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data_scaling/n1600_2/eval_v2/abide_dx__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,abide_dx,88,0.046415888336127774,train,0.8005698005698005,0.014653297432880005,0.7973858904053107,0.014954884597242443,0.7960871170173496,0.014984008038796993
+flat_mae,patch,logistic,abide_dx,88,0.046415888336127774,test,0.5483870967741935,0.04252426619473433,0.5386659580122243,0.0436022422417833,0.539390756302521,0.042862222303106184
+flat_mae,patch,logistic,abide_dx,89,0.3593813663804626,train,0.8717948717948718,0.011596692779189564,0.8700213956550362,0.011795748832502402,0.8689553340716132,0.011882472911993968
+flat_mae,patch,logistic,abide_dx,89,0.3593813663804626,test,0.6209677419354839,0.04133967249957996,0.620745753888202,0.04146532304612032,0.6244747899159664,0.04154678246826283
+flat_mae,patch,logistic,abide_dx,90,0.046415888336127774,train,0.7962962962962963,0.013954769312381966,0.7926573101589367,0.014253358538948631,0.7910299003322259,0.014246984319389828
+flat_mae,patch,logistic,abide_dx,90,0.046415888336127774,test,0.6370967741935484,0.04255374272342059,0.6217205613178767,0.045224811414170625,0.6234243697478992,0.043365788035303665
+flat_mae,patch,logistic,abide_dx,91,0.3593813663804626,train,0.8760683760683761,0.011977237795468996,0.8743113507506457,0.012181248253380057,0.8731266149870801,0.012256359387169938
+flat_mae,patch,logistic,abide_dx,91,0.3593813663804626,test,0.6370967741935484,0.042800322263367066,0.6330637206549615,0.04317420234066944,0.6328781512605042,0.042989377412215216
+flat_mae,patch,logistic,abide_dx,92,2.782559402207126,train,0.9772079772079773,0.005618143322032308,0.9769518681462999,0.005683489173943881,0.9766703580657069,0.0057568749581469215
+flat_mae,patch,logistic,abide_dx,92,2.782559402207126,test,0.5241935483870968,0.044851155733777556,0.5150792072645324,0.04591357469219585,0.5157563025210083,0.04511981989325819
+flat_mae,patch,logistic,abide_dx,93,0.005994842503189409,train,0.7037037037037037,0.016835624229474773,0.6953862329664462,0.017423872088956897,0.6940568475452196,0.017131257860077513
+flat_mae,patch,logistic,abide_dx,93,0.005994842503189409,test,0.5483870967741935,0.04337019697248149,0.5337093741606231,0.04475701523184902,0.5362394957983193,0.04356518080778565
+flat_mae,patch,logistic,abide_dx,94,0.046415888336127774,train,0.7763532763532763,0.015472041346927803,0.7716259534234142,0.015997982908974615,0.7696936138796604,0.015945171852928847
+flat_mae,patch,logistic,abide_dx,94,0.046415888336127774,test,0.5887096774193549,0.040720932317717645,0.5841388834089565,0.04137133522032405,0.5840336134453781,0.041185500746234706
+flat_mae,patch,logistic,abide_dx,95,0.3593813663804626,train,0.8717948717948718,0.013391779031142496,0.8703542583229364,0.01351249870922499,0.8701365817644887,0.013463795333396191
+flat_mae,patch,logistic,abide_dx,95,0.3593813663804626,test,0.5887096774193549,0.041694543570827164,0.5682392298764252,0.04483544905868884,0.5730042016806722,0.04233017465123211
+flat_mae,patch,logistic,abide_dx,96,0.3593813663804626,train,0.8732193732193733,0.012257917446511293,0.8715931955368574,0.012467581135410905,0.8708379475821336,0.012617869226476387
+flat_mae,patch,logistic,abide_dx,96,0.3593813663804626,test,0.5725806451612904,0.044288266756788985,0.5691904293674206,0.044770581744960285,0.569327731092437,0.04453422862670238
+flat_mae,patch,logistic,abide_dx,97,0.000774263682681127,train,0.6524216524216524,0.015692181483249835,0.623681805399325,0.01805094911672276,0.6307124400147656,0.016241855529289688
+flat_mae,patch,logistic,abide_dx,97,0.000774263682681127,test,0.6451612903225806,0.0397610546542926,0.6260964912280702,0.04415358256256588,0.6292016806722689,0.04122989235220067
+flat_mae,patch,logistic,abide_dx,98,0.3593813663804626,train,0.8732193732193733,0.011672006542275248,0.8715931955368574,0.011836403863057997,0.8708379475821336,0.011887270141183157
+flat_mae,patch,logistic,abide_dx,98,0.3593813663804626,test,0.6451612903225806,0.04620059866904033,0.6375232527238905,0.04765982748999892,0.6370798319327731,0.046916113422570196
+flat_mae,patch,logistic,abide_dx,99,0.046415888336127774,train,0.7735042735042735,0.014940029684594566,0.7692789476186441,0.015348082279262246,0.7677002583979329,0.015322523760673785
+flat_mae,patch,logistic,abide_dx,99,0.046415888336127774,test,0.6209677419354839,0.040610635617854586,0.6097756946769334,0.04246661188854553,0.6102941176470589,0.04128550682827888
+flat_mae,patch,logistic,abide_dx,100,0.3593813663804626,train,0.8789173789173789,0.012376402853927233,0.8773642878722796,0.01256515413728394,0.8765965300849021,0.012633115347542506
+flat_mae,patch,logistic,abide_dx,100,0.3593813663804626,test,0.6048387096774194,0.04521190711820668,0.6017043592264831,0.045754279310940796,0.601890756302521,0.04577271635893101

data_scaling/n1600_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 17:27:00
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:17:07    time: 3.5539  data: 2.8332  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:29    time: 0.1711  data: 0.0529  max mem: 2851
+extract (train)  [ 40/289]  eta: 0:01:01    time: 0.1552  data: 0.0425  max mem: 2851
+extract (train)  [ 60/289]  eta: 0:00:49    time: 0.1582  data: 0.0463  max mem: 2851
+extract (train)  [ 80/289]  eta: 0:00:42    time: 0.1573  data: 0.0470  max mem: 2851
+extract (train)  [100/289]  eta: 0:00:36    time: 0.1655  data: 0.0499  max mem: 2851
+extract (train)  [120/289]  eta: 0:00:31    time: 0.1360  data: 0.0358  max mem: 2851
+extract (train)  [140/289]  eta: 0:00:26    time: 0.1559  data: 0.0464  max mem: 2851
+extract (train)  [160/289]  eta: 0:00:23    time: 0.1600  data: 0.0465  max mem: 2851
+extract (train)  [180/289]  eta: 0:00:19    time: 0.1573  data: 0.0458  max mem: 2851
+extract (train)  [200/289]  eta: 0:00:15    time: 0.1380  data: 0.0375  max mem: 2851
+extract (train)  [220/289]  eta: 0:00:11    time: 0.1452  data: 0.0395  max mem: 2851
+extract (train)  [240/289]  eta: 0:00:08    time: 0.1548  data: 0.0458  max mem: 2851
+extract (train)  [260/289]  eta: 0:00:04    time: 0.1384  data: 0.0369  max mem: 2851
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1310  data: 0.0341  max mem: 2851
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1281  data: 0.0319  max mem: 2851
+extract (train) Total time: 0:00:47 (0.1640 s / it)
+extract (validation)  [ 0/62]  eta: 0:03:01    time: 2.9217  data: 2.7481  max mem: 2851
+extract (validation)  [20/62]  eta: 0:00:13    time: 0.1965  data: 0.0578  max mem: 2851
+extract (validation)  [40/62]  eta: 0:00:05    time: 0.1493  data: 0.0395  max mem: 2851
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1318  data: 0.0319  max mem: 2851
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1319  data: 0.0318  max mem: 2851
+extract (validation) Total time: 0:00:12 (0.2079 s / it)
+extract (test)  [ 0/62]  eta: 0:03:27    time: 3.3512  data: 3.1997  max mem: 2851
+extract (test)  [20/62]  eta: 0:00:14    time: 0.1958  data: 0.0567  max mem: 2851
+extract (test)  [40/62]  eta: 0:00:05    time: 0.1500  data: 0.0381  max mem: 2851
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1320  data: 0.0327  max mem: 2851
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1320  data: 0.0327  max mem: 2851
+extract (test) Total time: 0:00:13 (0.2143 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   | 0.77208 |  0.015403 | 0.76774 | 0.015903 | 0.76618 |   0.01592  |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.60484 |  0.043593 | 0.59317 | 0.04586  | 0.59505 |   0.044282 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.0431528362181822, "f1": 0.6522435897435898, "f1_std": 0.04498592414746896, "bacc": 0.6517857142857143, "bacc_std": 0.043978296880765294}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.044202659762426516, "f1": 0.5972691721349506, "f1_std": 0.045214227487996, "bacc": 0.5971638655462186, "bacc_std": 0.044688871491154095}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.043804491856016176, "f1": 0.6003223207091055, "f1_std": 0.04629448155661542, "bacc": 0.6013655462184874, "bacc_std": 0.044738576017664455}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.6935483870967742, "acc_std": 0.03885257227685982, "f1": 0.6853632478632479, "f1_std": 0.04055869820619437, "bacc": 0.6843487394957983, "bacc_std": 0.03982261308196209}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04241843492195212, "f1": 0.628161668839635, "f1_std": 0.042690805796799, "bacc": 0.6302521008403361, "bacc_std": 0.04300450833863724}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04566874746823453, "f1": 0.58994708994709, "f1_std": 0.0465323887490919, "bacc": 0.5898109243697479, "bacc_std": 0.04601098300584375}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04279020706913873, "f1": 0.6288435374149659, "f1_std": 0.04591184874750796, "bacc": 0.6307773109243697, "bacc_std": 0.043625348250164106}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 2.782559402207126, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04095014544627112, "f1": 0.5972691721349506, "f1_std": 0.0422564634297877, "bacc": 0.5971638655462186, "bacc_std": 0.04175652781428151}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.038429483240111056, "f1": 0.6231003039513678, "f1_std": 0.04216039341329129, "bacc": 0.6276260504201681, "bacc_std": 0.03938172725650939}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.039884393292412626, "f1": 0.6059684995855208, "f1_std": 0.044491399861951574, "bacc": 0.611344537815126, "bacc_std": 0.04103390154086692}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.04431692221006257, "f1": 0.5441176470588236, "f1_std": 0.04444483821621342, "bacc": 0.5441176470588236, "bacc_std": 0.04439615685123014}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04540333753495678, "f1": 0.5880946053680574, "f1_std": 0.046506377851099755, "bacc": 0.5882352941176471, "bacc_std": 0.04585144568940149}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.045869731392984156, "f1": 0.5929621848739496, "f1_std": 0.04635542738401937, "bacc": 0.5929621848739496, "bacc_std": 0.04629671716854991}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04238619763861987, "f1": 0.6092436974789917, "f1_std": 0.0429552220035364, "bacc": 0.6092436974789917, "bacc_std": 0.042957978813957874}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.039976745972157016, "f1": 0.6465831510572015, "f1_std": 0.041525834990039326, "bacc": 0.6460084033613445, "bacc_std": 0.04090075084303089}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.045042632135316195, "f1": 0.5989703649924097, "f1_std": 0.045892071499584144, "bacc": 0.5987394957983193, "bacc_std": 0.04558804180098735}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04297755809116786, "f1": 0.6242424242424243, "f1_std": 0.04364359287970895, "bacc": 0.6239495798319328, "bacc_std": 0.04353502875481593}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04620059866904033, "f1": 0.6375232527238905, "f1_std": 0.04765982748999892, "bacc": 0.6370798319327731, "bacc_std": 0.046916113422570196}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04521190711820668, "f1": 0.6017043592264831, "f1_std": 0.045754279310940796, "bacc": 0.601890756302521, "bacc_std": 0.04577271635893101}
+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 | 139.24 |  1020.3 | 0.81658 |  0.086267 | 0.8124  | 0.089871 | 0.81157 |   0.089904 |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 139.24 |  1020.3 | 0.61355 |  0.034035 | 0.60385 | 0.03579  | 0.60501 |   0.034694 |
+
+
+done! total time: 0:05:42

data_scaling/n1600_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 n1600_2; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_2/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

data_scaling/n1600_2/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n1600_2/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 17:27:04
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_2/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: 0:11:44    time: 4.6639  data: 3.6155  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:50    time: 0.1707  data: 0.0549  max mem: 2851
+extract (train)  [ 40/151]  eta: 0:00:32    time: 0.1955  data: 0.0660  max mem: 2851
+extract (train)  [ 60/151]  eta: 0:00:23    time: 0.2012  data: 0.0709  max mem: 2851
+extract (train)  [ 80/151]  eta: 0:00:17    time: 0.1756  data: 0.0562  max mem: 2851
+extract (train)  [100/151]  eta: 0:00:11    time: 0.1698  data: 0.0558  max mem: 2851
+extract (train)  [120/151]  eta: 0:00:06    time: 0.1727  data: 0.0601  max mem: 2851
+extract (train)  [140/151]  eta: 0:00:02    time: 0.1584  data: 0.0516  max mem: 2851
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1536  data: 0.0507  max mem: 2851
+extract (train) Total time: 0:00:31 (0.2078 s / it)
+extract (validation)  [ 0/32]  eta: 0:01:47    time: 3.3596  data: 3.2265  max mem: 2851
+extract (validation)  [20/32]  eta: 0:00:04    time: 0.1858  data: 0.0588  max mem: 2851
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1405  data: 0.0413  max mem: 2851
+extract (validation) Total time: 0:00:08 (0.2776 s / it)
+extract (test)  [ 0/33]  eta: 0:01:49    time: 3.3287  data: 3.1887  max mem: 2851
+extract (test)  [20/33]  eta: 0:00:04    time: 0.1789  data: 0.0618  max mem: 2851
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1505  data: 0.0475  max mem: 2851
+extract (test) Total time: 0:00:08 (0.2725 s / it)
+feature extraction time: 0:00:49
+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   | 0.74247 |  0.021883 | 0.73174 |  0.02315 | 0.7288  |   0.022673 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | test    | 0.63077 |  0.058435 | 0.61    |  0.06368 | 0.61052 |   0.060797 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05951296611743255, "f1": 0.5921814671814671, "f1_std": 0.060557871371843666, "bacc": 0.5921814671814671, "bacc_std": 0.060333039642536355}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.7230769230769231, "acc_std": 0.05269233239752369, "f1": 0.7075, "f1_std": 0.05731807859097356, "bacc": 0.7046332046332047, "bacc_std": 0.05525382420179031}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.06098498020538243, "f1": 0.5381034060279344, "f1_std": 0.06357402692545598, "bacc": 0.5386100386100386, "bacc_std": 0.062216525748380734}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.05419490576291177, "f1": 0.6973780936045086, "f1_std": 0.0567059027185894, "bacc": 0.6954633204633205, "bacc_std": 0.055737331674371164}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05959054767982138, "f1": 0.5830363506771205, "f1_std": 0.06008107483619911, "bacc": 0.5873552123552124, "bacc_std": 0.06092481709954507}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.05608597795418141, "f1": 0.675, "f1_std": 0.06100575639354203, "bacc": 0.6732625482625483, "bacc_std": 0.058483342350885016}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.7230769230769231, "acc_std": 0.05143731981569885, "f1": 0.7115384615384616, "f1_std": 0.05473232063757027, "bacc": 0.708976833976834, "bacc_std": 0.05374512707748828}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.05933178199775889, "f1": 0.675, "f1_std": 0.06491722618500859, "bacc": 0.6732625482625483, "bacc_std": 0.06184792140524965}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.05589084035423836, "f1": 0.6888384983623079, "f1_std": 0.06213466318821268, "bacc": 0.6867760617760618, "bacc_std": 0.05901026972610773}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06043768564524824, "f1": 0.5192307692307693, "f1_std": 0.06347770850874315, "bacc": 0.5207528957528957, "bacc_std": 0.061569687825021646}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.057382561316093816, "f1": 0.5644080416976918, "f1_std": 0.06127186018459559, "bacc": 0.5656370656370656, "bacc_std": 0.05883825588585533}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05722031335421914, "f1": 0.5578231292517006, "f1_std": 0.06223107583267175, "bacc": 0.5612934362934363, "bacc_std": 0.058498340928483106}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.061677535158234605, "f1": 0.5565302144249512, "f1_std": 0.0634337472393648, "bacc": 0.5564671814671815, "bacc_std": 0.06261822098209473}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.676923076923077, "acc_std": 0.05699870236247416, "f1": 0.6690909090909091, "f1_std": 0.05926425441306956, "bacc": 0.6684362934362934, "bacc_std": 0.05908643251096049}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.058581124653598736, "f1": 0.6285714285714286, "f1_std": 0.05863565020295843, "bacc": 0.6322393822393823, "bacc_std": 0.059052809051778656}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.06151146330842077, "f1": 0.6235521235521235, "f1_std": 0.06286955547843481, "bacc": 0.6235521235521235, "bacc_std": 0.0627218022753266}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.056698233695080945, "f1": 0.5626293995859213, "f1_std": 0.06485227074949784, "bacc": 0.5704633204633205, "bacc_std": 0.05869210376134052}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05668072316668363, "f1": 0.6474358974358974, "f1_std": 0.06034309128865844, "bacc": 0.6462355212355213, "bacc_std": 0.05855651474628569}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 19, "C": 0.005994842503189409, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.05606018743622443, "f1": 0.6888384983623079, "f1_std": 0.06223099279809405, "bacc": 0.6867760617760618, "bacc_std": 0.05891296189340408}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.0575744437059303, "f1": 0.606060606060606, "f1_std": 0.05939881892696783, "bacc": 0.6056949806949807, "bacc_std": 0.05902059409296603}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.06032985464052392, "f1": 0.6575670498084292, "f1_std": 0.0607889062497912, "bacc": 0.6592664092664093, "bacc_std": 0.061018000131592594}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 22, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.057305526030300674, "f1": 0.6375757575757576, "f1_std": 0.0593783660804841, "bacc": 0.6370656370656371, "bacc_std": 0.058848474177716985}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.054552891657231886, "f1": 0.6167649320687003, "f1_std": 0.06127594033820592, "bacc": 0.6196911196911197, "bacc_std": 0.056700602621853245}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05551321821377053, "f1": 0.6425000000000001, "f1_std": 0.06026547296728045, "bacc": 0.6418918918918919, "bacc_std": 0.057419416543665995}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 25, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06346399808753336, "f1": 0.5330459770114943, "f1_std": 0.06460857117096531, "bacc": 0.5337837837837838, "bacc_std": 0.06480760535194396}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 26, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05630411926452492, "f1": 0.61, "f1_std": 0.06054976421169517, "bacc": 0.6105212355212355, "bacc_std": 0.057915300924522344}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05414653796841029, "f1": 0.6289401836684041, "f1_std": 0.05779987911116773, "bacc": 0.6283783783783784, "bacc_std": 0.05557024417649122}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 28, "C": 0.000774263682681127, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05347344416408233, "f1": 0.5411764705882354, "f1_std": 0.061471351496398666, "bacc": 0.5526061776061776, "bacc_std": 0.055088492525465375}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.060369952163807084, "f1": 0.6018132810585641, "f1_std": 0.06300591168213855, "bacc": 0.6013513513513513, "bacc_std": 0.061727330592525866}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.058932604170522845, "f1": 0.5666666666666667, "f1_std": 0.05901042598523611, "bacc": 0.5694980694980695, "bacc_std": 0.05923300546295264}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 31, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.057460869113597136, "f1": 0.6018132810585641, "f1_std": 0.060374876811150495, "bacc": 0.6013513513513513, "bacc_std": 0.05926724663142683}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 32, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06382567975705158, "f1": 0.5565302144249512, "f1_std": 0.06602308962107237, "bacc": 0.5564671814671815, "bacc_std": 0.06501250041057197}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 33, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06291665191948309, "f1": 0.5374762808349146, "f1_std": 0.0631624393044961, "bacc": 0.5424710424710424, "bacc_std": 0.06397275686305928}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05968338553463384, "f1": 0.6094688776736361, "f1_std": 0.06123290717924041, "bacc": 0.61003861003861, "bacc_std": 0.06108689649469726}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05713391307856166, "f1": 0.5775, "f1_std": 0.0610147500990692, "bacc": 0.5791505791505791, "bacc_std": 0.05842154727156729}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 36, "C": 0.046415888336127774, "split": "test", "acc": 0.7230769230769231, "acc_std": 0.05187316347358619, "f1": 0.7115384615384616, "f1_std": 0.05540355444754897, "bacc": 0.708976833976834, "bacc_std": 0.0540688574255216}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 37, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05816605488451493, "f1": 0.6375757575757576, "f1_std": 0.05997637219945877, "bacc": 0.6370656370656371, "bacc_std": 0.05939996624858118}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 38, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.059492727415917115, "f1": 0.5810455956075435, "f1_std": 0.05998723265589573, "bacc": 0.583011583011583, "bacc_std": 0.06006467027900648}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 39, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.060541297949386993, "f1": 0.6018132810585641, "f1_std": 0.06327198085764672, "bacc": 0.6013513513513513, "bacc_std": 0.0620812608759971}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.046415888336127774, "split": "test", "acc": 0.676923076923077, "acc_std": 0.052731890803310935, "f1": 0.6431372549019607, "f1_std": 0.06252224056635451, "bacc": 0.6467181467181468, "bacc_std": 0.05586769003210275}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06022043137531967, "f1": 0.5565302144249512, "f1_std": 0.06298267291262516, "bacc": 0.5564671814671815, "bacc_std": 0.0617227274897446}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05924067050110185, "f1": 0.6595238095238095, "f1_std": 0.059465370132540156, "bacc": 0.6636100386100386, "bacc_std": 0.059370543474414485}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05213795991315086, "f1": 0.5427489177489178, "f1_std": 0.06436209478392713, "bacc": 0.5617760617760618, "bacc_std": 0.05420789204907884}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.06263614095589026, "f1": 0.6289401836684041, "f1_std": 0.06691682649089638, "bacc": 0.6283783783783784, "bacc_std": 0.06459699194819898}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.058151925080194414, "f1": 0.606060606060606, "f1_std": 0.059640238006162666, "bacc": 0.6056949806949807, "bacc_std": 0.059040269859032275}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.06055831753347518, "f1": 0.6139225469232596, "f1_std": 0.060596821892480425, "bacc": 0.6187258687258688, "bacc_std": 0.06059081320558718}
+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 | 1.7553 |  16.677 | 0.7869  |  0.06367  | 0.77931 | 0.066679 | 0.77669 |   0.066995 |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 1.7553 |  16.677 | 0.62631 |  0.055332 | 0.61123 | 0.057194 | 0.61268 |   0.055743 |
+
+
+done! total time: 0:04:39

