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data_scaling/n100_1/eval_v2/aabc_age__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/aabc_age__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/aabc_age__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,aabc_age,86,0.046415888336127774,train,0.7696850393700787,0.0187079969525949,0.7695562489906987,0.0188204919786749,0.7714512341073555,0.018560116012027544
+flat_mae,patch,logistic,aabc_age,86,0.046415888336127774,test,0.5769230769230769,0.06140344563698909,0.5511155851229381,0.06763417189296846,0.5707417582417582,0.06127242706798937
+flat_mae,patch,logistic,aabc_age,87,0.046415888336127774,train,0.7637795275590551,0.018561154625532376,0.7634473205958021,0.018775447583072435,0.7646001833563808,0.018538112068447014
+flat_mae,patch,logistic,aabc_age,87,0.046415888336127774,test,0.5192307692307693,0.0626757882907732,0.5047619047619047,0.0643092224487785,0.5217490842490843,0.06330880264362261
+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,train,0.5216535433070866,0.019486467354161967,0.5119971317693247,0.02013609136365991,0.5220993826106803,0.019493044315631616
+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,test,0.46153846153846156,0.06716070764135511,0.4619239043579815,0.06853027778192665,0.459478021978022,0.06717393339215923
+flat_mae,patch,logistic,aabc_age,89,0.046415888336127774,train,0.7736220472440944,0.017815925931997864,0.7724074893673964,0.018089262584226436,0.7751159642800289,0.017714593179729784
+flat_mae,patch,logistic,aabc_age,89,0.046415888336127774,test,0.40384615384615385,0.06600497560588693,0.4049919484702093,0.06606773787085492,0.40636446886446886,0.06622330238809408
+flat_mae,patch,logistic,aabc_age,90,0.046415888336127774,train,0.7618110236220472,0.018707406619682076,0.7614428610208973,0.018795613993448236,0.7625840543241227,0.01868041783726684
+flat_mae,patch,logistic,aabc_age,90,0.046415888336127774,test,0.5384615384615384,0.0654961040442133,0.5338625330192047,0.06676449771392655,0.5368589743589743,0.06552058374277724
+flat_mae,patch,logistic,aabc_age,91,0.3593813663804626,train,0.9566929133858267,0.009055011125694597,0.9571154457600566,0.008991752410842817,0.9568182297670262,0.009015516034605407
+flat_mae,patch,logistic,aabc_age,91,0.3593813663804626,test,0.5192307692307693,0.06821103695246229,0.5218864468864468,0.068838990051426,0.5176282051282051,0.0681584278720748
+flat_mae,patch,logistic,aabc_age,92,0.005994842503189409,train,0.6161417322834646,0.020434283034152338,0.6105168066729563,0.0212248528092576,0.6178652119626613,0.02042726139227659
+flat_mae,patch,logistic,aabc_age,92,0.005994842503189409,test,0.46153846153846156,0.05936393621616514,0.4197466453424871,0.059295708715106255,0.4562728937728938,0.058468550937216
+flat_mae,patch,logistic,aabc_age,93,0.046415888336127774,train,0.7696850393700787,0.018669709360782653,0.7702344998553865,0.01871478589232995,0.7710660850152189,0.018608888488541055
+flat_mae,patch,logistic,aabc_age,93,0.046415888336127774,test,0.4807692307692308,0.0579841209644819,0.4439172852001799,0.057442832091841464,0.4741300366300366,0.056977588302744485
+flat_mae,patch,logistic,aabc_age,94,0.3593813663804626,train,0.9488188976377953,0.009445763941837243,0.9490378265127188,0.009394611593150916,0.9486537402975513,0.009448458121186845
+flat_mae,patch,logistic,aabc_age,94,0.3593813663804626,test,0.4230769230769231,0.06303352165372808,0.4232967032967033,0.06318784343533682,0.4285714285714286,0.06354444211163705
+flat_mae,patch,logistic,aabc_age,95,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,95,2.782559402207126,test,0.5,0.06076897516986168,0.4882943143812709,0.059985409764482835,0.49793956043956045,0.060455248510281744
+flat_mae,patch,logistic,aabc_age,96,0.3593813663804626,train,0.9468503937007874,0.009859714114918575,0.9471976558717139,0.00977590908542793,0.946805192476251,0.009898052864061013
+flat_mae,patch,logistic,aabc_age,96,0.3593813663804626,test,0.4807692307692308,0.06804105965383378,0.4865281777046483,0.06927242914233601,0.47779304029304026,0.06833079529885855
+flat_mae,patch,logistic,aabc_age,97,0.3593813663804626,train,0.9566929133858267,0.009175890087284054,0.9570716825468174,0.00909429410809807,0.956768243096805,0.009169938030850887
+flat_mae,patch,logistic,aabc_age,97,0.3593813663804626,test,0.4807692307692308,0.07059018948884473,0.4739734299516908,0.07183076113009138,0.48054029304029305,0.07054593552334155
+flat_mae,patch,logistic,aabc_age,98,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,98,166.81005372000556,test,0.46153846153846156,0.06052007100196464,0.432378079436903,0.0641455240725304,0.45650183150183155,0.06001269449108668
+flat_mae,patch,logistic,aabc_age,99,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,99,2.782559402207126,test,0.5192307692307693,0.06529364712611468,0.5103943170581541,0.0676481013272437,0.5173992673992673,0.06502702977761378
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,train,0.781496062992126,0.018237234869723857,0.7819276426445609,0.01825968951869758,0.7827277234464095,0.018171258295191613
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,test,0.4423076923076923,0.06883050961059665,0.443452380952381,0.06975836751595894,0.443452380952381,0.06916150232463772

data_scaling/n100_1/eval_v2/aabc_age__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/aabc_age__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:17:38    time: 4.6410  data: 3.8504  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:32    time: 0.2338  data: 0.0851  max mem: 3393
+extract (train)  [ 40/228]  eta: 0:01:00    time: 0.1993  data: 0.0626  max mem: 3393
+extract (train)  [ 60/228]  eta: 0:00:48    time: 0.2091  data: 0.0708  max mem: 3393
+extract (train)  [ 80/228]  eta: 0:00:40    time: 0.2395  data: 0.0736  max mem: 3393
+extract (train)  [100/228]  eta: 0:00:35    time: 0.2761  data: 0.0861  max mem: 3393
+extract (train)  [120/228]  eta: 0:00:29    time: 0.2489  data: 0.0711  max mem: 3393
+extract (train)  [140/228]  eta: 0:00:23    time: 0.2572  data: 0.0744  max mem: 3393
+extract (train)  [160/228]  eta: 0:00:18    time: 0.2528  data: 0.0721  max mem: 3393
+extract (train)  [180/228]  eta: 0:00:12    time: 0.2406  data: 0.0645  max mem: 3393
+extract (train)  [200/228]  eta: 0:00:07    time: 0.2164  data: 0.0608  max mem: 3393
+extract (train)  [220/228]  eta: 0:00:02    time: 0.1768  data: 0.0459  max mem: 3393
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1804  data: 0.0493  max mem: 3393
+extract (train) Total time: 0:00:57 (0.2526 s / it)
+extract (validation)  [ 0/27]  eta: 0:02:22    time: 5.2807  data: 5.1311  max mem: 3393
+extract (validation)  [20/27]  eta: 0:00:02    time: 0.1794  data: 0.0477  max mem: 3393
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1738  data: 0.0487  max mem: 3393
+extract (validation) Total time: 0:00:10 (0.3808 s / it)
+extract (test)  [ 0/26]  eta: 0:02:12    time: 5.0865  data: 4.9448  max mem: 3393
+extract (test)  [20/26]  eta: 0:00:02    time: 0.1927  data: 0.0525  max mem: 3393
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1764  data: 0.0468  max mem: 3393
+extract (test) Total time: 0:00:10 (0.3896 s / it)
+feature extraction time: 0:01:18
+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 |
+|:---------|:-------|:---------|:----------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_age  |         | 0.046416 | train   | 0.77756 |  0.018467 | 0.77658 | 0.018722 | 0.77842 |    0.01841 |
+| flat_mae | patch  | logistic | aabc_age  |         | 0.046416 | test    | 0.32692 |  0.060837 | 0.30181 | 0.062122 | 0.31777 |    0.0602  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.05977634597783256, "f1": 0.4631702449023627, "f1_std": 0.05946382752046566, "bacc": 0.459478021978022, "bacc_std": 0.059706036399578}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06863056058458102, "f1": 0.5510351966873706, "f1_std": 0.0703759793197948, "bacc": 0.5547161172161172, "bacc_std": 0.06874149807948791}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 2.782559402207126, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06462796351920004, "f1": 0.4663098122074279, "f1_std": 0.06877797789400986, "bacc": 0.48031135531135527, "bacc_std": 0.06474615933079218}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.5, "acc_std": 0.06695759498837468, "f1": 0.49816017316017314, "f1_std": 0.06774619535906896, "bacc": 0.4983974358974359, "bacc_std": 0.06703422686044658}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.06322743534164309, "f1": 0.4908730158730158, "f1_std": 0.0644679259734538, "bacc": 0.502518315018315, "bacc_std": 0.06379346082694846}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.5961538461538461, "acc_std": 0.06340761111987263, "f1": 0.5869696969696969, "f1_std": 0.0658483348823564, "bacc": 0.594551282051282, "bacc_std": 0.06349343996725598}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.058037378284227385, "f1": 0.5427350427350428, "f1_std": 0.06065224133653189, "bacc": 0.5677655677655677, "bacc_std": 0.05772865258019097}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06266246340135877, "f1": 0.5286010840733479, "f1_std": 0.06463705513268113, "bacc": 0.538003663003663, "bacc_std": 0.0626834428347867}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06001078551975656, "f1": 0.5001406926406926, "f1_std": 0.06824358736636642, "bacc": 0.5306776556776557, "bacc_std": 0.05958088510955521}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.0686201705816219, "f1": 0.394487070190395, "f1_std": 0.0693189980143719, "bacc": 0.39743589743589747, "bacc_std": 0.06828845079323732}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.5961538461538461, "acc_std": 0.06757529221796377, "f1": 0.5981613756613757, "f1_std": 0.06737396562058631, "bacc": 0.5991300366300366, "bacc_std": 0.06789785360005285}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06263262850819974, "f1": 0.4051269030888596, "f1_std": 0.0616918049126719, "bacc": 0.4017857142857143, "bacc_std": 0.062352741510642885}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06955448887797913, "f1": 0.5180860805860806, "f1_std": 0.07014803801678113, "bacc": 0.5219780219780219, "bacc_std": 0.06962115684124948}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06559489940843241, "f1": 0.47792882404951376, "f1_std": 0.06624481139687308, "bacc": 0.486492673992674, "bacc_std": 0.0659872244753437}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06853820253771317, "f1": 0.528472430451441, "f1_std": 0.06729637025905505, "bacc": 0.5206043956043955, "bacc_std": 0.06880483633571846}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.0699058961714005, "f1": 0.5564405297351325, "f1_std": 0.07138443136941551, "bacc": 0.5592948717948718, "bacc_std": 0.0698578177391031}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.0635736887818022, "f1": 0.5005417344803534, "f1_std": 0.06924136037808454, "bacc": 0.5187728937728937, "bacc_std": 0.06355064479606871}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 166.81005372000556, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06052007100196464, "f1": 0.432378079436903, "f1_std": 0.0641455240725304, "bacc": 0.45650183150183155, "bacc_std": 0.06001269449108668}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06883050961059665, "f1": 0.443452380952381, "f1_std": 0.06975836751595894, "bacc": 0.443452380952381, "bacc_std": 0.06916150232463772}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_age  | train   |        100 | 3.8011 |  23.509 | 0.80073 |  0.13248  | 0.79929 | 0.13503  | 0.80145 |   0.13222  |
+| flat_mae | patch  | logistic | aabc_age  | test    |        100 | 3.8011 |  23.509 | 0.47846 |  0.060461 | 0.46927 | 0.058754 | 0.47774 |   0.060147 |
+
+
+done! total time: 0:06:02

data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:23:25    time: 5.9543  data: 5.0053  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:02:00    time: 0.2889  data: 0.0880  max mem: 3393
+extract (train)  [ 40/236]  eta: 0:01:18    time: 0.2346  data: 0.0696  max mem: 3393
+extract (train)  [ 60/236]  eta: 0:01:00    time: 0.2270  data: 0.0713  max mem: 3393
+extract (train)  [ 80/236]  eta: 0:00:48    time: 0.2157  data: 0.0674  max mem: 3393
+extract (train)  [100/236]  eta: 0:00:39    time: 0.2134  data: 0.0644  max mem: 3393
+extract (train)  [120/236]  eta: 0:00:32    time: 0.2098  data: 0.0646  max mem: 3393
+extract (train)  [140/236]  eta: 0:00:25    time: 0.2022  data: 0.0618  max mem: 3393
+extract (train)  [160/236]  eta: 0:00:20    time: 0.2406  data: 0.0816  max mem: 3393
+extract (train)  [180/236]  eta: 0:00:14    time: 0.2255  data: 0.0741  max mem: 3393
+extract (train)  [200/236]  eta: 0:00:09    time: 0.2022  data: 0.0638  max mem: 3393
+extract (train)  [220/236]  eta: 0:00:03    time: 0.1782  data: 0.0513  max mem: 3393
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1751  data: 0.0528  max mem: 3393
+extract (train) Total time: 0:00:57 (0.2452 s / it)
+extract (validation)  [ 0/29]  eta: 0:02:11    time: 4.5267  data: 4.3838  max mem: 3393
+extract (validation)  [20/29]  eta: 0:00:03    time: 0.1799  data: 0.0477  max mem: 3393
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1511  data: 0.0358  max mem: 3393
+extract (validation) Total time: 0:00:09 (0.3354 s / it)
+extract (test)  [ 0/28]  eta: 0:01:57    time: 4.2082  data: 4.0463  max mem: 3393
+extract (test)  [20/28]  eta: 0:00:03    time: 0.1872  data: 0.0561  max mem: 3393
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1564  data: 0.0416  max mem: 3393
+extract (test) Total time: 0:00:09 (0.3323 s / it)
+feature extraction time: 0:01:17
+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 |
+|:---------|:-------|:---------|:----------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.046416 | train   | 0.90359 |  0.012551 | 0.90085 | 0.012957 | 0.8992  |   0.013181 |
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.046416 | test    | 0.89091 |  0.043566 | 0.88791 | 0.044582 | 0.89394 |   0.043972 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06149502701119368, "f1": 0.7213779128672746, "f1_std": 0.06300308213097272, "bacc": 0.7228260869565217, "bacc_std": 0.06289869237919625}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.0443564226623942, "f1": 0.8699763593380614, "f1_std": 0.045191975404809126, "bacc": 0.8722826086956521, "bacc_std": 0.04480708812462005}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05761010012457569, "f1": 0.7518222839291913, "f1_std": 0.06180374554264843, "bacc": 0.7479619565217391, "bacc_std": 0.06099369797284896}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05893620027181375, "f1": 0.741263440860215, "f1_std": 0.0595342103879293, "bacc": 0.7445652173913043, "bacc_std": 0.05911429494702716}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 2.782559402207126, "split": "test", "acc": 0.8, "acc_std": 0.051905508553349286, "f1": 0.7931623931623932, "f1_std": 0.05410744959845042, "bacc": 0.7914402173913043, "bacc_std": 0.05394160432109722}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 21.54434690031882, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04207477012405101, "f1": 0.8863636363636364, "f1_std": 0.044531869567660164, "bacc": 0.8817934782608696, "bacc_std": 0.04542464887574152}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.052289075704362246, "f1": 0.8131793478260869, "f1_std": 0.05401904967266754, "bacc": 0.8131793478260869, "bacc_std": 0.05424543022018138}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 9.999999999999999e-05, "split": "test", "acc": 0.8, "acc_std": 0.04973411619853704, "f1": 0.7861435136090491, "f1_std": 0.05582870258167516, "bacc": 0.7792119565217391, "bacc_std": 0.05459630323040774}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 1291.5496650148827, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04829018345566536, "f1": 0.8521505376344086, "f1_std": 0.04891920635551357, "bacc": 0.8566576086956521, "bacc_std": 0.048501893291125496}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 2.782559402207126, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05168718369845356, "f1": 0.8131793478260869, "f1_std": 0.05349807856058431, "bacc": 0.8131793478260869, "bacc_std": 0.05351340645108859}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.000774263682681127, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04475054826756799, "f1": 0.8428571428571429, "f1_std": 0.05170393876454982, "bacc": 0.8322010869565217, "bacc_std": 0.0511362306276733}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 2.782559402207126, "split": "test", "acc": 0.8, "acc_std": 0.05329885800336446, "f1": 0.790003471017008, "f1_std": 0.05777860887834186, "bacc": 0.7853260869565217, "bacc_std": 0.05726455123030394}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.000774263682681127, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04000692502038587, "f1": 0.8863636363636364, "f1_std": 0.042140842076823906, "bacc": 0.8817934782608696, "bacc_std": 0.042894589293149245}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.000774263682681127, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.040937945379572065, "f1": 0.8639095086603039, "f1_std": 0.046265970892153466, "bacc": 0.8539402173913043, "bacc_std": 0.04712616725960955}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04723460000906673, "f1": 0.8521505376344086, "f1_std": 0.04782973209655773, "bacc": 0.8566576086956521, "bacc_std": 0.047585677645461656}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.000774263682681127, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.05158512182197912, "f1": 0.8307692307692308, "f1_std": 0.053759804661040966, "bacc": 0.8288043478260869, "bacc_std": 0.05371627302185273}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04871777383669437, "f1": 0.8250265111346766, "f1_std": 0.05486622904077203, "bacc": 0.8165760869565217, "bacc_std": 0.05417462407772356}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.046043816716810494, "f1": 0.8533333333333333, "f1_std": 0.046112478875184874, "bacc": 0.8627717391304348, "bacc_std": 0.044203453020826754}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04728620786810307, "f1": 0.8521505376344086, "f1_std": 0.04777001862252831, "bacc": 0.8566576086956521, "bacc_std": 0.047078335193565496}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05253682219120615, "f1": 0.76890756302521, "f1_std": 0.05781838617802826, "bacc": 0.7635869565217391, "bacc_std": 0.05646000861894566}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05519730500068678, "f1": 0.7758152173913043, "f1_std": 0.05733842600401388, "bacc": 0.7758152173913043, "bacc_std": 0.05760812171255835}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03949559657430599, "f1": 0.9071259709557582, "f1_std": 0.04025935007002016, "bacc": 0.9096467391304348, "bacc_std": 0.039641642436953145}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.046778450926408185, "f1": 0.8699763593380614, "f1_std": 0.047689220491704716, "bacc": 0.8722826086956521, "bacc_std": 0.04750025308359601}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04693274949549206, "f1": 0.8683760683760684, "f1_std": 0.04885433478739587, "bacc": 0.8661684782608696, "bacc_std": 0.049198830172519345}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.3593813663804626, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04945255343581676, "f1": 0.8541114058355437, "f1_std": 0.049307023009238574, "bacc": 0.8688858695652174, "bacc_std": 0.0449023901658249}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.04932115701122205, "f1": 0.790003471017008, "f1_std": 0.05312854744297095, "bacc": 0.7853260869565217, "bacc_std": 0.052592327630807345}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.3593813663804626, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04118667842080547, "f1": 0.8863636363636364, "f1_std": 0.04375628466388746, "bacc": 0.8817934782608696, "bacc_std": 0.04485277010331083}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 2.782559402207126, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05435577930451969, "f1": 0.8131793478260869, "f1_std": 0.05582871602760005, "bacc": 0.8131793478260869, "bacc_std": 0.05572861867880973}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.05016727061862457, "f1": 0.8328267477203647, "f1_std": 0.0512938906817724, "bacc": 0.8349184782608696, "bacc_std": 0.05122181428117861}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05996102315271698, "f1": 0.7433333333333334, "f1_std": 0.06006534985759663, "bacc": 0.7506793478260869, "bacc_std": 0.059464821669382245}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05339205995385176, "f1": 0.7727272727272727, "f1_std": 0.05604270875767791, "bacc": 0.7697010869565217, "bacc_std": 0.055565677264822325}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 2.782559402207126, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.045939507475673214, "f1": 0.8250265111346766, "f1_std": 0.05222977275672262, "bacc": 0.8165760869565217, "bacc_std": 0.05139437734216596}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 0.3593813663804626, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.03813600714970452, "f1": 0.8821428571428571, "f1_std": 0.04418446723805319, "bacc": 0.8695652173913043, "bacc_std": 0.045597399852907575}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.0498303369378418, "f1": 0.8074229691876751, "f1_std": 0.054379255472837715, "bacc": 0.8009510869565217, "bacc_std": 0.053777295347617576}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 35, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05688583899078882, "f1": 0.7758152173913043, "f1_std": 0.05873725405117418, "bacc": 0.7758152173913043, "bacc_std": 0.058762831721082986}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 36, "C": 0.3593813663804626, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.035979792308259026, "f1": 0.9252717391304348, "f1_std": 0.03705246831281112, "bacc": 0.9252717391304348, "bacc_std": 0.03727498515826867}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 37, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.06011843957438935, "f1": 0.7555555555555555, "f1_std": 0.06292865333477966, "bacc": 0.7540760869565217, "bacc_std": 0.0627078923575544}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 38, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04409090534208042, "f1": 0.8428571428571429, "f1_std": 0.05074466194245632, "bacc": 0.8322010869565217, "bacc_std": 0.050441006529502924}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 39, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.047545451069007316, "f1": 0.8328267477203647, "f1_std": 0.048523725641364576, "bacc": 0.8349184782608696, "bacc_std": 0.04831766565604586}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04765473622104646, "f1": 0.8328267477203647, "f1_std": 0.04866330498772358, "bacc": 0.8349184782608696, "bacc_std": 0.04840834013120089}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 41, "C": 166.81005372000556, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.051754027271729994, "f1": 0.8106060606060606, "f1_std": 0.054525964966139415, "bacc": 0.8070652173913043, "bacc_std": 0.05441409173470032}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 42, "C": 2.782559402207126, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.0440656910074827, "f1": 0.8683760683760684, "f1_std": 0.04611924236657043, "bacc": 0.8661684782608696, "bacc_std": 0.04647287192761871}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 43, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05739555083975016, "f1": 0.7727272727272727, "f1_std": 0.0605764611284322, "bacc": 0.7697010869565217, "bacc_std": 0.060242388016884287}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 0.000774263682681127, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04778580307169284, "f1": 0.8250265111346766, "f1_std": 0.053688991727804836, "bacc": 0.8165760869565217, "bacc_std": 0.0533121141149104}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04562225062473478, "f1": 0.8361469712015889, "f1_std": 0.04549945844739349, "bacc": 0.8532608695652174, "bacc_std": 0.041214952692404556}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_sex  | train   |        100 | 48.601 |  222.69 | 0.92707 |  0.059879 | 0.92447 | 0.062415 | 0.92295 |   0.064024 |
+| flat_mae | patch  | logistic | aabc_sex  | test    |        100 | 48.601 |  222.69 | 0.83    |  0.048096 | 0.82329 | 0.050205 | 0.82175 |   0.05046  |
+
+
+done! total time: 0:05:13

data_scaling/n100_1/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/abide_dx__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/abide_dx__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,abide_dx,86,0.046415888336127774,test,0.5967741935483871,0.04229229099033579,0.575109649122807,0.046302423147398096,0.5803571428571428,0.043275054042890386
+flat_mae,patch,logistic,abide_dx,87,0.046415888336127774,train,0.8304843304843305,0.014057598751624217,0.8266051446825204,0.01456555495075614,0.8238095238095238,0.014615419983092464
+flat_mae,patch,logistic,abide_dx,87,0.046415888336127774,test,0.6129032258064516,0.04334811775862246,0.6112852664576802,0.04340374643520204,0.6123949579831933,0.043336994569691284
+flat_mae,patch,logistic,abide_dx,88,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,88,21.54434690031882,test,0.6532258064516129,0.04201140769708803,0.6408702094699266,0.04403245814729608,0.641281512605042,0.042632654496572484
+flat_mae,patch,logistic,abide_dx,89,0.046415888336127774,train,0.8290598290598291,0.013471072160293994,0.8253731343283582,0.013880554867781243,0.82281284606866,0.013872785907616628
+flat_mae,patch,logistic,abide_dx,89,0.046415888336127774,test,0.6451612903225806,0.042155980488501624,0.6375232527238905,0.04324567946146056,0.6370798319327731,0.042520279992494615
+flat_mae,patch,logistic,abide_dx,90,0.005994842503189409,train,0.7307692307692307,0.016602432774135586,0.7228038433655146,0.017463002691974767,0.7209671465485419,0.017161058892233627
+flat_mae,patch,logistic,abide_dx,90,0.005994842503189409,test,0.6451612903225806,0.04162208888365049,0.6313513513513513,0.044689059767196886,0.6323529411764706,0.042720292647017676
+flat_mae,patch,logistic,abide_dx,91,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,91,21.54434690031882,test,0.6209677419354839,0.04307168902447912,0.6153389215233318,0.0440765702979215,0.6150210084033614,0.04364161769620354
+flat_mae,patch,logistic,abide_dx,92,0.046415888336127774,train,0.8304843304843305,0.013725276360614163,0.8262965173218997,0.014294612924173344,0.8232188999630861,0.014303592626264697
+flat_mae,patch,logistic,abide_dx,92,0.046415888336127774,test,0.5967741935483871,0.04407533931690264,0.5860042735042735,0.045495594106963855,0.5866596638655462,0.04455787723738546
+flat_mae,patch,logistic,abide_dx,93,0.046415888336127774,train,0.8262108262108262,0.013466745250890172,0.8223109289027943,0.013910672499754676,0.8196382428940568,0.013912663302811181
+flat_mae,patch,logistic,abide_dx,93,0.046415888336127774,test,0.6774193548387096,0.03966856706914758,0.6648648648648648,0.042001969120393834,0.6649159663865546,0.040471044778891255
+flat_mae,patch,logistic,abide_dx,94,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,94,2.782559402207126,test,0.532258064516129,0.045656099795001716,0.5291961246399581,0.04583128623186816,0.5294117647058824,0.045837414217238866
+flat_mae,patch,logistic,abide_dx,95,0.046415888336127774,train,0.8276353276353277,0.014133638675510447,0.8246894368275604,0.014417536256116848,0.8229974160206719,0.01441967433167325
+flat_mae,patch,logistic,abide_dx,95,0.046415888336127774,test,0.6370967741935484,0.042497781052436755,0.6190346145968457,0.046369912055995596,0.6218487394957983,0.04371573632814244
+flat_mae,patch,logistic,abide_dx,96,0.046415888336127774,train,0.8376068376068376,0.013853326082568446,0.8345120226308345,0.01426944374838113,0.8323366555924696,0.014361399249257698
+flat_mae,patch,logistic,abide_dx,96,0.046415888336127774,test,0.5967741935483871,0.04377530782806955,0.5810810810810811,0.04601122819692901,0.5835084033613446,0.04427082615087499
+flat_mae,patch,logistic,abide_dx,97,0.046415888336127774,train,0.8233618233618234,0.014192912981696726,0.8189155904673147,0.014779768284051795,0.8158730158730159,0.014784089426562803
+flat_mae,patch,logistic,abide_dx,97,0.046415888336127774,test,0.6854838709677419,0.041257355160488784,0.6838182412553122,0.0414973692037678,0.6848739495798319,0.04158528263363324
+flat_mae,patch,logistic,abide_dx,98,0.046415888336127774,train,0.8276353276353277,0.013894134154545719,0.824419828061987,0.0142551822082193,0.8224067921742341,0.014270304452547882
+flat_mae,patch,logistic,abide_dx,98,0.046415888336127774,test,0.6854838709677419,0.042673237549002915,0.6794591370053689,0.04394817526298988,0.6785714285714286,0.0434770135377859
+flat_mae,patch,logistic,abide_dx,99,0.046415888336127774,train,0.8376068376068376,0.014252639269792108,0.8341044776119404,0.014716586287897399,0.8314507198228129,0.014749307512276684
+flat_mae,patch,logistic,abide_dx,99,0.046415888336127774,test,0.6209677419354839,0.03970692582379918,0.6021028196900389,0.043327789247708734,0.6055672268907563,0.04067824674800565
+flat_mae,patch,logistic,abide_dx,100,0.3593813663804626,train,0.9430199430199431,0.008811152892149636,0.9423076923076923,0.008927890031547881,0.9415282392026578,0.008998866603267567
+flat_mae,patch,logistic,abide_dx,100,0.3593813663804626,test,0.5403225806451613,0.043174933954278336,0.5239442311578096,0.0454552281422764,0.5273109243697479,0.04376568664421706

data_scaling/n100_1/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/abide_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:27:14    time: 5.6563  data: 4.5977  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:02:12    time: 0.2333  data: 0.0662  max mem: 2851
+extract (train)  [ 40/289]  eta: 0:01:27    time: 0.2017  data: 0.0537  max mem: 2851
+extract (train)  [ 60/289]  eta: 0:01:08    time: 0.1954  data: 0.0528  max mem: 2851
+extract (train)  [ 80/289]  eta: 0:00:57    time: 0.1921  data: 0.0485  max mem: 2851
+extract (train)  [100/289]  eta: 0:00:48    time: 0.2028  data: 0.0536  max mem: 2851
+extract (train)  [120/289]  eta: 0:00:42    time: 0.1962  data: 0.0559  max mem: 2851
+extract (train)  [140/289]  eta: 0:00:35    time: 0.1928  data: 0.0530  max mem: 2851
+extract (train)  [160/289]  eta: 0:00:30    time: 0.1818  data: 0.0538  max mem: 2851
+extract (train)  [180/289]  eta: 0:00:24    time: 0.1720  data: 0.0541  max mem: 2851
+extract (train)  [200/289]  eta: 0:00:19    time: 0.1930  data: 0.0593  max mem: 2851
+extract (train)  [220/289]  eta: 0:00:15    time: 0.1644  data: 0.0495  max mem: 2851
+extract (train)  [240/289]  eta: 0:00:10    time: 0.1514  data: 0.0431  max mem: 2851
+extract (train)  [260/289]  eta: 0:00:06    time: 0.1699  data: 0.0540  max mem: 2851
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1693  data: 0.0527  max mem: 2851
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1533  data: 0.0454  max mem: 2851
+extract (train) Total time: 0:00:59 (0.2060 s / it)
+extract (validation)  [ 0/62]  eta: 0:03:47    time: 3.6731  data: 3.4786  max mem: 2851
+extract (validation)  [20/62]  eta: 0:00:15    time: 0.1991  data: 0.0646  max mem: 2851
+extract (validation)  [40/62]  eta: 0:00:05    time: 0.1585  data: 0.0464  max mem: 2851
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1439  data: 0.0393  max mem: 2851
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1438  data: 0.0394  max mem: 2851
+extract (validation) Total time: 0:00:14 (0.2292 s / it)
+extract (test)  [ 0/62]  eta: 0:03:41    time: 3.5699  data: 3.4403  max mem: 2851
+extract (test)  [20/62]  eta: 0:00:14    time: 0.1914  data: 0.0597  max mem: 2851
+extract (test)  [40/62]  eta: 0:00:05    time: 0.1486  data: 0.0446  max mem: 2851
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1508  data: 0.0470  max mem: 2851
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1512  data: 0.0471  max mem: 2851
+extract (test) Total time: 0:00:13 (0.2244 s / it)
+feature extraction time: 0:01:27
+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 |
+|:---------|:-------|:---------|:----------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | train   | 0.83191 |  0.013783 | 0.82814 | 0.014233 | 0.82546 |   0.014233 |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.64516 |  0.042547 | 0.62884 | 0.045943 | 0.63236 |   0.043281 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04146886800393249, "f1": 0.6743697478991597, "f1_std": 0.042101803153440975, "bacc": 0.6743697478991597, "bacc_std": 0.04199082684199459}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 166.81005372000556, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.0455501090554521, "f1": 0.607905138339921, "f1_std": 0.0463117668834, "bacc": 0.6076680672268908, "bacc_std": 0.04612301057932779}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.03995852102750445, "f1": 0.6575739206573719, "f1_std": 0.0428419115595645, "bacc": 0.657563025210084, "bacc_std": 0.04126270959722411}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.75, "acc_std": 0.039185967672560586, "f1": 0.743989343989344, "f1_std": 0.040534568921463954, "bacc": 0.7421218487394958, "bacc_std": 0.04011852093716733}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 166.81005372000556, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.044659222450059655, "f1": 0.5966692688004163, "f1_std": 0.04489314682196272, "bacc": 0.6039915966386555, "bacc_std": 0.04474387802729998}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.043163170796587685, "f1": 0.6169755573462261, "f1_std": 0.04548445342398305, "bacc": 0.6176470588235294, "bacc_std": 0.04408887390801531}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 2.782559402207126, "split": "test", "acc": 0.5403225806451613, "acc_std": 0.04471349422799569, "f1": 0.531517200238616, "f1_std": 0.04580299352797938, "bacc": 0.5320378151260504, "bacc_std": 0.04505668644572052}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 2.782559402207126, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04385751691332158, "f1": 0.6153389215233318, "f1_std": 0.0447049069090834, "bacc": 0.6150210084033614, "bacc_std": 0.044344378009941156}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04140343629387162, "f1": 0.6145945945945945, "f1_std": 0.04408104976490339, "bacc": 0.6160714285714286, "bacc_std": 0.04233808241921489}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.042767198863699764, "f1": 0.5808311791608669, "f1_std": 0.04401413339667804, "bacc": 0.5808823529411764, "bacc_std": 0.04348759078224899}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 21.54434690031882, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.03909238237868887, "f1": 0.6614052614052615, "f1_std": 0.04086012373219855, "bacc": 0.6607142857142857, "bacc_std": 0.040112417651475565}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 166.81005372000556, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04280479552913858, "f1": 0.6365057650967364, "f1_std": 0.04284617148876236, "bacc": 0.6391806722689075, "bacc_std": 0.04304017309731872}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04154595765287501, "f1": 0.6351748937561295, "f1_std": 0.0418265252425073, "bacc": 0.6360294117647058, "bacc_std": 0.04180595233866501}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04261491778100469, "f1": 0.6391534391534391, "f1_std": 0.04351267792688598, "bacc": 0.6386554621848739, "bacc_std": 0.043142452184953925}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.7016129032258065, "acc_std": 0.040825891072870185, "f1": 0.699246148803671, "f1_std": 0.04136071796031727, "bacc": 0.6995798319327731, "bacc_std": 0.04150559167230112}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 2.782559402207126, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.044721531152177835, "f1": 0.58994708994709, "f1_std": 0.04571495826405722, "bacc": 0.5898109243697479, "bacc_std": 0.04521711733573461}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.041257355160488784, "f1": 0.6838182412553122, "f1_std": 0.0414973692037678, "bacc": 0.6848739495798319, "bacc_std": 0.04158528263363324}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.042673237549002915, "f1": 0.6794591370053689, "f1_std": 0.04394817526298988, "bacc": 0.6785714285714286, "bacc_std": 0.0434770135377859}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.03970692582379918, "f1": 0.6021028196900389, "f1_std": 0.043327789247708734, "bacc": 0.6055672268907563, "bacc_std": 0.04067824674800565}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.5403225806451613, "acc_std": 0.043174933954278336, "f1": 0.5239442311578096, "f1_std": 0.0454552281422764, "bacc": 0.5273109243697479, "bacc_std": 0.04376568664421706}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | abide_dx  | train   |        100 | 273.58 |  1424.8 | 0.89637 |  0.089736 | 0.89403 | 0.092141 | 0.89277 |   0.093148 |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 273.58 |  1424.8 | 0.63137 |  0.040531 | 0.62356 | 0.042169 | 0.62425 |   0.041607 |
+
+
+done! total time: 0:05:37

data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:08:57    time: 3.5587  data: 2.9119  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:45    time: 0.1906  data: 0.0688  max mem: 2851
+extract (train)  [ 40/151]  eta: 0:00:28    time: 0.1500  data: 0.0460  max mem: 2851
+extract (train)  [ 60/151]  eta: 0:00:20    time: 0.1693  data: 0.0540  max mem: 2851
+extract (train)  [ 80/151]  eta: 0:00:15    time: 0.1931  data: 0.0651  max mem: 2851
+extract (train)  [100/151]  eta: 0:00:10    time: 0.1799  data: 0.0551  max mem: 2851
+extract (train)  [120/151]  eta: 0:00:06    time: 0.1709  data: 0.0454  max mem: 2851
+extract (train)  [140/151]  eta: 0:00:02    time: 0.1462  data: 0.0395  max mem: 2851
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1433  data: 0.0397  max mem: 2851
+extract (train) Total time: 0:00:29 (0.1949 s / it)
+extract (validation)  [ 0/32]  eta: 0:02:05    time: 3.9308  data: 3.7627  max mem: 2851
+extract (validation)  [20/32]  eta: 0:00:04    time: 0.1893  data: 0.0549  max mem: 2851
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1501  data: 0.0368  max mem: 2851
+extract (validation) Total time: 0:00:09 (0.3057 s / it)
+extract (test)  [ 0/33]  eta: 0:02:31    time: 4.5912  data: 4.4170  max mem: 2851
+extract (test)  [20/33]  eta: 0:00:05    time: 0.2187  data: 0.0624  max mem: 2851
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1505  data: 0.0376  max mem: 2851
+extract (test) Total time: 0:00:11 (0.3347 s / it)
+feature extraction time: 0:00:50
+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 |
+|:---------|:-------|:---------|:-----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | train   | 0.75342 |  0.022948 | 0.74065 | 0.024798 | 0.73707 |   0.024014 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | test    | 0.55385 |  0.058444 | 0.51679 | 0.064517 | 0.52558 |   0.059687 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.000774263682681127, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05592199977800863, "f1": 0.6233308138070043, "f1_std": 0.06137699339636488, "bacc": 0.6240347490347491, "bacc_std": 0.05772924777201049}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05212146941486298, "f1": 0.5626293995859213, "f1_std": 0.05930552243414796, "bacc": 0.5704633204633205, "bacc_std": 0.053839801358131795}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.058637500370532, "f1": 0.4715447154471545, "f1_std": 0.06344273228613832, "bacc": 0.4806949806949807, "bacc_std": 0.05942485940468532}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.06062131169772444, "f1": 0.49317738791423005, "f1_std": 0.06272135865273917, "bacc": 0.49372586872586877, "bacc_std": 0.06175414049858046}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.06371869390267444, "f1": 0.4980694980694981, "f1_std": 0.06477355862271233, "bacc": 0.4980694980694981, "bacc_std": 0.06442136154128993}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.05555027362136288, "f1": 0.675, "f1_std": 0.060370413881913915, "bacc": 0.6732625482625483, "bacc_std": 0.05797823497514282}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.06097727969541815, "f1": 0.6285714285714286, "f1_std": 0.061214017037599454, "bacc": 0.6322393822393823, "bacc_std": 0.06142607258506847}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.000774263682681127, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.057451320273631944, "f1": 0.5751633986928104, "f1_std": 0.06647510658167832, "bacc": 0.583976833976834, "bacc_std": 0.059476807941463546}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05804470017955912, "f1": 0.5775, "f1_std": 0.06307479098407857, "bacc": 0.5791505791505791, "bacc_std": 0.0598251637618992}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05567227859132672, "f1": 0.5376016260162602, "f1_std": 0.060809228779566224, "bacc": 0.5434362934362934, "bacc_std": 0.056797484303906326}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.000774263682681127, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05398413968267075, "f1": 0.6091503267973856, "f1_std": 0.06379710248533253, "bacc": 0.6153474903474904, "bacc_std": 0.05668988131247386}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05694594966660888, "f1": 0.5167905665214048, "f1_std": 0.06257514469709816, "bacc": 0.5255791505791506, "bacc_std": 0.057944237975501064}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.061208739755734286, "f1": 0.5644080416976918, "f1_std": 0.06512440546714307, "bacc": 0.5656370656370656, "bacc_std": 0.0627093391651264}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05496766128354243, "f1": 0.5578231292517006, "f1_std": 0.05927631735720825, "bacc": 0.5612934362934363, "bacc_std": 0.05608855919247021}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.000774263682681127, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05522630365491262, "f1": 0.5376016260162602, "f1_std": 0.06026844468590824, "bacc": 0.5434362934362934, "bacc_std": 0.056317148832879395}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.06342650594826058, "f1": 0.5115151515151515, "f1_std": 0.06447582847402317, "bacc": 0.5115830115830116, "bacc_std": 0.06411283571023087}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05643513111235446, "f1": 0.4953416149068323, "f1_std": 0.061578086433661056, "bacc": 0.5077220077220077, "bacc_std": 0.056904330535566865}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05677131536718794, "f1": 0.5125, "f1_std": 0.06028027939175216, "bacc": 0.5164092664092664, "bacc_std": 0.05723652531008247}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 19, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05722159562540318, "f1": 0.61, "f1_std": 0.062315168681252184, "bacc": 0.6105212355212355, "bacc_std": 0.05932084293447266}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05986892190987647, "f1": 0.588206627680312, "f1_std": 0.061730355708242535, "bacc": 0.5878378378378378, "bacc_std": 0.06078789253734003}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05885387380231196, "f1": 0.5921814671814671, "f1_std": 0.0602932980842606, "bacc": 0.5921814671814671, "bacc_std": 0.0603010309852992}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 22, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.0623122429487318, "f1": 0.5699583435432491, "f1_std": 0.06506969792083162, "bacc": 0.5699806949806949, "bacc_std": 0.06369169304035663}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05612273954014651, "f1": 0.4953416149068323, "f1_std": 0.06182355030375369, "bacc": 0.5077220077220077, "bacc_std": 0.05655550944069973}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05512124559529049, "f1": 0.5834401435529352, "f1_std": 0.061693828298321825, "bacc": 0.5883204633204633, "bacc_std": 0.05688057649079197}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 25, "C": 0.000774263682681127, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05180993028116951, "f1": 0.5308740978348035, "f1_std": 0.06106492626984262, "bacc": 0.5482625482625483, "bacc_std": 0.0531198821892957}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.0591932828429697, "f1": 0.5644080416976918, "f1_std": 0.06309039050705442, "bacc": 0.5656370656370656, "bacc_std": 0.06057421999314855}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 27, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05686609205876119, "f1": 0.578226387887527, "f1_std": 0.05762754332711848, "bacc": 0.5786679536679536, "bacc_std": 0.05744637902108996}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 28, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05602112027762175, "f1": 0.5045731707317074, "f1_std": 0.060522039312995976, "bacc": 0.5120656370656371, "bacc_std": 0.05672597675969969}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 29, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.06093861495717972, "f1": 0.5321419707123356, "f1_std": 0.0630920390037712, "bacc": 0.5342664092664092, "bacc_std": 0.06132734576405493}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 30, "C": 0.000774263682681127, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.055466358480406146, "f1": 0.4953416149068323, "f1_std": 0.06114050835152842, "bacc": 0.5077220077220077, "bacc_std": 0.05629714676004816}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05971225677754489, "f1": 0.5512820512820513, "f1_std": 0.0619344503175325, "bacc": 0.5521235521235521, "bacc_std": 0.06019661123843654}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 32, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.061004328103318024, "f1": 0.5192307692307693, "f1_std": 0.06329651155796842, "bacc": 0.5207528957528957, "bacc_std": 0.06170166317374117}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 33, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05839940828102597, "f1": 0.588206627680312, "f1_std": 0.06118411978512759, "bacc": 0.5878378378378378, "bacc_std": 0.060118277437519206}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06026418367753376, "f1": 0.545, "f1_std": 0.06455288468654581, "bacc": 0.5477799227799228, "bacc_std": 0.06164694593984234}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05755293113001982, "f1": 0.5644080416976918, "f1_std": 0.061254362284043046, "bacc": 0.5656370656370656, "bacc_std": 0.05863947982704052}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 36, "C": 0.000774263682681127, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.05655762940388309, "f1": 0.48, "f1_std": 0.060199042248191705, "bacc": 0.48503861003861004, "bacc_std": 0.057386717107884705}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 37, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.06328637644696512, "f1": 0.6139225469232596, "f1_std": 0.06359639287717067, "bacc": 0.6187258687258688, "bacc_std": 0.06406211548691089}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.059396340014352664, "f1": 0.5578231292517006, "f1_std": 0.06487338644697087, "bacc": 0.5612934362934363, "bacc_std": 0.060899097399728416}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.050336467893565995, "f1": 0.587737843551797, "f1_std": 0.06034799586167174, "bacc": 0.5974903474903475, "bacc_std": 0.052700909524064345}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.47692307692307695, "acc_std": 0.05648727981647804, "f1": 0.4475, "f1_std": 0.05853290819502478, "bacc": 0.45366795366795365, "bacc_std": 0.056497020949600026}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.000774263682681127, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.058690039274643294, "f1": 0.5501153550371699, "f1_std": 0.06433918671253566, "bacc": 0.556949806949807, "bacc_std": 0.05978113728777054}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.055750381960641276, "f1": 0.5411764705882354, "f1_std": 0.06305705051567974, "bacc": 0.5526061776061776, "bacc_std": 0.05693091925517808}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.06170662408219479, "f1": 0.6198830409356726, "f1_std": 0.0636041754942183, "bacc": 0.6192084942084942, "bacc_std": 0.06268430171898218}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.000774263682681127, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05749319486357757, "f1": 0.5376016260162602, "f1_std": 0.06369301065650654, "bacc": 0.5434362934362934, "bacc_std": 0.05917749624299527}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05732402658282685, "f1": 0.5966741126830479, "f1_std": 0.060057977956150636, "bacc": 0.597007722007722, "bacc_std": 0.05813559242875428}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset    | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:-----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | adhd200_dx | train   |        100 | 1.6858 |  16.679 | 0.75734 |  0.07425  | 0.74524 | 0.080653 | 0.74316 |   0.079428 |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 1.6858 |  16.679 | 0.58846 |  0.052815 | 0.56484 | 0.058318 | 0.56854 |   0.054735 |
+
+
+done! total time: 0:04:44

