913043, "bacc_std": 0.05945572371742091}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.3593813663804626, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.058907687974315726, "f1": 0.6464285714285715, "f1_std": 0.06639514832183285, "bacc": 0.6453804347826086, "bacc_std": 0.062254973307718786}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 2.782559402207126, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06021888449304569, "f1": 0.717948717948718, "f1_std": 0.0625694651430472, "bacc": 0.7167119565217391, "bacc_std": 0.062037027632182704}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06353437021287855, "f1": 0.6545454545454545, "f1_std": 0.06392942908424674, "bacc": 0.6725543478260869, "bacc_std": 0.06246582092074683}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06211278464639086, "f1": 0.6803418803418804, "f1_std": 0.06508362744696909, "bacc": 0.6793478260869565, "bacc_std": 0.06446216184070391}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 2.782559402207126, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.058024286736156186, "f1": 0.7213779128672746, "f1_std": 0.05931559505163625, "bacc": 0.7228260869565217, "bacc_std": 0.0594840199009972}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.054225619878795935, "f1": 0.7518222839291913, "f1_std": 0.05851623039717033, "bacc": 0.7479619565217391, "bacc_std": 0.05751172704125517}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 166.81005372000556, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.059796890380466726, "f1": 0.7348484848484849, "f1_std": 0.06316812856383607, "bacc": 0.7323369565217391, "bacc_std": 0.06213705276775625}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06293111542993815, "f1": 0.7010869565217391, "f1_std": 0.06554575080619554, "bacc": 0.7010869565217391, "bacc_std": 0.06522834796719641}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05945225461252414, "f1": 0.7384510869565217, "f1_std": 0.061182363246403866, "bacc": 0.7384510869565217, "bacc_std": 0.06086762836289121}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 2.782559402207126, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05900553272839746, "f1": 0.7555555555555555, "f1_std": 0.06178726967928884, "bacc": 0.7540760869565217, "bacc_std": 0.061731635565342884}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05576539997803965, "f1": 0.7020585048754062, "f1_std": 0.06433386053714032, "bacc": 0.6983695652173914, "bacc_std": 0.059721465459363056}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 166.81005372000556, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05958645361611527, "f1": 0.7083775185577943, "f1_std": 0.06588282213151217, "bacc": 0.7044836956521738, "bacc_std": 0.06327190386127284}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05491967387478187, "f1": 0.7250000000000001, "f1_std": 0.06147205840955743, "bacc": 0.7201086956521738, "bacc_std": 0.05870890557143403}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06202101483361698, "f1": 0.7043010752688172, "f1_std": 0.06229495341623531, "bacc": 0.7072010869565217, "bacc_std": 0.06190729193721373}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 88, "C": 2.782559402207126, "split": "test", "acc": 0.8, "acc_std": 0.052101502968438886, "f1": 0.7861435136090491, "f1_std": 0.0584260329978792, "bacc": 0.7792119565217391, "bacc_std": 0.05686486139613883}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 89, "C": 2.782559402207126, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06467581202549394, "f1": 0.6503847440615591, "f1_std": 0.06487558302088431, "bacc": 0.6542119565217391, "bacc_std": 0.06472700032768498}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 90, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.0640780928513878, "f1": 0.7043010752688172, "f1_std": 0.06465703683082466, "bacc": 0.7072010869565217, "bacc_std": 0.06462703412653957}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 91, "C": 0.3593813663804626, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06349557412831465, "f1": 0.637278722665741, "f1_std": 0.06779352809940102, "bacc": 0.6358695652173914, "bacc_std": 0.06603045241603711}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 94, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.0550744769973254, "f1": 0.7555555555555555, "f1_std": 0.05712765454081113, "bacc": 0.7540760869565217, "bacc_std": 0.05666945775899473}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.052975353695596555, "f1": 0.6783625730994152, "f1_std": 0.06330119779359644, "bacc": 0.6766304347826086, "bacc_std": 0.05724947768529123}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 96, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.061602565149982615, "f1": 0.696969696969697, "f1_std": 0.06549619848306926, "bacc": 0.6949728260869565, "bacc_std": 0.06445513043580543}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06142259524484107, "f1": 0.6533613445378151, "f1_std": 0.06691086394105263, "bacc": 0.6514945652173914, "bacc_std": 0.06445094374280624}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 10000.0, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06394223632918121, "f1": 0.7010869565217391, "f1_std": 0.06647000723424974, "bacc": 0.7010869565217391, "bacc_std": 0.06623997989281383}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05664777646573956, "f1": 0.7585275244849713, "f1_std": 0.05829144325285297, "bacc": 0.7601902173913043, "bacc_std": 0.05837175730642086}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05823473729741703, "f1": 0.717948717948718, "f1_std": 0.0610459969010364, "bacc": 0.7167119565217391, "bacc_std": 0.06063705678348756}
+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 | 108.92 |  999.64 | 0.90507 |  0.06979  | 0.90114 | 0.073704 | 0.89889 |   0.075225 |
+| schaefer400_mae | patch  | logistic | aabc_sex  | test    |        100 | 108.92 |  999.64 | 0.71182 |  0.051059 | 0.69937 | 0.053661 | 0.69909 |   0.053497 |
+
+
+done! total time: 0:04:45

input_space_v3/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic/eval_table.csv

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input_space_v3/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.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:50:39
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/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: 0:06:41    time: 1.3879  data: 1.0206  max mem: 1546
+extract (train)  [ 20/289]  eta: 0:00:29    time: 0.0469  data: 0.0019  max mem: 1715
+extract (train)  [ 40/289]  eta: 0:00:19    time: 0.0469  data: 0.0018  max mem: 1715
+extract (train)  [ 60/289]  eta: 0:00:15    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [ 80/289]  eta: 0:00:13    time: 0.0476  data: 0.0026  max mem: 1715
+extract (train)  [100/289]  eta: 0:00:11    time: 0.0478  data: 0.0028  max mem: 1715
+extract (train)  [120/289]  eta: 0:00:09    time: 0.0479  data: 0.0027  max mem: 1715
+extract (train)  [140/289]  eta: 0:00:08    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [160/289]  eta: 0:00:07    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [180/289]  eta: 0:00:05    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train)  [200/289]  eta: 0:00:04    time: 0.0479  data: 0.0027  max mem: 1715
+extract (train)  [220/289]  eta: 0:00:03    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [240/289]  eta: 0:00:02    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [260/289]  eta: 0:00:01    time: 0.0478  data: 0.0026  max mem: 1715
+extract (train)  [280/289]  eta: 0:00:00    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train)  [288/289]  eta: 0:00:00    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train) Total time: 0:00:15 (0.0528 s / it)
+extract (validation)  [ 0/62]  eta: 0:01:00    time: 0.9747  data: 0.9269  max mem: 1715
+extract (validation)  [20/62]  eta: 0:00:03    time: 0.0485  data: 0.0033  max mem: 1715
+extract (validation)  [40/62]  eta: 0:00:01    time: 0.0472  data: 0.0017  max mem: 1715
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.0478  data: 0.0027  max mem: 1715
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.0478  data: 0.0027  max mem: 1715
+extract (validation) Total time: 0:00:04 (0.0647 s / it)
+extract (test)  [ 0/62]  eta: 0:01:00    time: 0.9779  data: 0.9298  max mem: 1715
+extract (test)  [20/62]  eta: 0:00:03    time: 0.0483  data: 0.0032  max mem: 1715
+extract (test)  [40/62]  eta: 0:00:01    time: 0.0469  data: 0.0015  max mem: 1715
+extract (test)  [60/62]  eta: 0:00:00    time: 0.0479  data: 0.0027  max mem: 1715
+extract (test)  [61/62]  eta: 0:00:00    time: 0.0479  data: 0.0027  max mem: 1715
+extract (test) Total time: 0:00:04 (0.0647 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  |         | 0.046416 | train   | 0.7792  |  0.015248 | 0.77434 | 0.015786 | 0.77232 |   0.01575  |
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.54032 |  0.044917 | 0.5335  | 0.045268 | 0.53404 |   0.044899 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.000774263682681127, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04133967249957996, "f1": 0.6049081418208935, "f1_std": 0.043887789700325065, "bacc": 0.6071428571428572, "bacc_std": 0.04205176439681176}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04252908351762491, "f1": 0.6317074780542539, "f1_std": 0.04312279450579859, "bacc": 0.6313025210084033, "bacc_std": 0.042765057706642885}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.042365426286452984, "f1": 0.6137071651090342, "f1_std": 0.04362933346690014, "bacc": 0.6134453781512605, "bacc_std": 0.04296278547123904}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04252233299490828, "f1": 0.6539994685091681, "f1_std": 0.04387117898837935, "bacc": 0.6533613445378151, "bacc_std": 0.04324449144881075}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04528934609736776, "f1": 0.5603991596638656, "f1_std": 0.04567713452707315, "bacc": 0.5603991596638656, "bacc_std": 0.04560866240644633}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04121993433367434, "f1": 0.6598746081504703, "f1_std": 0.04127904284510793, "bacc": 0.6612394957983193, "bacc_std": 0.041150398126913186}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04486973572669157, "f1": 0.610369206598586, "f1_std": 0.045187247184266845, "bacc": 0.6108193277310925, "bacc_std": 0.045230791702583664}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 10000.0, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.042099119326595436, "f1": 0.6266038229903116, "f1_std": 0.042055018050377424, "bacc": 0.6271008403361344, "bacc_std": 0.041972148010330466}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04429548815118204, "f1": 0.5860042735042735, "f1_std": 0.04556953896910609, "bacc": 0.5866596638655462, "bacc_std": 0.04462163077925134}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.040853087592847705, "f1": 0.6575739206573719, "f1_std": 0.04330316985383098, "bacc": 0.657563025210084, "bacc_std": 0.04182126372895747}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.7419354838709677, "acc_std": 0.037647424400559425, "f1": 0.7350427350427351, "f1_std": 0.03938739660671578, "bacc": 0.7331932773109244, "bacc_std": 0.0388418971173392}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.532258064516129, "acc_std": 0.044314207196081874, "f1": 0.5311603650586701, "f1_std": 0.04433700959424755, "bacc": 0.532563025210084, "bacc_std": 0.044309793604802444}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04287385606426062, "f1": 0.6209430894308943, "f1_std": 0.04300806182721684, "bacc": 0.6260504201680672, "bacc_std": 0.04295876889503527}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 21.54434690031882, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.0455536498604529, "f1": 0.6041951664386684, "f1_std": 0.04564850746258425, "bacc": 0.6066176470588236, "bacc_std": 0.04586992526606758}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04498829481248735, "f1": 0.5718845677806006, "f1_std": 0.0450676730841927, "bacc": 0.5740546218487395, "bacc_std": 0.04525091512768182}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04522588698165948, "f1": 0.5498646953996436, "f1_std": 0.0460844399416263, "bacc": 0.5498949579831933, "bacc_std": 0.04566244158997847}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.0401830456686543, "f1": 0.6555555555555556, "f1_std": 0.04138230476570598, "bacc": 0.654936974789916, "bacc_std": 0.04100500198722404}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04194260485445684, "f1": 0.6119727891156463, "f1_std": 0.04538419753150055, "bacc": 0.6144957983193278, "bacc_std": 0.04297710979225176}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04288134295193053, "f1": 0.6448884448884449, "f1_std": 0.04430517758422626, "bacc": 0.6444327731092437, "bacc_std": 0.0436288899297231}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 20, "C": 2.782559402207126, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04480893360365979, "f1": 0.5616653574234092, "f1_std": 0.045108945326506195, "bacc": 0.5619747899159664, "bacc_std": 0.04509242830040524}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.042440642273006014, "f1": 0.5915678524374176, "f1_std": 0.04321383215684363, "bacc": 0.5913865546218487, "bacc_std": 0.042884677303322925}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.7258064516129032, "acc_std": 0.03818508447520801, "f1": 0.718482905982906, "f1_std": 0.040042686883643236, "bacc": 0.7169117647058824, "bacc_std": 0.039320602444917356}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 23, "C": 2.782559402207126, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04504552847726448, "f1": 0.6342182890855457, "f1_std": 0.04541856467004457, "bacc": 0.634453781512605, "bacc_std": 0.04541864822422763}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 24, "C": 2.782559402207126, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.046194908409425986, "f1": 0.5454307410316837, "f1_std": 0.04633763983231231, "bacc": 0.5456932773109244, "bacc_std": 0.04621544894280973}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.043823669949774914, "f1": 0.6045708211533352, "f1_std": 0.04520157005350238, "bacc": 0.6045168067226891, "bacc_std": 0.044451211222476125}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04170889154263009, "f1": 0.626380984265149, "f1_std": 0.04327792216850427, "bacc": 0.6265756302521008, "bacc_std": 0.04222169418827445}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 27, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.0431639995051181, "f1": 0.6356837606837606, "f1_std": 0.04498534411650205, "bacc": 0.6355042016806722, "bacc_std": 0.04403393482216018}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04112309887625523, "f1": 0.650475254015077, "f1_std": 0.04158982956270099, "bacc": 0.6507352941176471, "bacc_std": 0.04158768392069264}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 29, "C": 0.3593813663804626, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.04171768503329183, "f1": 0.6630211440312852, "f1_std": 0.042816654863171204, "bacc": 0.6622899159663866, "bacc_std": 0.042297022847090304}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 30, "C": 21.54434690031882, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04420193880793979, "f1": 0.5880946053680574, "f1_std": 0.04545523307697139, "bacc": 0.5882352941176471, "bacc_std": 0.044781321650508826}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.043583913227194605, "f1": 0.6301451580831179, "f1_std": 0.04440221995355648, "bacc": 0.6297268907563025, "bacc_std": 0.043882719389845795}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 32, "C": 2.782559402207126, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04581023098902068, "f1": 0.555142173797502, "f1_std": 0.046835008760859415, "bacc": 0.555672268907563, "bacc_std": 0.04611651971672173}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 33, "C": 0.3593813663804626, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.043309605219470526, "f1": 0.6342182890855457, "f1_std": 0.043578533277715374, "bacc": 0.634453781512605, "bacc_std": 0.043504059497335275}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 34, "C": 2.782559402207126, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.042648318047234464, "f1": 0.5802083333333333, "f1_std": 0.04260672118224077, "bacc": 0.5829831932773109, "bacc_std": 0.04279532141060965}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.041985859940741714, "f1": 0.6493719997369632, "f1_std": 0.04269903275074257, "bacc": 0.6491596638655461, "bacc_std": 0.04258201128636039}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 37, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04127804757057065, "f1": 0.58994708994709, "f1_std": 0.04261164708531172, "bacc": 0.5898109243697479, "bacc_std": 0.04199107472299986}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 38, "C": 0.046415888336127774, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04091290144541967, "f1": 0.6669352672575879, "f1_std": 0.0429983673911919, "bacc": 0.6664915966386555, "bacc_std": 0.041853832879946144}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 39, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.045682530582447305, "f1": 0.5662332519305657, "f1_std": 0.046353188970114985, "bacc": 0.5661764705882353, "bacc_std": 0.045982212757122845}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.043430123167394634, "f1": 0.5503626107977437, "f1_std": 0.045513377434316285, "bacc": 0.5525210084033614, "bacc_std": 0.044028400263822084}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 41, "C": 0.000774263682681127, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.03941366241037967, "f1": 0.6025641025641026, "f1_std": 0.0441456601909604, "bacc": 0.6097689075630253, "bacc_std": 0.040429503867679656}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 42, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.044040103283148946, "f1": 0.5854473942969518, "f1_std": 0.04429608683218574, "bacc": 0.585609243697479, "bacc_std": 0.04424947161999113}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 43, "C": 0.3593813663804626, "split": "test", "acc": 0.7096774193548387, "acc_std": 0.039349880703025676, "f1": 0.7059288537549406, "f1_std": 0.03985218559380381, "bacc": 0.7053571428571428, "bacc_std": 0.03956633935830353}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 44, "C": 0.000774263682681127, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.041444002690389836, "f1": 0.5978378378378378, "f1_std": 0.04437008808433499, "bacc": 0.5997899159663866, "bacc_std": 0.04242015603722995}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04485707154781722, "f1": 0.6153389215233318, "f1_std": 0.045382872770692195, "bacc": 0.6150210084033614, "bacc_std": 0.04518298786195225}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.0381368196566989, "f1": 0.6625850340136055, "f1_std": 0.04125200507247206, "bacc": 0.6633403361344539, "bacc_std": 0.03923856418469634}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04210243133413529, "f1": 0.6493719997369632, "f1_std": 0.04278365793001629, "bacc": 0.6491596638655461, "bacc_std": 0.04276614804426379}
+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 | 115.52 |  1006.9 | 0.82681 |  0.090262 | 0.8233  | 0.093359 | 0.82244 |   0.093318 |
+| schaefer400_mae | patch  | logistic | abide_dx  | test    |        100 | 115.52 |  1006.9 | 0.61782 |  0.046826 | 0.61088 | 0.046757 | 0.61144 |   0.046342 |
+
+
+done! total time: 0:05:12

input_space_v3/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,adhd200_dx,89,0.3593813663804626,train,0.8794520547945206,0.01657120654841575,0.8764387271496277,0.01712851379847758,0.8738322037003114,0.017351910640114467
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+schaefer400_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,train,0.7178082191780822,0.024022080291421284,0.7020266467991345,0.02603471857766384,0.6997771264578372,0.025009427760308536
+schaefer400_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,test,0.5692307692307692,0.05182783515680951,0.5289855072463768,0.058665718820653065,0.5390926640926641,0.05303647213713858
+schaefer400_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,train,0.7068493150684931,0.022176660500854567,0.6904548660923048,0.02403513299175073,0.6886334493496977,0.023089179439022424
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+schaefer400_mae,patch,logistic,adhd200_dx,92,0.005994842503189409,train,0.6931506849315069,0.022165186838822834,0.6755555555555555,0.02422111901547606,0.674345118153508,0.023110159115240773
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+schaefer400_mae,patch,logistic,adhd200_dx,93,0.046415888336127774,train,0.7780821917808219,0.020808286814394932,0.7690973984895464,0.02196535547818671,0.7653721682847896,0.02162322541347301
+schaefer400_mae,patch,logistic,adhd200_dx,93,0.046415888336127774,test,0.6461538461538462,0.05525368284065436,0.6167649320687003,0.06347365100146068,0.6196911196911197,0.05780424059210859
+schaefer400_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,train,0.7123287671232876,0.022287201584665385,0.6970379680793037,0.024409304367027516,0.6949227575257984,0.02348279164005827
+schaefer400_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,test,0.5692307692307692,0.046149907524252326,0.49444444444444446,0.057535076399235274,0.5260617760617761,0.04717678930350226
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+schaefer400_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,train,0.7123287671232876,0.022635591899523445,0.6970379680793037,0.02436778403283193,0.6949227575257984,0.023534954883900255
+schaefer400_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,test,0.5846153846153846,0.05481494203064056,0.5411764705882354,0.06447258756680768,0.5526061776061776,0.057063136134076
+schaefer400_mae,patch,logistic,adhd200_dx,98,0.046415888336127774,train,0.7863013698630137,0.020922252131769674,0.7783814920911696,0.022197402582745024,0.7748061305489405,0.022088980129311694
+schaefer400_mae,patch,logistic,adhd200_dx,98,0.046415888336127774,test,0.5692307692307692,0.05896363336134908,0.5376016260162602,0.06380976081499039,0.5434362934362934,0.05983190110664984
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+schaefer400_mae,patch,logistic,adhd200_dx,99,0.000774263682681127,test,0.49230769230769234,0.055405628492410874,0.4501409894898744,0.0595222075267008,0.46283783783783783,0.05562425448299605
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+schaefer400_mae,patch,logistic,adhd200_dx,100,0.000774263682681127,test,0.5846153846153846,0.05613930264774535,0.5501153550371699,0.06164385674589564,0.556949806949807,0.05728036071317441

input_space_v3/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.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:51:06
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/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: 0:02:43    time: 1.0807  data: 0.7212  max mem: 1546
+extract (train)  [ 20/151]  eta: 0:00:12    time: 0.0481  data: 0.0033  max mem: 1715
+extract (train)  [ 40/151]  eta: 0:00:08    time: 0.0461  data: 0.0012  max mem: 1715
+extract (train)  [ 60/151]  eta: 0:00:05    time: 0.0469  data: 0.0022  max mem: 1715
+extract (train)  [ 80/151]  eta: 0:00:04    time: 0.0467  data: 0.0020  max mem: 1715
+extract (train)  [100/151]  eta: 0:00:02    time: 0.0470  data: 0.0022  max mem: 1715
+extract (train)  [120/151]  eta: 0:00:01    time: 0.0469  data: 0.0021  max mem: 1715
+extract (train)  [140/151]  eta: 0:00:00    time: 0.0469  data: 0.0022  max mem: 1715
+extract (train)  [150/151]  eta: 0:00:00    time: 0.0460  data: 0.0021  max mem: 1715
+extract (train) Total time: 0:00:08 (0.0547 s / it)
+extract (validation)  [ 0/32]  eta: 0:00:23    time: 0.7248  data: 0.6777  max mem: 1715
+extract (validation)  [20/32]  eta: 0:00:00    time: 0.0473  data: 0.0026  max mem: 1715
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.0463  data: 0.0013  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.0712 s / it)
+extract (test)  [ 0/33]  eta: 0:00:24    time: 0.7336  data: 0.6857  max mem: 1715
+extract (test)  [20/33]  eta: 0:00:01    time: 0.0475  data: 0.0028  max mem: 1715
+extract (test)  [32/33]  eta: 0:00:00    time: 0.0453  data: 0.0015  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0701 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   | 0.6411  |  0.022894 | 0.61197 | 0.02616  | 0.61603 |   0.02392  |
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | test    | 0.58462 |  0.045952 | 0.50549 | 0.060102 | 0.53958 |   0.047686 |
+
+
+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": 0.059479270915496714, "f1": 0.5699583435432491, "f1_std": 0.06171399111953716, "bacc": 0.5699806949806949, "bacc_std": 0.06046929045537892}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05525368284065436, "f1": 0.6167649320687003, "f1_std": 0.06347365100146068, "bacc": 0.6196911196911197, "bacc_std": 0.05780424059210859}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.046149907524252326, "f1": 0.49444444444444446, "f1_std": 0.057535076399235274, "bacc": 0.5260617760617761, "bacc_std": 0.04717678930350226}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.05930883965766368, "f1": 0.4743935309973046, "f1_std": 0.06066949677480388, "bacc": 0.47586872586872586, "bacc_std": 0.05943977902876078}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.000774263682681127, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05704249428142281, "f1": 0.5905769715293525, "f1_std": 0.062359307632555296, "bacc": 0.5926640926640927, "bacc_std": 0.05861102085435265}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05481494203064056, "f1": 0.5411764705882354, "f1_std": 0.06447258756680768, "bacc": 0.5526061776061776, "bacc_std": 0.057063136134076}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05896363336134908, "f1": 0.5376016260162602, "f1_std": 0.06380976081499039, "bacc": 0.5434362934362934, "bacc_std": 0.05983190110664984}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.000774263682681127, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.055405628492410874, "f1": 0.4501409894898744, "f1_std": 0.0595222075267008, "bacc": 0.46283783783783783, "bacc_std": 0.05562425448299605}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.000774263682681127, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05613930264774535, "f1": 0.5501153550371699, "f1_std": 0.06164385674589564, "bacc": 0.556949806949807, "bacc_std": 0.05728036071317441}
+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 | 12.951 |  129.15 | 0.71247 |  0.069087 | 0.69453 | 0.077011 | 0.69428 |   0.074846 |
+| schaefer400_mae | patch  | logistic | adhd200_dx | test    |        100 | 12.951 |  129.15 | 0.59123 |  0.057325 | 0.56293 | 0.059573 | 0.56841 |   0.056222 |
+
+
+done! total time: 0:04:17

input_space_v3/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml

@@ -0,0 +1,31 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (adni_ad_vs_cn patch
+  logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/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/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,87,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,87,1291.5496650148827,test,0.7804878048780488,0.06544391043820943,0.7410526315789474,0.07133001010724702,0.7870967741935484,0.07602875373423436
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,88,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,88,21.54434690031882,test,0.7317073170731707,0.06890837444757018,0.6835087719298245,0.07424998091024113,0.7209677419354839,0.08086541091605402
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,89,2.782559402207126,train,0.989159891598916,0.005379192858322001,0.9845864661654136,0.007833592362695851,0.9767441860465116,0.01154024514372573
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,89,2.782559402207126,test,0.7560975609756098,0.06597594788245711,0.6693548387096775,0.08794397597210316,0.6693548387096775,0.09163375926845461
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,90,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,90,166.81005372000556,test,0.7804878048780488,0.06352296562978882,0.7280766396462786,0.07449733976478287,0.7532258064516129,0.08013962737079623
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,train,0.8699186991869918,0.013263362130105179,0.7785778577857786,0.027478803161630542,0.7371189087024406,0.02650377768280038
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,test,0.7073170731707317,0.06208150310647728,0.5729166666666666,0.08877570524063928,0.5693548387096774,0.08378445979054434
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,train,0.9376693766937669,0.01111081366513097,0.9042499182057154,0.018772582131318927,0.8703262388035171,0.023203754138581924
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,test,0.7073170731707317,0.06150093705606882,0.5340909090909092,0.09069619931494105,0.535483870967742,0.0775664985425399
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,93,0.3593813663804626,train,0.9322493224932249,0.011571239426381595,0.8969331322272499,0.019365013895193205,0.8667926698989235,0.02368938730951495
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,93,0.3593813663804626,test,0.6097560975609756,0.07677080237669119,0.5030303030303029,0.08414633635185076,0.5048387096774194,0.08954515658391513
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,1291.5496650148827,test,0.6829268292682927,0.07058472533294428,0.5839188134270101,0.08929523969993218,0.5870967741935484,0.0935334582081217
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,2.782559402207126,test,0.7317073170731707,0.05970339139015488,0.6232247284878863,0.08441819352414437,0.6193548387096774,0.08177196605733784
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,96,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,96,10000.0,test,0.7317073170731707,0.06395373104405778,0.6232247284878863,0.09080801109030458,0.6193548387096774,0.08720665613409041
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,2.782559402207126,train,0.997289972899729,0.002795713133899286,0.9961941891766453,0.003962179810576637,0.9941860465116279,0.005997779920981609
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,2.782559402207126,test,0.6829268292682927,0.0688132770830959,0.5839188134270101,0.08529306092881296,0.5870967741935484,0.08826281325651372
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,train,0.8509485094850948,0.014226248479928842,0.7447715582831345,0.029499315572034,0.7085627413920619,0.027310368751051882
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,test,0.8048780487804879,0.05176195916325809,0.6893939393939394,0.09014510790001097,0.667741935483871,0.08179970175419934
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,0.046415888336127774,train,0.8726287262872628,0.013775363915121946,0.7844652954700801,0.02801248560510012,0.7429328621908127,0.026977005694045417
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,0.3593813663804626,test,0.8292682926829268,0.05669467994518403,0.7602339181286549,0.08235820224386488,0.7516129032258064,0.08416266118642678

input_space_v3/schaefer400_lr3e-4_4/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:51:40
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (adni_ad_vs_cn patch
+  logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/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/input_space_v3/output/input_space_v3/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: 0:03:46    time: 1.3836  data: 1.0024  max mem: 1546
+extract (train)  [ 20/164]  eta: 0:00:16    time: 0.0478  data: 0.0030  max mem: 1715
+extract (train)  [ 40/164]  eta: 0:00:09    time: 0.0467  data: 0.0018  max mem: 1715
+extract (train)  [ 60/164]  eta: 0:00:07    time: 0.0477  data: 0.0028  max mem: 1715
+extract (train)  [ 80/164]  eta: 0:00:05    time: 0.0479  data: 0.0029  max mem: 1715
+extract (train)  [100/164]  eta: 0:00:03    time: 0.0477  data: 0.0028  max mem: 1715
+extract (train)  [120/164]  eta: 0:00:02    time: 0.0477  data: 0.0028  max mem: 1715
+extract (train)  [140/164]  eta: 0:00:01    time: 0.0477  data: 0.0027  max mem: 1715
+extract (train)  [160/164]  eta: 0:00:00    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [163/164]  eta: 0:00:00    time: 0.0476  data: 0.0026  max mem: 1715
+extract (train) Total time: 0:00:09 (0.0568 s / it)
+extract (validation)  [ 0/21]  eta: 0:00:21    time: 1.0145  data: 0.9673  max mem: 1715
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.0463  data: 0.0023  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.0983 s / it)
+extract (test)  [ 0/21]  eta: 0:00:21    time: 1.0066  data: 0.9600  max mem: 1715
+extract (test)  [20/21]  eta: 0:00:00    time: 0.0457  data: 0.0018  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0968 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 |         | 0.35938 | train   | 0.9187  |  0.011972 | 0.87322 | 0.021198 | 0.83553 |   0.024406 |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 0.35938 | test    | 0.78049 |  0.050352 | 0.63284 | 0.089045 | 0.61979 |   0.077785 |
+
+
+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": 0.6585365853658537, "acc_std": 0.05898759183490928, "f1": 0.5017361111111112, "f1_std": 0.07749938001503853, "bacc": 0.5032258064516129, "bacc_std": 0.07207778663154409}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.057344860934091274, "f1": 0.7602339181286549, "f1_std": 0.08307943848221006, "bacc": 0.7516129032258064, "bacc_std": 0.0853763566737078}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.05781693812651105, "f1": 0.5918552036199095, "f1_std": 0.08771289485861522, "bacc": 0.5854838709677419, "bacc_std": 0.07895621068031822}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05576126131050774, "f1": 0.6117424242424243, "f1_std": 0.09112448399410739, "bacc": 0.6016129032258064, "bacc_std": 0.07816555285063471}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 10000.0, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.0431749756767469, "f1": 0.569327731092437, "f1_std": 0.08621416580152994, "bacc": 0.567741935483871, "bacc_std": 0.06519079498104119}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 21.54434690031882, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0616952653805361, "f1": 0.5729166666666666, "f1_std": 0.0886974297591218, "bacc": 0.5693548387096774, "bacc_std": 0.08238184235650493}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.043276403672354885, "f1": 0.6554621848739496, "f1_std": 0.09605479191882184, "bacc": 0.6338709677419355, "bacc_std": 0.07609119508052505}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 1291.5496650148827, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.07083927900767473, "f1": 0.646551724137931, "f1_std": 0.07994488686873832, "bacc": 0.6709677419354838, "bacc_std": 0.0874128456093463}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 2.782559402207126, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06612538339825134, "f1": 0.6479313036690086, "f1_std": 0.08201870008584342, "bacc": 0.6532258064516129, "bacc_std": 0.08534806564939623}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06377106212122398, "f1": 0.6232247284878863, "f1_std": 0.08840428904223177, "bacc": 0.6193548387096774, "bacc_std": 0.08684897718862139}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.055080839406461524, "f1": 0.5340909090909092, "f1_std": 0.0819763501127481, "bacc": 0.535483870967742, "bacc_std": 0.07011775816728476}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05633155862678594, "f1": 0.7152777777777778, "f1_std": 0.08332761923120519, "bacc": 0.7016129032258065, "bacc_std": 0.08122601981831618}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.04755344530755557, "f1": 0.7144278606965174, "f1_std": 0.09696232034103233, "bacc": 0.6838709677419355, "bacc_std": 0.08283714267184365}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 14, "C": 21.54434690031882, "split": "test", "acc": 0.8536585365853658, "acc_std": 0.05620162140598357, "f1": 0.8136363636363637, "f1_std": 0.0695055727987665, "bacc": 0.8354838709677419, "bacc_std": 0.07377768973477998}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05823573092982004, "f1": 0.6440972222222222, "f1_std": 0.08518036797817734, "bacc": 0.635483870967742, "bacc_std": 0.08070953424344589}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.0465625279226088, "f1": 0.569327731092437, "f1_std": 0.0912876530340863, "bacc": 0.567741935483871, "bacc_std": 0.0687398800800781}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04011936384273493, "f1": 0.5886287625418061, "f1_std": 0.09433769462265607, "bacc": 0.5838709677419355, "bacc_std": 0.06687095210307459}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05669467994518403, "f1": 0.7602339181286549, "f1_std": 0.08235820224386488, "bacc": 0.7516129032258064, "bacc_std": 0.08416266118642678}
+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 | 750.93 |  2382.7 | 0.95165 |  0.055616 | 0.9195  | 0.09651  | 0.9017  |   0.11312  |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 750.93 |  2382.7 | 0.74927 |  0.061828 | 0.63788 | 0.082845 | 0.63571 |   0.079913 |
+
+
+done! total time: 0:04:17

input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_probe
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/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/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 14, "eval/id_best": 34, "eval/lr_best": 0.0015299999999999997, "eval/wd_best": 0.05, "eval/train/loss": 0.00019395968411117792, "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": 0.10955367237329483, "eval/validation/acc": 0.9774305555555556, "eval/validation/acc_std": 0.002316871788973775, "eval/validation/f1": 0.9716407335372177, "eval/validation/f1_std": 0.0032183791799482667, "eval/test/loss": 0.1295265257358551, "eval/test/acc": 0.9761904761904762, "eval/test/acc_std": 0.002228352195780635, "eval/test/f1": 0.9724951279436638, "eval/test/f1_std": 0.0027809355204929066}

input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 14, "eval/best/id_best": 34, "eval/best/lr_best": 0.0015299999999999997, "eval/best/wd_best": 0.05, "eval/best/train/loss": 0.00019395968411117792, "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": 0.10955367237329483, "eval/best/validation/acc": 0.9774305555555556, "eval/best/validation/acc_std": 0.002316871788973775, "eval/best/validation/f1": 0.9716407335372177, "eval/best/validation/f1_std": 0.0032183791799482667, "eval/best/test/loss": 0.1295265257358551, "eval/best/test/acc": 0.9761904761904762, "eval/best/test/acc_std": 0.002228352195780635, "eval/best/test/f1": 0.9724951279436638, "eval/best/test/f1_std": 0.0027809355204929066}

input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 34, "eval/last/lr_best": 0.0015299999999999997, "eval/last/wd_best": 0.05, "eval/last/train/loss": 0.00018113103578798473, "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": 0.10889282077550888, "eval/last/validation/acc": 0.9766865079365079, "eval/last/validation/acc_std": 0.0023204095748893252, "eval/last/validation/f1": 0.9710088469031694, "eval/last/validation/f1_std": 0.0032143852154166296, "eval/last/test/loss": 0.12786810100078583, "eval/last/test/acc": 0.9761904761904762, "eval/last/test/acc_std": 0.0022202586279317735, "eval/last/test/f1": 0.9722650152791493, "eval/last/test/f1_std": 0.0027856914832181346}

input_space_v3/schaefer400_lr3e-4_4/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,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,0.00019395968411117792,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,0.10955367237329483,0.9774305555555556,0.002316871788973775,0.9716407335372177,0.0032183791799482667
+schaefer400_mae,patch,attn,hcpya_task21,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,0.1295265257358551,0.9761904761904762,0.002228352195780635,0.9724951279436638,0.0027809355204929066

input_space_v3/schaefer400_lr3e-4_4/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,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,0.00019395968411117792,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,0.10955367237329483,0.9774305555555556,0.002316871788973775,0.9716407335372177,0.0032183791799482667
+schaefer400_mae,patch,attn,hcpya_task21,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,0.1295265257358551,0.9761904761904762,0.002228352195780635,0.9724951279436638,0.0027809355204929066

input_space_v3/schaefer400_lr3e-4_4/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,19,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,0.00018113103578798473,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,attn,hcpya_task21,last,19,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,0.10889282077550888,0.9766865079365079,0.0023204095748893252,0.9710088469031694,0.0032143852154166296
+schaefer400_mae,patch,attn,hcpya_task21,last,19,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,0.12786810100078583,0.9761904761904762,0.0022202586279317735,0.9722650152791493,0.0027856914832181346