data_scaling/n1600_2/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 n1600_2; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_2/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/n1600_2/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

data_scaling/n1600_2/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,84,0.046415888336127774,train,0.8807588075880759,0.014926278199596119,0.80606784519828,0.028057664062342183,0.7684690607280795,0.02880556355770536
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+flat_mae,patch,logistic,adni_ad_vs_cn,85,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,85,166.81005372000556,test,0.8536585365853658,0.05514491211834646,0.8136363636363637,0.06750273921524842,0.8354838709677419,0.07068315217985943
+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,0.6097560975609756,0.07421408971639466,0.5030303030303029,0.07881542073821315,0.5048387096774194,0.08409100565488528
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.3593813663804626,train,0.9728997289972899,0.008380434452473757,0.9611382593310305,0.01233841688510776,0.9499548031884295,0.015928802547203393
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.3593813663804626,test,0.926829268292683,0.03904889474549484,0.8972431077694235,0.05834781980143534,0.8838709677419355,0.06590370193881658
+flat_mae,patch,logistic,adni_ad_vs_cn,88,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,88,21.54434690031882,test,0.7317073170731707,0.06610206086743492,0.6479313036690086,0.08448959990360114,0.6532258064516129,0.08898612754188792
+flat_mae,patch,logistic,adni_ad_vs_cn,89,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,89,21.54434690031882,test,0.7560975609756098,0.06472336226466874,0.6693548387096775,0.08686003040889734,0.6693548387096775,0.08746798617562396
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,train,0.8807588075880759,0.014757341532918602,0.8101851851851851,0.026475162995654833,0.7765633988002301,0.02717876809139388
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.046415888336127774,test,0.8292682926829268,0.04369258789272879,0.7144278606965174,0.091493724904805,0.6838709677419355,0.07867075081845368
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.3593813663804626,train,0.978319783197832,0.007222994513231872,0.9689106074648244,0.010672191211094117,0.9575355411290984,0.01469491632284021
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.3593813663804626,test,0.6829268292682927,0.05960350851456386,0.5176470588235295,0.08104551449805994,0.5193548387096775,0.07309235209981871
+flat_mae,patch,logistic,adni_ad_vs_cn,92,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,92,2.782559402207126,test,0.6585365853658537,0.07254667208888303,0.5651515151515152,0.08505932672210377,0.5709677419354839,0.09123677963615709
+flat_mae,patch,logistic,adni_ad_vs_cn,93,0.046415888336127774,train,0.8970189701897019,0.013541975862992335,0.8394182317911132,0.02296696822261245,0.8073999506943874,0.024617267011224436
+flat_mae,patch,logistic,adni_ad_vs_cn,93,0.046415888336127774,test,0.6585365853658537,0.06554418701999315,0.5370967741935484,0.07931011148039589,0.5370967741935484,0.07953054106127054
+flat_mae,patch,logistic,adni_ad_vs_cn,94,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,94,166.81005372000556,test,0.6341463414634146,0.06464640472802312,0.48621553884711777,0.0763478122781493,0.48709677419354835,0.07351204669552634
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+flat_mae,patch,logistic,adni_ad_vs_cn,96,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,96,21.54434690031882,test,0.7317073170731707,0.061496293954488204,0.6232247284878863,0.0879879107187167,0.6193548387096774,0.08647087766816891
+flat_mae,patch,logistic,adni_ad_vs_cn,97,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,97,21.54434690031882,test,0.8048780487804879,0.053489667757534456,0.7152777777777778,0.0833327777601099,0.7016129032258065,0.08261376219779913
+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,train,0.8997289972899729,0.01406342185127451,0.8444297580930026,0.0240331947204859,0.8132139041827595,0.02611393909266364
+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,test,0.7804878048780488,0.04844361730000005,0.6328358208955224,0.09640045249703166,0.6177419354838709,0.07869924018583192
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+flat_mae,patch,logistic,adni_ad_vs_cn,99,0.3593813663804626,test,0.8048780487804879,0.05442564745168766,0.7152777777777778,0.0875328855736863,0.7016129032258065,0.0849325557242465
+flat_mae,patch,logistic,adni_ad_vs_cn,100,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,100,21.54434690031882,test,0.7073170731707317,0.06777721843281115,0.6272727272727273,0.08400184055419856,0.6370967741935484,0.0900869633617536

data_scaling/n1600_2/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 17:27:20
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_2/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/n1600_2/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: 0:10:43    time: 3.9217  data: 3.2257  max mem: 2698
+extract (train)  [ 20/164]  eta: 0:00:50    time: 0.1730  data: 0.0535  max mem: 2851
+extract (train)  [ 40/164]  eta: 0:00:31    time: 0.1577  data: 0.0477  max mem: 2851
+extract (train)  [ 60/164]  eta: 0:00:23    time: 0.1623  data: 0.0500  max mem: 2851
+extract (train)  [ 80/164]  eta: 0:00:17    time: 0.1517  data: 0.0471  max mem: 2851
+extract (train)  [100/164]  eta: 0:00:12    time: 0.1676  data: 0.0521  max mem: 2851
+extract (train)  [120/164]  eta: 0:00:08    time: 0.1594  data: 0.0516  max mem: 2851
+extract (train)  [140/164]  eta: 0:00:04    time: 0.1586  data: 0.0514  max mem: 2851
+extract (train)  [160/164]  eta: 0:00:00    time: 0.1578  data: 0.0499  max mem: 2851
+extract (train)  [163/164]  eta: 0:00:00    time: 0.1583  data: 0.0502  max mem: 2851
+extract (train) Total time: 0:00:30 (0.1858 s / it)
+extract (validation)  [ 0/21]  eta: 0:01:11    time: 3.4116  data: 3.3108  max mem: 2851
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.1270  data: 0.0301  max mem: 2851
+extract (validation) Total time: 0:00:06 (0.2956 s / it)
+extract (test)  [ 0/21]  eta: 0:01:09    time: 3.3283  data: 3.2283  max mem: 2851
+extract (test)  [20/21]  eta: 0:00:00    time: 0.1293  data: 0.0317  max mem: 2851
+extract (test) Total time: 0:00:06 (0.2940 s / it)
+feature extraction time: 0:00:42
+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 |         | 166.81 | train   | 1      |  0        | 1       | 0        | 1       |   0        |
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 166.81 | test    | 0.7561 |  0.066444 | 0.66935 | 0.082079 | 0.68403 |   0.087587 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06469079891765445, "f1": 0.6232247284878863, "f1_std": 0.08846292736020696, "bacc": 0.6193548387096774, "bacc_std": 0.0865868042614834}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.05854331262156867, "f1": 0.5729166666666666, "f1_std": 0.08482239339696974, "bacc": 0.5693548387096774, "bacc_std": 0.07898577303104107}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 166.81005372000556, "split": "test", "acc": 0.6097560975609756, "acc_std": 0.0729279875917472, "f1": 0.5287356321839081, "f1_std": 0.07832489599423027, "bacc": 0.5387096774193548, "bacc_std": 0.08644429308202833}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.9024390243902439, "acc_std": 0.04641101327788558, "f1": 0.8757575757575757, "f1_std": 0.055970167238343, "bacc": 0.9016129032258065, "bacc_std": 0.054836244631504105}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.031532878510004594, "f1": 0.4142857142857143, "f1_std": 0.01091927624162128, "bacc": 0.46774193548387094, "bacc_std": 0.020852387401777243}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 10000.0, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.058545202617753714, "f1": 0.7885040530582166, "f1_std": 0.06913710454046107, "bacc": 0.8193548387096774, "bacc_std": 0.07203354368065276}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 166.81005372000556, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06741745784916207, "f1": 0.603225806451613, "f1_std": 0.08849665824809386, "bacc": 0.603225806451613, "bacc_std": 0.08971656020968749}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 21.54434690031882, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.06977436226854498, "f1": 0.5467943994104643, "f1_std": 0.07799085079405843, "bacc": 0.5548387096774194, "bacc_std": 0.084664147408449}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0715238901693786, "f1": 0.646551724137931, "f1_std": 0.08068427648510763, "bacc": 0.6709677419354838, "bacc_std": 0.08941265225310366}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05111254800878185, "f1": 0.7402714932126697, "f1_std": 0.08698113045401043, "bacc": 0.717741935483871, "bacc_std": 0.08495326841726734}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 11, "C": 166.81005372000556, "split": "test", "acc": 0.8536585365853658, "acc_std": 0.04946889852700696, "f1": 0.7864583333333333, "f1_std": 0.07746488578439803, "bacc": 0.7677419354838709, "bacc_std": 0.07973092489753024}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.0631713720656874, "f1": 0.6693548387096775, "f1_std": 0.08495711994886397, "bacc": 0.6693548387096775, "bacc_std": 0.0861758444127456}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05630713768003152, "f1": 0.7152777777777778, "f1_std": 0.08739292803485292, "bacc": 0.7016129032258065, "bacc_std": 0.085625685107052}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 14, "C": 1291.5496650148827, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.05256379936956791, "f1": 0.6660633484162897, "f1_std": 0.08515324997134673, "bacc": 0.6516129032258065, "bacc_std": 0.08009516478187684}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05431288384514712, "f1": 0.7602339181286549, "f1_std": 0.07967697462654806, "bacc": 0.7516129032258064, "bacc_std": 0.08262059586811568}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 16, "C": 2.782559402207126, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.0629003347514001, "f1": 0.6917293233082706, "f1_std": 0.08812361422954909, "bacc": 0.685483870967742, "bacc_std": 0.08792433667778748}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 17, "C": 166.81005372000556, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06308150775828757, "f1": 0.6232247284878863, "f1_std": 0.09027877286351509, "bacc": 0.6193548387096774, "bacc_std": 0.08749595804548692}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 21.54434690031882, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.053489667757534456, "f1": 0.7152777777777778, "f1_std": 0.0833327777601099, "bacc": 0.7016129032258065, "bacc_std": 0.08261376219779913}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04844361730000005, "f1": 0.6328358208955224, "f1_std": 0.09640045249703166, "bacc": 0.6177419354838709, "bacc_std": 0.07869924018583192}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05442564745168766, "f1": 0.7152777777777778, "f1_std": 0.0875328855736863, "bacc": 0.7016129032258065, "bacc_std": 0.0849325557242465}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 21.54434690031882, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06777721843281115, "f1": 0.6272727272727273, "f1_std": 0.08400184055419856, "bacc": 0.6370967741935484, "bacc_std": 0.0900869633617536}
+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 | 463.09 |  1965.5 | 0.98241 |  0.031841 | 0.97331 | 0.050153 | 0.96708 |   0.059909 |
+| flat_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 463.09 |  1965.5 | 0.74976 |  0.06482  | 0.64816 | 0.088619 | 0.64755 |   0.086194 |
+
+
+done! total time: 0:04:27

data_scaling/n1600_2/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 n1600_2; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_2/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
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+- 0.1
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+- 0.14
+- 0.17
+- 0.2
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+- 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/n1600_2/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/n1600_2/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/eval_log.json

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data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 10, "eval/best/id_best": 33, "eval/best/lr_best": 0.00129, "eval/best/wd_best": 0.05, "eval/best/train/loss": 0.00019710067135747522, "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": 0.03448382019996643, "eval/best/validation/acc": 0.9910714285714286, "eval/best/validation/acc_std": 0.0014769365854009773, "eval/best/validation/f1": 0.9904264314256586, "eval/best/validation/f1_std": 0.0016872434093583122, "eval/best/test/loss": 0.05240816995501518, "eval/best/test/acc": 0.9853174603174604, "eval/best/test/acc_std": 0.0016792514289266332, "eval/best/test/f1": 0.9821627664531266, "eval/best/test/f1_std": 0.0022675074904741836}

data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 30, "eval/last/lr_best": 0.00081, "eval/last/wd_best": 0.05, "eval/last/train/loss": 0.00029725959757342935, "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": 0.03434709832072258, "eval/last/validation/acc": 0.9905753968253969, "eval/last/validation/acc_std": 0.001511543435763937, "eval/last/validation/f1": 0.9891538268026445, "eval/last/validation/f1_std": 0.0018910281754495686, "eval/last/test/loss": 0.0492265559732914, "eval/last/test/acc": 0.9859126984126985, "eval/last/test/acc_std": 0.001644677313903341, "eval/last/test/f1": 0.9827788452795835, "eval/last/test/f1_std": 0.0022399200939826427}

data_scaling/n1600_2/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,10,0.00129,0.05,33,"[4.3, 1.0]",train,0.00019710067135747522,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,10,0.00129,0.05,33,"[4.3, 1.0]",validation,0.03448382019996643,0.9910714285714286,0.0014769365854009773,0.9904264314256586,0.0016872434093583122
+flat_mae,patch,attn,hcpya_task21,best,10,0.00129,0.05,33,"[4.3, 1.0]",test,0.05240816995501518,0.9853174603174604,0.0016792514289266332,0.9821627664531266,0.0022675074904741836

data_scaling/n1600_2/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,10,0.00129,0.05,33,"[4.3, 1.0]",train,0.00019710067135747522,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,10,0.00129,0.05,33,"[4.3, 1.0]",validation,0.03448382019996643,0.9910714285714286,0.0014769365854009773,0.9904264314256586,0.0016872434093583122
+flat_mae,patch,attn,hcpya_task21,best,10,0.00129,0.05,33,"[4.3, 1.0]",test,0.05240816995501518,0.9853174603174604,0.0016792514289266332,0.9821627664531266,0.0022675074904741836

data_scaling/n1600_2/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,19,0.00081,0.05,30,"[2.7, 1.0]",train,0.00029725959757342935,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,last,19,0.00081,0.05,30,"[2.7, 1.0]",validation,0.03434709832072258,0.9905753968253969,0.001511543435763937,0.9891538268026445,0.0018910281754495686
+flat_mae,patch,attn,hcpya_task21,last,19,0.00081,0.05,30,"[2.7, 1.0]",test,0.0492265559732914,0.9859126984126985,0.001644677313903341,0.9827788452795835,0.0022399200939826427