data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,adni_ad_vs_cn,79,166.81005372000556,test,0.7317073170731707,0.07310011226290623,0.6835087719298245,0.0805400547130764,0.7209677419354839,0.08809901840965685
+flat_mae,patch,logistic,adni_ad_vs_cn,80,0.046415888336127774,train,0.9105691056910569,0.012735784972217341,0.8583822759783684,0.022770113024795417,0.8202810419919468,0.025352930869899863
+flat_mae,patch,logistic,adni_ad_vs_cn,80,0.046415888336127774,test,0.6829268292682927,0.06070582267462663,0.5176470588235295,0.08435734053755477,0.5193548387096775,0.07491420351052974
+flat_mae,patch,logistic,adni_ad_vs_cn,81,0.046415888336127774,train,0.9051490514905149,0.013827615252607776,0.8482106129164952,0.025599237060506364,0.8086531350152026,0.02769088952017803
+flat_mae,patch,logistic,adni_ad_vs_cn,81,0.046415888336127774,test,0.7560975609756098,0.046145516416076275,0.569327731092437,0.09205897134972776,0.567741935483871,0.0708568808587876
+flat_mae,patch,logistic,adni_ad_vs_cn,82,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,82,166.81005372000556,test,0.5365853658536586,0.07082409441561388,0.4533333333333333,0.07279841270606609,0.4564516129032258,0.08326315109882226
+flat_mae,patch,logistic,adni_ad_vs_cn,83,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,83,21.54434690031882,test,0.6585365853658537,0.07004815204555381,0.5370967741935484,0.08721829744942854,0.5370967741935484,0.08766481572873681
+flat_mae,patch,logistic,adni_ad_vs_cn,84,0.046415888336127774,train,0.8997289972899729,0.013564578752247886,0.8378060515342671,0.02525686517462975,0.7970252280384584,0.02687507504256071
+flat_mae,patch,logistic,adni_ad_vs_cn,84,0.046415888336127774,test,0.7073170731707317,0.05631768054685203,0.5340909090909092,0.08489509552642165,0.535483870967742,0.07134598877627318
+flat_mae,patch,logistic,adni_ad_vs_cn,85,0.005994842503189409,train,0.8319783197831978,0.012283487142275158,0.6783906882591093,0.03195450562114724,0.6476292217930808,0.024713430931630455
+flat_mae,patch,logistic,adni_ad_vs_cn,85,0.005994842503189409,test,0.8292682926829268,0.04609368934573893,0.7144278606965174,0.09715194382900151,0.6838709677419355,0.08277197662532954
+flat_mae,patch,logistic,adni_ad_vs_cn,86,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,86,2.782559402207126,test,0.6097560975609756,0.07384822933293496,0.5287356321839081,0.07901515972749347,0.5387096774193548,0.08786209152980412
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.005994842503189409,train,0.8373983739837398,0.014043630283189184,0.709204581275612,0.0307708015546038,0.6754458049141261,0.026053176572979966
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.005994842503189409,test,0.8048780487804879,0.04219653176835521,0.6554621848739496,0.09444583145394742,0.6338709677419355,0.07375745497515536
+flat_mae,patch,logistic,adni_ad_vs_cn,88,0.046415888336127774,train,0.8915989159891599,0.01365721484549814,0.825597882597599,0.024737636935395466,0.7876777056454927,0.025571413476553846
+flat_mae,patch,logistic,adni_ad_vs_cn,88,0.046415888336127774,test,0.7073170731707317,0.053473650435502104,0.5340909090909092,0.08129251418354416,0.535483870967742,0.06872655032869937
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,train,0.8861788617886179,0.013340822361947112,0.8148829431438127,0.025130247744407273,0.7760497986687485,0.02574157524931779
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,test,0.7804878048780488,0.051633633648387287,0.6328358208955224,0.09771917744260804,0.6177419354838709,0.08073474405101133
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,train,0.9051490514905149,0.013459861891547254,0.849799383613421,0.024147585760063126,0.8127003040512779,0.026411975974960488
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,test,0.7560975609756098,0.061220658817364285,0.6440972222222222,0.09100646715308956,0.635483870967742,0.085117169105809
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,train,0.8915989159891599,0.013883568020373735,0.825597882597599,0.02535716243262587,0.7876777056454927,0.02652909974683188
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,test,0.6829268292682927,0.05805337141099752,0.5176470588235295,0.07961094387672903,0.5193548387096775,0.07112574339554702
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.046415888336127774,train,0.9024390243902439,0.012441917111142798,0.8478699038021071,0.021445866844248384,0.8149806886350563,0.02386002787752125
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.046415888336127774,test,0.7560975609756098,0.045762753689251284,0.569327731092437,0.09125824157995051,0.567741935483871,0.06901454845192348
+flat_mae,patch,logistic,adni_ad_vs_cn,93,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,93,166.81005372000556,test,0.5853658536585366,0.07260809701767604,0.4863669859985261,0.07591322933603062,0.4887096774193548,0.08225891580837169
+flat_mae,patch,logistic,adni_ad_vs_cn,94,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,94,21.54434690031882,test,0.5365853658536586,0.07686597651901969,0.4533333333333333,0.07648612329943084,0.4564516129032258,0.08669000592912471
+flat_mae,patch,logistic,adni_ad_vs_cn,95,0.005994842503189409,train,0.8319783197831978,0.012824672903222692,0.6829193923938353,0.03311040816565228,0.6516763908291561,0.026039053136544622
+flat_mae,patch,logistic,adni_ad_vs_cn,95,0.005994842503189409,test,0.7804878048780488,0.0567092630140812,0.6660633484162897,0.09017070643628242,0.6516129032258065,0.08195789000218845
+flat_mae,patch,logistic,adni_ad_vs_cn,96,0.046415888336127774,train,0.8943089430894309,0.013953704008901753,0.8326335988835263,0.025365678466860674,0.79753882816994,0.02756974115611603
+flat_mae,patch,logistic,adni_ad_vs_cn,96,0.046415888336127774,test,0.7804878048780488,0.04687688128289074,0.6328358208955224,0.09245257687895993,0.6177419354838709,0.07655559629198395
+flat_mae,patch,logistic,adni_ad_vs_cn,97,0.046415888336127774,train,0.8943089430894309,0.012891983737511985,0.82903881107666,0.024041116410567706,0.7894444900977895,0.025393370243744207
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+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.005994842503189409,train,0.8482384823848238,0.013461498713769995,0.7320261437908497,0.029606614465914935,0.6946544498315391,0.026096245857829485
+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.005994842503189409,test,0.7804878048780488,0.03965646651063933,0.5886287625418061,0.09639304794876444,0.5838709677419355,0.06909493807987836
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+flat_mae,patch,logistic,adni_ad_vs_cn,100,0.005994842503189409,train,0.8319783197831978,0.013565626916949706,0.6995114006514658,0.030059363516223896,0.6678650669734572,0.02513195868951685
+flat_mae,patch,logistic,adni_ad_vs_cn,100,0.005994842503189409,test,0.7317073170731707,0.05093437473304996,0.5512437810945273,0.08953289818875948,0.5516129032258065,0.07170840616753693

data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt

@@ -0,0 +1,240 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:13:44    time: 5.0250  data: 3.8927  max mem: 2698
+extract (train)  [ 20/164]  eta: 0:01:06    time: 0.2328  data: 0.0694  max mem: 2851
+extract (train)  [ 40/164]  eta: 0:00:41    time: 0.1965  data: 0.0511  max mem: 2851
+extract (train)  [ 60/164]  eta: 0:00:30    time: 0.2030  data: 0.0542  max mem: 2851
+extract (train)  [ 80/164]  eta: 0:00:22    time: 0.1988  data: 0.0534  max mem: 2851
+extract (train)  [100/164]  eta: 0:00:16    time: 0.1840  data: 0.0487  max mem: 2851
+extract (train)  [120/164]  eta: 0:00:10    time: 0.1954  data: 0.0534  max mem: 2851
+extract (train)  [140/164]  eta: 0:00:05    time: 0.1655  data: 0.0420  max mem: 2851
+extract (train)  [160/164]  eta: 0:00:00    time: 0.1535  data: 0.0386  max mem: 2851
+extract (train)  [163/164]  eta: 0:00:00    time: 0.1538  data: 0.0389  max mem: 2851
+extract (train) Total time: 0:00:36 (0.2225 s / it)
+extract (validation)  [ 0/21]  eta: 0:01:22    time: 3.9315  data: 3.7871  max mem: 2851
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.1524  data: 0.0426  max mem: 2851
+extract (validation) Total time: 0:00:07 (0.3475 s / it)
+extract (test)  [ 0/21]  eta: 0:01:26    time: 4.1077  data: 3.9786  max mem: 2851
+extract (test)  [20/21]  eta: 0:00:00    time: 0.1403  data: 0.0363  max mem: 2851
+extract (test) Total time: 0:00:07 (0.3466 s / it)
+feature extraction time: 0:00:51
+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 |
+|:---------|:-------|:---------|:--------------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 0.0059948 | train   | 0.8374  |  0.013887 | 0.71289 | 0.030641 | 0.67902 |   0.026099 |
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 0.0059948 | test    | 0.80488 |  0.045315 | 0.65546 | 0.089037 | 0.63542 |   0.074996 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.02458149408150368, "f1": 0.5275288092189501, "f1_std": 0.0853385069223417, "bacc": 0.55, "bacc_std": 0.05039206286708255}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.053691586274246486, "f1": 0.5340909090909092, "f1_std": 0.0815666886203472, "bacc": 0.535483870967742, "bacc_std": 0.06864631115638925}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.034496152437859806, "f1": 0.4142857142857143, "f1_std": 0.012060548476957443, "bacc": 0.46774193548387094, "bacc_std": 0.02281197177342343}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.05483638526850474, "f1": 0.6660633484162897, "f1_std": 0.08796725538082396, "bacc": 0.6516129032258065, "bacc_std": 0.08135661019789861}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 166.81005372000556, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.05042770076222182, "f1": 0.5340909090909092, "f1_std": 0.08100053170660704, "bacc": 0.535483870967742, "bacc_std": 0.06748618388684266}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 166.81005372000556, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.059182835108209944, "f1": 0.6917293233082706, "f1_std": 0.08855310311989986, "bacc": 0.685483870967742, "bacc_std": 0.0871094635917502}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06159990651815801, "f1": 0.6232247284878863, "f1_std": 0.0828401107440817, "bacc": 0.6193548387096774, "bacc_std": 0.08152641828420908}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.0567092630140812, "f1": 0.6660633484162897, "f1_std": 0.09017070643628242, "bacc": 0.6516129032258065, "bacc_std": 0.08195789000218845}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04687688128289074, "f1": 0.6328358208955224, "f1_std": 0.09245257687895993, "bacc": 0.6177419354838709, "bacc_std": 0.07655559629198395}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.047619451231965614, "f1": 0.6328358208955224, "f1_std": 0.09322432896810552, "bacc": 0.6177419354838709, "bacc_std": 0.07658120736538476}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.03965646651063933, "f1": 0.5886287625418061, "f1_std": 0.09639304794876444, "bacc": 0.5838709677419355, "bacc_std": 0.06909493807987836}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05249331279469613, "f1": 0.6117424242424243, "f1_std": 0.09244498718636744, "bacc": 0.6016129032258064, "bacc_std": 0.07880278278336156}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.05093437473304996, "f1": 0.5512437810945273, "f1_std": 0.08953289818875948, "bacc": 0.5516129032258065, "bacc_std": 0.07170840616753693}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset       | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:--------------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | adni_ad_vs_cn | train   |        100 | 56.929 |  188.42 | 0.91794 |  0.06567  | 0.859   | 0.11796  | 0.83533 |   0.13249  |
+| flat_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 56.929 |  188.42 | 0.7322  |  0.060541 | 0.58168 | 0.073814 | 0.58039 |   0.065063 |
+
+
+done! total time: 0:04:35

data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn
+model: flat_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 16, "eval/id_best": 39, "eval/lr_best": 0.0036, "eval/wd_best": 0.05, "eval/train/loss": 2.4101665985654108e-05, "eval/train/acc": 1.0, "eval/train/acc_std": 0.0, "eval/train/f1": 1.0, "eval/train/f1_std": 0.0, "eval/validation/loss": 0.21449443697929382, "eval/validation/acc": 0.9749503968253969, "eval/validation/acc_std": 0.0024293595210807374, "eval/validation/f1": 0.9719248259294446, "eval/validation/f1_std": 0.003113844692680927, "eval/test/loss": 0.22988229990005493, "eval/test/acc": 0.9753968253968254, "eval/test/acc_std": 0.0021664230341538883, "eval/test/f1": 0.9700663006260797, "eval/test/f1_std": 0.002871808961477576}

data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 16, "eval/best/id_best": 39, "eval/best/lr_best": 0.0036, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.4101665985654108e-05, "eval/best/train/acc": 1.0, "eval/best/train/acc_std": 0.0, "eval/best/train/f1": 1.0, "eval/best/train/f1_std": 0.0, "eval/best/validation/loss": 0.21449443697929382, "eval/best/validation/acc": 0.9749503968253969, "eval/best/validation/acc_std": 0.0024293595210807374, "eval/best/validation/f1": 0.9719248259294446, "eval/best/validation/f1_std": 0.003113844692680927, "eval/best/test/loss": 0.22988229990005493, "eval/best/test/acc": 0.9753968253968254, "eval/best/test/acc_std": 0.0021664230341538883, "eval/best/test/f1": 0.9700663006260797, "eval/best/test/f1_std": 0.002871808961477576}

data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 39, "eval/last/lr_best": 0.0036, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.4328135623363778e-05, "eval/last/train/acc": 1.0, "eval/last/train/acc_std": 0.0, "eval/last/train/f1": 1.0, "eval/last/train/f1_std": 0.0, "eval/last/validation/loss": 0.21238309144973755, "eval/last/validation/acc": 0.9749503968253969, "eval/last/validation/acc_std": 0.0024293595210807374, "eval/last/validation/f1": 0.9719248259294446, "eval/last/validation/f1_std": 0.003113844692680927, "eval/last/test/loss": 0.2277032732963562, "eval/last/test/acc": 0.9751984126984127, "eval/last/test/acc_std": 0.002180191052337231, "eval/last/test/f1": 0.9698498582055118, "eval/last/test/f1_std": 0.0028872480090381815}

data_scaling/n100_1/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
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",train,2.4101665985654108e-05,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",validation,0.21449443697929382,0.9749503968253969,0.0024293595210807374,0.9719248259294446,0.003113844692680927
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",test,0.22988229990005493,0.9753968253968254,0.0021664230341538883,0.9700663006260797,0.002871808961477576

data_scaling/n100_1/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
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",train,2.4101665985654108e-05,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",validation,0.21449443697929382,0.9749503968253969,0.0024293595210807374,0.9719248259294446,0.003113844692680927
+flat_mae,patch,attn,hcpya_task21,best,16,0.0036,0.05,39,"[12, 1.0]",test,0.22988229990005493,0.9753968253968254,0.0021664230341538883,0.9700663006260797,0.002871808961477576

data_scaling/n100_1/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
+flat_mae,patch,attn,hcpya_task21,last,19,0.0036,0.05,39,"[12, 1.0]",train,2.4328135623363778e-05,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,last,19,0.0036,0.05,39,"[12, 1.0]",validation,0.21238309144973755,0.9749503968253969,0.0024293595210807374,0.9719248259294446,0.003113844692680927
+flat_mae,patch,attn,hcpya_task21,last,19,0.0036,0.05,39,"[12, 1.0]",test,0.2277032732963562,0.9751984126984127,0.002180191052337231,0.9698498582055118,0.0028872480090381815