input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/log.txt

@@ -0,0 +1,888 @@
+fMRI foundation model probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 20:42:23
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_probe
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/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/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/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: 0:06:17  lr: nan  time: 0.9449  data: 0.5538  max mem: 23058
+train: [0]  [ 20/400]  eta: 0:01:48  lr: 0.000003  loss: 2.9939 (2.9985)  grad: 0.3336 (0.3460)  time: 0.2525  data: 0.0021  max mem: 23762
+train: [0]  [ 40/400]  eta: 0:01:35  lr: 0.000006  loss: 2.9612 (2.9322)  grad: 0.3497 (0.3523)  time: 0.2455  data: 0.0021  max mem: 23762
+train: [0]  [ 60/400]  eta: 0:01:28  lr: 0.000009  loss: 2.7845 (2.8603)  grad: 0.3402 (0.3409)  time: 0.2455  data: 0.0026  max mem: 23762
+train: [0]  [ 80/400]  eta: 0:01:21  lr: 0.000012  loss: 2.6054 (2.7720)  grad: 0.3103 (0.3347)  time: 0.2455  data: 0.0020  max mem: 23762
+train: [0]  [100/400]  eta: 0:01:16  lr: 0.000015  loss: 2.4232 (2.6969)  grad: 0.2981 (0.3256)  time: 0.2462  data: 0.0027  max mem: 23762
+train: [0]  [120/400]  eta: 0:01:10  lr: 0.000018  loss: 2.3403 (2.6273)  grad: 0.2901 (0.3221)  time: 0.2456  data: 0.0020  max mem: 23762
+train: [0]  [140/400]  eta: 0:01:05  lr: 0.000021  loss: 2.2302 (2.5639)  grad: 0.2942 (0.3180)  time: 0.2458  data: 0.0026  max mem: 23762
+train: [0]  [160/400]  eta: 0:01:00  lr: 0.000024  loss: 2.1332 (2.5052)  grad: 0.2841 (0.3134)  time: 0.2453  data: 0.0021  max mem: 23762
+train: [0]  [180/400]  eta: 0:00:55  lr: 0.000027  loss: 2.0456 (2.4508)  grad: 0.2700 (0.3085)  time: 0.2457  data: 0.0026  max mem: 23762
+train: [0]  [200/400]  eta: 0:00:49  lr: 0.000030  loss: 1.9731 (2.3986)  grad: 0.2607 (0.3049)  time: 0.2455  data: 0.0020  max mem: 23762
+train: [0]  [220/400]  eta: 0:00:44  lr: 0.000033  loss: 1.8720 (2.3503)  grad: 0.2614 (0.3015)  time: 0.2461  data: 0.0026  max mem: 23762
+train: [0]  [240/400]  eta: 0:00:39  lr: 0.000036  loss: 1.8362 (2.3036)  grad: 0.2676 (0.2989)  time: 0.2452  data: 0.0020  max mem: 23762
+train: [0]  [260/400]  eta: 0:00:34  lr: 0.000039  loss: 1.7697 (2.2612)  grad: 0.2682 (0.2960)  time: 0.2462  data: 0.0026  max mem: 23762
+train: [0]  [280/400]  eta: 0:00:29  lr: 0.000042  loss: 1.7523 (2.2219)  grad: 0.2485 (0.2926)  time: 0.2460  data: 0.0021  max mem: 23762
+train: [0]  [300/400]  eta: 0:00:25  lr: 0.000045  loss: 1.6687 (2.1817)  grad: 0.2467 (0.2896)  time: 0.2696  data: 0.0255  max mem: 23762
+train: [0]  [320/400]  eta: 0:00:19  lr: 0.000048  loss: 1.5937 (2.1436)  grad: 0.2467 (0.2870)  time: 0.2459  data: 0.0026  max mem: 23762
+train: [0]  [340/400]  eta: 0:00:14  lr: 0.000051  loss: 1.5926 (2.1108)  grad: 0.2349 (0.2838)  time: 0.2454  data: 0.0018  max mem: 23762
+train: [0]  [360/400]  eta: 0:00:09  lr: 0.000054  loss: 1.5427 (2.0781)  grad: 0.2296 (0.2811)  time: 0.2464  data: 0.0026  max mem: 23762
+train: [0]  [380/400]  eta: 0:00:04  lr: 0.000057  loss: 1.4603 (2.0448)  grad: 0.2354 (0.2790)  time: 0.2452  data: 0.0018  max mem: 23762
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 1.4181 (2.0122)  grad: 0.2299 (0.2763)  time: 0.2454  data: 0.0019  max mem: 23762
+train: [0] Total time: 0:01:39 (0.2492 s / it)
+train: [0] Summary: lr: 0.000060  loss: 1.4181 (2.0122)  grad: 0.2299 (0.2763)
+eval (validation): [0]  [ 0/63]  eta: 0:00:32    time: 0.5221  data: 0.4024  max mem: 23762
+eval (validation): [0]  [20/63]  eta: 0:00:06    time: 0.1230  data: 0.0023  max mem: 23762
+eval (validation): [0]  [40/63]  eta: 0:00:03    time: 0.1231  data: 0.0021  max mem: 23762
+eval (validation): [0]  [60/63]  eta: 0:00:00    time: 0.1237  data: 0.0027  max mem: 23762
+eval (validation): [0]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0025  max mem: 23762
+eval (validation): [0] Total time: 0:00:08 (0.1307 s / it)
+cv: [0]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 0.149  acc: 0.955  f1: 0.949
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:04:38  lr: nan  time: 0.6960  data: 0.4608  max mem: 23762
+train: [1]  [ 20/400]  eta: 0:01:41  lr: 0.000063  loss: 1.3396 (1.3575)  grad: 0.2219 (0.2199)  time: 0.2468  data: 0.0024  max mem: 23762
+train: [1]  [ 40/400]  eta: 0:01:32  lr: 0.000066  loss: 1.3305 (1.3429)  grad: 0.2204 (0.2197)  time: 0.2463  data: 0.0024  max mem: 23762
+train: [1]  [ 60/400]  eta: 0:01:26  lr: 0.000069  loss: 1.3029 (1.3329)  grad: 0.2148 (0.2180)  time: 0.2467  data: 0.0027  max mem: 23762
+train: [1]  [ 80/400]  eta: 0:01:20  lr: 0.000072  loss: 1.2639 (1.3159)  grad: 0.2148 (0.2176)  time: 0.2458  data: 0.0023  max mem: 23762
+train: [1]  [100/400]  eta: 0:01:15  lr: 0.000075  loss: 1.2542 (1.3086)  grad: 0.2095 (0.2146)  time: 0.2464  data: 0.0028  max mem: 23762
+train: [1]  [120/400]  eta: 0:01:10  lr: 0.000078  loss: 1.2347 (1.2938)  grad: 0.2004 (0.2130)  time: 0.2463  data: 0.0023  max mem: 23762
+train: [1]  [140/400]  eta: 0:01:04  lr: 0.000081  loss: 1.2250 (1.2842)  grad: 0.2015 (0.2109)  time: 0.2467  data: 0.0027  max mem: 23762
+train: [1]  [160/400]  eta: 0:00:59  lr: 0.000084  loss: 1.2020 (1.2696)  grad: 0.1972 (0.2101)  time: 0.2457  data: 0.0023  max mem: 23762
+train: [1]  [180/400]  eta: 0:00:54  lr: 0.000087  loss: 1.1523 (1.2604)  grad: 0.2038 (0.2096)  time: 0.2468  data: 0.0028  max mem: 23762
+train: [1]  [200/400]  eta: 0:00:49  lr: 0.000090  loss: 1.1330 (1.2462)  grad: 0.2001 (0.2083)  time: 0.2462  data: 0.0022  max mem: 23762
+train: [1]  [220/400]  eta: 0:00:44  lr: 0.000093  loss: 1.1154 (1.2340)  grad: 0.1947 (0.2074)  time: 0.2470  data: 0.0028  max mem: 23762
+train: [1]  [240/400]  eta: 0:00:39  lr: 0.000096  loss: 1.0811 (1.2207)  grad: 0.1947 (0.2063)  time: 0.2460  data: 0.0023  max mem: 23762
+train: [1]  [260/400]  eta: 0:00:34  lr: 0.000099  loss: 1.0789 (1.2110)  grad: 0.1860 (0.2049)  time: 0.2474  data: 0.0028  max mem: 23762
+train: [1]  [280/400]  eta: 0:00:29  lr: 0.000102  loss: 1.0527 (1.1987)  grad: 0.1860 (0.2040)  time: 0.2465  data: 0.0024  max mem: 23762
+train: [1]  [300/400]  eta: 0:00:24  lr: 0.000105  loss: 1.0527 (1.1890)  grad: 0.1947 (0.2036)  time: 0.2700  data: 0.0259  max mem: 23762
+train: [1]  [320/400]  eta: 0:00:19  lr: 0.000108  loss: 1.0124 (1.1764)  grad: 0.1881 (0.2026)  time: 0.2460  data: 0.0026  max mem: 23762
+train: [1]  [340/400]  eta: 0:00:14  lr: 0.000111  loss: 0.9861 (1.1660)  grad: 0.1847 (0.2018)  time: 0.2468  data: 0.0020  max mem: 23762
+train: [1]  [360/400]  eta: 0:00:09  lr: 0.000114  loss: 0.9614 (1.1545)  grad: 0.1831 (0.2007)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [1]  [380/400]  eta: 0:00:04  lr: 0.000117  loss: 0.9525 (1.1432)  grad: 0.1862 (0.2001)  time: 0.2458  data: 0.0020  max mem: 23762
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 0.9518 (1.1339)  grad: 0.1893 (0.1996)  time: 0.2460  data: 0.0020  max mem: 23762
+train: [1] Total time: 0:01:39 (0.2490 s / it)
+train: [1] Summary: lr: 0.000120  loss: 0.9518 (1.1339)  grad: 0.1893 (0.1996)
+eval (validation): [1]  [ 0/63]  eta: 0:00:36    time: 0.5811  data: 0.4632  max mem: 23762
+eval (validation): [1]  [20/63]  eta: 0:00:06    time: 0.1224  data: 0.0019  max mem: 23762
+eval (validation): [1]  [40/63]  eta: 0:00:03    time: 0.1237  data: 0.0025  max mem: 23762
+eval (validation): [1]  [60/63]  eta: 0:00:00    time: 0.1227  data: 0.0017  max mem: 23762
+eval (validation): [1]  [62/63]  eta: 0:00:00    time: 0.1226  data: 0.0017  max mem: 23762
+eval (validation): [1] Total time: 0:00:08 (0.1313 s / it)
+cv: [1]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.123  acc: 0.965  f1: 0.956
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:04:24  lr: nan  time: 0.6604  data: 0.4245  max mem: 23762
+train: [2]  [ 20/400]  eta: 0:01:41  lr: 0.000123  loss: 0.8467 (0.8806)  grad: 0.1775 (0.1781)  time: 0.2468  data: 0.0025  max mem: 23762
+train: [2]  [ 40/400]  eta: 0:01:32  lr: 0.000126  loss: 0.8588 (0.8883)  grad: 0.1776 (0.1817)  time: 0.2473  data: 0.0027  max mem: 23762
+train: [2]  [ 60/400]  eta: 0:01:26  lr: 0.000129  loss: 0.8744 (0.8899)  grad: 0.1902 (0.1870)  time: 0.2459  data: 0.0020  max mem: 23762
+train: [2]  [ 80/400]  eta: 0:01:20  lr: 0.000132  loss: 0.9117 (0.8982)  grad: 0.1948 (0.1920)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [2]  [100/400]  eta: 0:01:15  lr: 0.000135  loss: 0.8916 (0.8924)  grad: 0.1905 (0.1927)  time: 0.2455  data: 0.0020  max mem: 23762
+train: [2]  [120/400]  eta: 0:01:09  lr: 0.000138  loss: 0.8664 (0.8892)  grad: 0.2007 (0.1965)  time: 0.2469  data: 0.0026  max mem: 23762
+train: [2]  [140/400]  eta: 0:01:04  lr: 0.000141  loss: 0.8674 (0.8888)  grad: 0.2188 (0.2012)  time: 0.2460  data: 0.0020  max mem: 23762
+train: [2]  [160/400]  eta: 0:00:59  lr: 0.000144  loss: 0.8386 (0.8818)  grad: 0.2202 (0.2041)  time: 0.2463  data: 0.0027  max mem: 23762
+train: [2]  [180/400]  eta: 0:00:54  lr: 0.000147  loss: 0.8508 (0.8831)  grad: 0.2291 (0.2094)  time: 0.2462  data: 0.0020  max mem: 23762
+train: [2]  [200/400]  eta: 0:00:49  lr: 0.000150  loss: 0.8780 (0.8832)  grad: 0.2475 (0.2136)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [2]  [220/400]  eta: 0:00:44  lr: 0.000153  loss: 0.9069 (0.8856)  grad: 0.2489 (0.2180)  time: 0.2459  data: 0.0020  max mem: 23762
+train: [2]  [240/400]  eta: 0:00:39  lr: 0.000156  loss: 0.8781 (0.8851)  grad: 0.2546 (0.2213)  time: 0.2467  data: 0.0027  max mem: 23762
+train: [2]  [260/400]  eta: 0:00:34  lr: 0.000159  loss: 0.8815 (0.8868)  grad: 0.2546 (0.2260)  time: 0.2462  data: 0.0021  max mem: 23762
+train: [2]  [280/400]  eta: 0:00:29  lr: 0.000162  loss: 0.9041 (0.8870)  grad: 0.2741 (0.2314)  time: 0.2471  data: 0.0028  max mem: 23762
+train: [2]  [300/400]  eta: 0:00:24  lr: 0.000165  loss: 0.8619 (0.8859)  grad: 0.2734 (0.2348)  time: 0.2714  data: 0.0268  max mem: 23762
+train: [2]  [320/400]  eta: 0:00:19  lr: 0.000168  loss: 0.8228 (0.8819)  grad: 0.2779 (0.2391)  time: 0.2467  data: 0.0029  max mem: 23762
+train: [2]  [340/400]  eta: 0:00:14  lr: 0.000171  loss: 0.7850 (0.8772)  grad: 0.2749 (0.2403)  time: 0.2460  data: 0.0017  max mem: 23762
+train: [2]  [360/400]  eta: 0:00:09  lr: 0.000174  loss: 0.7799 (0.8719)  grad: 0.2770 (0.2439)  time: 0.2466  data: 0.0027  max mem: 23762
+train: [2]  [380/400]  eta: 0:00:04  lr: 0.000177  loss: 0.8093 (0.8735)  grad: 0.3301 (0.2487)  time: 0.2462  data: 0.0018  max mem: 23762
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 0.8956 (0.8759)  grad: 0.3315 (0.2530)  time: 0.2463  data: 0.0020  max mem: 23762
+train: [2] Total time: 0:01:39 (0.2489 s / it)
+train: [2] Summary: lr: 0.000180  loss: 0.8956 (0.8759)  grad: 0.3315 (0.2530)
+eval (validation): [2]  [ 0/63]  eta: 0:00:33    time: 0.5305  data: 0.4123  max mem: 23762
+eval (validation): [2]  [20/63]  eta: 0:00:06    time: 0.1233  data: 0.0025  max mem: 23762
+eval (validation): [2]  [40/63]  eta: 0:00:03    time: 0.1233  data: 0.0024  max mem: 23762
+eval (validation): [2]  [60/63]  eta: 0:00:00    time: 0.1236  data: 0.0025  max mem: 23762
+eval (validation): [2]  [62/63]  eta: 0:00:00    time: 0.1232  data: 0.0024  max mem: 23762
+eval (validation): [2] Total time: 0:00:08 (0.1311 s / it)
+cv: [2]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 0.111  acc: 0.966  f1: 0.958
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:03:59  lr: nan  time: 0.5987  data: 0.3623  max mem: 23762
+train: [3]  [ 20/400]  eta: 0:01:40  lr: 0.000183  loss: 0.8415 (0.8384)  grad: 0.2961 (0.3070)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [3]  [ 40/400]  eta: 0:01:31  lr: 0.000186  loss: 0.8556 (0.8497)  grad: 0.3158 (0.3165)  time: 0.2462  data: 0.0024  max mem: 23762
+train: [3]  [ 60/400]  eta: 0:01:25  lr: 0.000189  loss: 0.8540 (0.8455)  grad: 0.3435 (0.3248)  time: 0.2467  data: 0.0023  max mem: 23762
+train: [3]  [ 80/400]  eta: 0:01:20  lr: 0.000192  loss: 0.8667 (0.8678)  grad: 0.3559 (0.3380)  time: 0.2469  data: 0.0023  max mem: 23762
+train: [3]  [100/400]  eta: 0:01:15  lr: 0.000195  loss: 0.8576 (0.8570)  grad: 0.3620 (0.3403)  time: 0.2463  data: 0.0023  max mem: 23762
+train: [3]  [120/400]  eta: 0:01:09  lr: 0.000198  loss: 0.8187 (0.8660)  grad: 0.3624 (0.3507)  time: 0.2459  data: 0.0023  max mem: 23762
+train: [3]  [140/400]  eta: 0:01:04  lr: 0.000201  loss: 0.8368 (0.8621)  grad: 0.3859 (0.3589)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [3]  [160/400]  eta: 0:00:59  lr: 0.000204  loss: 0.8434 (0.8759)  grad: 0.4023 (0.3696)  time: 0.2460  data: 0.0023  max mem: 23762
+train: [3]  [180/400]  eta: 0:00:54  lr: 0.000207  loss: 0.9515 (0.8898)  grad: 0.4516 (0.3801)  time: 0.2467  data: 0.0023  max mem: 23762
+train: [3]  [200/400]  eta: 0:00:49  lr: 0.000210  loss: 0.9941 (0.9115)  grad: 0.4609 (0.3942)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [3]  [220/400]  eta: 0:00:44  lr: 0.000213  loss: 0.9762 (0.9199)  grad: 0.4609 (0.4050)  time: 0.2462  data: 0.0024  max mem: 23762
+train: [3]  [240/400]  eta: 0:00:39  lr: 0.000216  loss: 0.9619 (0.9280)  grad: 0.5014 (0.4123)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [3]  [260/400]  eta: 0:00:34  lr: 0.000219  loss: 0.9017 (0.9298)  grad: 0.4808 (0.4193)  time: 0.2462  data: 0.0023  max mem: 23762
+train: [3]  [280/400]  eta: 0:00:29  lr: 0.000222  loss: 1.0077 (0.9366)  grad: 0.4974 (0.4266)  time: 0.2467  data: 0.0024  max mem: 23762
+train: [3]  [300/400]  eta: 0:00:24  lr: 0.000225  loss: 0.9586 (0.9339)  grad: 0.5037 (0.4377)  time: 0.2694  data: 0.0253  max mem: 23762
+train: [3]  [320/400]  eta: 0:00:19  lr: 0.000228  loss: 0.9232 (0.9371)  grad: 0.4815 (0.4371)  time: 0.2463  data: 0.0028  max mem: 23762
+train: [3]  [340/400]  eta: 0:00:14  lr: 0.000231  loss: 1.0443 (0.9463)  grad: 0.4366 (0.4468)  time: 0.2461  data: 0.0018  max mem: 23762
+train: [3]  [360/400]  eta: 0:00:09  lr: 0.000234  loss: 0.9597 (0.9441)  grad: 0.4506 (0.4483)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [3]  [380/400]  eta: 0:00:04  lr: 0.000237  loss: 0.9401 (0.9495)  grad: 0.4506 (0.4493)  time: 0.2456  data: 0.0018  max mem: 23762
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 0.9604 (0.9471)  grad: 0.4315 (0.4484)  time: 0.2458  data: 0.0019  max mem: 23762
+train: [3] Total time: 0:01:39 (0.2491 s / it)
+train: [3] Summary: lr: 0.000240  loss: 0.9604 (0.9471)  grad: 0.4315 (0.4484)
+eval (validation): [3]  [ 0/63]  eta: 0:00:35    time: 0.5601  data: 0.4400  max mem: 23762
+eval (validation): [3]  [20/63]  eta: 0:00:06    time: 0.1220  data: 0.0022  max mem: 23762
+eval (validation): [3]  [40/63]  eta: 0:00:03    time: 0.1231  data: 0.0021  max mem: 23762
+eval (validation): [3]  [60/63]  eta: 0:00:00    time: 0.1231  data: 0.0023  max mem: 23762
+eval (validation): [3]  [62/63]  eta: 0:00:00    time: 0.1225  data: 0.0019  max mem: 23762
+eval (validation): [3] Total time: 0:00:08 (0.1309 s / it)
+cv: [3]  best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00')  loss: 0.098  acc: 0.972  f1: 0.967
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:04:23  lr: nan  time: 0.6589  data: 0.4230  max mem: 23762
+train: [4]  [ 20/400]  eta: 0:01:41  lr: 0.000243  loss: 0.8117 (0.9404)  grad: 0.4854 (0.5049)  time: 0.2463  data: 0.0023  max mem: 23762
+train: [4]  [ 40/400]  eta: 0:01:32  lr: 0.000246  loss: 0.9320 (1.0042)  grad: 0.5218 (0.5303)  time: 0.2466  data: 0.0028  max mem: 23762
+train: [4]  [ 60/400]  eta: 0:01:26  lr: 0.000249  loss: 1.0183 (1.0173)  grad: 0.5335 (0.5368)  time: 0.2457  data: 0.0020  max mem: 23762
+train: [4]  [ 80/400]  eta: 0:01:20  lr: 0.000252  loss: 0.9189 (1.0147)  grad: 0.5335 (0.5411)  time: 0.2468  data: 0.0029  max mem: 23762
+train: [4]  [100/400]  eta: 0:01:15  lr: 0.000255  loss: 0.9781 (1.0333)  grad: 0.5594 (0.5461)  time: 0.2458  data: 0.0020  max mem: 23762
+train: [4]  [120/400]  eta: 0:01:09  lr: 0.000258  loss: 1.1687 (1.0812)  grad: 0.5467 (0.5658)  time: 0.2468  data: 0.0029  max mem: 23762
+train: [4]  [140/400]  eta: 0:01:04  lr: 0.000261  loss: 1.2797 (1.1044)  grad: 0.6702 (0.5923)  time: 0.2463  data: 0.0020  max mem: 23762
+train: [4]  [160/400]  eta: 0:00:59  lr: 0.000264  loss: 1.1570 (1.1192)  grad: 0.6367 (0.5966)  time: 0.2472  data: 0.0029  max mem: 23762
+train: [4]  [180/400]  eta: 0:00:54  lr: 0.000267  loss: 1.1696 (1.1361)  grad: 0.6509 (0.6119)  time: 0.2465  data: 0.0020  max mem: 23762
+train: [4]  [200/400]  eta: 0:00:49  lr: 0.000270  loss: 1.2242 (1.1551)  grad: 0.6628 (0.6141)  time: 0.2471  data: 0.0028  max mem: 23762
+train: [4]  [220/400]  eta: 0:00:44  lr: 0.000273  loss: 1.1304 (1.1448)  grad: 0.6186 (0.6265)  time: 0.2464  data: 0.0019  max mem: 23762
+train: [4]  [240/400]  eta: 0:00:39  lr: 0.000276  loss: 1.0519 (1.1469)  grad: 0.6276 (0.6286)  time: 0.2472  data: 0.0028  max mem: 23762
+train: [4]  [260/400]  eta: 0:00:34  lr: 0.000279  loss: 1.1275 (1.1637)  grad: 0.6549 (0.6366)  time: 0.2464  data: 0.0019  max mem: 23762
+train: [4]  [280/400]  eta: 0:00:29  lr: 0.000282  loss: 1.2131 (1.1790)  grad: 0.7065 (0.6421)  time: 0.2473  data: 0.0028  max mem: 23762
+train: [4]  [300/400]  eta: 0:00:24  lr: 0.000285  loss: 1.3277 (1.1884)  grad: 0.7021 (0.6563)  time: 0.2708  data: 0.0261  max mem: 23762
+train: [4]  [320/400]  eta: 0:00:19  lr: 0.000288  loss: 1.1445 (1.1824)  grad: 0.6775 (0.6599)  time: 0.2461  data: 0.0027  max mem: 23762
+train: [4]  [340/400]  eta: 0:00:14  lr: 0.000291  loss: 1.0375 (1.1810)  grad: 0.6888 (0.6617)  time: 0.2460  data: 0.0018  max mem: 23762
+train: [4]  [360/400]  eta: 0:00:09  lr: 0.000294  loss: 1.1799 (1.1887)  grad: 0.7166 (0.6660)  time: 0.2463  data: 0.0026  max mem: 23762
+train: [4]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 1.1799 (1.1808)  grad: 0.6842 (0.6641)  time: 0.2463  data: 0.0019  max mem: 23762
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 1.1045 (1.1805)  grad: 0.6518 (0.6637)  time: 0.2461  data: 0.0021  max mem: 23762
+train: [4] Total time: 0:01:39 (0.2490 s / it)
+train: [4] Summary: lr: 0.000300  loss: 1.1045 (1.1805)  grad: 0.6518 (0.6637)
+eval (validation): [4]  [ 0/63]  eta: 0:00:34    time: 0.5408  data: 0.4219  max mem: 23762
+eval (validation): [4]  [20/63]  eta: 0:00:06    time: 0.1224  data: 0.0017  max mem: 23762
+eval (validation): [4]  [40/63]  eta: 0:00:03    time: 0.1243  data: 0.0025  max mem: 23762
+eval (validation): [4]  [60/63]  eta: 0:00:00    time: 0.1230  data: 0.0016  max mem: 23762
+eval (validation): [4]  [62/63]  eta: 0:00:00    time: 0.1230  data: 0.0016  max mem: 23762
+eval (validation): [4] Total time: 0:00:08 (0.1310 s / it)
+cv: [4]  best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00')  loss: 0.097  acc: 0.970  f1: 0.963
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:04:31  lr: nan  time: 0.6777  data: 0.4415  max mem: 23762
+train: [5]  [ 20/400]  eta: 0:01:41  lr: 0.000300  loss: 0.9192 (1.0302)  grad: 0.5952 (0.6437)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [5]  [ 40/400]  eta: 0:01:32  lr: 0.000300  loss: 1.0518 (1.1104)  grad: 0.6247 (0.6943)  time: 0.2475  data: 0.0026  max mem: 23762
+train: [5]  [ 60/400]  eta: 0:01:26  lr: 0.000300  loss: 1.1102 (1.1801)  grad: 0.6234 (0.6680)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [5]  [ 80/400]  eta: 0:01:20  lr: 0.000300  loss: 1.1764 (1.1893)  grad: 0.6336 (0.6697)  time: 0.2464  data: 0.0024  max mem: 23762
+train: [5]  [100/400]  eta: 0:01:15  lr: 0.000300  loss: 1.1764 (1.2197)  grad: 0.6618 (0.6807)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [5]  [120/400]  eta: 0:01:10  lr: 0.000300  loss: 0.9974 (1.2042)  grad: 0.7005 (0.6981)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [5]  [140/400]  eta: 0:01:04  lr: 0.000300  loss: 0.9974 (1.1938)  grad: 0.7005 (0.6957)  time: 0.2467  data: 0.0025  max mem: 23762
+train: [5]  [160/400]  eta: 0:00:59  lr: 0.000299  loss: 1.0452 (1.1809)  grad: 0.6426 (0.6876)  time: 0.2469  data: 0.0025  max mem: 23762
+train: [5]  [180/400]  eta: 0:00:54  lr: 0.000299  loss: 0.9908 (1.1554)  grad: 0.5970 (0.6775)  time: 0.2469  data: 0.0026  max mem: 23762
+train: [5]  [200/400]  eta: 0:00:49  lr: 0.000299  loss: 0.9199 (1.1498)  grad: 0.5970 (0.6762)  time: 0.2467  data: 0.0024  max mem: 23762
+train: [5]  [220/400]  eta: 0:00:44  lr: 0.000299  loss: 1.0386 (1.1532)  grad: 0.6429 (0.6740)  time: 0.2469  data: 0.0026  max mem: 23762
+train: [5]  [240/400]  eta: 0:00:39  lr: 0.000299  loss: 0.9726 (1.1360)  grad: 0.6097 (0.6669)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [5]  [260/400]  eta: 0:00:34  lr: 0.000299  loss: 0.9726 (1.1416)  grad: 0.5762 (0.6627)  time: 0.2470  data: 0.0026  max mem: 23762
+train: [5]  [280/400]  eta: 0:00:29  lr: 0.000298  loss: 1.0787 (1.1384)  grad: 0.6077 (0.6619)  time: 0.2465  data: 0.0025  max mem: 23762
+train: [5]  [300/400]  eta: 0:00:24  lr: 0.000298  loss: 1.0825 (1.1333)  grad: 0.6433 (0.6622)  time: 0.2701  data: 0.0251  max mem: 23762
+train: [5]  [320/400]  eta: 0:00:19  lr: 0.000298  loss: 1.0825 (1.1309)  grad: 0.6412 (0.6611)  time: 0.2462  data: 0.0026  max mem: 23762
+train: [5]  [340/400]  eta: 0:00:14  lr: 0.000298  loss: 0.8836 (1.1202)  grad: 0.6146 (0.6562)  time: 0.2455  data: 0.0017  max mem: 23762
+train: [5]  [360/400]  eta: 0:00:09  lr: 0.000297  loss: 0.7511 (1.1033)  grad: 0.5172 (0.6490)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [5]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 0.7732 (1.0927)  grad: 0.5104 (0.6449)  time: 0.2456  data: 0.0017  max mem: 23762
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 0.8799 (1.0896)  grad: 0.5538 (0.6423)  time: 0.2456  data: 0.0017  max mem: 23762
+train: [5] Total time: 0:01:39 (0.2490 s / it)
+train: [5] Summary: lr: 0.000297  loss: 0.8799 (1.0896)  grad: 0.5538 (0.6423)
+eval (validation): [5]  [ 0/63]  eta: 0:00:36    time: 0.5739  data: 0.4552  max mem: 23762
+eval (validation): [5]  [20/63]  eta: 0:00:06    time: 0.1234  data: 0.0019  max mem: 23762
+eval (validation): [5]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0026  max mem: 23762
+eval (validation): [5]  [60/63]  eta: 0:00:00    time: 0.1235  data: 0.0018  max mem: 23762
+eval (validation): [5]  [62/63]  eta: 0:00:00    time: 0.1237  data: 0.0018  max mem: 23762
+eval (validation): [5] Total time: 0:00:08 (0.1320 s / it)
+cv: [5]  best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00')  loss: 0.092  acc: 0.973  f1: 0.968
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:04:30  lr: nan  time: 0.6751  data: 0.4399  max mem: 23762
+train: [6]  [ 20/400]  eta: 0:01:40  lr: 0.000296  loss: 0.8751 (0.9420)  grad: 0.5590 (0.5473)  time: 0.2447  data: 0.0021  max mem: 23762
+train: [6]  [ 40/400]  eta: 0:01:32  lr: 0.000296  loss: 0.8133 (0.8621)  grad: 0.5249 (0.5267)  time: 0.2464  data: 0.0026  max mem: 23762
+train: [6]  [ 60/400]  eta: 0:01:25  lr: 0.000296  loss: 0.7231 (0.8362)  grad: 0.4896 (0.5139)  time: 0.2465  data: 0.0025  max mem: 23762
+train: [6]  [ 80/400]  eta: 0:01:20  lr: 0.000295  loss: 0.7113 (0.8441)  grad: 0.5424 (0.5315)  time: 0.2459  data: 0.0025  max mem: 23762
+train: [6]  [100/400]  eta: 0:01:15  lr: 0.000295  loss: 0.8993 (0.8659)  grad: 0.5424 (0.5264)  time: 0.2457  data: 0.0025  max mem: 23762
+train: [6]  [120/400]  eta: 0:01:09  lr: 0.000295  loss: 0.8993 (0.8683)  grad: 0.5020 (0.5241)  time: 0.2464  data: 0.0024  max mem: 23762
+train: [6]  [140/400]  eta: 0:01:04  lr: 0.000294  loss: 0.7801 (0.8647)  grad: 0.5423 (0.5336)  time: 0.2459  data: 0.0025  max mem: 23762
+train: [6]  [160/400]  eta: 0:00:59  lr: 0.000294  loss: 0.9303 (0.8772)  grad: 0.5671 (0.5386)  time: 0.2463  data: 0.0024  max mem: 23762
+train: [6]  [180/400]  eta: 0:00:54  lr: 0.000293  loss: 0.9303 (0.8856)  grad: 0.5442 (0.5386)  time: 0.2465  data: 0.0025  max mem: 23762
+train: [6]  [200/400]  eta: 0:00:49  lr: 0.000293  loss: 0.8689 (0.8890)  grad: 0.5329 (0.5355)  time: 0.2459  data: 0.0025  max mem: 23762
+train: [6]  [220/400]  eta: 0:00:44  lr: 0.000292  loss: 0.8050 (0.8913)  grad: 0.5278 (0.5382)  time: 0.2459  data: 0.0025  max mem: 23762
+train: [6]  [240/400]  eta: 0:00:39  lr: 0.000292  loss: 0.8157 (0.8903)  grad: 0.5234 (0.5418)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [6]  [260/400]  eta: 0:00:34  lr: 0.000291  loss: 0.9870 (0.9026)  grad: 0.5234 (0.5438)  time: 0.2456  data: 0.0025  max mem: 23762
+train: [6]  [280/400]  eta: 0:00:29  lr: 0.000291  loss: 0.8973 (0.8957)  grad: 0.5405 (0.5424)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [6]  [300/400]  eta: 0:00:24  lr: 0.000290  loss: 0.6776 (0.8779)  grad: 0.4902 (0.5386)  time: 0.2688  data: 0.0251  max mem: 23762
+train: [6]  [320/400]  eta: 0:00:19  lr: 0.000290  loss: 0.6204 (0.8676)  grad: 0.4577 (0.5354)  time: 0.2474  data: 0.0026  max mem: 23762
+train: [6]  [340/400]  eta: 0:00:14  lr: 0.000289  loss: 0.6505 (0.8569)  grad: 0.4802 (0.5311)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [6]  [360/400]  eta: 0:00:09  lr: 0.000288  loss: 0.5948 (0.8446)  grad: 0.4783 (0.5277)  time: 0.2465  data: 0.0025  max mem: 23762
+train: [6]  [380/400]  eta: 0:00:04  lr: 0.000288  loss: 0.6322 (0.8391)  grad: 0.4680 (0.5242)  time: 0.2473  data: 0.0026  max mem: 23762
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 0.6555 (0.8325)  grad: 0.4561 (0.5207)  time: 0.2471  data: 0.0025  max mem: 23762
+train: [6] Total time: 0:01:39 (0.2487 s / it)
+train: [6] Summary: lr: 0.000287  loss: 0.6555 (0.8325)  grad: 0.4561 (0.5207)
+eval (validation): [6]  [ 0/63]  eta: 0:00:35    time: 0.5706  data: 0.4512  max mem: 23762
+eval (validation): [6]  [20/63]  eta: 0:00:06    time: 0.1230  data: 0.0022  max mem: 23762
+eval (validation): [6]  [40/63]  eta: 0:00:03    time: 0.1234  data: 0.0023  max mem: 23762
+eval (validation): [6]  [60/63]  eta: 0:00:00    time: 0.1232  data: 0.0022  max mem: 23762
+eval (validation): [6]  [62/63]  eta: 0:00:00    time: 0.1231  data: 0.0020  max mem: 23762
+eval (validation): [6] Total time: 0:00:08 (0.1315 s / it)
+cv: [6]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 0.096  acc: 0.973  f1: 0.966
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [7]  [  0/400]  eta: 0:04:37  lr: nan  time: 0.6944  data: 0.4586  max mem: 23762
+train: [7]  [ 20/400]  eta: 0:01:41  lr: 0.000286  loss: 0.6121 (0.5738)  grad: 0.4371 (0.4718)  time: 0.2451  data: 0.0019  max mem: 23762
+train: [7]  [ 40/400]  eta: 0:01:32  lr: 0.000286  loss: 0.5654 (0.5539)  grad: 0.4312 (0.4472)  time: 0.2470  data: 0.0024  max mem: 23762
+train: [7]  [ 60/400]  eta: 0:01:26  lr: 0.000285  loss: 0.5456 (0.6050)  grad: 0.4217 (0.4508)  time: 0.2454  data: 0.0017  max mem: 23762
+train: [7]  [ 80/400]  eta: 0:01:20  lr: 0.000284  loss: 0.5196 (0.5788)  grad: 0.4374 (0.4425)  time: 0.2458  data: 0.0024  max mem: 23762
+train: [7]  [100/400]  eta: 0:01:15  lr: 0.000284  loss: 0.5591 (0.5782)  grad: 0.4089 (0.4377)  time: 0.2450  data: 0.0016  max mem: 23762
+train: [7]  [120/400]  eta: 0:01:09  lr: 0.000283  loss: 0.5974 (0.5978)  grad: 0.3923 (0.4358)  time: 0.2460  data: 0.0024  max mem: 23762
+train: [7]  [140/400]  eta: 0:01:04  lr: 0.000282  loss: 0.6052 (0.5941)  grad: 0.4309 (0.4360)  time: 0.2454  data: 0.0016  max mem: 23762
+train: [7]  [160/400]  eta: 0:00:59  lr: 0.000282  loss: 0.5415 (0.5964)  grad: 0.4219 (0.4356)  time: 0.2458  data: 0.0024  max mem: 23762
+train: [7]  [180/400]  eta: 0:00:54  lr: 0.000281  loss: 0.4940 (0.5939)  grad: 0.4091 (0.4325)  time: 0.2456  data: 0.0016  max mem: 23762
+train: [7]  [200/400]  eta: 0:00:49  lr: 0.000280  loss: 0.5258 (0.5987)  grad: 0.3877 (0.4287)  time: 0.2465  data: 0.0024  max mem: 23762
+train: [7]  [220/400]  eta: 0:00:44  lr: 0.000279  loss: 0.5376 (0.5968)  grad: 0.4178 (0.4294)  time: 0.2457  data: 0.0017  max mem: 23762
+train: [7]  [240/400]  eta: 0:00:39  lr: 0.000278  loss: 0.6183 (0.6054)  grad: 0.4583 (0.4310)  time: 0.2466  data: 0.0024  max mem: 23762
+train: [7]  [260/400]  eta: 0:00:34  lr: 0.000278  loss: 0.6183 (0.6074)  grad: 0.4041 (0.4299)  time: 0.2460  data: 0.0017  max mem: 23762
+train: [7]  [280/400]  eta: 0:00:29  lr: 0.000277  loss: 0.6970 (0.6141)  grad: 0.3968 (0.4307)  time: 0.2471  data: 0.0025  max mem: 23762
+train: [7]  [300/400]  eta: 0:00:24  lr: 0.000276  loss: 0.7086 (0.6179)  grad: 0.4051 (0.4336)  time: 0.2678  data: 0.0235  max mem: 23762
+train: [7]  [320/400]  eta: 0:00:19  lr: 0.000275  loss: 0.5201 (0.6124)  grad: 0.3981 (0.4311)  time: 0.2469  data: 0.0029  max mem: 23762
+train: [7]  [340/400]  eta: 0:00:14  lr: 0.000274  loss: 0.4250 (0.6063)  grad: 0.3676 (0.4264)  time: 0.2456  data: 0.0021  max mem: 23762
+train: [7]  [360/400]  eta: 0:00:09  lr: 0.000273  loss: 0.4142 (0.5978)  grad: 0.3268 (0.4203)  time: 0.2467  data: 0.0028  max mem: 23762
+train: [7]  [380/400]  eta: 0:00:04  lr: 0.000272  loss: 0.4488 (0.5925)  grad: 0.3305 (0.4178)  time: 0.2460  data: 0.0021  max mem: 23762
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 0.4488 (0.5874)  grad: 0.3412 (0.4139)  time: 0.2463  data: 0.0022  max mem: 23762
+train: [7] Total time: 0:01:39 (0.2485 s / it)
+train: [7] Summary: lr: 0.000271  loss: 0.4488 (0.5874)  grad: 0.3412 (0.4139)
+eval (validation): [7]  [ 0/63]  eta: 0:00:35    time: 0.5657  data: 0.4476  max mem: 23762
+eval (validation): [7]  [20/63]  eta: 0:00:06    time: 0.1235  data: 0.0026  max mem: 23762
+eval (validation): [7]  [40/63]  eta: 0:00:03    time: 0.1232  data: 0.0022  max mem: 23762
+eval (validation): [7]  [60/63]  eta: 0:00:00    time: 0.1237  data: 0.0027  max mem: 23762
+eval (validation): [7]  [62/63]  eta: 0:00:00    time: 0.1233  data: 0.0024  max mem: 23762
+eval (validation): [7] Total time: 0:00:08 (0.1318 s / it)
+cv: [7]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.122  acc: 0.972  f1: 0.968
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:04:17  lr: nan  time: 0.6445  data: 0.4078  max mem: 23762
+train: [8]  [ 20/400]  eta: 0:01:40  lr: 0.000270  loss: 0.3811 (0.4079)  grad: 0.4028 (0.4213)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [8]  [ 40/400]  eta: 0:01:32  lr: 0.000270  loss: 0.4110 (0.4283)  grad: 0.3996 (0.4084)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [8]  [ 60/400]  eta: 0:01:25  lr: 0.000269  loss: 0.4436 (0.4394)  grad: 0.3413 (0.3859)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [8]  [ 80/400]  eta: 0:01:20  lr: 0.000268  loss: 0.4281 (0.4489)  grad: 0.3247 (0.3758)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [8]  [100/400]  eta: 0:01:15  lr: 0.000267  loss: 0.3980 (0.4270)  grad: 0.2914 (0.3581)  time: 0.2468  data: 0.0023  max mem: 23762
+train: [8]  [120/400]  eta: 0:01:09  lr: 0.000266  loss: 0.3624 (0.4304)  grad: 0.2849 (0.3511)  time: 0.2468  data: 0.0025  max mem: 23762
+train: [8]  [140/400]  eta: 0:01:04  lr: 0.000265  loss: 0.3667 (0.4435)  grad: 0.3269 (0.3480)  time: 0.2472  data: 0.0024  max mem: 23762
+train: [8]  [160/400]  eta: 0:00:59  lr: 0.000264  loss: 0.3667 (0.4452)  grad: 0.3315 (0.3466)  time: 0.2468  data: 0.0025  max mem: 23762
+train: [8]  [180/400]  eta: 0:00:54  lr: 0.000263  loss: 0.4638 (0.4526)  grad: 0.3443 (0.3498)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [8]  [200/400]  eta: 0:00:49  lr: 0.000262  loss: 0.4638 (0.4577)  grad: 0.3493 (0.3492)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [8]  [220/400]  eta: 0:00:44  lr: 0.000260  loss: 0.3755 (0.4511)  grad: 0.2964 (0.3431)  time: 0.2469  data: 0.0024  max mem: 23762
+train: [8]  [240/400]  eta: 0:00:39  lr: 0.000259  loss: 0.3981 (0.4510)  grad: 0.2988 (0.3428)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [8]  [260/400]  eta: 0:00:34  lr: 0.000258  loss: 0.4497 (0.4533)  grad: 0.3306 (0.3443)  time: 0.2464  data: 0.0023  max mem: 23762
+train: [8]  [280/400]  eta: 0:00:29  lr: 0.000257  loss: 0.4133 (0.4537)  grad: 0.3596 (0.3457)  time: 0.2468  data: 0.0026  max mem: 23762
+train: [8]  [300/400]  eta: 0:00:24  lr: 0.000256  loss: 0.3701 (0.4472)  grad: 0.3216 (0.3440)  time: 0.2690  data: 0.0252  max mem: 23762
+train: [8]  [320/400]  eta: 0:00:19  lr: 0.000255  loss: 0.3270 (0.4391)  grad: 0.2965 (0.3388)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [8]  [340/400]  eta: 0:00:14  lr: 0.000254  loss: 0.3270 (0.4327)  grad: 0.2689 (0.3363)  time: 0.2464  data: 0.0026  max mem: 23762
+train: [8]  [360/400]  eta: 0:00:09  lr: 0.000253  loss: 0.3368 (0.4250)  grad: 0.2689 (0.3323)  time: 0.2467  data: 0.0024  max mem: 23762
+train: [8]  [380/400]  eta: 0:00:04  lr: 0.000252  loss: 0.3369 (0.4231)  grad: 0.2704 (0.3301)  time: 0.2471  data: 0.0026  max mem: 23762
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 0.3030 (0.4168)  grad: 0.2709 (0.3267)  time: 0.2466  data: 0.0023  max mem: 23762
+train: [8] Total time: 0:01:39 (0.2490 s / it)
+train: [8] Summary: lr: 0.000250  loss: 0.3030 (0.4168)  grad: 0.2709 (0.3267)
+eval (validation): [8]  [ 0/63]  eta: 0:00:35    time: 0.5562  data: 0.4360  max mem: 23762
+eval (validation): [8]  [20/63]  eta: 0:00:06    time: 0.1234  data: 0.0025  max mem: 23762
+eval (validation): [8]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0025  max mem: 23762
+eval (validation): [8]  [60/63]  eta: 0:00:00    time: 0.1235  data: 0.0019  max mem: 23762
+eval (validation): [8]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0018  max mem: 23762
+eval (validation): [8] Total time: 0:00:08 (0.1316 s / it)
+cv: [8]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.138  acc: 0.974  f1: 0.968
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:04:37  lr: nan  time: 0.6938  data: 0.4627  max mem: 23762
+train: [9]  [ 20/400]  eta: 0:01:41  lr: 0.000249  loss: 0.3603 (0.3322)  grad: 0.2478 (0.2494)  time: 0.2458  data: 0.0026  max mem: 23762
+train: [9]  [ 40/400]  eta: 0:01:32  lr: 0.000248  loss: 0.2683 (0.3150)  grad: 0.2422 (0.2562)  time: 0.2461  data: 0.0025  max mem: 23762
+train: [9]  [ 60/400]  eta: 0:01:26  lr: 0.000247  loss: 0.2900 (0.3227)  grad: 0.2642 (0.2722)  time: 0.2452  data: 0.0018  max mem: 23762
+train: [9]  [ 80/400]  eta: 0:01:20  lr: 0.000246  loss: 0.3382 (0.3234)  grad: 0.2996 (0.2770)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [9]  [100/400]  eta: 0:01:15  lr: 0.000244  loss: 0.3575 (0.3335)  grad: 0.2938 (0.2766)  time: 0.2455  data: 0.0017  max mem: 23762
+train: [9]  [120/400]  eta: 0:01:09  lr: 0.000243  loss: 0.3327 (0.3332)  grad: 0.2728 (0.2781)  time: 0.2460  data: 0.0026  max mem: 23762
+train: [9]  [140/400]  eta: 0:01:04  lr: 0.000242  loss: 0.2989 (0.3251)  grad: 0.2861 (0.2781)  time: 0.2455  data: 0.0017  max mem: 23762
+train: [9]  [160/400]  eta: 0:00:59  lr: 0.000241  loss: 0.2807 (0.3274)  grad: 0.2741 (0.2754)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [9]  [180/400]  eta: 0:00:54  lr: 0.000240  loss: 0.3056 (0.3281)  grad: 0.2551 (0.2740)  time: 0.2453  data: 0.0017  max mem: 23762
+train: [9]  [200/400]  eta: 0:00:49  lr: 0.000238  loss: 0.3097 (0.3277)  grad: 0.2569 (0.2716)  time: 0.2462  data: 0.0025  max mem: 23762
+train: [9]  [220/400]  eta: 0:00:44  lr: 0.000237  loss: 0.3097 (0.3270)  grad: 0.2547 (0.2694)  time: 0.2455  data: 0.0017  max mem: 23762
+train: [9]  [240/400]  eta: 0:00:39  lr: 0.000236  loss: 0.3093 (0.3295)  grad: 0.2547 (0.2697)  time: 0.2458  data: 0.0024  max mem: 23762
+train: [9]  [260/400]  eta: 0:00:34  lr: 0.000234  loss: 0.2923 (0.3265)  grad: 0.2502 (0.2678)  time: 0.2454  data: 0.0017  max mem: 23762
+train: [9]  [280/400]  eta: 0:00:29  lr: 0.000233  loss: 0.2680 (0.3253)  grad: 0.2293 (0.2653)  time: 0.2458  data: 0.0026  max mem: 23762
+train: [9]  [300/400]  eta: 0:00:24  lr: 0.000232  loss: 0.2777 (0.3233)  grad: 0.2605 (0.2649)  time: 0.2688  data: 0.0245  max mem: 23762
+train: [9]  [320/400]  eta: 0:00:19  lr: 0.000230  loss: 0.2513 (0.3192)  grad: 0.2567 (0.2629)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [9]  [340/400]  eta: 0:00:14  lr: 0.000229  loss: 0.2513 (0.3181)  grad: 0.2360 (0.2629)  time: 0.2459  data: 0.0026  max mem: 23762
+train: [9]  [360/400]  eta: 0:00:09  lr: 0.000228  loss: 0.2535 (0.3149)  grad: 0.2220 (0.2603)  time: 0.2469  data: 0.0026  max mem: 23762
+train: [9]  [380/400]  eta: 0:00:04  lr: 0.000226  loss: 0.2227 (0.3110)  grad: 0.2132 (0.2593)  time: 0.2464  data: 0.0027  max mem: 23762
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 0.2592 (0.3094)  grad: 0.2175 (0.2578)  time: 0.2471  data: 0.0024  max mem: 23762
+train: [9] Total time: 0:01:39 (0.2485 s / it)
+train: [9] Summary: lr: 0.000225  loss: 0.2592 (0.3094)  grad: 0.2175 (0.2578)
+eval (validation): [9]  [ 0/63]  eta: 0:00:34    time: 0.5518  data: 0.4311  max mem: 23762
+eval (validation): [9]  [20/63]  eta: 0:00:06    time: 0.1232  data: 0.0024  max mem: 23762
+eval (validation): [9]  [40/63]  eta: 0:00:03    time: 0.1236  data: 0.0028  max mem: 23762
+eval (validation): [9]  [60/63]  eta: 0:00:00    time: 0.1238  data: 0.0021  max mem: 23762
+eval (validation): [9]  [62/63]  eta: 0:00:00    time: 0.1238  data: 0.0022  max mem: 23762
+eval (validation): [9] Total time: 0:00:08 (0.1315 s / it)
+cv: [9]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 0.092  acc: 0.973  f1: 0.967
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:04:25  lr: nan  time: 0.6636  data: 0.4262  max mem: 23762
+train: [10]  [ 20/400]  eta: 0:01:41  lr: 0.000224  loss: 0.2398 (0.2712)  grad: 0.1875 (0.1865)  time: 0.2459  data: 0.0026  max mem: 23762
+train: [10]  [ 40/400]  eta: 0:01:32  lr: 0.000222  loss: 0.2398 (0.2701)  grad: 0.1987 (0.1984)  time: 0.2467  data: 0.0027  max mem: 23762
+train: [10]  [ 60/400]  eta: 0:01:26  lr: 0.000221  loss: 0.2277 (0.2529)  grad: 0.2098 (0.2015)  time: 0.2458  data: 0.0019  max mem: 23762
+train: [10]  [ 80/400]  eta: 0:01:20  lr: 0.000220  loss: 0.2144 (0.2557)  grad: 0.2100 (0.2026)  time: 0.2472  data: 0.0028  max mem: 23762
+train: [10]  [100/400]  eta: 0:01:15  lr: 0.000218  loss: 0.2215 (0.2514)  grad: 0.2079 (0.2013)  time: 0.2462  data: 0.0020  max mem: 23762
+train: [10]  [120/400]  eta: 0:01:09  lr: 0.000217  loss: 0.2144 (0.2470)  grad: 0.1924 (0.1997)  time: 0.2473  data: 0.0029  max mem: 23762
+train: [10]  [140/400]  eta: 0:01:04  lr: 0.000215  loss: 0.2310 (0.2547)  grad: 0.2098 (0.2035)  time: 0.2463  data: 0.0020  max mem: 23762
+train: [10]  [160/400]  eta: 0:00:59  lr: 0.000214  loss: 0.2899 (0.2559)  grad: 0.2148 (0.2037)  time: 0.2472  data: 0.0029  max mem: 23762
+train: [10]  [180/400]  eta: 0:00:54  lr: 0.000213  loss: 0.2591 (0.2583)  grad: 0.1987 (0.2045)  time: 0.2459  data: 0.0019  max mem: 23762
+train: [10]  [200/400]  eta: 0:00:49  lr: 0.000211  loss: 0.2598 (0.2596)  grad: 0.2041 (0.2053)  time: 0.2470  data: 0.0028  max mem: 23762
+train: [10]  [220/400]  eta: 0:00:44  lr: 0.000210  loss: 0.2598 (0.2597)  grad: 0.1994 (0.2040)  time: 0.2465  data: 0.0019  max mem: 23762
+train: [10]  [240/400]  eta: 0:00:39  lr: 0.000208  loss: 0.2464 (0.2611)  grad: 0.1875 (0.2032)  time: 0.2472  data: 0.0028  max mem: 23762
+train: [10]  [260/400]  eta: 0:00:34  lr: 0.000207  loss: 0.2299 (0.2608)  grad: 0.1823 (0.2036)  time: 0.2464  data: 0.0019  max mem: 23762
+train: [10]  [280/400]  eta: 0:00:29  lr: 0.000205  loss: 0.2341 (0.2623)  grad: 0.2205 (0.2046)  time: 0.2470  data: 0.0029  max mem: 23762
+train: [10]  [300/400]  eta: 0:00:24  lr: 0.000204  loss: 0.2523 (0.2625)  grad: 0.1907 (0.2037)  time: 0.2703  data: 0.0260  max mem: 23762
+train: [10]  [320/400]  eta: 0:00:19  lr: 0.000202  loss: 0.2322 (0.2599)  grad: 0.1815 (0.2032)  time: 0.2464  data: 0.0028  max mem: 23762
+train: [10]  [340/400]  eta: 0:00:14  lr: 0.000201  loss: 0.2126 (0.2594)  grad: 0.1817 (0.2018)  time: 0.2470  data: 0.0027  max mem: 23762
+train: [10]  [360/400]  eta: 0:00:09  lr: 0.000199  loss: 0.2126 (0.2571)  grad: 0.1756 (0.2006)  time: 0.2462  data: 0.0026  max mem: 23762
+train: [10]  [380/400]  eta: 0:00:04  lr: 0.000198  loss: 0.2162 (0.2555)  grad: 0.1671 (0.1991)  time: 0.2469  data: 0.0026  max mem: 23762