data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/log.txt

@@ -0,0 +1,888 @@
+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 20:31:13
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_2/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/n1600_2/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/n1600_2/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: 0:21:53  lr: nan  time: 3.2826  data: 2.8245  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:42  lr: 0.000003  loss: 3.0793 (3.0856)  grad: 0.2613 (0.2688)  time: 0.4509  data: 0.0038  max mem: 22446
+train: [0]  [ 40/400]  eta: 0:03:07  lr: 0.000006  loss: 3.0506 (3.0400)  grad: 0.2627 (0.2686)  time: 0.4511  data: 0.0034  max mem: 22446
+train: [0]  [ 60/400]  eta: 0:02:49  lr: 0.000009  loss: 2.9155 (2.9795)  grad: 0.2627 (0.2648)  time: 0.4543  data: 0.0034  max mem: 22446
+train: [0]  [ 80/400]  eta: 0:02:36  lr: 0.000012  loss: 2.7663 (2.9134)  grad: 0.2463 (0.2581)  time: 0.4572  data: 0.0036  max mem: 22446
+train: [0]  [100/400]  eta: 0:02:24  lr: 0.000015  loss: 2.6399 (2.8426)  grad: 0.2304 (0.2540)  time: 0.4566  data: 0.0036  max mem: 22446
+train: [0]  [120/400]  eta: 0:02:13  lr: 0.000018  loss: 2.5161 (2.7750)  grad: 0.2309 (0.2498)  time: 0.4452  data: 0.0038  max mem: 22446
+train: [0]  [140/400]  eta: 0:02:02  lr: 0.000021  loss: 2.3793 (2.7104)  grad: 0.2283 (0.2481)  time: 0.4542  data: 0.0035  max mem: 22446
+train: [0]  [160/400]  eta: 0:01:52  lr: 0.000024  loss: 2.2742 (2.6530)  grad: 0.2213 (0.2439)  time: 0.4488  data: 0.0034  max mem: 22446
+train: [0]  [180/400]  eta: 0:01:43  lr: 0.000027  loss: 2.1739 (2.5940)  grad: 0.2100 (0.2399)  time: 0.4634  data: 0.0037  max mem: 22446
+train: [0]  [200/400]  eta: 0:01:33  lr: 0.000030  loss: 2.1018 (2.5384)  grad: 0.2101 (0.2371)  time: 0.4667  data: 0.0034  max mem: 22446
+train: [0]  [220/400]  eta: 0:01:24  lr: 0.000033  loss: 1.9979 (2.4864)  grad: 0.2001 (0.2338)  time: 0.4603  data: 0.0035  max mem: 22446
+train: [0]  [240/400]  eta: 0:01:14  lr: 0.000036  loss: 1.9105 (2.4342)  grad: 0.2043 (0.2318)  time: 0.4537  data: 0.0032  max mem: 22446
+train: [0]  [260/400]  eta: 0:01:05  lr: 0.000039  loss: 1.8331 (2.3864)  grad: 0.2054 (0.2297)  time: 0.4745  data: 0.0034  max mem: 22446
+train: [0]  [280/400]  eta: 0:00:56  lr: 0.000042  loss: 1.7999 (2.3438)  grad: 0.1931 (0.2266)  time: 0.4618  data: 0.0033  max mem: 22446
+train: [0]  [300/400]  eta: 0:00:47  lr: 0.000045  loss: 1.7587 (2.3023)  grad: 0.1788 (0.2236)  time: 0.6279  data: 0.1799  max mem: 22446
+train: [0]  [320/400]  eta: 0:00:38  lr: 0.000048  loss: 1.6959 (2.2618)  grad: 0.1755 (0.2209)  time: 0.4514  data: 0.0040  max mem: 22446
+train: [0]  [340/400]  eta: 0:00:28  lr: 0.000051  loss: 1.6209 (2.2235)  grad: 0.1859 (0.2191)  time: 0.4499  data: 0.0030  max mem: 22446
+train: [0]  [360/400]  eta: 0:00:18  lr: 0.000054  loss: 1.5972 (2.1882)  grad: 0.1826 (0.2170)  time: 0.4609  data: 0.0034  max mem: 22446
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 1.5659 (2.1544)  grad: 0.1765 (0.2148)  time: 0.4516  data: 0.0035  max mem: 22446
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 1.5113 (2.1205)  grad: 0.1762 (0.2131)  time: 0.4524  data: 0.0034  max mem: 22446
+train: [0] Total time: 0:03:08 (0.4720 s / it)
+train: [0] Summary: lr: 0.000060  loss: 1.5113 (2.1205)  grad: 0.1762 (0.2131)
+eval (validation): [0]  [ 0/63]  eta: 0:03:25    time: 3.2677  data: 3.0233  max mem: 22446
+eval (validation): [0]  [20/63]  eta: 0:00:21    time: 0.3611  data: 0.0035  max mem: 22446
+eval (validation): [0]  [40/63]  eta: 0:00:09    time: 0.3383  data: 0.0032  max mem: 22446
+eval (validation): [0]  [60/63]  eta: 0:00:01    time: 0.3400  data: 0.0034  max mem: 22446
+eval (validation): [0]  [62/63]  eta: 0:00:00    time: 0.3345  data: 0.0032  max mem: 22446
+eval (validation): [0] Total time: 0:00:24 (0.3967 s / it)
+cv: [0]  best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00')  loss: 0.063  acc: 0.981  f1: 0.977
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:32  lr: nan  time: 3.3813  data: 2.9989  max mem: 22446
+train: [1]  [ 20/400]  eta: 0:03:44  lr: 0.000063  loss: 1.4502 (1.4653)  grad: 0.1685 (0.1765)  time: 0.4518  data: 0.0024  max mem: 22446
+train: [1]  [ 40/400]  eta: 0:03:09  lr: 0.000066  loss: 1.4364 (1.4389)  grad: 0.1755 (0.1760)  time: 0.4590  data: 0.0031  max mem: 22446
+train: [1]  [ 60/400]  eta: 0:02:50  lr: 0.000069  loss: 1.3921 (1.4192)  grad: 0.1715 (0.1727)  time: 0.4508  data: 0.0035  max mem: 22446
+train: [1]  [ 80/400]  eta: 0:02:36  lr: 0.000072  loss: 1.3673 (1.4029)  grad: 0.1629 (0.1708)  time: 0.4528  data: 0.0035  max mem: 22446
+train: [1]  [100/400]  eta: 0:02:25  lr: 0.000075  loss: 1.3525 (1.3919)  grad: 0.1651 (0.1702)  time: 0.4716  data: 0.0036  max mem: 22446
+train: [1]  [120/400]  eta: 0:02:14  lr: 0.000078  loss: 1.3160 (1.3738)  grad: 0.1601 (0.1692)  time: 0.4560  data: 0.0037  max mem: 22446
+train: [1]  [140/400]  eta: 0:02:04  lr: 0.000081  loss: 1.2720 (1.3592)  grad: 0.1596 (0.1680)  time: 0.4535  data: 0.0035  max mem: 22446
+train: [1]  [160/400]  eta: 0:01:53  lr: 0.000084  loss: 1.2578 (1.3431)  grad: 0.1536 (0.1667)  time: 0.4576  data: 0.0035  max mem: 22446
+train: [1]  [180/400]  eta: 0:01:44  lr: 0.000087  loss: 1.2240 (1.3284)  grad: 0.1536 (0.1658)  time: 0.4726  data: 0.0035  max mem: 22446
+train: [1]  [200/400]  eta: 0:01:34  lr: 0.000090  loss: 1.1727 (1.3127)  grad: 0.1510 (0.1646)  time: 0.4648  data: 0.0034  max mem: 22446
+train: [1]  [220/400]  eta: 0:01:25  lr: 0.000093  loss: 1.1545 (1.2972)  grad: 0.1561 (0.1645)  time: 0.4615  data: 0.0034  max mem: 22446
+train: [1]  [240/400]  eta: 0:01:15  lr: 0.000096  loss: 1.1407 (1.2835)  grad: 0.1599 (0.1638)  time: 0.4533  data: 0.0034  max mem: 22446
+train: [1]  [260/400]  eta: 0:01:05  lr: 0.000099  loss: 1.1292 (1.2708)  grad: 0.1523 (0.1628)  time: 0.4563  data: 0.0035  max mem: 22446
+train: [1]  [280/400]  eta: 0:00:56  lr: 0.000102  loss: 1.0981 (1.2569)  grad: 0.1483 (0.1622)  time: 0.4669  data: 0.0036  max mem: 22446
+train: [1]  [300/400]  eta: 0:00:47  lr: 0.000105  loss: 1.0655 (1.2444)  grad: 0.1421 (0.1608)  time: 0.6132  data: 0.1706  max mem: 22446
+train: [1]  [320/400]  eta: 0:00:38  lr: 0.000108  loss: 1.0483 (1.2323)  grad: 0.1426 (0.1598)  time: 0.4714  data: 0.0036  max mem: 22446
+train: [1]  [340/400]  eta: 0:00:28  lr: 0.000111  loss: 1.0258 (1.2193)  grad: 0.1388 (0.1585)  time: 0.4450  data: 0.0036  max mem: 22446
+train: [1]  [360/400]  eta: 0:00:19  lr: 0.000114  loss: 1.0158 (1.2084)  grad: 0.1368 (0.1571)  time: 0.4677  data: 0.0037  max mem: 22446
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 1.0028 (1.1973)  grad: 0.1368 (0.1563)  time: 0.4536  data: 0.0035  max mem: 22446
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 0.9898 (1.1871)  grad: 0.1366 (0.1553)  time: 0.4679  data: 0.0035  max mem: 22446
+train: [1] Total time: 0:03:09 (0.4749 s / it)
+train: [1] Summary: lr: 0.000120  loss: 0.9898 (1.1871)  grad: 0.1366 (0.1553)
+eval (validation): [1]  [ 0/63]  eta: 0:03:25    time: 3.2653  data: 2.9827  max mem: 22446
+eval (validation): [1]  [20/63]  eta: 0:00:22    time: 0.3896  data: 0.0052  max mem: 22446
+eval (validation): [1]  [40/63]  eta: 0:00:10    time: 0.3391  data: 0.0029  max mem: 22446
+eval (validation): [1]  [60/63]  eta: 0:00:01    time: 0.3340  data: 0.0034  max mem: 22446
+eval (validation): [1]  [62/63]  eta: 0:00:00    time: 0.3320  data: 0.0034  max mem: 22446
+eval (validation): [1] Total time: 0:00:25 (0.4041 s / it)
+cv: [1]  best hparam: (16, 1.0) (041) ('041_lr1.6e+01_wd1.0e+00')  loss: 0.043  acc: 0.986  f1: 0.985
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:22:36  lr: nan  time: 3.3915  data: 3.0028  max mem: 22446
+train: [2]  [ 20/400]  eta: 0:03:42  lr: 0.000123  loss: 0.9103 (0.9337)  grad: 0.1432 (0.1444)  time: 0.4445  data: 0.0030  max mem: 22446
+train: [2]  [ 40/400]  eta: 0:03:09  lr: 0.000126  loss: 0.9349 (0.9414)  grad: 0.1442 (0.1459)  time: 0.4636  data: 0.0035  max mem: 22446
+train: [2]  [ 60/400]  eta: 0:02:50  lr: 0.000129  loss: 0.9349 (0.9338)  grad: 0.1451 (0.1468)  time: 0.4546  data: 0.0035  max mem: 22446
+train: [2]  [ 80/400]  eta: 0:02:36  lr: 0.000132  loss: 0.9172 (0.9313)  grad: 0.1473 (0.1484)  time: 0.4515  data: 0.0035  max mem: 22446
+train: [2]  [100/400]  eta: 0:02:25  lr: 0.000135  loss: 0.9172 (0.9268)  grad: 0.1483 (0.1503)  time: 0.4649  data: 0.0035  max mem: 22446
+train: [2]  [120/400]  eta: 0:02:13  lr: 0.000138  loss: 0.8985 (0.9233)  grad: 0.1619 (0.1543)  time: 0.4437  data: 0.0035  max mem: 22446
+train: [2]  [140/400]  eta: 0:02:03  lr: 0.000141  loss: 0.8791 (0.9169)  grad: 0.1697 (0.1568)  time: 0.4549  data: 0.0035  max mem: 22446
+train: [2]  [160/400]  eta: 0:01:53  lr: 0.000144  loss: 0.8764 (0.9163)  grad: 0.1662 (0.1608)  time: 0.4643  data: 0.0036  max mem: 22446
+train: [2]  [180/400]  eta: 0:01:44  lr: 0.000147  loss: 0.8778 (0.9122)  grad: 0.1667 (0.1639)  time: 0.4669  data: 0.0036  max mem: 22446
+train: [2]  [200/400]  eta: 0:01:34  lr: 0.000150  loss: 0.8460 (0.9038)  grad: 0.1801 (0.1652)  time: 0.4663  data: 0.0036  max mem: 22446
+train: [2]  [220/400]  eta: 0:01:24  lr: 0.000153  loss: 0.8460 (0.9021)  grad: 0.1694 (0.1659)  time: 0.4694  data: 0.0035  max mem: 22446
+train: [2]  [240/400]  eta: 0:01:15  lr: 0.000156  loss: 0.8728 (0.8975)  grad: 0.1668 (0.1664)  time: 0.4687  data: 0.0037  max mem: 22446
+train: [2]  [260/400]  eta: 0:01:05  lr: 0.000159  loss: 0.8281 (0.8946)  grad: 0.1924 (0.1701)  time: 0.4488  data: 0.0033  max mem: 22446
+train: [2]  [280/400]  eta: 0:00:56  lr: 0.000162  loss: 0.8242 (0.8906)  grad: 0.1854 (0.1710)  time: 0.4735  data: 0.0035  max mem: 22446
+train: [2]  [300/400]  eta: 0:00:47  lr: 0.000165  loss: 0.8059 (0.8854)  grad: 0.1850 (0.1724)  time: 0.6081  data: 0.1721  max mem: 22446
+train: [2]  [320/400]  eta: 0:00:38  lr: 0.000168  loss: 0.8031 (0.8834)  grad: 0.1927 (0.1747)  time: 0.4582  data: 0.0030  max mem: 22446
+train: [2]  [340/400]  eta: 0:00:28  lr: 0.000171  loss: 0.7996 (0.8810)  grad: 0.1931 (0.1772)  time: 0.4721  data: 0.0034  max mem: 22446
+train: [2]  [360/400]  eta: 0:00:19  lr: 0.000174  loss: 0.8172 (0.8783)  grad: 0.1895 (0.1785)  time: 0.4527  data: 0.0035  max mem: 22446
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 0.7978 (0.8738)  grad: 0.1951 (0.1812)  time: 0.4579  data: 0.0034  max mem: 22446
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 0.7580 (0.8663)  grad: 0.1940 (0.1830)  time: 0.4676  data: 0.0037  max mem: 22446
+train: [2] Total time: 0:03:10 (0.4751 s / it)
+train: [2] Summary: lr: 0.000180  loss: 0.7580 (0.8663)  grad: 0.1940 (0.1830)
+eval (validation): [2]  [ 0/63]  eta: 0:03:25    time: 3.2581  data: 2.9852  max mem: 22446
+eval (validation): [2]  [20/63]  eta: 0:00:23    time: 0.4020  data: 0.0192  max mem: 22446
+eval (validation): [2]  [40/63]  eta: 0:00:10    time: 0.3330  data: 0.0034  max mem: 22446
+eval (validation): [2]  [60/63]  eta: 0:00:01    time: 0.3449  data: 0.0033  max mem: 22446
+eval (validation): [2]  [62/63]  eta: 0:00:00    time: 0.3428  data: 0.0033  max mem: 22446
+eval (validation): [2] Total time: 0:00:25 (0.4099 s / it)
+cv: [2]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 0.039  acc: 0.986  f1: 0.984
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:22:31  lr: nan  time: 3.3790  data: 3.0318  max mem: 22446
+train: [3]  [ 20/400]  eta: 0:03:41  lr: 0.000183  loss: 0.6819 (0.6868)  grad: 0.1574 (0.1758)  time: 0.4431  data: 0.0023  max mem: 22446
+train: [3]  [ 40/400]  eta: 0:03:06  lr: 0.000186  loss: 0.7072 (0.7152)  grad: 0.1760 (0.1886)  time: 0.4508  data: 0.0036  max mem: 22446
+train: [3]  [ 60/400]  eta: 0:02:49  lr: 0.000189  loss: 0.7296 (0.7309)  grad: 0.1993 (0.1974)  time: 0.4588  data: 0.0034  max mem: 22446
+train: [3]  [ 80/400]  eta: 0:02:36  lr: 0.000192  loss: 0.7275 (0.7377)  grad: 0.2104 (0.2077)  time: 0.4565  data: 0.0033  max mem: 22446
+train: [3]  [100/400]  eta: 0:02:25  lr: 0.000195  loss: 0.7199 (0.7327)  grad: 0.2084 (0.2117)  time: 0.4690  data: 0.0033  max mem: 22446
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+train: [3]  [160/400]  eta: 0:01:53  lr: 0.000204  loss: 0.7313 (0.7452)  grad: 0.2599 (0.2283)  time: 0.4511  data: 0.0034  max mem: 22446
+train: [3]  [180/400]  eta: 0:01:43  lr: 0.000207  loss: 0.7313 (0.7509)  grad: 0.2798 (0.2401)  time: 0.4628  data: 0.0034  max mem: 22446
+train: [3]  [200/400]  eta: 0:01:34  lr: 0.000210  loss: 0.7939 (0.7582)  grad: 0.2751 (0.2454)  time: 0.4650  data: 0.0035  max mem: 22446
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+train: [3]  [260/400]  eta: 0:01:05  lr: 0.000219  loss: 0.7405 (0.7679)  grad: 0.3013 (0.2572)  time: 0.4441  data: 0.0034  max mem: 22446
+train: [3]  [280/400]  eta: 0:00:56  lr: 0.000222  loss: 0.7694 (0.7726)  grad: 0.3198 (0.2622)  time: 0.4719  data: 0.0036  max mem: 22446
+train: [3]  [300/400]  eta: 0:00:47  lr: 0.000225  loss: 0.7699 (0.7743)  grad: 0.3421 (0.2694)  time: 0.6147  data: 0.1761  max mem: 22446
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+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 0.6951 (0.7750)  grad: 0.3917 (0.2912)  time: 0.4484  data: 0.0036  max mem: 22446
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 0.7076 (0.7789)  grad: 0.3755 (0.2990)  time: 0.4541  data: 0.0037  max mem: 22446
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 0.7482 (0.7810)  grad: 0.3859 (0.3047)  time: 0.4513  data: 0.0035  max mem: 22446
+train: [3] Total time: 0:03:08 (0.4715 s / it)
+train: [3] Summary: lr: 0.000240  loss: 0.7482 (0.7810)  grad: 0.3859 (0.3047)
+eval (validation): [3]  [ 0/63]  eta: 0:03:21    time: 3.2032  data: 2.9665  max mem: 22446
+eval (validation): [3]  [20/63]  eta: 0:00:21    time: 0.3578  data: 0.0095  max mem: 22446
+eval (validation): [3]  [40/63]  eta: 0:00:09    time: 0.3308  data: 0.0030  max mem: 22446
+eval (validation): [3]  [60/63]  eta: 0:00:01    time: 0.3229  data: 0.0032  max mem: 22446
+eval (validation): [3]  [62/63]  eta: 0:00:00    time: 0.3223  data: 0.0033  max mem: 22446
+eval (validation): [3] Total time: 0:00:24 (0.3868 s / it)
+cv: [3]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 0.050  acc: 0.987  f1: 0.983
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:22:06  lr: nan  time: 3.3154  data: 2.9812  max mem: 22446
+train: [4]  [ 20/400]  eta: 0:03:47  lr: 0.000243  loss: 0.8400 (0.8424)  grad: 0.3664 (0.3835)  time: 0.4622  data: 0.0033  max mem: 22446
+train: [4]  [ 40/400]  eta: 0:03:10  lr: 0.000246  loss: 0.8617 (0.8576)  grad: 0.3655 (0.3879)  time: 0.4578  data: 0.0035  max mem: 22446
+train: [4]  [ 60/400]  eta: 0:02:52  lr: 0.000249  loss: 0.8618 (0.8615)  grad: 0.4320 (0.4447)  time: 0.4608  data: 0.0036  max mem: 22446
+train: [4]  [ 80/400]  eta: 0:02:37  lr: 0.000252  loss: 0.8268 (0.8491)  grad: 0.4873 (0.4414)  time: 0.4458  data: 0.0034  max mem: 22446
+train: [4]  [100/400]  eta: 0:02:26  lr: 0.000255  loss: 0.7847 (0.8464)  grad: 0.4573 (0.4466)  time: 0.4661  data: 0.0035  max mem: 22446
+train: [4]  [120/400]  eta: 0:02:15  lr: 0.000258  loss: 0.9560 (0.8744)  grad: 0.5118 (0.4705)  time: 0.4671  data: 0.0034  max mem: 22446
+train: [4]  [140/400]  eta: 0:02:05  lr: 0.000261  loss: 0.9882 (0.8835)  grad: 0.5149 (0.4741)  time: 0.4670  data: 0.0034  max mem: 22446
+train: [4]  [160/400]  eta: 0:01:55  lr: 0.000264  loss: 0.9228 (0.9028)  grad: 0.5041 (0.4777)  time: 0.4694  data: 0.0034  max mem: 22446
+train: [4]  [180/400]  eta: 0:01:44  lr: 0.000267  loss: 0.8186 (0.9013)  grad: 0.4937 (0.4825)  time: 0.4525  data: 0.0033  max mem: 22446
+train: [4]  [200/400]  eta: 0:01:35  lr: 0.000270  loss: 0.9442 (0.9088)  grad: 0.5808 (0.5078)  time: 0.4704  data: 0.0035  max mem: 22446
+train: [4]  [220/400]  eta: 0:01:25  lr: 0.000273  loss: 0.9458 (0.9180)  grad: 0.5224 (0.5071)  time: 0.4724  data: 0.0035  max mem: 22446
+train: [4]  [240/400]  eta: 0:01:15  lr: 0.000276  loss: 1.0117 (0.9288)  grad: 0.4950 (0.5126)  time: 0.4509  data: 0.0035  max mem: 22446
+train: [4]  [260/400]  eta: 0:01:06  lr: 0.000279  loss: 1.1067 (0.9724)  grad: 0.6189 (0.5230)  time: 0.4522  data: 0.0033  max mem: 22446
+train: [4]  [280/400]  eta: 0:00:56  lr: 0.000282  loss: 1.2937 (1.0005)  grad: 0.6516 (0.5362)  time: 0.4745  data: 0.0035  max mem: 22446
+train: [4]  [300/400]  eta: 0:00:48  lr: 0.000285  loss: 1.1053 (1.0161)  grad: 0.6596 (0.5411)  time: 0.6268  data: 0.1754  max mem: 22446
+train: [4]  [320/400]  eta: 0:00:38  lr: 0.000288  loss: 0.7909 (1.0049)  grad: 0.5730 (0.5417)  time: 0.4475  data: 0.0030  max mem: 22446
+train: [4]  [340/400]  eta: 0:00:28  lr: 0.000291  loss: 0.8072 (0.9947)  grad: 0.5258 (0.5407)  time: 0.4566  data: 0.0036  max mem: 22446
+train: [4]  [360/400]  eta: 0:00:19  lr: 0.000294  loss: 0.8520 (0.9971)  grad: 0.5354 (0.5431)  time: 0.4532  data: 0.0037  max mem: 22446
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 0.9749 (1.0067)  grad: 0.5807 (0.5467)  time: 0.4588  data: 0.0036  max mem: 22446
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 0.9048 (1.0051)  grad: 0.5807 (0.5489)  time: 0.4548  data: 0.0034  max mem: 22446
+train: [4] Total time: 0:03:10 (0.4758 s / it)
+train: [4] Summary: lr: 0.000300  loss: 0.9048 (1.0051)  grad: 0.5807 (0.5489)
+eval (validation): [4]  [ 0/63]  eta: 0:03:24    time: 3.2487  data: 2.9778  max mem: 22446
+eval (validation): [4]  [20/63]  eta: 0:00:20    time: 0.3446  data: 0.0040  max mem: 22446
+eval (validation): [4]  [40/63]  eta: 0:00:09    time: 0.3380  data: 0.0030  max mem: 22446
+eval (validation): [4]  [60/63]  eta: 0:00:01    time: 0.3242  data: 0.0034  max mem: 22446
+eval (validation): [4]  [62/63]  eta: 0:00:00    time: 0.3182  data: 0.0033  max mem: 22446
+eval (validation): [4] Total time: 0:00:24 (0.3861 s / it)
+cv: [4]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.038  acc: 0.988  f1: 0.987
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:21:54  lr: nan  time: 3.2864  data: 2.9516  max mem: 22446
+train: [5]  [ 20/400]  eta: 0:03:41  lr: 0.000300  loss: 0.7315 (0.8322)  grad: 0.4749 (0.5244)  time: 0.4480  data: 0.0033  max mem: 22446
+train: [5]  [ 40/400]  eta: 0:03:05  lr: 0.000300  loss: 0.9002 (0.9175)  grad: 0.4991 (0.5331)  time: 0.4438  data: 0.0034  max mem: 22446
+train: [5]  [ 60/400]  eta: 0:02:48  lr: 0.000300  loss: 0.9381 (0.9571)  grad: 0.5291 (0.5371)  time: 0.4529  data: 0.0036  max mem: 22446
+train: [5]  [ 80/400]  eta: 0:02:34  lr: 0.000300  loss: 0.8305 (0.9231)  grad: 0.5291 (0.5934)  time: 0.4470  data: 0.0034  max mem: 22446
+train: [5]  [100/400]  eta: 0:02:24  lr: 0.000300  loss: 0.8214 (0.9613)  grad: 0.5304 (0.5884)  time: 0.4689  data: 0.0034  max mem: 22446
+train: [5]  [120/400]  eta: 0:02:12  lr: 0.000300  loss: 0.9629 (0.9957)  grad: 0.5579 (0.5968)  time: 0.4476  data: 0.0034  max mem: 22446
+train: [5]  [140/400]  eta: 0:02:03  lr: 0.000300  loss: 1.0395 (0.9922)  grad: 0.5927 (0.5987)  time: 0.4659  data: 0.0035  max mem: 22446
+train: [5]  [160/400]  eta: 0:01:53  lr: 0.000299  loss: 0.9607 (1.0037)  grad: 0.5227 (0.5966)  time: 0.4582  data: 0.0035  max mem: 22446
+train: [5]  [180/400]  eta: 0:01:43  lr: 0.000299  loss: 0.9557 (0.9993)  grad: 0.5656 (0.6021)  time: 0.4562  data: 0.0035  max mem: 22446
+train: [5]  [200/400]  eta: 0:01:33  lr: 0.000299  loss: 0.8393 (1.0097)  grad: 0.5997 (0.5997)  time: 0.4490  data: 0.0033  max mem: 22446
+train: [5]  [220/400]  eta: 0:01:24  lr: 0.000299  loss: 0.8393 (1.0172)  grad: 0.5427 (0.5997)  time: 0.4780  data: 0.0035  max mem: 22446