data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/log.txt

@@ -0,0 +1,895 @@
+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 19:01:11
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn
+model: flat_mae
+representation: patch
+classifier: attn
+dataset: hcpya_task21
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:19:49  lr: nan  time: 2.9742  data: 2.4878  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:28  lr: 0.000003  loss: 3.0604 (3.0570)  grad: 0.2805 (0.2881)  time: 0.4268  data: 0.0033  max mem: 22446
+train: [0]  [ 40/400]  eta: 0:02:55  lr: 0.000006  loss: 3.0244 (3.0203)  grad: 0.2816 (0.2868)  time: 0.4262  data: 0.0044  max mem: 22446
+train: [0]  [ 60/400]  eta: 0:02:38  lr: 0.000009  loss: 2.9243 (2.9732)  grad: 0.2784 (0.2804)  time: 0.4181  data: 0.0043  max mem: 22446
+train: [0]  [ 80/400]  eta: 0:02:26  lr: 0.000012  loss: 2.8302 (2.9285)  grad: 0.2563 (0.2722)  time: 0.4360  data: 0.0044  max mem: 22446
+train: [0]  [100/400]  eta: 0:02:15  lr: 0.000015  loss: 2.7064 (2.8730)  grad: 0.2426 (0.2679)  time: 0.4313  data: 0.0043  max mem: 22446
+train: [0]  [120/400]  eta: 0:02:05  lr: 0.000018  loss: 2.6081 (2.8204)  grad: 0.2477 (0.2634)  time: 0.4256  data: 0.0036  max mem: 22446
+train: [0]  [140/400]  eta: 0:01:55  lr: 0.000021  loss: 2.4951 (2.7669)  grad: 0.2421 (0.2615)  time: 0.4294  data: 0.0043  max mem: 22446
+train: [0]  [160/400]  eta: 0:01:46  lr: 0.000024  loss: 2.4289 (2.7227)  grad: 0.2345 (0.2566)  time: 0.4315  data: 0.0041  max mem: 22446
+train: [0]  [180/400]  eta: 0:01:37  lr: 0.000027  loss: 2.3472 (2.6761)  grad: 0.2147 (0.2527)  time: 0.4268  data: 0.0041  max mem: 22446
+train: [0]  [200/400]  eta: 0:01:28  lr: 0.000030  loss: 2.2926 (2.6308)  grad: 0.2245 (0.2498)  time: 0.4536  data: 0.0041  max mem: 22446
+train: [0]  [220/400]  eta: 0:01:19  lr: 0.000033  loss: 2.1697 (2.5858)  grad: 0.2164 (0.2468)  time: 0.4279  data: 0.0042  max mem: 22446
+train: [0]  [240/400]  eta: 0:01:10  lr: 0.000036  loss: 2.0502 (2.5381)  grad: 0.2185 (0.2450)  time: 0.4361  data: 0.0044  max mem: 22446
+train: [0]  [260/400]  eta: 0:01:01  lr: 0.000039  loss: 1.9947 (2.4956)  grad: 0.2185 (0.2429)  time: 0.4371  data: 0.0042  max mem: 22446
+train: [0]  [280/400]  eta: 0:00:52  lr: 0.000042  loss: 1.9811 (2.4584)  grad: 0.2044 (0.2399)  time: 0.4263  data: 0.0043  max mem: 22446
+train: [0]  [300/400]  eta: 0:00:45  lr: 0.000045  loss: 1.9259 (2.4211)  grad: 0.1916 (0.2368)  time: 0.5963  data: 0.1548  max mem: 22446
+train: [0]  [320/400]  eta: 0:00:36  lr: 0.000048  loss: 1.8677 (2.3855)  grad: 0.1904 (0.2342)  time: 0.4487  data: 0.0055  max mem: 22446
+train: [0]  [340/400]  eta: 0:00:26  lr: 0.000051  loss: 1.7898 (2.3497)  grad: 0.2030 (0.2327)  time: 0.4318  data: 0.0041  max mem: 22446
+train: [0]  [360/400]  eta: 0:00:17  lr: 0.000054  loss: 1.7702 (2.3185)  grad: 0.1980 (0.2307)  time: 0.4327  data: 0.0041  max mem: 22446
+train: [0]  [380/400]  eta: 0:00:08  lr: 0.000057  loss: 1.7554 (2.2876)  grad: 0.1919 (0.2287)  time: 0.4349  data: 0.0042  max mem: 22446
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 1.6936 (2.2556)  grad: 0.1919 (0.2271)  time: 0.4288  data: 0.0043  max mem: 22446
+train: [0] Total time: 0:02:58 (0.4472 s / it)
+train: [0] Summary: lr: 0.000060  loss: 1.6936 (2.2556)  grad: 0.1919 (0.2271)
+eval (validation): [0]  [ 0/63]  eta: 0:03:04    time: 2.9235  data: 2.6993  max mem: 22446
+eval (validation): [0]  [20/63]  eta: 0:00:20    time: 0.3531  data: 0.0035  max mem: 22446
+eval (validation): [0]  [40/63]  eta: 0:00:09    time: 0.3226  data: 0.0033  max mem: 22446
+eval (validation): [0]  [60/63]  eta: 0:00:01    time: 0.3329  data: 0.0035  max mem: 22446
+eval (validation): [0]  [62/63]  eta: 0:00:00    time: 0.3287  data: 0.0035  max mem: 22446
+eval (validation): [0] Total time: 0:00:23 (0.3807 s / it)
+cv: [0]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.236  acc: 0.927  f1: 0.908
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:19:59  lr: nan  time: 2.9992  data: 2.6746  max mem: 22446
+train: [1]  [ 20/400]  eta: 0:03:29  lr: 0.000063  loss: 1.6350 (1.6435)  grad: 0.1856 (0.1887)  time: 0.4281  data: 0.0027  max mem: 22446
+train: [1]  [ 40/400]  eta: 0:02:56  lr: 0.000066  loss: 1.6187 (1.6202)  grad: 0.1856 (0.1876)  time: 0.4280  data: 0.0040  max mem: 22446
+train: [1]  [ 60/400]  eta: 0:02:41  lr: 0.000069  loss: 1.5733 (1.6040)  grad: 0.1840 (0.1872)  time: 0.4419  data: 0.0042  max mem: 22446
+train: [1]  [ 80/400]  eta: 0:02:28  lr: 0.000072  loss: 1.5458 (1.5897)  grad: 0.1853 (0.1876)  time: 0.4351  data: 0.0041  max mem: 22446
+train: [1]  [100/400]  eta: 0:02:17  lr: 0.000075  loss: 1.5351 (1.5794)  grad: 0.1857 (0.1875)  time: 0.4328  data: 0.0043  max mem: 22446
+train: [1]  [120/400]  eta: 0:02:07  lr: 0.000078  loss: 1.4814 (1.5608)  grad: 0.1768 (0.1860)  time: 0.4386  data: 0.0042  max mem: 22446
+train: [1]  [140/400]  eta: 0:01:57  lr: 0.000081  loss: 1.4694 (1.5467)  grad: 0.1758 (0.1843)  time: 0.4278  data: 0.0041  max mem: 22446
+train: [1]  [160/400]  eta: 0:01:47  lr: 0.000084  loss: 1.4384 (1.5305)  grad: 0.1777 (0.1840)  time: 0.4299  data: 0.0040  max mem: 22446
+train: [1]  [180/400]  eta: 0:01:38  lr: 0.000087  loss: 1.4133 (1.5177)  grad: 0.1787 (0.1831)  time: 0.4468  data: 0.0042  max mem: 22446
+train: [1]  [200/400]  eta: 0:01:29  lr: 0.000090  loss: 1.3917 (1.5038)  grad: 0.1755 (0.1827)  time: 0.4509  data: 0.0044  max mem: 22446
+train: [1]  [220/400]  eta: 0:01:20  lr: 0.000093  loss: 1.3538 (1.4878)  grad: 0.1784 (0.1831)  time: 0.4201  data: 0.0041  max mem: 22446
+train: [1]  [240/400]  eta: 0:01:11  lr: 0.000096  loss: 1.3241 (1.4756)  grad: 0.1814 (0.1829)  time: 0.4406  data: 0.0042  max mem: 22446
+train: [1]  [260/400]  eta: 0:01:02  lr: 0.000099  loss: 1.3241 (1.4638)  grad: 0.1730 (0.1820)  time: 0.4256  data: 0.0042  max mem: 22446
+train: [1]  [280/400]  eta: 0:00:53  lr: 0.000102  loss: 1.3022 (1.4503)  grad: 0.1716 (0.1817)  time: 0.4322  data: 0.0039  max mem: 22446
+train: [1]  [300/400]  eta: 0:00:45  lr: 0.000105  loss: 1.2647 (1.4368)  grad: 0.1678 (0.1802)  time: 0.6038  data: 0.1630  max mem: 22446
+train: [1]  [320/400]  eta: 0:00:36  lr: 0.000108  loss: 1.2554 (1.4253)  grad: 0.1572 (0.1790)  time: 0.4467  data: 0.0031  max mem: 22446
+train: [1]  [340/400]  eta: 0:00:27  lr: 0.000111  loss: 1.2301 (1.4130)  grad: 0.1544 (0.1775)  time: 0.4254  data: 0.0041  max mem: 22446
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 1.2083 (1.4029)  grad: 0.1539 (0.1762)  time: 0.4244  data: 0.0039  max mem: 22446
+train: [1]  [380/400]  eta: 0:00:08  lr: 0.000117  loss: 1.1917 (1.3920)  grad: 0.1584 (0.1757)  time: 0.4303  data: 0.0043  max mem: 22446
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 1.1857 (1.3825)  grad: 0.1600 (0.1749)  time: 0.4356  data: 0.0042  max mem: 22446
+train: [1] Total time: 0:02:59 (0.4493 s / it)
+train: [1] Summary: lr: 0.000120  loss: 1.1857 (1.3825)  grad: 0.1600 (0.1749)
+eval (validation): [1]  [ 0/63]  eta: 0:03:08    time: 2.9965  data: 2.7194  max mem: 22446
+eval (validation): [1]  [20/63]  eta: 0:00:19    time: 0.3205  data: 0.0056  max mem: 22446
+eval (validation): [1]  [40/63]  eta: 0:00:09    time: 0.3399  data: 0.0031  max mem: 22446
+eval (validation): [1]  [60/63]  eta: 0:00:01    time: 0.3141  data: 0.0034  max mem: 22446
+eval (validation): [1]  [62/63]  eta: 0:00:00    time: 0.3104  data: 0.0038  max mem: 22446
+eval (validation): [1] Total time: 0:00:23 (0.3710 s / it)
+cv: [1]  best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00')  loss: 0.198  acc: 0.948  f1: 0.941
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:20:35  lr: nan  time: 3.0893  data: 2.7634  max mem: 22446
+train: [2]  [ 20/400]  eta: 0:03:31  lr: 0.000123  loss: 1.1451 (1.1382)  grad: 0.1636 (0.1627)  time: 0.4289  data: 0.0025  max mem: 22446
+train: [2]  [ 40/400]  eta: 0:02:57  lr: 0.000126  loss: 1.1429 (1.1418)  grad: 0.1658 (0.1686)  time: 0.4290  data: 0.0037  max mem: 22446
+train: [2]  [ 60/400]  eta: 0:02:42  lr: 0.000129  loss: 1.1355 (1.1366)  grad: 0.1788 (0.1718)  time: 0.4481  data: 0.0042  max mem: 22446
+train: [2]  [ 80/400]  eta: 0:02:29  lr: 0.000132  loss: 1.1061 (1.1357)  grad: 0.1803 (0.1766)  time: 0.4295  data: 0.0041  max mem: 22446
+train: [2]  [100/400]  eta: 0:02:17  lr: 0.000135  loss: 1.1368 (1.1325)  grad: 0.1904 (0.1796)  time: 0.4318  data: 0.0042  max mem: 22446
+train: [2]  [120/400]  eta: 0:02:07  lr: 0.000138  loss: 1.1125 (1.1305)  grad: 0.1904 (0.1824)  time: 0.4266  data: 0.0041  max mem: 22446
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+train: [2]  [280/400]  eta: 0:00:53  lr: 0.000162  loss: 1.1009 (1.1125)  grad: 0.2430 (0.2141)  time: 0.4268  data: 0.0042  max mem: 22446
+train: [2]  [300/400]  eta: 0:00:45  lr: 0.000165  loss: 1.0975 (1.1096)  grad: 0.2522 (0.2167)  time: 0.5947  data: 0.1583  max mem: 22446
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+train: [2]  [380/400]  eta: 0:00:08  lr: 0.000177  loss: 1.0207 (1.0949)  grad: 0.2592 (0.2273)  time: 0.4312  data: 0.0041  max mem: 22446
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+train: [2] Total time: 0:02:59 (0.4498 s / it)
+train: [2] Summary: lr: 0.000180  loss: 0.9400 (1.0862)  grad: 0.2567 (0.2282)
+eval (validation): [2]  [ 0/63]  eta: 0:03:10    time: 3.0184  data: 2.7455  max mem: 22446
+eval (validation): [2]  [20/63]  eta: 0:00:19    time: 0.3283  data: 0.0034  max mem: 22446
+eval (validation): [2]  [40/63]  eta: 0:00:09    time: 0.3518  data: 0.0034  max mem: 22446
+eval (validation): [2]  [60/63]  eta: 0:00:01    time: 0.3230  data: 0.0037  max mem: 22446
+eval (validation): [2]  [62/63]  eta: 0:00:00    time: 0.3203  data: 0.0035  max mem: 22446
+eval (validation): [2] Total time: 0:00:23 (0.3801 s / it)
+cv: [2]  best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00')  loss: 0.154  acc: 0.953  f1: 0.944
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:19:44  lr: nan  time: 2.9605  data: 2.6279  max mem: 22446
+train: [3]  [ 20/400]  eta: 0:03:33  lr: 0.000183  loss: 0.8985 (0.9334)  grad: 0.2638 (0.2749)  time: 0.4423  data: 0.0028  max mem: 22446
+train: [3]  [ 40/400]  eta: 0:02:59  lr: 0.000186  loss: 0.9401 (0.9607)  grad: 0.2638 (0.2704)  time: 0.4320  data: 0.0039  max mem: 22446
+train: [3]  [ 60/400]  eta: 0:02:44  lr: 0.000189  loss: 1.0111 (0.9785)  grad: 0.2725 (0.2795)  time: 0.4521  data: 0.0041  max mem: 22446
+train: [3]  [ 80/400]  eta: 0:02:30  lr: 0.000192  loss: 1.0111 (0.9867)  grad: 0.2899 (0.2805)  time: 0.4364  data: 0.0044  max mem: 22446
+train: [3]  [100/400]  eta: 0:02:19  lr: 0.000195  loss: 1.0159 (0.9935)  grad: 0.2968 (0.2944)  time: 0.4302  data: 0.0042  max mem: 22446
+train: [3]  [120/400]  eta: 0:02:08  lr: 0.000198  loss: 1.0247 (1.0059)  grad: 0.3313 (0.3029)  time: 0.4437  data: 0.0041  max mem: 22446
+train: [3]  [140/400]  eta: 0:01:58  lr: 0.000201  loss: 1.0401 (1.0120)  grad: 0.3286 (0.3075)  time: 0.4330  data: 0.0042  max mem: 22446
+train: [3]  [160/400]  eta: 0:01:48  lr: 0.000204  loss: 1.0009 (1.0066)  grad: 0.3189 (0.3104)  time: 0.4368  data: 0.0041  max mem: 22446
+train: [3]  [180/400]  eta: 0:01:39  lr: 0.000207  loss: 0.9822 (1.0065)  grad: 0.3331 (0.3152)  time: 0.4383  data: 0.0044  max mem: 22446
+train: [3]  [200/400]  eta: 0:01:30  lr: 0.000210  loss: 1.0008 (1.0054)  grad: 0.3379 (0.3193)  time: 0.4309  data: 0.0043  max mem: 22446
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+train: [3]  [240/400]  eta: 0:01:11  lr: 0.000216  loss: 0.9682 (1.0106)  grad: 0.3534 (0.3243)  time: 0.4355  data: 0.0041  max mem: 22446
+train: [3]  [260/400]  eta: 0:01:02  lr: 0.000219  loss: 0.9116 (1.0067)  grad: 0.3566 (0.3350)  time: 0.4453  data: 0.0041  max mem: 22446
+train: [3]  [280/400]  eta: 0:00:53  lr: 0.000222  loss: 0.9277 (1.0087)  grad: 0.3708 (0.3398)  time: 0.4375  data: 0.0041  max mem: 22446
+train: [3]  [300/400]  eta: 0:00:45  lr: 0.000225  loss: 0.9756 (1.0067)  grad: 0.3851 (0.3436)  time: 0.6022  data: 0.1648  max mem: 22446
+train: [3]  [320/400]  eta: 0:00:36  lr: 0.000228  loss: 0.9567 (1.0040)  grad: 0.3885 (0.3460)  time: 0.4602  data: 0.0032  max mem: 22446
+train: [3]  [340/400]  eta: 0:00:27  lr: 0.000231  loss: 0.8645 (0.9968)  grad: 0.3781 (0.3491)  time: 0.4445  data: 0.0045  max mem: 22446
+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 0.8645 (0.9919)  grad: 0.3925 (0.3534)  time: 0.4399  data: 0.0043  max mem: 22446
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 0.9573 (0.9944)  grad: 0.4162 (0.3564)  time: 0.4286  data: 0.0042  max mem: 22446
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 0.9768 (0.9946)  grad: 0.4276 (0.3613)  time: 0.4437  data: 0.0042  max mem: 22446
+train: [3] Total time: 0:03:01 (0.4543 s / it)
+train: [3] Summary: lr: 0.000240  loss: 0.9768 (0.9946)  grad: 0.4276 (0.3613)
+eval (validation): [3]  [ 0/63]  eta: 0:03:03    time: 2.9170  data: 2.6954  max mem: 22446
+eval (validation): [3]  [20/63]  eta: 0:00:20    time: 0.3428  data: 0.0041  max mem: 22446
+eval (validation): [3]  [40/63]  eta: 0:00:09    time: 0.3285  data: 0.0033  max mem: 22446
+eval (validation): [3]  [60/63]  eta: 0:00:01    time: 0.3139  data: 0.0036  max mem: 22446
+eval (validation): [3]  [62/63]  eta: 0:00:00    time: 0.3102  data: 0.0035  max mem: 22446
+eval (validation): [3] Total time: 0:00:23 (0.3729 s / it)
+cv: [3]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.138  acc: 0.961  f1: 0.956
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:20:33  lr: nan  time: 3.0842  data: 2.6881  max mem: 22446
+train: [4]  [ 20/400]  eta: 0:03:41  lr: 0.000243  loss: 1.0320 (1.0686)  grad: 0.4708 (0.6196)  time: 0.4569  data: 0.0039  max mem: 22446
+train: [4]  [ 40/400]  eta: 0:03:05  lr: 0.000246  loss: 1.0883 (1.0787)  grad: 0.4750 (0.5689)  time: 0.4445  data: 0.0041  max mem: 22446
+train: [4]  [ 60/400]  eta: 0:02:46  lr: 0.000249  loss: 1.1250 (1.1039)  grad: 0.4953 (0.5540)  time: 0.4425  data: 0.0041  max mem: 22446
+train: [4]  [ 80/400]  eta: 0:02:33  lr: 0.000252  loss: 1.1405 (1.1195)  grad: 0.5304 (0.5571)  time: 0.4395  data: 0.0041  max mem: 22446
+train: [4]  [100/400]  eta: 0:02:20  lr: 0.000255  loss: 1.1310 (1.1165)  grad: 0.5805 (0.5683)  time: 0.4340  data: 0.0042  max mem: 22446
+train: [4]  [120/400]  eta: 0:02:09  lr: 0.000258  loss: 1.1531 (1.1645)  grad: 0.6258 (0.5903)  time: 0.4370  data: 0.0040  max mem: 22446
+train: [4]  [140/400]  eta: 0:01:59  lr: 0.000261  loss: 1.3799 (1.1901)  grad: 0.6844 (0.6014)  time: 0.4367  data: 0.0042  max mem: 22446
+train: [4]  [160/400]  eta: 0:01:50  lr: 0.000264  loss: 1.3964 (1.2197)  grad: 0.6939 (0.6186)  time: 0.4462  data: 0.0043  max mem: 22446
+train: [4]  [180/400]  eta: 0:01:40  lr: 0.000267  loss: 1.3964 (1.2430)  grad: 0.7467 (0.6390)  time: 0.4468  data: 0.0043  max mem: 22446
+train: [4]  [200/400]  eta: 0:01:31  lr: 0.000270  loss: 1.3879 (1.2483)  grad: 0.7736 (0.6533)  time: 0.4429  data: 0.0042  max mem: 22446
+train: [4]  [220/400]  eta: 0:01:21  lr: 0.000273  loss: 1.2907 (1.2704)  grad: 0.7798 (0.6681)  time: 0.4452  data: 0.0043  max mem: 22446
+train: [4]  [240/400]  eta: 0:01:12  lr: 0.000276  loss: 1.3249 (1.2845)  grad: 0.7531 (0.6779)  time: 0.4322  data: 0.0042  max mem: 22446
+train: [4]  [260/400]  eta: 0:01:03  lr: 0.000279  loss: 1.5321 (1.3180)  grad: 0.7472 (0.6864)  time: 0.4360  data: 0.0042  max mem: 22446
+train: [4]  [280/400]  eta: 0:00:54  lr: 0.000282  loss: 1.5914 (1.3430)  grad: 0.8731 (0.7214)  time: 0.4385  data: 0.0044  max mem: 22446
+train: [4]  [300/400]  eta: 0:00:46  lr: 0.000285  loss: 1.6206 (1.3738)  grad: 0.9968 (0.7340)  time: 0.5959  data: 0.1688  max mem: 22446
+train: [4]  [320/400]  eta: 0:00:36  lr: 0.000288  loss: 1.5577 (1.3837)  grad: 0.8603 (0.7411)  time: 0.4571  data: 0.0032  max mem: 22446
+train: [4]  [340/400]  eta: 0:00:27  lr: 0.000291  loss: 1.5551 (1.3976)  grad: 0.8092 (0.7488)  time: 0.4404  data: 0.0040  max mem: 22446
+train: [4]  [360/400]  eta: 0:00:18  lr: 0.000294  loss: 1.6228 (1.4172)  grad: 0.8419 (0.7576)  time: 0.4355  data: 0.0044  max mem: 22446
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 1.7056 (1.4442)  grad: 0.8512 (0.7672)  time: 0.4359  data: 0.0041  max mem: 22446
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 1.8587 (1.4695)  grad: 0.8999 (0.7772)  time: 0.4620  data: 0.0043  max mem: 22446
+train: [4] Total time: 0:03:03 (0.4575 s / it)
+train: [4] Summary: lr: 0.000300  loss: 1.8587 (1.4695)  grad: 0.8999 (0.7772)
+eval (validation): [4]  [ 0/63]  eta: 0:03:19    time: 3.1605  data: 2.8698  max mem: 22446
+eval (validation): [4]  [20/63]  eta: 0:00:22    time: 0.3948  data: 0.0036  max mem: 22446
+eval (validation): [4]  [40/63]  eta: 0:00:10    time: 0.3552  data: 0.0031  max mem: 22446
+eval (validation): [4]  [60/63]  eta: 0:00:01    time: 0.3011  data: 0.0031  max mem: 22446
+eval (validation): [4]  [62/63]  eta: 0:00:00    time: 0.3009  data: 0.0030  max mem: 22446
+eval (validation): [4] Total time: 0:00:25 (0.3983 s / it)
+cv: [4]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.154  acc: 0.959  f1: 0.955
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:19:46  lr: nan  time: 2.9665  data: 2.6192  max mem: 22446
+train: [5]  [ 20/400]  eta: 0:03:41  lr: 0.000300  loss: 1.5344 (1.7453)  grad: 1.0068 (1.0425)  time: 0.4651  data: 0.0043  max mem: 22446
+train: [5]  [ 40/400]  eta: 0:03:04  lr: 0.000300  loss: 1.5344 (1.6897)  grad: 0.9680 (0.9966)  time: 0.4398  data: 0.0041  max mem: 22446
+train: [5]  [ 60/400]  eta: 0:02:47  lr: 0.000300  loss: 1.6992 (1.7096)  grad: 0.9170 (0.9582)  time: 0.4482  data: 0.0039  max mem: 22446
+train: [5]  [ 80/400]  eta: 0:02:33  lr: 0.000300  loss: 1.6368 (1.6806)  grad: 0.8832 (0.9440)  time: 0.4397  data: 0.0043  max mem: 22446
+train: [5]  [100/400]  eta: 0:02:21  lr: 0.000300  loss: 1.6422 (1.7289)  grad: 0.8773 (0.9458)  time: 0.4374  data: 0.0042  max mem: 22446
+train: [5]  [120/400]  eta: 0:02:10  lr: 0.000300  loss: 1.7786 (1.7868)  grad: 0.8675 (0.9444)  time: 0.4316  data: 0.0042  max mem: 22446
+train: [5]  [140/400]  eta: 0:01:59  lr: 0.000300  loss: 1.8340 (1.7745)  grad: 0.8714 (0.9438)  time: 0.4318  data: 0.0042  max mem: 22446
+WARNING: classifier 47 (43, 1.0) diverged (loss=64.57 > 60.89) at step 1080. Freezing.
+train: [5]  [160/400]  eta: 0:01:50  lr: 0.000299  loss: 1.7311 (1.7961)  grad: 0.9095 (0.9409)  time: 0.4488  data: 0.0041  max mem: 22446
+train: [5]  [180/400]  eta: 0:01:40  lr: 0.000299  loss: 1.5576 (1.7667)  grad: 0.8536 (0.9273)  time: 0.4532  data: 0.0035  max mem: 22446
+train: [5]  [200/400]  eta: 0:01:31  lr: 0.000299  loss: 1.4012 (1.7187)  grad: 0.8315 (0.9181)  time: 0.4415  data: 0.0041  max mem: 22446
+train: [5]  [220/400]  eta: 0:01:22  lr: 0.000299  loss: 1.2471 (1.6852)  grad: 0.8118 (0.9082)  time: 0.4541  data: 0.0043  max mem: 22446
+train: [5]  [240/400]  eta: 0:01:12  lr: 0.000299  loss: 1.2317 (1.6541)  grad: 0.7589 (0.8915)  time: 0.4431  data: 0.0042  max mem: 22446
+train: [5]  [260/400]  eta: 0:01:03  lr: 0.000299  loss: 1.2317 (1.6478)  grad: 0.7161 (0.8806)  time: 0.4407  data: 0.0043  max mem: 22446
+train: [5]  [280/400]  eta: 0:00:54  lr: 0.000298  loss: 1.4918 (1.6572)  grad: 0.7285 (0.8733)  time: 0.4411  data: 0.0044  max mem: 22446
+train: [5]  [300/400]  eta: 0:00:46  lr: 0.000298  loss: 1.3761 (1.6271)  grad: 0.7339 (0.8634)  time: 0.5839  data: 0.1616  max mem: 22446
+train: [5]  [320/400]  eta: 0:00:36  lr: 0.000298  loss: 1.1743 (1.5978)  grad: 0.7339 (0.8575)  time: 0.4435  data: 0.0038  max mem: 22446
+train: [5]  [340/400]  eta: 0:00:27  lr: 0.000298  loss: 1.1626 (1.5699)  grad: 0.6456 (0.8466)  time: 0.4369  data: 0.0041  max mem: 22446
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 1.0458 (1.5417)  grad: 0.6462 (0.8387)  time: 0.4387  data: 0.0043  max mem: 22446
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 1.1163 (1.5238)  grad: 0.6816 (0.8299)  time: 0.4276  data: 0.0041  max mem: 22446
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 1.1439 (1.4962)  grad: 0.6045 (0.8190)  time: 0.4383  data: 0.0043  max mem: 22446
+train: [5] Total time: 0:03:02 (0.4562 s / it)
+train: [5] Summary: lr: 0.000297  loss: 1.1439 (1.4962)  grad: 0.6045 (0.8190)
+eval (validation): [5]  [ 0/63]  eta: 0:03:06    time: 2.9557  data: 2.6556  max mem: 22446
+eval (validation): [5]  [20/63]  eta: 0:00:22    time: 0.3904  data: 0.0042  max mem: 22446
+eval (validation): [5]  [40/63]  eta: 0:00:09    time: 0.3380  data: 0.0032  max mem: 22446
+eval (validation): [5]  [60/63]  eta: 0:00:01    time: 0.3148  data: 0.0034  max mem: 22446
+eval (validation): [5]  [62/63]  eta: 0:00:00    time: 0.3122  data: 0.0033  max mem: 22446
+eval (validation): [5] Total time: 0:00:24 (0.3919 s / it)
+cv: [5]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.134  acc: 0.962  f1: 0.957
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:21:24  lr: nan  time: 3.2123  data: 2.8703  max mem: 22446
+train: [6]  [ 20/400]  eta: 0:03:30  lr: 0.000296  loss: 0.9299 (0.9635)  grad: 0.6035 (0.6046)  time: 0.4200  data: 0.0032  max mem: 22446
+train: [6]  [ 40/400]  eta: 0:03:00  lr: 0.000296  loss: 0.9063 (0.9445)  grad: 0.5910 (0.5954)  time: 0.4478  data: 0.0039  max mem: 22446
+train: [6]  [ 60/400]  eta: 0:02:43  lr: 0.000296  loss: 0.9494 (0.9931)  grad: 0.5687 (0.5871)  time: 0.4407  data: 0.0042  max mem: 22446
+train: [6]  [ 80/400]  eta: 0:02:30  lr: 0.000295  loss: 1.0669 (1.0176)  grad: 0.5398 (0.5851)  time: 0.4377  data: 0.0042  max mem: 22446
+train: [6]  [100/400]  eta: 0:02:19  lr: 0.000295  loss: 1.0204 (1.0179)  grad: 0.5744 (0.5837)  time: 0.4380  data: 0.0043  max mem: 22446
+train: [6]  [120/400]  eta: 0:02:08  lr: 0.000295  loss: 0.9047 (0.9900)  grad: 0.5479 (0.5831)  time: 0.4307  data: 0.0043  max mem: 22446
+train: [6]  [140/400]  eta: 0:01:58  lr: 0.000294  loss: 0.8905 (0.9892)  grad: 0.5679 (0.5928)  time: 0.4310  data: 0.0041  max mem: 22446
+train: [6]  [160/400]  eta: 0:01:49  lr: 0.000294  loss: 0.8905 (0.9832)  grad: 0.6157 (0.5982)  time: 0.4548  data: 0.0045  max mem: 22446
+train: [6]  [180/400]  eta: 0:01:39  lr: 0.000293  loss: 0.9122 (0.9830)  grad: 0.6074 (0.5984)  time: 0.4478  data: 0.0044  max mem: 22446
+train: [6]  [200/400]  eta: 0:01:30  lr: 0.000293  loss: 0.9122 (0.9956)  grad: 0.5761 (0.5979)  time: 0.4287  data: 0.0043  max mem: 22446
+train: [6]  [220/400]  eta: 0:01:21  lr: 0.000292  loss: 0.8687 (0.9938)  grad: 0.5498 (0.5936)  time: 0.4508  data: 0.0044  max mem: 22446
+train: [6]  [240/400]  eta: 0:01:12  lr: 0.000292  loss: 0.7820 (0.9812)  grad: 0.5245 (0.5886)  time: 0.4403  data: 0.0041  max mem: 22446
+train: [6]  [260/400]  eta: 0:01:02  lr: 0.000291  loss: 0.7820 (0.9768)  grad: 0.5198 (0.5890)  time: 0.4422  data: 0.0043  max mem: 22446
+train: [6]  [280/400]  eta: 0:00:53  lr: 0.000291  loss: 0.7884 (0.9798)  grad: 0.5447 (0.5885)  time: 0.4382  data: 0.0043  max mem: 22446
+train: [6]  [300/400]  eta: 0:00:45  lr: 0.000290  loss: 0.8688 (0.9791)  grad: 0.5776 (0.5889)  time: 0.5972  data: 0.1724  max mem: 22446
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+train: [6]  [340/400]  eta: 0:00:27  lr: 0.000289  loss: 0.7806 (0.9619)  grad: 0.4707 (0.5793)  time: 0.4411  data: 0.0041  max mem: 22446
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+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 0.7214 (0.9419)  grad: 0.4321 (0.5662)  time: 0.4404  data: 0.0039  max mem: 22446
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 0.6874 (0.9259)  grad: 0.4343 (0.5621)  time: 0.4462  data: 0.0043  max mem: 22446
+train: [6] Total time: 0:03:02 (0.4559 s / it)
+train: [6] Summary: lr: 0.000287  loss: 0.6874 (0.9259)  grad: 0.4343 (0.5621)
+eval (validation): [6]  [ 0/63]  eta: 0:03:25    time: 3.2602  data: 2.9703  max mem: 22446
+eval (validation): [6]  [20/63]  eta: 0:00:22    time: 0.3807  data: 0.0041  max mem: 22446
+eval (validation): [6]  [40/63]  eta: 0:00:09    time: 0.3261  data: 0.0032  max mem: 22446
+eval (validation): [6]  [60/63]  eta: 0:00:01    time: 0.3160  data: 0.0034  max mem: 22446
+eval (validation): [6]  [62/63]  eta: 0:00:00    time: 0.3113  data: 0.0033  max mem: 22446
+eval (validation): [6] Total time: 0:00:24 (0.3908 s / it)
+cv: [6]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 0.158  acc: 0.963  f1: 0.960
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:21:25  lr: nan  time: 3.2125  data: 2.8221  max mem: 22446
+train: [7]  [ 20/400]  eta: 0:03:32  lr: 0.000286  loss: 0.6205 (0.7212)  grad: 0.4037 (0.4302)  time: 0.4253  data: 0.0031  max mem: 22446
+train: [7]  [ 40/400]  eta: 0:03:02  lr: 0.000286  loss: 0.6909 (0.7277)  grad: 0.4676 (0.4633)  time: 0.4527  data: 0.0044  max mem: 22446
+train: [7]  [ 60/400]  eta: 0:02:45  lr: 0.000285  loss: 0.7196 (0.7456)  grad: 0.4579 (0.4513)  time: 0.4422  data: 0.0042  max mem: 22446
+train: [7]  [ 80/400]  eta: 0:02:32  lr: 0.000284  loss: 0.6420 (0.7210)  grad: 0.4012 (0.4400)  time: 0.4435  data: 0.0044  max mem: 22446
+train: [7]  [100/400]  eta: 0:02:19  lr: 0.000284  loss: 0.5807 (0.7078)  grad: 0.4080 (0.4390)  time: 0.4321  data: 0.0041  max mem: 22446
+train: [7]  [120/400]  eta: 0:02:09  lr: 0.000283  loss: 0.5806 (0.6957)  grad: 0.4029 (0.4456)  time: 0.4347  data: 0.0042  max mem: 22446
+train: [7]  [140/400]  eta: 0:01:59  lr: 0.000282  loss: 0.6312 (0.6983)  grad: 0.4132 (0.4444)  time: 0.4373  data: 0.0041  max mem: 22446
+train: [7]  [160/400]  eta: 0:01:49  lr: 0.000282  loss: 0.6249 (0.6902)  grad: 0.4191 (0.4381)  time: 0.4572  data: 0.0041  max mem: 22446
+train: [7]  [180/400]  eta: 0:01:40  lr: 0.000281  loss: 0.6249 (0.6946)  grad: 0.3970 (0.4369)  time: 0.4424  data: 0.0042  max mem: 22446
+train: [7]  [200/400]  eta: 0:01:30  lr: 0.000280  loss: 0.6415 (0.6869)  grad: 0.3970 (0.4343)  time: 0.4355  data: 0.0044  max mem: 22446
+train: [7]  [220/400]  eta: 0:01:21  lr: 0.000279  loss: 0.5992 (0.6809)  grad: 0.4128 (0.4324)  time: 0.4472  data: 0.0042  max mem: 22446
+train: [7]  [240/400]  eta: 0:01:12  lr: 0.000278  loss: 0.6359 (0.6826)  grad: 0.4255 (0.4357)  time: 0.4398  data: 0.0044  max mem: 22446
+train: [7]  [260/400]  eta: 0:01:03  lr: 0.000278  loss: 0.6328 (0.6815)  grad: 0.4255 (0.4354)  time: 0.4348  data: 0.0043  max mem: 22446
+train: [7]  [280/400]  eta: 0:00:53  lr: 0.000277  loss: 0.6328 (0.6875)  grad: 0.4148 (0.4356)  time: 0.4340  data: 0.0042  max mem: 22446
+train: [7]  [300/400]  eta: 0:00:46  lr: 0.000276  loss: 0.7166 (0.6913)  grad: 0.4148 (0.4353)  time: 0.6147  data: 0.1687  max mem: 22446
+train: [7]  [320/400]  eta: 0:00:36  lr: 0.000275  loss: 0.6901 (0.6889)  grad: 0.3795 (0.4309)  time: 0.4467  data: 0.0034  max mem: 22446
+train: [7]  [340/400]  eta: 0:00:27  lr: 0.000274  loss: 0.5529 (0.6793)  grad: 0.3467 (0.4265)  time: 0.4460  data: 0.0043  max mem: 22446
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 0.5358 (0.6713)  grad: 0.3364 (0.4216)  time: 0.4392  data: 0.0043  max mem: 22446
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 0.5374 (0.6646)  grad: 0.3241 (0.4178)  time: 0.4542  data: 0.0043  max mem: 22446
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 0.5305 (0.6591)  grad: 0.3359 (0.4147)  time: 0.4495  data: 0.0044  max mem: 22446
+train: [7] Total time: 0:03:03 (0.4579 s / it)
+train: [7] Summary: lr: 0.000271  loss: 0.5305 (0.6591)  grad: 0.3359 (0.4147)
+eval (validation): [7]  [ 0/63]  eta: 0:03:19    time: 3.1658  data: 2.8753  max mem: 22446
+eval (validation): [7]  [20/63]  eta: 0:00:23    time: 0.4114  data: 0.0036  max mem: 22446
+eval (validation): [7]  [40/63]  eta: 0:00:10    time: 0.3518  data: 0.0033  max mem: 22446
+eval (validation): [7]  [60/63]  eta: 0:00:01    time: 0.3089  data: 0.0032  max mem: 22446
+eval (validation): [7]  [62/63]  eta: 0:00:00    time: 0.3066  data: 0.0032  max mem: 22446
+eval (validation): [7] Total time: 0:00:25 (0.4057 s / it)
+cv: [7]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.176  acc: 0.966  f1: 0.961
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [8]  [  0/400]  eta: 0:20:33  lr: nan  time: 3.0839  data: 2.7096  max mem: 22446
+train: [8]  [ 20/400]  eta: 0:03:39  lr: 0.000270  loss: 0.4559 (0.4897)  grad: 0.2795 (0.2841)  time: 0.4527  data: 0.0029  max mem: 22446
+train: [8]  [ 40/400]  eta: 0:03:03  lr: 0.000270  loss: 0.4643 (0.4905)  grad: 0.2853 (0.3325)  time: 0.4397  data: 0.0037  max mem: 22446
+train: [8]  [ 60/400]  eta: 0:02:45  lr: 0.000269  loss: 0.4838 (0.4862)  grad: 0.3121 (0.3258)  time: 0.4353  data: 0.0042  max mem: 22446
+train: [8]  [ 80/400]  eta: 0:02:31  lr: 0.000268  loss: 0.4667 (0.4863)  grad: 0.3243 (0.3239)  time: 0.4335  data: 0.0042  max mem: 22446
+train: [8]  [100/400]  eta: 0:02:19  lr: 0.000267  loss: 0.4512 (0.4820)  grad: 0.2965 (0.3187)  time: 0.4400  data: 0.0043  max mem: 22446
+train: [8]  [120/400]  eta: 0:02:09  lr: 0.000266  loss: 0.5146 (0.4998)  grad: 0.3244 (0.3231)  time: 0.4404  data: 0.0044  max mem: 22446
+train: [8]  [140/400]  eta: 0:01:59  lr: 0.000265  loss: 0.5262 (0.4984)  grad: 0.3391 (0.3249)  time: 0.4548  data: 0.0044  max mem: 22446
+train: [8]  [160/400]  eta: 0:01:50  lr: 0.000264  loss: 0.4858 (0.4981)  grad: 0.3159 (0.3235)  time: 0.4397  data: 0.0045  max mem: 22446
+train: [8]  [180/400]  eta: 0:01:40  lr: 0.000263  loss: 0.4858 (0.4946)  grad: 0.2983 (0.3210)  time: 0.4356  data: 0.0042  max mem: 22446
+train: [8]  [200/400]  eta: 0:01:30  lr: 0.000262  loss: 0.4501 (0.4953)  grad: 0.3042 (0.3220)  time: 0.4416  data: 0.0041  max mem: 22446
+train: [8]  [220/400]  eta: 0:01:21  lr: 0.000260  loss: 0.4706 (0.4964)  grad: 0.3051 (0.3202)  time: 0.4401  data: 0.0043  max mem: 22446
+train: [8]  [240/400]  eta: 0:01:12  lr: 0.000259  loss: 0.4898 (0.4958)  grad: 0.2938 (0.3193)  time: 0.4466  data: 0.0043  max mem: 22446
+train: [8]  [260/400]  eta: 0:01:03  lr: 0.000258  loss: 0.4746 (0.4934)  grad: 0.2925 (0.3156)  time: 0.4472  data: 0.0043  max mem: 22446
+train: [8]  [280/400]  eta: 0:00:54  lr: 0.000257  loss: 0.4749 (0.4958)  grad: 0.2974 (0.3150)  time: 0.4477  data: 0.0045  max mem: 22446
+train: [8]  [300/400]  eta: 0:00:46  lr: 0.000256  loss: 0.4907 (0.4977)  grad: 0.3083 (0.3146)  time: 0.6013  data: 0.1757  max mem: 22446
+train: [8]  [320/400]  eta: 0:00:36  lr: 0.000255  loss: 0.4136 (0.4929)  grad: 0.2821 (0.3099)  time: 0.4502  data: 0.0030  max mem: 22446
+train: [8]  [340/400]  eta: 0:00:27  lr: 0.000254  loss: 0.3957 (0.4894)  grad: 0.2282 (0.3060)  time: 0.4505  data: 0.0042  max mem: 22446
+train: [8]  [360/400]  eta: 0:00:18  lr: 0.000253  loss: 0.3900 (0.4832)  grad: 0.2226 (0.3005)  time: 0.4474  data: 0.0040  max mem: 22446
+train: [8]  [380/400]  eta: 0:00:09  lr: 0.000252  loss: 0.3668 (0.4781)  grad: 0.2068 (0.2958)  time: 0.4575  data: 0.0044  max mem: 22446
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 0.4143 (0.4752)  grad: 0.2122 (0.2934)  time: 0.4420  data: 0.0042  max mem: 22446
+train: [8] Total time: 0:03:03 (0.4593 s / it)
+train: [8] Summary: lr: 0.000250  loss: 0.4143 (0.4752)  grad: 0.2122 (0.2934)
+eval (validation): [8]  [ 0/63]  eta: 0:03:04    time: 2.9298  data: 2.6923  max mem: 22446
+eval (validation): [8]  [20/63]  eta: 0:00:20    time: 0.3576  data: 0.0030  max mem: 22446
+eval (validation): [8]  [40/63]  eta: 0:00:09    time: 0.3517  data: 0.0039  max mem: 22446
+eval (validation): [8]  [60/63]  eta: 0:00:01    time: 0.3359  data: 0.0036  max mem: 22446
+eval (validation): [8]  [62/63]  eta: 0:00:00    time: 0.3330  data: 0.0035  max mem: 22446
+eval (validation): [8] Total time: 0:00:24 (0.3921 s / it)
+cv: [8]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 0.160  acc: 0.967  f1: 0.965
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:20:41  lr: nan  time: 3.1031  data: 2.7579  max mem: 22446
+train: [9]  [ 20/400]  eta: 0:03:41  lr: 0.000249  loss: 0.4049 (0.4575)  grad: 0.2450 (0.2531)  time: 0.4558  data: 0.0027  max mem: 22446
+train: [9]  [ 40/400]  eta: 0:03:06  lr: 0.000248  loss: 0.4169 (0.4409)  grad: 0.2450 (0.2575)  time: 0.4522  data: 0.0038  max mem: 22446
+train: [9]  [ 60/400]  eta: 0:02:47  lr: 0.000247  loss: 0.4141 (0.4314)  grad: 0.2277 (0.2442)  time: 0.4435  data: 0.0043  max mem: 22446
+train: [9]  [ 80/400]  eta: 0:02:34  lr: 0.000246  loss: 0.4032 (0.4292)  grad: 0.2012 (0.2343)  time: 0.4461  data: 0.0040  max mem: 22446
+train: [9]  [100/400]  eta: 0:02:22  lr: 0.000244  loss: 0.3934 (0.4214)  grad: 0.2235 (0.2351)  time: 0.4527  data: 0.0044  max mem: 22446
+train: [9]  [120/400]  eta: 0:02:11  lr: 0.000243  loss: 0.4120 (0.4242)  grad: 0.2281 (0.2338)  time: 0.4446  data: 0.0043  max mem: 22446
+train: [9]  [140/400]  eta: 0:02:01  lr: 0.000242  loss: 0.4269 (0.4224)  grad: 0.2240 (0.2325)  time: 0.4433  data: 0.0042  max mem: 22446
+train: [9]  [160/400]  eta: 0:01:51  lr: 0.000241  loss: 0.3884 (0.4192)  grad: 0.2190 (0.2313)  time: 0.4427  data: 0.0042  max mem: 22446
+train: [9]  [180/400]  eta: 0:01:41  lr: 0.000240  loss: 0.3819 (0.4142)  grad: 0.2104 (0.2274)  time: 0.4470  data: 0.0041  max mem: 22446
+train: [9]  [200/400]  eta: 0:01:32  lr: 0.000238  loss: 0.3819 (0.4122)  grad: 0.2185 (0.2268)  time: 0.4466  data: 0.0043  max mem: 22446
+train: [9]  [220/400]  eta: 0:01:22  lr: 0.000237  loss: 0.3925 (0.4139)  grad: 0.2223 (0.2264)  time: 0.4408  data: 0.0044  max mem: 22446
+train: [9]  [240/400]  eta: 0:01:13  lr: 0.000236  loss: 0.4041 (0.4120)  grad: 0.2043 (0.2240)  time: 0.4472  data: 0.0043  max mem: 22446
+train: [9]  [260/400]  eta: 0:01:03  lr: 0.000234  loss: 0.3922 (0.4118)  grad: 0.2121 (0.2237)  time: 0.4469  data: 0.0043  max mem: 22446
+train: [9]  [280/400]  eta: 0:00:54  lr: 0.000233  loss: 0.3796 (0.4105)  grad: 0.2121 (0.2230)  time: 0.4430  data: 0.0043  max mem: 22446
+train: [9]  [300/400]  eta: 0:00:46  lr: 0.000232  loss: 0.3718 (0.4095)  grad: 0.2089 (0.2227)  time: 0.5915  data: 0.1684  max mem: 22446
+train: [9]  [320/400]  eta: 0:00:37  lr: 0.000230  loss: 0.3566 (0.4048)  grad: 0.2071 (0.2208)  time: 0.4562  data: 0.0032  max mem: 22446
+train: [9]  [340/400]  eta: 0:00:27  lr: 0.000229  loss: 0.3332 (0.4025)  grad: 0.1796 (0.2185)  time: 0.4560  data: 0.0039  max mem: 22446
+train: [9]  [360/400]  eta: 0:00:18  lr: 0.000228  loss: 0.3511 (0.3986)  grad: 0.1699 (0.2159)  time: 0.4255  data: 0.0039  max mem: 22446
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 0.3368 (0.3965)  grad: 0.1800 (0.2137)  time: 0.4682  data: 0.0044  max mem: 22446
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 0.3540 (0.3948)  grad: 0.1852 (0.2125)  time: 0.4485  data: 0.0041  max mem: 22446
+train: [9] Total time: 0:03:04 (0.4620 s / it)
+train: [9] Summary: lr: 0.000225  loss: 0.3540 (0.3948)  grad: 0.1852 (0.2125)
+eval (validation): [9]  [ 0/63]  eta: 0:03:32    time: 3.3751  data: 3.1331  max mem: 22446
+eval (validation): [9]  [20/63]  eta: 0:00:20    time: 0.3339  data: 0.0035  max mem: 22446
+eval (validation): [9]  [40/63]  eta: 0:00:09    time: 0.3181  data: 0.0026  max mem: 22446
+eval (validation): [9]  [60/63]  eta: 0:00:01    time: 0.3124  data: 0.0028  max mem: 22446
+eval (validation): [9]  [62/63]  eta: 0:00:00    time: 0.3106  data: 0.0027  max mem: 22446
+eval (validation): [9] Total time: 0:00:23 (0.3738 s / it)
+cv: [9]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.340  acc: 0.970  f1: 0.961
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [10]  [  0/400]  eta: 0:20:10  lr: nan  time: 3.0272  data: 2.6471  max mem: 22446
+train: [10]  [ 20/400]  eta: 0:03:31  lr: 0.000224  loss: 0.3918 (0.3881)  grad: 0.1630 (0.1731)  time: 0.4324  data: 0.0032  max mem: 22446
+train: [10]  [ 40/400]  eta: 0:02:58  lr: 0.000222  loss: 0.3295 (0.3590)  grad: 0.1740 (0.1774)  time: 0.4318  data: 0.0040  max mem: 22446
+train: [10]  [ 60/400]  eta: 0:02:42  lr: 0.000221  loss: 0.3295 (0.3529)  grad: 0.1740 (0.1764)  time: 0.4379  data: 0.0043  max mem: 22446
+train: [10]  [ 80/400]  eta: 0:02:29  lr: 0.000220  loss: 0.3513 (0.3561)  grad: 0.1917 (0.1786)  time: 0.4365  data: 0.0040  max mem: 22446
+train: [10]  [100/400]  eta: 0:02:18  lr: 0.000218  loss: 0.3513 (0.3557)  grad: 0.1793 (0.1771)  time: 0.4350  data: 0.0043  max mem: 22446
+train: [10]  [120/400]  eta: 0:02:07  lr: 0.000217  loss: 0.3297 (0.3525)  grad: 0.1657 (0.1783)  time: 0.4353  data: 0.0042  max mem: 22446
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+train: [10]  [160/400]  eta: 0:01:48  lr: 0.000214  loss: 0.3254 (0.3476)  grad: 0.1644 (0.1748)  time: 0.4330  data: 0.0042  max mem: 22446
+train: [10]  [180/400]  eta: 0:01:39  lr: 0.000213  loss: 0.3117 (0.3446)  grad: 0.1586 (0.1731)  time: 0.4440  data: 0.0043  max mem: 22446
+train: [10]  [200/400]  eta: 0:01:29  lr: 0.000211  loss: 0.3390 (0.3467)  grad: 0.1597 (0.1735)  time: 0.4406  data: 0.0044  max mem: 22446
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+train: [10]  [240/400]  eta: 0:01:11  lr: 0.000208  loss: 0.3234 (0.3441)  grad: 0.1553 (0.1707)  time: 0.4317  data: 0.0043  max mem: 22446
+train: [10]  [260/400]  eta: 0:01:02  lr: 0.000207  loss: 0.3295 (0.3433)  grad: 0.1716 (0.1718)  time: 0.4370  data: 0.0044  max mem: 22446
+train: [10]  [280/400]  eta: 0:00:53  lr: 0.000205  loss: 0.3260 (0.3424)  grad: 0.1633 (0.1706)  time: 0.4312  data: 0.0042  max mem: 22446
+train: [10]  [300/400]  eta: 0:00:45  lr: 0.000204  loss: 0.3195 (0.3422)  grad: 0.1467 (0.1698)  time: 0.6060  data: 0.1742  max mem: 22446
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+train: [10]  [340/400]  eta: 0:00:27  lr: 0.000201  loss: 0.3101 (0.3393)  grad: 0.1434 (0.1683)  time: 0.4383  data: 0.0043  max mem: 22446
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+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 0.3026 (0.3369)  grad: 0.1352 (0.1650)  time: 0.4479  data: 0.0043  max mem: 22446
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 0.3079 (0.3359)  grad: 0.1316 (0.1628)  time: 0.4325  data: 0.0045  max mem: 22446
+train: [10] Total time: 0:03:00 (0.4520 s / it)
+train: [10] Summary: lr: 0.000196  loss: 0.3079 (0.3359)  grad: 0.1316 (0.1628)
+eval (validation): [10]  [ 0/63]  eta: 0:03:12    time: 3.0566  data: 2.8131  max mem: 22446
+eval (validation): [10]  [20/63]  eta: 0:00:21    time: 0.3712  data: 0.0038  max mem: 22446
+eval (validation): [10]  [40/63]  eta: 0:00:09    time: 0.3289  data: 0.0030  max mem: 22446
+eval (validation): [10]  [60/63]  eta: 0:00:01    time: 0.3181  data: 0.0034  max mem: 22446
+eval (validation): [10]  [62/63]  eta: 0:00:00    time: 0.3159  data: 0.0032  max mem: 22446
+eval (validation): [10] Total time: 0:00:24 (0.3860 s / it)
+cv: [10]  best hparam: (16, 1.0) (041) ('041_lr1.6e+01_wd1.0e+00')  loss: 0.422  acc: 0.970  f1: 0.969
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:22:16  lr: nan  time: 3.3420  data: 2.9870  max mem: 22446
+train: [11]  [ 20/400]  eta: 0:03:41  lr: 0.000195  loss: 0.3065 (0.3165)  grad: 0.1008 (0.1089)  time: 0.4439  data: 0.0033  max mem: 22446
+train: [11]  [ 40/400]  eta: 0:03:04  lr: 0.000193  loss: 0.3065 (0.3136)  grad: 0.1073 (0.1096)  time: 0.4384  data: 0.0041  max mem: 22446
+train: [11]  [ 60/400]  eta: 0:02:45  lr: 0.000192  loss: 0.3004 (0.3127)  grad: 0.1073 (0.1105)  time: 0.4351  data: 0.0043  max mem: 22446
+train: [11]  [ 80/400]  eta: 0:02:31  lr: 0.000190  loss: 0.3004 (0.3105)  grad: 0.1237 (0.1145)  time: 0.4288  data: 0.0042  max mem: 22446
+train: [11]  [100/400]  eta: 0:02:19  lr: 0.000189  loss: 0.2982 (0.3060)  grad: 0.1237 (0.1166)  time: 0.4344  data: 0.0039  max mem: 22446
+train: [11]  [120/400]  eta: 0:02:09  lr: 0.000187  loss: 0.2956 (0.3057)  grad: 0.1156 (0.1182)  time: 0.4595  data: 0.0040  max mem: 22446
+train: [11]  [140/400]  eta: 0:01:59  lr: 0.000186  loss: 0.3014 (0.3085)  grad: 0.1200 (0.1195)  time: 0.4439  data: 0.0042  max mem: 22446
+train: [11]  [160/400]  eta: 0:01:49  lr: 0.000184  loss: 0.3184 (0.3097)  grad: 0.1221 (0.1195)  time: 0.4308  data: 0.0044  max mem: 22446
+train: [11]  [180/400]  eta: 0:01:40  lr: 0.000183  loss: 0.3129 (0.3089)  grad: 0.1236 (0.1213)  time: 0.4399  data: 0.0042  max mem: 22446
+train: [11]  [200/400]  eta: 0:01:31  lr: 0.000181  loss: 0.2890 (0.3066)  grad: 0.1262 (0.1214)  time: 0.4521  data: 0.0043  max mem: 22446
+train: [11]  [220/400]  eta: 0:01:21  lr: 0.000180  loss: 0.2836 (0.3050)  grad: 0.1222 (0.1223)  time: 0.4386  data: 0.0040  max mem: 22446
+train: [11]  [240/400]  eta: 0:01:12  lr: 0.000178  loss: 0.2813 (0.3030)  grad: 0.1187 (0.1222)  time: 0.4388  data: 0.0041  max mem: 22446
+train: [11]  [260/400]  eta: 0:01:03  lr: 0.000177  loss: 0.2954 (0.3033)  grad: 0.1178 (0.1231)  time: 0.4290  data: 0.0040  max mem: 22446
+train: [11]  [280/400]  eta: 0:00:53  lr: 0.000175  loss: 0.2995 (0.3039)  grad: 0.1272 (0.1246)  time: 0.4287  data: 0.0042  max mem: 22446
+train: [11]  [300/400]  eta: 0:00:46  lr: 0.000174  loss: 0.3064 (0.3050)  grad: 0.1299 (0.1246)  time: 0.6197  data: 0.1788  max mem: 22446
+train: [11]  [320/400]  eta: 0:00:36  lr: 0.000172  loss: 0.3064 (0.3039)  grad: 0.1131 (0.1237)  time: 0.4535  data: 0.0033  max mem: 22446
+train: [11]  [340/400]  eta: 0:00:27  lr: 0.000170  loss: 0.3016 (0.3030)  grad: 0.1053 (0.1224)  time: 0.4310  data: 0.0041  max mem: 22446
+train: [11]  [360/400]  eta: 0:00:18  lr: 0.000169  loss: 0.2875 (0.3021)  grad: 0.0923 (0.1211)  time: 0.4465  data: 0.0044  max mem: 22446
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 0.2801 (0.3015)  grad: 0.0946 (0.1199)  time: 0.4400  data: 0.0044  max mem: 22446
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 0.2920 (0.3018)  grad: 0.0979 (0.1193)  time: 0.4386  data: 0.0043  max mem: 22446
+train: [11] Total time: 0:03:02 (0.4562 s / it)
+train: [11] Summary: lr: 0.000166  loss: 0.2920 (0.3018)  grad: 0.0979 (0.1193)
+eval (validation): [11]  [ 0/63]  eta: 0:03:21    time: 3.1985  data: 2.9176  max mem: 22446
+eval (validation): [11]  [20/63]  eta: 0:00:20    time: 0.3427  data: 0.0033  max mem: 22446
+eval (validation): [11]  [40/63]  eta: 0:00:09    time: 0.3291  data: 0.0034  max mem: 22446
+eval (validation): [11]  [60/63]  eta: 0:00:01    time: 0.3104  data: 0.0033  max mem: 22446
+eval (validation): [11]  [62/63]  eta: 0:00:00    time: 0.3100  data: 0.0032  max mem: 22446
+eval (validation): [11] Total time: 0:00:23 (0.3769 s / it)
+cv: [11]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.198  acc: 0.971  f1: 0.967
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [12]  [  0/400]  eta: 0:22:14  lr: nan  time: 3.3353  data: 2.9935  max mem: 22446
+train: [12]  [ 20/400]  eta: 0:03:38  lr: 0.000164  loss: 0.2695 (0.2688)  grad: 0.0856 (0.0885)  time: 0.4363  data: 0.0031  max mem: 22446
+train: [12]  [ 40/400]  eta: 0:03:03  lr: 0.000163  loss: 0.2695 (0.2725)  grad: 0.0906 (0.0927)  time: 0.4409  data: 0.0038  max mem: 22446
+train: [12]  [ 60/400]  eta: 0:02:45  lr: 0.000161  loss: 0.2792 (0.2742)  grad: 0.0928 (0.0927)  time: 0.4368  data: 0.0041  max mem: 22446
+train: [12]  [ 80/400]  eta: 0:02:31  lr: 0.000160  loss: 0.2816 (0.2742)  grad: 0.0889 (0.0919)  time: 0.4348  data: 0.0044  max mem: 22446
+train: [12]  [100/400]  eta: 0:02:20  lr: 0.000158  loss: 0.2805 (0.2775)  grad: 0.0889 (0.0917)  time: 0.4449  data: 0.0042  max mem: 22446
+train: [12]  [120/400]  eta: 0:02:09  lr: 0.000156  loss: 0.2704 (0.2786)  grad: 0.0883 (0.0916)  time: 0.4430  data: 0.0042  max mem: 22446
+train: [12]  [140/400]  eta: 0:01:59  lr: 0.000155  loss: 0.2640 (0.2779)  grad: 0.0852 (0.0907)  time: 0.4402  data: 0.0041  max mem: 22446
+train: [12]  [160/400]  eta: 0:01:49  lr: 0.000153  loss: 0.2759 (0.2778)  grad: 0.0851 (0.0907)  time: 0.4330  data: 0.0043  max mem: 22446
+train: [12]  [180/400]  eta: 0:01:39  lr: 0.000152  loss: 0.2765 (0.2802)  grad: 0.0967 (0.0925)  time: 0.4356  data: 0.0041  max mem: 22446
+train: [12]  [200/400]  eta: 0:01:30  lr: 0.000150  loss: 0.2756 (0.2802)  grad: 0.1025 (0.0931)  time: 0.4509  data: 0.0043  max mem: 22446
+train: [12]  [220/400]  eta: 0:01:21  lr: 0.000149  loss: 0.2753 (0.2811)  grad: 0.0927 (0.0935)  time: 0.4377  data: 0.0043  max mem: 22446
+train: [12]  [240/400]  eta: 0:01:12  lr: 0.000147  loss: 0.2888 (0.2823)  grad: 0.1006 (0.0947)  time: 0.4347  data: 0.0043  max mem: 22446
+train: [12]  [260/400]  eta: 0:01:02  lr: 0.000145  loss: 0.2851 (0.2817)  grad: 0.0980 (0.0944)  time: 0.4303  data: 0.0040  max mem: 22446
+train: [12]  [280/400]  eta: 0:00:53  lr: 0.000144  loss: 0.2719 (0.2810)  grad: 0.0884 (0.0941)  time: 0.4491  data: 0.0043  max mem: 22446
+train: [12]  [300/400]  eta: 0:00:45  lr: 0.000142  loss: 0.2755 (0.2815)  grad: 0.0885 (0.0943)  time: 0.6015  data: 0.1767  max mem: 22446
+train: [12]  [320/400]  eta: 0:00:36  lr: 0.000141  loss: 0.2755 (0.2808)  grad: 0.0942 (0.0938)  time: 0.4376  data: 0.0042  max mem: 22446
+train: [12]  [340/400]  eta: 0:00:27  lr: 0.000139  loss: 0.2831 (0.2815)  grad: 0.0953 (0.0938)  time: 0.4371  data: 0.0039  max mem: 22446
+train: [12]  [360/400]  eta: 0:00:18  lr: 0.000138  loss: 0.2914 (0.2812)  grad: 0.0919 (0.0933)  time: 0.4514  data: 0.0043  max mem: 22446
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 0.2888 (0.2818)  grad: 0.0937 (0.0935)  time: 0.4427  data: 0.0046  max mem: 22446
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 0.2830 (0.2812)  grad: 0.0933 (0.0930)  time: 0.4400  data: 0.0041  max mem: 22446
+train: [12] Total time: 0:03:02 (0.4557 s / it)
+train: [12] Summary: lr: 0.000134  loss: 0.2830 (0.2812)  grad: 0.0933 (0.0930)
+eval (validation): [12]  [ 0/63]  eta: 0:03:12    time: 3.0590  data: 2.8225  max mem: 22446
+eval (validation): [12]  [20/63]  eta: 0:00:21    time: 0.3812  data: 0.0046  max mem: 22446
+eval (validation): [12]  [40/63]  eta: 0:00:09    time: 0.3337  data: 0.0031  max mem: 22446
+eval (validation): [12]  [60/63]  eta: 0:00:01    time: 0.3105  data: 0.0033  max mem: 22446
+eval (validation): [12]  [62/63]  eta: 0:00:00    time: 0.3127  data: 0.0033  max mem: 22446
+eval (validation): [12] Total time: 0:00:24 (0.3891 s / it)
+cv: [12]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.185  acc: 0.974  f1: 0.970
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:21:45  lr: nan  time: 3.2637  data: 2.9061  max mem: 22446
+train: [13]  [ 20/400]  eta: 0:03:41  lr: 0.000133  loss: 0.2853 (0.2840)  grad: 0.0727 (0.0791)  time: 0.4493  data: 0.0025  max mem: 22446
+train: [13]  [ 40/400]  eta: 0:03:04  lr: 0.000131  loss: 0.2623 (0.2687)  grad: 0.0760 (0.0804)  time: 0.4388  data: 0.0040  max mem: 22446
+train: [13]  [ 60/400]  eta: 0:02:46  lr: 0.000130  loss: 0.2563 (0.2666)  grad: 0.0778 (0.0819)  time: 0.4406  data: 0.0044  max mem: 22446
+train: [13]  [ 80/400]  eta: 0:02:32  lr: 0.000128  loss: 0.2630 (0.2689)  grad: 0.0731 (0.0793)  time: 0.4427  data: 0.0044  max mem: 22446
+train: [13]  [100/400]  eta: 0:02:20  lr: 0.000127  loss: 0.2735 (0.2693)  grad: 0.0736 (0.0799)  time: 0.4304  data: 0.0040  max mem: 22446
+train: [13]  [120/400]  eta: 0:02:09  lr: 0.000125  loss: 0.2645 (0.2684)  grad: 0.0827 (0.0800)  time: 0.4397  data: 0.0043  max mem: 22446
+train: [13]  [140/400]  eta: 0:01:59  lr: 0.000124  loss: 0.2518 (0.2694)  grad: 0.0822 (0.0805)  time: 0.4389  data: 0.0043  max mem: 22446
+train: [13]  [160/400]  eta: 0:01:49  lr: 0.000122  loss: 0.2528 (0.2686)  grad: 0.0740 (0.0799)  time: 0.4345  data: 0.0041  max mem: 22446
+train: [13]  [180/400]  eta: 0:01:39  lr: 0.000120  loss: 0.2746 (0.2706)  grad: 0.0782 (0.0802)  time: 0.4289  data: 0.0042  max mem: 22446
+train: [13]  [200/400]  eta: 0:01:30  lr: 0.000119  loss: 0.2853 (0.2718)  grad: 0.0874 (0.0810)  time: 0.4384  data: 0.0040  max mem: 22446
+train: [13]  [220/400]  eta: 0:01:21  lr: 0.000117  loss: 0.2726 (0.2709)  grad: 0.0850 (0.0813)  time: 0.4390  data: 0.0042  max mem: 22446
+train: [13]  [240/400]  eta: 0:01:11  lr: 0.000116  loss: 0.2674 (0.2702)  grad: 0.0818 (0.0814)  time: 0.4328  data: 0.0041  max mem: 22446
+train: [13]  [260/400]  eta: 0:01:02  lr: 0.000114  loss: 0.2704 (0.2706)  grad: 0.0778 (0.0812)  time: 0.4393  data: 0.0042  max mem: 22446
+train: [13]  [280/400]  eta: 0:00:53  lr: 0.000113  loss: 0.2568 (0.2697)  grad: 0.0732 (0.0810)  time: 0.4415  data: 0.0043  max mem: 22446
+train: [13]  [300/400]  eta: 0:00:45  lr: 0.000111  loss: 0.2568 (0.2699)  grad: 0.0772 (0.0818)  time: 0.6218  data: 0.1874  max mem: 22446
+train: [13]  [320/400]  eta: 0:00:36  lr: 0.000110  loss: 0.2613 (0.2695)  grad: 0.0805 (0.0819)  time: 0.4343  data: 0.0028  max mem: 22446
+train: [13]  [340/400]  eta: 0:00:27  lr: 0.000108  loss: 0.2613 (0.2689)  grad: 0.0768 (0.0818)  time: 0.4271  data: 0.0041  max mem: 22446
+train: [13]  [360/400]  eta: 0:00:18  lr: 0.000107  loss: 0.2419 (0.2677)  grad: 0.0768 (0.0815)  time: 0.4419  data: 0.0042  max mem: 22446
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 0.2532 (0.2678)  grad: 0.0750 (0.0813)  time: 0.4410  data: 0.0043  max mem: 22446
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 0.2674 (0.2678)  grad: 0.0714 (0.0810)  time: 0.4408  data: 0.0045  max mem: 22446
+train: [13] Total time: 0:03:01 (0.4547 s / it)
+train: [13] Summary: lr: 0.000104  loss: 0.2674 (0.2678)  grad: 0.0714 (0.0810)
+eval (validation): [13]  [ 0/63]  eta: 0:03:18    time: 3.1471  data: 2.8600  max mem: 22446
+eval (validation): [13]  [20/63]  eta: 0:00:20    time: 0.3416  data: 0.0106  max mem: 22446
+eval (validation): [13]  [40/63]  eta: 0:00:09    time: 0.3303  data: 0.0030  max mem: 22446
+eval (validation): [13]  [60/63]  eta: 0:00:01    time: 0.3134  data: 0.0032  max mem: 22446
+eval (validation): [13]  [62/63]  eta: 0:00:00    time: 0.3119  data: 0.0032  max mem: 22446
+eval (validation): [13] Total time: 0:00:23 (0.3768 s / it)
+cv: [13]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 0.189  acc: 0.974  f1: 0.971
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:20:56  lr: nan  time: 3.1416  data: 2.7477  max mem: 22446
+train: [14]  [ 20/400]  eta: 0:03:41  lr: 0.000102  loss: 0.2689 (0.2643)  grad: 0.0707 (0.0703)  time: 0.4543  data: 0.0027  max mem: 22446
+train: [14]  [ 40/400]  eta: 0:03:03  lr: 0.000101  loss: 0.2689 (0.2633)  grad: 0.0715 (0.0734)  time: 0.4347  data: 0.0042  max mem: 22446
+train: [14]  [ 60/400]  eta: 0:02:44  lr: 0.000099  loss: 0.2491 (0.2592)  grad: 0.0726 (0.0739)  time: 0.4333  data: 0.0043  max mem: 22446
+train: [14]  [ 80/400]  eta: 0:02:31  lr: 0.000098  loss: 0.2468 (0.2577)  grad: 0.0751 (0.0752)  time: 0.4370  data: 0.0044  max mem: 22446
+train: [14]  [100/400]  eta: 0:02:19  lr: 0.000096  loss: 0.2456 (0.2569)  grad: 0.0757 (0.0754)  time: 0.4310  data: 0.0044  max mem: 22446
+train: [14]  [120/400]  eta: 0:02:08  lr: 0.000095  loss: 0.2654 (0.2611)  grad: 0.0731 (0.0759)  time: 0.4369  data: 0.0041  max mem: 22446
+train: [14]  [140/400]  eta: 0:01:59  lr: 0.000093  loss: 0.2861 (0.2650)  grad: 0.0740 (0.0764)  time: 0.4473  data: 0.0043  max mem: 22446
+train: [14]  [160/400]  eta: 0:01:49  lr: 0.000092  loss: 0.2625 (0.2635)  grad: 0.0739 (0.0758)  time: 0.4394  data: 0.0044  max mem: 22446
+train: [14]  [180/400]  eta: 0:01:39  lr: 0.000090  loss: 0.2507 (0.2628)  grad: 0.0682 (0.0754)  time: 0.4334  data: 0.0041  max mem: 22446
+train: [14]  [200/400]  eta: 0:01:30  lr: 0.000089  loss: 0.2507 (0.2621)  grad: 0.0665 (0.0750)  time: 0.4508  data: 0.0043  max mem: 22446
+train: [14]  [220/400]  eta: 0:01:21  lr: 0.000088  loss: 0.2432 (0.2621)  grad: 0.0681 (0.0751)  time: 0.4486  data: 0.0042  max mem: 22446
+train: [14]  [240/400]  eta: 0:01:12  lr: 0.000086  loss: 0.2423 (0.2602)  grad: 0.0725 (0.0749)  time: 0.4398  data: 0.0042  max mem: 22446
+train: [14]  [260/400]  eta: 0:01:03  lr: 0.000085  loss: 0.2433 (0.2598)  grad: 0.0725 (0.0750)  time: 0.4310  data: 0.0042  max mem: 22446
+train: [14]  [280/400]  eta: 0:00:53  lr: 0.000083  loss: 0.2732 (0.2610)  grad: 0.0734 (0.0749)  time: 0.4462  data: 0.0042  max mem: 22446
+train: [14]  [300/400]  eta: 0:00:45  lr: 0.000082  loss: 0.2610 (0.2606)  grad: 0.0717 (0.0749)  time: 0.5902  data: 0.1699  max mem: 22446
+train: [14]  [320/400]  eta: 0:00:36  lr: 0.000081  loss: 0.2490 (0.2603)  grad: 0.0722 (0.0748)  time: 0.4385  data: 0.0039  max mem: 22446
+train: [14]  [340/400]  eta: 0:00:27  lr: 0.000079  loss: 0.2575 (0.2607)  grad: 0.0717 (0.0746)  time: 0.4209  data: 0.0040  max mem: 22446
+train: [14]  [360/400]  eta: 0:00:18  lr: 0.000078  loss: 0.2640 (0.2608)  grad: 0.0771 (0.0748)  time: 0.4371  data: 0.0043  max mem: 22446
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 0.2676 (0.2611)  grad: 0.0773 (0.0748)  time: 0.4315  data: 0.0044  max mem: 22446
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 0.2615 (0.2611)  grad: 0.0750 (0.0746)  time: 0.4302  data: 0.0040  max mem: 22446
+train: [14] Total time: 0:03:01 (0.4529 s / it)
+train: [14] Summary: lr: 0.000075  loss: 0.2615 (0.2611)  grad: 0.0750 (0.0746)
+eval (validation): [14]  [ 0/63]  eta: 0:03:11    time: 3.0328  data: 2.7844  max mem: 22446
+eval (validation): [14]  [20/63]  eta: 0:00:20    time: 0.3464  data: 0.0123  max mem: 22446
+eval (validation): [14]  [40/63]  eta: 0:00:09    time: 0.3429  data: 0.0039  max mem: 22446
+eval (validation): [14]  [60/63]  eta: 0:00:01    time: 0.3153  data: 0.0032  max mem: 22446
+eval (validation): [14]  [62/63]  eta: 0:00:00    time: 0.3128  data: 0.0035  max mem: 22446
+eval (validation): [14] Total time: 0:00:24 (0.3813 s / it)
+cv: [14]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 0.185  acc: 0.974  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:19:45  lr: nan  time: 2.9646  data: 2.6313  max mem: 22446
+train: [15]  [ 20/400]  eta: 0:03:35  lr: 0.000074  loss: 0.2601 (0.2522)  grad: 0.0622 (0.0657)  time: 0.4481  data: 0.0036  max mem: 22446
+train: [15]  [ 40/400]  eta: 0:03:02  lr: 0.000072  loss: 0.2623 (0.2593)  grad: 0.0672 (0.0707)  time: 0.4431  data: 0.0035  max mem: 22446
+train: [15]  [ 60/400]  eta: 0:02:45  lr: 0.000071  loss: 0.2575 (0.2572)  grad: 0.0702 (0.0705)  time: 0.4446  data: 0.0042  max mem: 22446
+train: [15]  [ 80/400]  eta: 0:02:31  lr: 0.000070  loss: 0.2491 (0.2560)  grad: 0.0690 (0.0706)  time: 0.4350  data: 0.0040  max mem: 22446
+train: [15]  [100/400]  eta: 0:02:19  lr: 0.000068  loss: 0.2464 (0.2541)  grad: 0.0699 (0.0708)  time: 0.4341  data: 0.0042  max mem: 22446
+train: [15]  [120/400]  eta: 0:02:09  lr: 0.000067  loss: 0.2364 (0.2521)  grad: 0.0699 (0.0709)  time: 0.4402  data: 0.0043  max mem: 22446
+train: [15]  [140/400]  eta: 0:01:59  lr: 0.000066  loss: 0.2444 (0.2531)  grad: 0.0701 (0.0711)  time: 0.4486  data: 0.0042  max mem: 22446
+train: [15]  [160/400]  eta: 0:01:50  lr: 0.000064  loss: 0.2512 (0.2525)  grad: 0.0757 (0.0720)  time: 0.4509  data: 0.0043  max mem: 22446
+train: [15]  [180/400]  eta: 0:01:40  lr: 0.000063  loss: 0.2512 (0.2532)  grad: 0.0706 (0.0722)  time: 0.4334  data: 0.0041  max mem: 22446
+train: [15]  [200/400]  eta: 0:01:31  lr: 0.000062  loss: 0.2445 (0.2525)  grad: 0.0673 (0.0717)  time: 0.4493  data: 0.0042  max mem: 22446
+train: [15]  [220/400]  eta: 0:01:21  lr: 0.000061  loss: 0.2349 (0.2521)  grad: 0.0697 (0.0716)  time: 0.4407  data: 0.0041  max mem: 22446
+train: [15]  [240/400]  eta: 0:01:12  lr: 0.000059  loss: 0.2372 (0.2509)  grad: 0.0718 (0.0718)  time: 0.4404  data: 0.0044  max mem: 22446
+train: [15]  [260/400]  eta: 0:01:03  lr: 0.000058  loss: 0.2482 (0.2519)  grad: 0.0702 (0.0715)  time: 0.4384  data: 0.0043  max mem: 22446
+train: [15]  [280/400]  eta: 0:00:54  lr: 0.000057  loss: 0.2607 (0.2526)  grad: 0.0701 (0.0718)  time: 0.4529  data: 0.0044  max mem: 22446
+train: [15]  [300/400]  eta: 0:00:46  lr: 0.000056  loss: 0.2409 (0.2521)  grad: 0.0717 (0.0719)  time: 0.6266  data: 0.1760  max mem: 22446
+train: [15]  [320/400]  eta: 0:00:36  lr: 0.000054  loss: 0.2409 (0.2522)  grad: 0.0738 (0.0720)  time: 0.4284  data: 0.0028  max mem: 22446
+train: [15]  [340/400]  eta: 0:00:27  lr: 0.000053  loss: 0.2562 (0.2529)  grad: 0.0776 (0.0722)  time: 0.4343  data: 0.0039  max mem: 22446
+train: [15]  [360/400]  eta: 0:00:18  lr: 0.000052  loss: 0.2519 (0.2525)  grad: 0.0720 (0.0721)  time: 0.4464  data: 0.0043  max mem: 22446
+train: [15]  [380/400]  eta: 0:00:09  lr: 0.000051  loss: 0.2409 (0.2517)  grad: 0.0675 (0.0718)  time: 0.4449  data: 0.0041  max mem: 22446
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 0.2495 (0.2525)  grad: 0.0662 (0.0717)  time: 0.4415  data: 0.0041  max mem: 22446
+train: [15] Total time: 0:03:03 (0.4579 s / it)
+train: [15] Summary: lr: 0.000050  loss: 0.2495 (0.2525)  grad: 0.0662 (0.0717)
+eval (validation): [15]  [ 0/63]  eta: 0:03:15    time: 3.0976  data: 2.8591  max mem: 22446
+eval (validation): [15]  [20/63]  eta: 0:00:20    time: 0.3339  data: 0.0038  max mem: 22446
+eval (validation): [15]  [40/63]  eta: 0:00:09    time: 0.3283  data: 0.0032  max mem: 22446
+eval (validation): [15]  [60/63]  eta: 0:00:01    time: 0.3173  data: 0.0033  max mem: 22446
+eval (validation): [15]  [62/63]  eta: 0:00:00    time: 0.3156  data: 0.0033  max mem: 22446
+eval (validation): [15] Total time: 0:00:23 (0.3745 s / it)
+cv: [15]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.218  acc: 0.974  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [16]  [  0/400]  eta: 0:19:30  lr: nan  time: 2.9273  data: 2.5563  max mem: 22446
+train: [16]  [ 20/400]  eta: 0:03:39  lr: 0.000048  loss: 0.2332 (0.2477)  grad: 0.0691 (0.0704)  time: 0.4603  data: 0.0033  max mem: 22446
+train: [16]  [ 40/400]  eta: 0:03:02  lr: 0.000047  loss: 0.2514 (0.2514)  grad: 0.0691 (0.0710)  time: 0.4352  data: 0.0041  max mem: 22446
+train: [16]  [ 60/400]  eta: 0:02:44  lr: 0.000046  loss: 0.2514 (0.2501)  grad: 0.0689 (0.0707)  time: 0.4345  data: 0.0043  max mem: 22446
+train: [16]  [ 80/400]  eta: 0:02:31  lr: 0.000045  loss: 0.2422 (0.2520)  grad: 0.0704 (0.0710)  time: 0.4383  data: 0.0041  max mem: 22446
+train: [16]  [100/400]  eta: 0:02:19  lr: 0.000044  loss: 0.2563 (0.2537)  grad: 0.0704 (0.0710)  time: 0.4345  data: 0.0044  max mem: 22446
+train: [16]  [120/400]  eta: 0:02:09  lr: 0.000043  loss: 0.2419 (0.2520)  grad: 0.0666 (0.0707)  time: 0.4448  data: 0.0041  max mem: 22446
+train: [16]  [140/400]  eta: 0:01:59  lr: 0.000042  loss: 0.2419 (0.2520)  grad: 0.0686 (0.0711)  time: 0.4496  data: 0.0043  max mem: 22446
+train: [16]  [160/400]  eta: 0:01:49  lr: 0.000041  loss: 0.2487 (0.2503)  grad: 0.0718 (0.0712)  time: 0.4357  data: 0.0039  max mem: 22446
+train: [16]  [180/400]  eta: 0:01:40  lr: 0.000040  loss: 0.2531 (0.2516)  grad: 0.0654 (0.0710)  time: 0.4346  data: 0.0041  max mem: 22446
+train: [16]  [200/400]  eta: 0:01:30  lr: 0.000039  loss: 0.2536 (0.2519)  grad: 0.0668 (0.0712)  time: 0.4531  data: 0.0044  max mem: 22446
+train: [16]  [220/400]  eta: 0:01:21  lr: 0.000038  loss: 0.2520 (0.2517)  grad: 0.0650 (0.0706)  time: 0.4369  data: 0.0043  max mem: 22446
+train: [16]  [240/400]  eta: 0:01:12  lr: 0.000036  loss: 0.2520 (0.2523)  grad: 0.0654 (0.0705)  time: 0.4384  data: 0.0042  max mem: 22446
+train: [16]  [260/400]  eta: 0:01:03  lr: 0.000035  loss: 0.2551 (0.2535)  grad: 0.0718 (0.0705)  time: 0.4434  data: 0.0041  max mem: 22446
+train: [16]  [280/400]  eta: 0:00:54  lr: 0.000034  loss: 0.2578 (0.2532)  grad: 0.0724 (0.0709)  time: 0.4476  data: 0.0043  max mem: 22446
+train: [16]  [300/400]  eta: 0:00:46  lr: 0.000033  loss: 0.2533 (0.2537)  grad: 0.0727 (0.0710)  time: 0.6010  data: 0.1673  max mem: 22446
+train: [16]  [320/400]  eta: 0:00:36  lr: 0.000032  loss: 0.2533 (0.2535)  grad: 0.0727 (0.0712)  time: 0.4231  data: 0.0031  max mem: 22446
+train: [16]  [340/400]  eta: 0:00:27  lr: 0.000031  loss: 0.2579 (0.2543)  grad: 0.0737 (0.0715)  time: 0.4456  data: 0.0034  max mem: 22446
+train: [16]  [360/400]  eta: 0:00:18  lr: 0.000031  loss: 0.2598 (0.2545)  grad: 0.0726 (0.0716)  time: 0.4337  data: 0.0040  max mem: 22446
+train: [16]  [380/400]  eta: 0:00:09  lr: 0.000030  loss: 0.2385 (0.2532)  grad: 0.0704 (0.0715)  time: 0.4355  data: 0.0041  max mem: 22446
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 0.2358 (0.2529)  grad: 0.0704 (0.0716)  time: 0.4341  data: 0.0040  max mem: 22446
+train: [16] Total time: 0:03:01 (0.4548 s / it)
+train: [16] Summary: lr: 0.000029  loss: 0.2358 (0.2529)  grad: 0.0704 (0.0716)
+eval (validation): [16]  [ 0/63]  eta: 0:03:13    time: 3.0753  data: 2.8447  max mem: 22446
+eval (validation): [16]  [20/63]  eta: 0:00:19    time: 0.3264  data: 0.0035  max mem: 22446
+eval (validation): [16]  [40/63]  eta: 0:00:09    time: 0.3415  data: 0.0030  max mem: 22446
+eval (validation): [16]  [60/63]  eta: 0:00:01    time: 0.3148  data: 0.0037  max mem: 22446
+eval (validation): [16]  [62/63]  eta: 0:00:00    time: 0.3110  data: 0.0037  max mem: 22446
+eval (validation): [16] Total time: 0:00:23 (0.3755 s / it)
+cv: [16]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.214  acc: 0.975  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [17]  [  0/400]  eta: 0:20:01  lr: nan  time: 3.0040  data: 2.6247  max mem: 22446
+train: [17]  [ 20/400]  eta: 0:03:39  lr: 0.000028  loss: 0.2449 (0.2437)  grad: 0.0708 (0.0716)  time: 0.4569  data: 0.0027  max mem: 22446
+train: [17]  [ 40/400]  eta: 0:03:05  lr: 0.000027  loss: 0.2480 (0.2500)  grad: 0.0707 (0.0715)  time: 0.4474  data: 0.0042  max mem: 22446
+train: [17]  [ 60/400]  eta: 0:02:45  lr: 0.000026  loss: 0.2561 (0.2518)  grad: 0.0657 (0.0695)  time: 0.4269  data: 0.0043  max mem: 22446
+train: [17]  [ 80/400]  eta: 0:02:31  lr: 0.000025  loss: 0.2609 (0.2540)  grad: 0.0676 (0.0705)  time: 0.4361  data: 0.0043  max mem: 22446
+train: [17]  [100/400]  eta: 0:02:19  lr: 0.000024  loss: 0.2524 (0.2537)  grad: 0.0703 (0.0711)  time: 0.4366  data: 0.0038  max mem: 22446
+train: [17]  [120/400]  eta: 0:02:09  lr: 0.000023  loss: 0.2460 (0.2507)  grad: 0.0650 (0.0702)  time: 0.4387  data: 0.0042  max mem: 22446
+train: [17]  [140/400]  eta: 0:01:59  lr: 0.000023  loss: 0.2363 (0.2496)  grad: 0.0664 (0.0703)  time: 0.4484  data: 0.0042  max mem: 22446
+train: [17]  [160/400]  eta: 0:01:50  lr: 0.000022  loss: 0.2476 (0.2495)  grad: 0.0709 (0.0707)  time: 0.4513  data: 0.0045  max mem: 22446
+train: [17]  [180/400]  eta: 0:01:40  lr: 0.000021  loss: 0.2476 (0.2495)  grad: 0.0701 (0.0707)  time: 0.4256  data: 0.0041  max mem: 22446
+train: [17]  [200/400]  eta: 0:01:30  lr: 0.000020  loss: 0.2416 (0.2495)  grad: 0.0687 (0.0707)  time: 0.4386  data: 0.0042  max mem: 22446
+train: [17]  [220/400]  eta: 0:01:21  lr: 0.000019  loss: 0.2425 (0.2494)  grad: 0.0670 (0.0704)  time: 0.4532  data: 0.0043  max mem: 22446
+train: [17]  [240/400]  eta: 0:01:12  lr: 0.000019  loss: 0.2425 (0.2495)  grad: 0.0670 (0.0703)  time: 0.4342  data: 0.0043  max mem: 22446
+train: [17]  [260/400]  eta: 0:01:03  lr: 0.000018  loss: 0.2406 (0.2497)  grad: 0.0695 (0.0702)  time: 0.4464  data: 0.0042  max mem: 22446
+train: [17]  [280/400]  eta: 0:00:54  lr: 0.000017  loss: 0.2377 (0.2488)  grad: 0.0683 (0.0700)  time: 0.4329  data: 0.0042  max mem: 22446
+train: [17]  [300/400]  eta: 0:00:45  lr: 0.000016  loss: 0.2366 (0.2488)  grad: 0.0718 (0.0705)  time: 0.5987  data: 0.1697  max mem: 22446
+train: [17]  [320/400]  eta: 0:00:36  lr: 0.000016  loss: 0.2493 (0.2492)  grad: 0.0718 (0.0705)  time: 0.4223  data: 0.0035  max mem: 22446
+train: [17]  [340/400]  eta: 0:00:27  lr: 0.000015  loss: 0.2439 (0.2491)  grad: 0.0657 (0.0703)  time: 0.4471  data: 0.0039  max mem: 22446
+train: [17]  [360/400]  eta: 0:00:18  lr: 0.000014  loss: 0.2355 (0.2482)  grad: 0.0641 (0.0699)  time: 0.4460  data: 0.0041  max mem: 22446
+train: [17]  [380/400]  eta: 0:00:09  lr: 0.000014  loss: 0.2355 (0.2483)  grad: 0.0648 (0.0699)  time: 0.4305  data: 0.0041  max mem: 22446
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 0.2543 (0.2483)  grad: 0.0703 (0.0699)  time: 0.4334  data: 0.0041  max mem: 22446
+train: [17] Total time: 0:03:01 (0.4545 s / it)
+train: [17] Summary: lr: 0.000013  loss: 0.2543 (0.2483)  grad: 0.0703 (0.0699)
+eval (validation): [17]  [ 0/63]  eta: 0:03:18    time: 3.1536  data: 2.8708  max mem: 22446
+eval (validation): [17]  [20/63]  eta: 0:00:21    time: 0.3564  data: 0.0156  max mem: 22446
+eval (validation): [17]  [40/63]  eta: 0:00:09    time: 0.3300  data: 0.0033  max mem: 22446
+eval (validation): [17]  [60/63]  eta: 0:00:01    time: 0.3181  data: 0.0033  max mem: 22446
+eval (validation): [17]  [62/63]  eta: 0:00:00    time: 0.3138  data: 0.0032  max mem: 22446
+eval (validation): [17] Total time: 0:00:24 (0.3839 s / it)
+cv: [17]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.213  acc: 0.975  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:21:45  lr: nan  time: 3.2627  data: 2.9243  max mem: 22446
+train: [18]  [ 20/400]  eta: 0:03:34  lr: 0.000012  loss: 0.2527 (0.2527)  grad: 0.0645 (0.0684)  time: 0.4304  data: 0.0025  max mem: 22446
+train: [18]  [ 40/400]  eta: 0:03:04  lr: 0.000012  loss: 0.2502 (0.2520)  grad: 0.0645 (0.0673)  time: 0.4567  data: 0.0041  max mem: 22446
+train: [18]  [ 60/400]  eta: 0:02:45  lr: 0.000011  loss: 0.2318 (0.2485)  grad: 0.0635 (0.0674)  time: 0.4319  data: 0.0041  max mem: 22446
+train: [18]  [ 80/400]  eta: 0:02:32  lr: 0.000011  loss: 0.2359 (0.2474)  grad: 0.0632 (0.0677)  time: 0.4449  data: 0.0041  max mem: 22446
+train: [18]  [100/400]  eta: 0:02:20  lr: 0.000010  loss: 0.2501 (0.2514)  grad: 0.0730 (0.0693)  time: 0.4319  data: 0.0042  max mem: 22446
+train: [18]  [120/400]  eta: 0:02:09  lr: 0.000009  loss: 0.2524 (0.2493)  grad: 0.0747 (0.0698)  time: 0.4343  data: 0.0034  max mem: 22446
+train: [18]  [140/400]  eta: 0:01:59  lr: 0.000009  loss: 0.2416 (0.2487)  grad: 0.0678 (0.0695)  time: 0.4348  data: 0.0042  max mem: 22446
+train: [18]  [160/400]  eta: 0:01:49  lr: 0.000008  loss: 0.2342 (0.2471)  grad: 0.0651 (0.0696)  time: 0.4505  data: 0.0042  max mem: 22446
+train: [18]  [180/400]  eta: 0:01:40  lr: 0.000008  loss: 0.2422 (0.2466)  grad: 0.0709 (0.0701)  time: 0.4481  data: 0.0043  max mem: 22446
+train: [18]  [200/400]  eta: 0:01:30  lr: 0.000007  loss: 0.2433 (0.2464)  grad: 0.0724 (0.0702)  time: 0.4354  data: 0.0044  max mem: 22446
+train: [18]  [220/400]  eta: 0:01:21  lr: 0.000007  loss: 0.2411 (0.2462)  grad: 0.0689 (0.0700)  time: 0.4574  data: 0.0042  max mem: 22446
+train: [18]  [240/400]  eta: 0:01:12  lr: 0.000006  loss: 0.2347 (0.2459)  grad: 0.0688 (0.0701)  time: 0.4398  data: 0.0041  max mem: 22446
+train: [18]  [260/400]  eta: 0:01:03  lr: 0.000006  loss: 0.2493 (0.2466)  grad: 0.0686 (0.0698)  time: 0.4469  data: 0.0043  max mem: 22446
+train: [18]  [280/400]  eta: 0:00:54  lr: 0.000006  loss: 0.2481 (0.2464)  grad: 0.0689 (0.0700)  time: 0.4465  data: 0.0043  max mem: 22446
+train: [18]  [300/400]  eta: 0:00:46  lr: 0.000005  loss: 0.2481 (0.2464)  grad: 0.0715 (0.0701)  time: 0.6226  data: 0.1880  max mem: 22446
+train: [18]  [320/400]  eta: 0:00:36  lr: 0.000005  loss: 0.2521 (0.2469)  grad: 0.0688 (0.0701)  time: 0.4337  data: 0.0029  max mem: 22446
+train: [18]  [340/400]  eta: 0:00:27  lr: 0.000004  loss: 0.2553 (0.2475)  grad: 0.0683 (0.0700)  time: 0.4460  data: 0.0043  max mem: 22446
+train: [18]  [360/400]  eta: 0:00:18  lr: 0.000004  loss: 0.2400 (0.2468)  grad: 0.0681 (0.0700)  time: 0.4355  data: 0.0043  max mem: 22446
+train: [18]  [380/400]  eta: 0:00:09  lr: 0.000004  loss: 0.2333 (0.2463)  grad: 0.0683 (0.0701)  time: 0.4420  data: 0.0042  max mem: 22446
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 0.2406 (0.2459)  grad: 0.0687 (0.0701)  time: 0.4346  data: 0.0043  max mem: 22446
+train: [18] Total time: 0:03:03 (0.4579 s / it)
+train: [18] Summary: lr: 0.000003  loss: 0.2406 (0.2459)  grad: 0.0687 (0.0701)
+eval (validation): [18]  [ 0/63]  eta: 0:03:17    time: 3.1390  data: 2.8547  max mem: 22446
+eval (validation): [18]  [20/63]  eta: 0:00:21    time: 0.3611  data: 0.0031  max mem: 22446
+eval (validation): [18]  [40/63]  eta: 0:00:09    time: 0.3288  data: 0.0034  max mem: 22446
+eval (validation): [18]  [60/63]  eta: 0:00:01    time: 0.3079  data: 0.0032  max mem: 22446
+eval (validation): [18]  [62/63]  eta: 0:00:00    time: 0.3082  data: 0.0032  max mem: 22446
+eval (validation): [18] Total time: 0:00:24 (0.3812 s / it)
+cv: [18]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.212  acc: 0.975  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:21:41  lr: nan  time: 3.2547  data: 2.8604  max mem: 22446
+train: [19]  [ 20/400]  eta: 0:03:40  lr: 0.000003  loss: 0.2619 (0.2496)  grad: 0.0680 (0.0682)  time: 0.4470  data: 0.0032  max mem: 22446
+train: [19]  [ 40/400]  eta: 0:03:08  lr: 0.000003  loss: 0.2576 (0.2515)  grad: 0.0705 (0.0725)  time: 0.4632  data: 0.0040  max mem: 22446
+train: [19]  [ 60/400]  eta: 0:02:48  lr: 0.000002  loss: 0.2496 (0.2475)  grad: 0.0702 (0.0709)  time: 0.4377  data: 0.0043  max mem: 22446
+train: [19]  [ 80/400]  eta: 0:02:34  lr: 0.000002  loss: 0.2386 (0.2459)  grad: 0.0664 (0.0695)  time: 0.4422  data: 0.0043  max mem: 22446
+train: [19]  [100/400]  eta: 0:02:22  lr: 0.000002  loss: 0.2436 (0.2489)  grad: 0.0683 (0.0690)  time: 0.4500  data: 0.0043  max mem: 22446
+train: [19]  [120/400]  eta: 0:02:11  lr: 0.000002  loss: 0.2489 (0.2482)  grad: 0.0659 (0.0685)  time: 0.4421  data: 0.0043  max mem: 22446
+train: [19]  [140/400]  eta: 0:02:01  lr: 0.000001  loss: 0.2366 (0.2460)  grad: 0.0642 (0.0680)  time: 0.4428  data: 0.0042  max mem: 22446
+train: [19]  [160/400]  eta: 0:01:51  lr: 0.000001  loss: 0.2374 (0.2467)  grad: 0.0655 (0.0679)  time: 0.4654  data: 0.0041  max mem: 22446
+train: [19]  [180/400]  eta: 0:01:42  lr: 0.000001  loss: 0.2466 (0.2460)  grad: 0.0677 (0.0681)  time: 0.4553  data: 0.0042  max mem: 22446
+train: [19]  [200/400]  eta: 0:01:32  lr: 0.000001  loss: 0.2363 (0.2455)  grad: 0.0661 (0.0679)  time: 0.4440  data: 0.0038  max mem: 22446
+train: [19]  [220/400]  eta: 0:01:22  lr: 0.000001  loss: 0.2356 (0.2463)  grad: 0.0679 (0.0683)  time: 0.4427  data: 0.0041  max mem: 22446
+train: [19]  [240/400]  eta: 0:01:13  lr: 0.000001  loss: 0.2372 (0.2454)  grad: 0.0684 (0.0681)  time: 0.4590  data: 0.0043  max mem: 22446
+train: [19]  [260/400]  eta: 0:01:04  lr: 0.000000  loss: 0.2334 (0.2452)  grad: 0.0679 (0.0683)  time: 0.4609  data: 0.0046  max mem: 22446
+train: [19]  [280/400]  eta: 0:00:55  lr: 0.000000  loss: 0.2463 (0.2467)  grad: 0.0684 (0.0683)  time: 0.4394  data: 0.0042  max mem: 22446
+train: [19]  [300/400]  eta: 0:00:47  lr: 0.000000  loss: 0.2576 (0.2476)  grad: 0.0689 (0.0685)  time: 0.6291  data: 0.1731  max mem: 22446
+train: [19]  [320/400]  eta: 0:00:37  lr: 0.000000  loss: 0.2563 (0.2474)  grad: 0.0695 (0.0686)  time: 0.4390  data: 0.0031  max mem: 22446
+train: [19]  [340/400]  eta: 0:00:28  lr: 0.000000  loss: 0.2386 (0.2472)  grad: 0.0671 (0.0685)  time: 0.4433  data: 0.0044  max mem: 22446
+train: [19]  [360/400]  eta: 0:00:18  lr: 0.000000  loss: 0.2430 (0.2470)  grad: 0.0693 (0.0687)  time: 0.4666  data: 0.0052  max mem: 22446
+train: [19]  [380/400]  eta: 0:00:09  lr: 0.000000  loss: 0.2466 (0.2472)  grad: 0.0702 (0.0689)  time: 0.4551  data: 0.0032  max mem: 22446
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 0.2467 (0.2474)  grad: 0.0666 (0.0689)  time: 0.4463  data: 0.0040  max mem: 22446
+train: [19] Total time: 0:03:06 (0.4661 s / it)
+train: [19] Summary: lr: 0.000000  loss: 0.2467 (0.2474)  grad: 0.0666 (0.0689)
+eval (validation): [19]  [ 0/63]  eta: 0:03:46    time: 3.6014  data: 3.3104  max mem: 22446
+eval (validation): [19]  [20/63]  eta: 0:00:22    time: 0.3581  data: 0.0026  max mem: 22446
+eval (validation): [19]  [40/63]  eta: 0:00:09    time: 0.3377  data: 0.0033  max mem: 22446
+eval (validation): [19]  [60/63]  eta: 0:00:01    time: 0.3238  data: 0.0035  max mem: 22446
+eval (validation): [19]  [62/63]  eta: 0:00:00    time: 0.3220  data: 0.0033  max mem: 22446
+eval (validation): [19] Total time: 0:00:24 (0.3959 s / it)
+cv: [19]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.212  acc: 0.975  f1: 0.972
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.9749503968253969, "hparam": [12, 1.0], "hparam_id": 39, "epoch": 19, "is_best": false, "best_score": 0.9749503968253969}
+eval (train): [20]  [  0/297]  eta: 0:14:41    time: 2.9682  data: 2.6869  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:20    time: 0.3830  data: 0.0042  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:01:47    time: 0.3251  data: 0.0028  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:33    time: 0.3507  data: 0.0034  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:24    time: 0.3692  data: 0.0038  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:15    time: 0.3488  data: 0.0036  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:06    time: 0.3358  data: 0.0033  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:00:57    time: 0.3230  data: 0.0030  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:50    time: 0.3820  data: 0.0038  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:42    time: 0.3537  data: 0.0036  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:35    time: 0.3367  data: 0.0034  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:27    time: 0.3392  data: 0.0037  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:20    time: 0.3562  data: 0.0036  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:13    time: 0.3341  data: 0.0035  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:06    time: 0.3496  data: 0.0032  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3360  data: 0.0033  max mem: 22446
+eval (train): [20] Total time: 0:01:46 (0.3589 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:02:47    time: 2.6586  data: 2.3930  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:18    time: 0.3201  data: 0.0030  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:09    time: 0.3547  data: 0.0027  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3275  data: 0.0036  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3268  data: 0.0036  max mem: 22446
+eval (validation): [20] Total time: 0:00:23 (0.3748 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:03:37    time: 2.7557  data: 2.4873  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:26    time: 0.3360  data: 0.0034  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:15    time: 0.3256  data: 0.0026  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:07    time: 0.3418  data: 0.0033  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3179  data: 0.0032  max mem: 22446
+eval (test): [20] Total time: 0:00:28 (0.3656 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.9749503968253969, "hparam": [12, 1.0], "hparam_id": 39, "epoch": 16, "is_best": true, "best_score": 0.9749503968253969}
+eval (train): [20]  [  0/297]  eta: 0:13:19    time: 2.6912  data: 2.4209  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:10    time: 0.3590  data: 0.0217  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:01:42    time: 0.3212  data: 0.0026  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:29    time: 0.3367  data: 0.0034  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:19    time: 0.3280  data: 0.0033  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:11    time: 0.3415  data: 0.0034  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:02    time: 0.3265  data: 0.0033  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:00:55    time: 0.3261  data: 0.0032  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:47    time: 0.3347  data: 0.0030  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:40    time: 0.3311  data: 0.0031  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:33    time: 0.3406  data: 0.0032  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:26    time: 0.3798  data: 0.0039  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:19    time: 0.3381  data: 0.0034  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:12    time: 0.3365  data: 0.0035  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:05    time: 0.3348  data: 0.0033  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3108  data: 0.0031  max mem: 22446
+eval (train): [20] Total time: 0:01:42 (0.3463 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:02:59    time: 2.8419  data: 2.5422  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:21    time: 0.3791  data: 0.0054  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:09    time: 0.3451  data: 0.0030  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3326  data: 0.0035  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3278  data: 0.0034  max mem: 22446
+eval (validation): [20] Total time: 0:00:24 (0.3951 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:03:51    time: 2.9361  data: 2.7017  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:29    time: 0.3842  data: 0.0046  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:16    time: 0.3234  data: 0.0032  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:07    time: 0.3445  data: 0.0037  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3193  data: 0.0034  max mem: 22446
+eval (test): [20] Total time: 0:00:29 (0.3780 s / it)
+eval results:
+
+| model    | repr   | clf   | dataset      | ckpt   |   epoch |     lr |   wd |   hparam_id | hparam    | split      |       loss |     acc |   acc_std |      f1 |    f1_std |
+|:---------|:-------|:------|:-------------|:-------|--------:|-------:|-----:|------------:|:----------|:-----------|-----------:|--------:|----------:|--------:|----------:|
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      16 | 0.0036 | 0.05 |          39 | [12, 1.0] | train      | 2.4102e-05 | 1       | 0         | 1       | 0         |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      16 | 0.0036 | 0.05 |          39 | [12, 1.0] | validation | 0.21449    | 0.97495 | 0.0024294 | 0.97192 | 0.0031138 |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      16 | 0.0036 | 0.05 |          39 | [12, 1.0] | test       | 0.22988    | 0.9754  | 0.0021664 | 0.97007 | 0.0028718 |
+
+
+done! total time: 1:17:19