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 0.2162 (0.2527)  grad: 0.1628 (0.1971)  time: 0.2464  data: 0.0024  max mem: 23762
+train: [10] Total time: 0:01:39 (0.2491 s / it)
+train: [10] Summary: lr: 0.000196  loss: 0.2162 (0.2527)  grad: 0.1628 (0.1971)
+eval (validation): [10]  [ 0/63]  eta: 0:00:35    time: 0.5692  data: 0.4489  max mem: 23762
+eval (validation): [10]  [20/63]  eta: 0:00:06    time: 0.1233  data: 0.0022  max mem: 23762
+eval (validation): [10]  [40/63]  eta: 0:00:03    time: 0.1231  data: 0.0024  max mem: 23762
+eval (validation): [10]  [60/63]  eta: 0:00:00    time: 0.1238  data: 0.0022  max mem: 23762
+eval (validation): [10]  [62/63]  eta: 0:00:00    time: 0.1237  data: 0.0020  max mem: 23762
+eval (validation): [10] Total time: 0:00:08 (0.1318 s / it)
+cv: [10]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.588  acc: 0.975  f1: 0.970
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:04:43  lr: nan  time: 0.7094  data: 0.4748  max mem: 23762
+train: [11]  [ 20/400]  eta: 0:01:42  lr: 0.000195  loss: 0.2176 (0.2495)  grad: 0.1460 (0.1608)  time: 0.2464  data: 0.0026  max mem: 23762
+train: [11]  [ 40/400]  eta: 0:01:32  lr: 0.000193  loss: 0.2097 (0.2267)  grad: 0.1460 (0.1535)  time: 0.2459  data: 0.0022  max mem: 23762
+train: [11]  [ 60/400]  eta: 0:01:26  lr: 0.000192  loss: 0.1994 (0.2167)  grad: 0.1542 (0.1574)  time: 0.2458  data: 0.0025  max mem: 23762
+train: [11]  [ 80/400]  eta: 0:01:20  lr: 0.000190  loss: 0.1824 (0.2129)  grad: 0.1623 (0.1596)  time: 0.2456  data: 0.0021  max mem: 23762
+train: [11]  [100/400]  eta: 0:01:15  lr: 0.000189  loss: 0.1917 (0.2079)  grad: 0.1560 (0.1562)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [11]  [120/400]  eta: 0:01:09  lr: 0.000187  loss: 0.1927 (0.2099)  grad: 0.1470 (0.1567)  time: 0.2459  data: 0.0022  max mem: 23762
+train: [11]  [140/400]  eta: 0:01:04  lr: 0.000186  loss: 0.1957 (0.2073)  grad: 0.1339 (0.1535)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [11]  [160/400]  eta: 0:00:59  lr: 0.000184  loss: 0.1846 (0.2093)  grad: 0.1372 (0.1549)  time: 0.2463  data: 0.0022  max mem: 23762
+train: [11]  [180/400]  eta: 0:00:54  lr: 0.000183  loss: 0.1905 (0.2100)  grad: 0.1421 (0.1539)  time: 0.2461  data: 0.0025  max mem: 23762
+train: [11]  [200/400]  eta: 0:00:49  lr: 0.000181  loss: 0.1916 (0.2092)  grad: 0.1400 (0.1534)  time: 0.2461  data: 0.0022  max mem: 23762
+train: [11]  [220/400]  eta: 0:00:44  lr: 0.000180  loss: 0.2002 (0.2112)  grad: 0.1645 (0.1544)  time: 0.2458  data: 0.0025  max mem: 23762
+train: [11]  [240/400]  eta: 0:00:39  lr: 0.000178  loss: 0.2006 (0.2115)  grad: 0.1645 (0.1545)  time: 0.2458  data: 0.0021  max mem: 23762
+train: [11]  [260/400]  eta: 0:00:34  lr: 0.000177  loss: 0.1918 (0.2091)  grad: 0.1474 (0.1539)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [11]  [280/400]  eta: 0:00:29  lr: 0.000175  loss: 0.1799 (0.2083)  grad: 0.1465 (0.1545)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [11]  [300/400]  eta: 0:00:24  lr: 0.000174  loss: 0.1909 (0.2088)  grad: 0.1606 (0.1554)  time: 0.2715  data: 0.0275  max mem: 23762
+train: [11]  [320/400]  eta: 0:00:19  lr: 0.000172  loss: 0.1909 (0.2083)  grad: 0.1430 (0.1541)  time: 0.2471  data: 0.0027  max mem: 23762
+train: [11]  [340/400]  eta: 0:00:14  lr: 0.000170  loss: 0.1887 (0.2072)  grad: 0.1307 (0.1543)  time: 0.2461  data: 0.0026  max mem: 23762
+train: [11]  [360/400]  eta: 0:00:09  lr: 0.000169  loss: 0.1887 (0.2071)  grad: 0.1490 (0.1541)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [11]  [380/400]  eta: 0:00:04  lr: 0.000167  loss: 0.1840 (0.2060)  grad: 0.1319 (0.1531)  time: 0.2463  data: 0.0026  max mem: 23762
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 0.1800 (0.2048)  grad: 0.1154 (0.1517)  time: 0.2467  data: 0.0024  max mem: 23762
+train: [11] Total time: 0:01:39 (0.2489 s / it)
+train: [11] Summary: lr: 0.000166  loss: 0.1800 (0.2048)  grad: 0.1154 (0.1517)
+eval (validation): [11]  [ 0/63]  eta: 0:00:35    time: 0.5630  data: 0.4426  max mem: 23762
+eval (validation): [11]  [20/63]  eta: 0:00:06    time: 0.1236  data: 0.0031  max mem: 23762
+eval (validation): [11]  [40/63]  eta: 0:00:03    time: 0.1229  data: 0.0025  max mem: 23762
+eval (validation): [11]  [60/63]  eta: 0:00:00    time: 0.1240  data: 0.0026  max mem: 23762
+eval (validation): [11]  [62/63]  eta: 0:00:00    time: 0.1239  data: 0.0025  max mem: 23762
+eval (validation): [11] Total time: 0:00:08 (0.1318 s / it)
+cv: [11]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.120  acc: 0.974  f1: 0.969
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:04:44  lr: nan  time: 0.7106  data: 0.4749  max mem: 23762
+train: [12]  [ 20/400]  eta: 0:01:41  lr: 0.000164  loss: 0.1570 (0.1578)  grad: 0.1137 (0.1317)  time: 0.2455  data: 0.0023  max mem: 23762
+train: [12]  [ 40/400]  eta: 0:01:32  lr: 0.000163  loss: 0.1650 (0.1638)  grad: 0.1137 (0.1206)  time: 0.2463  data: 0.0022  max mem: 23762
+train: [12]  [ 60/400]  eta: 0:01:26  lr: 0.000161  loss: 0.1878 (0.1742)  grad: 0.1177 (0.1224)  time: 0.2463  data: 0.0021  max mem: 23762
+train: [12]  [ 80/400]  eta: 0:01:20  lr: 0.000160  loss: 0.1878 (0.1745)  grad: 0.1177 (0.1216)  time: 0.2464  data: 0.0022  max mem: 23762
+train: [12]  [100/400]  eta: 0:01:15  lr: 0.000158  loss: 0.1743 (0.1757)  grad: 0.1236 (0.1232)  time: 0.2461  data: 0.0022  max mem: 23762
+train: [12]  [120/400]  eta: 0:01:10  lr: 0.000156  loss: 0.1712 (0.1747)  grad: 0.1229 (0.1216)  time: 0.2465  data: 0.0022  max mem: 23762
+train: [12]  [140/400]  eta: 0:01:04  lr: 0.000155  loss: 0.1663 (0.1753)  grad: 0.1147 (0.1223)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [12]  [160/400]  eta: 0:00:59  lr: 0.000153  loss: 0.1655 (0.1748)  grad: 0.1157 (0.1221)  time: 0.2460  data: 0.0022  max mem: 23762
+train: [12]  [180/400]  eta: 0:00:54  lr: 0.000152  loss: 0.1690 (0.1748)  grad: 0.1214 (0.1222)  time: 0.2467  data: 0.0024  max mem: 23762
+train: [12]  [200/400]  eta: 0:00:49  lr: 0.000150  loss: 0.1765 (0.1768)  grad: 0.1243 (0.1224)  time: 0.2463  data: 0.0022  max mem: 23762
+train: [12]  [220/400]  eta: 0:00:44  lr: 0.000149  loss: 0.1640 (0.1764)  grad: 0.1276 (0.1226)  time: 0.2463  data: 0.0024  max mem: 23762
+train: [12]  [240/400]  eta: 0:00:39  lr: 0.000147  loss: 0.1594 (0.1768)  grad: 0.1254 (0.1230)  time: 0.2467  data: 0.0022  max mem: 23762
+train: [12]  [260/400]  eta: 0:00:34  lr: 0.000145  loss: 0.1706 (0.1774)  grad: 0.1399 (0.1244)  time: 0.2465  data: 0.0024  max mem: 23762
+train: [12]  [280/400]  eta: 0:00:29  lr: 0.000144  loss: 0.1605 (0.1775)  grad: 0.1319 (0.1239)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [12]  [300/400]  eta: 0:00:24  lr: 0.000142  loss: 0.1824 (0.1783)  grad: 0.1234 (0.1242)  time: 0.2728  data: 0.0281  max mem: 23762
+train: [12]  [320/400]  eta: 0:00:19  lr: 0.000141  loss: 0.1666 (0.1774)  grad: 0.1161 (0.1233)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [12]  [340/400]  eta: 0:00:14  lr: 0.000139  loss: 0.1573 (0.1767)  grad: 0.1080 (0.1222)  time: 0.2474  data: 0.0028  max mem: 23762
+train: [12]  [360/400]  eta: 0:00:09  lr: 0.000138  loss: 0.1503 (0.1762)  grad: 0.1036 (0.1211)  time: 0.2462  data: 0.0021  max mem: 23762
+train: [12]  [380/400]  eta: 0:00:04  lr: 0.000136  loss: 0.1654 (0.1754)  grad: 0.1025 (0.1203)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 0.1602 (0.1743)  grad: 0.1025 (0.1195)  time: 0.2461  data: 0.0021  max mem: 23762
+train: [12] Total time: 0:01:39 (0.2491 s / it)
+train: [12] Summary: lr: 0.000134  loss: 0.1602 (0.1743)  grad: 0.1025 (0.1195)
+eval (validation): [12]  [ 0/63]  eta: 0:00:36    time: 0.5850  data: 0.4653  max mem: 23762
+eval (validation): [12]  [20/63]  eta: 0:00:06    time: 0.1233  data: 0.0022  max mem: 23762
+eval (validation): [12]  [40/63]  eta: 0:00:03    time: 0.1245  data: 0.0027  max mem: 23762
+eval (validation): [12]  [60/63]  eta: 0:00:00    time: 0.1219  data: 0.0018  max mem: 23762
+eval (validation): [12]  [62/63]  eta: 0:00:00    time: 0.1221  data: 0.0020  max mem: 23762
+eval (validation): [12] Total time: 0:00:08 (0.1318 s / it)
+cv: [12]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.110  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:04:51  lr: nan  time: 0.7299  data: 0.4937  max mem: 23762
+train: [13]  [ 20/400]  eta: 0:01:42  lr: 0.000133  loss: 0.1592 (0.1620)  grad: 0.1038 (0.1104)  time: 0.2455  data: 0.0022  max mem: 23762
+train: [13]  [ 40/400]  eta: 0:01:32  lr: 0.000131  loss: 0.1687 (0.1644)  grad: 0.1058 (0.1087)  time: 0.2461  data: 0.0024  max mem: 23762
+train: [13]  [ 60/400]  eta: 0:01:26  lr: 0.000130  loss: 0.1702 (0.1663)  grad: 0.1057 (0.1080)  time: 0.2459  data: 0.0024  max mem: 23762
+train: [13]  [ 80/400]  eta: 0:01:20  lr: 0.000128  loss: 0.1702 (0.1664)  grad: 0.1005 (0.1061)  time: 0.2455  data: 0.0024  max mem: 23762
+train: [13]  [100/400]  eta: 0:01:15  lr: 0.000127  loss: 0.1628 (0.1671)  grad: 0.0978 (0.1054)  time: 0.2460  data: 0.0024  max mem: 23762
+train: [13]  [120/400]  eta: 0:01:09  lr: 0.000125  loss: 0.1711 (0.1701)  grad: 0.0992 (0.1057)  time: 0.2458  data: 0.0024  max mem: 23762
+train: [13]  [140/400]  eta: 0:01:04  lr: 0.000124  loss: 0.1739 (0.1693)  grad: 0.0992 (0.1047)  time: 0.2462  data: 0.0024  max mem: 23762
+train: [13]  [160/400]  eta: 0:00:59  lr: 0.000122  loss: 0.1615 (0.1689)  grad: 0.0912 (0.1035)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [13]  [180/400]  eta: 0:00:54  lr: 0.000120  loss: 0.1625 (0.1681)  grad: 0.0953 (0.1028)  time: 0.2456  data: 0.0024  max mem: 23762
+train: [13]  [200/400]  eta: 0:00:49  lr: 0.000119  loss: 0.1622 (0.1679)  grad: 0.0921 (0.1015)  time: 0.2462  data: 0.0023  max mem: 23762
+train: [13]  [220/400]  eta: 0:00:44  lr: 0.000117  loss: 0.1622 (0.1690)  grad: 0.0911 (0.1007)  time: 0.2460  data: 0.0024  max mem: 23762
+train: [13]  [240/400]  eta: 0:00:39  lr: 0.000116  loss: 0.1675 (0.1685)  grad: 0.0984 (0.1008)  time: 0.2462  data: 0.0024  max mem: 23762
+train: [13]  [260/400]  eta: 0:00:34  lr: 0.000114  loss: 0.1477 (0.1668)  grad: 0.0947 (0.1004)  time: 0.2468  data: 0.0024  max mem: 23762
+train: [13]  [280/400]  eta: 0:00:29  lr: 0.000113  loss: 0.1473 (0.1666)  grad: 0.0912 (0.1004)  time: 0.2474  data: 0.0026  max mem: 23762
+train: [13]  [300/400]  eta: 0:00:24  lr: 0.000111  loss: 0.1678 (0.1672)  grad: 0.0926 (0.1003)  time: 0.2745  data: 0.0303  max mem: 23762
+train: [13]  [320/400]  eta: 0:00:19  lr: 0.000110  loss: 0.1607 (0.1657)  grad: 0.0926 (0.0999)  time: 0.2462  data: 0.0022  max mem: 23762
+train: [13]  [340/400]  eta: 0:00:14  lr: 0.000108  loss: 0.1451 (0.1647)  grad: 0.0923 (0.0992)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [13]  [360/400]  eta: 0:00:09  lr: 0.000107  loss: 0.1480 (0.1642)  grad: 0.0923 (0.0987)  time: 0.2457  data: 0.0021  max mem: 23762
+train: [13]  [380/400]  eta: 0:00:04  lr: 0.000105  loss: 0.1556 (0.1637)  grad: 0.0865 (0.0980)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 0.1540 (0.1632)  grad: 0.0824 (0.0972)  time: 0.2463  data: 0.0020  max mem: 23762
+train: [13] Total time: 0:01:39 (0.2490 s / it)
+train: [13] Summary: lr: 0.000104  loss: 0.1540 (0.1632)  grad: 0.0824 (0.0972)
+eval (validation): [13]  [ 0/63]  eta: 0:00:36    time: 0.5815  data: 0.4625  max mem: 23762
+eval (validation): [13]  [20/63]  eta: 0:00:06    time: 0.1234  data: 0.0019  max mem: 23762
+eval (validation): [13]  [40/63]  eta: 0:00:03    time: 0.1244  data: 0.0025  max mem: 23762
+eval (validation): [13]  [60/63]  eta: 0:00:00    time: 0.1225  data: 0.0016  max mem: 23762
+eval (validation): [13]  [62/63]  eta: 0:00:00    time: 0.1229  data: 0.0017  max mem: 23762
+eval (validation): [13] Total time: 0:00:08 (0.1321 s / it)
+cv: [13]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.110  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:04:39  lr: nan  time: 0.6993  data: 0.4619  max mem: 23762
+train: [14]  [ 20/400]  eta: 0:01:42  lr: 0.000102  loss: 0.1458 (0.1515)  grad: 0.0803 (0.0820)  time: 0.2471  data: 0.0028  max mem: 23762
+train: [14]  [ 40/400]  eta: 0:01:32  lr: 0.000101  loss: 0.1474 (0.1563)  grad: 0.0803 (0.0833)  time: 0.2457  data: 0.0026  max mem: 23762
+train: [14]  [ 60/400]  eta: 0:01:26  lr: 0.000099  loss: 0.1542 (0.1586)  grad: 0.0912 (0.0851)  time: 0.2459  data: 0.0019  max mem: 23762
+train: [14]  [ 80/400]  eta: 0:01:20  lr: 0.000098  loss: 0.1542 (0.1581)  grad: 0.0871 (0.0842)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [14]  [100/400]  eta: 0:01:15  lr: 0.000096  loss: 0.1452 (0.1558)  grad: 0.0800 (0.0841)  time: 0.2459  data: 0.0018  max mem: 23762
+train: [14]  [120/400]  eta: 0:01:09  lr: 0.000095  loss: 0.1530 (0.1580)  grad: 0.0818 (0.0849)  time: 0.2465  data: 0.0027  max mem: 23762
+train: [14]  [140/400]  eta: 0:01:04  lr: 0.000093  loss: 0.1530 (0.1565)  grad: 0.0885 (0.0849)  time: 0.2458  data: 0.0018  max mem: 23762
+train: [14]  [160/400]  eta: 0:00:59  lr: 0.000092  loss: 0.1396 (0.1550)  grad: 0.0806 (0.0842)  time: 0.2463  data: 0.0027  max mem: 23762
+train: [14]  [180/400]  eta: 0:00:54  lr: 0.000090  loss: 0.1348 (0.1539)  grad: 0.0828 (0.0845)  time: 0.2459  data: 0.0018  max mem: 23762
+train: [14]  [200/400]  eta: 0:00:49  lr: 0.000089  loss: 0.1423 (0.1550)  grad: 0.0884 (0.0849)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [14]  [220/400]  eta: 0:00:44  lr: 0.000088  loss: 0.1573 (0.1553)  grad: 0.0877 (0.0850)  time: 0.2458  data: 0.0018  max mem: 23762
+train: [14]  [240/400]  eta: 0:00:39  lr: 0.000086  loss: 0.1459 (0.1544)  grad: 0.0877 (0.0856)  time: 0.2463  data: 0.0026  max mem: 23762
+train: [14]  [260/400]  eta: 0:00:34  lr: 0.000085  loss: 0.1474 (0.1539)  grad: 0.0844 (0.0852)  time: 0.2465  data: 0.0018  max mem: 23762
+train: [14]  [280/400]  eta: 0:00:29  lr: 0.000083  loss: 0.1474 (0.1532)  grad: 0.0793 (0.0847)  time: 0.2468  data: 0.0027  max mem: 23762
+train: [14]  [300/400]  eta: 0:00:24  lr: 0.000082  loss: 0.1586 (0.1538)  grad: 0.0805 (0.0848)  time: 0.2738  data: 0.0303  max mem: 23762
+train: [14]  [320/400]  eta: 0:00:19  lr: 0.000081  loss: 0.1551 (0.1535)  grad: 0.0808 (0.0844)  time: 0.2465  data: 0.0024  max mem: 23762
+train: [14]  [340/400]  eta: 0:00:14  lr: 0.000079  loss: 0.1545 (0.1537)  grad: 0.0808 (0.0840)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [14]  [360/400]  eta: 0:00:09  lr: 0.000078  loss: 0.1534 (0.1535)  grad: 0.0778 (0.0834)  time: 0.2471  data: 0.0026  max mem: 23762
+train: [14]  [380/400]  eta: 0:00:04  lr: 0.000076  loss: 0.1523 (0.1540)  grad: 0.0750 (0.0831)  time: 0.2475  data: 0.0025  max mem: 23762
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 0.1523 (0.1538)  grad: 0.0701 (0.0824)  time: 0.2468  data: 0.0025  max mem: 23762
+train: [14] Total time: 0:01:39 (0.2492 s / it)
+train: [14] Summary: lr: 0.000075  loss: 0.1523 (0.1538)  grad: 0.0701 (0.0824)
+eval (validation): [14]  [ 0/63]  eta: 0:00:40    time: 0.6376  data: 0.5185  max mem: 23762
+eval (validation): [14]  [20/63]  eta: 0:00:06    time: 0.1224  data: 0.0016  max mem: 23762
+eval (validation): [14]  [40/63]  eta: 0:00:03    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [14]  [60/63]  eta: 0:00:00    time: 0.1230  data: 0.0016  max mem: 23762
+eval (validation): [14]  [62/63]  eta: 0:00:00    time: 0.1228  data: 0.0016  max mem: 23762
+eval (validation): [14] Total time: 0:00:08 (0.1325 s / it)
+cv: [14]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.110  acc: 0.977  f1: 0.972
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:05:03  lr: nan  time: 0.7598  data: 0.5222  max mem: 23762
+train: [15]  [ 20/400]  eta: 0:01:42  lr: 0.000074  loss: 0.1576 (0.1562)  grad: 0.0660 (0.0764)  time: 0.2462  data: 0.0018  max mem: 23762
+train: [15]  [ 40/400]  eta: 0:01:33  lr: 0.000072  loss: 0.1576 (0.1568)  grad: 0.0678 (0.0744)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [15]  [ 60/400]  eta: 0:01:26  lr: 0.000071  loss: 0.1543 (0.1562)  grad: 0.0740 (0.0761)  time: 0.2460  data: 0.0019  max mem: 23762
+train: [15]  [ 80/400]  eta: 0:01:20  lr: 0.000070  loss: 0.1553 (0.1565)  grad: 0.0778 (0.0764)  time: 0.2460  data: 0.0026  max mem: 23762
+train: [15]  [100/400]  eta: 0:01:15  lr: 0.000068  loss: 0.1459 (0.1551)  grad: 0.0782 (0.0764)  time: 0.2452  data: 0.0018  max mem: 23762
+train: [15]  [120/400]  eta: 0:01:10  lr: 0.000067  loss: 0.1456 (0.1547)  grad: 0.0700 (0.0751)  time: 0.2464  data: 0.0026  max mem: 23762
+train: [15]  [140/400]  eta: 0:01:04  lr: 0.000066  loss: 0.1456 (0.1540)  grad: 0.0658 (0.0757)  time: 0.2457  data: 0.0017  max mem: 23762
+train: [15]  [160/400]  eta: 0:00:59  lr: 0.000064  loss: 0.1456 (0.1528)  grad: 0.0769 (0.0760)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [15]  [180/400]  eta: 0:00:54  lr: 0.000063  loss: 0.1454 (0.1526)  grad: 0.0771 (0.0763)  time: 0.2456  data: 0.0018  max mem: 23762
+train: [15]  [200/400]  eta: 0:00:49  lr: 0.000062  loss: 0.1471 (0.1530)  grad: 0.0751 (0.0763)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [15]  [220/400]  eta: 0:00:44  lr: 0.000061  loss: 0.1569 (0.1537)  grad: 0.0744 (0.0763)  time: 0.2465  data: 0.0018  max mem: 23762
+train: [15]  [240/400]  eta: 0:00:39  lr: 0.000059  loss: 0.1505 (0.1524)  grad: 0.0704 (0.0757)  time: 0.2466  data: 0.0026  max mem: 23762
+train: [15]  [260/400]  eta: 0:00:34  lr: 0.000058  loss: 0.1373 (0.1521)  grad: 0.0673 (0.0755)  time: 0.2460  data: 0.0018  max mem: 23762
+train: [15]  [280/400]  eta: 0:00:29  lr: 0.000057  loss: 0.1439 (0.1521)  grad: 0.0714 (0.0753)  time: 0.2468  data: 0.0026  max mem: 23762
+train: [15]  [300/400]  eta: 0:00:24  lr: 0.000056  loss: 0.1439 (0.1516)  grad: 0.0682 (0.0748)  time: 0.2741  data: 0.0301  max mem: 23762
+train: [15]  [320/400]  eta: 0:00:19  lr: 0.000054  loss: 0.1422 (0.1506)  grad: 0.0640 (0.0745)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [15]  [340/400]  eta: 0:00:14  lr: 0.000053  loss: 0.1448 (0.1504)  grad: 0.0685 (0.0743)  time: 0.2459  data: 0.0019  max mem: 23762
+train: [15]  [360/400]  eta: 0:00:09  lr: 0.000052  loss: 0.1396 (0.1500)  grad: 0.0660 (0.0740)  time: 0.2467  data: 0.0026  max mem: 23762
+train: [15]  [380/400]  eta: 0:00:04  lr: 0.000051  loss: 0.1335 (0.1489)  grad: 0.0659 (0.0737)  time: 0.2453  data: 0.0017  max mem: 23762
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 0.1335 (0.1488)  grad: 0.0644 (0.0733)  time: 0.2455  data: 0.0018  max mem: 23762
+train: [15] Total time: 0:01:39 (0.2491 s / it)
+train: [15] Summary: lr: 0.000050  loss: 0.1335 (0.1488)  grad: 0.0644 (0.0733)
+eval (validation): [15]  [ 0/63]  eta: 0:00:35    time: 0.5610  data: 0.4421  max mem: 23762
+eval (validation): [15]  [20/63]  eta: 0:00:06    time: 0.1229  data: 0.0018  max mem: 23762
+eval (validation): [15]  [40/63]  eta: 0:00:03    time: 0.1235  data: 0.0026  max mem: 23762
+eval (validation): [15]  [60/63]  eta: 0:00:00    time: 0.1225  data: 0.0017  max mem: 23762
+eval (validation): [15]  [62/63]  eta: 0:00:00    time: 0.1227  data: 0.0017  max mem: 23762
+eval (validation): [15] Total time: 0:00:08 (0.1313 s / it)
+cv: [15]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.109  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:04:50  lr: nan  time: 0.7260  data: 0.4933  max mem: 23762
+train: [16]  [ 20/400]  eta: 0:01:42  lr: 0.000048  loss: 0.1527 (0.1500)  grad: 0.0597 (0.0641)  time: 0.2463  data: 0.0025  max mem: 23762
+train: [16]  [ 40/400]  eta: 0:01:32  lr: 0.000047  loss: 0.1463 (0.1477)  grad: 0.0656 (0.0674)  time: 0.2463  data: 0.0028  max mem: 23762
+train: [16]  [ 60/400]  eta: 0:01:26  lr: 0.000046  loss: 0.1388 (0.1469)  grad: 0.0708 (0.0674)  time: 0.2466  data: 0.0022  max mem: 23762
+train: [16]  [ 80/400]  eta: 0:01:20  lr: 0.000045  loss: 0.1502 (0.1487)  grad: 0.0664 (0.0670)  time: 0.2464  data: 0.0027  max mem: 23762
+train: [16]  [100/400]  eta: 0:01:15  lr: 0.000044  loss: 0.1386 (0.1463)  grad: 0.0673 (0.0682)  time: 0.2459  data: 0.0022  max mem: 23762
+train: [16]  [120/400]  eta: 0:01:10  lr: 0.000043  loss: 0.1330 (0.1458)  grad: 0.0690 (0.0686)  time: 0.2463  data: 0.0027  max mem: 23762
+train: [16]  [140/400]  eta: 0:01:04  lr: 0.000042  loss: 0.1401 (0.1459)  grad: 0.0672 (0.0690)  time: 0.2464  data: 0.0022  max mem: 23762
+train: [16]  [160/400]  eta: 0:00:59  lr: 0.000041  loss: 0.1467 (0.1478)  grad: 0.0702 (0.0697)  time: 0.2461  data: 0.0026  max mem: 23762
+train: [16]  [180/400]  eta: 0:00:54  lr: 0.000040  loss: 0.1562 (0.1483)  grad: 0.0694 (0.0697)  time: 0.2461  data: 0.0023  max mem: 23762
+train: [16]  [200/400]  eta: 0:00:49  lr: 0.000039  loss: 0.1526 (0.1494)  grad: 0.0641 (0.0698)  time: 0.2470  data: 0.0027  max mem: 23762
+train: [16]  [220/400]  eta: 0:00:44  lr: 0.000038  loss: 0.1546 (0.1494)  grad: 0.0703 (0.0701)  time: 0.2465  data: 0.0023  max mem: 23762
+train: [16]  [240/400]  eta: 0:00:39  lr: 0.000036  loss: 0.1359 (0.1485)  grad: 0.0664 (0.0698)  time: 0.2466  data: 0.0027  max mem: 23762
+train: [16]  [260/400]  eta: 0:00:34  lr: 0.000035  loss: 0.1441 (0.1485)  grad: 0.0694 (0.0701)  time: 0.2461  data: 0.0022  max mem: 23762
+train: [16]  [280/400]  eta: 0:00:29  lr: 0.000034  loss: 0.1441 (0.1482)  grad: 0.0714 (0.0702)  time: 0.2464  data: 0.0027  max mem: 23762
+train: [16]  [300/400]  eta: 0:00:24  lr: 0.000033  loss: 0.1381 (0.1487)  grad: 0.0705 (0.0704)  time: 0.2701  data: 0.0264  max mem: 23762
+train: [16]  [320/400]  eta: 0:00:19  lr: 0.000032  loss: 0.1504 (0.1489)  grad: 0.0716 (0.0707)  time: 0.2464  data: 0.0027  max mem: 23762
+train: [16]  [340/400]  eta: 0:00:14  lr: 0.000031  loss: 0.1504 (0.1489)  grad: 0.0623 (0.0703)  time: 0.2465  data: 0.0026  max mem: 23762
+train: [16]  [360/400]  eta: 0:00:09  lr: 0.000031  loss: 0.1489 (0.1489)  grad: 0.0697 (0.0704)  time: 0.2471  data: 0.0027  max mem: 23762
+train: [16]  [380/400]  eta: 0:00:04  lr: 0.000030  loss: 0.1492 (0.1486)  grad: 0.0697 (0.0701)  time: 0.2463  data: 0.0026  max mem: 23762
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 0.1284 (0.1478)  grad: 0.0634 (0.0698)  time: 0.2466  data: 0.0025  max mem: 23762
+train: [16] Total time: 0:01:39 (0.2490 s / it)
+train: [16] Summary: lr: 0.000029  loss: 0.1284 (0.1478)  grad: 0.0634 (0.0698)
+eval (validation): [16]  [ 0/63]  eta: 0:00:37    time: 0.5882  data: 0.4707  max mem: 23762
+eval (validation): [16]  [20/63]  eta: 0:00:06    time: 0.1229  data: 0.0021  max mem: 23762
+eval (validation): [16]  [40/63]  eta: 0:00:03    time: 0.1240  data: 0.0026  max mem: 23762
+eval (validation): [16]  [60/63]  eta: 0:00:00    time: 0.1225  data: 0.0018  max mem: 23762
+eval (validation): [16]  [62/63]  eta: 0:00:00    time: 0.1227  data: 0.0018  max mem: 23762
+eval (validation): [16] Total time: 0:00:08 (0.1318 s / it)
+cv: [16]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.109  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:04:33  lr: nan  time: 0.6835  data: 0.4485  max mem: 23762
+train: [17]  [ 20/400]  eta: 0:01:41  lr: 0.000028  loss: 0.1561 (0.1580)  grad: 0.0655 (0.0721)  time: 0.2467  data: 0.0030  max mem: 23762
+train: [17]  [ 40/400]  eta: 0:01:32  lr: 0.000027  loss: 0.1451 (0.1473)  grad: 0.0696 (0.0703)  time: 0.2458  data: 0.0023  max mem: 23762
+train: [17]  [ 60/400]  eta: 0:01:26  lr: 0.000026  loss: 0.1374 (0.1466)  grad: 0.0745 (0.0722)  time: 0.2467  data: 0.0029  max mem: 23762
+train: [17]  [ 80/400]  eta: 0:01:20  lr: 0.000025  loss: 0.1425 (0.1459)  grad: 0.0706 (0.0718)  time: 0.2458  data: 0.0023  max mem: 23762
+train: [17]  [100/400]  eta: 0:01:15  lr: 0.000024  loss: 0.1425 (0.1445)  grad: 0.0660 (0.0718)  time: 0.2464  data: 0.0030  max mem: 23762
+train: [17]  [120/400]  eta: 0:01:09  lr: 0.000023  loss: 0.1455 (0.1449)  grad: 0.0637 (0.0713)  time: 0.2459  data: 0.0022  max mem: 23762
+train: [17]  [140/400]  eta: 0:01:04  lr: 0.000023  loss: 0.1519 (0.1470)  grad: 0.0677 (0.0711)  time: 0.2458  data: 0.0028  max mem: 23762
+train: [17]  [160/400]  eta: 0:00:59  lr: 0.000022  loss: 0.1476 (0.1472)  grad: 0.0698 (0.0717)  time: 0.2462  data: 0.0022  max mem: 23762
+train: [17]  [180/400]  eta: 0:00:54  lr: 0.000021  loss: 0.1483 (0.1479)  grad: 0.0698 (0.0712)  time: 0.2466  data: 0.0029  max mem: 23762
+train: [17]  [200/400]  eta: 0:00:49  lr: 0.000020  loss: 0.1491 (0.1474)  grad: 0.0649 (0.0709)  time: 0.2461  data: 0.0022  max mem: 23762
+train: [17]  [220/400]  eta: 0:00:44  lr: 0.000019  loss: 0.1390 (0.1471)  grad: 0.0647 (0.0707)  time: 0.2465  data: 0.0028  max mem: 23762
+train: [17]  [240/400]  eta: 0:00:39  lr: 0.000019  loss: 0.1310 (0.1467)  grad: 0.0663 (0.0707)  time: 0.2456  data: 0.0022  max mem: 23762
+train: [17]  [260/400]  eta: 0:00:34  lr: 0.000018  loss: 0.1408 (0.1458)  grad: 0.0721 (0.0708)  time: 0.2465  data: 0.0029  max mem: 23762
+train: [17]  [280/400]  eta: 0:00:29  lr: 0.000017  loss: 0.1355 (0.1452)  grad: 0.0669 (0.0704)  time: 0.2456  data: 0.0021  max mem: 23762
+train: [17]  [300/400]  eta: 0:00:24  lr: 0.000016  loss: 0.1347 (0.1448)  grad: 0.0648 (0.0702)  time: 0.2707  data: 0.0272  max mem: 23762
+train: [17]  [320/400]  eta: 0:00:19  lr: 0.000016  loss: 0.1421 (0.1452)  grad: 0.0687 (0.0702)  time: 0.2462  data: 0.0025  max mem: 23762
+train: [17]  [340/400]  eta: 0:00:14  lr: 0.000015  loss: 0.1456 (0.1454)  grad: 0.0663 (0.0700)  time: 0.2464  data: 0.0025  max mem: 23762
+train: [17]  [360/400]  eta: 0:00:09  lr: 0.000014  loss: 0.1443 (0.1454)  grad: 0.0676 (0.0701)  time: 0.2462  data: 0.0026  max mem: 23762
+train: [17]  [380/400]  eta: 0:00:04  lr: 0.000014  loss: 0.1425 (0.1453)  grad: 0.0714 (0.0702)  time: 0.2455  data: 0.0024  max mem: 23762
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 0.1425 (0.1458)  grad: 0.0668 (0.0701)  time: 0.2460  data: 0.0024  max mem: 23762
+train: [17] Total time: 0:01:39 (0.2492 s / it)
+train: [17] Summary: lr: 0.000013  loss: 0.1425 (0.1458)  grad: 0.0668 (0.0701)
+eval (validation): [17]  [ 0/63]  eta: 0:00:34    time: 0.5497  data: 0.4303  max mem: 23762
+eval (validation): [17]  [20/63]  eta: 0:00:06    time: 0.1227  data: 0.0023  max mem: 23762
+eval (validation): [17]  [40/63]  eta: 0:00:03    time: 0.1244  data: 0.0025  max mem: 23762
+eval (validation): [17]  [60/63]  eta: 0:00:00    time: 0.1227  data: 0.0016  max mem: 23762
+eval (validation): [17]  [62/63]  eta: 0:00:00    time: 0.1228  data: 0.0016  max mem: 23762
+eval (validation): [17] Total time: 0:00:08 (0.1314 s / it)
+cv: [17]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 0.176  acc: 0.977  f1: 0.972
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:04:58  lr: nan  time: 0.7455  data: 0.5103  max mem: 23762
+train: [18]  [ 20/400]  eta: 0:01:42  lr: 0.000012  loss: 0.1332 (0.1441)  grad: 0.0633 (0.0680)  time: 0.2457  data: 0.0024  max mem: 23762
+train: [18]  [ 40/400]  eta: 0:01:32  lr: 0.000012  loss: 0.1332 (0.1391)  grad: 0.0667 (0.0689)  time: 0.2465  data: 0.0028  max mem: 23762
+train: [18]  [ 60/400]  eta: 0:01:26  lr: 0.000011  loss: 0.1347 (0.1413)  grad: 0.0694 (0.0694)  time: 0.2457  data: 0.0019  max mem: 23762
+train: [18]  [ 80/400]  eta: 0:01:20  lr: 0.000011  loss: 0.1345 (0.1389)  grad: 0.0701 (0.0697)  time: 0.2469  data: 0.0028  max mem: 23762
+train: [18]  [100/400]  eta: 0:01:15  lr: 0.000010  loss: 0.1350 (0.1403)  grad: 0.0648 (0.0691)  time: 0.2464  data: 0.0019  max mem: 23762
+train: [18]  [120/400]  eta: 0:01:10  lr: 0.000009  loss: 0.1376 (0.1426)  grad: 0.0684 (0.0697)  time: 0.2467  data: 0.0029  max mem: 23762
+train: [18]  [140/400]  eta: 0:01:04  lr: 0.000009  loss: 0.1369 (0.1443)  grad: 0.0676 (0.0690)  time: 0.2453  data: 0.0019  max mem: 23762
+train: [18]  [160/400]  eta: 0:00:59  lr: 0.000008  loss: 0.1369 (0.1434)  grad: 0.0660 (0.0690)  time: 0.2466  data: 0.0028  max mem: 23762
+train: [18]  [180/400]  eta: 0:00:54  lr: 0.000008  loss: 0.1346 (0.1445)  grad: 0.0656 (0.0686)  time: 0.2458  data: 0.0019  max mem: 23762
+train: [18]  [200/400]  eta: 0:00:49  lr: 0.000007  loss: 0.1363 (0.1434)  grad: 0.0655 (0.0687)  time: 0.2464  data: 0.0027  max mem: 23762
+train: [18]  [220/400]  eta: 0:00:44  lr: 0.000007  loss: 0.1363 (0.1433)  grad: 0.0693 (0.0690)  time: 0.2456  data: 0.0019  max mem: 23762
+train: [18]  [240/400]  eta: 0:00:39  lr: 0.000006  loss: 0.1406 (0.1443)  grad: 0.0693 (0.0688)  time: 0.2466  data: 0.0029  max mem: 23762
+train: [18]  [260/400]  eta: 0:00:34  lr: 0.000006  loss: 0.1661 (0.1456)  grad: 0.0628 (0.0686)  time: 0.2456  data: 0.0019  max mem: 23762
+train: [18]  [280/400]  eta: 0:00:29  lr: 0.000006  loss: 0.1366 (0.1450)  grad: 0.0646 (0.0686)  time: 0.2468  data: 0.0029  max mem: 23762
+train: [18]  [300/400]  eta: 0:00:24  lr: 0.000005  loss: 0.1366 (0.1453)  grad: 0.0682 (0.0687)  time: 0.2708  data: 0.0268  max mem: 23762
+train: [18]  [320/400]  eta: 0:00:19  lr: 0.000005  loss: 0.1404 (0.1444)  grad: 0.0652 (0.0684)  time: 0.2477  data: 0.0028  max mem: 23762
+train: [18]  [340/400]  eta: 0:00:14  lr: 0.000004  loss: 0.1313 (0.1445)  grad: 0.0650 (0.0685)  time: 0.2458  data: 0.0018  max mem: 23762
+train: [18]  [360/400]  eta: 0:00:09  lr: 0.000004  loss: 0.1321 (0.1449)  grad: 0.0676 (0.0684)  time: 0.2471  data: 0.0027  max mem: 23762
+train: [18]  [380/400]  eta: 0:00:04  lr: 0.000004  loss: 0.1376 (0.1449)  grad: 0.0687 (0.0688)  time: 0.2461  data: 0.0018  max mem: 23762
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 0.1321 (0.1445)  grad: 0.0704 (0.0688)  time: 0.2462  data: 0.0021  max mem: 23762
+train: [18] Total time: 0:01:39 (0.2490 s / it)
+train: [18] Summary: lr: 0.000003  loss: 0.1321 (0.1445)  grad: 0.0704 (0.0688)
+eval (validation): [18]  [ 0/63]  eta: 0:00:34    time: 0.5438  data: 0.4245  max mem: 23762
+eval (validation): [18]  [20/63]  eta: 0:00:06    time: 0.1230  data: 0.0025  max mem: 23762
+eval (validation): [18]  [40/63]  eta: 0:00:03    time: 0.1235  data: 0.0025  max mem: 23762
+eval (validation): [18]  [60/63]  eta: 0:00:00    time: 0.1239  data: 0.0024  max mem: 23762
+eval (validation): [18]  [62/63]  eta: 0:00:00    time: 0.1239  data: 0.0023  max mem: 23762
+eval (validation): [18] Total time: 0:00:08 (0.1314 s / it)
+cv: [18]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.109  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:05:10  lr: nan  time: 0.7751  data: 0.5398  max mem: 23762
+train: [19]  [ 20/400]  eta: 0:01:42  lr: 0.000003  loss: 0.1373 (0.1452)  grad: 0.0620 (0.0615)  time: 0.2453  data: 0.0019  max mem: 23762
+train: [19]  [ 40/400]  eta: 0:01:33  lr: 0.000003  loss: 0.1373 (0.1404)  grad: 0.0633 (0.0669)  time: 0.2466  data: 0.0029  max mem: 23762
+train: [19]  [ 60/400]  eta: 0:01:26  lr: 0.000002  loss: 0.1454 (0.1458)  grad: 0.0710 (0.0679)  time: 0.2460  data: 0.0020  max mem: 23762
+train: [19]  [ 80/400]  eta: 0:01:20  lr: 0.000002  loss: 0.1419 (0.1439)  grad: 0.0680 (0.0666)  time: 0.2466  data: 0.0028  max mem: 23762
+train: [19]  [100/400]  eta: 0:01:15  lr: 0.000002  loss: 0.1365 (0.1465)  grad: 0.0680 (0.0673)  time: 0.2459  data: 0.0019  max mem: 23762
+train: [19]  [120/400]  eta: 0:01:10  lr: 0.000002  loss: 0.1428 (0.1453)  grad: 0.0635 (0.0668)  time: 0.2467  data: 0.0029  max mem: 23762
+train: [19]  [140/400]  eta: 0:01:04  lr: 0.000001  loss: 0.1358 (0.1462)  grad: 0.0636 (0.0681)  time: 0.2456  data: 0.0019  max mem: 23762
+train: [19]  [160/400]  eta: 0:00:59  lr: 0.000001  loss: 0.1384 (0.1456)  grad: 0.0675 (0.0677)  time: 0.2471  data: 0.0029  max mem: 23762
+train: [19]  [180/400]  eta: 0:00:54  lr: 0.000001  loss: 0.1483 (0.1463)  grad: 0.0652 (0.0677)  time: 0.2459  data: 0.0020  max mem: 23762
+train: [19]  [200/400]  eta: 0:00:49  lr: 0.000001  loss: 0.1501 (0.1462)  grad: 0.0649 (0.0677)  time: 0.2468  data: 0.0029  max mem: 23762
+train: [19]  [220/400]  eta: 0:00:44  lr: 0.000001  loss: 0.1374 (0.1456)  grad: 0.0671 (0.0677)  time: 0.2459  data: 0.0019  max mem: 23762
+train: [19]  [240/400]  eta: 0:00:39  lr: 0.000001  loss: 0.1355 (0.1450)  grad: 0.0688 (0.0680)  time: 0.2469  data: 0.0029  max mem: 23762
+train: [19]  [260/400]  eta: 0:00:34  lr: 0.000000  loss: 0.1396 (0.1441)  grad: 0.0634 (0.0674)  time: 0.2458  data: 0.0020  max mem: 23762
+train: [19]  [280/400]  eta: 0:00:29  lr: 0.000000  loss: 0.1349 (0.1439)  grad: 0.0628 (0.0676)  time: 0.2474  data: 0.0029  max mem: 23762
+train: [19]  [300/400]  eta: 0:00:24  lr: 0.000000  loss: 0.1352 (0.1437)  grad: 0.0672 (0.0677)  time: 0.2720  data: 0.0273  max mem: 23762
+train: [19]  [320/400]  eta: 0:00:19  lr: 0.000000  loss: 0.1398 (0.1435)  grad: 0.0674 (0.0680)  time: 0.2474  data: 0.0028  max mem: 23762
+train: [19]  [340/400]  eta: 0:00:14  lr: 0.000000  loss: 0.1385 (0.1432)  grad: 0.0674 (0.0680)  time: 0.2465  data: 0.0029  max mem: 23762
+train: [19]  [360/400]  eta: 0:00:09  lr: 0.000000  loss: 0.1356 (0.1434)  grad: 0.0682 (0.0681)  time: 0.2469  data: 0.0027  max mem: 23762
+train: [19]  [380/400]  eta: 0:00:04  lr: 0.000000  loss: 0.1355 (0.1438)  grad: 0.0686 (0.0680)  time: 0.2463  data: 0.0029  max mem: 23762
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 0.1385 (0.1439)  grad: 0.0639 (0.0680)  time: 0.2465  data: 0.0028  max mem: 23762
+train: [19] Total time: 0:01:39 (0.2493 s / it)
+train: [19] Summary: lr: 0.000000  loss: 0.1385 (0.1439)  grad: 0.0639 (0.0680)
+eval (validation): [19]  [ 0/63]  eta: 0:00:42    time: 0.6738  data: 0.5546  max mem: 23762
+eval (validation): [19]  [20/63]  eta: 0:00:06    time: 0.1224  data: 0.0018  max mem: 23762
+eval (validation): [19]  [40/63]  eta: 0:00:03    time: 0.1238  data: 0.0026  max mem: 23762
+eval (validation): [19]  [60/63]  eta: 0:00:00    time: 0.1238  data: 0.0019  max mem: 23762
+eval (validation): [19]  [62/63]  eta: 0:00:00    time: 0.1236  data: 0.0019  max mem: 23762
+eval (validation): [19] Total time: 0:00:08 (0.1334 s / it)
+cv: [19]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.109  acc: 0.977  f1: 0.971
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.9766865079365079, "hparam": [5.1, 1.0], "hparam_id": 34, "epoch": 19, "is_best": false, "best_score": 0.9774305555555556}
+eval (train): [20]  [  0/297]  eta: 0:03:03    time: 0.6181  data: 0.4976  max mem: 23762
+eval (train): [20]  [ 20/297]  eta: 0:00:40    time: 0.1234  data: 0.0024  max mem: 23762
+eval (train): [20]  [ 40/297]  eta: 0:00:34    time: 0.1239  data: 0.0020  max mem: 23762
+eval (train): [20]  [ 60/297]  eta: 0:00:31    time: 0.1246  data: 0.0027  max mem: 23762
+eval (train): [20]  [ 80/297]  eta: 0:00:28    time: 0.1232  data: 0.0021  max mem: 23762
+eval (train): [20]  [100/297]  eta: 0:00:25    time: 0.1243  data: 0.0027  max mem: 23762
+eval (train): [20]  [120/297]  eta: 0:00:22    time: 0.1238  data: 0.0021  max mem: 23762
+eval (train): [20]  [140/297]  eta: 0:00:20    time: 0.1242  data: 0.0027  max mem: 23762
+eval (train): [20]  [160/297]  eta: 0:00:17    time: 0.1228  data: 0.0020  max mem: 23762
+eval (train): [20]  [180/297]  eta: 0:00:14    time: 0.1239  data: 0.0026  max mem: 23762
+eval (train): [20]  [200/297]  eta: 0:00:12    time: 0.1230  data: 0.0021  max mem: 23762
+eval (train): [20]  [220/297]  eta: 0:00:09    time: 0.1243  data: 0.0027  max mem: 23762
+eval (train): [20]  [240/297]  eta: 0:00:07    time: 0.1224  data: 0.0020  max mem: 23762
+eval (train): [20]  [260/297]  eta: 0:00:04    time: 0.1234  data: 0.0026  max mem: 23762
+eval (train): [20]  [280/297]  eta: 0:00:02    time: 0.1229  data: 0.0020  max mem: 23762
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.1223  data: 0.0020  max mem: 23762
+eval (train): [20] Total time: 0:00:37 (0.1256 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:00:41    time: 0.6587  data: 0.5392  max mem: 23762
+eval (validation): [20]  [20/63]  eta: 0:00:06    time: 0.1232  data: 0.0030  max mem: 23762
+eval (validation): [20]  [40/63]  eta: 0:00:03    time: 0.1236  data: 0.0024  max mem: 23762
+eval (validation): [20]  [60/63]  eta: 0:00:00    time: 0.1236  data: 0.0024  max mem: 23762
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.1236  data: 0.0024  max mem: 23762
+eval (validation): [20] Total time: 0:00:08 (0.1333 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:00:50    time: 0.6356  data: 0.5148  max mem: 23762
+eval (test): [20]  [20/79]  eta: 0:00:08    time: 0.1226  data: 0.0021  max mem: 23762
+eval (test): [20]  [40/79]  eta: 0:00:05    time: 0.1233  data: 0.0023  max mem: 23762
+eval (test): [20]  [60/79]  eta: 0:00:02    time: 0.1241  data: 0.0022  max mem: 23762
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.1227  data: 0.0021  max mem: 23762
+eval (test): [20] Total time: 0:00:10 (0.1307 s / it)
+evaluating best checkpoint: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.9774305555555556, "hparam": [5.1, 1.0], "hparam_id": 34, "epoch": 14, "is_best": true, "best_score": 0.9774305555555556}
+eval (train): [20]  [  0/297]  eta: 0:03:03    time: 0.6189  data: 0.5004  max mem: 23762
+eval (train): [20]  [ 20/297]  eta: 0:00:40    time: 0.1231  data: 0.0026  max mem: 23762
+eval (train): [20]  [ 40/297]  eta: 0:00:34    time: 0.1233  data: 0.0026  max mem: 23762
+eval (train): [20]  [ 60/297]  eta: 0:00:31    time: 0.1222  data: 0.0020  max mem: 23762
+eval (train): [20]  [ 80/297]  eta: 0:00:27    time: 0.1231  data: 0.0025  max mem: 23762
+eval (train): [20]  [100/297]  eta: 0:00:25    time: 0.1237  data: 0.0020  max mem: 23762
+eval (train): [20]  [120/297]  eta: 0:00:22    time: 0.1242  data: 0.0025  max mem: 23762
+eval (train): [20]  [140/297]  eta: 0:00:19    time: 0.1238  data: 0.0019  max mem: 23762
+eval (train): [20]  [160/297]  eta: 0:00:17    time: 0.1240  data: 0.0026  max mem: 23762
+eval (train): [20]  [180/297]  eta: 0:00:14    time: 0.1230  data: 0.0019  max mem: 23762
+eval (train): [20]  [200/297]  eta: 0:00:12    time: 0.1241  data: 0.0026  max mem: 23762
+eval (train): [20]  [220/297]  eta: 0:00:09    time: 0.1238  data: 0.0019  max mem: 23762
+eval (train): [20]  [240/297]  eta: 0:00:07    time: 0.1236  data: 0.0025  max mem: 23762
+eval (train): [20]  [260/297]  eta: 0:00:04    time: 0.1232  data: 0.0018  max mem: 23762
+eval (train): [20]  [280/297]  eta: 0:00:02    time: 0.1237  data: 0.0024  max mem: 23762
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.1216  data: 0.0019  max mem: 23762
+eval (train): [20] Total time: 0:00:37 (0.1254 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:00:44    time: 0.7061  data: 0.5810  max mem: 23762
+eval (validation): [20]  [20/63]  eta: 0:00:06    time: 0.1237  data: 0.0028  max mem: 23762
+eval (validation): [20]  [40/63]  eta: 0:00:03    time: 0.1241  data: 0.0024  max mem: 23762
+eval (validation): [20]  [60/63]  eta: 0:00:00    time: 0.1234  data: 0.0024  max mem: 23762
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.1234  data: 0.0024  max mem: 23762
+eval (validation): [20] Total time: 0:00:08 (0.1343 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:00:49    time: 0.6209  data: 0.5028  max mem: 23762
+eval (test): [20]  [20/79]  eta: 0:00:08    time: 0.1230  data: 0.0025  max mem: 23762
+eval (test): [20]  [40/79]  eta: 0:00:05    time: 0.1239  data: 0.0027  max mem: 23762
+eval (test): [20]  [60/79]  eta: 0:00:02    time: 0.1230  data: 0.0020  max mem: 23762
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.1216  data: 0.0020  max mem: 23762
+eval (test): [20] Total time: 0:00:10 (0.1304 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   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | train      | 0.00019396 | 1       | 0         | 1       | 0         |
+| schaefer400_mae | patch  | attn  | hcpya_task21 | best   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | validation | 0.10955    | 0.97743 | 0.0023169 | 0.97164 | 0.0032184 |
+| schaefer400_mae | patch  | attn  | hcpya_task21 | best   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | test       | 0.12953    | 0.97619 | 0.0022284 | 0.9725  | 0.0027809 |
+
+
+done! total time: 0:39:21