+train: [5]  [240/400]  eta: 0:01:14  lr: 0.000299  loss: 0.7732 (1.0038)  grad: 0.5708 (0.5976)  time: 0.4576  data: 0.0035  max mem: 22446
+train: [5]  [260/400]  eta: 0:01:05  lr: 0.000299  loss: 0.9256 (1.0310)  grad: 0.5894 (0.6035)  time: 0.4588  data: 0.0034  max mem: 22446
+train: [5]  [280/400]  eta: 0:00:56  lr: 0.000298  loss: 1.2650 (1.0378)  grad: 0.6023 (0.6050)  time: 0.4669  data: 0.0036  max mem: 22446
+train: [5]  [300/400]  eta: 0:00:47  lr: 0.000298  loss: 1.0125 (1.0285)  grad: 0.6440 (0.6051)  time: 0.6260  data: 0.1726  max mem: 22446
+train: [5]  [320/400]  eta: 0:00:38  lr: 0.000298  loss: 0.7394 (1.0134)  grad: 0.6216 (0.6071)  time: 0.4539  data: 0.0030  max mem: 22446
+train: [5]  [340/400]  eta: 0:00:28  lr: 0.000298  loss: 0.7838 (1.0154)  grad: 0.5512 (0.6025)  time: 0.4512  data: 0.0035  max mem: 22446
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 0.7838 (1.0124)  grad: 0.5402 (0.6014)  time: 0.4460  data: 0.0036  max mem: 22446
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 0.7773 (1.0050)  grad: 0.5139 (0.5965)  time: 0.4594  data: 0.0035  max mem: 22446
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 0.6969 (0.9883)  grad: 0.4846 (0.5894)  time: 0.4543  data: 0.0035  max mem: 22446
+train: [5] Total time: 0:03:08 (0.4719 s / it)
+train: [5] Summary: lr: 0.000297  loss: 0.6969 (0.9883)  grad: 0.4846 (0.5894)
+eval (validation): [5]  [ 0/63]  eta: 0:03:22    time: 3.2076  data: 2.9710  max mem: 22446
+eval (validation): [5]  [20/63]  eta: 0:00:21    time: 0.3616  data: 0.0040  max mem: 22446
+eval (validation): [5]  [40/63]  eta: 0:00:09    time: 0.3442  data: 0.0029  max mem: 22446
+eval (validation): [5]  [60/63]  eta: 0:00:01    time: 0.3364  data: 0.0035  max mem: 22446
+eval (validation): [5]  [62/63]  eta: 0:00:00    time: 0.3332  data: 0.0035  max mem: 22446
+eval (validation): [5] Total time: 0:00:24 (0.3962 s / it)
+cv: [5]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 0.039  acc: 0.988  f1: 0.984
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [6]  [  0/400]  eta: 0:22:12  lr: nan  time: 3.3315  data: 2.9422  max mem: 22446
+train: [6]  [ 20/400]  eta: 0:03:47  lr: 0.000296  loss: 0.5519 (0.6398)  grad: 0.4581 (0.4818)  time: 0.4611  data: 0.0027  max mem: 22446
+train: [6]  [ 40/400]  eta: 0:03:09  lr: 0.000296  loss: 0.6211 (0.6948)  grad: 0.4581 (0.4796)  time: 0.4518  data: 0.0036  max mem: 22446
+train: [6]  [ 60/400]  eta: 0:02:50  lr: 0.000296  loss: 0.7665 (0.7386)  grad: 0.4900 (0.4952)  time: 0.4482  data: 0.0037  max mem: 22446
+train: [6]  [ 80/400]  eta: 0:02:35  lr: 0.000295  loss: 0.7199 (0.7632)  grad: 0.5269 (0.5054)  time: 0.4410  data: 0.0034  max mem: 22446
+train: [6]  [100/400]  eta: 0:02:24  lr: 0.000295  loss: 0.6951 (0.7398)  grad: 0.4409 (0.4970)  time: 0.4703  data: 0.0034  max mem: 22446
+train: [6]  [120/400]  eta: 0:02:14  lr: 0.000295  loss: 0.5027 (0.7061)  grad: 0.4192 (0.4835)  time: 0.4604  data: 0.0033  max mem: 22446
+train: [6]  [140/400]  eta: 0:02:03  lr: 0.000294  loss: 0.5098 (0.6925)  grad: 0.4049 (0.4752)  time: 0.4594  data: 0.0033  max mem: 22446
+train: [6]  [160/400]  eta: 0:01:54  lr: 0.000294  loss: 0.5456 (0.6917)  grad: 0.4128 (0.4773)  time: 0.4666  data: 0.0034  max mem: 22446
+train: [6]  [180/400]  eta: 0:01:44  lr: 0.000293  loss: 0.5800 (0.7052)  grad: 0.4291 (0.4731)  time: 0.4571  data: 0.0034  max mem: 22446
+train: [6]  [200/400]  eta: 0:01:34  lr: 0.000293  loss: 0.6749 (0.7367)  grad: 0.4562 (0.4722)  time: 0.4529  data: 0.0035  max mem: 22446
+train: [6]  [220/400]  eta: 0:01:24  lr: 0.000292  loss: 0.6610 (0.7276)  grad: 0.4270 (0.4674)  time: 0.4647  data: 0.0035  max mem: 22446
+train: [6]  [240/400]  eta: 0:01:15  lr: 0.000292  loss: 0.5853 (0.7202)  grad: 0.3932 (0.4632)  time: 0.4620  data: 0.0035  max mem: 22446
+train: [6]  [260/400]  eta: 0:01:05  lr: 0.000291  loss: 0.5853 (0.7166)  grad: 0.4905 (0.4716)  time: 0.4607  data: 0.0034  max mem: 22446
+train: [6]  [280/400]  eta: 0:00:56  lr: 0.000291  loss: 0.6855 (0.7257)  grad: 0.5250 (0.4747)  time: 0.4650  data: 0.0036  max mem: 22446
+train: [6]  [300/400]  eta: 0:00:47  lr: 0.000290  loss: 0.7234 (0.7206)  grad: 0.4271 (0.4704)  time: 0.6169  data: 0.1751  max mem: 22446
+train: [6]  [320/400]  eta: 0:00:38  lr: 0.000290  loss: 0.5136 (0.7084)  grad: 0.3800 (0.4626)  time: 0.4664  data: 0.0031  max mem: 22446
+train: [6]  [340/400]  eta: 0:00:28  lr: 0.000289  loss: 0.4416 (0.6983)  grad: 0.3206 (0.4542)  time: 0.4495  data: 0.0036  max mem: 22446
+train: [6]  [360/400]  eta: 0:00:18  lr: 0.000288  loss: 0.4175 (0.6847)  grad: 0.3337 (0.4505)  time: 0.4517  data: 0.0036  max mem: 22446
+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 0.4251 (0.6735)  grad: 0.3637 (0.4443)  time: 0.4589  data: 0.0034  max mem: 22446
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 0.3505 (0.6577)  grad: 0.3096 (0.4373)  time: 0.4526  data: 0.0036  max mem: 22446
+train: [6] Total time: 0:03:09 (0.4734 s / it)
+train: [6] Summary: lr: 0.000287  loss: 0.3505 (0.6577)  grad: 0.3096 (0.4373)
+eval (validation): [6]  [ 0/63]  eta: 0:03:26    time: 3.2713  data: 3.0348  max mem: 22446
+eval (validation): [6]  [20/63]  eta: 0:00:20    time: 0.3455  data: 0.0030  max mem: 22446
+eval (validation): [6]  [40/63]  eta: 0:00:09    time: 0.3389  data: 0.0031  max mem: 22446
+eval (validation): [6]  [60/63]  eta: 0:00:01    time: 0.3383  data: 0.0034  max mem: 22446
+eval (validation): [6]  [62/63]  eta: 0:00:00    time: 0.3368  data: 0.0034  max mem: 22446
+eval (validation): [6] Total time: 0:00:24 (0.3913 s / it)
+cv: [6]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.036  acc: 0.989  f1: 0.987
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:21:57  lr: nan  time: 3.2936  data: 2.9541  max mem: 22446
+train: [7]  [ 20/400]  eta: 0:03:39  lr: 0.000286  loss: 0.4367 (0.5227)  grad: 0.3094 (0.3377)  time: 0.4427  data: 0.0034  max mem: 22446
+train: [7]  [ 40/400]  eta: 0:03:07  lr: 0.000286  loss: 0.4158 (0.4634)  grad: 0.3412 (0.3455)  time: 0.4610  data: 0.0035  max mem: 22446
+train: [7]  [ 60/400]  eta: 0:02:49  lr: 0.000285  loss: 0.3907 (0.4661)  grad: 0.3391 (0.3330)  time: 0.4539  data: 0.0034  max mem: 22446
+train: [7]  [ 80/400]  eta: 0:02:36  lr: 0.000284  loss: 0.4121 (0.4511)  grad: 0.3113 (0.3291)  time: 0.4536  data: 0.0033  max mem: 22446
+train: [7]  [100/400]  eta: 0:02:24  lr: 0.000284  loss: 0.3634 (0.4439)  grad: 0.3113 (0.3498)  time: 0.4628  data: 0.0035  max mem: 22446
+train: [7]  [120/400]  eta: 0:02:13  lr: 0.000283  loss: 0.3358 (0.4297)  grad: 0.3230 (0.3424)  time: 0.4515  data: 0.0034  max mem: 22446
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+train: [7]  [160/400]  eta: 0:01:53  lr: 0.000282  loss: 0.4422 (0.4366)  grad: 0.3202 (0.3399)  time: 0.4634  data: 0.0036  max mem: 22446
+train: [7]  [180/400]  eta: 0:01:43  lr: 0.000281  loss: 0.3719 (0.4418)  grad: 0.3265 (0.3447)  time: 0.4565  data: 0.0035  max mem: 22446
+train: [7]  [200/400]  eta: 0:01:33  lr: 0.000280  loss: 0.3642 (0.4322)  grad: 0.3296 (0.3436)  time: 0.4624  data: 0.0035  max mem: 22446
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+train: [7]  [240/400]  eta: 0:01:15  lr: 0.000278  loss: 0.3720 (0.4307)  grad: 0.3024 (0.3364)  time: 0.4661  data: 0.0034  max mem: 22446
+train: [7]  [260/400]  eta: 0:01:05  lr: 0.000278  loss: 0.3731 (0.4241)  grad: 0.3504 (0.3404)  time: 0.4572  data: 0.0033  max mem: 22446
+train: [7]  [280/400]  eta: 0:00:56  lr: 0.000277  loss: 0.3731 (0.4287)  grad: 0.3346 (0.3360)  time: 0.4753  data: 0.0035  max mem: 22446
+train: [7]  [300/400]  eta: 0:00:47  lr: 0.000276  loss: 0.3920 (0.4284)  grad: 0.2930 (0.3344)  time: 0.6232  data: 0.1761  max mem: 22446
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+train: [7]  [340/400]  eta: 0:00:28  lr: 0.000274  loss: 0.3434 (0.4220)  grad: 0.2913 (0.3285)  time: 0.4528  data: 0.0037  max mem: 22446
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 0.2805 (0.4143)  grad: 0.2489 (0.3243)  time: 0.4392  data: 0.0036  max mem: 22446
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 0.2485 (0.4059)  grad: 0.2390 (0.3207)  time: 0.4444  data: 0.0036  max mem: 22446
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 0.2403 (0.4004)  grad: 0.2390 (0.3172)  time: 0.4508  data: 0.0035  max mem: 22446
+train: [7] Total time: 0:03:08 (0.4717 s / it)
+train: [7] Summary: lr: 0.000271  loss: 0.2403 (0.4004)  grad: 0.2390 (0.3172)
+eval (validation): [7]  [ 0/63]  eta: 0:03:28    time: 3.3063  data: 3.0332  max mem: 22446
+eval (validation): [7]  [20/63]  eta: 0:00:21    time: 0.3669  data: 0.0034  max mem: 22446
+eval (validation): [7]  [40/63]  eta: 0:00:09    time: 0.3372  data: 0.0032  max mem: 22446
+eval (validation): [7]  [60/63]  eta: 0:00:01    time: 0.3401  data: 0.0034  max mem: 22446
+eval (validation): [7]  [62/63]  eta: 0:00:00    time: 0.3375  data: 0.0034  max mem: 22446
+eval (validation): [7] Total time: 0:00:25 (0.3990 s / it)
+cv: [7]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.037  acc: 0.990  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [8]  [  0/400]  eta: 0:21:45  lr: nan  time: 3.2637  data: 2.9279  max mem: 22446
+train: [8]  [ 20/400]  eta: 0:03:37  lr: 0.000270  loss: 0.2201 (0.2445)  grad: 0.1800 (0.2088)  time: 0.4390  data: 0.0037  max mem: 22446
+train: [8]  [ 40/400]  eta: 0:03:05  lr: 0.000270  loss: 0.2201 (0.2560)  grad: 0.1964 (0.2092)  time: 0.4519  data: 0.0033  max mem: 22446
+train: [8]  [ 60/400]  eta: 0:02:47  lr: 0.000269  loss: 0.2297 (0.2553)  grad: 0.2025 (0.2049)  time: 0.4486  data: 0.0035  max mem: 22446
+train: [8]  [ 80/400]  eta: 0:02:33  lr: 0.000268  loss: 0.2621 (0.2652)  grad: 0.2269 (0.2115)  time: 0.4411  data: 0.0034  max mem: 22446
+train: [8]  [100/400]  eta: 0:02:23  lr: 0.000267  loss: 0.2621 (0.2631)  grad: 0.2279 (0.2118)  time: 0.4639  data: 0.0034  max mem: 22446
+train: [8]  [120/400]  eta: 0:02:11  lr: 0.000266  loss: 0.2623 (0.2840)  grad: 0.2523 (0.2232)  time: 0.4397  data: 0.0035  max mem: 22446
+train: [8]  [140/400]  eta: 0:02:01  lr: 0.000265  loss: 0.2998 (0.2867)  grad: 0.2538 (0.2266)  time: 0.4523  data: 0.0035  max mem: 22446
+train: [8]  [160/400]  eta: 0:01:52  lr: 0.000264  loss: 0.2784 (0.2840)  grad: 0.2289 (0.2280)  time: 0.4717  data: 0.0035  max mem: 22446
+train: [8]  [180/400]  eta: 0:01:42  lr: 0.000263  loss: 0.2784 (0.2853)  grad: 0.2219 (0.2264)  time: 0.4631  data: 0.0035  max mem: 22446
+train: [8]  [200/400]  eta: 0:01:33  lr: 0.000262  loss: 0.2128 (0.2803)  grad: 0.2186 (0.2270)  time: 0.4565  data: 0.0035  max mem: 22446
+train: [8]  [220/400]  eta: 0:01:24  lr: 0.000260  loss: 0.2128 (0.2778)  grad: 0.2186 (0.2260)  time: 0.4767  data: 0.0035  max mem: 22446
+train: [8]  [240/400]  eta: 0:01:14  lr: 0.000259  loss: 0.2416 (0.2782)  grad: 0.2130 (0.2267)  time: 0.4729  data: 0.0034  max mem: 22446
+train: [8]  [260/400]  eta: 0:01:05  lr: 0.000258  loss: 0.2494 (0.2764)  grad: 0.2269 (0.2269)  time: 0.4620  data: 0.0034  max mem: 22446
+train: [8]  [280/400]  eta: 0:00:56  lr: 0.000257  loss: 0.2494 (0.2811)  grad: 0.2380 (0.2288)  time: 0.4620  data: 0.0035  max mem: 22446
+train: [8]  [300/400]  eta: 0:00:47  lr: 0.000256  loss: 0.3187 (0.2870)  grad: 0.2539 (0.2322)  time: 0.6272  data: 0.1750  max mem: 22446
+train: [8]  [320/400]  eta: 0:00:38  lr: 0.000255  loss: 0.2553 (0.2837)  grad: 0.2327 (0.2299)  time: 0.4511  data: 0.0032  max mem: 22446
+train: [8]  [340/400]  eta: 0:00:28  lr: 0.000254  loss: 0.2327 (0.2812)  grad: 0.1887 (0.2285)  time: 0.4455  data: 0.0035  max mem: 22446
+train: [8]  [360/400]  eta: 0:00:18  lr: 0.000253  loss: 0.2214 (0.2774)  grad: 0.1972 (0.2274)  time: 0.4473  data: 0.0035  max mem: 22446
+train: [8]  [380/400]  eta: 0:00:09  lr: 0.000252  loss: 0.1832 (0.2732)  grad: 0.1972 (0.2255)  time: 0.4419  data: 0.0036  max mem: 22446
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 0.1982 (0.2719)  grad: 0.1807 (0.2236)  time: 0.4492  data: 0.0036  max mem: 22446
+train: [8] Total time: 0:03:08 (0.4705 s / it)
+train: [8] Summary: lr: 0.000250  loss: 0.1982 (0.2719)  grad: 0.1807 (0.2236)
+eval (validation): [8]  [ 0/63]  eta: 0:03:20    time: 3.1820  data: 2.9426  max mem: 22446
+eval (validation): [8]  [20/63]  eta: 0:00:21    time: 0.3727  data: 0.0154  max mem: 22446
+eval (validation): [8]  [40/63]  eta: 0:00:09    time: 0.3496  data: 0.0031  max mem: 22446
+eval (validation): [8]  [60/63]  eta: 0:00:01    time: 0.3264  data: 0.0032  max mem: 22446
+eval (validation): [8]  [62/63]  eta: 0:00:00    time: 0.3247  data: 0.0032  max mem: 22446
+eval (validation): [8] Total time: 0:00:25 (0.3976 s / it)
+cv: [8]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.033  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:22:26  lr: nan  time: 3.3654  data: 2.9812  max mem: 22446
+train: [9]  [ 20/400]  eta: 0:03:44  lr: 0.000249  loss: 0.2486 (0.2869)  grad: 0.2140 (0.2282)  time: 0.4525  data: 0.0027  max mem: 22446
+train: [9]  [ 40/400]  eta: 0:03:09  lr: 0.000248  loss: 0.2055 (0.2498)  grad: 0.1978 (0.2084)  time: 0.4574  data: 0.0034  max mem: 22446
+train: [9]  [ 60/400]  eta: 0:02:50  lr: 0.000247  loss: 0.2059 (0.2508)  grad: 0.1881 (0.1988)  time: 0.4530  data: 0.0036  max mem: 22446
+train: [9]  [ 80/400]  eta: 0:02:36  lr: 0.000246  loss: 0.2240 (0.2452)  grad: 0.1586 (0.1935)  time: 0.4542  data: 0.0035  max mem: 22446
+train: [9]  [100/400]  eta: 0:02:25  lr: 0.000244  loss: 0.2061 (0.2396)  grad: 0.1619 (0.1892)  time: 0.4557  data: 0.0034  max mem: 22446
+train: [9]  [120/400]  eta: 0:02:13  lr: 0.000243  loss: 0.2381 (0.2432)  grad: 0.1773 (0.1909)  time: 0.4505  data: 0.0034  max mem: 22446
+train: [9]  [140/400]  eta: 0:02:03  lr: 0.000242  loss: 0.2419 (0.2389)  grad: 0.1993 (0.1916)  time: 0.4540  data: 0.0034  max mem: 22446
+train: [9]  [160/400]  eta: 0:01:53  lr: 0.000241  loss: 0.1947 (0.2356)  grad: 0.2003 (0.1933)  time: 0.4622  data: 0.0035  max mem: 22446
+train: [9]  [180/400]  eta: 0:01:43  lr: 0.000240  loss: 0.1849 (0.2304)  grad: 0.1924 (0.1910)  time: 0.4582  data: 0.0034  max mem: 22446
+train: [9]  [200/400]  eta: 0:01:34  lr: 0.000238  loss: 0.1686 (0.2274)  grad: 0.1714 (0.1886)  time: 0.4605  data: 0.0034  max mem: 22446
+train: [9]  [220/400]  eta: 0:01:24  lr: 0.000237  loss: 0.1817 (0.2267)  grad: 0.1644 (0.1858)  time: 0.4696  data: 0.0034  max mem: 22446
+train: [9]  [240/400]  eta: 0:01:15  lr: 0.000236  loss: 0.1982 (0.2245)  grad: 0.1644 (0.1836)  time: 0.4749  data: 0.0034  max mem: 22446
+train: [9]  [260/400]  eta: 0:01:05  lr: 0.000234  loss: 0.1995 (0.2252)  grad: 0.1732 (0.1840)  time: 0.4695  data: 0.0035  max mem: 22446
+train: [9]  [280/400]  eta: 0:00:56  lr: 0.000233  loss: 0.2125 (0.2258)  grad: 0.1734 (0.1838)  time: 0.4604  data: 0.0036  max mem: 22446
+train: [9]  [300/400]  eta: 0:00:48  lr: 0.000232  loss: 0.1958 (0.2247)  grad: 0.1754 (0.1842)  time: 0.6332  data: 0.1855  max mem: 22446
+train: [9]  [320/400]  eta: 0:00:38  lr: 0.000230  loss: 0.1679 (0.2216)  grad: 0.1524 (0.1830)  time: 0.4528  data: 0.0032  max mem: 22446
+train: [9]  [340/400]  eta: 0:00:28  lr: 0.000229  loss: 0.1678 (0.2221)  grad: 0.1524 (0.1834)  time: 0.4632  data: 0.0033  max mem: 22446
+train: [9]  [360/400]  eta: 0:00:19  lr: 0.000228  loss: 0.2033 (0.2204)  grad: 0.1767 (0.1830)  time: 0.4631  data: 0.0033  max mem: 22446
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 0.1850 (0.2180)  grad: 0.1643 (0.1816)  time: 0.4660  data: 0.0036  max mem: 22446
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 0.1647 (0.2153)  grad: 0.1348 (0.1797)  time: 0.4518  data: 0.0035  max mem: 22446
+train: [9] Total time: 0:03:10 (0.4757 s / it)
+train: [9] Summary: lr: 0.000225  loss: 0.1647 (0.2153)  grad: 0.1348 (0.1797)
+eval (validation): [9]  [ 0/63]  eta: 0:03:29    time: 3.3241  data: 3.0339  max mem: 22446
+eval (validation): [9]  [20/63]  eta: 0:00:21    time: 0.3609  data: 0.0049  max mem: 22446
+eval (validation): [9]  [40/63]  eta: 0:00:09    time: 0.3375  data: 0.0028  max mem: 22446
+eval (validation): [9]  [60/63]  eta: 0:00:01    time: 0.3422  data: 0.0032  max mem: 22446
+eval (validation): [9]  [62/63]  eta: 0:00:00    time: 0.3426  data: 0.0032  max mem: 22446
+eval (validation): [9] Total time: 0:00:25 (0.3986 s / it)
+cv: [9]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.032  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:22:32  lr: nan  time: 3.3815  data: 2.9995  max mem: 22446
+train: [10]  [ 20/400]  eta: 0:03:52  lr: 0.000224  loss: 0.1899 (0.2106)  grad: 0.1315 (0.1432)  time: 0.4733  data: 0.0023  max mem: 22446
+train: [10]  [ 40/400]  eta: 0:03:16  lr: 0.000222  loss: 0.1747 (0.1892)  grad: 0.1315 (0.1408)  time: 0.4740  data: 0.0036  max mem: 22446
+train: [10]  [ 60/400]  eta: 0:02:55  lr: 0.000221  loss: 0.1531 (0.1821)  grad: 0.1135 (0.1354)  time: 0.4616  data: 0.0036  max mem: 22446
+train: [10]  [ 80/400]  eta: 0:02:40  lr: 0.000220  loss: 0.1441 (0.1768)  grad: 0.1317 (0.1377)  time: 0.4583  data: 0.0035  max mem: 22446
+train: [10]  [100/400]  eta: 0:02:28  lr: 0.000218  loss: 0.1441 (0.1745)  grad: 0.1320 (0.1355)  time: 0.4683  data: 0.0036  max mem: 22446
+train: [10]  [120/400]  eta: 0:02:17  lr: 0.000217  loss: 0.1554 (0.1745)  grad: 0.1398 (0.1393)  time: 0.4654  data: 0.0036  max mem: 22446
+train: [10]  [140/400]  eta: 0:02:06  lr: 0.000215  loss: 0.1806 (0.1775)  grad: 0.1482 (0.1405)  time: 0.4578  data: 0.0034  max mem: 22446
+train: [10]  [160/400]  eta: 0:01:56  lr: 0.000214  loss: 0.1547 (0.1740)  grad: 0.1342 (0.1378)  time: 0.4856  data: 0.0035  max mem: 22446
+train: [10]  [180/400]  eta: 0:01:46  lr: 0.000213  loss: 0.1460 (0.1721)  grad: 0.1152 (0.1359)  time: 0.4795  data: 0.0036  max mem: 22446
+train: [10]  [200/400]  eta: 0:01:36  lr: 0.000211  loss: 0.1572 (0.1724)  grad: 0.1307 (0.1369)  time: 0.4659  data: 0.0034  max mem: 22446
+train: [10]  [220/400]  eta: 0:01:26  lr: 0.000210  loss: 0.1603 (0.1712)  grad: 0.1332 (0.1345)  time: 0.4573  data: 0.0033  max mem: 22446
+train: [10]  [240/400]  eta: 0:01:16  lr: 0.000208  loss: 0.1411 (0.1701)  grad: 0.1270 (0.1342)  time: 0.4710  data: 0.0034  max mem: 22446
+train: [10]  [260/400]  eta: 0:01:07  lr: 0.000207  loss: 0.1466 (0.1690)  grad: 0.1270 (0.1341)  time: 0.4728  data: 0.0034  max mem: 22446
+train: [10]  [280/400]  eta: 0:00:57  lr: 0.000205  loss: 0.1469 (0.1680)  grad: 0.1113 (0.1325)  time: 0.5183  data: 0.0038  max mem: 22446
+train: [10]  [300/400]  eta: 0:00:49  lr: 0.000204  loss: 0.1455 (0.1685)  grad: 0.1077 (0.1314)  time: 0.6343  data: 0.1917  max mem: 22446
+train: [10]  [320/400]  eta: 0:00:39  lr: 0.000202  loss: 0.1376 (0.1673)  grad: 0.1109 (0.1302)  time: 0.4630  data: 0.0030  max mem: 22446
+train: [10]  [340/400]  eta: 0:00:29  lr: 0.000201  loss: 0.1376 (0.1655)  grad: 0.1075 (0.1287)  time: 0.4536  data: 0.0033  max mem: 22446
+train: [10]  [360/400]  eta: 0:00:19  lr: 0.000199  loss: 0.1258 (0.1646)  grad: 0.0936 (0.1265)  time: 0.4544  data: 0.0034  max mem: 22446
+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 0.1314 (0.1638)  grad: 0.0912 (0.1249)  time: 0.4436  data: 0.0032  max mem: 22446
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 0.1324 (0.1628)  grad: 0.0985 (0.1232)  time: 0.4704  data: 0.0035  max mem: 22446
+train: [10] Total time: 0:03:13 (0.4840 s / it)
+train: [10] Summary: lr: 0.000196  loss: 0.1324 (0.1628)  grad: 0.0985 (0.1232)
+eval (validation): [10]  [ 0/63]  eta: 0:03:32    time: 3.3784  data: 3.0833  max mem: 22446
+eval (validation): [10]  [20/63]  eta: 0:00:23    time: 0.3995  data: 0.0133  max mem: 22446