data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn
+model: flat_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null

data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 6, "eval/id_best": 22, "eval/lr_best": 0.00021599999999999996, "eval/wd_best": 0.05, "eval/train/loss": 2.149545192718506, "eval/train/acc": 0.3522849503672516, "eval/train/acc_std": 0.002339245471254718, "eval/train/f1": 0.29712255139127103, "eval/train/f1_std": 0.002368208750693375, "eval/validation/loss": 2.4543910026550293, "eval/validation/acc": 0.2593207825765965, "eval/validation/acc_std": 0.0056060123391739455, "eval/validation/f1": 0.20393290461595417, "eval/validation/f1_std": 0.004958709413239012, "eval/test/loss": 2.3971211910247803, "eval/test/acc": 0.2717996289424861, "eval/test/acc_std": 0.005388663388073568, "eval/test/f1": 0.20969777817050883, "eval/test/f1_std": 0.005083817199895967, "eval/testid/loss": 2.3186991214752197, "eval/testid/acc": 0.29053402737613265, "eval/testid/acc_std": 0.005916682826613068, "eval/testid/f1": 0.2350341574968312, "eval/testid/f1_std": 0.00553450952962364}

data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 19, "eval/last/lr_best": 0.00013199999999999998, "eval/last/wd_best": 0.05, "eval/last/train/loss": 1.9709069728851318, "eval/last/train/acc": 0.40711146624051137, "eval/last/train/acc_std": 0.002404276709929451, "eval/last/train/f1": 0.3544355887063153, "eval/last/train/f1_std": 0.0026238197370713873, "eval/last/validation/loss": 2.500664472579956, "eval/last/validation/acc": 0.25489110372831303, "eval/last/validation/acc_std": 0.005362370961265093, "eval/last/validation/f1": 0.19612619050533206, "eval/last/validation/f1_std": 0.00468147949882978, "eval/last/test/loss": 2.443652629852295, "eval/last/test/acc": 0.26679035250463823, "eval/last/test/acc_std": 0.005297818937253951, "eval/last/test/f1": 0.20887364491654814, "eval/last/test/f1_std": 0.005278194895978472, "eval/last/testid/loss": 2.232384443283081, "eval/last/testid/acc": 0.316560632350106, "eval/last/testid/acc_std": 0.006037131988037881, "eval/last/testid/f1": 0.2627563921704213, "eval/last/testid/f1_std": 0.005846194636393452}

data_scaling/n100_1/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
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",train,2.149545192718506,0.3522849503672516,0.002339245471254718,0.29712255139127103,0.002368208750693375
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",validation,2.4543910026550293,0.2593207825765965,0.0056060123391739455,0.20393290461595417,0.004958709413239012
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",test,2.3971211910247803,0.2717996289424861,0.005388663388073568,0.20969777817050883,0.005083817199895967
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",testid,2.3186991214752197,0.29053402737613265,0.005916682826613068,0.2350341574968312,0.00553450952962364

data_scaling/n100_1/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
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",train,2.149545192718506,0.3522849503672516,0.002339245471254718,0.29712255139127103,0.002368208750693375
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",validation,2.4543910026550293,0.2593207825765965,0.0056060123391739455,0.20393290461595417,0.004958709413239012
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",test,2.3971211910247803,0.2717996289424861,0.005388663388073568,0.20969777817050883,0.005083817199895967
+flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",testid,2.3186991214752197,0.29053402737613265,0.005916682826613068,0.2350341574968312,0.00553450952962364

data_scaling/n100_1/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
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",train,1.9709069728851318,0.40711146624051137,0.002404276709929451,0.3544355887063153,0.0026238197370713873
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",validation,2.500664472579956,0.25489110372831303,0.005362370961265093,0.19612619050533206,0.00468147949882978
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",test,2.443652629852295,0.26679035250463823,0.005297818937253951,0.20887364491654814,0.005278194895978472
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",testid,2.232384443283081,0.316560632350106,0.006037131988037881,0.2627563921704213,0.005846194636393452