input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_probe
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 14, "eval/id_best": 34, "eval/lr_best": 0.0015299999999999997, "eval/wd_best": 0.05, "eval/train/loss": 1.9670344591140747, "eval/train/acc": 0.40333138695104337, "eval/train/acc_std": 0.0026705670985659436, "eval/train/f1": 0.36488406561346504, "eval/train/f1_std": 0.0029587538628118434, "eval/validation/loss": 2.471343755722046, "eval/validation/acc": 0.27297895902547065, "eval/validation/acc_std": 0.005515787844998848, "eval/validation/f1": 0.22316686675760744, "eval/validation/f1_std": 0.005289161537842078, "eval/test/loss": 2.4036474227905273, "eval/test/acc": 0.2795918367346939, "eval/test/acc_std": 0.005624072836466225, "eval/test/f1": 0.2318438123796105, "eval/test/f1_std": 0.005410665710674907, "eval/testid/loss": 2.452451467514038, "eval/testid/acc": 0.2693271640640062, "eval/testid/acc_std": 0.005866914667657412, "eval/testid/f1": 0.22693466292582212, "eval/testid/f1_std": 0.0057518178516242635}

input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 14, "eval/best/id_best": 34, "eval/best/lr_best": 0.0015299999999999997, "eval/best/wd_best": 0.05, "eval/best/train/loss": 1.9670344591140747, "eval/best/train/acc": 0.40333138695104337, "eval/best/train/acc_std": 0.0026705670985659436, "eval/best/train/f1": 0.36488406561346504, "eval/best/train/f1_std": 0.0029587538628118434, "eval/best/validation/loss": 2.471343755722046, "eval/best/validation/acc": 0.27297895902547065, "eval/best/validation/acc_std": 0.005515787844998848, "eval/best/validation/f1": 0.22316686675760744, "eval/best/validation/f1_std": 0.005289161537842078, "eval/best/test/loss": 2.4036474227905273, "eval/best/test/acc": 0.2795918367346939, "eval/best/test/acc_std": 0.005624072836466225, "eval/best/test/f1": 0.2318438123796105, "eval/best/test/f1_std": 0.005410665710674907, "eval/best/testid/loss": 2.452451467514038, "eval/best/testid/acc": 0.2693271640640062, "eval/best/testid/acc_std": 0.005866914667657412, "eval/best/testid/f1": 0.22693466292582212, "eval/best/testid/f1_std": 0.0057518178516242635}

input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 26, "eval/last/lr_best": 0.00041999999999999996, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.0389087200164795, "eval/last/train/acc": 0.3829558376102523, "eval/last/train/acc_std": 0.002444420706681879, "eval/last/train/f1": 0.33580460057272427, "eval/last/train/f1_std": 0.002662318702841523, "eval/last/validation/loss": 2.4561188220977783, "eval/last/validation/acc": 0.27002583979328165, "eval/last/validation/acc_std": 0.005248648602814061, "eval/last/validation/f1": 0.21516639364731602, "eval/last/validation/f1_std": 0.004993621897337917, "eval/last/test/loss": 2.4004769325256348, "eval/last/test/acc": 0.27569573283859, "eval/last/test/acc_std": 0.005238845914993417, "eval/last/test/f1": 0.21541184546598338, "eval/last/test/f1_std": 0.0051583830915957725, "eval/last/testid/loss": 2.407186269760132, "eval/last/testid/acc": 0.2768459610564874, "eval/last/testid/acc_std": 0.005687605589617059, "eval/last/testid/f1": 0.2310769353043027, "eval/last/testid/f1_std": 0.005615343683740769}

input_space_v3/schaefer400_lr3e-4_4/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,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,1.9670344591140747,0.40333138695104337,0.0026705670985659436,0.36488406561346504,0.0029587538628118434
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,2.471343755722046,0.27297895902547065,0.005515787844998848,0.22316686675760744,0.005289161537842078
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,2.4036474227905273,0.2795918367346939,0.005624072836466225,0.2318438123796105,0.005410665710674907
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",testid,2.452451467514038,0.2693271640640062,0.005866914667657412,0.22693466292582212,0.0057518178516242635

input_space_v3/schaefer400_lr3e-4_4/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,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,1.9670344591140747,0.40333138695104337,0.0026705670985659436,0.36488406561346504,0.0029587538628118434
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,2.471343755722046,0.27297895902547065,0.005515787844998848,0.22316686675760744,0.005289161537842078
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,2.4036474227905273,0.2795918367346939,0.005624072836466225,0.2318438123796105,0.005410665710674907
+schaefer400_mae,patch,attn,nsd_cococlip,best,14,0.0015299999999999997,0.05,34,"[5.1, 1.0]",testid,2.452451467514038,0.2693271640640062,0.005866914667657412,0.22693466292582212,0.0057518178516242635

input_space_v3/schaefer400_lr3e-4_4/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,19,0.00041999999999999996,0.05,26,"[1.4, 1.0]",train,2.0389087200164795,0.3829558376102523,0.002444420706681879,0.33580460057272427,0.002662318702841523
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00041999999999999996,0.05,26,"[1.4, 1.0]",validation,2.4561188220977783,0.27002583979328165,0.005248648602814061,0.21516639364731602,0.004993621897337917
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00041999999999999996,0.05,26,"[1.4, 1.0]",test,2.4004769325256348,0.27569573283859,0.005238845914993417,0.21541184546598338,0.0051583830915957725
+schaefer400_mae,patch,attn,nsd_cococlip,last,19,0.00041999999999999996,0.05,26,"[1.4, 1.0]",testid,2.407186269760132,0.2768459610564874,0.005687605589617059,0.2310769353043027,0.005615343683740769