+eval (validation): [10]  [40/63]  eta: 0:00:10    time: 0.3337  data: 0.0034  max mem: 22446
+eval (validation): [10]  [60/63]  eta: 0:00:01    time: 0.3548  data: 0.0035  max mem: 22446
+eval (validation): [10]  [62/63]  eta: 0:00:00    time: 0.3593  data: 0.0035  max mem: 22446
+eval (validation): [10] Total time: 0:00:26 (0.4162 s / it)
+cv: [10]  best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00')  loss: 0.034  acc: 0.991  f1: 0.990
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:22:41  lr: nan  time: 3.4050  data: 3.0617  max mem: 22446
+train: [11]  [ 20/400]  eta: 0:03:48  lr: 0.000195  loss: 0.1558 (0.1529)  grad: 0.0907 (0.0879)  time: 0.4601  data: 0.0023  max mem: 22446
+train: [11]  [ 40/400]  eta: 0:03:11  lr: 0.000193  loss: 0.1482 (0.1486)  grad: 0.0835 (0.0918)  time: 0.4587  data: 0.0034  max mem: 22446
+train: [11]  [ 60/400]  eta: 0:02:53  lr: 0.000192  loss: 0.1358 (0.1494)  grad: 0.0788 (0.0944)  time: 0.4639  data: 0.0035  max mem: 22446
+train: [11]  [ 80/400]  eta: 0:02:38  lr: 0.000190  loss: 0.1335 (0.1463)  grad: 0.0922 (0.0950)  time: 0.4526  data: 0.0036  max mem: 22446
+train: [11]  [100/400]  eta: 0:02:26  lr: 0.000189  loss: 0.1333 (0.1459)  grad: 0.0930 (0.0956)  time: 0.4605  data: 0.0037  max mem: 22446
+train: [11]  [120/400]  eta: 0:02:15  lr: 0.000187  loss: 0.1337 (0.1438)  grad: 0.0930 (0.0953)  time: 0.4559  data: 0.0036  max mem: 22446
+train: [11]  [140/400]  eta: 0:02:05  lr: 0.000186  loss: 0.1337 (0.1441)  grad: 0.0940 (0.0959)  time: 0.4678  data: 0.0035  max mem: 22446
+train: [11]  [160/400]  eta: 0:01:54  lr: 0.000184  loss: 0.1386 (0.1454)  grad: 0.0918 (0.0958)  time: 0.4663  data: 0.0035  max mem: 22446
+train: [11]  [180/400]  eta: 0:01:45  lr: 0.000183  loss: 0.1349 (0.1440)  grad: 0.0846 (0.0955)  time: 0.4677  data: 0.0035  max mem: 22446
+train: [11]  [200/400]  eta: 0:01:35  lr: 0.000181  loss: 0.1272 (0.1429)  grad: 0.0826 (0.0961)  time: 0.4669  data: 0.0036  max mem: 22446
+train: [11]  [220/400]  eta: 0:01:25  lr: 0.000180  loss: 0.1257 (0.1422)  grad: 0.0888 (0.0957)  time: 0.4757  data: 0.0036  max mem: 22446
+train: [11]  [240/400]  eta: 0:01:16  lr: 0.000178  loss: 0.1257 (0.1410)  grad: 0.0888 (0.0956)  time: 0.4689  data: 0.0036  max mem: 22446
+train: [11]  [260/400]  eta: 0:01:06  lr: 0.000177  loss: 0.1323 (0.1406)  grad: 0.0879 (0.0944)  time: 0.4646  data: 0.0036  max mem: 22446
+train: [11]  [280/400]  eta: 0:00:56  lr: 0.000175  loss: 0.1332 (0.1406)  grad: 0.0847 (0.0941)  time: 0.4624  data: 0.0037  max mem: 22446
+train: [11]  [300/400]  eta: 0:00:48  lr: 0.000174  loss: 0.1402 (0.1412)  grad: 0.0895 (0.0938)  time: 0.6531  data: 0.1855  max mem: 22446
+train: [11]  [320/400]  eta: 0:00:38  lr: 0.000172  loss: 0.1344 (0.1406)  grad: 0.0873 (0.0927)  time: 0.4693  data: 0.0033  max mem: 22446
+train: [11]  [340/400]  eta: 0:00:29  lr: 0.000170  loss: 0.1274 (0.1395)  grad: 0.0693 (0.0911)  time: 0.4691  data: 0.0036  max mem: 22446
+train: [11]  [360/400]  eta: 0:00:19  lr: 0.000169  loss: 0.1217 (0.1385)  grad: 0.0702 (0.0904)  time: 0.4587  data: 0.0036  max mem: 22446
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 0.1217 (0.1378)  grad: 0.0750 (0.0898)  time: 0.4669  data: 0.0036  max mem: 22446
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 0.1256 (0.1374)  grad: 0.0804 (0.0894)  time: 0.4776  data: 0.0037  max mem: 22446
+train: [11] Total time: 0:03:12 (0.4819 s / it)
+train: [11] Summary: lr: 0.000166  loss: 0.1256 (0.1374)  grad: 0.0804 (0.0894)
+eval (validation): [11]  [ 0/63]  eta: 0:03:25    time: 3.2696  data: 3.0301  max mem: 22446
+eval (validation): [11]  [20/63]  eta: 0:00:22    time: 0.3742  data: 0.0039  max mem: 22446
+eval (validation): [11]  [40/63]  eta: 0:00:09    time: 0.3462  data: 0.0029  max mem: 22446
+eval (validation): [11]  [60/63]  eta: 0:00:01    time: 0.3437  data: 0.0033  max mem: 22446
+eval (validation): [11]  [62/63]  eta: 0:00:00    time: 0.3372  data: 0.0034  max mem: 22446
+eval (validation): [11] Total time: 0:00:25 (0.4059 s / it)
+cv: [11]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.034  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:23:17  lr: nan  time: 3.4931  data: 3.0887  max mem: 22446
+train: [12]  [ 20/400]  eta: 0:03:50  lr: 0.000164  loss: 0.1181 (0.1245)  grad: 0.0572 (0.0721)  time: 0.4621  data: 0.0030  max mem: 22446
+train: [12]  [ 40/400]  eta: 0:03:13  lr: 0.000163  loss: 0.1181 (0.1241)  grad: 0.0581 (0.0701)  time: 0.4641  data: 0.0036  max mem: 22446
+train: [12]  [ 60/400]  eta: 0:02:52  lr: 0.000161  loss: 0.1192 (0.1216)  grad: 0.0768 (0.0716)  time: 0.4507  data: 0.0033  max mem: 22446
+train: [12]  [ 80/400]  eta: 0:02:41  lr: 0.000160  loss: 0.1111 (0.1200)  grad: 0.0545 (0.0671)  time: 0.4882  data: 0.0036  max mem: 22446
+train: [12]  [100/400]  eta: 0:02:29  lr: 0.000158  loss: 0.1182 (0.1217)  grad: 0.0510 (0.0667)  time: 0.4828  data: 0.0036  max mem: 22446
+train: [12]  [120/400]  eta: 0:02:17  lr: 0.000156  loss: 0.1262 (0.1230)  grad: 0.0623 (0.0667)  time: 0.4582  data: 0.0034  max mem: 22446
+train: [12]  [140/400]  eta: 0:02:07  lr: 0.000155  loss: 0.1231 (0.1232)  grad: 0.0623 (0.0667)  time: 0.4653  data: 0.0035  max mem: 22446
+train: [12]  [160/400]  eta: 0:01:56  lr: 0.000153  loss: 0.1195 (0.1225)  grad: 0.0539 (0.0658)  time: 0.4674  data: 0.0034  max mem: 22446
+train: [12]  [180/400]  eta: 0:01:46  lr: 0.000152  loss: 0.1195 (0.1223)  grad: 0.0584 (0.0662)  time: 0.4630  data: 0.0033  max mem: 22446
+train: [12]  [200/400]  eta: 0:01:36  lr: 0.000150  loss: 0.1083 (0.1217)  grad: 0.0682 (0.0660)  time: 0.4568  data: 0.0034  max mem: 22446
+train: [12]  [220/400]  eta: 0:01:26  lr: 0.000149  loss: 0.1107 (0.1226)  grad: 0.0729 (0.0669)  time: 0.4675  data: 0.0034  max mem: 22446
+train: [12]  [240/400]  eta: 0:01:16  lr: 0.000147  loss: 0.1254 (0.1235)  grad: 0.0733 (0.0673)  time: 0.4549  data: 0.0033  max mem: 22446
+train: [12]  [260/400]  eta: 0:01:06  lr: 0.000145  loss: 0.1254 (0.1232)  grad: 0.0707 (0.0670)  time: 0.4566  data: 0.0033  max mem: 22446
+train: [12]  [280/400]  eta: 0:00:57  lr: 0.000144  loss: 0.1141 (0.1225)  grad: 0.0565 (0.0667)  time: 0.4717  data: 0.0035  max mem: 22446
+train: [12]  [300/400]  eta: 0:00:48  lr: 0.000142  loss: 0.1106 (0.1224)  grad: 0.0685 (0.0668)  time: 0.6338  data: 0.1858  max mem: 22446
+train: [12]  [320/400]  eta: 0:00:38  lr: 0.000141  loss: 0.1209 (0.1221)  grad: 0.0615 (0.0663)  time: 0.4587  data: 0.0033  max mem: 22446
+train: [12]  [340/400]  eta: 0:00:28  lr: 0.000139  loss: 0.1181 (0.1220)  grad: 0.0573 (0.0663)  time: 0.4599  data: 0.0037  max mem: 22446
+train: [12]  [360/400]  eta: 0:00:19  lr: 0.000138  loss: 0.1160 (0.1219)  grad: 0.0642 (0.0664)  time: 0.4699  data: 0.0036  max mem: 22446
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 0.1139 (0.1223)  grad: 0.0642 (0.0669)  time: 0.4640  data: 0.0036  max mem: 22446
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 0.1120 (0.1219)  grad: 0.0566 (0.0665)  time: 0.4800  data: 0.0036  max mem: 22446
+train: [12] Total time: 0:03:12 (0.4816 s / it)
+train: [12] Summary: lr: 0.000134  loss: 0.1120 (0.1219)  grad: 0.0566 (0.0665)
+eval (validation): [12]  [ 0/63]  eta: 0:03:30    time: 3.3425  data: 3.0527  max mem: 22446
+eval (validation): [12]  [20/63]  eta: 0:00:22    time: 0.3843  data: 0.0033  max mem: 22446
+eval (validation): [12]  [40/63]  eta: 0:00:10    time: 0.3684  data: 0.0036  max mem: 22446
+eval (validation): [12]  [60/63]  eta: 0:00:01    time: 0.3382  data: 0.0033  max mem: 22446
+eval (validation): [12]  [62/63]  eta: 0:00:00    time: 0.3365  data: 0.0033  max mem: 22446
+eval (validation): [12] Total time: 0:00:26 (0.4154 s / it)
+cv: [12]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.036  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:23:08  lr: nan  time: 3.4721  data: 3.1273  max mem: 22446
+train: [13]  [ 20/400]  eta: 0:03:49  lr: 0.000133  loss: 0.1133 (0.1188)  grad: 0.0587 (0.0634)  time: 0.4602  data: 0.0035  max mem: 22446
+train: [13]  [ 40/400]  eta: 0:03:12  lr: 0.000131  loss: 0.1117 (0.1128)  grad: 0.0488 (0.0564)  time: 0.4604  data: 0.0033  max mem: 22446
+train: [13]  [ 60/400]  eta: 0:02:53  lr: 0.000130  loss: 0.1052 (0.1113)  grad: 0.0493 (0.0560)  time: 0.4622  data: 0.0035  max mem: 22446
+train: [13]  [ 80/400]  eta: 0:02:39  lr: 0.000128  loss: 0.1052 (0.1116)  grad: 0.0508 (0.0561)  time: 0.4609  data: 0.0035  max mem: 22446
+train: [13]  [100/400]  eta: 0:02:27  lr: 0.000127  loss: 0.1081 (0.1114)  grad: 0.0508 (0.0590)  time: 0.4693  data: 0.0034  max mem: 22446
+train: [13]  [120/400]  eta: 0:02:16  lr: 0.000125  loss: 0.1087 (0.1114)  grad: 0.0603 (0.0606)  time: 0.4661  data: 0.0036  max mem: 22446
+train: [13]  [140/400]  eta: 0:02:06  lr: 0.000124  loss: 0.1087 (0.1118)  grad: 0.0580 (0.0605)  time: 0.4785  data: 0.0035  max mem: 22446
+train: [13]  [160/400]  eta: 0:01:56  lr: 0.000122  loss: 0.1030 (0.1116)  grad: 0.0486 (0.0606)  time: 0.4740  data: 0.0035  max mem: 22446
+train: [13]  [180/400]  eta: 0:01:46  lr: 0.000120  loss: 0.1156 (0.1127)  grad: 0.0526 (0.0602)  time: 0.4693  data: 0.0035  max mem: 22446
+train: [13]  [200/400]  eta: 0:01:36  lr: 0.000119  loss: 0.1156 (0.1132)  grad: 0.0570 (0.0602)  time: 0.4622  data: 0.0035  max mem: 22446
+train: [13]  [220/400]  eta: 0:01:26  lr: 0.000117  loss: 0.1058 (0.1128)  grad: 0.0570 (0.0597)  time: 0.4732  data: 0.0036  max mem: 22446
+train: [13]  [240/400]  eta: 0:01:16  lr: 0.000116  loss: 0.1033 (0.1125)  grad: 0.0518 (0.0593)  time: 0.4668  data: 0.0035  max mem: 22446
+train: [13]  [260/400]  eta: 0:01:07  lr: 0.000114  loss: 0.1065 (0.1123)  grad: 0.0518 (0.0594)  time: 0.4717  data: 0.0036  max mem: 22446
+train: [13]  [280/400]  eta: 0:00:57  lr: 0.000113  loss: 0.1065 (0.1118)  grad: 0.0614 (0.0594)  time: 0.4687  data: 0.0037  max mem: 22446
+train: [13]  [300/400]  eta: 0:00:48  lr: 0.000111  loss: 0.1070 (0.1119)  grad: 0.0615 (0.0596)  time: 0.6396  data: 0.1773  max mem: 22446
+train: [13]  [320/400]  eta: 0:00:39  lr: 0.000110  loss: 0.1102 (0.1119)  grad: 0.0584 (0.0589)  time: 0.4711  data: 0.0032  max mem: 22446
+train: [13]  [340/400]  eta: 0:00:29  lr: 0.000108  loss: 0.1102 (0.1116)  grad: 0.0445 (0.0585)  time: 0.4685  data: 0.0037  max mem: 22446
+train: [13]  [360/400]  eta: 0:00:19  lr: 0.000107  loss: 0.0960 (0.1108)  grad: 0.0463 (0.0580)  time: 0.4611  data: 0.0036  max mem: 22446
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 0.0960 (0.1106)  grad: 0.0484 (0.0575)  time: 0.4671  data: 0.0037  max mem: 22446
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 0.1061 (0.1107)  grad: 0.0475 (0.0571)  time: 0.4620  data: 0.0036  max mem: 22446
+train: [13] Total time: 0:03:13 (0.4834 s / it)
+train: [13] Summary: lr: 0.000104  loss: 0.1061 (0.1107)  grad: 0.0475 (0.0571)
+eval (validation): [13]  [ 0/63]  eta: 0:03:27    time: 3.2961  data: 3.0522  max mem: 22446
+eval (validation): [13]  [20/63]  eta: 0:00:20    time: 0.3375  data: 0.0041  max mem: 22446
+eval (validation): [13]  [40/63]  eta: 0:00:09    time: 0.3522  data: 0.0030  max mem: 22446
+eval (validation): [13]  [60/63]  eta: 0:00:01    time: 0.3428  data: 0.0032  max mem: 22446
+eval (validation): [13]  [62/63]  eta: 0:00:00    time: 0.3432  data: 0.0031  max mem: 22446
+eval (validation): [13] Total time: 0:00:25 (0.3969 s / it)
+cv: [13]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.033  acc: 0.991  f1: 0.990
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:22:51  lr: nan  time: 3.4276  data: 3.0348  max mem: 22446
+train: [14]  [ 20/400]  eta: 0:03:52  lr: 0.000102  loss: 0.1116 (0.1108)  grad: 0.0462 (0.0454)  time: 0.4707  data: 0.0043  max mem: 22446
+train: [14]  [ 40/400]  eta: 0:03:13  lr: 0.000101  loss: 0.1082 (0.1092)  grad: 0.0480 (0.0500)  time: 0.4581  data: 0.0032  max mem: 22446
+train: [14]  [ 60/400]  eta: 0:02:54  lr: 0.000099  loss: 0.1026 (0.1071)  grad: 0.0511 (0.0499)  time: 0.4659  data: 0.0037  max mem: 22446
+train: [14]  [ 80/400]  eta: 0:02:40  lr: 0.000098  loss: 0.0979 (0.1061)  grad: 0.0475 (0.0508)  time: 0.4595  data: 0.0035  max mem: 22446
+train: [14]  [100/400]  eta: 0:02:27  lr: 0.000096  loss: 0.0967 (0.1055)  grad: 0.0474 (0.0499)  time: 0.4505  data: 0.0035  max mem: 22446
+train: [14]  [120/400]  eta: 0:02:15  lr: 0.000095  loss: 0.1075 (0.1077)  grad: 0.0467 (0.0499)  time: 0.4564  data: 0.0036  max mem: 22446
+train: [14]  [140/400]  eta: 0:02:05  lr: 0.000093  loss: 0.1146 (0.1090)  grad: 0.0446 (0.0505)  time: 0.4710  data: 0.0036  max mem: 22446
+train: [14]  [160/400]  eta: 0:01:55  lr: 0.000092  loss: 0.0969 (0.1078)  grad: 0.0429 (0.0499)  time: 0.4817  data: 0.0036  max mem: 22446
+train: [14]  [180/400]  eta: 0:01:46  lr: 0.000090  loss: 0.0982 (0.1070)  grad: 0.0429 (0.0494)  time: 0.4778  data: 0.0036  max mem: 22446
+train: [14]  [200/400]  eta: 0:01:35  lr: 0.000089  loss: 0.1003 (0.1071)  grad: 0.0442 (0.0494)  time: 0.4570  data: 0.0034  max mem: 22446
+train: [14]  [220/400]  eta: 0:01:26  lr: 0.000088  loss: 0.0996 (0.1066)  grad: 0.0473 (0.0492)  time: 0.4847  data: 0.0034  max mem: 22446
+train: [14]  [240/400]  eta: 0:01:16  lr: 0.000086  loss: 0.0996 (0.1062)  grad: 0.0477 (0.0492)  time: 0.4695  data: 0.0035  max mem: 22446
+train: [14]  [260/400]  eta: 0:01:06  lr: 0.000085  loss: 0.0952 (0.1055)  grad: 0.0481 (0.0493)  time: 0.4584  data: 0.0036  max mem: 22446
+train: [14]  [280/400]  eta: 0:00:57  lr: 0.000083  loss: 0.1063 (0.1063)  grad: 0.0479 (0.0492)  time: 0.4511  data: 0.0035  max mem: 22446
+train: [14]  [300/400]  eta: 0:00:48  lr: 0.000082  loss: 0.1100 (0.1063)  grad: 0.0452 (0.0491)  time: 0.6314  data: 0.1802  max mem: 22446
+train: [14]  [320/400]  eta: 0:00:38  lr: 0.000081  loss: 0.1005 (0.1061)  grad: 0.0463 (0.0489)  time: 0.4569  data: 0.0032  max mem: 22446
+train: [14]  [340/400]  eta: 0:00:28  lr: 0.000079  loss: 0.1101 (0.1065)  grad: 0.0463 (0.0488)  time: 0.4536  data: 0.0035  max mem: 22446
+train: [14]  [360/400]  eta: 0:00:19  lr: 0.000078  loss: 0.1120 (0.1067)  grad: 0.0490 (0.0489)  time: 0.4712  data: 0.0037  max mem: 22446
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 0.1064 (0.1067)  grad: 0.0490 (0.0488)  time: 0.4693  data: 0.0036  max mem: 22446
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 0.1042 (0.1067)  grad: 0.0454 (0.0486)  time: 0.4604  data: 0.0036  max mem: 22446
+train: [14] Total time: 0:03:12 (0.4802 s / it)
+train: [14] Summary: lr: 0.000075  loss: 0.1042 (0.1067)  grad: 0.0454 (0.0486)
+eval (validation): [14]  [ 0/63]  eta: 0:03:43    time: 3.5462  data: 3.2983  max mem: 22446
+eval (validation): [14]  [20/63]  eta: 0:00:22    time: 0.3818  data: 0.0079  max mem: 22446
+eval (validation): [14]  [40/63]  eta: 0:00:10    time: 0.3803  data: 0.0030  max mem: 22446
+eval (validation): [14]  [60/63]  eta: 0:00:01    time: 0.3505  data: 0.0034  max mem: 22446
+eval (validation): [14]  [62/63]  eta: 0:00:00    time: 0.3432  data: 0.0034  max mem: 22446
+eval (validation): [14] Total time: 0:00:26 (0.4253 s / it)
+cv: [14]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.037  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:22:45  lr: nan  time: 3.4134  data: 3.0225  max mem: 22446
+train: [15]  [ 20/400]  eta: 0:03:47  lr: 0.000074  loss: 0.1031 (0.1084)  grad: 0.0415 (0.0472)  time: 0.4573  data: 0.0037  max mem: 22446
+train: [15]  [ 40/400]  eta: 0:03:16  lr: 0.000072  loss: 0.1095 (0.1085)  grad: 0.0450 (0.0490)  time: 0.4884  data: 0.0035  max mem: 22446
+train: [15]  [ 60/400]  eta: 0:02:57  lr: 0.000071  loss: 0.0977 (0.1030)  grad: 0.0484 (0.0482)  time: 0.4770  data: 0.0035  max mem: 22446
+train: [15]  [ 80/400]  eta: 0:02:42  lr: 0.000070  loss: 0.0910 (0.1017)  grad: 0.0453 (0.0480)  time: 0.4611  data: 0.0035  max mem: 22446
+train: [15]  [100/400]  eta: 0:02:29  lr: 0.000068  loss: 0.0910 (0.1009)  grad: 0.0441 (0.0476)  time: 0.4686  data: 0.0035  max mem: 22446
+train: [15]  [120/400]  eta: 0:02:18  lr: 0.000067  loss: 0.0937 (0.1006)  grad: 0.0450 (0.0475)  time: 0.4684  data: 0.0034  max mem: 22446
+train: [15]  [140/400]  eta: 0:02:07  lr: 0.000066  loss: 0.0972 (0.1008)  grad: 0.0455 (0.0472)  time: 0.4762  data: 0.0035  max mem: 22446
+train: [15]  [160/400]  eta: 0:01:57  lr: 0.000064  loss: 0.0992 (0.1006)  grad: 0.0477 (0.0476)  time: 0.4699  data: 0.0033  max mem: 22446
+train: [15]  [180/400]  eta: 0:01:47  lr: 0.000063  loss: 0.1001 (0.1012)  grad: 0.0470 (0.0474)  time: 0.4729  data: 0.0035  max mem: 22446
+train: [15]  [200/400]  eta: 0:01:36  lr: 0.000062  loss: 0.1073 (0.1017)  grad: 0.0421 (0.0472)  time: 0.4624  data: 0.0033  max mem: 22446
+train: [15]  [220/400]  eta: 0:01:27  lr: 0.000061  loss: 0.0982 (0.1012)  grad: 0.0447 (0.0472)  time: 0.4770  data: 0.0035  max mem: 22446
+train: [15]  [240/400]  eta: 0:01:17  lr: 0.000059  loss: 0.0963 (0.1009)  grad: 0.0468 (0.0472)  time: 0.4701  data: 0.0035  max mem: 22446
+train: [15]  [260/400]  eta: 0:01:07  lr: 0.000058  loss: 0.0933 (0.1010)  grad: 0.0431 (0.0467)  time: 0.4603  data: 0.0034  max mem: 22446
+train: [15]  [280/400]  eta: 0:00:57  lr: 0.000057  loss: 0.0933 (0.1010)  grad: 0.0425 (0.0467)  time: 0.4649  data: 0.0035  max mem: 22446
+train: [15]  [300/400]  eta: 0:00:49  lr: 0.000056  loss: 0.0951 (0.1009)  grad: 0.0438 (0.0466)  time: 0.6397  data: 0.1902  max mem: 22446
+train: [15]  [320/400]  eta: 0:00:39  lr: 0.000054  loss: 0.0951 (0.1007)  grad: 0.0457 (0.0468)  time: 0.4576  data: 0.0031  max mem: 22446
+train: [15]  [340/400]  eta: 0:00:29  lr: 0.000053  loss: 0.0975 (0.1010)  grad: 0.0513 (0.0471)  time: 0.4633  data: 0.0035  max mem: 22446
+train: [15]  [360/400]  eta: 0:00:19  lr: 0.000052  loss: 0.0934 (0.1011)  grad: 0.0475 (0.0471)  time: 0.4737  data: 0.0036  max mem: 22446
+train: [15]  [380/400]  eta: 0:00:09  lr: 0.000051  loss: 0.0898 (0.1005)  grad: 0.0441 (0.0470)  time: 0.4598  data: 0.0035  max mem: 22446
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 0.0996 (0.1010)  grad: 0.0419 (0.0468)  time: 0.4720  data: 0.0035  max mem: 22446
+train: [15] Total time: 0:03:13 (0.4847 s / it)
+train: [15] Summary: lr: 0.000050  loss: 0.0996 (0.1010)  grad: 0.0419 (0.0468)
+eval (validation): [15]  [ 0/63]  eta: 0:03:36    time: 3.4315  data: 3.1268  max mem: 22446
+eval (validation): [15]  [20/63]  eta: 0:00:22    time: 0.3814  data: 0.0039  max mem: 22446
+eval (validation): [15]  [40/63]  eta: 0:00:10    time: 0.3853  data: 0.0042  max mem: 22446
+eval (validation): [15]  [60/63]  eta: 0:00:01    time: 0.3452  data: 0.0031  max mem: 22446
+eval (validation): [15]  [62/63]  eta: 0:00:00    time: 0.3441  data: 0.0031  max mem: 22446
+eval (validation): [15] Total time: 0:00:26 (0.4246 s / it)
+cv: [15]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.037  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:22:59  lr: nan  time: 3.4500  data: 3.1024  max mem: 22446
+train: [16]  [ 20/400]  eta: 0:03:50  lr: 0.000048  loss: 0.1053 (0.1033)  grad: 0.0430 (0.0458)  time: 0.4634  data: 0.0033  max mem: 22446
+train: [16]  [ 40/400]  eta: 0:03:12  lr: 0.000047  loss: 0.1046 (0.1042)  grad: 0.0444 (0.0464)  time: 0.4589  data: 0.0033  max mem: 22446
+train: [16]  [ 60/400]  eta: 0:02:54  lr: 0.000046  loss: 0.1032 (0.1030)  grad: 0.0431 (0.0449)  time: 0.4721  data: 0.0037  max mem: 22446
+train: [16]  [ 80/400]  eta: 0:02:40  lr: 0.000045  loss: 0.1012 (0.1026)  grad: 0.0423 (0.0447)  time: 0.4619  data: 0.0037  max mem: 22446
+train: [16]  [100/400]  eta: 0:02:27  lr: 0.000044  loss: 0.0995 (0.1023)  grad: 0.0429 (0.0444)  time: 0.4607  data: 0.0035  max mem: 22446