data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/log.txt

@@ -0,0 +1,962 @@
+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 19:02:46
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn
+model: flat_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:24:22  lr: nan  time: 3.6561  data: 3.0336  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:45  lr: 0.000003  loss: 3.1519 (3.1628)  grad: 0.1580 (0.1621)  time: 0.4407  data: 0.0025  max mem: 22448
+train: [0]  [ 40/400]  eta: 0:03:06  lr: 0.000006  loss: 3.1580 (3.1585)  grad: 0.1580 (0.1621)  time: 0.4383  data: 0.0047  max mem: 22448
+train: [0]  [ 60/400]  eta: 0:02:49  lr: 0.000009  loss: 3.1619 (3.1619)  grad: 0.1616 (0.1618)  time: 0.4630  data: 0.0050  max mem: 22448
+train: [0]  [ 80/400]  eta: 0:02:36  lr: 0.000012  loss: 3.1601 (3.1598)  grad: 0.1616 (0.1612)  time: 0.4523  data: 0.0049  max mem: 22448
+train: [0]  [100/400]  eta: 0:02:24  lr: 0.000015  loss: 3.1596 (3.1616)  grad: 0.1545 (0.1603)  time: 0.4540  data: 0.0049  max mem: 22448
+train: [0]  [120/400]  eta: 0:02:13  lr: 0.000018  loss: 3.1596 (3.1599)  grad: 0.1434 (0.1580)  time: 0.4460  data: 0.0049  max mem: 22448
+train: [0]  [140/400]  eta: 0:02:02  lr: 0.000021  loss: 3.1511 (3.1589)  grad: 0.1458 (0.1577)  time: 0.4405  data: 0.0049  max mem: 22448
+train: [0]  [160/400]  eta: 0:01:52  lr: 0.000024  loss: 3.1429 (3.1557)  grad: 0.1611 (0.1589)  time: 0.4633  data: 0.0050  max mem: 22448
+train: [0]  [180/400]  eta: 0:01:43  lr: 0.000027  loss: 3.1319 (3.1533)  grad: 0.1592 (0.1587)  time: 0.4573  data: 0.0049  max mem: 22448
+train: [0]  [200/400]  eta: 0:01:33  lr: 0.000030  loss: 3.1268 (3.1521)  grad: 0.1463 (0.1575)  time: 0.4533  data: 0.0049  max mem: 22448
+train: [0]  [220/400]  eta: 0:01:23  lr: 0.000033  loss: 3.1423 (3.1509)  grad: 0.1529 (0.1574)  time: 0.4350  data: 0.0047  max mem: 22448
+train: [0]  [240/400]  eta: 0:01:14  lr: 0.000036  loss: 3.1324 (3.1482)  grad: 0.1529 (0.1570)  time: 0.4751  data: 0.0050  max mem: 22448
+train: [0]  [260/400]  eta: 0:01:04  lr: 0.000039  loss: 3.1130 (3.1459)  grad: 0.1520 (0.1566)  time: 0.4507  data: 0.0047  max mem: 22448
+train: [0]  [280/400]  eta: 0:00:55  lr: 0.000042  loss: 3.0948 (3.1420)  grad: 0.1531 (0.1565)  time: 0.4396  data: 0.0048  max mem: 22448
+train: [0]  [300/400]  eta: 0:00:46  lr: 0.000045  loss: 3.0791 (3.1372)  grad: 0.1551 (0.1566)  time: 0.4553  data: 0.0050  max mem: 22448
+train: [0]  [320/400]  eta: 0:00:36  lr: 0.000048  loss: 3.0771 (3.1337)  grad: 0.1596 (0.1573)  time: 0.4377  data: 0.0048  max mem: 22448
+train: [0]  [340/400]  eta: 0:00:27  lr: 0.000051  loss: 3.0740 (3.1304)  grad: 0.1646 (0.1576)  time: 0.4606  data: 0.0049  max mem: 22448
+train: [0]  [360/400]  eta: 0:00:18  lr: 0.000054  loss: 3.0641 (3.1265)  grad: 0.1655 (0.1585)  time: 0.4525  data: 0.0049  max mem: 22448
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 3.0629 (3.1230)  grad: 0.1732 (0.1594)  time: 0.4442  data: 0.0048  max mem: 22448
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.0623 (3.1203)  grad: 0.1803 (0.1603)  time: 0.4450  data: 0.0048  max mem: 22448
+train: [0] Total time: 0:03:03 (0.4588 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.0623 (3.1203)  grad: 0.1803 (0.1603)
+eval (validation): [0]  [ 0/85]  eta: 0:04:32    time: 3.2014  data: 2.9043  max mem: 22448
+eval (validation): [0]  [20/85]  eta: 0:00:31    time: 0.3559  data: 0.0034  max mem: 22448
+eval (validation): [0]  [40/85]  eta: 0:00:18    time: 0.3468  data: 0.0042  max mem: 22448
+eval (validation): [0]  [60/85]  eta: 0:00:09    time: 0.3227  data: 0.0040  max mem: 22448
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3265  data: 0.0041  max mem: 22448
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3233  data: 0.0041  max mem: 22448
+eval (validation): [0] Total time: 0:00:31 (0.3740 s / it)
+cv: [0]  best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00')  loss: 2.639  acc: 0.220  f1: 0.148
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:21:32  lr: nan  time: 3.2320  data: 2.8733  max mem: 22448
+train: [1]  [ 20/400]  eta: 0:03:39  lr: 0.000063  loss: 3.0144 (3.0179)  grad: 0.1794 (0.1732)  time: 0.4451  data: 0.0036  max mem: 22448
+train: [1]  [ 40/400]  eta: 0:03:04  lr: 0.000066  loss: 3.0271 (3.0211)  grad: 0.1682 (0.1685)  time: 0.4467  data: 0.0042  max mem: 22448
+train: [1]  [ 60/400]  eta: 0:02:46  lr: 0.000069  loss: 3.0078 (3.0076)  grad: 0.1625 (0.1693)  time: 0.4435  data: 0.0043  max mem: 22448
+train: [1]  [ 80/400]  eta: 0:02:33  lr: 0.000072  loss: 3.0029 (3.0081)  grad: 0.1749 (0.1726)  time: 0.4486  data: 0.0046  max mem: 22448
+train: [1]  [100/400]  eta: 0:02:21  lr: 0.000075  loss: 2.9950 (3.0013)  grad: 0.1791 (0.1751)  time: 0.4442  data: 0.0048  max mem: 22448
+train: [1]  [120/400]  eta: 0:02:11  lr: 0.000078  loss: 2.9887 (2.9998)  grad: 0.1811 (0.1766)  time: 0.4429  data: 0.0048  max mem: 22448
+train: [1]  [140/400]  eta: 0:02:01  lr: 0.000081  loss: 2.9887 (2.9964)  grad: 0.1816 (0.1778)  time: 0.4620  data: 0.0049  max mem: 22448
+train: [1]  [160/400]  eta: 0:01:51  lr: 0.000084  loss: 2.9905 (2.9968)  grad: 0.1816 (0.1784)  time: 0.4571  data: 0.0050  max mem: 22448
+train: [1]  [180/400]  eta: 0:01:42  lr: 0.000087  loss: 3.0006 (2.9962)  grad: 0.1799 (0.1795)  time: 0.4469  data: 0.0048  max mem: 22448
+train: [1]  [200/400]  eta: 0:01:32  lr: 0.000090  loss: 2.9776 (2.9943)  grad: 0.1806 (0.1801)  time: 0.4475  data: 0.0047  max mem: 22448
+train: [1]  [220/400]  eta: 0:01:23  lr: 0.000093  loss: 2.9423 (2.9875)  grad: 0.1938 (0.1822)  time: 0.4518  data: 0.0050  max mem: 22448
+train: [1]  [240/400]  eta: 0:01:13  lr: 0.000096  loss: 2.9295 (2.9846)  grad: 0.1976 (0.1828)  time: 0.4476  data: 0.0046  max mem: 22448
+train: [1]  [260/400]  eta: 0:01:04  lr: 0.000099  loss: 2.9593 (2.9822)  grad: 0.1875 (0.1834)  time: 0.4455  data: 0.0046  max mem: 22448
+train: [1]  [280/400]  eta: 0:00:54  lr: 0.000102  loss: 2.9502 (2.9789)  grad: 0.1845 (0.1837)  time: 0.4466  data: 0.0046  max mem: 22448
+train: [1]  [300/400]  eta: 0:00:45  lr: 0.000105  loss: 2.9451 (2.9777)  grad: 0.1860 (0.1842)  time: 0.4488  data: 0.0045  max mem: 22448
+train: [1]  [320/400]  eta: 0:00:36  lr: 0.000108  loss: 2.9189 (2.9736)  grad: 0.1937 (0.1850)  time: 0.4992  data: 0.0052  max mem: 22448
+train: [1]  [340/400]  eta: 0:00:27  lr: 0.000111  loss: 2.8834 (2.9690)  grad: 0.1939 (0.1854)  time: 0.4611  data: 0.0045  max mem: 22448
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 2.9085 (2.9676)  grad: 0.1896 (0.1857)  time: 0.4450  data: 0.0047  max mem: 22448
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 2.9266 (2.9644)  grad: 0.1926 (0.1865)  time: 0.4447  data: 0.0046  max mem: 22448
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 2.9182 (2.9625)  grad: 0.2047 (0.1879)  time: 0.4658  data: 0.0049  max mem: 22448
+train: [1] Total time: 0:03:03 (0.4594 s / it)
+train: [1] Summary: lr: 0.000120  loss: 2.9182 (2.9625)  grad: 0.2047 (0.1879)
+eval (validation): [1]  [ 0/85]  eta: 0:04:22    time: 3.0869  data: 2.8611  max mem: 22448
+eval (validation): [1]  [20/85]  eta: 0:00:30    time: 0.3401  data: 0.0038  max mem: 22448
+eval (validation): [1]  [40/85]  eta: 0:00:18    time: 0.3290  data: 0.0037  max mem: 22448
+eval (validation): [1]  [60/85]  eta: 0:00:09    time: 0.3287  data: 0.0038  max mem: 22448
+eval (validation): [1]  [80/85]  eta: 0:00:01    time: 0.3472  data: 0.0041  max mem: 22448
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.3268  data: 0.0040  max mem: 22448
+eval (validation): [1] Total time: 0:00:31 (0.3699 s / it)
+cv: [1]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 2.536  acc: 0.235  f1: 0.165
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:21:41  lr: nan  time: 3.2532  data: 2.9077  max mem: 22448
+train: [2]  [ 20/400]  eta: 0:03:44  lr: 0.000123  loss: 2.9181 (2.9037)  grad: 0.2269 (0.2260)  time: 0.4587  data: 0.0047  max mem: 22448
+train: [2]  [ 40/400]  eta: 0:03:08  lr: 0.000126  loss: 2.9207 (2.9125)  grad: 0.2261 (0.2254)  time: 0.4509  data: 0.0046  max mem: 22448
+train: [2]  [ 60/400]  eta: 0:02:49  lr: 0.000129  loss: 2.9207 (2.9162)  grad: 0.2217 (0.2256)  time: 0.4461  data: 0.0051  max mem: 22448
+train: [2]  [ 80/400]  eta: 0:02:34  lr: 0.000132  loss: 2.9104 (2.9158)  grad: 0.2426 (0.2356)  time: 0.4414  data: 0.0048  max mem: 22448
+train: [2]  [100/400]  eta: 0:02:23  lr: 0.000135  loss: 2.9231 (2.9290)  grad: 0.2881 (0.2873)  time: 0.4628  data: 0.0050  max mem: 22448
+train: [2]  [120/400]  eta: 0:02:13  lr: 0.000138  loss: 3.1841 (3.0388)  grad: 0.7536 (0.4742)  time: 0.4631  data: 0.0051  max mem: 22448
+train: [2]  [140/400]  eta: 0:02:03  lr: 0.000141  loss: 3.6530 (3.1418)  grad: 1.3606 (0.6330)  time: 0.4530  data: 0.0050  max mem: 22448
+WARNING: classifier 48 (50, 1.0) diverged (loss=72.02 > 63.56) at step 474. Freezing.
+train: [2]  [160/400]  eta: 0:01:52  lr: 0.000144  loss: 3.6530 (3.1647)  grad: 1.3231 (0.6670)  time: 0.4472  data: 0.0047  max mem: 22448
+train: [2]  [180/400]  eta: 0:01:42  lr: 0.000147  loss: 2.8721 (3.1288)  grad: 0.2185 (0.6168)  time: 0.4509  data: 0.0048  max mem: 22448
+train: [2]  [200/400]  eta: 0:01:33  lr: 0.000150  loss: 2.8460 (3.0995)  grad: 0.2182 (0.5777)  time: 0.4515  data: 0.0050  max mem: 22448
+train: [2]  [220/400]  eta: 0:01:23  lr: 0.000153  loss: 2.8595 (3.0805)  grad: 0.2325 (0.5473)  time: 0.4472  data: 0.0049  max mem: 22448
+train: [2]  [240/400]  eta: 0:01:14  lr: 0.000156  loss: 2.8802 (3.0653)  grad: 0.2362 (0.5224)  time: 0.4465  data: 0.0047  max mem: 22448
+train: [2]  [260/400]  eta: 0:01:04  lr: 0.000159  loss: 2.8863 (3.0506)  grad: 0.2608 (0.5032)  time: 0.4414  data: 0.0046  max mem: 22448
+train: [2]  [280/400]  eta: 0:00:55  lr: 0.000162  loss: 2.9041 (3.0612)  grad: 0.3344 (0.5355)  time: 0.4559  data: 0.0049  max mem: 22448
+WARNING: classifier 47 (43, 1.0) diverged (loss=83.21 > 63.56) at step 547. Freezing.
+train: [2]  [300/400]  eta: 0:00:46  lr: 0.000165  loss: 3.2146 (3.0856)  grad: 1.2067 (0.5799)  time: 0.4445  data: 0.0046  max mem: 22448
+train: [2]  [320/400]  eta: 0:00:36  lr: 0.000168  loss: 2.8553 (3.0685)  grad: 0.2255 (0.5569)  time: 0.4429  data: 0.0047  max mem: 22448
+train: [2]  [340/400]  eta: 0:00:27  lr: 0.000171  loss: 2.8163 (3.0550)  grad: 0.2160 (0.5371)  time: 0.4524  data: 0.0049  max mem: 22448
+train: [2]  [360/400]  eta: 0:00:18  lr: 0.000174  loss: 2.8163 (3.0434)  grad: 0.2136 (0.5193)  time: 0.4425  data: 0.0049  max mem: 22448
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 2.8585 (3.0334)  grad: 0.2267 (0.5042)  time: 0.4481  data: 0.0051  max mem: 22448
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 2.8165 (3.0219)  grad: 0.2225 (0.4897)  time: 0.4608  data: 0.0052  max mem: 22448
+train: [2] Total time: 0:03:03 (0.4581 s / it)
+train: [2] Summary: lr: 0.000180  loss: 2.8165 (3.0219)  grad: 0.2225 (0.4897)
+eval (validation): [2]  [ 0/85]  eta: 0:04:33    time: 3.2132  data: 2.9321  max mem: 22448
+eval (validation): [2]  [20/85]  eta: 0:00:32    time: 0.3697  data: 0.0046  max mem: 22448
+eval (validation): [2]  [40/85]  eta: 0:00:18    time: 0.3195  data: 0.0036  max mem: 22448
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3287  data: 0.0042  max mem: 22448
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3384  data: 0.0040  max mem: 22448
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3296  data: 0.0039  max mem: 22448
+eval (validation): [2] Total time: 0:00:31 (0.3746 s / it)
+cv: [2]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.517  acc: 0.249  f1: 0.180
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:21:41  lr: nan  time: 3.2534  data: 2.8967  max mem: 22448
+train: [3]  [ 20/400]  eta: 0:03:44  lr: 0.000183  loss: 2.7604 (2.7882)  grad: 0.2162 (0.2180)  time: 0.4567  data: 0.0030  max mem: 22448
+train: [3]  [ 40/400]  eta: 0:03:04  lr: 0.000186  loss: 2.8115 (2.8209)  grad: 0.2297 (0.2349)  time: 0.4333  data: 0.0045  max mem: 22448
+train: [3]  [ 60/400]  eta: 0:02:46  lr: 0.000189  loss: 2.8123 (2.8167)  grad: 0.2445 (0.2443)  time: 0.4417  data: 0.0047  max mem: 22448
+train: [3]  [ 80/400]  eta: 0:02:34  lr: 0.000192  loss: 2.8316 (2.8335)  grad: 0.3046 (0.2782)  time: 0.4613  data: 0.0049  max mem: 22448
+train: [3]  [100/400]  eta: 0:02:23  lr: 0.000195  loss: 2.9843 (2.9334)  grad: 0.4968 (0.4842)  time: 0.4576  data: 0.0047  max mem: 22448
+WARNING: classifier 46 (36, 1.0) diverged (loss=94.80 > 63.56) at step 651. Freezing.
+train: [3]  [120/400]  eta: 0:02:12  lr: 0.000198  loss: 2.9929 (2.9399)  grad: 0.8691 (0.4758)  time: 0.4481  data: 0.0047  max mem: 22448
+train: [3]  [140/400]  eta: 0:02:01  lr: 0.000201  loss: 2.8016 (2.9238)  grad: 0.2601 (0.4460)  time: 0.4425  data: 0.0049  max mem: 22448
+train: [3]  [160/400]  eta: 0:01:51  lr: 0.000204  loss: 2.8400 (2.9124)  grad: 0.2652 (0.4221)  time: 0.4380  data: 0.0049  max mem: 22448
+train: [3]  [180/400]  eta: 0:01:41  lr: 0.000207  loss: 2.8116 (2.8996)  grad: 0.2462 (0.4025)  time: 0.4485  data: 0.0049  max mem: 22448
+train: [3]  [200/400]  eta: 0:01:32  lr: 0.000210  loss: 2.8082 (2.8938)  grad: 0.2462 (0.3873)  time: 0.4447  data: 0.0050  max mem: 22448
+train: [3]  [220/400]  eta: 0:01:22  lr: 0.000213  loss: 2.8050 (2.8869)  grad: 0.2496 (0.3750)  time: 0.4420  data: 0.0049  max mem: 22448
+train: [3]  [240/400]  eta: 0:01:13  lr: 0.000216  loss: 2.7951 (2.8804)  grad: 0.2546 (0.3656)  time: 0.4362  data: 0.0048  max mem: 22448
+train: [3]  [260/400]  eta: 0:01:04  lr: 0.000219  loss: 2.8098 (2.8735)  grad: 0.2655 (0.3574)  time: 0.4540  data: 0.0049  max mem: 22448
+train: [3]  [280/400]  eta: 0:00:54  lr: 0.000222  loss: 2.8051 (2.8705)  grad: 0.2740 (0.3612)  time: 0.4536  data: 0.0051  max mem: 22448
+WARNING: classifier 45 (31, 1.0) diverged (loss=77.13 > 63.56) at step 746. Freezing.
+train: [3]  [300/400]  eta: 0:00:45  lr: 0.000225  loss: 2.8441 (2.8920)  grad: 0.3470 (0.3925)  time: 0.4384  data: 0.0050  max mem: 22448
+train: [3]  [320/400]  eta: 0:00:36  lr: 0.000228  loss: 2.7798 (2.8841)  grad: 0.2366 (0.3826)  time: 0.4344  data: 0.0050  max mem: 22448
+train: [3]  [340/400]  eta: 0:00:27  lr: 0.000231  loss: 2.7798 (2.8799)  grad: 0.2368 (0.3745)  time: 0.4278  data: 0.0046  max mem: 22448
+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 2.8335 (2.8777)  grad: 0.2456 (0.3671)  time: 0.4638  data: 0.0050  max mem: 22448
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 2.7839 (2.8724)  grad: 0.2471 (0.3607)  time: 0.4419  data: 0.0049  max mem: 22448
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.7739 (2.8672)  grad: 0.2367 (0.3542)  time: 0.4469  data: 0.0050  max mem: 22448
+train: [3] Total time: 0:03:01 (0.4532 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.7739 (2.8672)  grad: 0.2367 (0.3542)
+eval (validation): [3]  [ 0/85]  eta: 0:04:24    time: 3.1063  data: 2.8833  max mem: 22448
+eval (validation): [3]  [20/85]  eta: 0:00:30    time: 0.3408  data: 0.0044  max mem: 22448
+eval (validation): [3]  [40/85]  eta: 0:00:18    time: 0.3426  data: 0.0038  max mem: 22448
+eval (validation): [3]  [60/85]  eta: 0:00:09    time: 0.3425  data: 0.0041  max mem: 22448
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3249  data: 0.0040  max mem: 22448
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3160  data: 0.0040  max mem: 22448
+eval (validation): [3] Total time: 0:00:31 (0.3719 s / it)
+cv: [3]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 2.506  acc: 0.244  f1: 0.167
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [4]  [  0/400]  eta: 0:21:02  lr: nan  time: 3.1561  data: 2.8189  max mem: 22448
+train: [4]  [ 20/400]  eta: 0:03:36  lr: 0.000243  loss: 2.7207 (2.7299)  grad: 0.2352 (0.2420)  time: 0.4410  data: 0.0036  max mem: 22448
+train: [4]  [ 40/400]  eta: 0:03:03  lr: 0.000246  loss: 2.7207 (2.7326)  grad: 0.2380 (0.2399)  time: 0.4448  data: 0.0049  max mem: 22448
+train: [4]  [ 60/400]  eta: 0:02:46  lr: 0.000249  loss: 2.7319 (2.7349)  grad: 0.2340 (0.2378)  time: 0.4457  data: 0.0050  max mem: 22448
+train: [4]  [ 80/400]  eta: 0:02:32  lr: 0.000252  loss: 2.7451 (2.7394)  grad: 0.2320 (0.2364)  time: 0.4467  data: 0.0049  max mem: 22448
+train: [4]  [100/400]  eta: 0:02:21  lr: 0.000255  loss: 2.7635 (2.7477)  grad: 0.2383 (0.2385)  time: 0.4517  data: 0.0049  max mem: 22448
+train: [4]  [120/400]  eta: 0:02:11  lr: 0.000258  loss: 2.7635 (2.7456)  grad: 0.2436 (0.2407)  time: 0.4436  data: 0.0050  max mem: 22448
+train: [4]  [140/400]  eta: 0:02:00  lr: 0.000261  loss: 2.7507 (2.7438)  grad: 0.2556 (0.2439)  time: 0.4447  data: 0.0048  max mem: 22448
+train: [4]  [160/400]  eta: 0:01:50  lr: 0.000264  loss: 2.7507 (2.7460)  grad: 0.2728 (0.2471)  time: 0.4318  data: 0.0046  max mem: 22448
+train: [4]  [180/400]  eta: 0:01:41  lr: 0.000267  loss: 2.7529 (2.7507)  grad: 0.2735 (0.2502)  time: 0.4523  data: 0.0049  max mem: 22448
+train: [4]  [200/400]  eta: 0:01:31  lr: 0.000270  loss: 2.7445 (2.7471)  grad: 0.2782 (0.2534)  time: 0.4372  data: 0.0049  max mem: 22448
+train: [4]  [220/400]  eta: 0:01:22  lr: 0.000273  loss: 2.8204 (2.7679)  grad: 0.2995 (0.2841)  time: 0.4383  data: 0.0047  max mem: 22448
+WARNING: classifier 44 (26, 1.0) diverged (loss=111.36 > 63.56) at step 918. Freezing.
+train: [4]  [240/400]  eta: 0:01:12  lr: 0.000276  loss: 3.0038 (2.8326)  grad: 0.7440 (0.3773)  time: 0.4324  data: 0.0045  max mem: 22448
+train: [4]  [260/400]  eta: 0:01:03  lr: 0.000279  loss: 2.8522 (2.8280)  grad: 0.2674 (0.3681)  time: 0.4368  data: 0.0046  max mem: 22448
+train: [4]  [280/400]  eta: 0:00:54  lr: 0.000282  loss: 2.7859 (2.8230)  grad: 0.2579 (0.3615)  time: 0.4395  data: 0.0047  max mem: 22448
+train: [4]  [300/400]  eta: 0:00:45  lr: 0.000285  loss: 2.7917 (2.8209)  grad: 0.2946 (0.3581)  time: 0.4471  data: 0.0046  max mem: 22448
+train: [4]  [320/400]  eta: 0:00:36  lr: 0.000288  loss: 2.8104 (2.8217)  grad: 0.3349 (0.3616)  time: 0.4362  data: 0.0045  max mem: 22448
+train: [4]  [340/400]  eta: 0:00:26  lr: 0.000291  loss: 2.9292 (2.8480)  grad: 0.5442 (0.4060)  time: 0.4347  data: 0.0044  max mem: 22448
+WARNING: classifier 43 (22, 1.0) diverged (loss=65.61 > 63.56) at step 972. Freezing.
+train: [4]  [360/400]  eta: 0:00:17  lr: 0.000294  loss: 3.1947 (2.8573)  grad: 0.9933 (0.4096)  time: 0.4592  data: 0.0047  max mem: 22448
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.7724 (2.8517)  grad: 0.2295 (0.4001)  time: 0.4512  data: 0.0048  max mem: 22448
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.7312 (2.8454)  grad: 0.2290 (0.3916)  time: 0.4521  data: 0.0049  max mem: 22448
+train: [4] Total time: 0:03:00 (0.4504 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.7312 (2.8454)  grad: 0.2290 (0.3916)
+eval (validation): [4]  [ 0/85]  eta: 0:04:29    time: 3.1683  data: 2.9239  max mem: 22448
+eval (validation): [4]  [20/85]  eta: 0:00:30    time: 0.3388  data: 0.0045  max mem: 22448
+eval (validation): [4]  [40/85]  eta: 0:00:18    time: 0.3466  data: 0.0040  max mem: 22448
+eval (validation): [4]  [60/85]  eta: 0:00:09    time: 0.3353  data: 0.0041  max mem: 22448
+eval (validation): [4]  [80/85]  eta: 0:00:01    time: 0.3237  data: 0.0043  max mem: 22448
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3179  data: 0.0042  max mem: 22448
+eval (validation): [4] Total time: 0:00:31 (0.3716 s / it)
+cv: [4]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 2.518  acc: 0.244  f1: 0.192
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:21:16  lr: nan  time: 3.1914  data: 2.8522  max mem: 22448
+train: [5]  [ 20/400]  eta: 0:03:36  lr: 0.000300  loss: 2.6548 (2.6472)  grad: 0.2402 (0.2394)  time: 0.4377  data: 0.0047  max mem: 22448
+train: [5]  [ 40/400]  eta: 0:03:01  lr: 0.000300  loss: 2.6654 (2.7022)  grad: 0.2468 (0.2454)  time: 0.4374  data: 0.0045  max mem: 22448
+train: [5]  [ 60/400]  eta: 0:02:46  lr: 0.000300  loss: 2.7419 (2.7135)  grad: 0.2498 (0.2476)  time: 0.4562  data: 0.0049  max mem: 22448
+train: [5]  [ 80/400]  eta: 0:02:32  lr: 0.000300  loss: 2.7170 (2.7098)  grad: 0.2475 (0.2469)  time: 0.4443  data: 0.0051  max mem: 22448
+train: [5]  [100/400]  eta: 0:02:21  lr: 0.000300  loss: 2.7170 (2.7152)  grad: 0.2484 (0.2486)  time: 0.4480  data: 0.0052  max mem: 22448
+train: [5]  [120/400]  eta: 0:02:10  lr: 0.000300  loss: 2.6884 (2.7062)  grad: 0.2457 (0.2472)  time: 0.4438  data: 0.0051  max mem: 22448
+train: [5]  [140/400]  eta: 0:02:00  lr: 0.000300  loss: 2.6658 (2.6962)  grad: 0.2334 (0.2444)  time: 0.4338  data: 0.0048  max mem: 22448
+train: [5]  [160/400]  eta: 0:01:50  lr: 0.000299  loss: 2.6267 (2.6913)  grad: 0.2309 (0.2445)  time: 0.4542  data: 0.0048  max mem: 22448
+train: [5]  [180/400]  eta: 0:01:41  lr: 0.000299  loss: 2.6869 (2.6968)  grad: 0.2353 (0.2443)  time: 0.4481  data: 0.0048  max mem: 22448
+train: [5]  [200/400]  eta: 0:01:31  lr: 0.000299  loss: 2.7007 (2.6926)  grad: 0.2402 (0.2441)  time: 0.4407  data: 0.0047  max mem: 22448
+train: [5]  [220/400]  eta: 0:01:22  lr: 0.000299  loss: 2.6409 (2.6907)  grad: 0.2409 (0.2433)  time: 0.4400  data: 0.0048  max mem: 22448
+train: [5]  [240/400]  eta: 0:01:12  lr: 0.000299  loss: 2.6610 (2.6904)  grad: 0.2347 (0.2432)  time: 0.4343  data: 0.0050  max mem: 22448
+train: [5]  [260/400]  eta: 0:01:03  lr: 0.000299  loss: 2.6586 (2.6878)  grad: 0.2358 (0.2425)  time: 0.4331  data: 0.0046  max mem: 22448
+train: [5]  [280/400]  eta: 0:00:54  lr: 0.000298  loss: 2.6896 (2.6886)  grad: 0.2438 (0.2431)  time: 0.4485  data: 0.0048  max mem: 22448
+train: [5]  [300/400]  eta: 0:00:45  lr: 0.000298  loss: 2.6713 (2.6845)  grad: 0.2447 (0.2431)  time: 0.4354  data: 0.0047  max mem: 22448
+train: [5]  [320/400]  eta: 0:00:36  lr: 0.000298  loss: 2.6473 (2.6853)  grad: 0.2475 (0.2435)  time: 0.4347  data: 0.0045  max mem: 22448
+train: [5]  [340/400]  eta: 0:00:27  lr: 0.000298  loss: 2.6729 (2.6838)  grad: 0.2474 (0.2436)  time: 0.4492  data: 0.0049  max mem: 22448
+train: [5]  [360/400]  eta: 0:00:17  lr: 0.000297  loss: 2.6499 (2.6825)  grad: 0.2485 (0.2442)  time: 0.4399  data: 0.0044  max mem: 22448
+train: [5]  [380/400]  eta: 0:00:08  lr: 0.000297  loss: 2.6561 (2.6818)  grad: 0.2542 (0.2447)  time: 0.4364  data: 0.0045  max mem: 22448
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.6150 (2.6780)  grad: 0.2390 (0.2441)  time: 0.4512  data: 0.0047  max mem: 22448
+train: [5] Total time: 0:02:59 (0.4499 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.6150 (2.6780)  grad: 0.2390 (0.2441)
+eval (validation): [5]  [ 0/85]  eta: 0:04:29    time: 3.1688  data: 2.9115  max mem: 22448
+eval (validation): [5]  [20/85]  eta: 0:00:29    time: 0.3210  data: 0.0039  max mem: 22448
+eval (validation): [5]  [40/85]  eta: 0:00:17    time: 0.3377  data: 0.0033  max mem: 22448
+eval (validation): [5]  [60/85]  eta: 0:00:09    time: 0.3499  data: 0.0042  max mem: 22448
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3324  data: 0.0042  max mem: 22448
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3164  data: 0.0041  max mem: 22448
+eval (validation): [5] Total time: 0:00:31 (0.3702 s / it)
+cv: [5]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 2.459  acc: 0.256  f1: 0.186
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:20:57  lr: nan  time: 3.1442  data: 2.8218  max mem: 22448
+train: [6]  [ 20/400]  eta: 0:03:38  lr: 0.000296  loss: 2.6091 (2.6031)  grad: 0.2371 (0.2383)  time: 0.4472  data: 0.0039  max mem: 22448
+train: [6]  [ 40/400]  eta: 0:03:03  lr: 0.000296  loss: 2.6091 (2.6133)  grad: 0.2445 (0.2419)  time: 0.4387  data: 0.0045  max mem: 22448
+train: [6]  [ 60/400]  eta: 0:02:45  lr: 0.000296  loss: 2.5949 (2.6077)  grad: 0.2445 (0.2440)  time: 0.4436  data: 0.0047  max mem: 22448
+train: [6]  [ 80/400]  eta: 0:02:33  lr: 0.000295  loss: 2.6019 (2.6007)  grad: 0.2489 (0.2452)  time: 0.4529  data: 0.0049  max mem: 22448
+train: [6]  [100/400]  eta: 0:02:21  lr: 0.000295  loss: 2.6093 (2.6026)  grad: 0.2489 (0.2452)  time: 0.4487  data: 0.0050  max mem: 22448
+train: [6]  [120/400]  eta: 0:02:10  lr: 0.000295  loss: 2.6246 (2.6067)  grad: 0.2499 (0.2460)  time: 0.4301  data: 0.0050  max mem: 22448
+train: [6]  [140/400]  eta: 0:02:00  lr: 0.000294  loss: 2.6455 (2.6157)  grad: 0.2517 (0.2467)  time: 0.4479  data: 0.0048  max mem: 22448
+train: [6]  [160/400]  eta: 0:01:50  lr: 0.000294  loss: 2.6512 (2.6206)  grad: 0.2517 (0.2471)  time: 0.4400  data: 0.0050  max mem: 22448
+train: [6]  [180/400]  eta: 0:01:40  lr: 0.000293  loss: 2.6290 (2.6201)  grad: 0.2542 (0.2488)  time: 0.4401  data: 0.0050  max mem: 22448
+train: [6]  [200/400]  eta: 0:01:31  lr: 0.000293  loss: 2.6338 (2.6222)  grad: 0.2548 (0.2490)  time: 0.4381  data: 0.0049  max mem: 22448
+train: [6]  [220/400]  eta: 0:01:21  lr: 0.000292  loss: 2.6296 (2.6202)  grad: 0.2542 (0.2498)  time: 0.4361  data: 0.0044  max mem: 22448
+train: [6]  [240/400]  eta: 0:01:12  lr: 0.000292  loss: 2.6350 (2.6221)  grad: 0.2546 (0.2506)  time: 0.4263  data: 0.0045  max mem: 22448
+train: [6]  [260/400]  eta: 0:01:03  lr: 0.000291  loss: 2.6392 (2.6204)  grad: 0.2516 (0.2505)  time: 0.4451  data: 0.0049  max mem: 22448
+train: [6]  [280/400]  eta: 0:00:54  lr: 0.000291  loss: 2.5977 (2.6200)  grad: 0.2449 (0.2505)  time: 0.4392  data: 0.0049  max mem: 22448
+train: [6]  [300/400]  eta: 0:00:44  lr: 0.000290  loss: 2.6447 (2.6225)  grad: 0.2475 (0.2507)  time: 0.4412  data: 0.0047  max mem: 22448
+train: [6]  [320/400]  eta: 0:00:35  lr: 0.000290  loss: 2.6362 (2.6233)  grad: 0.2533 (0.2513)  time: 0.4437  data: 0.0051  max mem: 22448
+train: [6]  [340/400]  eta: 0:00:27  lr: 0.000289  loss: 2.6362 (2.6243)  grad: 0.2543 (0.2516)  time: 0.4606  data: 0.0049  max mem: 22448
+train: [6]  [360/400]  eta: 0:00:18  lr: 0.000288  loss: 2.6068 (2.6218)  grad: 0.2499 (0.2514)  time: 0.4497  data: 0.0047  max mem: 22448
+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 2.5907 (2.6235)  grad: 0.2477 (0.2514)  time: 0.4508  data: 0.0050  max mem: 22448
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.6067 (2.6230)  grad: 0.2447 (0.2509)  time: 0.4421  data: 0.0050  max mem: 22448
+train: [6] Total time: 0:03:00 (0.4504 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.6067 (2.6230)  grad: 0.2447 (0.2509)
+eval (validation): [6]  [ 0/85]  eta: 0:04:03    time: 2.8665  data: 2.6291  max mem: 22448
+eval (validation): [6]  [20/85]  eta: 0:00:29    time: 0.3377  data: 0.0043  max mem: 22448
+eval (validation): [6]  [40/85]  eta: 0:00:19    time: 0.3955  data: 0.0043  max mem: 22448
+eval (validation): [6]  [60/85]  eta: 0:00:10    time: 0.3531  data: 0.0042  max mem: 22448
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3299  data: 0.0043  max mem: 22448
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3171  data: 0.0040  max mem: 22448
+eval (validation): [6] Total time: 0:00:32 (0.3848 s / it)
+cv: [6]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.454  acc: 0.259  f1: 0.204
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:22:14  lr: nan  time: 3.3369  data: 2.9492  max mem: 22448
+train: [7]  [ 20/400]  eta: 0:03:42  lr: 0.000286  loss: 2.5263 (2.5395)  grad: 0.2433 (0.2488)  time: 0.4492  data: 0.0037  max mem: 22448
+train: [7]  [ 40/400]  eta: 0:03:04  lr: 0.000286  loss: 2.5308 (2.5493)  grad: 0.2496 (0.2534)  time: 0.4370  data: 0.0047  max mem: 22448
+train: [7]  [ 60/400]  eta: 0:02:47  lr: 0.000285  loss: 2.5383 (2.5414)  grad: 0.2590 (0.2564)  time: 0.4463  data: 0.0049  max mem: 22448
+train: [7]  [ 80/400]  eta: 0:02:33  lr: 0.000284  loss: 2.5383 (2.5541)  grad: 0.2538 (0.2537)  time: 0.4383  data: 0.0049  max mem: 22448
+train: [7]  [100/400]  eta: 0:02:23  lr: 0.000284  loss: 2.5388 (2.5488)  grad: 0.2466 (0.2531)  time: 0.4713  data: 0.0053  max mem: 22448
+train: [7]  [120/400]  eta: 0:02:12  lr: 0.000283  loss: 2.5392 (2.5502)  grad: 0.2495 (0.2538)  time: 0.4555  data: 0.0048  max mem: 22448
+train: [7]  [140/400]  eta: 0:02:02  lr: 0.000282  loss: 2.5548 (2.5512)  grad: 0.2495 (0.2533)  time: 0.4479  data: 0.0045  max mem: 22448
+train: [7]  [160/400]  eta: 0:01:51  lr: 0.000282  loss: 2.5249 (2.5503)  grad: 0.2497 (0.2532)  time: 0.4281  data: 0.0046  max mem: 22448
+train: [7]  [180/400]  eta: 0:01:41  lr: 0.000281  loss: 2.5918 (2.5552)  grad: 0.2537 (0.2541)  time: 0.4463  data: 0.0048  max mem: 22448
+train: [7]  [200/400]  eta: 0:01:32  lr: 0.000280  loss: 2.5708 (2.5540)  grad: 0.2534 (0.2540)  time: 0.4381  data: 0.0048  max mem: 22448
+train: [7]  [220/400]  eta: 0:01:22  lr: 0.000279  loss: 2.5268 (2.5504)  grad: 0.2521 (0.2541)  time: 0.4406  data: 0.0048  max mem: 22448
+train: [7]  [240/400]  eta: 0:01:13  lr: 0.000278  loss: 2.5508 (2.5535)  grad: 0.2502 (0.2544)  time: 0.4352  data: 0.0045  max mem: 22448
+train: [7]  [260/400]  eta: 0:01:03  lr: 0.000278  loss: 2.5721 (2.5516)  grad: 0.2526 (0.2542)  time: 0.4510  data: 0.0047  max mem: 22448
+train: [7]  [280/400]  eta: 0:00:54  lr: 0.000277  loss: 2.4895 (2.5490)  grad: 0.2526 (0.2541)  time: 0.4439  data: 0.0049  max mem: 22448
+train: [7]  [300/400]  eta: 0:00:45  lr: 0.000276  loss: 2.4859 (2.5485)  grad: 0.2538 (0.2544)  time: 0.4449  data: 0.0049  max mem: 22448
+train: [7]  [320/400]  eta: 0:00:36  lr: 0.000275  loss: 2.4943 (2.5476)  grad: 0.2541 (0.2542)  time: 0.4321  data: 0.0047  max mem: 22448
+train: [7]  [340/400]  eta: 0:00:27  lr: 0.000274  loss: 2.5208 (2.5458)  grad: 0.2514 (0.2539)  time: 0.4368  data: 0.0044  max mem: 22448
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 2.5208 (2.5467)  grad: 0.2542 (0.2541)  time: 0.4690  data: 0.0049  max mem: 22448
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 2.5465 (2.5474)  grad: 0.2556 (0.2546)  time: 0.4580  data: 0.0051  max mem: 22448
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.5791 (2.5483)  grad: 0.2657 (0.2554)  time: 0.4508  data: 0.0050  max mem: 22448
+train: [7] Total time: 0:03:01 (0.4538 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.5791 (2.5483)  grad: 0.2657 (0.2554)
+eval (validation): [7]  [ 0/85]  eta: 0:04:17    time: 3.0240  data: 2.7466  max mem: 22448
+eval (validation): [7]  [20/85]  eta: 0:00:30    time: 0.3398  data: 0.0039  max mem: 22448
+eval (validation): [7]  [40/85]  eta: 0:00:18    time: 0.3325  data: 0.0040  max mem: 22448
+eval (validation): [7]  [60/85]  eta: 0:00:09    time: 0.3475  data: 0.0044  max mem: 22448
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3328  data: 0.0043  max mem: 22448
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3252  data: 0.0041  max mem: 22448
+eval (validation): [7] Total time: 0:00:31 (0.3719 s / it)
+cv: [7]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.475  acc: 0.259  f1: 0.201
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:22:06  lr: nan  time: 3.3166  data: 2.9272  max mem: 22448
+train: [8]  [ 20/400]  eta: 0:03:50  lr: 0.000270  loss: 2.4062 (2.4251)  grad: 0.2437 (0.2473)  time: 0.4709  data: 0.0047  max mem: 22448
+train: [8]  [ 40/400]  eta: 0:03:12  lr: 0.000270  loss: 2.4397 (2.4577)  grad: 0.2518 (0.2530)  time: 0.4571  data: 0.0046  max mem: 22448
+train: [8]  [ 60/400]  eta: 0:02:50  lr: 0.000269  loss: 2.4670 (2.4715)  grad: 0.2527 (0.2523)  time: 0.4336  data: 0.0048  max mem: 22448
+train: [8]  [ 80/400]  eta: 0:02:36  lr: 0.000268  loss: 2.5111 (2.4823)  grad: 0.2535 (0.2557)  time: 0.4513  data: 0.0049  max mem: 22448
+train: [8]  [100/400]  eta: 0:02:25  lr: 0.000267  loss: 2.4778 (2.4818)  grad: 0.2611 (0.2579)  time: 0.4682  data: 0.0052  max mem: 22448
+train: [8]  [120/400]  eta: 0:02:14  lr: 0.000266  loss: 2.4582 (2.4815)  grad: 0.2630 (0.2601)  time: 0.4532  data: 0.0050  max mem: 22448
+train: [8]  [140/400]  eta: 0:02:03  lr: 0.000265  loss: 2.4692 (2.4863)  grad: 0.2662 (0.2609)  time: 0.4520  data: 0.0049  max mem: 22448
+train: [8]  [160/400]  eta: 0:01:52  lr: 0.000264  loss: 2.4929 (2.4873)  grad: 0.2690 (0.2628)  time: 0.4351  data: 0.0048  max mem: 22448
+train: [8]  [180/400]  eta: 0:01:42  lr: 0.000263  loss: 2.4720 (2.4843)  grad: 0.2644 (0.2625)  time: 0.4445  data: 0.0050  max mem: 22448
+train: [8]  [200/400]  eta: 0:01:33  lr: 0.000262  loss: 2.4755 (2.4864)  grad: 0.2579 (0.2625)  time: 0.4457  data: 0.0048  max mem: 22448
+train: [8]  [220/400]  eta: 0:01:23  lr: 0.000260  loss: 2.4903 (2.4883)  grad: 0.2639 (0.2627)  time: 0.4541  data: 0.0049  max mem: 22448
+train: [8]  [240/400]  eta: 0:01:13  lr: 0.000259  loss: 2.5035 (2.4883)  grad: 0.2643 (0.2634)  time: 0.4370  data: 0.0048  max mem: 22448
+train: [8]  [260/400]  eta: 0:01:04  lr: 0.000258  loss: 2.5035 (2.4903)  grad: 0.2623 (0.2636)  time: 0.4590  data: 0.0047  max mem: 22448
+train: [8]  [280/400]  eta: 0:00:55  lr: 0.000257  loss: 2.4978 (2.4896)  grad: 0.2567 (0.2636)  time: 0.4445  data: 0.0049  max mem: 22448
+train: [8]  [300/400]  eta: 0:00:45  lr: 0.000256  loss: 2.4563 (2.4897)  grad: 0.2549 (0.2635)  time: 0.4439  data: 0.0049  max mem: 22448
+train: [8]  [320/400]  eta: 0:00:36  lr: 0.000255  loss: 2.4834 (2.4893)  grad: 0.2532 (0.2628)  time: 0.4495  data: 0.0050  max mem: 22448
+train: [8]  [340/400]  eta: 0:00:27  lr: 0.000254  loss: 2.4834 (2.4894)  grad: 0.2589 (0.2630)  time: 0.4414  data: 0.0049  max mem: 22448
+train: [8]  [360/400]  eta: 0:00:18  lr: 0.000253  loss: 2.4833 (2.4896)  grad: 0.2574 (0.2625)  time: 0.4453  data: 0.0047  max mem: 22448
+train: [8]  [380/400]  eta: 0:00:09  lr: 0.000252  loss: 2.5094 (2.4902)  grad: 0.2498 (0.2621)  time: 0.4558  data: 0.0051  max mem: 22448
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.5094 (2.4908)  grad: 0.2586 (0.2630)  time: 0.4542  data: 0.0050  max mem: 22448
+train: [8] Total time: 0:03:03 (0.4576 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.5094 (2.4908)  grad: 0.2586 (0.2630)
+eval (validation): [8]  [ 0/85]  eta: 0:04:14    time: 2.9894  data: 2.7522  max mem: 22448
+eval (validation): [8]  [20/85]  eta: 0:00:33    time: 0.3946  data: 0.0059  max mem: 22448
+eval (validation): [8]  [40/85]  eta: 0:00:19    time: 0.3611  data: 0.0035  max mem: 22448
+eval (validation): [8]  [60/85]  eta: 0:00:10    time: 0.3535  data: 0.0042  max mem: 22448
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3274  data: 0.0039  max mem: 22448
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3228  data: 0.0038  max mem: 22448
+eval (validation): [8] Total time: 0:00:33 (0.3911 s / it)
+cv: [8]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.502  acc: 0.251  f1: 0.188
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:21:38  lr: nan  time: 3.2469  data: 2.8583  max mem: 22448
+train: [9]  [ 20/400]  eta: 0:03:49  lr: 0.000249  loss: 2.4419 (2.4208)  grad: 0.2682 (0.2734)  time: 0.4724  data: 0.0037  max mem: 22448
+train: [9]  [ 40/400]  eta: 0:03:11  lr: 0.000248  loss: 2.4481 (2.4527)  grad: 0.2574 (0.2649)  time: 0.4540  data: 0.0049  max mem: 22448
+train: [9]  [ 60/400]  eta: 0:02:50  lr: 0.000247  loss: 2.4541 (2.4427)  grad: 0.2501 (0.2601)  time: 0.4448  data: 0.0050  max mem: 22448
+train: [9]  [ 80/400]  eta: 0:02:35  lr: 0.000246  loss: 2.4350 (2.4542)  grad: 0.2589 (0.2631)  time: 0.4398  data: 0.0051  max mem: 22448
+train: [9]  [100/400]  eta: 0:02:23  lr: 0.000244  loss: 2.4600 (2.4557)  grad: 0.2683 (0.2640)  time: 0.4481  data: 0.0050  max mem: 22448
+train: [9]  [120/400]  eta: 0:02:13  lr: 0.000243  loss: 2.4221 (2.4518)  grad: 0.2603 (0.2637)  time: 0.4528  data: 0.0048  max mem: 22448
+train: [9]  [140/400]  eta: 0:02:02  lr: 0.000242  loss: 2.4338 (2.4500)  grad: 0.2603 (0.2642)  time: 0.4608  data: 0.0049  max mem: 22448
+train: [9]  [160/400]  eta: 0:01:52  lr: 0.000241  loss: 2.4408 (2.4456)  grad: 0.2622 (0.2644)  time: 0.4419  data: 0.0049  max mem: 22448
+train: [9]  [180/400]  eta: 0:01:42  lr: 0.000240  loss: 2.4343 (2.4475)  grad: 0.2622 (0.2654)  time: 0.4499  data: 0.0050  max mem: 22448
+train: [9]  [200/400]  eta: 0:01:32  lr: 0.000238  loss: 2.4170 (2.4453)  grad: 0.2623 (0.2655)  time: 0.4330  data: 0.0049  max mem: 22448
+train: [9]  [220/400]  eta: 0:01:22  lr: 0.000237  loss: 2.4096 (2.4435)  grad: 0.2630 (0.2659)  time: 0.4292  data: 0.0046  max mem: 22448
+train: [9]  [240/400]  eta: 0:01:13  lr: 0.000236  loss: 2.4544 (2.4472)  grad: 0.2636 (0.2654)  time: 0.4321  data: 0.0045  max mem: 22448
+train: [9]  [260/400]  eta: 0:01:04  lr: 0.000234  loss: 2.4521 (2.4455)  grad: 0.2584 (0.2647)  time: 0.4467  data: 0.0046  max mem: 22448
+train: [9]  [280/400]  eta: 0:00:54  lr: 0.000233  loss: 2.4405 (2.4455)  grad: 0.2603 (0.2648)  time: 0.4270  data: 0.0046  max mem: 22448
+train: [9]  [300/400]  eta: 0:00:45  lr: 0.000232  loss: 2.4401 (2.4456)  grad: 0.2702 (0.2652)  time: 0.4278  data: 0.0047  max mem: 22448
+train: [9]  [320/400]  eta: 0:00:36  lr: 0.000230  loss: 2.4291 (2.4464)  grad: 0.2626 (0.2648)  time: 0.4322  data: 0.0047  max mem: 22448
+train: [9]  [340/400]  eta: 0:00:27  lr: 0.000229  loss: 2.4254 (2.4450)  grad: 0.2595 (0.2650)  time: 0.4339  data: 0.0046  max mem: 22448
+train: [9]  [360/400]  eta: 0:00:17  lr: 0.000228  loss: 2.4469 (2.4455)  grad: 0.2683 (0.2654)  time: 0.4295  data: 0.0046  max mem: 22448
+train: [9]  [380/400]  eta: 0:00:08  lr: 0.000226  loss: 2.4469 (2.4443)  grad: 0.2615 (0.2651)  time: 0.4436  data: 0.0048  max mem: 22448
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.4595 (2.4457)  grad: 0.2584 (0.2652)  time: 0.4331  data: 0.0048  max mem: 22448
+train: [9] Total time: 0:02:59 (0.4491 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.4595 (2.4457)  grad: 0.2584 (0.2652)
+eval (validation): [9]  [ 0/85]  eta: 0:04:05    time: 2.8930  data: 2.6655  max mem: 22448
+eval (validation): [9]  [20/85]  eta: 0:00:29    time: 0.3322  data: 0.0033  max mem: 22448
+eval (validation): [9]  [40/85]  eta: 0:00:17    time: 0.3396  data: 0.0035  max mem: 22448
+eval (validation): [9]  [60/85]  eta: 0:00:09    time: 0.3252  data: 0.0039  max mem: 22448
+eval (validation): [9]  [80/85]  eta: 0:00:01    time: 0.3120  data: 0.0039  max mem: 22448
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3057  data: 0.0038  max mem: 22448
+eval (validation): [9] Total time: 0:00:30 (0.3596 s / it)
+cv: [9]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.490  acc: 0.255  f1: 0.189
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:23:23  lr: nan  time: 3.5081  data: 3.1778  max mem: 22448
+train: [10]  [ 20/400]  eta: 0:03:38  lr: 0.000224  loss: 2.3762 (2.3771)  grad: 0.2533 (0.2665)  time: 0.4293  data: 0.0030  max mem: 22448
+train: [10]  [ 40/400]  eta: 0:03:01  lr: 0.000222  loss: 2.3671 (2.3710)  grad: 0.2608 (0.2661)  time: 0.4293  data: 0.0042  max mem: 22448
+train: [10]  [ 60/400]  eta: 0:02:43  lr: 0.000221  loss: 2.3872 (2.3936)  grad: 0.2562 (0.2624)  time: 0.4302  data: 0.0049  max mem: 22448
+train: [10]  [ 80/400]  eta: 0:02:32  lr: 0.000220  loss: 2.4268 (2.3876)  grad: 0.2535 (0.2610)  time: 0.4602  data: 0.0050  max mem: 22448
+train: [10]  [100/400]  eta: 0:02:21  lr: 0.000218  loss: 2.3715 (2.3857)  grad: 0.2595 (0.2614)  time: 0.4504  data: 0.0051  max mem: 22448
+train: [10]  [120/400]  eta: 0:02:10  lr: 0.000217  loss: 2.3715 (2.3857)  grad: 0.2608 (0.2618)  time: 0.4515  data: 0.0050  max mem: 22448
+train: [10]  [140/400]  eta: 0:02:00  lr: 0.000215  loss: 2.3801 (2.3889)  grad: 0.2608 (0.2624)  time: 0.4482  data: 0.0049  max mem: 22448
+train: [10]  [160/400]  eta: 0:01:50  lr: 0.000214  loss: 2.3877 (2.3878)  grad: 0.2595 (0.2627)  time: 0.4271  data: 0.0047  max mem: 22448
+train: [10]  [180/400]  eta: 0:01:40  lr: 0.000213  loss: 2.3877 (2.3907)  grad: 0.2633 (0.2630)  time: 0.4531  data: 0.0050  max mem: 22448
+train: [10]  [200/400]  eta: 0:01:31  lr: 0.000211  loss: 2.3971 (2.3926)  grad: 0.2636 (0.2635)  time: 0.4664  data: 0.0051  max mem: 22448
+train: [10]  [220/400]  eta: 0:01:22  lr: 0.000210  loss: 2.3971 (2.3925)  grad: 0.2616 (0.2637)  time: 0.4467  data: 0.0051  max mem: 22448
+train: [10]  [240/400]  eta: 0:01:13  lr: 0.000208  loss: 2.3783 (2.3930)  grad: 0.2607 (0.2635)  time: 0.4409  data: 0.0049  max mem: 22448
+train: [10]  [260/400]  eta: 0:01:03  lr: 0.000207  loss: 2.3934 (2.3933)  grad: 0.2592 (0.2635)  time: 0.4524  data: 0.0051  max mem: 22448
+train: [10]  [280/400]  eta: 0:00:54  lr: 0.000205  loss: 2.4219 (2.3945)  grad: 0.2613 (0.2639)  time: 0.4400  data: 0.0049  max mem: 22448
+train: [10]  [300/400]  eta: 0:00:45  lr: 0.000204  loss: 2.3808 (2.3925)  grad: 0.2636 (0.2638)  time: 0.4412  data: 0.0048  max mem: 22448
+train: [10]  [320/400]  eta: 0:00:36  lr: 0.000202  loss: 2.3748 (2.3922)  grad: 0.2641 (0.2642)  time: 0.4603  data: 0.0049  max mem: 22448
+train: [10]  [340/400]  eta: 0:00:27  lr: 0.000201  loss: 2.3775 (2.3932)  grad: 0.2658 (0.2642)  time: 0.4508  data: 0.0050  max mem: 22448
+train: [10]  [360/400]  eta: 0:00:18  lr: 0.000199  loss: 2.3941 (2.3922)  grad: 0.2572 (0.2637)  time: 0.4528  data: 0.0050  max mem: 22448
+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 2.3642 (2.3915)  grad: 0.2572 (0.2638)  time: 0.4466  data: 0.0048  max mem: 22448
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.3765 (2.3930)  grad: 0.2685 (0.2641)  time: 0.4457  data: 0.0047  max mem: 22448
+train: [10] Total time: 0:03:01 (0.4545 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.3765 (2.3930)  grad: 0.2685 (0.2641)
+eval (validation): [10]  [ 0/85]  eta: 0:04:34    time: 3.2316  data: 2.9840  max mem: 22448
+eval (validation): [10]  [20/85]  eta: 0:00:32    time: 0.3573  data: 0.0147  max mem: 22448
+eval (validation): [10]  [40/85]  eta: 0:00:19    time: 0.3583  data: 0.0039  max mem: 22448
+eval (validation): [10]  [60/85]  eta: 0:00:10    time: 0.3529  data: 0.0039  max mem: 22448
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.3285  data: 0.0040  max mem: 22448
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.3195  data: 0.0038  max mem: 22448
+eval (validation): [10] Total time: 0:00:32 (0.3848 s / it)
+cv: [10]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.486  acc: 0.253  f1: 0.191
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:20:32  lr: nan  time: 3.0803  data: 2.7516  max mem: 22448
+train: [11]  [ 20/400]  eta: 0:03:43  lr: 0.000195  loss: 2.3221 (2.3272)  grad: 0.2521 (0.2582)  time: 0.4632  data: 0.0049  max mem: 22448
+train: [11]  [ 40/400]  eta: 0:03:06  lr: 0.000193  loss: 2.3441 (2.3425)  grad: 0.2567 (0.2595)  time: 0.4443  data: 0.0042  max mem: 22448
+train: [11]  [ 60/400]  eta: 0:02:45  lr: 0.000192  loss: 2.3197 (2.3375)  grad: 0.2632 (0.2632)  time: 0.4229  data: 0.0047  max mem: 22448
+train: [11]  [ 80/400]  eta: 0:02:32  lr: 0.000190  loss: 2.3190 (2.3493)  grad: 0.2665 (0.2656)  time: 0.4451  data: 0.0047  max mem: 22448
+train: [11]  [100/400]  eta: 0:02:20  lr: 0.000189  loss: 2.3190 (2.3439)  grad: 0.2641 (0.2651)  time: 0.4374  data: 0.0047  max mem: 22448