input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/log.txt

@@ -0,0 +1,969 @@
+fMRI foundation model probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 20:42:23
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_probe
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/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: 0:05:42  lr: nan  time: 0.8561  data: 0.4560  max mem: 23058
+train: [0]  [ 20/400]  eta: 0:01:46  lr: 0.000003  loss: 3.2045 (3.2027)  grad: 0.1820 (0.1910)  time: 0.2527  data: 0.0047  max mem: 23764
+train: [0]  [ 40/400]  eta: 0:01:34  lr: 0.000006  loss: 3.1899 (3.1958)  grad: 0.1820 (0.1843)  time: 0.2438  data: 0.0034  max mem: 23764
+train: [0]  [ 60/400]  eta: 0:01:27  lr: 0.000009  loss: 3.1801 (3.1900)  grad: 0.1719 (0.1786)  time: 0.2442  data: 0.0033  max mem: 23764
+train: [0]  [ 80/400]  eta: 0:01:21  lr: 0.000012  loss: 3.1808 (3.1889)  grad: 0.1607 (0.1739)  time: 0.2442  data: 0.0033  max mem: 23764
+train: [0]  [100/400]  eta: 0:01:15  lr: 0.000015  loss: 3.1749 (3.1830)  grad: 0.1623 (0.1734)  time: 0.2443  data: 0.0034  max mem: 23764
+train: [0]  [120/400]  eta: 0:01:10  lr: 0.000018  loss: 3.1610 (3.1819)  grad: 0.1715 (0.1719)  time: 0.2442  data: 0.0034  max mem: 23764
+train: [0]  [140/400]  eta: 0:01:04  lr: 0.000021  loss: 3.1689 (3.1796)  grad: 0.1681 (0.1701)  time: 0.2447  data: 0.0033  max mem: 23764
+train: [0]  [160/400]  eta: 0:00:59  lr: 0.000024  loss: 3.1665 (3.1790)  grad: 0.1691 (0.1704)  time: 0.2442  data: 0.0033  max mem: 23764
+train: [0]  [180/400]  eta: 0:00:54  lr: 0.000027  loss: 3.1758 (3.1792)  grad: 0.1644 (0.1693)  time: 0.2447  data: 0.0033  max mem: 23764
+train: [0]  [200/400]  eta: 0:00:49  lr: 0.000030  loss: 3.1745 (3.1765)  grad: 0.1614 (0.1694)  time: 0.2451  data: 0.0034  max mem: 23764
+train: [0]  [220/400]  eta: 0:00:44  lr: 0.000033  loss: 3.1737 (3.1771)  grad: 0.1703 (0.1705)  time: 0.2444  data: 0.0033  max mem: 23764
+train: [0]  [240/400]  eta: 0:00:39  lr: 0.000036  loss: 3.1737 (3.1751)  grad: 0.1732 (0.1703)  time: 0.2445  data: 0.0033  max mem: 23764
+train: [0]  [260/400]  eta: 0:00:34  lr: 0.000039  loss: 3.1577 (3.1736)  grad: 0.1700 (0.1698)  time: 0.2446  data: 0.0034  max mem: 23764
+train: [0]  [280/400]  eta: 0:00:29  lr: 0.000042  loss: 3.1607 (3.1734)  grad: 0.1602 (0.1693)  time: 0.2445  data: 0.0034  max mem: 23764
+train: [0]  [300/400]  eta: 0:00:24  lr: 0.000045  loss: 3.1570 (3.1716)  grad: 0.1579 (0.1684)  time: 0.2446  data: 0.0034  max mem: 23764
+train: [0]  [320/400]  eta: 0:00:19  lr: 0.000048  loss: 3.1387 (3.1694)  grad: 0.1553 (0.1679)  time: 0.2445  data: 0.0033  max mem: 23764
+train: [0]  [340/400]  eta: 0:00:14  lr: 0.000051  loss: 3.1275 (3.1675)  grad: 0.1574 (0.1676)  time: 0.2448  data: 0.0033  max mem: 23764
+train: [0]  [360/400]  eta: 0:00:09  lr: 0.000054  loss: 3.1300 (3.1660)  grad: 0.1636 (0.1682)  time: 0.2446  data: 0.0033  max mem: 23764
+train: [0]  [380/400]  eta: 0:00:04  lr: 0.000057  loss: 3.1284 (3.1634)  grad: 0.1752 (0.1687)  time: 0.2446  data: 0.0033  max mem: 23764
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.1136 (3.1617)  grad: 0.1727 (0.1690)  time: 0.2450  data: 0.0033  max mem: 23764
+train: [0] Total time: 0:01:38 (0.2467 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.1136 (3.1617)  grad: 0.1727 (0.1690)
+eval (validation): [0]  [ 0/85]  eta: 0:00:51    time: 0.6090  data: 0.4909  max mem: 23764
+eval (validation): [0]  [20/85]  eta: 0:00:09    time: 0.1218  data: 0.0026  max mem: 23764
+eval (validation): [0]  [40/85]  eta: 0:00:06    time: 0.1225  data: 0.0033  max mem: 23764
+eval (validation): [0]  [60/85]  eta: 0:00:03    time: 0.1226  data: 0.0033  max mem: 23764
+eval (validation): [0]  [80/85]  eta: 0:00:00    time: 0.1230  data: 0.0035  max mem: 23764
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.1210  data: 0.0034  max mem: 23764
+eval (validation): [0] Total time: 0:00:10 (0.1288 s / it)
+cv: [0]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 2.778  acc: 0.172  f1: 0.103
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:04:30  lr: nan  time: 0.6753  data: 0.4419  max mem: 23764
+train: [1]  [ 20/400]  eta: 0:01:40  lr: 0.000063  loss: 3.0828 (3.0918)  grad: 0.1652 (0.1701)  time: 0.2447  data: 0.0032  max mem: 23764
+train: [1]  [ 40/400]  eta: 0:01:31  lr: 0.000066  loss: 3.0807 (3.0917)  grad: 0.1652 (0.1718)  time: 0.2450  data: 0.0034  max mem: 23764
+train: [1]  [ 60/400]  eta: 0:01:25  lr: 0.000069  loss: 3.0873 (3.0954)  grad: 0.1741 (0.1765)  time: 0.2448  data: 0.0034  max mem: 23764
+train: [1]  [ 80/400]  eta: 0:01:20  lr: 0.000072  loss: 3.0883 (3.0878)  grad: 0.1870 (0.1795)  time: 0.2448  data: 0.0033  max mem: 23764
+train: [1]  [100/400]  eta: 0:01:14  lr: 0.000075  loss: 3.0783 (3.0836)  grad: 0.1831 (0.1797)  time: 0.2449  data: 0.0034  max mem: 23764
+train: [1]  [120/400]  eta: 0:01:09  lr: 0.000078  loss: 3.0794 (3.0867)  grad: 0.1829 (0.1807)  time: 0.2449  data: 0.0034  max mem: 23764
+train: [1]  [140/400]  eta: 0:01:04  lr: 0.000081  loss: 3.0957 (3.0860)  grad: 0.1831 (0.1810)  time: 0.2450  data: 0.0034  max mem: 23764
+train: [1]  [160/400]  eta: 0:00:59  lr: 0.000084  loss: 3.0659 (3.0819)  grad: 0.1823 (0.1813)  time: 0.2453  data: 0.0034  max mem: 23764
+train: [1]  [180/400]  eta: 0:00:54  lr: 0.000087  loss: 3.0533 (3.0799)  grad: 0.1820 (0.1826)  time: 0.2449  data: 0.0033  max mem: 23764
+train: [1]  [200/400]  eta: 0:00:49  lr: 0.000090  loss: 3.0610 (3.0781)  grad: 0.1855 (0.1831)  time: 0.2450  data: 0.0034  max mem: 23764
+train: [1]  [220/400]  eta: 0:00:44  lr: 0.000093  loss: 3.0498 (3.0758)  grad: 0.1869 (0.1834)  time: 0.2451  data: 0.0034  max mem: 23764
+train: [1]  [240/400]  eta: 0:00:39  lr: 0.000096  loss: 3.0365 (3.0728)  grad: 0.1869 (0.1834)  time: 0.2449  data: 0.0034  max mem: 23764
+train: [1]  [260/400]  eta: 0:00:34  lr: 0.000099  loss: 3.0219 (3.0703)  grad: 0.1820 (0.1831)  time: 0.2452  data: 0.0033  max mem: 23764
+train: [1]  [280/400]  eta: 0:00:29  lr: 0.000102  loss: 3.0482 (3.0687)  grad: 0.1867 (0.1842)  time: 0.2449  data: 0.0034  max mem: 23764
+train: [1]  [300/400]  eta: 0:00:24  lr: 0.000105  loss: 3.0594 (3.0676)  grad: 0.1919 (0.1845)  time: 0.2454  data: 0.0034  max mem: 23764
+train: [1]  [320/400]  eta: 0:00:19  lr: 0.000108  loss: 3.0207 (3.0642)  grad: 0.1919 (0.1856)  time: 0.2454  data: 0.0034  max mem: 23764
+train: [1]  [340/400]  eta: 0:00:14  lr: 0.000111  loss: 3.0155 (3.0617)  grad: 0.1976 (0.1864)  time: 0.2453  data: 0.0034  max mem: 23764
+train: [1]  [360/400]  eta: 0:00:09  lr: 0.000114  loss: 3.0155 (3.0594)  grad: 0.1975 (0.1872)  time: 0.2452  data: 0.0034  max mem: 23764
+train: [1]  [380/400]  eta: 0:00:04  lr: 0.000117  loss: 3.0238 (3.0570)  grad: 0.2046 (0.1889)  time: 0.2452  data: 0.0034  max mem: 23764
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 3.0238 (3.0552)  grad: 0.2170 (0.1900)  time: 0.2456  data: 0.0033  max mem: 23764
+train: [1] Total time: 0:01:38 (0.2464 s / it)
+train: [1] Summary: lr: 0.000120  loss: 3.0238 (3.0552)  grad: 0.2170 (0.1900)
+eval (validation): [1]  [ 0/85]  eta: 0:00:52    time: 0.6205  data: 0.5039  max mem: 23764
+eval (validation): [1]  [20/85]  eta: 0:00:09    time: 0.1212  data: 0.0019  max mem: 23764
+eval (validation): [1]  [40/85]  eta: 0:00:06    time: 0.1231  data: 0.0033  max mem: 23764
+eval (validation): [1]  [60/85]  eta: 0:00:03    time: 0.1232  data: 0.0035  max mem: 23764
+eval (validation): [1]  [80/85]  eta: 0:00:00    time: 0.1231  data: 0.0035  max mem: 23764
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.1211  data: 0.0034  max mem: 23764
+eval (validation): [1] Total time: 0:00:10 (0.1292 s / it)
+cv: [1]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 2.655  acc: 0.212  f1: 0.131
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:04:55  lr: nan  time: 0.7386  data: 0.5053  max mem: 23764
+train: [2]  [ 20/400]  eta: 0:01:41  lr: 0.000123  loss: 2.9416 (2.9675)  grad: 0.2169 (0.2226)  time: 0.2441  data: 0.0023  max mem: 23764
+train: [2]  [ 40/400]  eta: 0:01:32  lr: 0.000126  loss: 2.9880 (2.9795)  grad: 0.2258 (0.2253)  time: 0.2452  data: 0.0035  max mem: 23764
+train: [2]  [ 60/400]  eta: 0:01:26  lr: 0.000129  loss: 3.0038 (2.9905)  grad: 0.2334 (0.2397)  time: 0.2455  data: 0.0035  max mem: 23764
+train: [2]  [ 80/400]  eta: 0:01:20  lr: 0.000132  loss: 3.0624 (3.0407)  grad: 0.2929 (0.3220)  time: 0.2453  data: 0.0034  max mem: 23764
+train: [2]  [100/400]  eta: 0:01:14  lr: 0.000135  loss: 3.4510 (3.2151)  grad: 0.9575 (0.6058)  time: 0.2453  data: 0.0034  max mem: 23764
+WARNING: classifier 48 (50, 1.0) diverged (loss=122.00 > 63.56) at step 453. Freezing.
+train: [2]  [120/400]  eta: 0:01:09  lr: 0.000138  loss: 3.7481 (3.3453)  grad: 1.6596 (0.7689)  time: 0.2437  data: 0.0034  max mem: 23764
+WARNING: classifier 47 (43, 1.0) diverged (loss=66.82 > 63.56) at step 465. Freezing.
+train: [2]  [140/400]  eta: 0:01:04  lr: 0.000141  loss: 3.6607 (3.3589)  grad: 1.3045 (0.8055)  time: 0.2414  data: 0.0033  max mem: 23764
+train: [2]  [160/400]  eta: 0:00:59  lr: 0.000144  loss: 2.9847 (3.3106)  grad: 0.2007 (0.7288)  time: 0.2405  data: 0.0035  max mem: 23764
+train: [2]  [180/400]  eta: 0:00:54  lr: 0.000147  loss: 2.9691 (3.2749)  grad: 0.1947 (0.6695)  time: 0.2403  data: 0.0034  max mem: 23764
+train: [2]  [200/400]  eta: 0:00:49  lr: 0.000150  loss: 2.9909 (3.2464)  grad: 0.2070 (0.6239)  time: 0.2399  data: 0.0033  max mem: 23764
+train: [2]  [220/400]  eta: 0:00:44  lr: 0.000153  loss: 2.9946 (3.2244)  grad: 0.2333 (0.5983)  time: 0.2402  data: 0.0034  max mem: 23764
+train: [2]  [240/400]  eta: 0:00:39  lr: 0.000156  loss: 3.1541 (3.2489)  grad: 0.5880 (0.6710)  time: 0.2402  data: 0.0033  max mem: 23764
+WARNING: classifier 46 (36, 1.0) diverged (loss=73.02 > 63.56) at step 525. Freezing.
+train: [2]  [260/400]  eta: 0:00:34  lr: 0.000159  loss: 3.4535 (3.2678)  grad: 1.3747 (0.7057)  time: 0.2390  data: 0.0033  max mem: 23764
+train: [2]  [280/400]  eta: 0:00:29  lr: 0.000162  loss: 3.1597 (3.2634)  grad: 0.6192 (0.7073)  time: 0.2382  data: 0.0035  max mem: 23764
+WARNING: classifier 45 (31, 1.0) diverged (loss=69.11 > 63.56) at step 550. Freezing.
+train: [2]  [300/400]  eta: 0:00:24  lr: 0.000165  loss: 3.4452 (3.2943)  grad: 1.1462 (0.7700)  time: 0.2380  data: 0.0034  max mem: 23764
+train: [2]  [320/400]  eta: 0:00:19  lr: 0.000168  loss: 3.0478 (3.2750)  grad: 0.2219 (0.7345)  time: 0.2353  data: 0.0033  max mem: 23764
+train: [2]  [340/400]  eta: 0:00:14  lr: 0.000171  loss: 2.9880 (3.2562)  grad: 0.1998 (0.7027)  time: 0.2354  data: 0.0033  max mem: 23764
+train: [2]  [360/400]  eta: 0:00:09  lr: 0.000174  loss: 2.9497 (3.2394)  grad: 0.1840 (0.6740)  time: 0.2356  data: 0.0034  max mem: 23764
+train: [2]  [380/400]  eta: 0:00:04  lr: 0.000177  loss: 2.9143 (3.2233)  grad: 0.1897 (0.6491)  time: 0.2358  data: 0.0034  max mem: 23764
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 2.9476 (3.2105)  grad: 0.2058 (0.6269)  time: 0.2358  data: 0.0034  max mem: 23764
+train: [2] Total time: 0:01:36 (0.2417 s / it)
+train: [2] Summary: lr: 0.000180  loss: 2.9476 (3.2105)  grad: 0.2058 (0.6269)
+eval (validation): [2]  [ 0/85]  eta: 0:00:48    time: 0.5647  data: 0.4476  max mem: 23764
+eval (validation): [2]  [20/85]  eta: 0:00:09    time: 0.1216  data: 0.0028  max mem: 23764
+eval (validation): [2]  [40/85]  eta: 0:00:05    time: 0.1229  data: 0.0035  max mem: 23764
+eval (validation): [2]  [60/85]  eta: 0:00:03    time: 0.1226  data: 0.0035  max mem: 23764
+eval (validation): [2]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0034  max mem: 23764
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.1208  data: 0.0034  max mem: 23764
+eval (validation): [2] Total time: 0:00:10 (0.1281 s / it)
+cv: [2]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 2.612  acc: 0.223  f1: 0.137
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:04:52  lr: nan  time: 0.7304  data: 0.5057  max mem: 23764
+train: [3]  [ 20/400]  eta: 0:01:38  lr: 0.000183  loss: 2.9428 (2.9448)  grad: 0.2104 (0.2119)  time: 0.2345  data: 0.0027  max mem: 23764
+train: [3]  [ 40/400]  eta: 0:01:28  lr: 0.000186  loss: 2.9414 (2.9442)  grad: 0.2094 (0.2090)  time: 0.2356  data: 0.0035  max mem: 23764
+train: [3]  [ 60/400]  eta: 0:01:22  lr: 0.000189  loss: 2.9600 (2.9569)  grad: 0.2234 (0.2315)  time: 0.2354  data: 0.0035  max mem: 23764
+train: [3]  [ 80/400]  eta: 0:01:17  lr: 0.000192  loss: 2.9779 (2.9740)  grad: 0.2876 (0.2972)  time: 0.2354  data: 0.0034  max mem: 23764
+train: [3]  [100/400]  eta: 0:01:12  lr: 0.000195  loss: 3.1538 (3.0828)  grad: 0.8684 (0.5047)  time: 0.2350  data: 0.0034  max mem: 23764
+train: [3]  [120/400]  eta: 0:01:06  lr: 0.000198  loss: 3.6568 (3.2043)  grad: 1.5463 (0.7148)  time: 0.2353  data: 0.0035  max mem: 23764
+WARNING: classifier 44 (26, 1.0) diverged (loss=65.52 > 63.56) at step 661. Freezing.
+train: [3]  [140/400]  eta: 0:01:01  lr: 0.000201  loss: 3.5090 (3.1860)  grad: 1.1747 (0.6729)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [3]  [160/400]  eta: 0:00:57  lr: 0.000204  loss: 2.9504 (3.1569)  grad: 0.2019 (0.6145)  time: 0.2329  data: 0.0035  max mem: 23764
+train: [3]  [180/400]  eta: 0:00:52  lr: 0.000207  loss: 2.9720 (3.1379)  grad: 0.2128 (0.5710)  time: 0.2329  data: 0.0034  max mem: 23764
+train: [3]  [200/400]  eta: 0:00:47  lr: 0.000210  loss: 2.9759 (3.1183)  grad: 0.2128 (0.5340)  time: 0.2331  data: 0.0035  max mem: 23764
+train: [3]  [220/400]  eta: 0:00:42  lr: 0.000213  loss: 2.9297 (3.1002)  grad: 0.1999 (0.5048)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [3]  [240/400]  eta: 0:00:37  lr: 0.000216  loss: 2.9317 (3.0862)  grad: 0.2012 (0.4795)  time: 0.2334  data: 0.0035  max mem: 23764
+train: [3]  [260/400]  eta: 0:00:33  lr: 0.000219  loss: 2.9596 (3.0771)  grad: 0.2008 (0.4583)  time: 0.2330  data: 0.0034  max mem: 23764
+train: [3]  [280/400]  eta: 0:00:28  lr: 0.000222  loss: 2.9596 (3.0678)  grad: 0.1996 (0.4403)  time: 0.2335  data: 0.0035  max mem: 23764
+train: [3]  [300/400]  eta: 0:00:23  lr: 0.000225  loss: 2.9582 (3.0593)  grad: 0.2000 (0.4248)  time: 0.2329  data: 0.0034  max mem: 23764
+train: [3]  [320/400]  eta: 0:00:18  lr: 0.000228  loss: 2.9352 (3.0511)  grad: 0.1956 (0.4102)  time: 0.2329  data: 0.0034  max mem: 23764
+train: [3]  [340/400]  eta: 0:00:14  lr: 0.000231  loss: 2.9173 (3.0429)  grad: 0.1939 (0.3980)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [3]  [360/400]  eta: 0:00:09  lr: 0.000234  loss: 2.9266 (3.0379)  grad: 0.2038 (0.3878)  time: 0.2330  data: 0.0034  max mem: 23764
+train: [3]  [380/400]  eta: 0:00:04  lr: 0.000237  loss: 2.9266 (3.0312)  grad: 0.2173 (0.3794)  time: 0.2332  data: 0.0034  max mem: 23764
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.9113 (3.0257)  grad: 0.2268 (0.3719)  time: 0.2334  data: 0.0035  max mem: 23764
+train: [3] Total time: 0:01:34 (0.2352 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.9113 (3.0257)  grad: 0.2268 (0.3719)
+eval (validation): [3]  [ 0/85]  eta: 0:00:47    time: 0.5578  data: 0.4408  max mem: 23764
+eval (validation): [3]  [20/85]  eta: 0:00:09    time: 0.1218  data: 0.0028  max mem: 23764
+eval (validation): [3]  [40/85]  eta: 0:00:05    time: 0.1228  data: 0.0034  max mem: 23764
+eval (validation): [3]  [60/85]  eta: 0:00:03    time: 0.1228  data: 0.0035  max mem: 23764
+eval (validation): [3]  [80/85]  eta: 0:00:00    time: 0.1224  data: 0.0034  max mem: 23764
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.1204  data: 0.0033  max mem: 23764
+eval (validation): [3] Total time: 0:00:10 (0.1281 s / it)
+cv: [3]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.602  acc: 0.236  f1: 0.158
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:04:57  lr: nan  time: 0.7425  data: 0.5174  max mem: 23764
+train: [4]  [ 20/400]  eta: 0:01:37  lr: 0.000243  loss: 2.8810 (2.8896)  grad: 0.2294 (0.2270)  time: 0.2320  data: 0.0022  max mem: 23764
+train: [4]  [ 40/400]  eta: 0:01:28  lr: 0.000246  loss: 2.8740 (2.8800)  grad: 0.2287 (0.2274)  time: 0.2330  data: 0.0035  max mem: 23764
+train: [4]  [ 60/400]  eta: 0:01:21  lr: 0.000249  loss: 2.8740 (2.8857)  grad: 0.2403 (0.2364)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [4]  [ 80/400]  eta: 0:01:16  lr: 0.000252  loss: 2.8742 (2.8848)  grad: 0.2487 (0.2382)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [4]  [100/400]  eta: 0:01:11  lr: 0.000255  loss: 2.9055 (2.8952)  grad: 0.2546 (0.2535)  time: 0.2331  data: 0.0034  max mem: 23764
+train: [4]  [120/400]  eta: 0:01:06  lr: 0.000258  loss: 2.9909 (2.9649)  grad: 0.4709 (0.3766)  time: 0.2331  data: 0.0034  max mem: 23764
+WARNING: classifier 43 (22, 1.0) diverged (loss=100.60 > 63.56) at step 864. Freezing.
+train: [4]  [140/400]  eta: 0:01:01  lr: 0.000261  loss: 3.1925 (3.0180)  grad: 0.6004 (0.4483)  time: 0.2316  data: 0.0034  max mem: 23764
+train: [4]  [160/400]  eta: 0:00:56  lr: 0.000264  loss: 2.9569 (3.0130)  grad: 0.3712 (0.4460)  time: 0.2306  data: 0.0035  max mem: 23764
+train: [4]  [180/400]  eta: 0:00:51  lr: 0.000267  loss: 3.0953 (3.0382)  grad: 0.6379 (0.4959)  time: 0.2308  data: 0.0035  max mem: 23764
+WARNING: classifier 42 (19, 1.0) diverged (loss=63.76 > 63.56) at step 896. Freezing.
+train: [4]  [200/400]  eta: 0:00:46  lr: 0.000270  loss: 3.1791 (3.0763)  grad: 0.9056 (0.5486)  time: 0.2294  data: 0.0034  max mem: 23764
+train: [4]  [220/400]  eta: 0:00:42  lr: 0.000273  loss: 2.9314 (3.0605)  grad: 0.2341 (0.5191)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [4]  [240/400]  eta: 0:00:37  lr: 0.000276  loss: 2.9182 (3.0493)  grad: 0.2058 (0.4925)  time: 0.2286  data: 0.0035  max mem: 23764
+train: [4]  [260/400]  eta: 0:00:32  lr: 0.000279  loss: 2.9097 (3.0377)  grad: 0.1966 (0.4696)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [4]  [280/400]  eta: 0:00:27  lr: 0.000282  loss: 2.8732 (3.0242)  grad: 0.2053 (0.4514)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [4]  [300/400]  eta: 0:00:23  lr: 0.000285  loss: 2.8867 (3.0176)  grad: 0.2143 (0.4360)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [4]  [320/400]  eta: 0:00:18  lr: 0.000288  loss: 2.8957 (3.0081)  grad: 0.2104 (0.4216)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [4]  [340/400]  eta: 0:00:13  lr: 0.000291  loss: 2.8673 (3.0017)  grad: 0.2020 (0.4087)  time: 0.2285  data: 0.0034  max mem: 23764
+train: [4]  [360/400]  eta: 0:00:09  lr: 0.000294  loss: 2.8939 (2.9966)  grad: 0.2003 (0.3974)  time: 0.2281  data: 0.0033  max mem: 23764
+train: [4]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 2.8868 (2.9893)  grad: 0.1994 (0.3875)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.8611 (2.9828)  grad: 0.2098 (0.3786)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [4] Total time: 0:01:32 (0.2317 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.8611 (2.9828)  grad: 0.2098 (0.3786)
+eval (validation): [4]  [ 0/85]  eta: 0:00:49    time: 0.5792  data: 0.4628  max mem: 23764
+eval (validation): [4]  [20/85]  eta: 0:00:09    time: 0.1211  data: 0.0022  max mem: 23764
+eval (validation): [4]  [40/85]  eta: 0:00:05    time: 0.1223  data: 0.0033  max mem: 23764
+eval (validation): [4]  [60/85]  eta: 0:00:03    time: 0.1223  data: 0.0034  max mem: 23764
+eval (validation): [4]  [80/85]  eta: 0:00:00    time: 0.1224  data: 0.0034  max mem: 23764
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.1202  data: 0.0034  max mem: 23764
+eval (validation): [4] Total time: 0:00:10 (0.1279 s / it)
+cv: [4]  best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00')  loss: 2.505  acc: 0.240  f1: 0.173
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:04:28  lr: nan  time: 0.6701  data: 0.4535  max mem: 23764
+train: [5]  [ 20/400]  eta: 0:01:34  lr: 0.000300  loss: 2.8613 (2.8581)  grad: 0.1990 (0.2015)  time: 0.2273  data: 0.0030  max mem: 23764
+train: [5]  [ 40/400]  eta: 0:01:25  lr: 0.000300  loss: 2.8561 (2.8547)  grad: 0.2044 (0.2035)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [5]  [ 60/400]  eta: 0:01:19  lr: 0.000300  loss: 2.8688 (2.8734)  grad: 0.2147 (0.2089)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [5]  [ 80/400]  eta: 0:01:14  lr: 0.000300  loss: 2.8791 (2.8729)  grad: 0.2189 (0.2114)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [5]  [100/400]  eta: 0:01:09  lr: 0.000300  loss: 2.8540 (2.8714)  grad: 0.2189 (0.2135)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [5]  [120/400]  eta: 0:01:04  lr: 0.000300  loss: 2.8421 (2.8692)  grad: 0.2204 (0.2149)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [5]  [140/400]  eta: 0:01:00  lr: 0.000300  loss: 2.8596 (2.8708)  grad: 0.2182 (0.2149)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [5]  [160/400]  eta: 0:00:55  lr: 0.000299  loss: 2.8731 (2.8719)  grad: 0.2160 (0.2147)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [5]  [180/400]  eta: 0:00:50  lr: 0.000299  loss: 2.8664 (2.8704)  grad: 0.2099 (0.2143)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [5]  [200/400]  eta: 0:00:45  lr: 0.000299  loss: 2.8653 (2.8689)  grad: 0.2061 (0.2133)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [5]  [220/400]  eta: 0:00:41  lr: 0.000299  loss: 2.8653 (2.8681)  grad: 0.2073 (0.2133)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [5]  [240/400]  eta: 0:00:36  lr: 0.000299  loss: 2.8458 (2.8657)  grad: 0.2173 (0.2141)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [5]  [260/400]  eta: 0:00:32  lr: 0.000299  loss: 2.8434 (2.8648)  grad: 0.2217 (0.2150)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [5]  [280/400]  eta: 0:00:27  lr: 0.000298  loss: 2.8405 (2.8620)  grad: 0.2080 (0.2141)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [5]  [300/400]  eta: 0:00:22  lr: 0.000298  loss: 2.8372 (2.8602)  grad: 0.1991 (0.2135)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [5]  [320/400]  eta: 0:00:18  lr: 0.000298  loss: 2.8372 (2.8577)  grad: 0.1991 (0.2129)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [5]  [340/400]  eta: 0:00:13  lr: 0.000298  loss: 2.8663 (2.8592)  grad: 0.1996 (0.2121)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [5]  [360/400]  eta: 0:00:09  lr: 0.000297  loss: 2.8421 (2.8570)  grad: 0.1973 (0.2114)  time: 0.2276  data: 0.0035  max mem: 23764
+train: [5]  [380/400]  eta: 0:00:04  lr: 0.000297  loss: 2.8085 (2.8558)  grad: 0.2013 (0.2111)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.8174 (2.8540)  grad: 0.2035 (0.2106)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [5] Total time: 0:01:31 (0.2292 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.8174 (2.8540)  grad: 0.2035 (0.2106)
+eval (validation): [5]  [ 0/85]  eta: 0:00:55    time: 0.6521  data: 0.5329  max mem: 23764
+eval (validation): [5]  [20/85]  eta: 0:00:09    time: 0.1216  data: 0.0026  max mem: 23764
+eval (validation): [5]  [40/85]  eta: 0:00:06    time: 0.1221  data: 0.0032  max mem: 23764
+eval (validation): [5]  [60/85]  eta: 0:00:03    time: 0.1227  data: 0.0034  max mem: 23764
+eval (validation): [5]  [80/85]  eta: 0:00:00    time: 0.1222  data: 0.0033  max mem: 23764
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.1202  data: 0.0033  max mem: 23764
+eval (validation): [5] Total time: 0:00:10 (0.1292 s / it)
+cv: [5]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.504  acc: 0.256  f1: 0.173
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:04:24  lr: nan  time: 0.6620  data: 0.4441  max mem: 23764
+train: [6]  [ 20/400]  eta: 0:01:34  lr: 0.000296  loss: 2.8160 (2.8406)  grad: 0.1908 (0.1976)  time: 0.2274  data: 0.0031  max mem: 23764
+train: [6]  [ 40/400]  eta: 0:01:25  lr: 0.000296  loss: 2.8053 (2.8259)  grad: 0.1903 (0.1944)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [6]  [ 60/400]  eta: 0:01:19  lr: 0.000296  loss: 2.7890 (2.8084)  grad: 0.1939 (0.1978)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [6]  [ 80/400]  eta: 0:01:14  lr: 0.000295  loss: 2.7710 (2.8027)  grad: 0.2041 (0.2024)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [6]  [100/400]  eta: 0:01:09  lr: 0.000295  loss: 2.8031 (2.8094)  grad: 0.2163 (0.2051)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [6]  [120/400]  eta: 0:01:04  lr: 0.000295  loss: 2.8227 (2.8093)  grad: 0.2067 (0.2046)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [6]  [140/400]  eta: 0:01:00  lr: 0.000294  loss: 2.7918 (2.8086)  grad: 0.2015 (0.2052)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [6]  [160/400]  eta: 0:00:55  lr: 0.000294  loss: 2.7750 (2.8042)  grad: 0.2097 (0.2066)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [6]  [180/400]  eta: 0:00:50  lr: 0.000293  loss: 2.7830 (2.8028)  grad: 0.2037 (0.2065)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [6]  [200/400]  eta: 0:00:45  lr: 0.000293  loss: 2.7851 (2.8025)  grad: 0.2000 (0.2066)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [6]  [220/400]  eta: 0:00:41  lr: 0.000292  loss: 2.8166 (2.8048)  grad: 0.2156 (0.2083)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [6]  [240/400]  eta: 0:00:36  lr: 0.000292  loss: 2.8427 (2.8104)  grad: 0.2246 (0.2092)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [6]  [260/400]  eta: 0:00:32  lr: 0.000291  loss: 2.8446 (2.8117)  grad: 0.2206 (0.2097)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [6]  [280/400]  eta: 0:00:27  lr: 0.000291  loss: 2.8326 (2.8126)  grad: 0.2182 (0.2104)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [6]  [300/400]  eta: 0:00:22  lr: 0.000290  loss: 2.8203 (2.8125)  grad: 0.2133 (0.2106)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [6]  [320/400]  eta: 0:00:18  lr: 0.000290  loss: 2.8053 (2.8117)  grad: 0.2038 (0.2104)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [6]  [340/400]  eta: 0:00:13  lr: 0.000289  loss: 2.7927 (2.8125)  grad: 0.2077 (0.2111)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [6]  [360/400]  eta: 0:00:09  lr: 0.000288  loss: 2.7892 (2.8117)  grad: 0.2159 (0.2111)  time: 0.2286  data: 0.0035  max mem: 23764
+train: [6]  [380/400]  eta: 0:00:04  lr: 0.000288  loss: 2.7704 (2.8092)  grad: 0.2000 (0.2104)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.7704 (2.8083)  grad: 0.2000 (0.2104)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [6] Total time: 0:01:31 (0.2291 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.7704 (2.8083)  grad: 0.2000 (0.2104)
+eval (validation): [6]  [ 0/85]  eta: 0:00:51    time: 0.6103  data: 0.4915  max mem: 23764
+eval (validation): [6]  [20/85]  eta: 0:00:09    time: 0.1208  data: 0.0022  max mem: 23764
+eval (validation): [6]  [40/85]  eta: 0:00:06    time: 0.1221  data: 0.0033  max mem: 23764
+eval (validation): [6]  [60/85]  eta: 0:00:03    time: 0.1227  data: 0.0036  max mem: 23764
+eval (validation): [6]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0035  max mem: 23764
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.1207  data: 0.0035  max mem: 23764
+eval (validation): [6] Total time: 0:00:10 (0.1284 s / it)
+cv: [6]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 2.461  acc: 0.265  f1: 0.184
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:04:27  lr: nan  time: 0.6695  data: 0.4516  max mem: 23764
+train: [7]  [ 20/400]  eta: 0:01:34  lr: 0.000286  loss: 2.7812 (2.7660)  grad: 0.2015 (0.2137)  time: 0.2272  data: 0.0028  max mem: 23764
+train: [7]  [ 40/400]  eta: 0:01:25  lr: 0.000286  loss: 2.7477 (2.7636)  grad: 0.2029 (0.2105)  time: 0.2274  data: 0.0032  max mem: 23764
+train: [7]  [ 60/400]  eta: 0:01:19  lr: 0.000285  loss: 2.7437 (2.7652)  grad: 0.2096 (0.2127)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [7]  [ 80/400]  eta: 0:01:14  lr: 0.000284  loss: 2.7834 (2.7761)  grad: 0.2167 (0.2138)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [7]  [100/400]  eta: 0:01:09  lr: 0.000284  loss: 2.7717 (2.7705)  grad: 0.2167 (0.2140)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [7]  [120/400]  eta: 0:01:04  lr: 0.000283  loss: 2.7687 (2.7760)  grad: 0.2138 (0.2149)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [7]  [140/400]  eta: 0:01:00  lr: 0.000282  loss: 2.7575 (2.7722)  grad: 0.2091 (0.2139)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [7]  [160/400]  eta: 0:00:55  lr: 0.000282  loss: 2.7300 (2.7711)  grad: 0.2152 (0.2155)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [7]  [180/400]  eta: 0:00:50  lr: 0.000281  loss: 2.7772 (2.7727)  grad: 0.2175 (0.2158)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [7]  [200/400]  eta: 0:00:46  lr: 0.000280  loss: 2.7772 (2.7742)  grad: 0.2175 (0.2167)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [7]  [220/400]  eta: 0:00:41  lr: 0.000279  loss: 2.7833 (2.7761)  grad: 0.2173 (0.2164)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [7]  [240/400]  eta: 0:00:36  lr: 0.000278  loss: 2.7697 (2.7743)  grad: 0.2121 (0.2163)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [7]  [260/400]  eta: 0:00:32  lr: 0.000278  loss: 2.7482 (2.7731)  grad: 0.2131 (0.2161)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [7]  [280/400]  eta: 0:00:27  lr: 0.000277  loss: 2.7498 (2.7726)  grad: 0.2089 (0.2157)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [7]  [300/400]  eta: 0:00:22  lr: 0.000276  loss: 2.7595 (2.7707)  grad: 0.2149 (0.2159)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [7]  [320/400]  eta: 0:00:18  lr: 0.000275  loss: 2.7644 (2.7714)  grad: 0.2195 (0.2164)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [7]  [340/400]  eta: 0:00:13  lr: 0.000274  loss: 2.7799 (2.7735)  grad: 0.2195 (0.2170)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [7]  [360/400]  eta: 0:00:09  lr: 0.000273  loss: 2.7661 (2.7715)  grad: 0.2111 (0.2167)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [7]  [380/400]  eta: 0:00:04  lr: 0.000272  loss: 2.7495 (2.7702)  grad: 0.2111 (0.2168)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.7520 (2.7693)  grad: 0.2144 (0.2169)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [7] Total time: 0:01:31 (0.2293 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.7520 (2.7693)  grad: 0.2144 (0.2169)
+eval (validation): [7]  [ 0/85]  eta: 0:00:46    time: 0.5500  data: 0.4329  max mem: 23764
+eval (validation): [7]  [20/85]  eta: 0:00:09    time: 0.1219  data: 0.0031  max mem: 23764
+eval (validation): [7]  [40/85]  eta: 0:00:05    time: 0.1227  data: 0.0034  max mem: 23764
+eval (validation): [7]  [60/85]  eta: 0:00:03    time: 0.1221  data: 0.0033  max mem: 23764
+eval (validation): [7]  [80/85]  eta: 0:00:00    time: 0.1221  data: 0.0034  max mem: 23764
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.1202  data: 0.0034  max mem: 23764
+eval (validation): [7] Total time: 0:00:10 (0.1278 s / it)
+cv: [7]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.452  acc: 0.264  f1: 0.215
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:04:30  lr: nan  time: 0.6764  data: 0.4584  max mem: 23764
+train: [8]  [ 20/400]  eta: 0:01:34  lr: 0.000270  loss: 2.7139 (2.7346)  grad: 0.2047 (0.2071)  time: 0.2276  data: 0.0029  max mem: 23764
+train: [8]  [ 40/400]  eta: 0:01:25  lr: 0.000270  loss: 2.7206 (2.7343)  grad: 0.2089 (0.2079)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [8]  [ 60/400]  eta: 0:01:19  lr: 0.000269  loss: 2.7304 (2.7232)  grad: 0.2098 (0.2094)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [8]  [ 80/400]  eta: 0:01:14  lr: 0.000268  loss: 2.7451 (2.7396)  grad: 0.2108 (0.2098)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [8]  [100/400]  eta: 0:01:09  lr: 0.000267  loss: 2.7598 (2.7397)  grad: 0.2125 (0.2116)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [8]  [120/400]  eta: 0:01:04  lr: 0.000266  loss: 2.7390 (2.7387)  grad: 0.2185 (0.2132)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [8]  [140/400]  eta: 0:01:00  lr: 0.000265  loss: 2.7469 (2.7424)  grad: 0.2164 (0.2140)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [8]  [160/400]  eta: 0:00:55  lr: 0.000264  loss: 2.7365 (2.7354)  grad: 0.2068 (0.2128)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [8]  [180/400]  eta: 0:00:50  lr: 0.000263  loss: 2.7255 (2.7375)  grad: 0.2015 (0.2122)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [8]  [200/400]  eta: 0:00:46  lr: 0.000262  loss: 2.7617 (2.7396)  grad: 0.2060 (0.2119)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [8]  [220/400]  eta: 0:00:41  lr: 0.000260  loss: 2.6890 (2.7337)  grad: 0.2079 (0.2122)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [8]  [240/400]  eta: 0:00:36  lr: 0.000259  loss: 2.6797 (2.7305)  grad: 0.2077 (0.2120)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [8]  [260/400]  eta: 0:00:32  lr: 0.000258  loss: 2.7027 (2.7294)  grad: 0.2096 (0.2123)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [8]  [280/400]  eta: 0:00:27  lr: 0.000257  loss: 2.7285 (2.7315)  grad: 0.2139 (0.2127)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [8]  [300/400]  eta: 0:00:22  lr: 0.000256  loss: 2.7278 (2.7317)  grad: 0.2147 (0.2130)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [8]  [320/400]  eta: 0:00:18  lr: 0.000255  loss: 2.6923 (2.7305)  grad: 0.2161 (0.2132)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [8]  [340/400]  eta: 0:00:13  lr: 0.000254  loss: 2.6923 (2.7292)  grad: 0.2182 (0.2135)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [8]  [360/400]  eta: 0:00:09  lr: 0.000253  loss: 2.6864 (2.7284)  grad: 0.2114 (0.2138)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [8]  [380/400]  eta: 0:00:04  lr: 0.000252  loss: 2.6896 (2.7267)  grad: 0.2106 (0.2135)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.7046 (2.7268)  grad: 0.2112 (0.2133)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [8] Total time: 0:01:31 (0.2292 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.7046 (2.7268)  grad: 0.2112 (0.2133)
+eval (validation): [8]  [ 0/85]  eta: 0:00:46    time: 0.5456  data: 0.4288  max mem: 23764
+eval (validation): [8]  [20/85]  eta: 0:00:09    time: 0.1215  data: 0.0027  max mem: 23764
+eval (validation): [8]  [40/85]  eta: 0:00:05    time: 0.1220  data: 0.0031  max mem: 23764
+eval (validation): [8]  [60/85]  eta: 0:00:03    time: 0.1226  data: 0.0032  max mem: 23764
+eval (validation): [8]  [80/85]  eta: 0:00:00    time: 0.1224  data: 0.0033  max mem: 23764
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.1204  data: 0.0033  max mem: 23764
+eval (validation): [8] Total time: 0:00:10 (0.1277 s / it)
+cv: [8]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 2.478  acc: 0.266  f1: 0.208
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:04:41  lr: nan  time: 0.7037  data: 0.4852  max mem: 23764
+train: [9]  [ 20/400]  eta: 0:01:35  lr: 0.000249  loss: 2.6635 (2.6791)  grad: 0.2149 (0.2162)  time: 0.2278  data: 0.0029  max mem: 23764
+train: [9]  [ 40/400]  eta: 0:01:26  lr: 0.000248  loss: 2.6635 (2.6754)  grad: 0.2202 (0.2215)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [9]  [ 60/400]  eta: 0:01:20  lr: 0.000247  loss: 2.6810 (2.6809)  grad: 0.2228 (0.2223)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [9]  [ 80/400]  eta: 0:01:14  lr: 0.000246  loss: 2.6946 (2.6892)  grad: 0.2180 (0.2189)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [9]  [100/400]  eta: 0:01:09  lr: 0.000244  loss: 2.7109 (2.6972)  grad: 0.2098 (0.2179)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [9]  [120/400]  eta: 0:01:04  lr: 0.000243  loss: 2.7027 (2.6925)  grad: 0.2101 (0.2169)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [9]  [140/400]  eta: 0:01:00  lr: 0.000242  loss: 2.7156 (2.7005)  grad: 0.2115 (0.2174)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [9]  [160/400]  eta: 0:00:55  lr: 0.000241  loss: 2.7159 (2.7013)  grad: 0.2189 (0.2170)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [9]  [180/400]  eta: 0:00:50  lr: 0.000240  loss: 2.6732 (2.6938)  grad: 0.2155 (0.2164)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [9]  [200/400]  eta: 0:00:46  lr: 0.000238  loss: 2.6616 (2.6960)  grad: 0.2149 (0.2164)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [9]  [220/400]  eta: 0:00:41  lr: 0.000237  loss: 2.6746 (2.6941)  grad: 0.2095 (0.2159)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [9]  [240/400]  eta: 0:00:36  lr: 0.000236  loss: 2.6746 (2.6979)  grad: 0.2093 (0.2161)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [9]  [260/400]  eta: 0:00:32  lr: 0.000234  loss: 2.6824 (2.6970)  grad: 0.2127 (0.2159)  time: 0.2276  data: 0.0032  max mem: 23764
+train: [9]  [280/400]  eta: 0:00:27  lr: 0.000233  loss: 2.6747 (2.6964)  grad: 0.2095 (0.2152)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [9]  [300/400]  eta: 0:00:22  lr: 0.000232  loss: 2.6837 (2.6975)  grad: 0.2145 (0.2159)  time: 0.2282  data: 0.0033  max mem: 23764
+train: [9]  [320/400]  eta: 0:00:18  lr: 0.000230  loss: 2.7120 (2.6979)  grad: 0.2174 (0.2158)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [9]  [340/400]  eta: 0:00:13  lr: 0.000229  loss: 2.7095 (2.6993)  grad: 0.2069 (0.2152)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [9]  [360/400]  eta: 0:00:09  lr: 0.000228  loss: 2.7104 (2.7004)  grad: 0.2033 (0.2143)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [9]  [380/400]  eta: 0:00:04  lr: 0.000226  loss: 2.7104 (2.6997)  grad: 0.1966 (0.2137)  time: 0.2282  data: 0.0033  max mem: 23764
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.7023 (2.6988)  grad: 0.1966 (0.2129)  time: 0.2280  data: 0.0033  max mem: 23764
+train: [9] Total time: 0:01:31 (0.2293 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.7023 (2.6988)  grad: 0.1966 (0.2129)
+eval (validation): [9]  [ 0/85]  eta: 0:00:50    time: 0.5942  data: 0.4758  max mem: 23764
+eval (validation): [9]  [20/85]  eta: 0:00:09    time: 0.1210  data: 0.0022  max mem: 23764
+eval (validation): [9]  [40/85]  eta: 0:00:06    time: 0.1226  data: 0.0033  max mem: 23764
+eval (validation): [9]  [60/85]  eta: 0:00:03    time: 0.1220  data: 0.0031  max mem: 23764
+eval (validation): [9]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0034  max mem: 23764
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.1206  data: 0.0035  max mem: 23764
+eval (validation): [9] Total time: 0:00:10 (0.1283 s / it)
+cv: [9]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.455  acc: 0.262  f1: 0.206
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:04:13  lr: nan  time: 0.6339  data: 0.4165  max mem: 23764
+train: [10]  [ 20/400]  eta: 0:01:33  lr: 0.000224  loss: 2.6715 (2.6757)  grad: 0.2081 (0.2088)  time: 0.2274  data: 0.0031  max mem: 23764
+train: [10]  [ 40/400]  eta: 0:01:25  lr: 0.000222  loss: 2.6715 (2.6812)  grad: 0.2081 (0.2126)  time: 0.2285  data: 0.0035  max mem: 23764
+train: [10]  [ 60/400]  eta: 0:01:19  lr: 0.000221  loss: 2.6791 (2.6861)  grad: 0.2082 (0.2115)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [10]  [ 80/400]  eta: 0:01:14  lr: 0.000220  loss: 2.6978 (2.6898)  grad: 0.2082 (0.2116)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [10]  [100/400]  eta: 0:01:09  lr: 0.000218  loss: 2.6505 (2.6795)  grad: 0.2114 (0.2122)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [10]  [120/400]  eta: 0:01:04  lr: 0.000217  loss: 2.6393 (2.6878)  grad: 0.2114 (0.2124)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [10]  [140/400]  eta: 0:01:00  lr: 0.000215  loss: 2.6751 (2.6821)  grad: 0.2128 (0.2130)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [10]  [160/400]  eta: 0:00:55  lr: 0.000214  loss: 2.6488 (2.6811)  grad: 0.2164 (0.2133)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [10]  [180/400]  eta: 0:00:50  lr: 0.000213  loss: 2.6488 (2.6812)  grad: 0.2187 (0.2143)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [10]  [200/400]  eta: 0:00:46  lr: 0.000211  loss: 2.6356 (2.6780)  grad: 0.2163 (0.2145)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [10]  [220/400]  eta: 0:00:41  lr: 0.000210  loss: 2.6549 (2.6787)  grad: 0.2216 (0.2163)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [10]  [240/400]  eta: 0:00:36  lr: 0.000208  loss: 2.6998 (2.6813)  grad: 0.2220 (0.2163)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [10]  [260/400]  eta: 0:00:32  lr: 0.000207  loss: 2.7190 (2.6851)  grad: 0.2179 (0.2170)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [10]  [280/400]  eta: 0:00:27  lr: 0.000205  loss: 2.7064 (2.6843)  grad: 0.2087 (0.2167)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [10]  [300/400]  eta: 0:00:22  lr: 0.000204  loss: 2.6633 (2.6838)  grad: 0.2087 (0.2168)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [10]  [320/400]  eta: 0:00:18  lr: 0.000202  loss: 2.6732 (2.6836)  grad: 0.2116 (0.2162)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [10]  [340/400]  eta: 0:00:13  lr: 0.000201  loss: 2.6725 (2.6819)  grad: 0.2116 (0.2168)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [10]  [360/400]  eta: 0:00:09  lr: 0.000199  loss: 2.6453 (2.6812)  grad: 0.2134 (0.2165)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [10]  [380/400]  eta: 0:00:04  lr: 0.000198  loss: 2.6583 (2.6800)  grad: 0.2134 (0.2164)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.6954 (2.6799)  grad: 0.2205 (0.2170)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [10] Total time: 0:01:31 (0.2292 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.6954 (2.6799)  grad: 0.2205 (0.2170)
+eval (validation): [10]  [ 0/85]  eta: 0:00:45    time: 0.5329  data: 0.4158  max mem: 23764
+eval (validation): [10]  [20/85]  eta: 0:00:09    time: 0.1214  data: 0.0027  max mem: 23764
+eval (validation): [10]  [40/85]  eta: 0:00:05    time: 0.1226  data: 0.0034  max mem: 23764
+eval (validation): [10]  [60/85]  eta: 0:00:03    time: 0.1219  data: 0.0032  max mem: 23764
+eval (validation): [10]  [80/85]  eta: 0:00:00    time: 0.1225  data: 0.0032  max mem: 23764
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.1205  data: 0.0033  max mem: 23764
+eval (validation): [10] Total time: 0:00:10 (0.1275 s / it)
+cv: [10]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.455  acc: 0.264  f1: 0.198
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:04:45  lr: nan  time: 0.7129  data: 0.4965  max mem: 23764
+train: [11]  [ 20/400]  eta: 0:01:35  lr: 0.000195  loss: 2.6414 (2.6422)  grad: 0.2339 (0.2261)  time: 0.2270  data: 0.0022  max mem: 23764
+train: [11]  [ 40/400]  eta: 0:01:26  lr: 0.000193  loss: 2.6722 (2.6633)  grad: 0.2119 (0.2167)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [11]  [ 60/400]  eta: 0:01:20  lr: 0.000192  loss: 2.6861 (2.6688)  grad: 0.2048 (0.2133)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [11]  [ 80/400]  eta: 0:01:14  lr: 0.000190  loss: 2.6611 (2.6578)  grad: 0.2086 (0.2154)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [11]  [100/400]  eta: 0:01:09  lr: 0.000189  loss: 2.6050 (2.6554)  grad: 0.2220 (0.2159)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [11]  [120/400]  eta: 0:01:04  lr: 0.000187  loss: 2.6428 (2.6568)  grad: 0.2179 (0.2168)  time: 0.2290  data: 0.0036  max mem: 23764
+train: [11]  [140/400]  eta: 0:01:00  lr: 0.000186  loss: 2.6488 (2.6595)  grad: 0.2235 (0.2184)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [11]  [160/400]  eta: 0:00:55  lr: 0.000184  loss: 2.6468 (2.6578)  grad: 0.2235 (0.2177)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [11]  [180/400]  eta: 0:00:50  lr: 0.000183  loss: 2.6454 (2.6601)  grad: 0.2208 (0.2186)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [11]  [200/400]  eta: 0:00:46  lr: 0.000181  loss: 2.6314 (2.6547)  grad: 0.2177 (0.2180)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [11]  [220/400]  eta: 0:00:41  lr: 0.000180  loss: 2.6314 (2.6573)  grad: 0.2107 (0.2175)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [11]  [240/400]  eta: 0:00:36  lr: 0.000178  loss: 2.6542 (2.6577)  grad: 0.2095 (0.2170)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [11]  [260/400]  eta: 0:00:32  lr: 0.000177  loss: 2.6542 (2.6586)  grad: 0.2208 (0.2178)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [11]  [280/400]  eta: 0:00:27  lr: 0.000175  loss: 2.6329 (2.6534)  grad: 0.2138 (0.2173)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [11]  [300/400]  eta: 0:00:22  lr: 0.000174  loss: 2.6181 (2.6533)  grad: 0.2138 (0.2175)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [11]  [320/400]  eta: 0:00:18  lr: 0.000172  loss: 2.6454 (2.6542)  grad: 0.2217 (0.2178)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [11]  [340/400]  eta: 0:00:13  lr: 0.000170  loss: 2.6058 (2.6505)  grad: 0.2150 (0.2179)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [11]  [360/400]  eta: 0:00:09  lr: 0.000169  loss: 2.6094 (2.6499)  grad: 0.2141 (0.2176)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [11]  [380/400]  eta: 0:00:04  lr: 0.000167  loss: 2.6258 (2.6502)  grad: 0.2103 (0.2173)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.6576 (2.6503)  grad: 0.2116 (0.2175)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [11] Total time: 0:01:31 (0.2295 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.6576 (2.6503)  grad: 0.2116 (0.2175)
+eval (validation): [11]  [ 0/85]  eta: 0:00:46    time: 0.5518  data: 0.4329  max mem: 23764
+eval (validation): [11]  [20/85]  eta: 0:00:09    time: 0.1215  data: 0.0024  max mem: 23764
+eval (validation): [11]  [40/85]  eta: 0:00:05    time: 0.1225  data: 0.0033  max mem: 23764
+eval (validation): [11]  [60/85]  eta: 0:00:03    time: 0.1226  data: 0.0033  max mem: 23764
+eval (validation): [11]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0034  max mem: 23764
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.1207  data: 0.0034  max mem: 23764
+eval (validation): [11] Total time: 0:00:10 (0.1282 s / it)
+cv: [11]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.466  acc: 0.270  f1: 0.198
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [12]  [  0/400]  eta: 0:05:08  lr: nan  time: 0.7718  data: 0.5535  max mem: 23764
+train: [12]  [ 20/400]  eta: 0:01:36  lr: 0.000164  loss: 2.6179 (2.6417)  grad: 0.2146 (0.2197)  time: 0.2280  data: 0.0029  max mem: 23764
+train: [12]  [ 40/400]  eta: 0:01:26  lr: 0.000163  loss: 2.6179 (2.6318)  grad: 0.2143 (0.2173)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [12]  [ 60/400]  eta: 0:01:20  lr: 0.000161  loss: 2.6378 (2.6409)  grad: 0.2135 (0.2185)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [12]  [ 80/400]  eta: 0:01:15  lr: 0.000160  loss: 2.6019 (2.6292)  grad: 0.2101 (0.2162)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [12]  [100/400]  eta: 0:01:10  lr: 0.000158  loss: 2.6019 (2.6243)  grad: 0.1998 (0.2142)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [12]  [120/400]  eta: 0:01:05  lr: 0.000156  loss: 2.6026 (2.6223)  grad: 0.2050 (0.2126)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [12]  [140/400]  eta: 0:01:00  lr: 0.000155  loss: 2.6110 (2.6185)  grad: 0.2100 (0.2135)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [12]  [160/400]  eta: 0:00:55  lr: 0.000153  loss: 2.6165 (2.6199)  grad: 0.2187 (0.2140)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [12]  [180/400]  eta: 0:00:50  lr: 0.000152  loss: 2.6235 (2.6200)  grad: 0.2178 (0.2141)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [12]  [200/400]  eta: 0:00:46  lr: 0.000150  loss: 2.6300 (2.6211)  grad: 0.2164 (0.2145)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [12]  [220/400]  eta: 0:00:41  lr: 0.000149  loss: 2.6349 (2.6216)  grad: 0.2150 (0.2149)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [12]  [240/400]  eta: 0:00:36  lr: 0.000147  loss: 2.6456 (2.6233)  grad: 0.2130 (0.2146)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [12]  [260/400]  eta: 0:00:32  lr: 0.000145  loss: 2.5995 (2.6219)  grad: 0.2128 (0.2146)  time: 0.2287  data: 0.0036  max mem: 23764
+train: [12]  [280/400]  eta: 0:00:27  lr: 0.000144  loss: 2.5747 (2.6162)  grad: 0.2101 (0.2141)  time: 0.2284  data: 0.0036  max mem: 23764
+train: [12]  [300/400]  eta: 0:00:22  lr: 0.000142  loss: 2.5935 (2.6173)  grad: 0.2101 (0.2142)  time: 0.2285  data: 0.0035  max mem: 23764
+train: [12]  [320/400]  eta: 0:00:18  lr: 0.000141  loss: 2.6276 (2.6153)  grad: 0.2148 (0.2140)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [12]  [340/400]  eta: 0:00:13  lr: 0.000139  loss: 2.6298 (2.6181)  grad: 0.2111 (0.2139)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [12]  [360/400]  eta: 0:00:09  lr: 0.000138  loss: 2.6479 (2.6205)  grad: 0.2111 (0.2140)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [12]  [380/400]  eta: 0:00:04  lr: 0.000136  loss: 2.6430 (2.6220)  grad: 0.2129 (0.2139)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.6242 (2.6219)  grad: 0.2129 (0.2140)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [12] Total time: 0:01:31 (0.2298 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.6242 (2.6219)  grad: 0.2129 (0.2140)
+eval (validation): [12]  [ 0/85]  eta: 0:00:42    time: 0.5037  data: 0.3869  max mem: 23764
+eval (validation): [12]  [20/85]  eta: 0:00:09    time: 0.1217  data: 0.0031  max mem: 23764
+eval (validation): [12]  [40/85]  eta: 0:00:05    time: 0.1225  data: 0.0033  max mem: 23764
+eval (validation): [12]  [60/85]  eta: 0:00:03    time: 0.1225  data: 0.0034  max mem: 23764
+eval (validation): [12]  [80/85]  eta: 0:00:00    time: 0.1224  data: 0.0034  max mem: 23764
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.1205  data: 0.0034  max mem: 23764
+eval (validation): [12] Total time: 0:00:10 (0.1274 s / it)
+cv: [12]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.491  acc: 0.270  f1: 0.212
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:05:06  lr: nan  time: 0.7653  data: 0.5498  max mem: 23764
+train: [13]  [ 20/400]  eta: 0:01:35  lr: 0.000133  loss: 2.6222 (2.6068)  grad: 0.2151 (0.2161)  time: 0.2267  data: 0.0025  max mem: 23764
+train: [13]  [ 40/400]  eta: 0:01:26  lr: 0.000131  loss: 2.5996 (2.6038)  grad: 0.2157 (0.2179)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [13]  [ 60/400]  eta: 0:01:20  lr: 0.000130  loss: 2.5590 (2.5851)  grad: 0.2174 (0.2181)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [13]  [ 80/400]  eta: 0:01:14  lr: 0.000128  loss: 2.5676 (2.5924)  grad: 0.2196 (0.2200)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [13]  [100/400]  eta: 0:01:09  lr: 0.000127  loss: 2.5886 (2.5889)  grad: 0.2206 (0.2202)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [13]  [120/400]  eta: 0:01:04  lr: 0.000125  loss: 2.5931 (2.5954)  grad: 0.2170 (0.2201)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [13]  [140/400]  eta: 0:01:00  lr: 0.000124  loss: 2.6152 (2.5950)  grad: 0.2147 (0.2197)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [13]  [160/400]  eta: 0:00:55  lr: 0.000122  loss: 2.6240 (2.6038)  grad: 0.2198 (0.2201)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [13]  [180/400]  eta: 0:00:50  lr: 0.000120  loss: 2.6254 (2.6028)  grad: 0.2198 (0.2198)  time: 0.2274  data: 0.0034  max mem: 23764
+train: [13]  [200/400]  eta: 0:00:46  lr: 0.000119  loss: 2.5744 (2.6022)  grad: 0.2144 (0.2199)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [13]  [220/400]  eta: 0:00:41  lr: 0.000117  loss: 2.5747 (2.6005)  grad: 0.2171 (0.2199)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [13]  [240/400]  eta: 0:00:36  lr: 0.000116  loss: 2.6022 (2.6009)  grad: 0.2172 (0.2198)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [13]  [260/400]  eta: 0:00:32  lr: 0.000114  loss: 2.6206 (2.6026)  grad: 0.2191 (0.2199)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [13]  [280/400]  eta: 0:00:27  lr: 0.000113  loss: 2.6199 (2.6027)  grad: 0.2241 (0.2206)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [13]  [300/400]  eta: 0:00:22  lr: 0.000111  loss: 2.5992 (2.6036)  grad: 0.2241 (0.2205)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [13]  [320/400]  eta: 0:00:18  lr: 0.000110  loss: 2.6304 (2.6054)  grad: 0.2160 (0.2202)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [13]  [340/400]  eta: 0:00:13  lr: 0.000108  loss: 2.5901 (2.6032)  grad: 0.2148 (0.2202)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [13]  [360/400]  eta: 0:00:09  lr: 0.000107  loss: 2.5780 (2.6026)  grad: 0.2148 (0.2200)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [13]  [380/400]  eta: 0:00:04  lr: 0.000105  loss: 2.6090 (2.6042)  grad: 0.2106 (0.2193)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.6212 (2.6058)  grad: 0.2126 (0.2192)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [13] Total time: 0:01:31 (0.2293 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.6212 (2.6058)  grad: 0.2126 (0.2192)
+eval (validation): [13]  [ 0/85]  eta: 0:00:52    time: 0.6223  data: 0.5056  max mem: 23764
+eval (validation): [13]  [20/85]  eta: 0:00:09    time: 0.1212  data: 0.0022  max mem: 23764
+eval (validation): [13]  [40/85]  eta: 0:00:06    time: 0.1223  data: 0.0031  max mem: 23764
+eval (validation): [13]  [60/85]  eta: 0:00:03    time: 0.1222  data: 0.0033  max mem: 23764
+eval (validation): [13]  [80/85]  eta: 0:00:00    time: 0.1225  data: 0.0034  max mem: 23764
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.1204  data: 0.0034  max mem: 23764
+eval (validation): [13] Total time: 0:00:10 (0.1285 s / it)
+cv: [13]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.473  acc: 0.265  f1: 0.216
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:04:33  lr: nan  time: 0.6835  data: 0.4641  max mem: 23764
+train: [14]  [ 20/400]  eta: 0:01:34  lr: 0.000102  loss: 2.5696 (2.5994)  grad: 0.2104 (0.2156)  time: 0.2276  data: 0.0032  max mem: 23764
+train: [14]  [ 40/400]  eta: 0:01:26  lr: 0.000101  loss: 2.5965 (2.6114)  grad: 0.2154 (0.2174)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [14]  [ 60/400]  eta: 0:01:20  lr: 0.000099  loss: 2.6004 (2.6132)  grad: 0.2168 (0.2175)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [14]  [ 80/400]  eta: 0:01:14  lr: 0.000098  loss: 2.5965 (2.6051)  grad: 0.2125 (0.2161)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [14]  [100/400]  eta: 0:01:09  lr: 0.000096  loss: 2.5639 (2.5948)  grad: 0.2104 (0.2166)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [14]  [120/400]  eta: 0:01:04  lr: 0.000095  loss: 2.5822 (2.6006)  grad: 0.2193 (0.2183)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [14]  [140/400]  eta: 0:01:00  lr: 0.000093  loss: 2.6106 (2.5984)  grad: 0.2225 (0.2187)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [14]  [160/400]  eta: 0:00:55  lr: 0.000092  loss: 2.6001 (2.5950)  grad: 0.2234 (0.2201)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [14]  [180/400]  eta: 0:00:50  lr: 0.000090  loss: 2.5757 (2.5916)  grad: 0.2236 (0.2204)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [14]  [200/400]  eta: 0:00:46  lr: 0.000089  loss: 2.5757 (2.5897)  grad: 0.2199 (0.2204)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [14]  [220/400]  eta: 0:00:41  lr: 0.000088  loss: 2.5842 (2.5892)  grad: 0.2129 (0.2197)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [14]  [240/400]  eta: 0:00:36  lr: 0.000086  loss: 2.5628 (2.5858)  grad: 0.2077 (0.2187)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [14]  [260/400]  eta: 0:00:32  lr: 0.000085  loss: 2.5586 (2.5860)  grad: 0.2023 (0.2179)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [14]  [280/400]  eta: 0:00:27  lr: 0.000083  loss: 2.5891 (2.5894)  grad: 0.2067 (0.2176)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [14]  [300/400]  eta: 0:00:22  lr: 0.000082  loss: 2.5995 (2.5900)  grad: 0.2172 (0.2179)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [14]  [320/400]  eta: 0:00:18  lr: 0.000081  loss: 2.5840 (2.5918)  grad: 0.2205 (0.2182)  time: 0.2284  data: 0.0034  max mem: 23764
+train: [14]  [340/400]  eta: 0:00:13  lr: 0.000079  loss: 2.5722 (2.5908)  grad: 0.2111 (0.2179)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [14]  [360/400]  eta: 0:00:09  lr: 0.000078  loss: 2.5564 (2.5904)  grad: 0.2105 (0.2178)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [14]  [380/400]  eta: 0:00:04  lr: 0.000076  loss: 2.5830 (2.5923)  grad: 0.2106 (0.2178)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.5936 (2.5906)  grad: 0.2157 (0.2176)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [14] Total time: 0:01:31 (0.2294 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.5936 (2.5906)  grad: 0.2157 (0.2176)
+eval (validation): [14]  [ 0/85]  eta: 0:00:50    time: 0.5942  data: 0.4777  max mem: 23764
+eval (validation): [14]  [20/85]  eta: 0:00:09    time: 0.1216  data: 0.0031  max mem: 23764
+eval (validation): [14]  [40/85]  eta: 0:00:06    time: 0.1223  data: 0.0033  max mem: 23764
+eval (validation): [14]  [60/85]  eta: 0:00:03    time: 0.1223  data: 0.0033  max mem: 23764
+eval (validation): [14]  [80/85]  eta: 0:00:00    time: 0.1223  data: 0.0034  max mem: 23764
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.1203  data: 0.0034  max mem: 23764
+eval (validation): [14] Total time: 0:00:10 (0.1283 s / it)
+cv: [14]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.471  acc: 0.273  f1: 0.223
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:04:40  lr: nan  time: 0.7006  data: 0.4830  max mem: 23764
+train: [15]  [ 20/400]  eta: 0:01:34  lr: 0.000074  loss: 2.5147 (2.5209)  grad: 0.2038 (0.2080)  time: 0.2270  data: 0.0029  max mem: 23764
+train: [15]  [ 40/400]  eta: 0:01:26  lr: 0.000072  loss: 2.5558 (2.5529)  grad: 0.2138 (0.2113)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [15]  [ 60/400]  eta: 0:01:19  lr: 0.000071  loss: 2.5841 (2.5619)  grad: 0.2138 (0.2111)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [15]  [ 80/400]  eta: 0:01:14  lr: 0.000070  loss: 2.5658 (2.5618)  grad: 0.2137 (0.2114)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [15]  [100/400]  eta: 0:01:09  lr: 0.000068  loss: 2.5573 (2.5565)  grad: 0.2155 (0.2126)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [15]  [120/400]  eta: 0:01:04  lr: 0.000067  loss: 2.5695 (2.5616)  grad: 0.2155 (0.2131)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [15]  [140/400]  eta: 0:01:00  lr: 0.000066  loss: 2.5695 (2.5674)  grad: 0.2140 (0.2132)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [15]  [160/400]  eta: 0:00:55  lr: 0.000064  loss: 2.5593 (2.5700)  grad: 0.2181 (0.2148)  time: 0.2281  data: 0.0033  max mem: 23764
+train: [15]  [180/400]  eta: 0:00:50  lr: 0.000063  loss: 2.5593 (2.5688)  grad: 0.2241 (0.2149)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [15]  [200/400]  eta: 0:00:46  lr: 0.000062  loss: 2.5621 (2.5667)  grad: 0.2147 (0.2158)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [15]  [220/400]  eta: 0:00:41  lr: 0.000061  loss: 2.5810 (2.5659)  grad: 0.2147 (0.2159)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [15]  [240/400]  eta: 0:00:36  lr: 0.000059  loss: 2.5541 (2.5659)  grad: 0.2114 (0.2154)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [15]  [260/400]  eta: 0:00:32  lr: 0.000058  loss: 2.5562 (2.5661)  grad: 0.2101 (0.2157)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [15]  [280/400]  eta: 0:00:27  lr: 0.000057  loss: 2.5812 (2.5685)  grad: 0.2140 (0.2158)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [15]  [300/400]  eta: 0:00:22  lr: 0.000056  loss: 2.5739 (2.5667)  grad: 0.2070 (0.2150)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [15]  [320/400]  eta: 0:00:18  lr: 0.000054  loss: 2.5403 (2.5674)  grad: 0.2075 (0.2152)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [15]  [340/400]  eta: 0:00:13  lr: 0.000053  loss: 2.5617 (2.5690)  grad: 0.2172 (0.2155)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [15]  [360/400]  eta: 0:00:09  lr: 0.000052  loss: 2.5590 (2.5687)  grad: 0.2175 (0.2156)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [15]  [380/400]  eta: 0:00:04  lr: 0.000051  loss: 2.5263 (2.5674)  grad: 0.2230 (0.2161)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.5280 (2.5665)  grad: 0.2190 (0.2160)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [15] Total time: 0:01:31 (0.2293 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.5280 (2.5665)  grad: 0.2190 (0.2160)
+eval (validation): [15]  [ 0/85]  eta: 0:00:55    time: 0.6481  data: 0.5302  max mem: 23764
+eval (validation): [15]  [20/85]  eta: 0:00:09    time: 0.1214  data: 0.0022  max mem: 23764
+eval (validation): [15]  [40/85]  eta: 0:00:06    time: 0.1224  data: 0.0032  max mem: 23764
+eval (validation): [15]  [60/85]  eta: 0:00:03    time: 0.1224  data: 0.0031  max mem: 23764
+eval (validation): [15]  [80/85]  eta: 0:00:00    time: 0.1227  data: 0.0034  max mem: 23764
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.1207  data: 0.0034  max mem: 23764
+eval (validation): [15] Total time: 0:00:10 (0.1290 s / it)
+cv: [15]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 2.475  acc: 0.267  f1: 0.221
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:04:58  lr: nan  time: 0.7458  data: 0.5263  max mem: 23764
+train: [16]  [ 20/400]  eta: 0:01:35  lr: 0.000048  loss: 2.5469 (2.5499)  grad: 0.2141 (0.2137)  time: 0.2266  data: 0.0026  max mem: 23764
+train: [16]  [ 40/400]  eta: 0:01:26  lr: 0.000047  loss: 2.5459 (2.5482)  grad: 0.2114 (0.2136)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [16]  [ 60/400]  eta: 0:01:20  lr: 0.000046  loss: 2.5399 (2.5476)  grad: 0.2155 (0.2166)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [16]  [ 80/400]  eta: 0:01:14  lr: 0.000045  loss: 2.5249 (2.5382)  grad: 0.2161 (0.2157)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [16]  [100/400]  eta: 0:01:09  lr: 0.000044  loss: 2.5527 (2.5480)  grad: 0.2132 (0.2157)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [16]  [120/400]  eta: 0:01:04  lr: 0.000043  loss: 2.5892 (2.5566)  grad: 0.2106 (0.2151)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [16]  [140/400]  eta: 0:01:00  lr: 0.000042  loss: 2.5782 (2.5557)  grad: 0.2106 (0.2155)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [16]  [160/400]  eta: 0:00:55  lr: 0.000041  loss: 2.5658 (2.5579)  grad: 0.2163 (0.2158)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [16]  [180/400]  eta: 0:00:50  lr: 0.000040  loss: 2.5343 (2.5537)  grad: 0.2174 (0.2161)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [16]  [200/400]  eta: 0:00:46  lr: 0.000039  loss: 2.5265 (2.5519)  grad: 0.2121 (0.2160)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [16]  [220/400]  eta: 0:00:41  lr: 0.000038  loss: 2.5577 (2.5548)  grad: 0.2121 (0.2163)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [16]  [240/400]  eta: 0:00:36  lr: 0.000036  loss: 2.5309 (2.5521)  grad: 0.2135 (0.2162)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [16]  [260/400]  eta: 0:00:32  lr: 0.000035  loss: 2.5160 (2.5516)  grad: 0.2126 (0.2163)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [16]  [280/400]  eta: 0:00:27  lr: 0.000034  loss: 2.5215 (2.5507)  grad: 0.2158 (0.2164)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [16]  [300/400]  eta: 0:00:22  lr: 0.000033  loss: 2.5068 (2.5490)  grad: 0.2150 (0.2163)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [16]  [320/400]  eta: 0:00:18  lr: 0.000032  loss: 2.5490 (2.5515)  grad: 0.2143 (0.2163)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [16]  [340/400]  eta: 0:00:13  lr: 0.000031  loss: 2.5612 (2.5517)  grad: 0.2123 (0.2162)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [16]  [360/400]  eta: 0:00:09  lr: 0.000031  loss: 2.5370 (2.5502)  grad: 0.2063 (0.2155)  time: 0.2282  data: 0.0033  max mem: 23764
+train: [16]  [380/400]  eta: 0:00:04  lr: 0.000030  loss: 2.5337 (2.5502)  grad: 0.2019 (0.2149)  time: 0.2278  data: 0.0033  max mem: 23764
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.5587 (2.5516)  grad: 0.2090 (0.2147)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [16] Total time: 0:01:31 (0.2293 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.5587 (2.5516)  grad: 0.2090 (0.2147)
+eval (validation): [16]  [ 0/85]  eta: 0:00:48    time: 0.5667  data: 0.4489  max mem: 23764
+eval (validation): [16]  [20/85]  eta: 0:00:09    time: 0.1218  data: 0.0034  max mem: 23764
+eval (validation): [16]  [40/85]  eta: 0:00:05    time: 0.1221  data: 0.0031  max mem: 23764
+eval (validation): [16]  [60/85]  eta: 0:00:03    time: 0.1223  data: 0.0032  max mem: 23764
+eval (validation): [16]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0032  max mem: 23764
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.1204  data: 0.0032  max mem: 23764
+eval (validation): [16] Total time: 0:00:10 (0.1280 s / it)
+cv: [16]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.450  acc: 0.267  f1: 0.210
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:04:54  lr: nan  time: 0.7363  data: 0.5181  max mem: 23764
+train: [17]  [ 20/400]  eta: 0:01:35  lr: 0.000028  loss: 2.5267 (2.5336)  grad: 0.2140 (0.2186)  time: 0.2261  data: 0.0019  max mem: 23764
+train: [17]  [ 40/400]  eta: 0:01:26  lr: 0.000027  loss: 2.5267 (2.5412)  grad: 0.2147 (0.2180)  time: 0.2281  data: 0.0035  max mem: 23764
+train: [17]  [ 60/400]  eta: 0:01:20  lr: 0.000026  loss: 2.5295 (2.5429)  grad: 0.2103 (0.2164)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [17]  [ 80/400]  eta: 0:01:14  lr: 0.000025  loss: 2.5296 (2.5333)  grad: 0.2103 (0.2185)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [17]  [100/400]  eta: 0:01:09  lr: 0.000024  loss: 2.5296 (2.5306)  grad: 0.2213 (0.2178)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [17]  [120/400]  eta: 0:01:04  lr: 0.000023  loss: 2.5411 (2.5362)  grad: 0.2110 (0.2172)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [17]  [140/400]  eta: 0:01:00  lr: 0.000023  loss: 2.5499 (2.5379)  grad: 0.2154 (0.2178)  time: 0.2282  data: 0.0034  max mem: 23764
+train: [17]  [160/400]  eta: 0:00:55  lr: 0.000022  loss: 2.5187 (2.5348)  grad: 0.2154 (0.2176)  time: 0.2284  data: 0.0035  max mem: 23764
+train: [17]  [180/400]  eta: 0:00:50  lr: 0.000021  loss: 2.4992 (2.5294)  grad: 0.2119 (0.2172)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [17]  [200/400]  eta: 0:00:46  lr: 0.000020  loss: 2.5251 (2.5313)  grad: 0.2157 (0.2175)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [17]  [220/400]  eta: 0:00:41  lr: 0.000019  loss: 2.5555 (2.5354)  grad: 0.2157 (0.2172)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [17]  [240/400]  eta: 0:00:36  lr: 0.000019  loss: 2.5999 (2.5375)  grad: 0.2150 (0.2173)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [17]  [260/400]  eta: 0:00:32  lr: 0.000018  loss: 2.5442 (2.5363)  grad: 0.2108 (0.2170)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [17]  [280/400]  eta: 0:00:27  lr: 0.000017  loss: 2.5286 (2.5365)  grad: 0.2099 (0.2168)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [17]  [300/400]  eta: 0:00:22  lr: 0.000016  loss: 2.5286 (2.5359)  grad: 0.2126 (0.2169)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [17]  [320/400]  eta: 0:00:18  lr: 0.000016  loss: 2.4765 (2.5317)  grad: 0.2117 (0.2162)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [17]  [340/400]  eta: 0:00:13  lr: 0.000015  loss: 2.4697 (2.5305)  grad: 0.2090 (0.2158)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [17]  [360/400]  eta: 0:00:09  lr: 0.000014  loss: 2.5442 (2.5324)  grad: 0.2105 (0.2160)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [17]  [380/400]  eta: 0:00:04  lr: 0.000014  loss: 2.5655 (2.5334)  grad: 0.2112 (0.2157)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.5399 (2.5319)  grad: 0.2082 (0.2153)  time: 0.2274  data: 0.0033  max mem: 23764
+train: [17] Total time: 0:01:31 (0.2293 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.5399 (2.5319)  grad: 0.2082 (0.2153)
+eval (validation): [17]  [ 0/85]  eta: 0:00:47    time: 0.5534  data: 0.4337  max mem: 23764
+eval (validation): [17]  [20/85]  eta: 0:00:09    time: 0.1215  data: 0.0031  max mem: 23764
+eval (validation): [17]  [40/85]  eta: 0:00:05    time: 0.1222  data: 0.0030  max mem: 23764
+eval (validation): [17]  [60/85]  eta: 0:00:03    time: 0.1225  data: 0.0033  max mem: 23764
+eval (validation): [17]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0034  max mem: 23764
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.1206  data: 0.0034  max mem: 23764
+eval (validation): [17] Total time: 0:00:10 (0.1279 s / it)
+cv: [17]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 2.463  acc: 0.273  f1: 0.220
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:04:11  lr: nan  time: 0.6281  data: 0.4119  max mem: 23764
+train: [18]  [ 20/400]  eta: 0:01:33  lr: 0.000012  loss: 2.4647 (2.4956)  grad: 0.2123 (0.2076)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [18]  [ 40/400]  eta: 0:01:25  lr: 0.000012  loss: 2.4927 (2.5155)  grad: 0.2169 (0.2106)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [18]  [ 60/400]  eta: 0:01:19  lr: 0.000011  loss: 2.4914 (2.5144)  grad: 0.2094 (0.2099)  time: 0.2277  data: 0.0035  max mem: 23764
+train: [18]  [ 80/400]  eta: 0:01:14  lr: 0.000011  loss: 2.4702 (2.5181)  grad: 0.2032 (0.2090)  time: 0.2275  data: 0.0034  max mem: 23764
+train: [18]  [100/400]  eta: 0:01:09  lr: 0.000010  loss: 2.5090 (2.5121)  grad: 0.2064 (0.2102)  time: 0.2273  data: 0.0033  max mem: 23764
+train: [18]  [120/400]  eta: 0:01:04  lr: 0.000009  loss: 2.5090 (2.5182)  grad: 0.2117 (0.2094)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [18]  [140/400]  eta: 0:00:59  lr: 0.000009  loss: 2.5072 (2.5124)  grad: 0.2102 (0.2096)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [18]  [160/400]  eta: 0:00:55  lr: 0.000008  loss: 2.5310 (2.5184)  grad: 0.2130 (0.2099)  time: 0.2280  data: 0.0035  max mem: 23764
+train: [18]  [180/400]  eta: 0:00:50  lr: 0.000008  loss: 2.5556 (2.5234)  grad: 0.2052 (0.2097)  time: 0.2282  data: 0.0035  max mem: 23764
+train: [18]  [200/400]  eta: 0:00:45  lr: 0.000007  loss: 2.5553 (2.5238)  grad: 0.2098 (0.2108)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [18]  [220/400]  eta: 0:00:41  lr: 0.000007  loss: 2.5123 (2.5207)  grad: 0.2131 (0.2112)  time: 0.2283  data: 0.0035  max mem: 23764
+train: [18]  [240/400]  eta: 0:00:36  lr: 0.000006  loss: 2.4838 (2.5168)  grad: 0.2069 (0.2111)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [18]  [260/400]  eta: 0:00:32  lr: 0.000006  loss: 2.4838 (2.5182)  grad: 0.2121 (0.2117)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [18]  [280/400]  eta: 0:00:27  lr: 0.000006  loss: 2.5025 (2.5161)  grad: 0.2110 (0.2113)  time: 0.2278  data: 0.0035  max mem: 23764
+train: [18]  [300/400]  eta: 0:00:22  lr: 0.000005  loss: 2.5267 (2.5182)  grad: 0.2054 (0.2111)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [18]  [320/400]  eta: 0:00:18  lr: 0.000005  loss: 2.5304 (2.5186)  grad: 0.2068 (0.2112)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [18]  [340/400]  eta: 0:00:13  lr: 0.000004  loss: 2.5057 (2.5191)  grad: 0.2083 (0.2113)  time: 0.2279  data: 0.0035  max mem: 23764
+train: [18]  [360/400]  eta: 0:00:09  lr: 0.000004  loss: 2.4899 (2.5179)  grad: 0.2083 (0.2115)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [18]  [380/400]  eta: 0:00:04  lr: 0.000004  loss: 2.4906 (2.5180)  grad: 0.2196 (0.2116)  time: 0.2277  data: 0.0034  max mem: 23764
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.5330 (2.5192)  grad: 0.2139 (0.2117)  time: 0.2278  data: 0.0034  max mem: 23764
+train: [18] Total time: 0:01:31 (0.2290 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.5330 (2.5192)  grad: 0.2139 (0.2117)
+eval (validation): [18]  [ 0/85]  eta: 0:00:50    time: 0.5973  data: 0.4806  max mem: 23764
+eval (validation): [18]  [20/85]  eta: 0:00:09    time: 0.1211  data: 0.0021  max mem: 23764
+eval (validation): [18]  [40/85]  eta: 0:00:05    time: 0.1222  data: 0.0031  max mem: 23764
+eval (validation): [18]  [60/85]  eta: 0:00:03    time: 0.1221  data: 0.0032  max mem: 23764
+eval (validation): [18]  [80/85]  eta: 0:00:00    time: 0.1225  data: 0.0032  max mem: 23764
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.1207  data: 0.0032  max mem: 23764
+eval (validation): [18] Total time: 0:00:10 (0.1281 s / it)
+cv: [18]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.457  acc: 0.269  f1: 0.214
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:04:58  lr: nan  time: 0.7473  data: 0.5291  max mem: 23764
+train: [19]  [ 20/400]  eta: 0:01:35  lr: 0.000003  loss: 2.5280 (2.5379)  grad: 0.2115 (0.2148)  time: 0.2264  data: 0.0020  max mem: 23764
+train: [19]  [ 40/400]  eta: 0:01:26  lr: 0.000003  loss: 2.5483 (2.5367)  grad: 0.2111 (0.2145)  time: 0.2275  data: 0.0032  max mem: 23764
+train: [19]  [ 60/400]  eta: 0:01:20  lr: 0.000002  loss: 2.5077 (2.5312)  grad: 0.2114 (0.2150)  time: 0.2281  data: 0.0034  max mem: 23764
+train: [19]  [ 80/400]  eta: 0:01:14  lr: 0.000002  loss: 2.5193 (2.5303)  grad: 0.2128 (0.2146)  time: 0.2280  data: 0.0033  max mem: 23764
+train: [19]  [100/400]  eta: 0:01:09  lr: 0.000002  loss: 2.5193 (2.5249)  grad: 0.2087 (0.2128)  time: 0.2274  data: 0.0032  max mem: 23764
+train: [19]  [120/400]  eta: 0:01:04  lr: 0.000002  loss: 2.4808 (2.5199)  grad: 0.2056 (0.2116)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [19]  [140/400]  eta: 0:01:00  lr: 0.000001  loss: 2.5076 (2.5190)  grad: 0.2025 (0.2115)  time: 0.2282  data: 0.0033  max mem: 23764
+train: [19]  [160/400]  eta: 0:00:55  lr: 0.000001  loss: 2.5219 (2.5187)  grad: 0.2049 (0.2111)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [19]  [180/400]  eta: 0:00:50  lr: 0.000001  loss: 2.5022 (2.5155)  grad: 0.2054 (0.2102)  time: 0.2283  data: 0.0034  max mem: 23764
+train: [19]  [200/400]  eta: 0:00:46  lr: 0.000001  loss: 2.5022 (2.5144)  grad: 0.2054 (0.2100)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [19]  [220/400]  eta: 0:00:41  lr: 0.000001  loss: 2.4800 (2.5120)  grad: 0.2054 (0.2098)  time: 0.2279  data: 0.0033  max mem: 23764
+train: [19]  [240/400]  eta: 0:00:36  lr: 0.000001  loss: 2.4800 (2.5102)  grad: 0.2082 (0.2099)  time: 0.2276  data: 0.0033  max mem: 23764
+train: [19]  [260/400]  eta: 0:00:32  lr: 0.000000  loss: 2.5072 (2.5130)  grad: 0.2051 (0.2096)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [19]  [280/400]  eta: 0:00:27  lr: 0.000000  loss: 2.5300 (2.5131)  grad: 0.2036 (0.2095)  time: 0.2279  data: 0.0034  max mem: 23764
+train: [19]  [300/400]  eta: 0:00:22  lr: 0.000000  loss: 2.5121 (2.5151)  grad: 0.2060 (0.2098)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [19]  [320/400]  eta: 0:00:18  lr: 0.000000  loss: 2.5695 (2.5200)  grad: 0.2083 (0.2099)  time: 0.2276  data: 0.0034  max mem: 23764
+train: [19]  [340/400]  eta: 0:00:13  lr: 0.000000  loss: 2.5420 (2.5191)  grad: 0.2070 (0.2100)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [19]  [360/400]  eta: 0:00:09  lr: 0.000000  loss: 2.4890 (2.5173)  grad: 0.2087 (0.2100)  time: 0.2280  data: 0.0034  max mem: 23764
+train: [19]  [380/400]  eta: 0:00:04  lr: 0.000000  loss: 2.5074 (2.5177)  grad: 0.2133 (0.2102)  time: 0.2277  data: 0.0033  max mem: 23764
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.5154 (2.5165)  grad: 0.2136 (0.2103)  time: 0.2275  data: 0.0033  max mem: 23764
+train: [19] Total time: 0:01:31 (0.2292 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.5154 (2.5165)  grad: 0.2136 (0.2103)
+eval (validation): [19]  [ 0/85]  eta: 0:00:46    time: 0.5508  data: 0.4304  max mem: 23764
+eval (validation): [19]  [20/85]  eta: 0:00:09    time: 0.1213  data: 0.0027  max mem: 23764
+eval (validation): [19]  [40/85]  eta: 0:00:05    time: 0.1227  data: 0.0034  max mem: 23764
+eval (validation): [19]  [60/85]  eta: 0:00:03    time: 0.1226  data: 0.0033  max mem: 23764
+eval (validation): [19]  [80/85]  eta: 0:00:00    time: 0.1226  data: 0.0033  max mem: 23764
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.1205  data: 0.0033  max mem: 23764
+eval (validation): [19] Total time: 0:00:10 (0.1280 s / it)
+cv: [19]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.456  acc: 0.270  f1: 0.215
+saving checkpoint experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.27002583979328165, "hparam": [1.4, 1.0], "hparam_id": 26, "epoch": 19, "is_best": false, "best_score": 0.27297895902547065}
+eval (train): [20]  [  0/509]  eta: 0:05:31    time: 0.6509  data: 0.5313  max mem: 23764
+eval (train): [20]  [ 20/509]  eta: 0:01:11    time: 0.1214  data: 0.0027  max mem: 23764
+eval (train): [20]  [ 40/509]  eta: 0:01:03    time: 0.1225  data: 0.0033  max mem: 23764
+eval (train): [20]  [ 60/509]  eta: 0:00:58    time: 0.1222  data: 0.0033  max mem: 23764
+eval (train): [20]  [ 80/509]  eta: 0:00:55    time: 0.1224  data: 0.0033  max mem: 23764
+eval (train): [20]  [100/509]  eta: 0:00:52    time: 0.1226  data: 0.0033  max mem: 23764
+eval (train): [20]  [120/509]  eta: 0:00:49    time: 0.1229  data: 0.0034  max mem: 23764
+eval (train): [20]  [140/509]  eta: 0:00:46    time: 0.1227  data: 0.0033  max mem: 23764
+eval (train): [20]  [160/509]  eta: 0:00:43    time: 0.1221  data: 0.0032  max mem: 23764
+eval (train): [20]  [180/509]  eta: 0:00:41    time: 0.1222  data: 0.0034  max mem: 23764
+eval (train): [20]  [200/509]  eta: 0:00:38    time: 0.1228  data: 0.0034  max mem: 23764
+eval (train): [20]  [220/509]  eta: 0:00:36    time: 0.1225  data: 0.0034  max mem: 23764
+eval (train): [20]  [240/509]  eta: 0:00:33    time: 0.1221  data: 0.0034  max mem: 23764
+eval (train): [20]  [260/509]  eta: 0:00:30    time: 0.1225  data: 0.0033  max mem: 23764
+eval (train): [20]  [280/509]  eta: 0:00:28    time: 0.1226  data: 0.0033  max mem: 23764
+eval (train): [20]  [300/509]  eta: 0:00:25    time: 0.1221  data: 0.0033  max mem: 23764
+eval (train): [20]  [320/509]  eta: 0:00:23    time: 0.1220  data: 0.0032  max mem: 23764
+eval (train): [20]  [340/509]  eta: 0:00:20    time: 0.1226  data: 0.0034  max mem: 23764
+eval (train): [20]  [360/509]  eta: 0:00:18    time: 0.1224  data: 0.0034  max mem: 23764
+eval (train): [20]  [380/509]  eta: 0:00:15    time: 0.1223  data: 0.0034  max mem: 23764
+eval (train): [20]  [400/509]  eta: 0:00:13    time: 0.1223  data: 0.0033  max mem: 23764
+eval (train): [20]  [420/509]  eta: 0:00:11    time: 0.1224  data: 0.0032  max mem: 23764
+eval (train): [20]  [440/509]  eta: 0:00:08    time: 0.1218  data: 0.0033  max mem: 23764
+eval (train): [20]  [460/509]  eta: 0:00:06    time: 0.1217  data: 0.0032  max mem: 23764
+eval (train): [20]  [480/509]  eta: 0:00:03    time: 0.1221  data: 0.0033  max mem: 23764
+eval (train): [20]  [500/509]  eta: 0:00:01    time: 0.1224  data: 0.0032  max mem: 23764
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.1189  data: 0.0032  max mem: 23764
+eval (train): [20] Total time: 0:01:02 (0.1235 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:01:01    time: 0.7252  data: 0.6071  max mem: 23764
+eval (validation): [20]  [20/85]  eta: 0:00:09    time: 0.1211  data: 0.0021  max mem: 23764
+eval (validation): [20]  [40/85]  eta: 0:00:06    time: 0.1223  data: 0.0031  max mem: 23764
+eval (validation): [20]  [60/85]  eta: 0:00:03    time: 0.1222  data: 0.0032  max mem: 23764
+eval (validation): [20]  [80/85]  eta: 0:00:00    time: 0.1223  data: 0.0033  max mem: 23764
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.1201  data: 0.0032  max mem: 23764
+eval (validation): [20] Total time: 0:00:11 (0.1297 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:00:57    time: 0.6732  data: 0.5551  max mem: 23764
+eval (test): [20]  [20/85]  eta: 0:00:09    time: 0.1218  data: 0.0030  max mem: 23764
+eval (test): [20]  [40/85]  eta: 0:00:06    time: 0.1218  data: 0.0033  max mem: 23764
+eval (test): [20]  [60/85]  eta: 0:00:03    time: 0.1223  data: 0.0033  max mem: 23764
+eval (test): [20]  [80/85]  eta: 0:00:00    time: 0.1219  data: 0.0032  max mem: 23764
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.1189  data: 0.0032  max mem: 23764
+eval (test): [20] Total time: 0:00:10 (0.1288 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:00:59    time: 0.7255  data: 0.6068  max mem: 23764
+eval (testid): [20]  [20/82]  eta: 0:00:09    time: 0.1210  data: 0.0024  max mem: 23764
+eval (testid): [20]  [40/82]  eta: 0:00:05    time: 0.1220  data: 0.0033  max mem: 23764
+eval (testid): [20]  [60/82]  eta: 0:00:02    time: 0.1220  data: 0.0033  max mem: 23764
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.1221  data: 0.0033  max mem: 23764
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.1174  data: 0.0033  max mem: 23764
+eval (testid): [20] Total time: 0:00:10 (0.1291 s / it)
+evaluating best checkpoint: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.27297895902547065, "hparam": [5.1, 1.0], "hparam_id": 34, "epoch": 14, "is_best": true, "best_score": 0.27297895902547065}
+eval (train): [20]  [  0/509]  eta: 0:05:57    time: 0.7021  data: 0.5828  max mem: 23764
+eval (train): [20]  [ 20/509]  eta: 0:01:12    time: 0.1209  data: 0.0024  max mem: 23764
+eval (train): [20]  [ 40/509]  eta: 0:01:03    time: 0.1223  data: 0.0033  max mem: 23764
+eval (train): [20]  [ 60/509]  eta: 0:00:58    time: 0.1224  data: 0.0033  max mem: 23764
+eval (train): [20]  [ 80/509]  eta: 0:00:55    time: 0.1220  data: 0.0031  max mem: 23764
+eval (train): [20]  [100/509]  eta: 0:00:52    time: 0.1220  data: 0.0033  max mem: 23764
+eval (train): [20]  [120/509]  eta: 0:00:49    time: 0.1225  data: 0.0033  max mem: 23764
+eval (train): [20]  [140/509]  eta: 0:00:46    time: 0.1224  data: 0.0034  max mem: 23764
+eval (train): [20]  [160/509]  eta: 0:00:43    time: 0.1226  data: 0.0033  max mem: 23764
+eval (train): [20]  [180/509]  eta: 0:00:41    time: 0.1225  data: 0.0034  max mem: 23764
+eval (train): [20]  [200/509]  eta: 0:00:38    time: 0.1226  data: 0.0034  max mem: 23764
+eval (train): [20]  [220/509]  eta: 0:00:36    time: 0.1227  data: 0.0033  max mem: 23764
+eval (train): [20]  [240/509]  eta: 0:00:33    time: 0.1225  data: 0.0033  max mem: 23764
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+eval (train): [20] Total time: 0:01:02 (0.1237 s / it)
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+eval (test): [20] Total time: 0:00:10 (0.1287 s / it)
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+eval (testid): [20]  [20/82]  eta: 0:00:09    time: 0.1216  data: 0.0029  max mem: 23764
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+eval (testid): [20] Total time: 0:00:10 (0.1284 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 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | train      | 1.967  | 0.40333 | 0.0026706 | 0.36488 | 0.0029588 |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | validation | 2.4713 | 0.27298 | 0.0055158 | 0.22317 | 0.0052892 |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | test       | 2.4036 | 0.27959 | 0.0056241 | 0.23184 | 0.0054107 |
+| schaefer400_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.00153 | 0.05 |          34 | [5.1, 1.0] | testid     | 2.4525 | 0.26933 | 0.0058669 | 0.22693 | 0.0057518 |
+
+
+done! total time: 0:39:34