+train: [16]  [120/400]  eta: 0:02:16  lr: 0.000043  loss: 0.0897 (0.1010)  grad: 0.0437 (0.0444)  time: 0.4661  data: 0.0035  max mem: 22446
+train: [16]  [140/400]  eta: 0:02:06  lr: 0.000042  loss: 0.0954 (0.1009)  grad: 0.0443 (0.0449)  time: 0.4782  data: 0.0036  max mem: 22446
+train: [16]  [160/400]  eta: 0:01:56  lr: 0.000041  loss: 0.0954 (0.1005)  grad: 0.0456 (0.0450)  time: 0.4710  data: 0.0037  max mem: 22446
+train: [16]  [180/400]  eta: 0:01:46  lr: 0.000040  loss: 0.0984 (0.1004)  grad: 0.0428 (0.0448)  time: 0.4644  data: 0.0033  max mem: 22446
+train: [16]  [200/400]  eta: 0:01:36  lr: 0.000039  loss: 0.0990 (0.1011)  grad: 0.0442 (0.0452)  time: 0.4614  data: 0.0035  max mem: 22446
+train: [16]  [220/400]  eta: 0:01:26  lr: 0.000038  loss: 0.0974 (0.1009)  grad: 0.0448 (0.0449)  time: 0.4781  data: 0.0036  max mem: 22446
+train: [16]  [240/400]  eta: 0:01:16  lr: 0.000036  loss: 0.0956 (0.1006)  grad: 0.0412 (0.0446)  time: 0.4617  data: 0.0036  max mem: 22446
+train: [16]  [260/400]  eta: 0:01:06  lr: 0.000035  loss: 0.0971 (0.1011)  grad: 0.0429 (0.0446)  time: 0.4638  data: 0.0036  max mem: 22446
+train: [16]  [280/400]  eta: 0:00:57  lr: 0.000034  loss: 0.1000 (0.1010)  grad: 0.0458 (0.0451)  time: 0.4621  data: 0.0035  max mem: 22446
+train: [16]  [300/400]  eta: 0:00:48  lr: 0.000033  loss: 0.1000 (0.1012)  grad: 0.0496 (0.0454)  time: 0.6434  data: 0.1939  max mem: 22446
+train: [16]  [320/400]  eta: 0:00:38  lr: 0.000032  loss: 0.0976 (0.1011)  grad: 0.0461 (0.0455)  time: 0.4651  data: 0.0031  max mem: 22446
+train: [16]  [340/400]  eta: 0:00:29  lr: 0.000031  loss: 0.1016 (0.1017)  grad: 0.0461 (0.0457)  time: 0.4561  data: 0.0032  max mem: 22446
+train: [16]  [360/400]  eta: 0:00:19  lr: 0.000031  loss: 0.1022 (0.1015)  grad: 0.0441 (0.0457)  time: 0.4751  data: 0.0035  max mem: 22446
+train: [16]  [380/400]  eta: 0:00:09  lr: 0.000030  loss: 0.0944 (0.1010)  grad: 0.0419 (0.0455)  time: 0.4643  data: 0.0036  max mem: 22446
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 0.0922 (0.1008)  grad: 0.0451 (0.0456)  time: 0.4607  data: 0.0035  max mem: 22446
+train: [16] Total time: 0:03:12 (0.4822 s / it)
+train: [16] Summary: lr: 0.000029  loss: 0.0922 (0.1008)  grad: 0.0451 (0.0456)
+eval (validation): [16]  [ 0/63]  eta: 0:03:35    time: 3.4179  data: 3.1153  max mem: 22446
+eval (validation): [16]  [20/63]  eta: 0:00:23    time: 0.4105  data: 0.0040  max mem: 22446
+eval (validation): [16]  [40/63]  eta: 0:00:11    time: 0.4261  data: 0.0037  max mem: 22446
+eval (validation): [16]  [60/63]  eta: 0:00:01    time: 0.3383  data: 0.0034  max mem: 22446
+eval (validation): [16]  [62/63]  eta: 0:00:00    time: 0.3354  data: 0.0033  max mem: 22446
+eval (validation): [16] Total time: 0:00:27 (0.4420 s / it)
+cv: [16]  best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00')  loss: 0.037  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:22:57  lr: nan  time: 3.4439  data: 3.0922  max mem: 22446
+train: [17]  [ 20/400]  eta: 0:03:48  lr: 0.000028  loss: 0.0888 (0.0935)  grad: 0.0430 (0.0452)  time: 0.4592  data: 0.0034  max mem: 22446
+train: [17]  [ 40/400]  eta: 0:03:13  lr: 0.000027  loss: 0.0986 (0.1003)  grad: 0.0468 (0.0465)  time: 0.4704  data: 0.0037  max mem: 22446
+train: [17]  [ 60/400]  eta: 0:02:53  lr: 0.000026  loss: 0.1003 (0.1021)  grad: 0.0458 (0.0455)  time: 0.4547  data: 0.0035  max mem: 22446
+train: [17]  [ 80/400]  eta: 0:02:40  lr: 0.000025  loss: 0.1069 (0.1038)  grad: 0.0437 (0.0454)  time: 0.4749  data: 0.0036  max mem: 22446
+train: [17]  [100/400]  eta: 0:02:28  lr: 0.000024  loss: 0.1025 (0.1027)  grad: 0.0433 (0.0450)  time: 0.4682  data: 0.0037  max mem: 22446
+train: [17]  [120/400]  eta: 0:02:16  lr: 0.000023  loss: 0.0986 (0.1017)  grad: 0.0400 (0.0443)  time: 0.4584  data: 0.0036  max mem: 22446
+train: [17]  [140/400]  eta: 0:02:06  lr: 0.000023  loss: 0.0992 (0.1013)  grad: 0.0400 (0.0444)  time: 0.4816  data: 0.0037  max mem: 22446
+train: [17]  [160/400]  eta: 0:01:57  lr: 0.000022  loss: 0.0986 (0.1011)  grad: 0.0431 (0.0447)  time: 0.4875  data: 0.0038  max mem: 22446
+train: [17]  [180/400]  eta: 0:01:47  lr: 0.000021  loss: 0.0986 (0.1010)  grad: 0.0448 (0.0446)  time: 0.4871  data: 0.0037  max mem: 22446
+train: [17]  [200/400]  eta: 0:01:37  lr: 0.000020  loss: 0.0924 (0.1004)  grad: 0.0412 (0.0447)  time: 0.4652  data: 0.0035  max mem: 22446
+train: [17]  [220/400]  eta: 0:01:27  lr: 0.000019  loss: 0.0924 (0.1006)  grad: 0.0438 (0.0447)  time: 0.4675  data: 0.0035  max mem: 22446
+train: [17]  [240/400]  eta: 0:01:17  lr: 0.000019  loss: 0.0950 (0.1006)  grad: 0.0433 (0.0447)  time: 0.4782  data: 0.0035  max mem: 22446
+train: [17]  [260/400]  eta: 0:01:07  lr: 0.000018  loss: 0.0960 (0.1007)  grad: 0.0433 (0.0447)  time: 0.4749  data: 0.0035  max mem: 22446
+train: [17]  [280/400]  eta: 0:00:57  lr: 0.000017  loss: 0.0941 (0.1004)  grad: 0.0428 (0.0445)  time: 0.4717  data: 0.0036  max mem: 22446
+train: [17]  [300/400]  eta: 0:00:49  lr: 0.000016  loss: 0.0904 (0.1003)  grad: 0.0449 (0.0447)  time: 0.6522  data: 0.2033  max mem: 22446
+train: [17]  [320/400]  eta: 0:00:39  lr: 0.000016  loss: 0.0937 (0.1002)  grad: 0.0465 (0.0449)  time: 0.4555  data: 0.0034  max mem: 22446
+train: [17]  [340/400]  eta: 0:00:29  lr: 0.000015  loss: 0.0976 (0.1002)  grad: 0.0426 (0.0446)  time: 0.4670  data: 0.0034  max mem: 22446
+train: [17]  [360/400]  eta: 0:00:19  lr: 0.000014  loss: 0.0960 (0.0998)  grad: 0.0407 (0.0445)  time: 0.4688  data: 0.0036  max mem: 22446
+train: [17]  [380/400]  eta: 0:00:09  lr: 0.000014  loss: 0.0960 (0.1000)  grad: 0.0420 (0.0444)  time: 0.4629  data: 0.0036  max mem: 22446
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 0.0966 (0.0997)  grad: 0.0447 (0.0444)  time: 0.4680  data: 0.0036  max mem: 22446
+train: [17] Total time: 0:03:14 (0.4865 s / it)
+train: [17] Summary: lr: 0.000013  loss: 0.0966 (0.0997)  grad: 0.0447 (0.0444)
+eval (validation): [17]  [ 0/63]  eta: 0:03:23    time: 3.2368  data: 2.9509  max mem: 22446
+eval (validation): [17]  [20/63]  eta: 0:00:22    time: 0.3914  data: 0.0046  max mem: 22446
+eval (validation): [17]  [40/63]  eta: 0:00:10    time: 0.3782  data: 0.0036  max mem: 22446
+eval (validation): [17]  [60/63]  eta: 0:00:01    time: 0.3620  data: 0.0037  max mem: 22446
+eval (validation): [17]  [62/63]  eta: 0:00:00    time: 0.3611  data: 0.0036  max mem: 22446
+eval (validation): [17] Total time: 0:00:26 (0.4266 s / it)
+cv: [17]  best hparam: (1, 1.0) (024) ('024_lr1.0e+00_wd1.0e+00')  loss: 0.033  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:23:25  lr: nan  time: 3.5140  data: 3.1123  max mem: 22446
+train: [18]  [ 20/400]  eta: 0:03:54  lr: 0.000012  loss: 0.0869 (0.0945)  grad: 0.0415 (0.0428)  time: 0.4715  data: 0.0031  max mem: 22446
+train: [18]  [ 40/400]  eta: 0:03:16  lr: 0.000012  loss: 0.0964 (0.0979)  grad: 0.0441 (0.0434)  time: 0.4721  data: 0.0032  max mem: 22446
+train: [18]  [ 60/400]  eta: 0:02:57  lr: 0.000011  loss: 0.0951 (0.0979)  grad: 0.0452 (0.0435)  time: 0.4755  data: 0.0035  max mem: 22446
+train: [18]  [ 80/400]  eta: 0:02:43  lr: 0.000011  loss: 0.0963 (0.0989)  grad: 0.0419 (0.0437)  time: 0.4707  data: 0.0036  max mem: 22446
+train: [18]  [100/400]  eta: 0:02:30  lr: 0.000010  loss: 0.0947 (0.0989)  grad: 0.0436 (0.0440)  time: 0.4607  data: 0.0036  max mem: 22446
+train: [18]  [120/400]  eta: 0:02:18  lr: 0.000009  loss: 0.0944 (0.0976)  grad: 0.0432 (0.0440)  time: 0.4730  data: 0.0035  max mem: 22446
+train: [18]  [140/400]  eta: 0:02:08  lr: 0.000009  loss: 0.0986 (0.0983)  grad: 0.0428 (0.0442)  time: 0.4758  data: 0.0037  max mem: 22446
+train: [18]  [160/400]  eta: 0:01:57  lr: 0.000008  loss: 0.0989 (0.0982)  grad: 0.0417 (0.0441)  time: 0.4703  data: 0.0035  max mem: 22446
+train: [18]  [180/400]  eta: 0:01:47  lr: 0.000008  loss: 0.0941 (0.0979)  grad: 0.0449 (0.0445)  time: 0.4572  data: 0.0038  max mem: 22446
+train: [18]  [200/400]  eta: 0:01:36  lr: 0.000007  loss: 0.0935 (0.0974)  grad: 0.0449 (0.0446)  time: 0.4686  data: 0.0035  max mem: 22446
+train: [18]  [220/400]  eta: 0:01:26  lr: 0.000007  loss: 0.0887 (0.0969)  grad: 0.0442 (0.0443)  time: 0.4664  data: 0.0036  max mem: 22446
+train: [18]  [240/400]  eta: 0:01:16  lr: 0.000006  loss: 0.0937 (0.0968)  grad: 0.0421 (0.0445)  time: 0.4558  data: 0.0034  max mem: 22446
+train: [18]  [260/400]  eta: 0:01:07  lr: 0.000006  loss: 0.0953 (0.0974)  grad: 0.0431 (0.0444)  time: 0.4697  data: 0.0035  max mem: 22446
+train: [18]  [280/400]  eta: 0:00:57  lr: 0.000006  loss: 0.0961 (0.0975)  grad: 0.0462 (0.0448)  time: 0.4647  data: 0.0036  max mem: 22446
+train: [18]  [300/400]  eta: 0:00:48  lr: 0.000005  loss: 0.0952 (0.0972)  grad: 0.0467 (0.0449)  time: 0.6215  data: 0.1738  max mem: 22446
+train: [18]  [320/400]  eta: 0:00:39  lr: 0.000005  loss: 0.0943 (0.0971)  grad: 0.0446 (0.0449)  time: 0.4757  data: 0.0033  max mem: 22446
+train: [18]  [340/400]  eta: 0:00:29  lr: 0.000004  loss: 0.0964 (0.0974)  grad: 0.0433 (0.0448)  time: 0.4655  data: 0.0035  max mem: 22446
+train: [18]  [360/400]  eta: 0:00:19  lr: 0.000004  loss: 0.0964 (0.0972)  grad: 0.0444 (0.0450)  time: 0.4736  data: 0.0036  max mem: 22446
+train: [18]  [380/400]  eta: 0:00:09  lr: 0.000004  loss: 0.0959 (0.0968)  grad: 0.0451 (0.0449)  time: 0.4711  data: 0.0036  max mem: 22446
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 0.0934 (0.0966)  grad: 0.0424 (0.0449)  time: 0.4561  data: 0.0036  max mem: 22446
+train: [18] Total time: 0:03:13 (0.4838 s / it)
+train: [18] Summary: lr: 0.000003  loss: 0.0934 (0.0966)  grad: 0.0424 (0.0449)
+eval (validation): [18]  [ 0/63]  eta: 0:03:35    time: 3.4144  data: 3.1118  max mem: 22446
+eval (validation): [18]  [20/63]  eta: 0:00:24    time: 0.4195  data: 0.0051  max mem: 22446
+eval (validation): [18]  [40/63]  eta: 0:00:10    time: 0.3799  data: 0.0033  max mem: 22446
+eval (validation): [18]  [60/63]  eta: 0:00:01    time: 0.3303  data: 0.0033  max mem: 22446
+eval (validation): [18]  [62/63]  eta: 0:00:00    time: 0.3277  data: 0.0032  max mem: 22446
+eval (validation): [18] Total time: 0:00:26 (0.4275 s / it)
+cv: [18]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.034  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:25:39  lr: nan  time: 3.8477  data: 3.4987  max mem: 22446
+train: [19]  [ 20/400]  eta: 0:03:59  lr: 0.000003  loss: 0.0993 (0.0948)  grad: 0.0386 (0.0386)  time: 0.4699  data: 0.0027  max mem: 22446
+train: [19]  [ 40/400]  eta: 0:03:17  lr: 0.000003  loss: 0.0993 (0.0969)  grad: 0.0420 (0.0434)  time: 0.4629  data: 0.0030  max mem: 22446
+train: [19]  [ 60/400]  eta: 0:02:58  lr: 0.000002  loss: 0.0915 (0.0964)  grad: 0.0440 (0.0430)  time: 0.4719  data: 0.0037  max mem: 22446
+train: [19]  [ 80/400]  eta: 0:02:43  lr: 0.000002  loss: 0.0915 (0.0965)  grad: 0.0433 (0.0437)  time: 0.4714  data: 0.0036  max mem: 22446
+train: [19]  [100/400]  eta: 0:02:31  lr: 0.000002  loss: 0.0955 (0.0980)  grad: 0.0439 (0.0436)  time: 0.4748  data: 0.0036  max mem: 22446
+train: [19]  [120/400]  eta: 0:02:19  lr: 0.000002  loss: 0.0956 (0.0970)  grad: 0.0419 (0.0433)  time: 0.4710  data: 0.0036  max mem: 22446
+train: [19]  [140/400]  eta: 0:02:08  lr: 0.000001  loss: 0.0946 (0.0966)  grad: 0.0414 (0.0433)  time: 0.4819  data: 0.0037  max mem: 22446
+train: [19]  [160/400]  eta: 0:01:58  lr: 0.000001  loss: 0.0963 (0.0967)  grad: 0.0421 (0.0433)  time: 0.4701  data: 0.0037  max mem: 22446
+train: [19]  [180/400]  eta: 0:01:47  lr: 0.000001  loss: 0.0975 (0.0969)  grad: 0.0421 (0.0434)  time: 0.4626  data: 0.0036  max mem: 22446
+train: [19]  [200/400]  eta: 0:01:37  lr: 0.000001  loss: 0.0945 (0.0972)  grad: 0.0437 (0.0436)  time: 0.4781  data: 0.0035  max mem: 22446
+train: [19]  [220/400]  eta: 0:01:27  lr: 0.000001  loss: 0.0983 (0.0980)  grad: 0.0463 (0.0438)  time: 0.4853  data: 0.0035  max mem: 22446
+train: [19]  [240/400]  eta: 0:01:17  lr: 0.000001  loss: 0.0983 (0.0976)  grad: 0.0424 (0.0438)  time: 0.4731  data: 0.0036  max mem: 22446
+train: [19]  [260/400]  eta: 0:01:07  lr: 0.000000  loss: 0.0940 (0.0976)  grad: 0.0426 (0.0439)  time: 0.4697  data: 0.0038  max mem: 22446
+train: [19]  [280/400]  eta: 0:00:58  lr: 0.000000  loss: 0.0995 (0.0980)  grad: 0.0426 (0.0439)  time: 0.4724  data: 0.0038  max mem: 22446
+train: [19]  [300/400]  eta: 0:00:49  lr: 0.000000  loss: 0.0998 (0.0981)  grad: 0.0422 (0.0439)  time: 0.6435  data: 0.1916  max mem: 22446
+train: [19]  [320/400]  eta: 0:00:39  lr: 0.000000  loss: 0.0930 (0.0978)  grad: 0.0442 (0.0441)  time: 0.4681  data: 0.0033  max mem: 22446
+train: [19]  [340/400]  eta: 0:00:29  lr: 0.000000  loss: 0.0911 (0.0975)  grad: 0.0431 (0.0441)  time: 0.4620  data: 0.0030  max mem: 22446
+train: [19]  [360/400]  eta: 0:00:19  lr: 0.000000  loss: 0.0934 (0.0975)  grad: 0.0429 (0.0442)  time: 0.4739  data: 0.0035  max mem: 22446
+train: [19]  [380/400]  eta: 0:00:09  lr: 0.000000  loss: 0.0969 (0.0975)  grad: 0.0436 (0.0441)  time: 0.4688  data: 0.0035  max mem: 22446
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 0.0977 (0.0974)  grad: 0.0421 (0.0441)  time: 0.4633  data: 0.0035  max mem: 22446
+train: [19] Total time: 0:03:15 (0.4885 s / it)
+train: [19] Summary: lr: 0.000000  loss: 0.0977 (0.0974)  grad: 0.0421 (0.0441)
+eval (validation): [19]  [ 0/63]  eta: 0:03:29    time: 3.3190  data: 3.0798  max mem: 22446
+eval (validation): [19]  [20/63]  eta: 0:00:22    time: 0.3778  data: 0.0038  max mem: 22446
+eval (validation): [19]  [40/63]  eta: 0:00:10    time: 0.3582  data: 0.0032  max mem: 22446
+eval (validation): [19]  [60/63]  eta: 0:00:01    time: 0.3490  data: 0.0032  max mem: 22446
+eval (validation): [19]  [62/63]  eta: 0:00:00    time: 0.3466  data: 0.0032  max mem: 22446
+eval (validation): [19] Total time: 0:00:25 (0.4125 s / it)
+cv: [19]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 0.034  acc: 0.991  f1: 0.989
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.9905753968253969, "hparam": [2.7, 1.0], "hparam_id": 30, "epoch": 19, "is_best": false, "best_score": 0.9910714285714286}
+eval (train): [20]  [  0/297]  eta: 0:15:21    time: 3.1025  data: 2.8191  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:28    time: 0.4068  data: 0.0034  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:01:56    time: 0.3700  data: 0.0033  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:41    time: 0.3791  data: 0.0037  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:29    time: 0.3657  data: 0.0038  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:19    time: 0.3711  data: 0.0036  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:10    time: 0.3696  data: 0.0038  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:01:02    time: 0.3822  data: 0.0036  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:53    time: 0.3709  data: 0.0035  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:45    time: 0.3691  data: 0.0037  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:37    time: 0.3618  data: 0.0036  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:29    time: 0.3893  data: 0.0036  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:22    time: 0.4020  data: 0.0038  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:14    time: 0.4004  data: 0.0040  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:06    time: 0.4098  data: 0.0042  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3592  data: 0.0036  max mem: 22446
+eval (train): [20] Total time: 0:01:56 (0.3909 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:03:19    time: 3.1688  data: 2.8713  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:21    time: 0.3622  data: 0.0116  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:09    time: 0.3524  data: 0.0038  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3643  data: 0.0028  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3649  data: 0.0026  max mem: 22446
+eval (validation): [20] Total time: 0:00:25 (0.4082 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:04:11    time: 3.1872  data: 2.9039  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:29    time: 0.3709  data: 0.0034  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:16    time: 0.3609  data: 0.0031  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:07    time: 0.3725  data: 0.0041  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3218  data: 0.0031  max mem: 22446
+eval (test): [20] Total time: 0:00:31 (0.3975 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.9910714285714286, "hparam": [4.3, 1.0], "hparam_id": 33, "epoch": 10, "is_best": true, "best_score": 0.9910714285714286}
+eval (train): [20]  [  0/297]  eta: 0:15:42    time: 3.1720  data: 2.8740  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:26    time: 0.3980  data: 0.0030  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:02:01    time: 0.4098  data: 0.0039  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:43    time: 0.3667  data: 0.0035  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:31    time: 0.3820  data: 0.0037  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:24    time: 0.4390  data: 0.0039  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:13    time: 0.3636  data: 0.0036  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:01:04    time: 0.3939  data: 0.0038  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:55    time: 0.3702  data: 0.0038  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:46    time: 0.3518  data: 0.0035  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:38    time: 0.3639  data: 0.0034  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:30    time: 0.3500  data: 0.0034  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:22    time: 0.3896  data: 0.0037  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:14    time: 0.3710  data: 0.0033  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:06    time: 0.3803  data: 0.0037  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3467  data: 0.0033  max mem: 22446
+eval (train): [20] Total time: 0:01:55 (0.3900 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:03:20    time: 3.1901  data: 2.8919  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:21    time: 0.3545  data: 0.0041  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:10    time: 0.3812  data: 0.0030  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3563  data: 0.0034  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3527  data: 0.0029  max mem: 22446
+eval (validation): [20] Total time: 0:00:26 (0.4133 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:04:05    time: 3.1021  data: 2.8274  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:30    time: 0.3853  data: 0.0053  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:17    time: 0.3548  data: 0.0031  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:08    time: 0.3945  data: 0.0036  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3404  data: 0.0035  max mem: 22446
+eval (test): [20] Total time: 0:00:32 (0.4085 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   |      10 | 0.00129 | 0.05 |          33 | [4.3, 1.0] | train      | 0.0001971 | 1       | 0         | 1       | 0         |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      10 | 0.00129 | 0.05 |          33 | [4.3, 1.0] | validation | 0.034484  | 0.99107 | 0.0014769 | 0.99043 | 0.0016872 |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      10 | 0.00129 | 0.05 |          33 | [4.3, 1.0] | test       | 0.052408  | 0.98532 | 0.0016793 | 0.98216 | 0.0022675 |
+
+
+done! total time: 1:20:34