+train: [11]  [120/400]  eta: 0:02:09  lr: 0.000187  loss: 2.2651 (2.3337)  grad: 0.2570 (0.2656)  time: 0.4310  data: 0.0048  max mem: 22448
+train: [11]  [140/400]  eta: 0:01:59  lr: 0.000186  loss: 2.3265 (2.3388)  grad: 0.2669 (0.2663)  time: 0.4522  data: 0.0048  max mem: 22448
+train: [11]  [160/400]  eta: 0:01:50  lr: 0.000184  loss: 2.3583 (2.3440)  grad: 0.2707 (0.2668)  time: 0.4439  data: 0.0047  max mem: 22448
+train: [11]  [180/400]  eta: 0:01:40  lr: 0.000183  loss: 2.3185 (2.3434)  grad: 0.2771 (0.2686)  time: 0.4423  data: 0.0045  max mem: 22448
+train: [11]  [200/400]  eta: 0:01:30  lr: 0.000181  loss: 2.3296 (2.3480)  grad: 0.2698 (0.2689)  time: 0.4356  data: 0.0046  max mem: 22448
+train: [11]  [220/400]  eta: 0:01:21  lr: 0.000180  loss: 2.4275 (2.3557)  grad: 0.2663 (0.2686)  time: 0.4327  data: 0.0047  max mem: 22448
+train: [11]  [240/400]  eta: 0:01:12  lr: 0.000178  loss: 2.4314 (2.3575)  grad: 0.2638 (0.2691)  time: 0.4284  data: 0.0046  max mem: 22448
+train: [11]  [260/400]  eta: 0:01:03  lr: 0.000177  loss: 2.3593 (2.3588)  grad: 0.2722 (0.2695)  time: 0.4415  data: 0.0048  max mem: 22448
+train: [11]  [280/400]  eta: 0:00:53  lr: 0.000175  loss: 2.3593 (2.3597)  grad: 0.2722 (0.2695)  time: 0.4327  data: 0.0047  max mem: 22448
+train: [11]  [300/400]  eta: 0:00:44  lr: 0.000174  loss: 2.3685 (2.3617)  grad: 0.2653 (0.2693)  time: 0.4242  data: 0.0046  max mem: 22448
+train: [11]  [320/400]  eta: 0:00:35  lr: 0.000172  loss: 2.3642 (2.3621)  grad: 0.2719 (0.2697)  time: 0.4265  data: 0.0044  max mem: 22448
+train: [11]  [340/400]  eta: 0:00:26  lr: 0.000170  loss: 2.3518 (2.3623)  grad: 0.2726 (0.2703)  time: 0.4284  data: 0.0047  max mem: 22448
+train: [11]  [360/400]  eta: 0:00:17  lr: 0.000169  loss: 2.3458 (2.3599)  grad: 0.2671 (0.2704)  time: 0.4304  data: 0.0046  max mem: 22448
+train: [11]  [380/400]  eta: 0:00:08  lr: 0.000167  loss: 2.2947 (2.3555)  grad: 0.2653 (0.2699)  time: 0.4294  data: 0.0048  max mem: 22448
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.3337 (2.3590)  grad: 0.2560 (0.2693)  time: 0.4176  data: 0.0046  max mem: 22448
+train: [11] Total time: 0:02:57 (0.4426 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.3337 (2.3590)  grad: 0.2560 (0.2693)
+eval (validation): [11]  [ 0/85]  eta: 0:05:02    time: 3.5538  data: 3.2683  max mem: 22448
+eval (validation): [11]  [20/85]  eta: 0:00:32    time: 0.3460  data: 0.0029  max mem: 22448
+eval (validation): [11]  [40/85]  eta: 0:00:19    time: 0.3432  data: 0.0041  max mem: 22448
+eval (validation): [11]  [60/85]  eta: 0:00:10    time: 0.3878  data: 0.0045  max mem: 22448
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.3519  data: 0.0044  max mem: 22448
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.3411  data: 0.0041  max mem: 22448
+eval (validation): [11] Total time: 0:00:33 (0.3965 s / it)
+cv: [11]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.486  acc: 0.257  f1: 0.191
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:21:03  lr: nan  time: 3.1593  data: 2.7814  max mem: 22448
+train: [12]  [ 20/400]  eta: 0:03:36  lr: 0.000164  loss: 2.2754 (2.2639)  grad: 0.2538 (0.2526)  time: 0.4401  data: 0.0028  max mem: 22448
+train: [12]  [ 40/400]  eta: 0:03:04  lr: 0.000163  loss: 2.2754 (2.2827)  grad: 0.2579 (0.2603)  time: 0.4535  data: 0.0052  max mem: 22448
+train: [12]  [ 60/400]  eta: 0:02:46  lr: 0.000161  loss: 2.2671 (2.2817)  grad: 0.2553 (0.2582)  time: 0.4401  data: 0.0049  max mem: 22448
+train: [12]  [ 80/400]  eta: 0:02:32  lr: 0.000160  loss: 2.2869 (2.2873)  grad: 0.2543 (0.2571)  time: 0.4415  data: 0.0048  max mem: 22448
+train: [12]  [100/400]  eta: 0:02:20  lr: 0.000158  loss: 2.2886 (2.2843)  grad: 0.2591 (0.2575)  time: 0.4368  data: 0.0048  max mem: 22448
+train: [12]  [120/400]  eta: 0:02:09  lr: 0.000156  loss: 2.2884 (2.2903)  grad: 0.2567 (0.2571)  time: 0.4364  data: 0.0048  max mem: 22448
+train: [12]  [140/400]  eta: 0:01:59  lr: 0.000155  loss: 2.3124 (2.2944)  grad: 0.2630 (0.2601)  time: 0.4349  data: 0.0049  max mem: 22448
+train: [12]  [160/400]  eta: 0:01:49  lr: 0.000153  loss: 2.3338 (2.2947)  grad: 0.2775 (0.2621)  time: 0.4353  data: 0.0051  max mem: 22448
+train: [12]  [180/400]  eta: 0:01:40  lr: 0.000152  loss: 2.3032 (2.2956)  grad: 0.2702 (0.2632)  time: 0.4391  data: 0.0049  max mem: 22448
+train: [12]  [200/400]  eta: 0:01:30  lr: 0.000150  loss: 2.3187 (2.3012)  grad: 0.2687 (0.2638)  time: 0.4412  data: 0.0050  max mem: 22448
+train: [12]  [220/400]  eta: 0:01:21  lr: 0.000149  loss: 2.3361 (2.3052)  grad: 0.2641 (0.2638)  time: 0.4264  data: 0.0048  max mem: 22448
+train: [12]  [240/400]  eta: 0:01:11  lr: 0.000147  loss: 2.3019 (2.3028)  grad: 0.2715 (0.2657)  time: 0.4319  data: 0.0047  max mem: 22448
+train: [12]  [260/400]  eta: 0:01:02  lr: 0.000145  loss: 2.2742 (2.3032)  grad: 0.2726 (0.2657)  time: 0.4453  data: 0.0050  max mem: 22448
+train: [12]  [280/400]  eta: 0:00:53  lr: 0.000144  loss: 2.2822 (2.2999)  grad: 0.2628 (0.2658)  time: 0.4396  data: 0.0049  max mem: 22448
+train: [12]  [300/400]  eta: 0:00:44  lr: 0.000142  loss: 2.2839 (2.3029)  grad: 0.2721 (0.2668)  time: 0.4308  data: 0.0045  max mem: 22448
+train: [12]  [320/400]  eta: 0:00:35  lr: 0.000141  loss: 2.3311 (2.3052)  grad: 0.2727 (0.2673)  time: 0.4454  data: 0.0049  max mem: 22448
+train: [12]  [340/400]  eta: 0:00:26  lr: 0.000139  loss: 2.3253 (2.3051)  grad: 0.2697 (0.2673)  time: 0.4450  data: 0.0049  max mem: 22448
+train: [12]  [360/400]  eta: 0:00:17  lr: 0.000138  loss: 2.3050 (2.3049)  grad: 0.2652 (0.2671)  time: 0.4440  data: 0.0047  max mem: 22448
+train: [12]  [380/400]  eta: 0:00:08  lr: 0.000136  loss: 2.3050 (2.3051)  grad: 0.2651 (0.2672)  time: 0.4251  data: 0.0046  max mem: 22448
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.2878 (2.3039)  grad: 0.2697 (0.2676)  time: 0.4578  data: 0.0049  max mem: 22448
+train: [12] Total time: 0:02:58 (0.4469 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.2878 (2.3039)  grad: 0.2697 (0.2676)
+eval (validation): [12]  [ 0/85]  eta: 0:04:31    time: 3.1951  data: 2.9591  max mem: 22448
+eval (validation): [12]  [20/85]  eta: 0:00:31    time: 0.3421  data: 0.0046  max mem: 22448
+eval (validation): [12]  [40/85]  eta: 0:00:18    time: 0.3430  data: 0.0035  max mem: 22448
+eval (validation): [12]  [60/85]  eta: 0:00:09    time: 0.3429  data: 0.0034  max mem: 22448
+eval (validation): [12]  [80/85]  eta: 0:00:01    time: 0.3326  data: 0.0042  max mem: 22448
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.3262  data: 0.0041  max mem: 22448
+eval (validation): [12] Total time: 0:00:31 (0.3755 s / it)
+cv: [12]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.514  acc: 0.248  f1: 0.187
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:20:39  lr: nan  time: 3.0988  data: 2.7719  max mem: 22448
+train: [13]  [ 20/400]  eta: 0:03:27  lr: 0.000133  loss: 2.2602 (2.2652)  grad: 0.2614 (0.2659)  time: 0.4197  data: 0.0029  max mem: 22448
+train: [13]  [ 40/400]  eta: 0:02:55  lr: 0.000131  loss: 2.2783 (2.2799)  grad: 0.2628 (0.2661)  time: 0.4263  data: 0.0045  max mem: 22448
+train: [13]  [ 60/400]  eta: 0:02:40  lr: 0.000130  loss: 2.3031 (2.2801)  grad: 0.2667 (0.2659)  time: 0.4380  data: 0.0047  max mem: 22448
+train: [13]  [ 80/400]  eta: 0:02:27  lr: 0.000128  loss: 2.2457 (2.2738)  grad: 0.2643 (0.2664)  time: 0.4252  data: 0.0048  max mem: 22448
+train: [13]  [100/400]  eta: 0:02:16  lr: 0.000127  loss: 2.2381 (2.2668)  grad: 0.2577 (0.2650)  time: 0.4321  data: 0.0046  max mem: 22448
+train: [13]  [120/400]  eta: 0:02:05  lr: 0.000125  loss: 2.2540 (2.2623)  grad: 0.2583 (0.2661)  time: 0.4260  data: 0.0047  max mem: 22448
+train: [13]  [140/400]  eta: 0:01:56  lr: 0.000124  loss: 2.2839 (2.2690)  grad: 0.2706 (0.2675)  time: 0.4285  data: 0.0048  max mem: 22448
+train: [13]  [160/400]  eta: 0:01:46  lr: 0.000122  loss: 2.2497 (2.2681)  grad: 0.2704 (0.2686)  time: 0.4291  data: 0.0047  max mem: 22448
+train: [13]  [180/400]  eta: 0:01:37  lr: 0.000120  loss: 2.2568 (2.2752)  grad: 0.2784 (0.2694)  time: 0.4262  data: 0.0046  max mem: 22448
+train: [13]  [200/400]  eta: 0:01:28  lr: 0.000119  loss: 2.2465 (2.2721)  grad: 0.2691 (0.2690)  time: 0.4184  data: 0.0046  max mem: 22448
+train: [13]  [220/400]  eta: 0:01:19  lr: 0.000117  loss: 2.2330 (2.2720)  grad: 0.2676 (0.2697)  time: 0.4265  data: 0.0048  max mem: 22448
+train: [13]  [240/400]  eta: 0:01:10  lr: 0.000116  loss: 2.2752 (2.2716)  grad: 0.2682 (0.2698)  time: 0.4434  data: 0.0050  max mem: 22448
+train: [13]  [260/400]  eta: 0:01:01  lr: 0.000114  loss: 2.2752 (2.2724)  grad: 0.2618 (0.2690)  time: 0.4300  data: 0.0048  max mem: 22448
+train: [13]  [280/400]  eta: 0:00:52  lr: 0.000113  loss: 2.2395 (2.2689)  grad: 0.2544 (0.2679)  time: 0.4279  data: 0.0046  max mem: 22448
+train: [13]  [300/400]  eta: 0:00:43  lr: 0.000111  loss: 2.2395 (2.2689)  grad: 0.2495 (0.2667)  time: 0.4403  data: 0.0048  max mem: 22448
+train: [13]  [320/400]  eta: 0:00:35  lr: 0.000110  loss: 2.2648 (2.2688)  grad: 0.2635 (0.2671)  time: 0.4387  data: 0.0047  max mem: 22448
+train: [13]  [340/400]  eta: 0:00:26  lr: 0.000108  loss: 2.2447 (2.2678)  grad: 0.2660 (0.2668)  time: 0.4409  data: 0.0049  max mem: 22448
+train: [13]  [360/400]  eta: 0:00:17  lr: 0.000107  loss: 2.2358 (2.2681)  grad: 0.2717 (0.2673)  time: 0.4322  data: 0.0047  max mem: 22448
+train: [13]  [380/400]  eta: 0:00:08  lr: 0.000105  loss: 2.2687 (2.2682)  grad: 0.2784 (0.2676)  time: 0.4325  data: 0.0048  max mem: 22448
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.2641 (2.2681)  grad: 0.2676 (0.2676)  time: 0.4346  data: 0.0046  max mem: 22448
+train: [13] Total time: 0:02:55 (0.4381 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.2641 (2.2681)  grad: 0.2676 (0.2676)
+eval (validation): [13]  [ 0/85]  eta: 0:04:29    time: 3.1673  data: 2.9159  max mem: 22448
+eval (validation): [13]  [20/85]  eta: 0:00:32    time: 0.3645  data: 0.0044  max mem: 22448
+eval (validation): [13]  [40/85]  eta: 0:00:18    time: 0.3318  data: 0.0037  max mem: 22448
+eval (validation): [13]  [60/85]  eta: 0:00:09    time: 0.3346  data: 0.0040  max mem: 22448
+eval (validation): [13]  [80/85]  eta: 0:00:01    time: 0.3428  data: 0.0042  max mem: 22448
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3394  data: 0.0041  max mem: 22448
+eval (validation): [13] Total time: 0:00:32 (0.3783 s / it)
+cv: [13]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.498  acc: 0.255  f1: 0.194
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:20:28  lr: nan  time: 3.0701  data: 2.7446  max mem: 22448
+train: [14]  [ 20/400]  eta: 0:03:33  lr: 0.000102  loss: 2.1962 (2.1854)  grad: 0.2539 (0.2553)  time: 0.4377  data: 0.0043  max mem: 22448
+train: [14]  [ 40/400]  eta: 0:02:59  lr: 0.000101  loss: 2.2267 (2.2096)  grad: 0.2521 (0.2565)  time: 0.4295  data: 0.0047  max mem: 22448
+train: [14]  [ 60/400]  eta: 0:02:43  lr: 0.000099  loss: 2.2295 (2.2137)  grad: 0.2664 (0.2600)  time: 0.4453  data: 0.0050  max mem: 22448
+train: [14]  [ 80/400]  eta: 0:02:29  lr: 0.000098  loss: 2.2056 (2.2224)  grad: 0.2598 (0.2590)  time: 0.4299  data: 0.0048  max mem: 22448
+train: [14]  [100/400]  eta: 0:02:18  lr: 0.000096  loss: 2.2163 (2.2247)  grad: 0.2566 (0.2597)  time: 0.4299  data: 0.0048  max mem: 22448
+train: [14]  [120/400]  eta: 0:02:07  lr: 0.000095  loss: 2.2055 (2.2196)  grad: 0.2627 (0.2605)  time: 0.4256  data: 0.0048  max mem: 22448
+train: [14]  [140/400]  eta: 0:01:57  lr: 0.000093  loss: 2.2033 (2.2199)  grad: 0.2661 (0.2619)  time: 0.4281  data: 0.0048  max mem: 22448
+train: [14]  [160/400]  eta: 0:01:47  lr: 0.000092  loss: 2.2026 (2.2171)  grad: 0.2661 (0.2629)  time: 0.4334  data: 0.0047  max mem: 22448
+train: [14]  [180/400]  eta: 0:01:38  lr: 0.000090  loss: 2.1921 (2.2145)  grad: 0.2678 (0.2633)  time: 0.4461  data: 0.0048  max mem: 22448
+train: [14]  [200/400]  eta: 0:01:29  lr: 0.000089  loss: 2.2036 (2.2140)  grad: 0.2679 (0.2640)  time: 0.4346  data: 0.0051  max mem: 22448
+train: [14]  [220/400]  eta: 0:01:20  lr: 0.000088  loss: 2.2003 (2.2143)  grad: 0.2716 (0.2644)  time: 0.4335  data: 0.0046  max mem: 22448
+train: [14]  [240/400]  eta: 0:01:11  lr: 0.000086  loss: 2.2058 (2.2183)  grad: 0.2665 (0.2646)  time: 0.4337  data: 0.0047  max mem: 22448
+train: [14]  [260/400]  eta: 0:01:02  lr: 0.000085  loss: 2.2390 (2.2192)  grad: 0.2609 (0.2646)  time: 0.4448  data: 0.0049  max mem: 22448
+train: [14]  [280/400]  eta: 0:00:53  lr: 0.000083  loss: 2.2292 (2.2181)  grad: 0.2597 (0.2638)  time: 0.4344  data: 0.0045  max mem: 22448
+train: [14]  [300/400]  eta: 0:00:44  lr: 0.000082  loss: 2.2292 (2.2236)  grad: 0.2578 (0.2639)  time: 0.4301  data: 0.0045  max mem: 22448
+train: [14]  [320/400]  eta: 0:00:35  lr: 0.000081  loss: 2.2795 (2.2245)  grad: 0.2708 (0.2646)  time: 0.4275  data: 0.0045  max mem: 22448
+train: [14]  [340/400]  eta: 0:00:26  lr: 0.000079  loss: 2.2563 (2.2248)  grad: 0.2708 (0.2651)  time: 0.4307  data: 0.0047  max mem: 22448
+train: [14]  [360/400]  eta: 0:00:17  lr: 0.000078  loss: 2.2123 (2.2249)  grad: 0.2698 (0.2649)  time: 0.4301  data: 0.0043  max mem: 22448
+train: [14]  [380/400]  eta: 0:00:08  lr: 0.000076  loss: 2.2075 (2.2242)  grad: 0.2676 (0.2652)  time: 0.4366  data: 0.0046  max mem: 22448
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.2188 (2.2248)  grad: 0.2722 (0.2658)  time: 0.4315  data: 0.0045  max mem: 22448
+train: [14] Total time: 0:02:56 (0.4408 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.2188 (2.2248)  grad: 0.2722 (0.2658)
+eval (validation): [14]  [ 0/85]  eta: 0:04:20    time: 3.0663  data: 2.8202  max mem: 22448
+eval (validation): [14]  [20/85]  eta: 0:00:30    time: 0.3452  data: 0.0042  max mem: 22448
+eval (validation): [14]  [40/85]  eta: 0:00:18    time: 0.3456  data: 0.0036  max mem: 22448
+eval (validation): [14]  [60/85]  eta: 0:00:09    time: 0.3658  data: 0.0041  max mem: 22448
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3310  data: 0.0039  max mem: 22448
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.3266  data: 0.0038  max mem: 22448
+eval (validation): [14] Total time: 0:00:32 (0.3809 s / it)
+cv: [14]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.494  acc: 0.256  f1: 0.189
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:21:21  lr: nan  time: 3.2043  data: 2.8051  max mem: 22448
+train: [15]  [ 20/400]  eta: 0:03:36  lr: 0.000074  loss: 2.1671 (2.1823)  grad: 0.2583 (0.2603)  time: 0.4385  data: 0.0041  max mem: 22448
+train: [15]  [ 40/400]  eta: 0:03:02  lr: 0.000072  loss: 2.1671 (2.1830)  grad: 0.2570 (0.2596)  time: 0.4418  data: 0.0042  max mem: 22448
+train: [15]  [ 60/400]  eta: 0:02:47  lr: 0.000071  loss: 2.1585 (2.1769)  grad: 0.2630 (0.2604)  time: 0.4648  data: 0.0047  max mem: 22448
+train: [15]  [ 80/400]  eta: 0:02:33  lr: 0.000070  loss: 2.1192 (2.1644)  grad: 0.2612 (0.2592)  time: 0.4399  data: 0.0045  max mem: 22448
+train: [15]  [100/400]  eta: 0:02:21  lr: 0.000068  loss: 2.1240 (2.1609)  grad: 0.2542 (0.2600)  time: 0.4373  data: 0.0048  max mem: 22448
+train: [15]  [120/400]  eta: 0:02:10  lr: 0.000067  loss: 2.1624 (2.1680)  grad: 0.2617 (0.2609)  time: 0.4397  data: 0.0048  max mem: 22448
+train: [15]  [140/400]  eta: 0:02:00  lr: 0.000066  loss: 2.2120 (2.1780)  grad: 0.2682 (0.2630)  time: 0.4397  data: 0.0049  max mem: 22448
+train: [15]  [160/400]  eta: 0:01:50  lr: 0.000064  loss: 2.2120 (2.1775)  grad: 0.2673 (0.2633)  time: 0.4528  data: 0.0046  max mem: 22448
+train: [15]  [180/400]  eta: 0:01:40  lr: 0.000063  loss: 2.2238 (2.1873)  grad: 0.2669 (0.2641)  time: 0.4386  data: 0.0047  max mem: 22448
+train: [15]  [200/400]  eta: 0:01:31  lr: 0.000062  loss: 2.1891 (2.1864)  grad: 0.2605 (0.2635)  time: 0.4475  data: 0.0049  max mem: 22448
+train: [15]  [220/400]  eta: 0:01:22  lr: 0.000061  loss: 2.1834 (2.1846)  grad: 0.2560 (0.2633)  time: 0.4388  data: 0.0048  max mem: 22448
+train: [15]  [240/400]  eta: 0:01:12  lr: 0.000059  loss: 2.1971 (2.1843)  grad: 0.2643 (0.2638)  time: 0.4373  data: 0.0048  max mem: 22448
+train: [15]  [260/400]  eta: 0:01:03  lr: 0.000058  loss: 2.2054 (2.1893)  grad: 0.2646 (0.2637)  time: 0.4343  data: 0.0047  max mem: 22448
+train: [15]  [280/400]  eta: 0:00:54  lr: 0.000057  loss: 2.2289 (2.1864)  grad: 0.2531 (0.2627)  time: 0.4532  data: 0.0049  max mem: 22448
+train: [15]  [300/400]  eta: 0:00:45  lr: 0.000056  loss: 2.1760 (2.1865)  grad: 0.2462 (0.2626)  time: 0.4313  data: 0.0049  max mem: 22448
+train: [15]  [320/400]  eta: 0:00:36  lr: 0.000054  loss: 2.2031 (2.1869)  grad: 0.2636 (0.2628)  time: 0.4345  data: 0.0048  max mem: 22448
+train: [15]  [340/400]  eta: 0:00:26  lr: 0.000053  loss: 2.1991 (2.1881)  grad: 0.2665 (0.2631)  time: 0.4331  data: 0.0045  max mem: 22448
+train: [15]  [360/400]  eta: 0:00:17  lr: 0.000052  loss: 2.1897 (2.1885)  grad: 0.2705 (0.2636)  time: 0.4303  data: 0.0046  max mem: 22448
+train: [15]  [380/400]  eta: 0:00:08  lr: 0.000051  loss: 2.1554 (2.1862)  grad: 0.2563 (0.2631)  time: 0.4458  data: 0.0047  max mem: 22448
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.1540 (2.1881)  grad: 0.2554 (0.2631)  time: 0.4316  data: 0.0046  max mem: 22448
+train: [15] Total time: 0:02:59 (0.4481 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.1540 (2.1881)  grad: 0.2554 (0.2631)
+eval (validation): [15]  [ 0/85]  eta: 0:04:18    time: 3.0380  data: 2.7982  max mem: 22448
+eval (validation): [15]  [20/85]  eta: 0:00:30    time: 0.3475  data: 0.0044  max mem: 22448
+eval (validation): [15]  [40/85]  eta: 0:00:18    time: 0.3460  data: 0.0035  max mem: 22448
+eval (validation): [15]  [60/85]  eta: 0:00:09    time: 0.3446  data: 0.0041  max mem: 22448
+eval (validation): [15]  [80/85]  eta: 0:00:01    time: 0.3387  data: 0.0039  max mem: 22448
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.3314  data: 0.0038  max mem: 22448
+eval (validation): [15] Total time: 0:00:32 (0.3789 s / it)
+cv: [15]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.491  acc: 0.255  f1: 0.191
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:21:56  lr: nan  time: 3.2906  data: 2.9057  max mem: 22448
+train: [16]  [ 20/400]  eta: 0:03:44  lr: 0.000048  loss: 2.1336 (2.1480)  grad: 0.2460 (0.2501)  time: 0.4558  data: 0.0047  max mem: 22448
+train: [16]  [ 40/400]  eta: 0:03:04  lr: 0.000047  loss: 2.1336 (2.1339)  grad: 0.2461 (0.2498)  time: 0.4312  data: 0.0048  max mem: 22448
+train: [16]  [ 60/400]  eta: 0:02:46  lr: 0.000046  loss: 2.1079 (2.1377)  grad: 0.2500 (0.2524)  time: 0.4449  data: 0.0047  max mem: 22448
+train: [16]  [ 80/400]  eta: 0:02:33  lr: 0.000045  loss: 2.1734 (2.1510)  grad: 0.2609 (0.2558)  time: 0.4447  data: 0.0048  max mem: 22448
+train: [16]  [100/400]  eta: 0:02:20  lr: 0.000044  loss: 2.1455 (2.1474)  grad: 0.2631 (0.2565)  time: 0.4310  data: 0.0048  max mem: 22448
+train: [16]  [120/400]  eta: 0:02:10  lr: 0.000043  loss: 2.1178 (2.1486)  grad: 0.2539 (0.2566)  time: 0.4489  data: 0.0048  max mem: 22448
+train: [16]  [140/400]  eta: 0:02:00  lr: 0.000042  loss: 2.1359 (2.1483)  grad: 0.2499 (0.2564)  time: 0.4361  data: 0.0044  max mem: 22448
+train: [16]  [160/400]  eta: 0:01:50  lr: 0.000041  loss: 2.1588 (2.1545)  grad: 0.2548 (0.2578)  time: 0.4329  data: 0.0045  max mem: 22448
+train: [16]  [180/400]  eta: 0:01:40  lr: 0.000040  loss: 2.1584 (2.1542)  grad: 0.2611 (0.2580)  time: 0.4331  data: 0.0048  max mem: 22448
+train: [16]  [200/400]  eta: 0:01:30  lr: 0.000039  loss: 2.1306 (2.1498)  grad: 0.2542 (0.2569)  time: 0.4437  data: 0.0047  max mem: 22448
+train: [16]  [220/400]  eta: 0:01:21  lr: 0.000038  loss: 2.1416 (2.1506)  grad: 0.2367 (0.2564)  time: 0.4617  data: 0.0047  max mem: 22448
+train: [16]  [240/400]  eta: 0:01:12  lr: 0.000036  loss: 2.1517 (2.1504)  grad: 0.2552 (0.2570)  time: 0.4433  data: 0.0048  max mem: 22448
+train: [16]  [260/400]  eta: 0:01:03  lr: 0.000035  loss: 2.1799 (2.1543)  grad: 0.2734 (0.2581)  time: 0.4404  data: 0.0046  max mem: 22448
+train: [16]  [280/400]  eta: 0:00:54  lr: 0.000034  loss: 2.1802 (2.1567)  grad: 0.2621 (0.2582)  time: 0.4330  data: 0.0044  max mem: 22448
+train: [16]  [300/400]  eta: 0:00:45  lr: 0.000033  loss: 2.2044 (2.1602)  grad: 0.2580 (0.2583)  time: 0.4420  data: 0.0048  max mem: 22448
+train: [16]  [320/400]  eta: 0:00:36  lr: 0.000032  loss: 2.2058 (2.1642)  grad: 0.2581 (0.2587)  time: 0.4388  data: 0.0046  max mem: 22448
+train: [16]  [340/400]  eta: 0:00:26  lr: 0.000031  loss: 2.1436 (2.1636)  grad: 0.2604 (0.2587)  time: 0.4393  data: 0.0048  max mem: 22448
+train: [16]  [360/400]  eta: 0:00:17  lr: 0.000031  loss: 2.1366 (2.1636)  grad: 0.2554 (0.2589)  time: 0.4331  data: 0.0048  max mem: 22448
+train: [16]  [380/400]  eta: 0:00:08  lr: 0.000030  loss: 2.1705 (2.1631)  grad: 0.2570 (0.2594)  time: 0.4363  data: 0.0048  max mem: 22448
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.1705 (2.1640)  grad: 0.2660 (0.2598)  time: 0.4379  data: 0.0046  max mem: 22448
+train: [16] Total time: 0:02:59 (0.4480 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.1705 (2.1640)  grad: 0.2660 (0.2598)
+eval (validation): [16]  [ 0/85]  eta: 0:05:23    time: 3.8099  data: 3.5242  max mem: 22448
+eval (validation): [16]  [20/85]  eta: 0:00:32    time: 0.3413  data: 0.0029  max mem: 22448
+eval (validation): [16]  [40/85]  eta: 0:00:18    time: 0.3308  data: 0.0038  max mem: 22448
+eval (validation): [16]  [60/85]  eta: 0:00:09    time: 0.3232  data: 0.0039  max mem: 22448
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3373  data: 0.0040  max mem: 22448
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.3350  data: 0.0040  max mem: 22448
+eval (validation): [16] Total time: 0:00:32 (0.3775 s / it)
+cv: [16]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.492  acc: 0.255  f1: 0.192
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:22:00  lr: nan  time: 3.3006  data: 2.9709  max mem: 22448
+train: [17]  [ 20/400]  eta: 0:03:37  lr: 0.000028  loss: 2.1130 (2.1000)  grad: 0.2396 (0.2514)  time: 0.4347  data: 0.0038  max mem: 22448
+train: [17]  [ 40/400]  eta: 0:03:02  lr: 0.000027  loss: 2.1320 (2.1276)  grad: 0.2523 (0.2540)  time: 0.4369  data: 0.0042  max mem: 22448
+train: [17]  [ 60/400]  eta: 0:02:44  lr: 0.000026  loss: 2.1563 (2.1415)  grad: 0.2523 (0.2525)  time: 0.4406  data: 0.0046  max mem: 22448
+train: [17]  [ 80/400]  eta: 0:02:32  lr: 0.000025  loss: 2.1294 (2.1296)  grad: 0.2450 (0.2525)  time: 0.4521  data: 0.0050  max mem: 22448
+train: [17]  [100/400]  eta: 0:02:20  lr: 0.000024  loss: 2.1294 (2.1415)  grad: 0.2497 (0.2519)  time: 0.4322  data: 0.0048  max mem: 22448
+train: [17]  [120/400]  eta: 0:02:09  lr: 0.000023  loss: 2.1505 (2.1407)  grad: 0.2497 (0.2521)  time: 0.4434  data: 0.0048  max mem: 22448
+train: [17]  [140/400]  eta: 0:01:59  lr: 0.000023  loss: 2.1214 (2.1398)  grad: 0.2542 (0.2521)  time: 0.4333  data: 0.0047  max mem: 22448
+train: [17]  [160/400]  eta: 0:01:49  lr: 0.000022  loss: 2.1485 (2.1410)  grad: 0.2542 (0.2526)  time: 0.4279  data: 0.0047  max mem: 22448
+train: [17]  [180/400]  eta: 0:01:39  lr: 0.000021  loss: 2.1454 (2.1394)  grad: 0.2521 (0.2524)  time: 0.4299  data: 0.0048  max mem: 22448
+train: [17]  [200/400]  eta: 0:01:30  lr: 0.000020  loss: 2.1176 (2.1374)  grad: 0.2501 (0.2520)  time: 0.4295  data: 0.0046  max mem: 22448
+train: [17]  [220/400]  eta: 0:01:21  lr: 0.000019  loss: 2.1325 (2.1377)  grad: 0.2562 (0.2529)  time: 0.4533  data: 0.0050  max mem: 22448
+train: [17]  [240/400]  eta: 0:01:12  lr: 0.000019  loss: 2.1650 (2.1371)  grad: 0.2580 (0.2536)  time: 0.4491  data: 0.0049  max mem: 22448
+train: [17]  [260/400]  eta: 0:01:02  lr: 0.000018  loss: 2.1147 (2.1383)  grad: 0.2614 (0.2540)  time: 0.4392  data: 0.0049  max mem: 22448
+train: [17]  [280/400]  eta: 0:00:53  lr: 0.000017  loss: 2.1159 (2.1406)  grad: 0.2606 (0.2541)  time: 0.4255  data: 0.0050  max mem: 22448
+train: [17]  [300/400]  eta: 0:00:44  lr: 0.000016  loss: 2.1592 (2.1416)  grad: 0.2555 (0.2540)  time: 0.4354  data: 0.0048  max mem: 22448
+train: [17]  [320/400]  eta: 0:00:35  lr: 0.000016  loss: 2.1541 (2.1413)  grad: 0.2499 (0.2534)  time: 0.4427  data: 0.0050  max mem: 22448
+train: [17]  [340/400]  eta: 0:00:26  lr: 0.000015  loss: 2.1200 (2.1412)  grad: 0.2476 (0.2537)  time: 0.4345  data: 0.0047  max mem: 22448
+train: [17]  [360/400]  eta: 0:00:17  lr: 0.000014  loss: 2.1388 (2.1426)  grad: 0.2546 (0.2538)  time: 0.4274  data: 0.0049  max mem: 22448
+train: [17]  [380/400]  eta: 0:00:08  lr: 0.000014  loss: 2.1405 (2.1421)  grad: 0.2613 (0.2545)  time: 0.4343  data: 0.0049  max mem: 22448
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.1051 (2.1411)  grad: 0.2574 (0.2543)  time: 0.4394  data: 0.0046  max mem: 22448
+train: [17] Total time: 0:02:57 (0.4446 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.1051 (2.1411)  grad: 0.2574 (0.2543)
+eval (validation): [17]  [ 0/85]  eta: 0:04:18    time: 3.0464  data: 2.8049  max mem: 22448
+eval (validation): [17]  [20/85]  eta: 0:00:31    time: 0.3518  data: 0.0053  max mem: 22448
+eval (validation): [17]  [40/85]  eta: 0:00:18    time: 0.3285  data: 0.0035  max mem: 22448
+eval (validation): [17]  [60/85]  eta: 0:00:09    time: 0.3324  data: 0.0038  max mem: 22448
+eval (validation): [17]  [80/85]  eta: 0:00:01    time: 0.3341  data: 0.0040  max mem: 22448
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3283  data: 0.0041  max mem: 22448
+eval (validation): [17] Total time: 0:00:31 (0.3714 s / it)
+cv: [17]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.497  acc: 0.258  f1: 0.200
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:22:11  lr: nan  time: 3.3280  data: 2.9492  max mem: 22448
+train: [18]  [ 20/400]  eta: 0:03:48  lr: 0.000012  loss: 2.1487 (2.1724)  grad: 0.2537 (0.2570)  time: 0.4658  data: 0.0043  max mem: 22448
+train: [18]  [ 40/400]  eta: 0:03:08  lr: 0.000012  loss: 2.1314 (2.1232)  grad: 0.2517 (0.2519)  time: 0.4431  data: 0.0045  max mem: 22448
+train: [18]  [ 60/400]  eta: 0:02:47  lr: 0.000011  loss: 2.0644 (2.1163)  grad: 0.2568 (0.2561)  time: 0.4286  data: 0.0047  max mem: 22448
+train: [18]  [ 80/400]  eta: 0:02:35  lr: 0.000011  loss: 2.0850 (2.1167)  grad: 0.2555 (0.2540)  time: 0.4629  data: 0.0050  max mem: 22448
+train: [18]  [100/400]  eta: 0:02:22  lr: 0.000010  loss: 2.1296 (2.1192)  grad: 0.2466 (0.2526)  time: 0.4335  data: 0.0047  max mem: 22448
+train: [18]  [120/400]  eta: 0:02:11  lr: 0.000009  loss: 2.1021 (2.1125)  grad: 0.2466 (0.2517)  time: 0.4449  data: 0.0047  max mem: 22448
+train: [18]  [140/400]  eta: 0:02:01  lr: 0.000009  loss: 2.1021 (2.1186)  grad: 0.2528 (0.2521)  time: 0.4362  data: 0.0047  max mem: 22448
+train: [18]  [160/400]  eta: 0:01:50  lr: 0.000008  loss: 2.1372 (2.1204)  grad: 0.2521 (0.2511)  time: 0.4298  data: 0.0046  max mem: 22448
+train: [18]  [180/400]  eta: 0:01:40  lr: 0.000008  loss: 2.1037 (2.1174)  grad: 0.2519 (0.2518)  time: 0.4305  data: 0.0048  max mem: 22448
+train: [18]  [200/400]  eta: 0:01:31  lr: 0.000007  loss: 2.1037 (2.1199)  grad: 0.2524 (0.2518)  time: 0.4386  data: 0.0047  max mem: 22448
+train: [18]  [220/400]  eta: 0:01:21  lr: 0.000007  loss: 2.1222 (2.1213)  grad: 0.2559 (0.2519)  time: 0.4336  data: 0.0049  max mem: 22448
+train: [18]  [240/400]  eta: 0:01:12  lr: 0.000006  loss: 2.1315 (2.1243)  grad: 0.2547 (0.2518)  time: 0.4544  data: 0.0050  max mem: 22448
+train: [18]  [260/400]  eta: 0:01:03  lr: 0.000006  loss: 2.1424 (2.1251)  grad: 0.2487 (0.2515)  time: 0.4451  data: 0.0051  max mem: 22448
+train: [18]  [280/400]  eta: 0:00:54  lr: 0.000006  loss: 2.1265 (2.1243)  grad: 0.2487 (0.2516)  time: 0.4327  data: 0.0047  max mem: 22448
+train: [18]  [300/400]  eta: 0:00:45  lr: 0.000005  loss: 2.1124 (2.1236)  grad: 0.2501 (0.2515)  time: 0.4318  data: 0.0048  max mem: 22448
+train: [18]  [320/400]  eta: 0:00:35  lr: 0.000005  loss: 2.1631 (2.1267)  grad: 0.2510 (0.2514)  time: 0.4422  data: 0.0047  max mem: 22448
+train: [18]  [340/400]  eta: 0:00:26  lr: 0.000004  loss: 2.1007 (2.1226)  grad: 0.2482 (0.2512)  time: 0.4393  data: 0.0046  max mem: 22448
+train: [18]  [360/400]  eta: 0:00:17  lr: 0.000004  loss: 2.0712 (2.1214)  grad: 0.2482 (0.2512)  time: 0.4308  data: 0.0050  max mem: 22448
+train: [18]  [380/400]  eta: 0:00:08  lr: 0.000004  loss: 2.0843 (2.1206)  grad: 0.2540 (0.2512)  time: 0.4331  data: 0.0045  max mem: 22448
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.0843 (2.1187)  grad: 0.2538 (0.2514)  time: 0.4248  data: 0.0047  max mem: 22448
+train: [18] Total time: 0:02:58 (0.4470 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.0843 (2.1187)  grad: 0.2538 (0.2514)
+eval (validation): [18]  [ 0/85]  eta: 0:04:18    time: 3.0418  data: 2.7874  max mem: 22448
+eval (validation): [18]  [20/85]  eta: 0:00:30    time: 0.3444  data: 0.0051  max mem: 22448
+eval (validation): [18]  [40/85]  eta: 0:00:17    time: 0.3228  data: 0.0038  max mem: 22448
+eval (validation): [18]  [60/85]  eta: 0:00:09    time: 0.3362  data: 0.0039  max mem: 22448
+eval (validation): [18]  [80/85]  eta: 0:00:01    time: 0.3186  data: 0.0038  max mem: 22448
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3098  data: 0.0036  max mem: 22448
+eval (validation): [18] Total time: 0:00:30 (0.3647 s / it)
+cv: [18]  best hparam: (0.27, 1.0) (016) ('016_lr2.7e-01_wd1.0e+00')  loss: 2.486  acc: 0.255  f1: 0.188
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:22:08  lr: nan  time: 3.3201  data: 2.9909  max mem: 22448
+train: [19]  [ 20/400]  eta: 0:03:48  lr: 0.000003  loss: 2.1223 (2.1588)  grad: 0.2419 (0.2476)  time: 0.4648  data: 0.0046  max mem: 22448
+train: [19]  [ 40/400]  eta: 0:03:09  lr: 0.000003  loss: 2.1118 (2.1131)  grad: 0.2426 (0.2478)  time: 0.4472  data: 0.0047  max mem: 22448
+train: [19]  [ 60/400]  eta: 0:02:47  lr: 0.000002  loss: 2.1118 (2.1243)  grad: 0.2426 (0.2475)  time: 0.4231  data: 0.0043  max mem: 22448
+train: [19]  [ 80/400]  eta: 0:02:35  lr: 0.000002  loss: 2.1271 (2.1259)  grad: 0.2443 (0.2470)  time: 0.4647  data: 0.0048  max mem: 22448
+train: [19]  [100/400]  eta: 0:02:22  lr: 0.000002  loss: 2.1224 (2.1257)  grad: 0.2450 (0.2471)  time: 0.4317  data: 0.0047  max mem: 22448
+train: [19]  [120/400]  eta: 0:02:10  lr: 0.000002  loss: 2.1157 (2.1305)  grad: 0.2459 (0.2482)  time: 0.4288  data: 0.0046  max mem: 22448
+train: [19]  [140/400]  eta: 0:02:00  lr: 0.000001  loss: 2.1615 (2.1265)  grad: 0.2442 (0.2475)  time: 0.4346  data: 0.0047  max mem: 22448
+train: [19]  [160/400]  eta: 0:01:49  lr: 0.000001  loss: 2.1208 (2.1201)  grad: 0.2393 (0.2467)  time: 0.4253  data: 0.0046  max mem: 22448
+train: [19]  [180/400]  eta: 0:01:39  lr: 0.000001  loss: 2.0657 (2.1161)  grad: 0.2436 (0.2480)  time: 0.4248  data: 0.0048  max mem: 22448
+train: [19]  [200/400]  eta: 0:01:30  lr: 0.000001  loss: 2.0748 (2.1154)  grad: 0.2494 (0.2477)  time: 0.4395  data: 0.0048  max mem: 22448
+train: [19]  [220/400]  eta: 0:01:21  lr: 0.000001  loss: 2.1060 (2.1127)  grad: 0.2440 (0.2472)  time: 0.4323  data: 0.0047  max mem: 22448
+train: [19]  [240/400]  eta: 0:01:12  lr: 0.000001  loss: 2.0971 (2.1107)  grad: 0.2440 (0.2476)  time: 0.4493  data: 0.0048  max mem: 22448
+train: [19]  [260/400]  eta: 0:01:03  lr: 0.000000  loss: 2.0866 (2.1094)  grad: 0.2514 (0.2478)  time: 0.4451  data: 0.0049  max mem: 22448
+train: [19]  [280/400]  eta: 0:00:53  lr: 0.000000  loss: 2.1012 (2.1122)  grad: 0.2505 (0.2480)  time: 0.4368  data: 0.0050  max mem: 22448
+train: [19]  [300/400]  eta: 0:00:44  lr: 0.000000  loss: 2.1300 (2.1121)  grad: 0.2505 (0.2480)  time: 0.4329  data: 0.0046  max mem: 22448
+train: [19]  [320/400]  eta: 0:00:35  lr: 0.000000  loss: 2.1194 (2.1140)  grad: 0.2475 (0.2483)  time: 0.4364  data: 0.0048  max mem: 22448
+train: [19]  [340/400]  eta: 0:00:26  lr: 0.000000  loss: 2.1350 (2.1163)  grad: 0.2428 (0.2480)  time: 0.4467  data: 0.0049  max mem: 22448
+train: [19]  [360/400]  eta: 0:00:17  lr: 0.000000  loss: 2.1351 (2.1171)  grad: 0.2418 (0.2478)  time: 0.4323  data: 0.0047  max mem: 22448
+train: [19]  [380/400]  eta: 0:00:08  lr: 0.000000  loss: 2.1288 (2.1168)  grad: 0.2416 (0.2476)  time: 0.4299  data: 0.0045  max mem: 22448
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.1288 (2.1193)  grad: 0.2433 (0.2478)  time: 0.4285  data: 0.0044  max mem: 22448
+train: [19] Total time: 0:02:58 (0.4456 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.1288 (2.1193)  grad: 0.2433 (0.2478)
+eval (validation): [19]  [ 0/85]  eta: 0:04:18    time: 3.0384  data: 2.8058  max mem: 22448
+eval (validation): [19]  [20/85]  eta: 0:00:31    time: 0.3567  data: 0.0054  max mem: 22448
+eval (validation): [19]  [40/85]  eta: 0:00:18    time: 0.3270  data: 0.0036  max mem: 22448
+eval (validation): [19]  [60/85]  eta: 0:00:09    time: 0.3312  data: 0.0040  max mem: 22448
+eval (validation): [19]  [80/85]  eta: 0:00:01    time: 0.3212  data: 0.0039  max mem: 22448
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.3102  data: 0.0038  max mem: 22448
+eval (validation): [19] Total time: 0:00:31 (0.3673 s / it)
+cv: [19]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.501  acc: 0.255  f1: 0.196
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.25489110372831303, "hparam": [0.44, 1.0], "hparam_id": 19, "epoch": 19, "is_best": false, "best_score": 0.2593207825765965}
+eval (train): [20]  [  0/509]  eta: 0:29:44    time: 3.5065  data: 3.1857  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:20    time: 0.3839  data: 0.0035  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:26    time: 0.3448  data: 0.0038  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:03    time: 0.3392  data: 0.0037  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:47    time: 0.3359  data: 0.0040  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:36    time: 0.3570  data: 0.0041  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:25    time: 0.3288  data: 0.0039  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:16    time: 0.3453  data: 0.0042  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:08    time: 0.3474  data: 0.0044  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:01:59    time: 0.3330  data: 0.0041  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:01:51    time: 0.3394  data: 0.0040  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:44    time: 0.3571  data: 0.0042  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:36    time: 0.3360  data: 0.0041  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:28    time: 0.3327  data: 0.0042  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:21    time: 0.3390  data: 0.0041  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:13    time: 0.3316  data: 0.0041  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:06    time: 0.3353  data: 0.0040  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:00:59    time: 0.3613  data: 0.0042  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:52    time: 0.3484  data: 0.0042  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:45    time: 0.3390  data: 0.0041  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:38    time: 0.3319  data: 0.0040  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:31    time: 0.3366  data: 0.0037  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:24    time: 0.3420  data: 0.0041  max mem: 22448
+eval (train): [20]  [460/509]  eta: 0:00:17    time: 0.3463  data: 0.0038  max mem: 22448
+eval (train): [20]  [480/509]  eta: 0:00:10    time: 0.3337  data: 0.0040  max mem: 22448
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3347  data: 0.0041  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3132  data: 0.0042  max mem: 22448
+eval (train): [20] Total time: 0:02:57 (0.3494 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:00    time: 2.8248  data: 2.5967  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:28    time: 0.3250  data: 0.0036  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:18    time: 0.3542  data: 0.0040  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:09    time: 0.3453  data: 0.0041  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:01    time: 0.3284  data: 0.0038  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3239  data: 0.0038  max mem: 22448
+eval (validation): [20] Total time: 0:00:31 (0.3695 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:25    time: 3.1216  data: 2.8832  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:30    time: 0.3320  data: 0.0042  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:18    time: 0.3508  data: 0.0047  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:09    time: 0.3393  data: 0.0037  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3080  data: 0.0037  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.2965  data: 0.0035  max mem: 22448
+eval (test): [20] Total time: 0:00:31 (0.3656 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:08    time: 3.0328  data: 2.7329  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:31    time: 0.3877  data: 0.0063  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:17    time: 0.3302  data: 0.0037  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:08    time: 0.3327  data: 0.0040  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3177  data: 0.0041  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3036  data: 0.0039  max mem: 22448
+eval (testid): [20] Total time: 0:00:30 (0.3755 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.2593207825765965, "hparam": [0.72, 1.0], "hparam_id": 22, "epoch": 6, "is_best": true, "best_score": 0.2593207825765965}
+eval (train): [20]  [  0/509]  eta: 0:24:01    time: 2.8325  data: 2.5566  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:03:52    time: 0.3572  data: 0.0053  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:09    time: 0.3307  data: 0.0041  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:02:50    time: 0.3307  data: 0.0040  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:37    time: 0.3265  data: 0.0041  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:27    time: 0.3336  data: 0.0042  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:18    time: 0.3382  data: 0.0040  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:10    time: 0.3332  data: 0.0039  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:03    time: 0.3561  data: 0.0045  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:01:57    time: 0.3756  data: 0.0041  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:01:49    time: 0.3509  data: 0.0039  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:42    time: 0.3328  data: 0.0041  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:34    time: 0.3308  data: 0.0039  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:27    time: 0.3455  data: 0.0042  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:20    time: 0.3399  data: 0.0040  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:13    time: 0.3353  data: 0.0040  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:05    time: 0.3328  data: 0.0041  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:00:58    time: 0.3390  data: 0.0041  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:51    time: 0.3392  data: 0.0040  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:44    time: 0.3345  data: 0.0040  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:37    time: 0.3420  data: 0.0040  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:30    time: 0.3390  data: 0.0042  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:23    time: 0.3511  data: 0.0040  max mem: 22448
+eval (train): [20]  [460/509]  eta: 0:00:17    time: 0.3729  data: 0.0042  max mem: 22448
+eval (train): [20]  [480/509]  eta: 0:00:10    time: 0.3654  data: 0.0040  max mem: 22448
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3470  data: 0.0040  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3323  data: 0.0039  max mem: 22448
+eval (train): [20] Total time: 0:02:57 (0.3492 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:03:54    time: 2.7607  data: 2.4762  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:33    time: 0.4033  data: 0.0048  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:19    time: 0.3427  data: 0.0039  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:09    time: 0.3352  data: 0.0041  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:01    time: 0.3152  data: 0.0039  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3102  data: 0.0038  max mem: 22448
+eval (validation): [20] Total time: 0:00:32 (0.3788 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:03:59    time: 2.8217  data: 2.5590  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:30    time: 0.3459  data: 0.0048  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:17    time: 0.3318  data: 0.0038  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:09    time: 0.3326  data: 0.0040  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3230  data: 0.0040  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3086  data: 0.0038  max mem: 22448
+eval (test): [20] Total time: 0:00:30 (0.3632 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:03:46    time: 2.7608  data: 2.5377  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:28    time: 0.3425  data: 0.0050  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:16    time: 0.3341  data: 0.0038  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:08    time: 0.3497  data: 0.0041  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3272  data: 0.0041  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3147  data: 0.0039  max mem: 22448
+eval (testid): [20] Total time: 0:00:30 (0.3686 s / it)
+eval results:
+
+| model    | repr   | clf   | dataset      | ckpt   |   epoch |       lr |   wd |   hparam_id | hparam      | split      |   loss |     acc |   acc_std |      f1 |    f1_std |
+|:---------|:-------|:------|:-------------|:-------|--------:|---------:|-----:|------------:|:------------|:-----------|-------:|--------:|----------:|--------:|----------:|
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       6 | 0.000216 | 0.05 |          22 | [0.72, 1.0] | train      | 2.1495 | 0.35228 | 0.0023392 | 0.29712 | 0.0023682 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       6 | 0.000216 | 0.05 |          22 | [0.72, 1.0] | validation | 2.4544 | 0.25932 | 0.005606  | 0.20393 | 0.0049587 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       6 | 0.000216 | 0.05 |          22 | [0.72, 1.0] | test       | 2.3971 | 0.2718  | 0.0053887 | 0.2097  | 0.0050838 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       6 | 0.000216 | 0.05 |          22 | [0.72, 1.0] | testid     | 2.3187 | 0.29053 | 0.0059167 | 0.23503 | 0.0055345 |
+
+
+done! total time: 1:22:40