input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+schaefer400_mae,patch,logistic,ppmi_dx,74,2.782559402207126,train,0.9644128113879004,0.0076784874628207595,0.9620583032905308,0.008258495641545373,0.9580523442517662,0.00917919003118108
+schaefer400_mae,patch,logistic,ppmi_dx,74,2.782559402207126,test,0.64,0.04808415955384892,0.6179966044142615,0.050994126161810656,0.6179966044142615,0.050948293067049504
+schaefer400_mae,patch,logistic,ppmi_dx,75,2.782559402207126,train,0.9750889679715302,0.006530196747489498,0.9736344749011461,0.0069240769694388,0.9728109612502676,0.007301904816814242
+schaefer400_mae,patch,logistic,ppmi_dx,75,2.782559402207126,test,0.65,0.04753245627989364,0.630450849963045,0.04942555369184664,0.6311544991511036,0.049559203644479
+schaefer400_mae,patch,logistic,ppmi_dx,76,0.3593813663804626,train,0.8718861209964412,0.013807489356349512,0.8614853762734144,0.015257914678590046,0.8542068079640335,0.015745563248241227
+schaefer400_mae,patch,logistic,ppmi_dx,76,0.3593813663804626,test,0.67,0.04731927302907346,0.6515679442508711,0.04978572228728442,0.652376910016978,0.0501074343090345
+schaefer400_mae,patch,logistic,ppmi_dx,77,0.046415888336127774,train,0.797153024911032,0.015640395709139714,0.7733870967741936,0.01851189009677351,0.7630726825090987,0.018156710662111514
+schaefer400_mae,patch,logistic,ppmi_dx,77,0.046415888336127774,test,0.67,0.0419302802280166,0.6239316239316239,0.05028884683854266,0.6218166383701189,0.046340494694008076
+schaefer400_mae,patch,logistic,ppmi_dx,78,0.046415888336127774,train,0.7935943060498221,0.015756912058058844,0.770579954954955,0.018348060298322907,0.7610522372083066,0.018034104051672002
+schaefer400_mae,patch,logistic,ppmi_dx,78,0.046415888336127774,test,0.65,0.042764003554391396,0.5944849959448499,0.05154595778290465,0.5955008488964346,0.04679168398471484
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+schaefer400_mae,patch,logistic,ppmi_dx,80,0.046415888336127774,train,0.7900355871886121,0.01615113450052917,0.764826872065478,0.019128670444446872,0.754683151359452,0.0186264441633261
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+schaefer400_mae,patch,logistic,ppmi_dx,81,2.782559402207126,train,0.9679715302491103,0.007255249437067478,0.9659164420485176,0.00777210779859812,0.9626819738813959,0.00850249229942543
+schaefer400_mae,patch,logistic,ppmi_dx,81,2.782559402207126,test,0.64,0.04740664510382484,0.609375,0.05084349755482623,0.6078098471986417,0.04966760335365781
+schaefer400_mae,patch,logistic,ppmi_dx,82,2.782559402207126,train,0.9644128113879004,0.007688950380508817,0.9621293800539084,0.00825374736706273,0.9589220723613787,0.009087356528515729
+schaefer400_mae,patch,logistic,ppmi_dx,82,2.782559402207126,test,0.64,0.04529443232892979,0.6179966044142615,0.048125987448669826,0.6179966044142615,0.048289239772645244
+schaefer400_mae,patch,logistic,ppmi_dx,83,0.046415888336127774,train,0.7900355871886121,0.015686633731646443,0.7666244369369369,0.018138298420852815,0.7572923356882895,0.01771864602239082
+schaefer400_mae,patch,logistic,ppmi_dx,83,0.046415888336127774,test,0.6,0.050961161682206574,0.5796553173602353,0.052575649924400204,0.5806451612903225,0.05260510024552758
+schaefer400_mae,patch,logistic,ppmi_dx,84,0.3593813663804626,train,0.8825622775800712,0.01289562018857068,0.8727582090371447,0.014316751846103449,0.8646167844144723,0.014937241007900361
+schaefer400_mae,patch,logistic,ppmi_dx,84,0.3593813663804626,test,0.62,0.04961401011811079,0.5876736111111112,0.053574749274393665,0.5865874363327674,0.05227204469160952
+schaefer400_mae,patch,logistic,ppmi_dx,85,2.782559402207126,train,0.9768683274021353,0.006221628910793233,0.9754518360722937,0.006633741610621758,0.9733863198458574,0.007270168811530103
+schaefer400_mae,patch,logistic,ppmi_dx,85,2.782559402207126,test,0.67,0.047392400234636776,0.648,0.050133295753351424,0.6472835314091681,0.04984712678477032
+schaefer400_mae,patch,logistic,ppmi_dx,86,2.782559402207126,train,0.9786476868327402,0.006200534295471783,0.9773195044190646,0.006627947904776557,0.9748314065510597,0.0073990863100927
+schaefer400_mae,patch,logistic,ppmi_dx,86,2.782559402207126,test,0.67,0.043896583010526,0.6296711929076422,0.051026491249499206,0.6269100169779287,0.04766025910145885
+schaefer400_mae,patch,logistic,ppmi_dx,87,0.046415888336127774,train,0.8078291814946619,0.014984133146326217,0.785314091680815,0.01782116184154502,0.7743523870691501,0.01769679616842315
+schaefer400_mae,patch,logistic,ppmi_dx,87,0.046415888336127774,test,0.63,0.04537054551137776,0.5713127099988413,0.05487851114088273,0.5742784380305602,0.04918355459381006
+schaefer400_mae,patch,logistic,ppmi_dx,88,0.3593813663804626,train,0.8718861209964412,0.013910722036841823,0.8605862653491545,0.015559192664031033,0.8515976236351959,0.01604336227278903
+schaefer400_mae,patch,logistic,ppmi_dx,88,0.3593813663804626,test,0.72,0.04404533573489933,0.7057587221521648,0.04641826599886166,0.7079796264855688,0.04736224314064924
+schaefer400_mae,patch,logistic,ppmi_dx,89,2.782559402207126,train,0.9679715302491103,0.007309974972363402,0.9659164420485176,0.007840048266107355,0.9626819738813959,0.008641631886482025
+schaefer400_mae,patch,logistic,ppmi_dx,89,2.782559402207126,test,0.69,0.043687069025055904,0.6726850385386971,0.045632007042836854,0.6735993208828523,0.04592807770674667
+schaefer400_mae,patch,logistic,ppmi_dx,90,0.005994842503189409,train,0.7295373665480427,0.015511762779061376,0.683671051072402,0.019634400626537492,0.6777189038749732,0.017677101906882528
+schaefer400_mae,patch,logistic,ppmi_dx,90,0.005994842503189409,test,0.63,0.040598522140590294,0.5713127099988413,0.048269871643456495,0.5742784380305602,0.043385310417490656
+schaefer400_mae,patch,logistic,ppmi_dx,91,0.046415888336127774,train,0.7811387900355872,0.016603963219203672,0.753890520694259,0.01980184298356536,0.7439788053949904,0.019091422735931843
+schaefer400_mae,patch,logistic,ppmi_dx,91,0.046415888336127774,test,0.66,0.04227658926640132,0.6155585707824514,0.050008902834102066,0.6137521222410866,0.04616163231989117
+schaefer400_mae,patch,logistic,ppmi_dx,92,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,ppmi_dx,92,21.54434690031882,test,0.63,0.04664581010122989,0.6129302228266555,0.048202926412376355,0.615025466893039,0.04901609529162338
+schaefer400_mae,patch,logistic,ppmi_dx,93,2.782559402207126,train,0.9608540925266904,0.00783201178217466,0.9582641336195838,0.008429785861279196,0.9542924427317491,0.009255999129799587
+schaefer400_mae,patch,logistic,ppmi_dx,93,2.782559402207126,test,0.69,0.042881697727585356,0.6656239887822242,0.046702849373539246,0.6634125636672326,0.046154590145467085
+schaefer400_mae,patch,logistic,ppmi_dx,94,2.782559402207126,train,0.9679715302491103,0.007220509023635607,0.9659792566285967,0.0077058178374904245,0.9635517019910083,0.008247446664232766
+schaefer400_mae,patch,logistic,ppmi_dx,94,2.782559402207126,test,0.71,0.0427668048841622,0.6871966346672419,0.04669101678078479,0.684634974533107,0.04598768452021123
+schaefer400_mae,patch,logistic,ppmi_dx,95,0.046415888336127774,train,0.7882562277580071,0.01652647889001837,0.7649403370648296,0.019031763718784754,0.7558472489830872,0.0186054934378432
+schaefer400_mae,patch,logistic,ppmi_dx,95,0.046415888336127774,test,0.64,0.04631583314591243,0.609375,0.050565708876191544,0.6078098471986417,0.049402180534132645
+schaefer400_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,train,0.7864768683274022,0.016618776617876235,0.7632587724840979,0.01917661828490741,0.7544021622778848,0.018805982792077246
+schaefer400_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,test,0.65,0.03859274543227004,0.5872154735228211,0.04887003368032839,0.5904074702886248,0.04254767609222367
+schaefer400_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,train,0.7740213523131673,0.016668636467901825,0.7438073188678906,0.020220508591497512,0.7338498180261186,0.019261985906069718
+schaefer400_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,test,0.7,0.04218689844015556,0.66078697421981,0.049536168399212474,0.6561969439728353,0.046743735827461025
+schaefer400_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,train,0.8718861209964412,0.01341670048773388,0.861190773495067,0.014892799653931576,0.8533370798544209,0.015430949408967304
+schaefer400_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,test,0.67,0.04311426214143064,0.6349153667441089,0.04895514724399681,0.6320033955857385,0.04707954122022614
+schaefer400_mae,patch,logistic,ppmi_dx,99,0.3593813663804626,train,0.8861209964412812,0.012818910405174336,0.8749461094499686,0.01459583350221901,0.8631583172768144,0.0151908609619551
+schaefer400_mae,patch,logistic,ppmi_dx,99,0.3593813663804626,test,0.7,0.044437209633369196,0.6816638370118846,0.04692845798508045,0.6816638370118846,0.046894034417380596
+schaefer400_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,train,0.7864768683274022,0.015381489375961083,0.7626689189189189,0.01784897798747685,0.7535324341682723,0.017486175837500492
+schaefer400_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,test,0.67,0.04167301285004481,0.6176572818908586,0.051661932805161355,0.616723259762309,0.04644690761919531