data_scaling/n1600_2/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 n1600_2; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_2/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/n1600_2/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/n1600_2/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null

data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 9, "eval/id_best": 20, "eval/lr_best": 0.000156, "eval/wd_best": 0.05, "eval/train/loss": 2.15356707572937, "eval/train/acc": 0.35210055625557024, "eval/train/acc_std": 0.00239365501018213, "eval/train/f1": 0.29116828032140274, "eval/train/f1_std": 0.0025240654563469436, "eval/validation/loss": 2.4080796241760254, "eval/validation/acc": 0.2809154669619786, "eval/validation/acc_std": 0.005195654784022603, "eval/validation/f1": 0.21711416929166086, "eval/validation/f1_std": 0.004896303689000226, "eval/test/loss": 2.2583935260772705, "eval/test/acc": 0.3144712430426716, "eval/test/acc_std": 0.005443737680268145, "eval/test/f1": 0.24011658233127722, "eval/test/f1_std": 0.005197456601917186, "eval/testid/loss": 2.302957057952881, "eval/testid/acc": 0.2943898207056102, "eval/testid/acc_std": 0.005361265369718957, "eval/testid/f1": 0.23200289884076616, "eval/testid/f1_std": 0.005260274209010906}

data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 9, "eval/best/id_best": 20, "eval/best/lr_best": 0.000156, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.15356707572937, "eval/best/train/acc": 0.35210055625557024, "eval/best/train/acc_std": 0.00239365501018213, "eval/best/train/f1": 0.29116828032140274, "eval/best/train/f1_std": 0.0025240654563469436, "eval/best/validation/loss": 2.4080796241760254, "eval/best/validation/acc": 0.2809154669619786, "eval/best/validation/acc_std": 0.005195654784022603, "eval/best/validation/f1": 0.21711416929166086, "eval/best/validation/f1_std": 0.004896303689000226, "eval/best/test/loss": 2.2583935260772705, "eval/best/test/acc": 0.3144712430426716, "eval/best/test/acc_std": 0.005443737680268145, "eval/best/test/f1": 0.24011658233127722, "eval/best/test/f1_std": 0.005197456601917186, "eval/best/testid/loss": 2.302957057952881, "eval/best/testid/acc": 0.2943898207056102, "eval/best/testid/acc_std": 0.005361265369718957, "eval/best/testid/f1": 0.23200289884076616, "eval/best/testid/f1_std": 0.005260274209010906}

data_scaling/n1600_2/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": 2.064777135848999, "eval/last/train/acc": 0.37948308184025326, "eval/last/train/acc_std": 0.002434940063542126, "eval/last/train/f1": 0.3238407947909492, "eval/last/train/f1_std": 0.0026363171754022387, "eval/last/validation/loss": 2.4168779850006104, "eval/last/validation/acc": 0.27593207825765964, "eval/last/validation/acc_std": 0.005262876378711739, "eval/last/validation/f1": 0.2179382766803268, "eval/last/validation/f1_std": 0.005082834230840219, "eval/last/test/loss": 2.246542453765869, "eval/last/test/acc": 0.3144712430426716, "eval/last/test/acc_std": 0.005464534112605879, "eval/last/test/f1": 0.24478123920677533, "eval/last/test/f1_std": 0.005296571386319866, "eval/last/testid/loss": 2.2572391033172607, "eval/last/testid/acc": 0.3125120493541546, "eval/last/testid/acc_std": 0.005627194296653273, "eval/last/testid/f1": 0.25326776283952385, "eval/last/testid/f1_std": 0.005628573746060338}

data_scaling/n1600_2/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,9,0.000156,0.05,20,"[0.52, 1.0]",train,2.15356707572937,0.35210055625557024,0.00239365501018213,0.29116828032140274,0.0025240654563469436
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",validation,2.4080796241760254,0.2809154669619786,0.005195654784022603,0.21711416929166086,0.004896303689000226
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",test,2.2583935260772705,0.3144712430426716,0.005443737680268145,0.24011658233127722,0.005197456601917186
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",testid,2.302957057952881,0.2943898207056102,0.005361265369718957,0.23200289884076616,0.005260274209010906

data_scaling/n1600_2/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,9,0.000156,0.05,20,"[0.52, 1.0]",train,2.15356707572937,0.35210055625557024,0.00239365501018213,0.29116828032140274,0.0025240654563469436
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",validation,2.4080796241760254,0.2809154669619786,0.005195654784022603,0.21711416929166086,0.004896303689000226
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",test,2.2583935260772705,0.3144712430426716,0.005443737680268145,0.24011658233127722,0.005197456601917186
+flat_mae,patch,attn,nsd_cococlip,best,9,0.000156,0.05,20,"[0.52, 1.0]",testid,2.302957057952881,0.2943898207056102,0.005361265369718957,0.23200289884076616,0.005260274209010906

data_scaling/n1600_2/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,2.064777135848999,0.37948308184025326,0.002434940063542126,0.3238407947909492,0.0026363171754022387
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",validation,2.4168779850006104,0.27593207825765964,0.005262876378711739,0.2179382766803268,0.005082834230840219
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",test,2.246542453765869,0.3144712430426716,0.005464534112605879,0.24478123920677533,0.005296571386319866
+flat_mae,patch,attn,nsd_cococlip,last,19,0.00013199999999999998,0.05,19,"[0.44, 1.0]",testid,2.2572391033172607,0.3125120493541546,0.005627194296653273,0.25326776283952385,0.005628573746060338