data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,ppmi_dx,82,0.046415888336127774,test,0.65,0.047420750732142566,0.6266666666666667,0.05017708720828885,0.6260611205432938,0.049959845422387614
+flat_mae,patch,logistic,ppmi_dx,83,0.046415888336127774,train,0.8398576512455516,0.014849670326495902,0.8245504495504495,0.01692616571822227,0.8151493256262042,0.017219574139481632
+flat_mae,patch,logistic,ppmi_dx,83,0.046415888336127774,test,0.62,0.04809858209968356,0.5924495924495925,0.05110399683576373,0.5916808149405772,0.05065560960688852
+flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,train,0.7330960854092526,0.015916743976719307,0.6858228980322003,0.02070469799996081,0.6797393491757653,0.018448344020047795
+flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,test,0.61,0.04359656408479916,0.5555555555555556,0.05019481121478349,0.5581494057724957,0.04646210237699096
+flat_mae,patch,logistic,ppmi_dx,85,0.046415888336127774,train,0.8451957295373665,0.01405864037639887,0.8281496581902537,0.016280248403064322,0.8160056733033612,0.016375844316428547
+flat_mae,patch,logistic,ppmi_dx,85,0.046415888336127774,test,0.6,0.043735290098500544,0.554367201426025,0.048788939462689335,0.5551782682512734,0.0462102156354296
+flat_mae,patch,logistic,ppmi_dx,86,0.046415888336127774,train,0.8220640569395018,0.015464036399913172,0.8036749807866974,0.01780013740807638,0.7937406336972811,0.01785266318710941
+flat_mae,patch,logistic,ppmi_dx,86,0.046415888336127774,test,0.6,0.041724793588464884,0.5324918186068257,0.05026501052872356,0.5398981324278438,0.04452678186115027
+flat_mae,patch,logistic,ppmi_dx,87,0.046415888336127774,train,0.8220640569395018,0.01562784594970618,0.805056055056055,0.017741812286441984,0.7963498180261186,0.01789614540527924
+flat_mae,patch,logistic,ppmi_dx,87,0.046415888336127774,test,0.65,0.04274405689683655,0.6011396011396011,0.050555574860665566,0.6005942275042444,0.04653504447221476
+flat_mae,patch,logistic,ppmi_dx,88,0.046415888336127774,train,0.8238434163701067,0.014879318060685412,0.8034569366581532,0.01758845605238574,0.7917068079640335,0.017494057450042205
+flat_mae,patch,logistic,ppmi_dx,88,0.046415888336127774,test,0.61,0.043659244152870993,0.5623386825272135,0.04954666519971509,0.5632427843803056,0.046481906238997506
+flat_mae,patch,logistic,ppmi_dx,89,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,ppmi_dx,89,21.54434690031882,test,0.51,0.05236296019134136,0.4916485112563544,0.05262210003655906,0.49278438030560273,0.05341068976833675
+flat_mae,patch,logistic,ppmi_dx,90,0.046415888336127774,train,0.8469750889679716,0.01450867421185242,0.8303354536136035,0.016782060459225086,0.818320488118176,0.016873889855704115
+flat_mae,patch,logistic,ppmi_dx,90,0.046415888336127774,test,0.61,0.043107307965123506,0.5555555555555556,0.04899647449743055,0.5581494057724957,0.04521097628062286
+flat_mae,patch,logistic,ppmi_dx,91,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,ppmi_dx,91,2.782559402207126,test,0.61,0.04964383143956558,0.5882166613873931,0.05135756352447227,0.5887096774193548,0.05126609182981499
+flat_mae,patch,logistic,ppmi_dx,92,0.005994842503189409,train,0.7064056939501779,0.016280772801829293,0.6491686056409965,0.021433463345506595,0.6476263112823807,0.01854037364897599
+flat_mae,patch,logistic,ppmi_dx,92,0.005994842503189409,test,0.69,0.03949227772615805,0.6343908479773559,0.04982908824110038,0.6328522920203735,0.04426083734549907
+flat_mae,patch,logistic,ppmi_dx,93,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,ppmi_dx,93,1291.5496650148827,test,0.55,0.05254483799575368,0.5396419437340154,0.05244518371601439,0.5454159592529711,0.05370534823532489
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,train,0.7224199288256228,0.01669566420745893,0.6743094045796989,0.021490480043872186,0.6693293727253264,0.01916286957275452
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,test,0.65,0.04489399068917799,0.6072270227808326,0.050701481816167286,0.6056876061120543,0.04772605754437407
+flat_mae,patch,logistic,ppmi_dx,95,0.046415888336127774,train,0.8291814946619217,0.01565815108235777,0.8106070179872783,0.01803821214173158,0.7995209805180903,0.017962417221423867
+flat_mae,patch,logistic,ppmi_dx,95,0.046415888336127774,test,0.61,0.044300139954632195,0.568536342515765,0.04929422400818932,0.5683361629881154,0.04690704147544208
+flat_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,train,0.8327402135231317,0.015013738468554154,0.8150072837292694,0.017503984862139783,0.80415061014772,0.017722068247193242
+flat_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,test,0.65,0.04376045703600455,0.6072270227808326,0.051153868812847336,0.6056876061120543,0.04775070104428475
+flat_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,train,0.8434163701067615,0.013691952414556544,0.8268153294486777,0.015811130366771873,0.8154303147077713,0.016014978356046813
+flat_mae,patch,logistic,ppmi_dx,97,0.046415888336127774,test,0.65,0.045345866404778286,0.6178622120318812,0.05065254394796493,0.615874363327674,0.0491317090897063
+flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,train,0.9377224199288257,0.010005697392955584,0.9330138978283153,0.010977425614209339,0.9259393063583815,0.012036394818641891
+flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,test,0.65,0.04737467677990004,0.6178622120318812,0.05269243287187057,0.615874363327674,0.051420597784539994
+flat_mae,patch,logistic,ppmi_dx,99,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,ppmi_dx,99,2.782559402207126,test,0.62,0.04729131421307723,0.6100164203612479,0.04782446991481695,0.6171477079796265,0.04903924505282701
+flat_mae,patch,logistic,ppmi_dx,100,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,ppmi_dx,100,166.81005372000556,test,0.56,0.04648431563441587,0.548440065681445,0.04687901875875589,0.5534804753820034,0.04806801242217095