input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic/log.txt

@@ -0,0 +1,247 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:51:41
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_4; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/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: input_space_v3/schaefer400_lr3e-4_4/eval_v2/ppmi_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/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: 0:04:06    time: 1.0629  data: 0.7004  max mem: 1546
+extract (train)  [ 20/232]  eta: 0:00:20    time: 0.0482  data: 0.0037  max mem: 1715
+extract (train)  [ 40/232]  eta: 0:00:13    time: 0.0465  data: 0.0013  max mem: 1715
+extract (train)  [ 60/232]  eta: 0:00:10    time: 0.0470  data: 0.0024  max mem: 1715
+extract (train)  [ 80/232]  eta: 0:00:09    time: 0.0469  data: 0.0022  max mem: 1715
+extract (train)  [100/232]  eta: 0:00:07    time: 0.0471  data: 0.0023  max mem: 1715
+extract (train)  [120/232]  eta: 0:00:06    time: 0.0472  data: 0.0023  max mem: 1715
+extract (train)  [140/232]  eta: 0:00:05    time: 0.0472  data: 0.0024  max mem: 1715
+extract (train)  [160/232]  eta: 0:00:03    time: 0.0470  data: 0.0022  max mem: 1715
+extract (train)  [180/232]  eta: 0:00:02    time: 0.0472  data: 0.0023  max mem: 1715
+extract (train)  [200/232]  eta: 0:00:01    time: 0.0472  data: 0.0023  max mem: 1715
+extract (train)  [220/232]  eta: 0:00:00    time: 0.0472  data: 0.0023  max mem: 1715
+extract (train)  [231/232]  eta: 0:00:00    time: 0.0462  data: 0.0023  max mem: 1715
+extract (train) Total time: 0:00:12 (0.0521 s / it)
+extract (validation)  [ 0/50]  eta: 0:00:33    time: 0.6795  data: 0.6328  max mem: 1715
+extract (validation)  [20/50]  eta: 0:00:02    time: 0.0473  data: 0.0026  max mem: 1715
+extract (validation)  [40/50]  eta: 0:00:00    time: 0.0468  data: 0.0013  max mem: 1715
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.0464  data: 0.0022  max mem: 1715
+extract (validation) Total time: 0:00:03 (0.0613 s / it)
+extract (test)  [ 0/50]  eta: 0:00:37    time: 0.7583  data: 0.7117  max mem: 1715
+extract (test)  [20/50]  eta: 0:00:02    time: 0.0477  data: 0.0029  max mem: 1715
+extract (test)  [40/50]  eta: 0:00:00    time: 0.0466  data: 0.0013  max mem: 1715
+extract (test)  [49/50]  eta: 0:00:00    time: 0.0473  data: 0.0021  max mem: 1715
+extract (test) Total time: 0:00:03 (0.0638 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   |         | 0.046416 | train   | 0.80071 |  0.015973 | 0.77793 | 0.018702 | 0.76758 |   0.018279 |
+| schaefer400_mae | patch  | logistic | ppmi_dx   |         | 0.046416 | test    | 0.62    |  0.042229 | 0.56342 | 0.049317 | 0.56456 |   0.045466 |
+
+
+evaluating random splits (n=100)
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.03859274543227004, "f1": 0.5872154735228211, "f1_std": 0.04887003368032839, "bacc": 0.5904074702886248, "bacc_std": 0.04254767609222367}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.7, "acc_std": 0.04218689844015556, "f1": 0.66078697421981, "f1_std": 0.049536168399212474, "bacc": 0.6561969439728353, "bacc_std": 0.046743735827461025}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.67, "acc_std": 0.04311426214143064, "f1": 0.6349153667441089, "f1_std": 0.04895514724399681, "bacc": 0.6320033955857385, "bacc_std": 0.04707954122022614}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.7, "acc_std": 0.044437209633369196, "f1": 0.6816638370118846, "f1_std": 0.04692845798508045, "bacc": 0.6816638370118846, "bacc_std": 0.046894034417380596}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04167301285004481, "f1": 0.6176572818908586, "f1_std": 0.051661932805161355, "bacc": 0.616723259762309, "bacc_std": 0.04644690761919531}
+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 | 118.12 |  1006.7 | 0.88342 |  0.076434 | 0.87126 | 0.086219 | 0.86435 |   0.089395 |
+| schaefer400_mae | patch  | logistic | ppmi_dx   | test    |        100 | 118.12 |  1006.7 | 0.6604  |  0.038453 | 0.63045 | 0.040549 | 0.63007 |   0.039338 |
+
+
+done! total time: 0:04:44

input_space_v3/schaefer400_lr3e-4_4/pretrain/config.yaml

@@ -0,0 +1,102 @@
+name: input_space_v3/schaefer400_lr3e-4_4/pretrain
+notes: input_space ablation v3 schaefer400_lr3e-4_4; use tube masking and on-disk
+  data (input_space=schaefer400 base_lr=3e-4 seed=5404)
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_4/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: 5
+device: cuda
+presend_cuda: false
+seed: 5404
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input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,aabc_age,72,0.3593813663804626,train,0.8543307086614174,0.015220574277331422,0.8555025344342331,0.015099707210784637,0.8549783606865508,0.015188146447168923
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+schaefer400_mae,patch,logistic,aabc_age,73,0.3593813663804626,train,0.8759842519685039,0.014751913341593825,0.876439657698538,0.014721974173486155,0.8765030763978526,0.014712267176118865
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+schaefer400_mae,patch,logistic,aabc_age,74,0.3593813663804626,train,0.8779527559055118,0.014361501053351787,0.8787364725073605,0.014263802368984236,0.877648933905395,0.014423005814797297
+schaefer400_mae,patch,logistic,aabc_age,74,0.3593813663804626,test,0.38461538461538464,0.06628627174977642,0.39488801054018446,0.06545198246070037,0.3841575091575091,0.06618283044501609
+schaefer400_mae,patch,logistic,aabc_age,75,0.3593813663804626,train,0.8700787401574803,0.013834791317649184,0.8708947140750053,0.013749874478744876,0.8713749474960154,0.013708357577040012
+schaefer400_mae,patch,logistic,aabc_age,75,0.3593813663804626,test,0.46153846153846156,0.06738429706633631,0.4654469926209056,0.06720652617682049,0.46130952380952384,0.06751935812544503
+schaefer400_mae,patch,logistic,aabc_age,76,0.005994842503189409,train,0.5846456692913385,0.02153796606618101,0.5815474607406377,0.021891361802926775,0.5861598777496264,0.02150506714154347
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+schaefer400_mae,patch,logistic,aabc_age,78,0.046415888336127774,train,0.7086614173228346,0.01888265276938469,0.7084905293770267,0.018953609267044695,0.7093392601301524,0.01884047470435598
+schaefer400_mae,patch,logistic,aabc_age,78,0.046415888336127774,test,0.5576923076923077,0.05708359643758021,0.5219390592722072,0.061261955791601534,0.5526556776556777,0.05656887480525382
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+schaefer400_mae,patch,logistic,aabc_age,79,0.005994842503189409,test,0.46153846153846156,0.0643847877031055,0.4501176359872012,0.06550912046728838,0.4608516483516484,0.06438739243042113
+schaefer400_mae,patch,logistic,aabc_age,80,0.046415888336127774,train,0.7047244094488189,0.020422411614444372,0.7047489235357612,0.020555751061716785,0.7046542984220995,0.02045893636367772
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+schaefer400_mae,patch,logistic,aabc_age,81,2.782559402207126,train,0.9960629921259843,0.002602049488638342,0.9959166666666667,0.00270016858631863,0.9958677685950413,0.002731076736008856
+schaefer400_mae,patch,logistic,aabc_age,81,2.782559402207126,test,0.40384615384615385,0.061087421593345036,0.4011627519573546,0.0592794049595518,0.40453296703296704,0.061196216034599175
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+schaefer400_mae,patch,logistic,aabc_age,82,0.005994842503189409,test,0.6153846153846154,0.0614400535279291,0.6091280972417017,0.06544251749802465,0.6137820512820513,0.0615154816620873
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+schaefer400_mae,patch,logistic,aabc_age,83,0.3593813663804626,test,0.4423076923076923,0.06330422226212891,0.4428530923267765,0.062843741876233,0.44024725274725274,0.06318993023978073
+schaefer400_mae,patch,logistic,aabc_age,84,0.3593813663804626,train,0.8681102362204725,0.015633403685909895,0.8684174910623472,0.015689010627289477,0.8689912905719146,0.015576829018019296
+schaefer400_mae,patch,logistic,aabc_age,84,0.3593813663804626,test,0.4807692307692308,0.062302694481418466,0.46559603262884686,0.06437284331410378,0.4773351648351648,0.061904850423336044
+schaefer400_mae,patch,logistic,aabc_age,85,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_age,85,21.54434690031882,test,0.4230769230769231,0.06667809936486824,0.4111952861952862,0.06580401137978047,0.41666666666666663,0.06639882776430987
+schaefer400_mae,patch,logistic,aabc_age,86,2.782559402207126,train,0.9980314960629921,0.001891419878011833,0.9979586395374027,0.001962083532110422,0.9979338842975207,0.0019852092934504392
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+schaefer400_mae,patch,logistic,aabc_age,87,0.046415888336127774,train,0.7007874015748031,0.019514324318758956,0.6995362588013405,0.019764712985428017,0.7012423785311926,0.019510046172296135
+schaefer400_mae,patch,logistic,aabc_age,87,0.046415888336127774,test,0.5384615384615384,0.06902593629685007,0.5324872286079183,0.07067480659298313,0.5412087912087913,0.0694456339663516
+schaefer400_mae,patch,logistic,aabc_age,88,9.999999999999999e-05,train,0.47244094488188976,0.018972105164729313,0.4463777364890267,0.019676214226593127,0.47004371975626646,0.018906527079826967
+schaefer400_mae,patch,logistic,aabc_age,88,9.999999999999999e-05,test,0.5,0.06196921358184773,0.47878001628001626,0.06393996750383486,0.49221611721611724,0.0614663963690272
+schaefer400_mae,patch,logistic,aabc_age,89,0.3593813663804626,train,0.8759842519685039,0.015105496296118051,0.8771258181700443,0.014984715584507374,0.8772557533818319,0.014991210066410757
+schaefer400_mae,patch,logistic,aabc_age,89,0.3593813663804626,test,0.4230769230769231,0.06576562068364691,0.4196164021164021,0.06613331232573087,0.4226190476190476,0.06567552220639956
+schaefer400_mae,patch,logistic,aabc_age,90,0.000774263682681127,train,0.5,0.02115432365997124,0.48582875630941713,0.021448769716684565,0.49778152684463994,0.02107615381114782
+schaefer400_mae,patch,logistic,aabc_age,90,0.000774263682681127,test,0.5192307692307693,0.058458552555326795,0.48797099484955275,0.06136211969662976,0.5155677655677656,0.05795048715848529
+schaefer400_mae,patch,logistic,aabc_age,91,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_age,91,166.81005372000556,test,0.38461538461538464,0.06842527129269778,0.38344086021505375,0.06793180529171292,0.38118131868131866,0.06818088179211074
+schaefer400_mae,patch,logistic,aabc_age,92,0.3593813663804626,train,0.8562992125984252,0.015519453331443847,0.8576777648386558,0.015376265568510211,0.8565593539564509,0.01555498768978676
+schaefer400_mae,patch,logistic,aabc_age,92,0.3593813663804626,test,0.5384615384615384,0.06614490100524818,0.5351612903225806,0.06602781476189502,0.5441849816849818,0.06649986072136316
+schaefer400_mae,patch,logistic,aabc_age,93,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_age,93,21.54434690031882,test,0.36538461538461536,0.06700934978478662,0.3751149425287356,0.06757918857242934,0.3695054945054945,0.06742026768829444
+schaefer400_mae,patch,logistic,aabc_age,94,0.000774263682681127,train,0.5295275590551181,0.02148029295205191,0.5186106359854254,0.022061563822850645,0.5283733690200598,0.02135784487101062
+schaefer400_mae,patch,logistic,aabc_age,94,0.000774263682681127,test,0.4423076923076923,0.06692860277805361,0.4277408170914543,0.06604026159862814,0.4358974358974359,0.06623559249079634
+schaefer400_mae,patch,logistic,aabc_age,95,0.046415888336127774,train,0.7283464566929134,0.019683219994662937,0.7279122020664871,0.01983365195222757,0.72899780681986,0.01964137153919848
+schaefer400_mae,patch,logistic,aabc_age,95,0.046415888336127774,test,0.40384615384615385,0.060193180136218445,0.3847420695246782,0.058189691979617944,0.3999542124542125,0.05957820932724175
+schaefer400_mae,patch,logistic,aabc_age,96,0.046415888336127774,train,0.6850393700787402,0.01972104541699715,0.6839578142728474,0.019938193776650372,0.6859836177978438,0.019576831168382318
+schaefer400_mae,patch,logistic,aabc_age,96,0.046415888336127774,test,0.5576923076923077,0.06815900093372865,0.5534029434800823,0.07065266999402625,0.5560897435897436,0.06821181384737274
+schaefer400_mae,patch,logistic,aabc_age,97,0.3593813663804626,train,0.8582677165354331,0.014847547656072465,0.8595772497934945,0.014741137048986329,0.8591781999620246,0.014812208565748972
+schaefer400_mae,patch,logistic,aabc_age,97,0.3593813663804626,test,0.4807692307692308,0.06511949643976102,0.4790660225442834,0.06503914243693802,0.4832875457875458,0.06513568830311377
+schaefer400_mae,patch,logistic,aabc_age,98,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_age,98,1291.5496650148827,test,0.4807692307692308,0.06779069554420329,0.48770753512132825,0.0670843920309626,0.480540293040293,0.06762755163725821
+schaefer400_mae,patch,logistic,aabc_age,99,0.3593813663804626,train,0.8838582677165354,0.01426041588520202,0.8849302559708017,0.014120043076324031,0.8841148355642333,0.014192715674540519
+schaefer400_mae,patch,logistic,aabc_age,99,0.3593813663804626,test,0.4230769230769231,0.06605873081705221,0.4291157992432355,0.06483547464693883,0.42422161172161177,0.06632012172662613
+schaefer400_mae,patch,logistic,aabc_age,100,0.3593813663804626,train,0.8700787401574803,0.014413976204552982,0.8714346319332544,0.014296773529747148,0.8715925153771944,0.014229893447696798
+schaefer400_mae,patch,logistic,aabc_age,100,0.3593813663804626,test,0.4423076923076923,0.0630691716625578,0.4403418803418804,0.06251534068757549,0.4478021978021978,0.06342877410047554

input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:53:07
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_age__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: 0:05:00    time: 1.3172  data: 0.9298  max mem: 1814
+extract (train)  [ 20/228]  eta: 0:00:24    time: 0.0583  data: 0.0041  max mem: 2020
+extract (train)  [ 40/228]  eta: 0:00:16    time: 0.0563  data: 0.0017  max mem: 2020
+extract (train)  [ 60/228]  eta: 0:00:13    time: 0.0569  data: 0.0026  max mem: 2020
+extract (train)  [ 80/228]  eta: 0:00:10    time: 0.0571  data: 0.0028  max mem: 2020
+extract (train)  [100/228]  eta: 0:00:08    time: 0.0570  data: 0.0026  max mem: 2020
+extract (train)  [120/228]  eta: 0:00:07    time: 0.0572  data: 0.0027  max mem: 2020
+extract (train)  [140/228]  eta: 0:00:05    time: 0.0570  data: 0.0025  max mem: 2020
+extract (train)  [160/228]  eta: 0:00:04    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [180/228]  eta: 0:00:03    time: 0.0571  data: 0.0025  max mem: 2020
+extract (train)  [200/228]  eta: 0:00:01    time: 0.0572  data: 0.0026  max mem: 2020
+extract (train)  [220/228]  eta: 0:00:00    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [227/228]  eta: 0:00:00    time: 0.0561  data: 0.0026  max mem: 2020
+extract (train) Total time: 0:00:14 (0.0632 s / it)
+extract (validation)  [ 0/27]  eta: 0:00:25    time: 0.9449  data: 0.8891  max mem: 2020
+extract (validation)  [20/27]  eta: 0:00:00    time: 0.0570  data: 0.0025  max mem: 2020
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.0554  data: 0.0022  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0932 s / it)
+extract (test)  [ 0/26]  eta: 0:00:24    time: 0.9309  data: 0.8748  max mem: 2020
+extract (test)  [20/26]  eta: 0:00:00    time: 0.0570  data: 0.0026  max mem: 2020
+extract (test)  [25/26]  eta: 0:00:00    time: 0.0566  data: 0.0018  max mem: 2020
+extract (test) Total time: 0:00:02 (0.0943 s / it)
+feature extraction time: 0:00:19
+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  |         | 0.35938 | train   | 0.86024 |  0.014965 | 0.85979 | 0.015081 | 0.86077 |   0.014876 |
+| schaefer400_mae | patch  | logistic | aabc_age  |         | 0.35938 | test    | 0.40385 |  0.069256 | 0.40083 | 0.067443 | 0.39629 |   0.068945 |
+
+
+evaluating random splits (n=100)
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06511949643976102, "f1": 0.4790660225442834, "f1_std": 0.06503914243693802, "bacc": 0.4832875457875458, "bacc_std": 0.06513568830311377}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 1291.5496650148827, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06779069554420329, "f1": 0.48770753512132825, "f1_std": 0.0670843920309626, "bacc": 0.480540293040293, "bacc_std": 0.06762755163725821}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06605873081705221, "f1": 0.4291157992432355, "f1_std": 0.06483547464693883, "bacc": 0.42422161172161177, "bacc_std": 0.06632012172662613}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.0630691716625578, "f1": 0.4403418803418804, "f1_std": 0.06251534068757549, "bacc": 0.4478021978021978, "bacc_std": 0.06342877410047554}
+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 | 36.976 |  184.44 | 0.78193 |  0.17037  | 0.77948 | 0.17487  | 0.78217 |   0.17064  |
+| schaefer400_mae | patch  | logistic | aabc_age  | test    |        100 | 36.976 |  184.44 | 0.45269 |  0.059911 | 0.4454  | 0.061107 | 0.45176 |   0.059774 |
+
+
+done! total time: 0:05:57

input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+schaefer400_mae,patch,logistic,aabc_sex,85,0.3593813663804626,test,0.7090909090909091,0.06433626536660575,0.696969696969697,0.06737550226339467,0.6949728260869565,0.06634713989090778
+schaefer400_mae,patch,logistic,aabc_sex,86,2.782559402207126,train,0.9735349716446124,0.007005053852637154,0.9727640482494851,0.0072337953023710365,0.9710425276239045,0.0076651404127845
+schaefer400_mae,patch,logistic,aabc_sex,86,2.782559402207126,test,0.8727272727272727,0.04432636288125826,0.8639095086603039,0.04979987213903247,0.8539402173913043,0.0502930692667086
+schaefer400_mae,patch,logistic,aabc_sex,87,0.3593813663804626,train,0.9054820415879017,0.012928495688390045,0.9025997937840624,0.013364887753679028,0.9006638529851403,0.01355576986975738
+schaefer400_mae,patch,logistic,aabc_sex,87,0.3593813663804626,test,0.7272727272727273,0.059350746199017305,0.7136410968413746,0.06414769112547014,0.7105978260869565,0.06270748185963265
+schaefer400_mae,patch,logistic,aabc_sex,88,0.3593813663804626,train,0.9111531190926276,0.01159537068445672,0.9083820662768032,0.012030014118203718,0.906173979307717,0.012327591570700439
+schaefer400_mae,patch,logistic,aabc_sex,88,0.3593813663804626,test,0.8,0.055126253288727056,0.7931623931623932,0.05818270272140872,0.7914402173913043,0.05839274611047426
+schaefer400_mae,patch,logistic,aabc_sex,89,2.782559402207126,train,0.9735349716446124,0.0071208693234362476,0.9727992595639654,0.007335926208214127,0.9716506931621677,0.007635863876822352
+schaefer400_mae,patch,logistic,aabc_sex,89,2.782559402207126,test,0.7090909090909091,0.06128077637986491,0.696969696969697,0.06482770205739143,0.6949728260869565,0.06368648530360337
+schaefer400_mae,patch,logistic,aabc_sex,90,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_sex,90,21.54434690031882,test,0.6909090909090909,0.061559018244873996,0.687186349949816,0.06252056386921154,0.6915760869565217,0.0627798146008943
+schaefer400_mae,patch,logistic,aabc_sex,91,0.3593813663804626,train,0.9149338374291115,0.011924495913701066,0.9122807017543859,0.0123719150877443,0.9100501187021894,0.012695992656327551
+schaefer400_mae,patch,logistic,aabc_sex,91,0.3593813663804626,test,0.6909090909090909,0.06016282040162087,0.6754599097535579,0.0640361456926671,0.6732336956521738,0.0627564048181493
+schaefer400_mae,patch,logistic,aabc_sex,92,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_sex,92,21.54434690031882,test,0.6909090909090909,0.05326669528369751,0.6538319141058867,0.06394282289609395,0.654891304347826,0.05739349898211958
+schaefer400_mae,patch,logistic,aabc_sex,93,0.3593813663804626,train,0.9054820415879017,0.012407439759109216,0.9023322107999527,0.012952063066938444,0.899447521908614,0.013341593462626127
+schaefer400_mae,patch,logistic,aabc_sex,93,0.3593813663804626,test,0.7818181818181819,0.05450670138494686,0.76890756302521,0.0597348902012752,0.7635869565217391,0.058281242176294476
+schaefer400_mae,patch,logistic,aabc_sex,94,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,aabc_sex,94,166.81005372000556,test,0.7818181818181819,0.05720757044047998,0.7758152173913043,0.05883709505523212,0.7758152173913043,0.05825279883566188
+schaefer400_mae,patch,logistic,aabc_sex,95,0.046415888336127774,train,0.8355387523629489,0.016489629788207342,0.828106266877318,0.017512549613801637,0.8231777015739031,0.01758070839436819
+schaefer400_mae,patch,logistic,aabc_sex,95,0.046415888336127774,test,0.7090909090909091,0.058840487550721514,0.6918767507002801,0.06380584412632603,0.6888586956521738,0.061941917779090305
+schaefer400_mae,patch,logistic,aabc_sex,96,0.3593813663804626,train,0.8998109640831758,0.013771315194275931,0.8965443442002915,0.014323110018447974,0.8939373955860371,0.01465670986745737
+schaefer400_mae,patch,logistic,aabc_sex,96,0.3593813663804626,test,0.6727272727272727,0.06232939425688135,0.6637228260869565,0.06411785488996406,0.6637228260869565,0.06387389208350654
+schaefer400_mae,patch,logistic,aabc_sex,97,2.782559402207126,train,0.9848771266540642,0.0052989434693223375,0.9844954424221108,0.005431423918293854,0.9844954424221108,0.005444810755012787
+schaefer400_mae,patch,logistic,aabc_sex,97,2.782559402207126,test,0.6363636363636364,0.06529985761821139,0.6263586956521738,0.0671848016859898,0.6263586956521738,0.06681061038776072
+schaefer400_mae,patch,logistic,aabc_sex,98,0.3593813663804626,train,0.9017013232514177,0.012978042487258097,0.8984254992319508,0.013503623375703684,0.8955713825141417,0.013775275571766511
+schaefer400_mae,patch,logistic,aabc_sex,98,0.3593813663804626,test,0.7818181818181819,0.056410432186822776,0.7727272727272727,0.05954406834304758,0.7697010869565217,0.05932454670390113
+schaefer400_mae,patch,logistic,aabc_sex,99,0.3593813663804626,train,0.8998109640831758,0.012764627852578469,0.896250328415428,0.013348251100201306,0.8927210645095108,0.01369658329930941
+schaefer400_mae,patch,logistic,aabc_sex,99,0.3593813663804626,test,0.7818181818181819,0.056038522759418355,0.7782258064516129,0.056961686894503864,0.7819293478260869,0.05689888366891556
+schaefer400_mae,patch,logistic,aabc_sex,100,0.3593813663804626,train,0.8979206049149339,0.012591821336595409,0.8943690095846646,0.013130190911564832,0.8910870775814063,0.013413413527959491
+schaefer400_mae,patch,logistic,aabc_sex,100,0.3593813663804626,test,0.8,0.05177144942153551,0.790003471017008,0.05583045076055521,0.7853260869565217,0.05520512523968409

input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:53:14
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/aabc_sex__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: 0:05:12    time: 1.3253  data: 0.9634  max mem: 1814
+extract (train)  [ 20/236]  eta: 0:00:25    time: 0.0586  data: 0.0043  max mem: 2020
+extract (train)  [ 40/236]  eta: 0:00:17    time: 0.0564  data: 0.0019  max mem: 2020
+extract (train)  [ 60/236]  eta: 0:00:13    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [ 80/236]  eta: 0:00:11    time: 0.0572  data: 0.0029  max mem: 2020
+extract (train)  [100/236]  eta: 0:00:09    time: 0.0574  data: 0.0029  max mem: 2020
+extract (train)  [120/236]  eta: 0:00:07    time: 0.0575  data: 0.0030  max mem: 2020
+extract (train)  [140/236]  eta: 0:00:06    time: 0.0575  data: 0.0030  max mem: 2020
+extract (train)  [160/236]  eta: 0:00:04    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [180/236]  eta: 0:00:03    time: 0.0569  data: 0.0025  max mem: 2020
+extract (train)  [200/236]  eta: 0:00:02    time: 0.0573  data: 0.0028  max mem: 2020
+extract (train)  [220/236]  eta: 0:00:01    time: 0.0571  data: 0.0027  max mem: 2020
+extract (train)  [235/236]  eta: 0:00:00    time: 0.0560  data: 0.0026  max mem: 2020
+extract (train) Total time: 0:00:14 (0.0632 s / it)
+extract (validation)  [ 0/29]  eta: 0:00:23    time: 0.8062  data: 0.7502  max mem: 2020
+extract (validation)  [20/29]  eta: 0:00:00    time: 0.0569  data: 0.0026  max mem: 2020
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.0568  data: 0.0023  max mem: 2020
+extract (validation) Total time: 0:00:02 (0.0862 s / it)
+extract (test)  [ 0/28]  eta: 0:00:25    time: 0.8997  data: 0.8433  max mem: 2020
+extract (test)  [20/28]  eta: 0:00:00    time: 0.0565  data: 0.0020  max mem: 2020
+extract (test)  [27/28]  eta: 0:00:00    time: 0.0553  data: 0.0020  max mem: 2020
+extract (test) Total time: 0:00:02 (0.0887 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  |         | 166.81 | train   | 1       |  0        | 1       | 0        | 1       |   0        |
+| schaefer400_mae | patch  | logistic | aabc_sex  |         | 166.81 | test    | 0.74545 |  0.054907 | 0.74537 | 0.055188 | 0.77273 |   0.051547 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.5818181818181818, "acc_std": 0.06132535101967173, "f1": 0.5767815322850451, "f1_std": 0.06210837832738263, "bacc": 0.5794836956521738, "bacc_std": 0.06309080219912422}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.058476741155765066, "f1": 0.7348484848484849, "f1_std": 0.06182678567352823, "bacc": 0.7323369565217391, "bacc_std": 0.06110183319826374}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06025263616879562, "f1": 0.6590909090909092, "f1_std": 0.06312544640768902, "bacc": 0.6576086956521738, "bacc_std": 0.06235164296393374}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.054334671517601164, "f1": 0.7518222839291913, "f1_std": 0.05877095373750099, "bacc": 0.7479619565217391, "bacc_std": 0.05789429582129034}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05684690183232402, "f1": 0.741263440860215, "f1_std": 0.05772318554358334, "bacc": 0.7445652173913043, "bacc_std": 0.0577971691341746}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 1291.5496650148827, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06436080117022107, "f1": 0.6637228260869565, "f1_std": 0.06646749376562722, "bacc": 0.6637228260869565, "bacc_std": 0.06646119571614856}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06048688403692052, "f1": 0.6803418803418804, "f1_std": 0.06363952954698616, "bacc": 0.6793478260869565, "bacc_std": 0.0630306971881853}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05976787052511943, "f1": 0.6842283012495778, "f1_std": 0.060854256874517065, "bacc": 0.6854619565217391, "bacc_std": 0.06091629264473343}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06190234911896545, "f1": 0.696969696969697, "f1_std": 0.06495901454248161, "bacc": 0.6949728260869565, "bacc_std": 0.06383202848605929}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05750671969359194, "f1": 0.7555555555555555, "f1_std": 0.06006195178618151, "bacc": 0.7540760869565217, "bacc_std": 0.05981381490059422}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 2.782559402207126, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05356248841576775, "f1": 0.7727272727272727, "f1_std": 0.056766778376353626, "bacc": 0.7697010869565217, "bacc_std": 0.05633714859151024}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05639261425176848, "f1": 0.7303921568627451, "f1_std": 0.061578965760204885, "bacc": 0.7262228260869565, "bacc_std": 0.060116710685063954}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06598672543689603, "f1": 0.6503847440615591, "f1_std": 0.06642543983876828, "bacc": 0.6542119565217391, "bacc_std": 0.06632421577330738}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.05111563296961405, "f1": 0.790003471017008, "f1_std": 0.05557828272861398, "bacc": 0.7853260869565217, "bacc_std": 0.05521509008264813}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 1291.5496650148827, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05754450954272176, "f1": 0.7758152173913043, "f1_std": 0.05932469535242386, "bacc": 0.7758152173913043, "bacc_std": 0.05912362567692594}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05177144942153551, "f1": 0.790003471017008, "f1_std": 0.05583045076055521, "bacc": 0.7853260869565217, "bacc_std": 0.05520512523968409}
+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 | 144.65 |  1019.9 | 0.92359 |  0.056519 | 0.92076 | 0.05899  | 0.91847 |   0.0605   |
+| schaefer400_mae | patch  | logistic | aabc_sex  | test    |        100 | 144.65 |  1019.9 | 0.73582 |  0.055694 | 0.72542 | 0.057722 | 0.72479 |   0.057309 |
+
+
+done! total time: 0:04:41

input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+schaefer400_mae,patch,logistic,abide_dx,95,0.3593813663804626,train,0.8717948717948718,0.012326955485584231,0.869554445224429,0.012600210557122942,0.8674787744555186,0.012665019648693982
+schaefer400_mae,patch,logistic,abide_dx,95,0.3593813663804626,test,0.6370967741935484,0.041853129432436006,0.6283716283716283,0.04335751605813021,0.6281512605042017,0.04256520919585717
+schaefer400_mae,patch,logistic,abide_dx,96,0.3593813663804626,train,0.8917378917378918,0.01186718272086137,0.8904539369399707,0.012032692938245751,0.8899963086009597,0.012143167751682012
+schaefer400_mae,patch,logistic,abide_dx,96,0.3593813663804626,test,0.6129032258064516,0.04195595034069225,0.607905138339921,0.042820326268639006,0.6076680672268908,0.04241198733682637
+schaefer400_mae,patch,logistic,abide_dx,97,0.3593813663804626,train,0.8717948717948718,0.012530884889400079,0.869554445224429,0.012850594538471344,0.8674787744555186,0.012982210420761812
+schaefer400_mae,patch,logistic,abide_dx,97,0.3593813663804626,test,0.6370967741935484,0.044403583186403806,0.626380984265149,0.04639037112403154,0.6265756302521008,0.045197269776163077
+schaefer400_mae,patch,logistic,abide_dx,98,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,abide_dx,98,166.81005372000556,test,0.6532258064516129,0.042003617108007346,0.6465831510572015,0.043210181024631346,0.6460084033613445,0.042712794721401416
+schaefer400_mae,patch,logistic,abide_dx,99,0.3593813663804626,train,0.8817663817663818,0.01187497197024741,0.8799220996012241,0.012128489449950439,0.8782945736434109,0.012283786948731232
+schaefer400_mae,patch,logistic,abide_dx,99,0.3593813663804626,test,0.6693548387096774,0.039128300926601786,0.6595915634415801,0.04084197151629543,0.6591386554621849,0.0399058974474195
+schaefer400_mae,patch,logistic,abide_dx,100,2.782559402207126,train,0.9658119658119658,0.006817839422556555,0.9653621269561928,0.006929878496488899,0.9642672572905131,0.007173850703579849
+schaefer400_mae,patch,logistic,abide_dx,100,2.782559402207126,test,0.5967741935483871,0.0432972567880392,0.5860042735042735,0.04463466222119215,0.5866596638655462,0.043640091637309544

input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:52:08
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/abide_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: 0:06:39    time: 1.3838  data: 1.0120  max mem: 1546
+extract (train)  [ 20/289]  eta: 0:00:29    time: 0.0475  data: 0.0026  max mem: 1715
+extract (train)  [ 40/289]  eta: 0:00:19    time: 0.0467  data: 0.0016  max mem: 1715
+extract (train)  [ 60/289]  eta: 0:00:15    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [ 80/289]  eta: 0:00:13    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train)  [100/289]  eta: 0:00:11    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [120/289]  eta: 0:00:09    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [140/289]  eta: 0:00:08    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [160/289]  eta: 0:00:07    time: 0.0479  data: 0.0027  max mem: 1715
+extract (train)  [180/289]  eta: 0:00:05    time: 0.0479  data: 0.0028  max mem: 1715
+extract (train)  [200/289]  eta: 0:00:04    time: 0.0475  data: 0.0025  max mem: 1715
+extract (train)  [220/289]  eta: 0:00:03    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train)  [240/289]  eta: 0:00:02    time: 0.0478  data: 0.0026  max mem: 1715
+extract (train)  [260/289]  eta: 0:00:01    time: 0.0478  data: 0.0027  max mem: 1715
+extract (train)  [280/289]  eta: 0:00:00    time: 0.0476  data: 0.0025  max mem: 1715
+extract (train)  [288/289]  eta: 0:00:00    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train) Total time: 0:00:15 (0.0529 s / it)
+extract (validation)  [ 0/62]  eta: 0:01:02    time: 1.0003  data: 0.9537  max mem: 1715
+extract (validation)  [20/62]  eta: 0:00:03    time: 0.0474  data: 0.0023  max mem: 1715
+extract (validation)  [40/62]  eta: 0:00:01    time: 0.0469  data: 0.0016  max mem: 1715
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.0477  data: 0.0026  max mem: 1715
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.0476  data: 0.0026  max mem: 1715
+extract (validation) Total time: 0:00:04 (0.0646 s / it)
+extract (test)  [ 0/62]  eta: 0:01:01    time: 0.9908  data: 0.9441  max mem: 1715
+extract (test)  [20/62]  eta: 0:00:03    time: 0.0479  data: 0.0028  max mem: 1715
+extract (test)  [40/62]  eta: 0:00:01    time: 0.0470  data: 0.0017  max mem: 1715
+extract (test)  [60/62]  eta: 0:00:00    time: 0.0477  data: 0.0024  max mem: 1715
+extract (test)  [61/62]  eta: 0:00:00    time: 0.0477  data: 0.0025  max mem: 1715
+extract (test) Total time: 0:00:04 (0.0650 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  |         | 0.046416 | train   | 0.78917 |  0.016473 | 0.78406 | 0.017137 | 0.78164 |   0.017088 |
+| schaefer400_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.60484 |  0.042962 | 0.60045 | 0.043361 | 0.60029 |   0.043072 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04277570181742136, "f1": 0.6521171788347361, "f1_std": 0.042937663452469584, "bacc": 0.6538865546218487, "bacc_std": 0.043046379600220994}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04371145098175399, "f1": 0.5826018084614877, "f1_std": 0.04446920146766699, "bacc": 0.5824579831932774, "bacc_std": 0.04419550965955549}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.0441375911337786, "f1": 0.5915678524374176, "f1_std": 0.04485533146910449, "bacc": 0.5913865546218487, "bacc_std": 0.04458398891785179}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.717741935483871, "acc_std": 0.039287604519575595, "f1": 0.7172822617419061, "f1_std": 0.039349441868543104, "bacc": 0.7205882352941176, "bacc_std": 0.03931769835623903}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04378139278047383, "f1": 0.6189604445897352, "f1_std": 0.04390558985901045, "bacc": 0.6197478991596639, "bacc_std": 0.04387470746119514}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04274721817628153, "f1": 0.5836690840719849, "f1_std": 0.04431043432164995, "bacc": 0.5850840336134454, "bacc_std": 0.04308646598352888}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04166676933736918, "f1": 0.5571428571428572, "f1_std": 0.04253756775068311, "bacc": 0.5572478991596639, "bacc_std": 0.04194639057446817}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04371534304431099, "f1": 0.6375232527238905, "f1_std": 0.04492945033608443, "bacc": 0.6370798319327731, "bacc_std": 0.04430417306815964}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.040596729337255895, "f1": 0.6119727891156463, "f1_std": 0.04305726699418026, "bacc": 0.6144957983193278, "bacc_std": 0.04123225512962687}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.043211393239783465, "f1": 0.6242424242424243, "f1_std": 0.04399870621291534, "bacc": 0.6239495798319328, "bacc_std": 0.04368356375885031}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 2.782559402207126, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.04151268251478531, "f1": 0.6829891838741396, "f1_std": 0.04181143760874901, "bacc": 0.6832983193277311, "bacc_std": 0.04171592451689405}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 2.782559402207126, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04419271249503808, "f1": 0.6598746081504703, "f1_std": 0.044422516586425, "bacc": 0.6612394957983193, "bacc_std": 0.04448129910478992}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.040876517687188196, "f1": 0.650475254015077, "f1_std": 0.04121773762552959, "bacc": 0.6507352941176471, "bacc_std": 0.04124791253467657}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 2.782559402207126, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04287251811561063, "f1": 0.6688034188034189, "f1_std": 0.04468336642185707, "bacc": 0.6680672268907563, "bacc_std": 0.043690177910986484}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 2.782559402207126, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.043938216366167696, "f1": 0.6667322189446083, "f1_std": 0.044233479003216565, "bacc": 0.6670168067226891, "bacc_std": 0.04404910847592929}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.042133107543839296, "f1": 0.6375232527238905, "f1_std": 0.043348830733932614, "bacc": 0.6370798319327731, "bacc_std": 0.04271638829817567}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04353901930306938, "f1": 0.6266038229903116, "f1_std": 0.043732771938606495, "bacc": 0.6271008403361344, "bacc_std": 0.043717303700272214}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.042096415769083455, "f1": 0.5836690840719849, "f1_std": 0.044027500703064375, "bacc": 0.5850840336134454, "bacc_std": 0.042653090227866404}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04281308745998586, "f1": 0.6045708211533352, "f1_std": 0.04395983085024517, "bacc": 0.6045168067226891, "bacc_std": 0.04329564160364163}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.042696823423869296, "f1": 0.6418067226890756, "f1_std": 0.043125881186761014, "bacc": 0.6418067226890756, "bacc_std": 0.042999804713678855}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 21, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04604943504824165, "f1": 0.5854473942969518, "f1_std": 0.04644695188768889, "bacc": 0.585609243697479, "bacc_std": 0.04660082399878789}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.7016129032258065, "acc_std": 0.0390457554642124, "f1": 0.6928021426180114, "f1_std": 0.04098631951992946, "bacc": 0.6917016806722689, "bacc_std": 0.03997803471202815}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 23, "C": 2.782559402207126, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.043541827462954916, "f1": 0.6137071651090342, "f1_std": 0.04523502117564678, "bacc": 0.6134453781512605, "bacc_std": 0.044507416629252776}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 24, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04375879872872496, "f1": 0.602745995423341, "f1_std": 0.044088728309380536, "bacc": 0.6034663865546219, "bacc_std": 0.044230478901827026}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.0432972567880392, "f1": 0.5860042735042735, "f1_std": 0.04463466222119215, "bacc": 0.5866596638655462, "bacc_std": 0.043640091637309544}
+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 | 35.437 |  183.96 | 0.90632 |  0.062648 | 0.90478 | 0.064003 | 0.90358 |   0.064378 |
+| schaefer400_mae | patch  | logistic | abide_dx  | test    |        100 | 35.437 |  183.96 | 0.63589 |  0.041223 | 0.63054 | 0.042377 | 0.63103 |   0.042269 |
+
+
+done! total time: 0:05:12

input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:52:35
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/adhd200_dx__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: 0:03:19    time: 1.3241  data: 0.9527  max mem: 1546
+extract (train)  [ 20/151]  eta: 0:00:14    time: 0.0473  data: 0.0025  max mem: 1715
+extract (train)  [ 40/151]  eta: 0:00:08    time: 0.0467  data: 0.0015  max mem: 1715
+extract (train)  [ 60/151]  eta: 0:00:06    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [ 80/151]  eta: 0:00:04    time: 0.0475  data: 0.0024  max mem: 1715
+extract (train)  [100/151]  eta: 0:00:03    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [120/151]  eta: 0:00:01    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [140/151]  eta: 0:00:00    time: 0.0475  data: 0.0024  max mem: 1715
+extract (train)  [150/151]  eta: 0:00:00    time: 0.0466  data: 0.0025  max mem: 1715
+extract (train) Total time: 0:00:08 (0.0567 s / it)
+extract (validation)  [ 0/32]  eta: 0:00:31    time: 0.9830  data: 0.9363  max mem: 1715
+extract (validation)  [20/32]  eta: 0:00:01    time: 0.0475  data: 0.0024  max mem: 1715
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.0468  data: 0.0017  max mem: 1715
+extract (validation) Total time: 0:00:02 (0.0796 s / it)
+extract (test)  [ 0/33]  eta: 0:00:23    time: 0.7201  data: 0.6732  max mem: 1715
+extract (test)  [20/33]  eta: 0:00:01    time: 0.0479  data: 0.0028  max mem: 1715
+extract (test)  [32/33]  eta: 0:00:00    time: 0.0458  data: 0.0015  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0701 s / it)
+feature extraction time: 0:00:13
+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   | 0.6411 |  0.022311 | 0.61069 | 0.02586  | 0.61531 |   0.023438 |
+| schaefer400_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | test    | 0.6    |  0.047523 | 0.53056 | 0.061765 | 0.55743 |   0.049753 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05930800957712204, "f1": 0.588206627680312, "f1_std": 0.061785474008127415, "bacc": 0.5878378378378378, "bacc_std": 0.06066401515737633}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.000774263682681127, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.0534355863960631, "f1": 0.5834401435529352, "f1_std": 0.06015387275336823, "bacc": 0.5883204633204633, "bacc_std": 0.05511950166621306}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.059880749344107524, "f1": 0.5833333333333333, "f1_std": 0.06296432289966705, "bacc": 0.5834942084942085, "bacc_std": 0.0610898886585815}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05659183456687873, "f1": 0.6289401836684041, "f1_std": 0.06006898925310009, "bacc": 0.6283783783783784, "bacc_std": 0.05795419996463281}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.05788786548504099, "f1": 0.4743935309973046, "f1_std": 0.06078710244285137, "bacc": 0.47586872586872586, "bacc_std": 0.05937129315385526}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 1291.5496650148827, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06127076389988736, "f1": 0.5810455956075435, "f1_std": 0.06134912877820336, "bacc": 0.583011583011583, "bacc_std": 0.06116984846722491}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05811313186083294, "f1": 0.6515594541910331, "f1_std": 0.06027774020726413, "bacc": 0.6505791505791505, "bacc_std": 0.05975128609601933}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05667642194717066, "f1": 0.6233308138070043, "f1_std": 0.06171706112321735, "bacc": 0.6240347490347491, "bacc_std": 0.058584353514905665}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05577214846479108, "f1": 0.6515594541910331, "f1_std": 0.05804734825782125, "bacc": 0.6505791505791505, "bacc_std": 0.0573204114689213}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 21.54434690031882, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06111342447357678, "f1": 0.4532564287430906, "f1_std": 0.0622326944382772, "bacc": 0.4531853281853282, "bacc_std": 0.06223034864311548}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.000774263682681127, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05453477040780452, "f1": 0.4846723044397463, "f1_std": 0.06268427876895537, "bacc": 0.5033783783783784, "bacc_std": 0.055453587566434}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.057291374031886666, "f1": 0.5045731707317074, "f1_std": 0.06274641584392682, "bacc": 0.5120656370656371, "bacc_std": 0.058367441761144896}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.06247329180817281, "f1": 0.4980694980694981, "f1_std": 0.0636988288359473, "bacc": 0.4980694980694981, "bacc_std": 0.06344560959041061}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 0.000774263682681127, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.056004767337228006, "f1": 0.5289855072463768, "f1_std": 0.06264252840034157, "bacc": 0.5390926640926641, "bacc_std": 0.05733999708351218}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06408777265243577, "f1": 0.5357142857142857, "f1_std": 0.0643352908694299, "bacc": 0.5381274131274132, "bacc_std": 0.06492691157059999}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05519111496969084, "f1": 0.5321419707123356, "f1_std": 0.05819561603906534, "bacc": 0.5342664092664092, "bacc_std": 0.056058794440585945}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.06004653028124579, "f1": 0.5321419707123356, "f1_std": 0.06403041179720198, "bacc": 0.5342664092664092, "bacc_std": 0.061351001598744036}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05763762742058834, "f1": 0.6153846153846154, "f1_std": 0.06039437429320805, "bacc": 0.6148648648648649, "bacc_std": 0.058790434573141134}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.059011782997502954, "f1": 0.5644080416976918, "f1_std": 0.06259337400941269, "bacc": 0.5656370656370656, "bacc_std": 0.06024812042281892}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.053184141427762885, "f1": 0.5626293995859213, "f1_std": 0.0612213717246963, "bacc": 0.5704633204633205, "bacc_std": 0.05508640914775699}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.056829653064712805, "f1": 0.5578231292517006, "f1_std": 0.062080486354216334, "bacc": 0.5612934362934363, "bacc_std": 0.05848821595385719}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.058836522087568593, "f1": 0.5565302144249512, "f1_std": 0.06053558556606232, "bacc": 0.5564671814671815, "bacc_std": 0.05960007053974142}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05549257853253306, "f1": 0.5411764705882354, "f1_std": 0.06483492959080744, "bacc": 0.5526061776061776, "bacc_std": 0.057586947852453475}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05385708899952687, "f1": 0.5751633986928104, "f1_std": 0.06254440094152726, "bacc": 0.583976833976834, "bacc_std": 0.055940035045308034}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05975047918898735, "f1": 0.5745454545454545, "f1_std": 0.061171184512016884, "bacc": 0.5743243243243243, "bacc_std": 0.06068898726972423}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05705908906537515, "f1": 0.545, "f1_std": 0.061072232475683066, "bacc": 0.5477799227799228, "bacc_std": 0.05830974324880991}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.058269124650830006, "f1": 0.61, "f1_std": 0.06280045224217205, "bacc": 0.6105212355212355, "bacc_std": 0.06009463340896401}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 28, "C": 1291.5496650148827, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06138037386541392, "f1": 0.5830363506771205, "f1_std": 0.061500961940370934, "bacc": 0.5873552123552124, "bacc_std": 0.06160659534228227}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 29, "C": 0.000774263682681127, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.059564486460787, "f1": 0.5125, "f1_std": 0.06353401869706421, "bacc": 0.5164092664092664, "bacc_std": 0.06054669157916681}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 30, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05554629392885514, "f1": 0.5834401435529352, "f1_std": 0.061803412330808084, "bacc": 0.5883204633204633, "bacc_std": 0.057133912414297716}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.06051890799454391, "f1": 0.543030303030303, "f1_std": 0.06244175473194057, "bacc": 0.542953667953668, "bacc_std": 0.06170262323901965}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 32, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.056196198991258196, "f1": 0.5578231292517006, "f1_std": 0.060534501568752855, "bacc": 0.5612934362934363, "bacc_std": 0.05731855270645089}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 33, "C": 0.000774263682681127, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05504679880948208, "f1": 0.6036585365853658, "f1_std": 0.0622859594737131, "bacc": 0.6061776061776062, "bacc_std": 0.0576838164741637}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 34, "C": 1291.5496650148827, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.06229735146858171, "f1": 0.49317738791423005, "f1_std": 0.0632627553184902, "bacc": 0.49372586872586877, "bacc_std": 0.062494987619702735}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 35, "C": 0.000774263682681127, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.04510163670130049, "f1": 0.5902987119758838, "f1_std": 0.05873973324747347, "bacc": 0.6066602316602316, "bacc_std": 0.048003024233525815}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 36, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05743825910895503, "f1": 0.5321419707123356, "f1_std": 0.060324015289273517, "bacc": 0.5342664092664092, "bacc_std": 0.05842052167573008}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 37, "C": 0.046415888336127774, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.0632829356211325, "f1": 0.5157414083153088, "f1_std": 0.06448323326523092, "bacc": 0.515926640926641, "bacc_std": 0.06443678316670466}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 38, "C": 0.3593813663804626, "split": "test", "acc": 0.4461538461538462, "acc_std": 0.06176397908511105, "f1": 0.4353281853281853, "f1_std": 0.0615321949793778, "bacc": 0.4353281853281853, "bacc_std": 0.061532127667687}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 92, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05582064261553995, "f1": 0.5751633986928104, "f1_std": 0.06325218224251768, "bacc": 0.583976833976834, "bacc_std": 0.057315382621738215}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 2.782559402207126, "split": "test", "acc": 0.676923076923077, "acc_std": 0.050705405101516304, "f1": 0.6431372549019607, "f1_std": 0.06026750584776436, "bacc": 0.6467181467181468, "bacc_std": 0.053834878759401794}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.04884014257378062, "f1": 0.4477939161414086, "f1_std": 0.05750684273230914, "bacc": 0.4811776061776062, "bacc_std": 0.04907768177954589}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.3593813663804626, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.06085749383776017, "f1": 0.4743935309973046, "f1_std": 0.06264081756903984, "bacc": 0.47586872586872586, "bacc_std": 0.06139694670045797}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.000774263682681127, "split": "test", "acc": 0.6, "acc_std": 0.05325558336703593, "f1": 0.5626293995859213, "f1_std": 0.0607067157007782, "bacc": 0.5704633204633205, "bacc_std": 0.05489248151536986}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06307151196302317, "f1": 0.5294401544401545, "f1_std": 0.06410891854092937, "bacc": 0.5294401544401545, "bacc_std": 0.06380029853886249}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.47692307692307695, "acc_std": 0.05890014416152578, "f1": 0.4475, "f1_std": 0.061245965941725405, "bacc": 0.45366795366795365, "bacc_std": 0.05916057951335641}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.05790066171658951, "f1": 0.4834657780056396, "f1_std": 0.06344865153347881, "bacc": 0.4942084942084942, "bacc_std": 0.05867741280952552}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.000774263682681127, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.056776518189754106, "f1": 0.5250692869740489, "f1_std": 0.06052812389573289, "bacc": 0.5299227799227799, "bacc_std": 0.057572735053660694}
+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 | 167.47 |  1032.6 | 0.77211 |  0.11917  | 0.75799 | 0.12868  | 0.75734 |    0.1276  |
+| schaefer400_mae | patch  | logistic | adhd200_dx | test    |        100 | 167.47 |  1032.6 | 0.57431 |  0.060006 | 0.54978 | 0.060634 | 0.55441 |    0.05871 |
+
+
+done! total time: 0:04:11

input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml

@@ -0,0 +1,31 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (adni_ad_vs_cn patch
+  logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,74,1291.5496650148827,test,0.7073170731707317,0.05635360443571542,0.5340909090909092,0.0839976187672097,0.535483870967742,0.07123246258052687
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,75,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,75,10000.0,test,0.6829268292682927,0.06513325927216167,0.5839188134270101,0.08291742546309858,0.5870967741935484,0.08611149175020956
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,76,2.782559402207126,train,0.997289972899729,0.002719095937129655,0.9961941891766453,0.0038489893434846757,0.9941860465116279,0.005833409306981639
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,76,2.782559402207126,test,0.6097560975609756,0.06451715136109272,0.47096774193548385,0.07157593888561671,0.47096774193548385,0.07132371506662638
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,77,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,77,2.782559402207126,test,0.7317073170731707,0.06447317276161335,0.6232247284878863,0.0898661500194403,0.6193548387096774,0.08916178393729195
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,78,0.3593813663804626,train,0.940379403794038,0.01182608008889758,0.9088602478893479,0.019908422739641354,0.8761401922918892,0.024629555480876628
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,79,21.54434690031882,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,82,0.046415888336127774,train,0.8617886178861789,0.013523758609020243,0.7604536602130828,0.029060755772138742,0.7196770482373244,0.027195451255774706
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,82,0.046415888336127774,test,0.7560975609756098,0.044894058650138714,0.569327731092437,0.09243883314748594,0.567741935483871,0.06986309929483607
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,84,0.046415888336127774,train,0.8590785907859079,0.014851837648694294,0.7629237916378373,0.029473665917138323,0.7260046018571781,0.027909121842181545
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,84,0.046415888336127774,test,0.7804878048780488,0.03824452463406266,0.5886287625418061,0.09158305202897976,0.5838709677419355,0.06490606804525054
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+schaefer400_mae,patch,logistic,adni_ad_vs_cn,86,2.782559402207126,train,0.997289972899729,0.00261142576187957,0.9961941891766453,0.0036960167262702743,0.9941860465116279,0.005602419221706741
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,86,2.782559402207126,test,0.6585365853658537,0.07004475496280167,0.5651515151515152,0.07997384708160622,0.5709677419354839,0.0850343640519469
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,87,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,87,166.81005372000556,test,0.6585365853658537,0.0777085725558095,0.6057692307692308,0.08141065524671776,0.6387096774193548,0.0910848527793028
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,88,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,88,2.782559402207126,test,0.7560975609756098,0.0674812243920421,0.7184065934065934,0.07085803363670977,0.7709677419354839,0.07603409361792701
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,89,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,89,1291.5496650148827,test,0.7073170731707317,0.06688932676072672,0.603225806451613,0.08819213522837899,0.603225806451613,0.08950891711721441
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,train,0.8563685636856369,0.013964705223266915,0.7510596861037919,0.029578946457994994,0.7120963102966554,0.026944904793377456
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,test,0.7804878048780488,0.05771117237601123,0.6660633484162897,0.09577631319695809,0.6516129032258065,0.08879341502334981
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,91,0.005994842503189409,train,0.8428184281842819,0.012983059625969882,0.7152171598892911,0.03056154205801915,0.6789793738187198,0.025865718649588875
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,91,0.005994842503189409,test,0.6829268292682927,0.05926801164241307,0.5176470588235295,0.08319903167003775,0.5193548387096775,0.07503016278659909
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,train,0.943089430894309,0.010589099287912159,0.9125760122747837,0.018025969034316237,0.877906976744186,0.022717311844416218
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,test,0.7073170731707317,0.06003090617847744,0.5729166666666666,0.08447612940596604,0.5693548387096774,0.07759703294154968
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,93,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,93,21.54434690031882,test,0.6585365853658537,0.06948430762800852,0.5370967741935484,0.08539311166256754,0.5370967741935484,0.0857942933418722
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,2.782559402207126,train,0.997289972899729,0.002670822108771276,0.9961941891766453,0.003781768462999844,0.9941860465116279,0.005729845105445348
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,94,2.782559402207126,test,0.7073170731707317,0.05453771978400406,0.5340909090909092,0.08340132552013683,0.535483870967742,0.0711915633995461
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,95,166.81005372000556,test,0.6829268292682927,0.06817802221149444,0.5839188134270101,0.08501136838652229,0.5870967741935484,0.08799907624148584
+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,0.7804878048780488,0.052923097541816115,0.6660633484162897,0.08823052907415198,0.6516129032258065,0.07958705754988364
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,train,0.94579945799458,0.010298460983650264,0.9179416473937021,0.01691217222133154,0.8877680992686334,0.021389987248587438
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,test,0.7073170731707317,0.05844505038355861,0.5729166666666666,0.08346071394179921,0.5693548387096774,0.0779226417399565
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,train,0.8617886178861789,0.013829330692735032,0.7633336267716337,0.02873333396377275,0.7237242172733996,0.02674287632839373
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,test,0.7804878048780488,0.05463577959231227,0.6660633484162897,0.08712870208073502,0.6516129032258065,0.08185685044405377
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,0.3593813663804626,train,0.94579945799458,0.010950607542676322,0.9187153053132435,0.017805363503171096,0.8918152683047087,0.02240271063292631
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,99,0.3593813663804626,test,0.7317073170731707,0.049415645380065376,0.5512437810945273,0.08574928326323948,0.5516129032258065,0.0677123855672591
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,2.782559402207126,train,0.997289972899729,0.0026877130120796697,0.9961941891766453,0.0038054863671016896,0.9941860465116279,0.0057660819852174226
+schaefer400_mae,patch,logistic,adni_ad_vs_cn,100,2.782559402207126,test,0.7317073170731707,0.0679905511859195,0.6479313036690086,0.08619995770817873,0.6532258064516129,0.08896899949987255

input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev86+gf97f52698
+sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-04-08 22:52:46
+config:
+output_root: experiments/input_space_v3/output
+name_prefix: eval_logistic
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (adni_ad_vs_cn patch
+  logistic)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/adni_ad_vs_cn__patch__logistic
+model: schaefer400_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: 0:03:35    time: 1.3160  data: 0.9485  max mem: 1546
+extract (train)  [ 20/164]  eta: 0:00:15    time: 0.0482  data: 0.0031  max mem: 1715
+extract (train)  [ 40/164]  eta: 0:00:09    time: 0.0471  data: 0.0018  max mem: 1715
+extract (train)  [ 60/164]  eta: 0:00:07    time: 0.0478  data: 0.0025  max mem: 1715
+extract (train)  [ 80/164]  eta: 0:00:05    time: 0.0478  data: 0.0026  max mem: 1715
+extract (train)  [100/164]  eta: 0:00:03    time: 0.0477  data: 0.0025  max mem: 1715
+extract (train)  [120/164]  eta: 0:00:02    time: 0.0478  data: 0.0026  max mem: 1715
+extract (train)  [140/164]  eta: 0:00:01    time: 0.0477  data: 0.0026  max mem: 1715
+extract (train)  [160/164]  eta: 0:00:00    time: 0.0478  data: 0.0026  max mem: 1715
+extract (train)  [163/164]  eta: 0:00:00    time: 0.0479  data: 0.0027  max mem: 1715
+extract (train) Total time: 0:00:09 (0.0565 s / it)
+extract (validation)  [ 0/21]  eta: 0:00:20    time: 0.9740  data: 0.9275  max mem: 1715
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.0455  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: 0.9977  data: 0.9508  max mem: 1715
+extract (test)  [20/21]  eta: 0:00:00    time: 0.0462  data: 0.0020  max mem: 1715
+extract (test) Total time: 0:00:02 (0.0967 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 |         | 0.35938 | train   | 0.93767 |  0.011131 | 0.90608 | 0.018285 | 0.87576 |   0.022247 |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn |         | 0.35938 | test    | 0.70732 |  0.053512 | 0.48319 | 0.072019 | 0.49306 |   0.061087 |
+
+
+evaluating random splits (n=100)
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.054668194965167394, "f1": 0.6117424242424243, "f1_std": 0.09047744000399176, "bacc": 0.6016129032258064, "bacc_std": 0.07729677123186976}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05943009130506411, "f1": 0.7354838709677419, "f1_std": 0.08128792632814752, "bacc": 0.7354838709677419, "bacc_std": 0.08529458599221737}
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+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.05463577959231227, "f1": 0.6660633484162897, "f1_std": 0.08712870208073502, "bacc": 0.6516129032258065, "bacc_std": 0.08185685044405377}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.049415645380065376, "f1": 0.5512437810945273, "f1_std": 0.08574928326323948, "bacc": 0.5516129032258065, "bacc_std": 0.0677123855672591}
+{"model": "schaefer400_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.0679905511859195, "f1": 0.6479313036690086, "f1_std": 0.08619995770817873, "bacc": 0.6532258064516129, "bacc_std": 0.08896899949987255}
+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 | 414.92 |  1449.9 | 0.96054 |  0.053508 | 0.93437 | 0.09364  | 0.91988 |   0.10832  |
+| schaefer400_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 414.92 |  1449.9 | 0.73659 |  0.057905 | 0.62557 | 0.077435 | 0.62665 |   0.077473 |
+
+
+done! total time: 0:04:05

input_space_v3/schaefer400_lr3e-4_5/eval_v2/hcpya_task21__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/input_space_v3/output
+name_prefix: eval_probe
+remote_root: null
+notes: input_space ablation v3 schaefer400_lr3e-4_5; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/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: input_space_v3/schaefer400_lr3e-4_5/eval_v2/hcpya_task21__patch__attn
+model: schaefer400_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/input_space_v3/output/input_space_v3/schaefer400_lr3e-4_5/eval_v2/hcpya_task21__patch__attn
+remote_dir: null