data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic/log.txt

@@ -0,0 +1,247 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:14:51
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_1; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_1/pretrain/checkpoint-best.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: data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:15:50    time: 4.0984  data: 3.1572  max mem: 2698
+extract (train)  [ 20/232]  eta: 0:01:19    time: 0.1910  data: 0.0570  max mem: 2851
+extract (train)  [ 40/232]  eta: 0:00:52    time: 0.1593  data: 0.0408  max mem: 2851
+extract (train)  [ 60/232]  eta: 0:00:40    time: 0.1710  data: 0.0474  max mem: 2851
+extract (train)  [ 80/232]  eta: 0:00:33    time: 0.1616  data: 0.0465  max mem: 2851
+extract (train)  [100/232]  eta: 0:00:27    time: 0.1655  data: 0.0530  max mem: 2851
+extract (train)  [120/232]  eta: 0:00:22    time: 0.1876  data: 0.0589  max mem: 2851
+extract (train)  [140/232]  eta: 0:00:18    time: 0.1800  data: 0.0506  max mem: 2851
+extract (train)  [160/232]  eta: 0:00:14    time: 0.1925  data: 0.0534  max mem: 2851
+extract (train)  [180/232]  eta: 0:00:10    time: 0.2024  data: 0.0632  max mem: 2851
+extract (train)  [200/232]  eta: 0:00:06    time: 0.1715  data: 0.0480  max mem: 2851
+extract (train)  [220/232]  eta: 0:00:02    time: 0.1521  data: 0.0419  max mem: 2851
+extract (train)  [231/232]  eta: 0:00:00    time: 0.1486  data: 0.0428  max mem: 2851
+extract (train) Total time: 0:00:44 (0.1931 s / it)
+extract (validation)  [ 0/50]  eta: 0:03:11    time: 3.8263  data: 3.6765  max mem: 2851
+extract (validation)  [20/50]  eta: 0:00:12    time: 0.2290  data: 0.0722  max mem: 2851
+extract (validation)  [40/50]  eta: 0:00:02    time: 0.1440  data: 0.0380  max mem: 2851
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.1391  data: 0.0372  max mem: 2851
+extract (validation) Total time: 0:00:12 (0.2567 s / it)
+extract (test)  [ 0/50]  eta: 0:02:51    time: 3.4253  data: 3.2832  max mem: 2851
+extract (test)  [20/50]  eta: 0:00:11    time: 0.2397  data: 0.0810  max mem: 2851
+extract (test)  [40/50]  eta: 0:00:02    time: 0.1458  data: 0.0374  max mem: 2851
+extract (test)  [49/50]  eta: 0:00:00    time: 0.1422  data: 0.0359  max mem: 2851
+extract (test) Total time: 0:00:12 (0.2549 s / it)
+feature extraction time: 0:01:10
+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 |
+|:---------|:-------|:---------|:----------|:--------|-------:|:--------|------:|----------:|------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | ppmi_dx   |         | 166.81 | train   |  1    |  0        | 1     | 0        | 1       |   0        |
+| flat_mae | patch  | logistic | ppmi_dx   |         | 166.81 | test    |  0.61 |  0.045349 | 0.584 | 0.048204 | 0.58451 |   0.048452 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.58, "acc_std": 0.044871835264450675, "f1": 0.5320855614973261, "f1_std": 0.049716709346924605, "bacc": 0.533955857385399, "bacc_std": 0.04689572784084943}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.68, "acc_std": 0.04383087496274743, "f1": 0.64349376114082, "f1_std": 0.049361267973810206, "bacc": 0.6400679117147707, "bacc_std": 0.046832702276007804}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.62, "acc_std": 0.04313728781460421, "f1": 0.5634191176470589, "f1_std": 0.051475836009069076, "bacc": 0.566213921901528, "bacc_std": 0.04668143894243826}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.045346536802715165, "f1": 0.5847828526540231, "f1_std": 0.051753349076231824, "bacc": 0.5844651952461799, "bacc_std": 0.04856952809719456}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.045750873215710315, "f1": 0.6349153667441089, "f1_std": 0.051908247279991666, "bacc": 0.6320033955857385, "bacc_std": 0.049463479795888755}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.03946064368456247, "f1": 0.5215311004784688, "f1_std": 0.051577959939088355, "bacc": 0.5377758913412564, "bacc_std": 0.04260651693152204}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.04457737542745199, "f1": 0.5713127099988413, "f1_std": 0.054527324952209744, "bacc": 0.5742784380305602, "bacc_std": 0.04892112598139636}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.042708776615585704, "f1": 0.5792426367461431, "f1_std": 0.05178780181432718, "bacc": 0.5823429541595926, "bacc_std": 0.04575574659518016}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.68, "acc_std": 0.04373073976049341, "f1": 0.6567996567996568, "f1_std": 0.0470589595166625, "bacc": 0.6553480475382003, "bacc_std": 0.04667073981917745}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.59, "acc_std": 0.04532538361668878, "f1": 0.5523528769516323, "f1_std": 0.04982810946459481, "bacc": 0.5522071307300509, "bacc_std": 0.048314562916418856}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.66, "acc_std": 0.04725853996898338, "f1": 0.6392190152801358, "f1_std": 0.050487211120466446, "bacc": 0.6392190152801358, "bacc_std": 0.050499895651942524}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.6, "acc_std": 0.04486635710641103, "f1": 0.554367201426025, "f1_std": 0.05150497499532059, "bacc": 0.5551782682512734, "bacc_std": 0.048465272646286146}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.6, "acc_std": 0.04560822294279837, "f1": 0.5659722222222222, "f1_std": 0.049707614897828926, "bacc": 0.565365025466893, "bacc_std": 0.048196183487145106}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.043452829597162033, "f1": 0.5906626839252129, "f1_std": 0.0488504414528103, "bacc": 0.5895585738539898, "bacc_std": 0.04660594579854282}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.65, "acc_std": 0.04737467677990004, "f1": 0.6178622120318812, "f1_std": 0.05269243287187057, "bacc": 0.615874363327674, "bacc_std": 0.051420597784539994}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 166.81005372000556, "split": "test", "acc": 0.56, "acc_std": 0.04648431563441587, "f1": 0.548440065681445, "f1_std": 0.04687901875875589, "bacc": 0.5534804753820034, "bacc_std": 0.04806801242217095}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | ppmi_dx   | train   |        100 | 133.32 |  1013.4 | 0.87509 |  0.092832 | 0.85872 | 0.10889  | 0.85189 |   0.11074  |
+| flat_mae | patch  | logistic | ppmi_dx   | test    |        100 | 133.32 |  1013.4 | 0.6234  |  0.042503 | 0.58387 | 0.042874 | 0.585   |   0.040479 |
+
+
+done! total time: 0:05:16

data_scaling/n100_1/pretrain/config.yaml

@@ -0,0 +1,109 @@
+name: data_scaling/n100_1/pretrain
+notes: data scaling experiment n100_1 (seed=1644)
+output_dir: experiments/data_scaling/output/data_scaling/n100_1/pretrain
+input_space: flat
+patch_size: 16
+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.flat.wds/hcpya-all-flat-{00000..00099}.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.${input_space}.arrow/train
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+  hcp-val:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/test
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+  nsd-val:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/nsd-cococlip.${input_space}.arrow/testid
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+train_dataset: hcp-train
+eval_datasets:
+- hcp-train-subset
+- hcp-val
+- nsd-val
+val_dataset: hcp-val
+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: 0.001
+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: 20
+checkpoint_period: 5
+plot_period: 5
+device: cuda
+presend_cuda: false
+seed: 1644
+debug: false
+wandb: true
+wandb_entity: null
+wandb_project: fMRI-foundation-model
+rank: 0
+world_size: 1
+gpu: 0
+distributed: true
+dist_backend: nccl
+in_chans: 1
+img_size:
+- 224
+- 560

data_scaling/n100_1/pretrain/log.json

@@ -0,0 +1,100 @@
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data_scaling/n100_2/eval_v2/aabc_age__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/aabc_age__patch__logistic
+remote_dir: null

data_scaling/n100_2/eval_v2/aabc_age__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,aabc_age,77,1291.5496650148827,test,0.28846153846153844,0.05916859874057418,0.2911111111111111,0.05803216146891045,0.2921245421245421,0.060031161701079354
+flat_mae,patch,logistic,aabc_age,78,0.046415888336127774,train,0.7755905511811023,0.017269636909348105,0.7746217731762193,0.017465429967709032,0.7764793896687502,0.01722414536712377
+flat_mae,patch,logistic,aabc_age,78,0.046415888336127774,test,0.5192307692307693,0.06662955081208426,0.5177368927368927,0.06763986049126285,0.5203754578754579,0.06664932977589097
+flat_mae,patch,logistic,aabc_age,79,0.3593813663804626,train,0.9488188976377953,0.009457391124643917,0.9491780081520822,0.00941523184363529,0.9494064172815306,0.009382158193993437
+flat_mae,patch,logistic,aabc_age,79,0.3593813663804626,test,0.5,0.06829854192003199,0.4891123188405797,0.07054129583679214,0.49977106227106227,0.06848999177919732
+flat_mae,patch,logistic,aabc_age,80,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,80,2.782559402207126,test,0.4807692307692308,0.06404569143756647,0.46820652173913047,0.06702503585763457,0.48031135531135527,0.06447789533868917
+flat_mae,patch,logistic,aabc_age,81,0.005994842503189409,train,0.6023622047244095,0.02089475493231657,0.5973333295391334,0.02139186911623388,0.6036523353964123,0.020808476946390552
+flat_mae,patch,logistic,aabc_age,81,0.005994842503189409,test,0.4423076923076923,0.0678471131974734,0.44387205387205386,0.0682979138336547,0.44184981684981683,0.0681352535013533
+flat_mae,patch,logistic,aabc_age,82,0.046415888336127774,train,0.7637795275590551,0.019362825525682574,0.7623483559159976,0.019560961389692846,0.7645501966861594,0.019350711631388278
+flat_mae,patch,logistic,aabc_age,82,0.046415888336127774,test,0.4807692307692308,0.06997911776435113,0.47590928093645485,0.07249045709628528,0.4819139194139194,0.07019223824932229
+flat_mae,patch,logistic,aabc_age,83,0.046415888336127774,train,0.7598425196850394,0.019312483992072995,0.7589434435059746,0.019526597788451353,0.7607355065028223,0.019305673250931833
+flat_mae,patch,logistic,aabc_age,83,0.046415888336127774,test,0.5,0.06466774435345116,0.49526315789473685,0.06634727683976535,0.5009157509157509,0.06497779932992374
+flat_mae,patch,logistic,aabc_age,84,0.046415888336127774,train,0.7539370078740157,0.019340007305369996,0.7525170527084777,0.019612162830091668,0.7549722951577421,0.019261030458028416
+flat_mae,patch,logistic,aabc_age,84,0.046415888336127774,test,0.5961538461538461,0.06039636093508601,0.5714342188488298,0.06655937063362495,0.5899725274725275,0.0601873509585229
+flat_mae,patch,logistic,aabc_age,85,0.3593813663804626,train,0.9566929133858267,0.008954642838843807,0.9570470941797867,0.008876931145055398,0.957688501291742,0.008730090498968907
+flat_mae,patch,logistic,aabc_age,85,0.3593813663804626,test,0.5192307692307693,0.06615244855410977,0.5155998389694042,0.06688702004769408,0.5203754578754579,0.06632328181957486
+flat_mae,patch,logistic,aabc_age,86,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,86,166.81005372000556,test,0.40384615384615385,0.06534664055598163,0.3926767676767676,0.06480110062160613,0.4001831501831502,0.06503095664004291
+flat_mae,patch,logistic,aabc_age,87,0.005994842503189409,train,0.59251968503937,0.02098837175732073,0.5854808457586839,0.021679897778776526,0.5935893114354158,0.02093613757205373
+flat_mae,patch,logistic,aabc_age,87,0.005994842503189409,test,0.5192307692307693,0.06704342626922612,0.5131107717314614,0.06865560456611554,0.5203754578754579,0.06731428691124802
+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,train,0.5039370078740157,0.019169832275347087,0.49603689624778546,0.019717049394028248,0.5052596286074313,0.019160084789034835
+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,test,0.40384615384615385,0.06513072880230596,0.40765188834154353,0.06528391816037106,0.40476190476190477,0.06555862088858541
+flat_mae,patch,logistic,aabc_age,89,0.3593813663804626,train,0.9389763779527559,0.010238778450829376,0.9393092878406839,0.01020725867437514,0.9395109745314918,0.010161278443428842
+flat_mae,patch,logistic,aabc_age,89,0.3593813663804626,test,0.4230769230769231,0.06903874008943163,0.4214179817628093,0.06914661722095265,0.4226190476190476,0.06888014958609126
+flat_mae,patch,logistic,aabc_age,90,0.005994842503189409,train,0.5826771653543307,0.020335122068342232,0.5746679561156899,0.020857728871288724,0.5824884347387459,0.02034210325074099
+flat_mae,patch,logistic,aabc_age,90,0.005994842503189409,test,0.5192307692307693,0.05722937343211048,0.5027504105090312,0.0586227211082685,0.5185439560439561,0.057194070904264135
+flat_mae,patch,logistic,aabc_age,91,9.999999999999999e-05,train,0.4645669291338583,0.020715088371275957,0.4345767399942766,0.02118948769364514,0.461711649075834,0.02050871926609143
+flat_mae,patch,logistic,aabc_age,91,9.999999999999999e-05,test,0.4807692307692308,0.061425376865566914,0.4623093681917211,0.06510462002039004,0.48008241758241754,0.06123783960321217
+flat_mae,patch,logistic,aabc_age,92,0.3593813663804626,train,0.9488188976377953,0.009808504213870205,0.9490533715475912,0.009763961291829016,0.9491388627301305,0.009764641609814094
+flat_mae,patch,logistic,aabc_age,92,0.3593813663804626,test,0.4807692307692308,0.06661227564249843,0.47619754018772253,0.06654371636383805,0.4773351648351648,0.0664904315868978
+flat_mae,patch,logistic,aabc_age,93,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,93,21.54434690031882,test,0.4230769230769231,0.06753916242846414,0.42211538461538456,0.06865787228361037,0.4212454212454212,0.06773237890586586
+flat_mae,patch,logistic,aabc_age,94,0.046415888336127774,train,0.7716535433070866,0.017107241078426264,0.7717795767837063,0.0171905073995465,0.7728146594960768,0.017016950059914795
+flat_mae,patch,logistic,aabc_age,94,0.046415888336127774,test,0.34615384615384615,0.06637569019242444,0.35288461538461535,0.06486463459379457,0.34684065934065933,0.06651024144614247
+flat_mae,patch,logistic,aabc_age,95,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,95,2.782559402207126,test,0.46153846153846156,0.05924169479654396,0.4387218963831867,0.05603668959993173,0.4507783882783883,0.05842812438041194
+flat_mae,patch,logistic,aabc_age,96,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,96,2.782559402207126,test,0.40384615384615385,0.0685907488543635,0.4145995184226069,0.06835365638902038,0.4049908424908425,0.06898085546644915
+flat_mae,patch,logistic,aabc_age,97,0.046415888336127774,train,0.765748031496063,0.0186924241556156,0.7646156420747882,0.019027306141239604,0.7667339069293752,0.01865581545159486
+flat_mae,patch,logistic,aabc_age,97,0.046415888336127774,test,0.5,0.0692350426031526,0.5060606060606061,0.06886307104317241,0.49977106227106227,0.06920692514242509
+flat_mae,patch,logistic,aabc_age,98,0.046415888336127774,train,0.7716535433070866,0.01810033750360572,0.7708232856164412,0.01837892910803931,0.7724471316042341,0.018080788107461984
+flat_mae,patch,logistic,aabc_age,98,0.046415888336127774,test,0.46153846153846156,0.06318296667699697,0.4399590373783922,0.0664860703718155,0.4608516483516484,0.0631334149535645
+flat_mae,patch,logistic,aabc_age,99,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,99,166.81005372000556,test,0.40384615384615385,0.0622755258850566,0.3788425925925926,0.06434918818962469,0.4001831501831502,0.0621385077996204
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,train,0.7578740157480315,0.01869684997919163,0.7563946802075788,0.01904281591326563,0.7583518495787214,0.018608595496722002
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,test,0.38461538461538464,0.06438277729202367,0.38095048629531386,0.06294293271682548,0.3882783882783883,0.06505580136793902

data_scaling/n100_2/eval_v2/aabc_age__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:21:25
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/aabc_age__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:20:06    time: 5.2902  data: 4.2558  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:43    time: 0.2586  data: 0.0851  max mem: 3393
+extract (train)  [ 40/228]  eta: 0:01:07    time: 0.2112  data: 0.0650  max mem: 3393
+extract (train)  [ 60/228]  eta: 0:00:51    time: 0.2039  data: 0.0653  max mem: 3393
+extract (train)  [ 80/228]  eta: 0:00:42    time: 0.2171  data: 0.0678  max mem: 3393
+extract (train)  [100/228]  eta: 0:00:35    time: 0.2441  data: 0.0786  max mem: 3393
+extract (train)  [120/228]  eta: 0:00:28    time: 0.2101  data: 0.0638  max mem: 3393
+extract (train)  [140/228]  eta: 0:00:22    time: 0.2126  data: 0.0697  max mem: 3393
+extract (train)  [160/228]  eta: 0:00:17    time: 0.2328  data: 0.0776  max mem: 3393
+extract (train)  [180/228]  eta: 0:00:12    time: 0.2137  data: 0.0701  max mem: 3393
+extract (train)  [200/228]  eta: 0:00:06    time: 0.2059  data: 0.0685  max mem: 3393
+extract (train)  [220/228]  eta: 0:00:01    time: 0.1876  data: 0.0582  max mem: 3393
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1868  data: 0.0621  max mem: 3393
+extract (train) Total time: 0:00:55 (0.2418 s / it)
+extract (validation)  [ 0/27]  eta: 0:02:01    time: 4.5147  data: 4.3468  max mem: 3393
+extract (validation)  [20/27]  eta: 0:00:02    time: 0.1919  data: 0.0574  max mem: 3393
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1765  data: 0.0524  max mem: 3393
+extract (validation) Total time: 0:00:09 (0.3640 s / it)
+extract (test)  [ 0/26]  eta: 0:01:51    time: 4.2763  data: 4.1459  max mem: 3393
+extract (test)  [20/26]  eta: 0:00:02    time: 0.1959  data: 0.0617  max mem: 3393
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1771  data: 0.0522  max mem: 3393
+extract (test) Total time: 0:00:09 (0.3638 s / it)
+feature extraction time: 0:01:14
+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 |
+|:---------|:-------|:---------|:----------|:--------|--------:|:--------|--------:|----------:|--------:|----------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_age  |         | 0.35938 | train   | 0.95472 |  0.009455 | 0.95512 | 0.0093696 | 0.95517 |  0.0093153 |
+| flat_mae | patch  | logistic | aabc_age  |         | 0.35938 | test    | 0.40385 |  0.063832 | 0.40286 | 0.06334   | 0.39904 |  0.063379  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06595897057123137, "f1": 0.5358441558441558, "f1_std": 0.06717106461657657, "bacc": 0.5325091575091575, "bacc_std": 0.06617728086574842}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06721002804509521, "f1": 0.5675574425574426, "f1_std": 0.06817371597283524, "bacc": 0.5723443223443223, "bacc_std": 0.06698985260497134}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06693412817683442, "f1": 0.499147465437788, "f1_std": 0.06990882852633595, "bacc": 0.5157967032967034, "bacc_std": 0.06686377372462708}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06450470144784594, "f1": 0.556098901098901, "f1_std": 0.06430645548951695, "bacc": 0.55746336996337, "bacc_std": 0.0647567830407725}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 9.999999999999999e-05, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.0627101319635637, "f1": 0.34598214285714285, "f1_std": 0.059353558737149315, "bacc": 0.3628663003663004, "bacc_std": 0.06191586042056673}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06826033528178033, "f1": 0.5756988172580377, "f1_std": 0.06915858341630031, "bacc": 0.5798992673992673, "bacc_std": 0.06827255846154588}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06012377519081171, "f1": 0.3931623931623932, "f1_std": 0.058130363102626044, "bacc": 0.4178113553113553, "bacc_std": 0.05928273027395515}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 96, "C": 2.782559402207126, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.0685907488543635, "f1": 0.4145995184226069, "f1_std": 0.06835365638902038, "bacc": 0.4049908424908425, "bacc_std": 0.06898085546644915}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.0692350426031526, "f1": 0.5060606060606061, "f1_std": 0.06886307104317241, "bacc": 0.49977106227106227, "bacc_std": 0.06920692514242509}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06318296667699697, "f1": 0.4399590373783922, "f1_std": 0.0664860703718155, "bacc": 0.4608516483516484, "bacc_std": 0.0631334149535645}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 166.81005372000556, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.0622755258850566, "f1": 0.3788425925925926, "f1_std": 0.06434918818962469, "bacc": 0.4001831501831502, "bacc_std": 0.0621385077996204}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06438277729202367, "f1": 0.38095048629531386, "f1_std": 0.06294293271682548, "bacc": 0.3882783882783883, "bacc_std": 0.06505580136793902}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_age  | train   |        100 | 31.821 |  183.12 | 0.77687 |  0.17579  | 0.77329 | 0.18077  | 0.77742 |   0.17582  |
+| flat_mae | patch  | logistic | aabc_age  | test    |        100 | 31.821 |  183.12 | 0.45538 |  0.065821 | 0.44701 | 0.066311 | 0.45406 |   0.065461 |
+
+
+done! total time: 0:06:15

data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
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+flat_mae,patch,logistic,aabc_sex,3,0.046415888336127774,test,0.8181818181818182,0.052352827167180684,0.8106060606060606,0.055255867133056843,0.8070652173913043,0.05527593548034441
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+flat_mae,patch,logistic,aabc_sex,95,0.046415888336127774,train,0.8941398865784499,0.013072462249462319,0.8907637393433434,0.013543539288050957,0.8884272692634603,0.013700867066224833
+flat_mae,patch,logistic,aabc_sex,95,0.046415888336127774,test,0.8545454545454545,0.04715616398365001,0.8505434782608696,0.04852117259162437,0.8505434782608696,0.04846336403198105
+flat_mae,patch,logistic,aabc_sex,96,0.005994842503189409,train,0.8525519848771267,0.015020749701250464,0.8474219027334043,0.015680880714440317,0.8445734048477388,0.015852415660447042
+flat_mae,patch,logistic,aabc_sex,96,0.005994842503189409,test,0.8363636363636363,0.047183958613211126,0.8281846581048247,0.050439839703354736,0.8226902173913043,0.0503876761686454
+flat_mae,patch,logistic,aabc_sex,97,0.046415888336127774,train,0.9017013232514177,0.013131673010175947,0.8988378934980876,0.013560671256001569,0.8973958791289312,0.013780733563817168
+flat_mae,patch,logistic,aabc_sex,97,0.046415888336127774,test,0.8363636363636363,0.05089946662273915,0.8281846581048247,0.05504237988674656,0.8226902173913043,0.054660020624436936
+flat_mae,patch,logistic,aabc_sex,98,0.046415888336127774,train,0.9054820415879017,0.0122705011928615,0.9024676244136995,0.012753802381410358,0.900055687446877,0.013047887210339209
+flat_mae,patch,logistic,aabc_sex,98,0.046415888336127774,test,0.8545454545454545,0.04318694323653505,0.8428571428571429,0.04934919170430577,0.8322010869565217,0.04915764495381051
+flat_mae,patch,logistic,aabc_sex,99,0.046415888336127774,train,0.888468809073724,0.014487969183690668,0.885294582446701,0.01491969458753007,0.8841334740174096,0.015061389677431586
+flat_mae,patch,logistic,aabc_sex,99,0.046415888336127774,test,0.8727272727272727,0.04243117519111594,0.8639095086603039,0.04796021701826644,0.8539402173913043,0.0485811429626717
+flat_mae,patch,logistic,aabc_sex,100,0.046415888336127774,train,0.9017013232514177,0.013009822610794334,0.8988378934980876,0.013476037569042087,0.8973958791289312,0.01384432120686867
+flat_mae,patch,logistic,aabc_sex,100,0.046415888336127774,test,0.8363636363636363,0.0477708580206914,0.8343927735028438,0.04796789134675685,0.8410326086956521,0.04703773459180098

data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:21:25
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/aabc_sex__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:21:12    time: 5.3937  data: 4.3900  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:01:47    time: 0.2520  data: 0.0875  max mem: 3393
+extract (train)  [ 40/236]  eta: 0:01:08    time: 0.2000  data: 0.0581  max mem: 3393
+extract (train)  [ 60/236]  eta: 0:00:53    time: 0.1972  data: 0.0606  max mem: 3393
+extract (train)  [ 80/236]  eta: 0:00:43    time: 0.1975  data: 0.0636  max mem: 3393
+extract (train)  [100/236]  eta: 0:00:35    time: 0.1946  data: 0.0626  max mem: 3393
+extract (train)  [120/236]  eta: 0:00:29    time: 0.2038  data: 0.0662  max mem: 3393
+extract (train)  [140/236]  eta: 0:00:23    time: 0.1991  data: 0.0644  max mem: 3393
+extract (train)  [160/236]  eta: 0:00:18    time: 0.2136  data: 0.0711  max mem: 3393
+extract (train)  [180/236]  eta: 0:00:13    time: 0.2065  data: 0.0662  max mem: 3393
+extract (train)  [200/236]  eta: 0:00:08    time: 0.1888  data: 0.0580  max mem: 3393
+extract (train)  [220/236]  eta: 0:00:03    time: 0.1887  data: 0.0574  max mem: 3393
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1655  data: 0.0476  max mem: 3393
+extract (train) Total time: 0:00:53 (0.2253 s / it)
+extract (validation)  [ 0/29]  eta: 0:02:03    time: 4.2749  data: 4.1036  max mem: 3393
+extract (validation)  [20/29]  eta: 0:00:03    time: 0.1957  data: 0.0644  max mem: 3393
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1671  data: 0.0487  max mem: 3393
+extract (validation) Total time: 0:00:09 (0.3384 s / it)
+extract (test)  [ 0/28]  eta: 0:02:02    time: 4.3761  data: 4.2380  max mem: 3393
+extract (test)  [20/28]  eta: 0:00:03    time: 0.1803  data: 0.0558  max mem: 3393
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1656  data: 0.0495  max mem: 3393
+extract (test) Total time: 0:00:09 (0.3354 s / it)
+feature extraction time: 0:01:12
+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 |
+|:---------|:-------|:---------|:----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.0059948 | train   | 0.85066 |  0.015184 | 0.84513 | 0.015887 | 0.84144 |   0.016013 |
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.0059948 | test    | 0.94545 |  0.03095  | 0.94359 | 0.031936 | 0.94697 |   0.031185 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.056578581351679307, "f1": 0.795677136102668, "f1_std": 0.05770622541448077, "bacc": 0.7975543478260869, "bacc_std": 0.057531510328103504}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04185546164387017, "f1": 0.8863636363636364, "f1_std": 0.04451911429115712, "bacc": 0.8817934782608696, "bacc_std": 0.04550598917833532}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.052352827167180684, "f1": 0.8106060606060606, "f1_std": 0.055255867133056843, "bacc": 0.8070652173913043, "bacc_std": 0.05527593548034441}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.055190800526989235, "f1": 0.795677136102668, "f1_std": 0.05625457232869017, "bacc": 0.7975543478260869, "bacc_std": 0.05639537432986651}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.04880151738597429, "f1": 0.8074229691876751, "f1_std": 0.054151786829327905, "bacc": 0.8009510869565217, "bacc_std": 0.05389055283631942}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.044912417984964884, "f1": 0.84593837535014, "f1_std": 0.0498201997648846, "bacc": 0.8383152173913043, "bacc_std": 0.04998019523000291}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.0449613828792356, "f1": 0.8541114058355437, "f1_std": 0.044850175549859435, "bacc": 0.8688858695652174, "bacc_std": 0.04119394256590823}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.0518452114511381, "f1": 0.7975911676145868, "f1_std": 0.052363410990702755, "bacc": 0.8036684782608696, "bacc_std": 0.052132023208475944}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.049884163339341606, "f1": 0.8505434782608696, "f1_std": 0.051407018477685355, "bacc": 0.8505434782608696, "bacc_std": 0.0516869322426776}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.055338907748019396, "f1": 0.7758152173913043, "f1_std": 0.05716734045773888, "bacc": 0.7758152173913043, "bacc_std": 0.05736597600423305}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04265062370563106, "f1": 0.8639095086603039, "f1_std": 0.048343068688850646, "bacc": 0.8539402173913043, "bacc_std": 0.048852019205630315}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.055386712611277956, "f1": 0.7472605160834218, "f1_std": 0.06180043800728465, "bacc": 0.7418478260869565, "bacc_std": 0.059676324679529456}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04000692502038587, "f1": 0.8863636363636364, "f1_std": 0.042140842076823906, "bacc": 0.8817934782608696, "bacc_std": 0.042894589293149245}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04908809419539054, "f1": 0.8281846581048247, "f1_std": 0.05332071079417537, "bacc": 0.8226902173913043, "bacc_std": 0.05347310577605193}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 2.782559402207126, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05118464387484824, "f1": 0.8151881720430108, "f1_std": 0.05184347598531246, "bacc": 0.8192934782608696, "bacc_std": 0.051584230899385304}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.054112939745812015, "f1": 0.8106060606060606, "f1_std": 0.057943527476893096, "bacc": 0.8070652173913043, "bacc_std": 0.057885118648511626}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.05438395485301288, "f1": 0.790003471017008, "f1_std": 0.058112736329989687, "bacc": 0.7853260869565217, "bacc_std": 0.05755937638407434}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.000774263682681127, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.049592268125699154, "f1": 0.8354935194416749, "f1_std": 0.049605209140049414, "bacc": 0.8471467391304348, "bacc_std": 0.0473093973962291}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.000774263682681127, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.040461797102864676, "f1": 0.8891129032258065, "f1_std": 0.040851989063660825, "bacc": 0.8940217391304348, "bacc_std": 0.03975129124146655}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05427574530972024, "f1": 0.7758152173913043, "f1_std": 0.056078224076130036, "bacc": 0.7758152173913043, "bacc_std": 0.05608240264929922}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05859420894861581, "f1": 0.7585275244849713, "f1_std": 0.05981368638743842, "bacc": 0.7601902173913043, "bacc_std": 0.059561309448005216}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 2.782559402207126, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05582616466458199, "f1": 0.7758152173913043, "f1_std": 0.05766073721373179, "bacc": 0.7758152173913043, "bacc_std": 0.05792203347656141}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.046415888336127774, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04329336406310817, "f1": 0.8863636363636364, "f1_std": 0.04600345194279069, "bacc": 0.8817934782608696, "bacc_std": 0.04690130994074899}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.049836745048455866, "f1": 0.8307692307692308, "f1_std": 0.0520850090114269, "bacc": 0.8288043478260869, "bacc_std": 0.05229418111497062}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.005994842503189409, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.040793112366291996, "f1": 0.8891129032258065, "f1_std": 0.0412495107682933, "bacc": 0.8940217391304348, "bacc_std": 0.04021462504716063}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05157326490433354, "f1": 0.7555555555555555, "f1_std": 0.0539028146360495, "bacc": 0.7540760869565217, "bacc_std": 0.05366836425070444}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04091985312981083, "f1": 0.8891129032258065, "f1_std": 0.041313964584468135, "bacc": 0.8940217391304348, "bacc_std": 0.040196585462621726}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04318694323653505, "f1": 0.8428571428571429, "f1_std": 0.04934919170430577, "bacc": 0.8322010869565217, "bacc_std": 0.04915764495381051}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04243117519111594, "f1": 0.8639095086603039, "f1_std": 0.04796021701826644, "bacc": 0.8539402173913043, "bacc_std": 0.0485811429626717}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.0477708580206914, "f1": 0.8343927735028438, "f1_std": 0.04796789134675685, "bacc": 0.8410326086956521, "bacc_std": 0.04703773459180098}
+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_sex  | train   |        100 | 2.0939 |  16.783 | 0.90754 |  0.041485 | 0.90464 | 0.043041 | 0.90319 |   0.044167 |
+| flat_mae | patch  | logistic | aabc_sex  | test    |        100 | 2.0939 |  16.783 | 0.84218 |  0.045271 | 0.83635 | 0.046957 | 0.8349  |   0.047388 |
+
+
+done! total time: 0:05:08

data_scaling/n100_2/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/abide_dx__patch__logistic
+remote_dir: null

data_scaling/n100_2/eval_v2/abide_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n100_2/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:20:47
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/abide_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (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 (flat)
+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: 0:18:51    time: 3.9149  data: 2.8983  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:36    time: 0.1813  data: 0.0516  max mem: 2851
+extract (train)  [ 40/289]  eta: 0:01:05    time: 0.1619  data: 0.0427  max mem: 2851
+extract (train)  [ 60/289]  eta: 0:00:52    time: 0.1584  data: 0.0440  max mem: 2851
+extract (train)  [ 80/289]  eta: 0:00:44    time: 0.1705  data: 0.0484  max mem: 2851
+extract (train)  [100/289]  eta: 0:00:38    time: 0.1658  data: 0.0473  max mem: 2851
+extract (train)  [120/289]  eta: 0:00:33    time: 0.1632  data: 0.0470  max mem: 2851
+extract (train)  [140/289]  eta: 0:00:28    time: 0.1558  data: 0.0433  max mem: 2851
+extract (train)  [160/289]  eta: 0:00:24    time: 0.1497  data: 0.0434  max mem: 2851
+extract (train)  [180/289]  eta: 0:00:19    time: 0.1384  data: 0.0363  max mem: 2851
+extract (train)  [200/289]  eta: 0:00:16    time: 0.1674  data: 0.0518  max mem: 2851
+extract (train)  [220/289]  eta: 0:00:12    time: 0.1493  data: 0.0447  max mem: 2851
+extract (train)  [240/289]  eta: 0:00:08    time: 0.1428  data: 0.0398  max mem: 2851
+extract (train)  [260/289]  eta: 0:00:04    time: 0.1468  data: 0.0426  max mem: 2851
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1358  data: 0.0381  max mem: 2851
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1305  data: 0.0341  max mem: 2851
+extract (train) Total time: 0:00:49 (0.1696 s / it)
+extract (validation)  [ 0/62]  eta: 0:02:55    time: 2.8363  data: 2.6949  max mem: 2851
+extract (validation)  [20/62]  eta: 0:00:12    time: 0.1776  data: 0.0538  max mem: 2851
+extract (validation)  [40/62]  eta: 0:00:04    time: 0.1283  data: 0.0302  max mem: 2851
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1224  data: 0.0279  max mem: 2851
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1225  data: 0.0280  max mem: 2851
+extract (validation) Total time: 0:00:11 (0.1903 s / it)
+extract (test)  [ 0/62]  eta: 0:02:59    time: 2.8981  data: 2.7294  max mem: 2851
+extract (test)  [20/62]  eta: 0:00:13    time: 0.1815  data: 0.0560  max mem: 2851
+extract (test)  [40/62]  eta: 0:00:05    time: 0.1407  data: 0.0362  max mem: 2851
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1231  data: 0.0278  max mem: 2851
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1230  data: 0.0278  max mem: 2851
+extract (test) Total time: 0:00:12 (0.1971 s / it)
+feature extraction time: 0:01:13
+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 |
+|:---------|:-------|:---------|:----------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | train   | 0.80627 |  0.015141 | 0.80192 | 0.015637 | 0.79954 |   0.015586 |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.56452 |  0.042812 | 0.54757 | 0.045293 | 0.5525  |   0.043289 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.04102128517817421, "f1": 0.665680278818965, "f1_std": 0.04169803613646133, "bacc": 0.6654411764705883, "bacc_std": 0.041606463632044254}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.0417965284668889, "f1": 0.5989703649924097, "f1_std": 0.04291791215825841, "bacc": 0.5987394957983193, "bacc_std": 0.04262387677414299}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04076409570245765, "f1": 0.5612903225806452, "f1_std": 0.04445626420623248, "bacc": 0.5698529411764706, "bacc_std": 0.04131059744761275}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.03934624443441346, "f1": 0.6402321083172147, "f1_std": 0.044116583354234795, "bacc": 0.64390756302521, "bacc_std": 0.0408005159962814}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04282559979636986, "f1": 0.6045708211533352, "f1_std": 0.044950056443147646, "bacc": 0.6045168067226891, "bacc_std": 0.04387190556616899}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.5403225806451613, "acc_std": 0.04314246599962288, "f1": 0.5174438451560046, "f1_std": 0.04578826320600653, "bacc": 0.5241596638655462, "bacc_std": 0.04351560866736028}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 21.54434690031882, "split": "test", "acc": 0.5, "acc_std": 0.04366643498414733, "f1": 0.4952731092436975, "f1_std": 0.0438758130226648, "bacc": 0.4952731092436975, "bacc_std": 0.043689338689098314}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04040926167006869, "f1": 0.5951020408163266, "f1_std": 0.04373843005228634, "bacc": 0.5982142857142857, "bacc_std": 0.0413143483497391}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04170680515520102, "f1": 0.5649122807017544, "f1_std": 0.04569147272867051, "bacc": 0.5714285714285714, "bacc_std": 0.04257401197252157}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.03629863345696188, "f1": 0.6502235982112143, "f1_std": 0.04095946839687293, "bacc": 0.6528361344537815, "bacc_std": 0.03779286709210886}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04371905327367831, "f1": 0.5808311791608669, "f1_std": 0.04470783054647527, "bacc": 0.5808823529411764, "bacc_std": 0.04420417934353756}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04257679295869103, "f1": 0.58994708994709, "f1_std": 0.0436151067391576, "bacc": 0.5898109243697479, "bacc_std": 0.043112283000548514}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04377616357560976, "f1": 0.602745995423341, "f1_std": 0.04425991374838376, "bacc": 0.6034663865546219, "bacc_std": 0.04433334251989069}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.5241935483870968, "acc_std": 0.045477853639943805, "f1": 0.5171275823377994, "f1_std": 0.046300404150686626, "bacc": 0.5173319327731093, "bacc_std": 0.046039506101780654}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 21.54434690031882, "split": "test", "acc": 0.532258064516129, "acc_std": 0.04534603915922273, "f1": 0.5317708333333333, "f1_std": 0.04543235546116987, "bacc": 0.5341386554621849, "bacc_std": 0.04571020023722273}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.041465263849871224, "f1": 0.5953379953379954, "f1_std": 0.04272093208442295, "bacc": 0.5955882352941176, "bacc_std": 0.04190185462332464}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.0407589644640678, "f1": 0.6137071651090342, "f1_std": 0.042077578968094685, "bacc": 0.6134453781512605, "bacc_std": 0.04143952081163773}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.042782667749094935, "f1": 0.6169755573462261, "f1_std": 0.04534568202756216, "bacc": 0.6176470588235294, "bacc_std": 0.043806821965557526}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04395568196612561, "f1": 0.6704756842944459, "f1_std": 0.04540993216993206, "bacc": 0.6696428571428572, "bacc_std": 0.04471632139990081}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04390415016447685, "f1": 0.5826018084614877, "f1_std": 0.044566216475924486, "bacc": 0.5824579831932774, "bacc_std": 0.04419474301124303}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04515753440602835, "f1": 0.543354536324071, "f1_std": 0.04659959505733285, "bacc": 0.5451680672268907, "bacc_std": 0.04545476604023936}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.0448650276673965, "f1": 0.6137071651090342, "f1_std": 0.046064729604353996, "bacc": 0.6134453781512605, "bacc_std": 0.04541941103690125}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 23, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04433101706346667, "f1": 0.5978378378378378, "f1_std": 0.0477766744434893, "bacc": 0.5997899159663866, "bacc_std": 0.045562213885503466}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04131618078919207, "f1": 0.5623043623043623, "f1_std": 0.042878280911061335, "bacc": 0.5630252100840336, "bacc_std": 0.04189339417535167}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 25, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04252230852346108, "f1": 0.6145945945945945, "f1_std": 0.04489702597393315, "bacc": 0.6160714285714286, "bacc_std": 0.04329447387249303}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 26, "C": 0.005994842503189409, "split": "test", "acc": 0.5241935483870968, "acc_std": 0.04522576043437906, "f1": 0.5040336248389939, "f1_std": 0.04781662517804316, "bacc": 0.509453781512605, "bacc_std": 0.04566142862818599}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 27, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04170089159768197, "f1": 0.6227513227513227, "f1_std": 0.042457301424484936, "bacc": 0.6223739495798319, "bacc_std": 0.04197596464307421}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 28, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.03901578898090218, "f1": 0.6091008771929824, "f1_std": 0.04159192534523863, "bacc": 0.6129201680672269, "bacc_std": 0.039487962469163994}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 29, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04283434927283793, "f1": 0.6191239316239316, "f1_std": 0.0442311426472211, "bacc": 0.6192226890756303, "bacc_std": 0.043314701088726114}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 30, "C": 0.046415888336127774, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04558269976877408, "f1": 0.6405797101449275, "f1_std": 0.04678344372427798, "bacc": 0.6402310924369747, "bacc_std": 0.046439919558325965}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 31, "C": 2.782559402207126, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04335975874540744, "f1": 0.6209430894308943, "f1_std": 0.04346652391177319, "bacc": 0.6260504201680672, "bacc_std": 0.043234307946630045}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04127803181487563, "f1": 0.6025641025641025, "f1_std": 0.04271368895383393, "bacc": 0.6029411764705883, "bacc_std": 0.041887584324418525}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 36, "C": 0.046415888336127774, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04428945033560675, "f1": 0.5694444444444444, "f1_std": 0.04564890660558863, "bacc": 0.5703781512605042, "bacc_std": 0.044565591700589866}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 37, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04137811986219123, "f1": 0.6336287939833467, "f1_std": 0.043660283066259784, "bacc": 0.6339285714285714, "bacc_std": 0.04226887753049954}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 38, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04091238322240008, "f1": 0.6119727891156463, "f1_std": 0.043653611195713454, "bacc": 0.6144957983193278, "bacc_std": 0.04162319810088019}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04438279470899801, "f1": 0.5670158474348643, "f1_std": 0.046594465829281895, "bacc": 0.5688025210084033, "bacc_std": 0.045086392579567994}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.040200667313433615, "f1": 0.5716183895827798, "f1_std": 0.04134474350809993, "bacc": 0.571953781512605, "bacc_std": 0.040676244832990274}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 41, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.03962143966022965, "f1": 0.6003223207091055, "f1_std": 0.04163695881856695, "bacc": 0.6013655462184874, "bacc_std": 0.04017483608439783}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 42, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04593219904891668, "f1": 0.6063492063492064, "f1_std": 0.04717151177435496, "bacc": 0.60609243697479, "bacc_std": 0.04649034602613794}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 43, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.043689091688546684, "f1": 0.5989703649924097, "f1_std": 0.044308197613895345, "bacc": 0.5987394957983193, "bacc_std": 0.044005016493470786}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 44, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.040428489944190765, "f1": 0.575109649122807, "f1_std": 0.04347277815711393, "bacc": 0.5803571428571428, "bacc_std": 0.04096890419973279}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 45, "C": 21.54434690031882, "split": "test", "acc": 0.5080645161290323, "acc_std": 0.04655616314456096, "f1": 0.5041625696492953, "f1_std": 0.046565353862755336, "bacc": 0.5042016806722689, "bacc_std": 0.04650182441315453}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 46, "C": 0.046415888336127774, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.045556253889782725, "f1": 0.5454307410316837, "f1_std": 0.04563901672215012, "bacc": 0.5456932773109244, "bacc_std": 0.045511104675065764}
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+eval results (random splits):
+
+| model    | repr   | clf      | dataset   | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | abide_dx  | train   |        100 | 13.686 |  129.13 | 0.78261 |  0.08352  | 0.77624 | 0.087093 | 0.77465 |   0.087009 |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 13.686 |  129.13 | 0.60685 |  0.037902 | 0.59672 | 0.038298 | 0.59782 |   0.03761  |
+
+
+done! total time: 0:05:32

data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.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: data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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