data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/log.txt

@@ -0,0 +1,964 @@
+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 20:43:35
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_2/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/n1600_2/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/n1600_2/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: 0:23:09  lr: nan  time: 3.4730  data: 2.9792  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:47  lr: 0.000003  loss: 3.1823 (3.1872)  grad: 0.1940 (0.1954)  time: 0.4562  data: 0.0033  max mem: 22448
+train: [0]  [ 40/400]  eta: 0:03:12  lr: 0.000006  loss: 3.1747 (3.1809)  grad: 0.1922 (0.1942)  time: 0.4643  data: 0.0038  max mem: 22448
+train: [0]  [ 60/400]  eta: 0:02:53  lr: 0.000009  loss: 3.1746 (3.1793)  grad: 0.1878 (0.1927)  time: 0.4602  data: 0.0044  max mem: 22448
+train: [0]  [ 80/400]  eta: 0:02:38  lr: 0.000012  loss: 3.1673 (3.1757)  grad: 0.1861 (0.1905)  time: 0.4489  data: 0.0041  max mem: 22448
+train: [0]  [100/400]  eta: 0:02:27  lr: 0.000015  loss: 3.1684 (3.1753)  grad: 0.1810 (0.1882)  time: 0.4739  data: 0.0044  max mem: 22448
+train: [0]  [120/400]  eta: 0:02:15  lr: 0.000018  loss: 3.1707 (3.1738)  grad: 0.1684 (0.1853)  time: 0.4510  data: 0.0042  max mem: 22448
+train: [0]  [140/400]  eta: 0:02:04  lr: 0.000021  loss: 3.1590 (3.1719)  grad: 0.1704 (0.1847)  time: 0.4581  data: 0.0041  max mem: 22448
+train: [0]  [160/400]  eta: 0:01:54  lr: 0.000024  loss: 3.1554 (3.1689)  grad: 0.1898 (0.1859)  time: 0.4616  data: 0.0041  max mem: 22448
+train: [0]  [180/400]  eta: 0:01:44  lr: 0.000027  loss: 3.1387 (3.1663)  grad: 0.1804 (0.1853)  time: 0.4510  data: 0.0043  max mem: 22448
+train: [0]  [200/400]  eta: 0:01:34  lr: 0.000030  loss: 3.1575 (3.1663)  grad: 0.1694 (0.1835)  time: 0.4533  data: 0.0043  max mem: 22448
+train: [0]  [220/400]  eta: 0:01:24  lr: 0.000033  loss: 3.1611 (3.1660)  grad: 0.1677 (0.1822)  time: 0.4605  data: 0.0042  max mem: 22448
+train: [0]  [240/400]  eta: 0:01:15  lr: 0.000036  loss: 3.1590 (3.1652)  grad: 0.1682 (0.1812)  time: 0.4515  data: 0.0044  max mem: 22448
+train: [0]  [260/400]  eta: 0:01:05  lr: 0.000039  loss: 3.1448 (3.1636)  grad: 0.1669 (0.1799)  time: 0.4587  data: 0.0044  max mem: 22448
+train: [0]  [280/400]  eta: 0:00:56  lr: 0.000042  loss: 3.1336 (3.1613)  grad: 0.1657 (0.1790)  time: 0.4518  data: 0.0043  max mem: 22448
+train: [0]  [300/400]  eta: 0:00:46  lr: 0.000045  loss: 3.1185 (3.1574)  grad: 0.1667 (0.1786)  time: 0.4564  data: 0.0041  max mem: 22448
+train: [0]  [320/400]  eta: 0:00:37  lr: 0.000048  loss: 3.1048 (3.1548)  grad: 0.1776 (0.1788)  time: 0.4564  data: 0.0042  max mem: 22448
+train: [0]  [340/400]  eta: 0:00:27  lr: 0.000051  loss: 3.1107 (3.1520)  grad: 0.1794 (0.1784)  time: 0.4524  data: 0.0042  max mem: 22448
+train: [0]  [360/400]  eta: 0:00:18  lr: 0.000054  loss: 3.0924 (3.1485)  grad: 0.1757 (0.1788)  time: 0.4589  data: 0.0042  max mem: 22448
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 3.0889 (3.1457)  grad: 0.1858 (0.1794)  time: 0.4525  data: 0.0042  max mem: 22448
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.0962 (3.1435)  grad: 0.1879 (0.1798)  time: 0.4684  data: 0.0043  max mem: 22448
+train: [0] Total time: 0:03:06 (0.4651 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.0962 (3.1435)  grad: 0.1879 (0.1798)
+eval (validation): [0]  [ 0/85]  eta: 0:04:37    time: 3.2606  data: 3.0182  max mem: 22448
+eval (validation): [0]  [20/85]  eta: 0:00:32    time: 0.3626  data: 0.0046  max mem: 22448
+eval (validation): [0]  [40/85]  eta: 0:00:19    time: 0.3516  data: 0.0040  max mem: 22448
+eval (validation): [0]  [60/85]  eta: 0:00:10    time: 0.3515  data: 0.0044  max mem: 22448
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3325  data: 0.0042  max mem: 22448
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3262  data: 0.0040  max mem: 22448
+eval (validation): [0] Total time: 0:00:32 (0.3859 s / it)
+cv: [0]  best hparam: (36, 1.0) (046) ('046_lr3.6e+01_wd1.0e+00')  loss: 2.641  acc: 0.223  f1: 0.142
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:37  lr: nan  time: 3.3932  data: 3.0102  max mem: 22448
+train: [1]  [ 20/400]  eta: 0:03:51  lr: 0.000063  loss: 3.0482 (3.0469)  grad: 0.1877 (0.1833)  time: 0.4706  data: 0.0032  max mem: 22448
+train: [1]  [ 40/400]  eta: 0:03:12  lr: 0.000066  loss: 3.0558 (3.0508)  grad: 0.1803 (0.1800)  time: 0.4577  data: 0.0041  max mem: 22448
+train: [1]  [ 60/400]  eta: 0:02:52  lr: 0.000069  loss: 3.0323 (3.0377)  grad: 0.1757 (0.1803)  time: 0.4490  data: 0.0041  max mem: 22448
+train: [1]  [ 80/400]  eta: 0:02:40  lr: 0.000072  loss: 3.0216 (3.0372)  grad: 0.1816 (0.1827)  time: 0.4799  data: 0.0043  max mem: 22448
+train: [1]  [100/400]  eta: 0:02:27  lr: 0.000075  loss: 3.0250 (3.0333)  grad: 0.1861 (0.1850)  time: 0.4559  data: 0.0041  max mem: 22448
+train: [1]  [120/400]  eta: 0:02:16  lr: 0.000078  loss: 3.0250 (3.0310)  grad: 0.1947 (0.1868)  time: 0.4585  data: 0.0041  max mem: 22448
+train: [1]  [140/400]  eta: 0:02:05  lr: 0.000081  loss: 3.0189 (3.0286)  grad: 0.1934 (0.1880)  time: 0.4652  data: 0.0041  max mem: 22448
+train: [1]  [160/400]  eta: 0:01:55  lr: 0.000084  loss: 3.0157 (3.0292)  grad: 0.1920 (0.1883)  time: 0.4516  data: 0.0040  max mem: 22448
+train: [1]  [180/400]  eta: 0:01:44  lr: 0.000087  loss: 3.0157 (3.0288)  grad: 0.1879 (0.1889)  time: 0.4529  data: 0.0041  max mem: 22448
+train: [1]  [200/400]  eta: 0:01:34  lr: 0.000090  loss: 2.9991 (3.0264)  grad: 0.1922 (0.1898)  time: 0.4474  data: 0.0042  max mem: 22448
+train: [1]  [220/400]  eta: 0:01:24  lr: 0.000093  loss: 2.9692 (3.0198)  grad: 0.2071 (0.1923)  time: 0.4424  data: 0.0041  max mem: 22448
+train: [1]  [240/400]  eta: 0:01:15  lr: 0.000096  loss: 2.9689 (3.0169)  grad: 0.2005 (0.1925)  time: 0.4651  data: 0.0042  max mem: 22448
+train: [1]  [260/400]  eta: 0:01:05  lr: 0.000099  loss: 2.9831 (3.0157)  grad: 0.2002 (0.1936)  time: 0.4604  data: 0.0042  max mem: 22448
+train: [1]  [280/400]  eta: 0:00:56  lr: 0.000102  loss: 2.9792 (3.0126)  grad: 0.2031 (0.1944)  time: 0.4477  data: 0.0041  max mem: 22448
+train: [1]  [300/400]  eta: 0:00:46  lr: 0.000105  loss: 2.9698 (3.0120)  grad: 0.2033 (0.1956)  time: 0.4536  data: 0.0041  max mem: 22448
+train: [1]  [320/400]  eta: 0:00:37  lr: 0.000108  loss: 2.9574 (3.0083)  grad: 0.2103 (0.1965)  time: 0.4479  data: 0.0042  max mem: 22448
+train: [1]  [340/400]  eta: 0:00:27  lr: 0.000111  loss: 2.9405 (3.0040)  grad: 0.2054 (0.1970)  time: 0.4509  data: 0.0041  max mem: 22448
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 2.9349 (3.0022)  grad: 0.2028 (0.1974)  time: 0.4529  data: 0.0040  max mem: 22448
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 2.9439 (2.9987)  grad: 0.2131 (0.1985)  time: 0.4610  data: 0.0040  max mem: 22448
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 2.9426 (2.9970)  grad: 0.2200 (0.2004)  time: 0.4612  data: 0.0041  max mem: 22448
+train: [1] Total time: 0:03:05 (0.4641 s / it)
+train: [1] Summary: lr: 0.000120  loss: 2.9426 (2.9970)  grad: 0.2200 (0.2004)
+eval (validation): [1]  [ 0/85]  eta: 0:04:34    time: 3.2330  data: 2.9556  max mem: 22448
+eval (validation): [1]  [20/85]  eta: 0:00:32    time: 0.3631  data: 0.0060  max mem: 22448
+eval (validation): [1]  [40/85]  eta: 0:00:19    time: 0.3497  data: 0.0036  max mem: 22448
+eval (validation): [1]  [60/85]  eta: 0:00:10    time: 0.3461  data: 0.0042  max mem: 22448
+eval (validation): [1]  [80/85]  eta: 0:00:01    time: 0.3245  data: 0.0043  max mem: 22448
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.3202  data: 0.0042  max mem: 22448
+eval (validation): [1] Total time: 0:00:32 (0.3818 s / it)
+cv: [1]  best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00')  loss: 2.490  acc: 0.246  f1: 0.181
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:23:05  lr: nan  time: 3.4638  data: 3.0694  max mem: 22448
+train: [2]  [ 20/400]  eta: 0:03:55  lr: 0.000123  loss: 2.9809 (2.9619)  grad: 0.2647 (0.2720)  time: 0.4778  data: 0.0042  max mem: 22448
+train: [2]  [ 40/400]  eta: 0:03:14  lr: 0.000126  loss: 2.9725 (2.9624)  grad: 0.2605 (0.2627)  time: 0.4588  data: 0.0045  max mem: 22448
+train: [2]  [ 60/400]  eta: 0:02:56  lr: 0.000129  loss: 2.9666 (2.9597)  grad: 0.2428 (0.2582)  time: 0.4699  data: 0.0043  max mem: 22448
+train: [2]  [ 80/400]  eta: 0:02:40  lr: 0.000132  loss: 2.9457 (2.9720)  grad: 0.2861 (0.2970)  time: 0.4544  data: 0.0043  max mem: 22448
+train: [2]  [100/400]  eta: 0:02:26  lr: 0.000135  loss: 3.1073 (3.0334)  grad: 0.6328 (0.4338)  time: 0.4392  data: 0.0041  max mem: 22448
+WARNING: classifier 48 (50, 1.0) diverged (loss=77.90 > 63.56) at step 457. Freezing.
+train: [2]  [120/400]  eta: 0:02:16  lr: 0.000138  loss: 3.3690 (3.1358)  grad: 1.1020 (0.5774)  time: 0.4774  data: 0.0044  max mem: 22448
+train: [2]  [140/400]  eta: 0:02:05  lr: 0.000141  loss: 2.9726 (3.1090)  grad: 0.2475 (0.5298)  time: 0.4569  data: 0.0042  max mem: 22448
+train: [2]  [160/400]  eta: 0:01:55  lr: 0.000144  loss: 2.9726 (3.1037)  grad: 0.2722 (0.5506)  time: 0.4535  data: 0.0041  max mem: 22448
+train: [2]  [180/400]  eta: 0:01:44  lr: 0.000147  loss: 3.2309 (3.1356)  grad: 0.9312 (0.6057)  time: 0.4556  data: 0.0040  max mem: 22448
+WARNING: classifier 47 (43, 1.0) diverged (loss=74.30 > 63.56) at step 497. Freezing.
+train: [2]  [200/400]  eta: 0:01:34  lr: 0.000150  loss: 3.4198 (3.1721)  grad: 1.0744 (0.6590)  time: 0.4456  data: 0.0043  max mem: 22448
+train: [2]  [220/400]  eta: 0:01:24  lr: 0.000153  loss: 2.9148 (3.1480)  grad: 0.2389 (0.6201)  time: 0.4535  data: 0.0041  max mem: 22448
+train: [2]  [240/400]  eta: 0:01:15  lr: 0.000156  loss: 2.9148 (3.1297)  grad: 0.2284 (0.5879)  time: 0.4601  data: 0.0041  max mem: 22448
+train: [2]  [260/400]  eta: 0:01:05  lr: 0.000159  loss: 2.9173 (3.1114)  grad: 0.2282 (0.5599)  time: 0.4460  data: 0.0043  max mem: 22448
+train: [2]  [280/400]  eta: 0:00:56  lr: 0.000162  loss: 2.8858 (3.0957)  grad: 0.2482 (0.5407)  time: 0.4621  data: 0.0043  max mem: 22448
+train: [2]  [300/400]  eta: 0:00:46  lr: 0.000165  loss: 2.9523 (3.0975)  grad: 0.3308 (0.5561)  time: 0.4496  data: 0.0043  max mem: 22448
+train: [2]  [320/400]  eta: 0:00:37  lr: 0.000168  loss: 3.3174 (3.1347)  grad: 1.1231 (0.6102)  time: 0.4519  data: 0.0043  max mem: 22448
+WARNING: classifier 46 (36, 1.0) diverged (loss=68.20 > 63.56) at step 561. Freezing.
+train: [2]  [340/400]  eta: 0:00:27  lr: 0.000171  loss: 3.3174 (3.1291)  grad: 1.1231 (0.5985)  time: 0.4415  data: 0.0043  max mem: 22448
+train: [2]  [360/400]  eta: 0:00:18  lr: 0.000174  loss: 2.8845 (3.1156)  grad: 0.2162 (0.5772)  time: 0.4478  data: 0.0042  max mem: 22448
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 2.9000 (3.1041)  grad: 0.2193 (0.5589)  time: 0.4560  data: 0.0042  max mem: 22448
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 2.8982 (3.0918)  grad: 0.2278 (0.5418)  time: 0.4584  data: 0.0041  max mem: 22448
+train: [2] Total time: 0:03:05 (0.4637 s / it)
+train: [2] Summary: lr: 0.000180  loss: 2.8982 (3.0918)  grad: 0.2278 (0.5418)
+eval (validation): [2]  [ 0/85]  eta: 0:04:38    time: 3.2724  data: 2.9955  max mem: 22448
+eval (validation): [2]  [20/85]  eta: 0:00:31    time: 0.3453  data: 0.0143  max mem: 22448
+eval (validation): [2]  [40/85]  eta: 0:00:18    time: 0.3440  data: 0.0041  max mem: 22448
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3354  data: 0.0040  max mem: 22448
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3268  data: 0.0041  max mem: 22448
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3177  data: 0.0039  max mem: 22448
+eval (validation): [2] Total time: 0:00:31 (0.3757 s / it)
+cv: [2]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 2.486  acc: 0.260  f1: 0.188
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:22:48  lr: nan  time: 3.4208  data: 3.0374  max mem: 22448
+train: [3]  [ 20/400]  eta: 0:03:48  lr: 0.000183  loss: 2.8095 (2.8298)  grad: 0.2160 (0.2141)  time: 0.4602  data: 0.0035  max mem: 22448
+train: [3]  [ 40/400]  eta: 0:03:10  lr: 0.000186  loss: 2.8499 (2.8568)  grad: 0.2233 (0.2250)  time: 0.4557  data: 0.0045  max mem: 22448
+train: [3]  [ 60/400]  eta: 0:02:51  lr: 0.000189  loss: 2.8476 (2.8542)  grad: 0.2338 (0.2280)  time: 0.4533  data: 0.0045  max mem: 22448
+train: [3]  [ 80/400]  eta: 0:02:36  lr: 0.000192  loss: 2.8405 (2.8534)  grad: 0.2267 (0.2276)  time: 0.4424  data: 0.0044  max mem: 22448
+train: [3]  [100/400]  eta: 0:02:24  lr: 0.000195  loss: 2.8209 (2.8482)  grad: 0.2278 (0.2292)  time: 0.4520  data: 0.0044  max mem: 22448
+train: [3]  [120/400]  eta: 0:02:13  lr: 0.000198  loss: 2.8167 (2.8499)  grad: 0.2486 (0.2410)  time: 0.4454  data: 0.0043  max mem: 22448
+train: [3]  [140/400]  eta: 0:02:02  lr: 0.000201  loss: 2.9401 (2.8857)  grad: 0.4544 (0.3217)  time: 0.4427  data: 0.0043  max mem: 22448
+WARNING: classifier 45 (31, 1.0) diverged (loss=73.75 > 63.56) at step 675. Freezing.
+train: [3]  [160/400]  eta: 0:01:52  lr: 0.000204  loss: 2.9928 (2.9409)  grad: 0.7464 (0.3888)  time: 0.4450  data: 0.0041  max mem: 22448
+train: [3]  [180/400]  eta: 0:01:42  lr: 0.000207  loss: 2.8623 (2.9298)  grad: 0.2358 (0.3730)  time: 0.4452  data: 0.0044  max mem: 22448
+train: [3]  [200/400]  eta: 0:01:32  lr: 0.000210  loss: 2.8478 (2.9265)  grad: 0.2428 (0.3707)  time: 0.4470  data: 0.0042  max mem: 22448
+train: [3]  [220/400]  eta: 0:01:23  lr: 0.000213  loss: 2.9737 (2.9471)  grad: 0.4413 (0.4194)  time: 0.4545  data: 0.0043  max mem: 22448
+WARNING: classifier 44 (26, 1.0) diverged (loss=82.27 > 63.56) at step 715. Freezing.
+train: [3]  [240/400]  eta: 0:01:13  lr: 0.000216  loss: 3.0790 (2.9783)  grad: 0.7342 (0.4651)  time: 0.4521  data: 0.0045  max mem: 22448
+train: [3]  [260/400]  eta: 0:01:04  lr: 0.000219  loss: 2.8538 (2.9664)  grad: 0.2163 (0.4456)  time: 0.4405  data: 0.0041  max mem: 22448
+train: [3]  [280/400]  eta: 0:00:55  lr: 0.000222  loss: 2.8292 (2.9569)  grad: 0.2163 (0.4296)  time: 0.4544  data: 0.0042  max mem: 22448
+train: [3]  [300/400]  eta: 0:00:45  lr: 0.000225  loss: 2.8428 (2.9493)  grad: 0.2155 (0.4152)  time: 0.4440  data: 0.0043  max mem: 22448
+train: [3]  [320/400]  eta: 0:00:36  lr: 0.000228  loss: 2.8179 (2.9407)  grad: 0.2216 (0.4037)  time: 0.4447  data: 0.0044  max mem: 22448
+train: [3]  [340/400]  eta: 0:00:27  lr: 0.000231  loss: 2.8179 (2.9348)  grad: 0.2317 (0.3942)  time: 0.4439  data: 0.0043  max mem: 22448
+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 2.8583 (2.9324)  grad: 0.2417 (0.3858)  time: 0.4554  data: 0.0043  max mem: 22448
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 2.8701 (2.9271)  grad: 0.2422 (0.3782)  time: 0.4440  data: 0.0042  max mem: 22448
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.8143 (2.9216)  grad: 0.2351 (0.3708)  time: 0.4477  data: 0.0043  max mem: 22448
+train: [3] Total time: 0:03:02 (0.4562 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.8143 (2.9216)  grad: 0.2351 (0.3708)
+eval (validation): [3]  [ 0/85]  eta: 0:04:50    time: 3.4124  data: 3.1082  max mem: 22448
+eval (validation): [3]  [20/85]  eta: 0:00:32    time: 0.3519  data: 0.0029  max mem: 22448
+eval (validation): [3]  [40/85]  eta: 0:00:18    time: 0.3347  data: 0.0043  max mem: 22448
+eval (validation): [3]  [60/85]  eta: 0:00:09    time: 0.3371  data: 0.0043  max mem: 22448
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3351  data: 0.0040  max mem: 22448
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3322  data: 0.0039  max mem: 22448
+eval (validation): [3] Total time: 0:00:32 (0.3789 s / it)
+cv: [3]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 2.488  acc: 0.265  f1: 0.183
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:22:21  lr: nan  time: 3.3530  data: 2.9824  max mem: 22448
+train: [4]  [ 20/400]  eta: 0:03:41  lr: 0.000243  loss: 2.7579 (2.7855)  grad: 0.2294 (0.2313)  time: 0.4434  data: 0.0046  max mem: 22448
+train: [4]  [ 40/400]  eta: 0:03:05  lr: 0.000246  loss: 2.7797 (2.7891)  grad: 0.2314 (0.2314)  time: 0.4425  data: 0.0034  max mem: 22448
+train: [4]  [ 60/400]  eta: 0:02:49  lr: 0.000249  loss: 2.7797 (2.7949)  grad: 0.2314 (0.2322)  time: 0.4656  data: 0.0042  max mem: 22448
+train: [4]  [ 80/400]  eta: 0:02:34  lr: 0.000252  loss: 2.7809 (2.7957)  grad: 0.2277 (0.2301)  time: 0.4306  data: 0.0039  max mem: 22448
+train: [4]  [100/400]  eta: 0:02:22  lr: 0.000255  loss: 2.8349 (2.8040)  grad: 0.2277 (0.2305)  time: 0.4536  data: 0.0041  max mem: 22448
+train: [4]  [120/400]  eta: 0:02:12  lr: 0.000258  loss: 2.8349 (2.8007)  grad: 0.2309 (0.2319)  time: 0.4509  data: 0.0041  max mem: 22448
+train: [4]  [140/400]  eta: 0:02:01  lr: 0.000261  loss: 2.7836 (2.7990)  grad: 0.2462 (0.2358)  time: 0.4294  data: 0.0042  max mem: 22448
+train: [4]  [160/400]  eta: 0:01:51  lr: 0.000264  loss: 2.8006 (2.7995)  grad: 0.2557 (0.2384)  time: 0.4425  data: 0.0043  max mem: 22448
+train: [4]  [180/400]  eta: 0:01:41  lr: 0.000267  loss: 2.8044 (2.8015)  grad: 0.2556 (0.2406)  time: 0.4366  data: 0.0042  max mem: 22448
+train: [4]  [200/400]  eta: 0:01:31  lr: 0.000270  loss: 2.7937 (2.7994)  grad: 0.2507 (0.2415)  time: 0.4403  data: 0.0041  max mem: 22448
+train: [4]  [220/400]  eta: 0:01:22  lr: 0.000273  loss: 2.7859 (2.8019)  grad: 0.2570 (0.2466)  time: 0.4588  data: 0.0042  max mem: 22448
+train: [4]  [240/400]  eta: 0:01:13  lr: 0.000276  loss: 2.8743 (2.8129)  grad: 0.3717 (0.2744)  time: 0.4467  data: 0.0043  max mem: 22448
+WARNING: classifier 43 (22, 1.0) diverged (loss=74.30 > 63.56) at step 928. Freezing.
+train: [4]  [260/400]  eta: 0:01:03  lr: 0.000279  loss: 3.0250 (2.8613)  grad: 0.6979 (0.3442)  time: 0.4423  data: 0.0041  max mem: 22448
+train: [4]  [280/400]  eta: 0:00:54  lr: 0.000282  loss: 2.8694 (2.8588)  grad: 0.2715 (0.3373)  time: 0.4512  data: 0.0042  max mem: 22448
+train: [4]  [300/400]  eta: 0:00:45  lr: 0.000285  loss: 2.8241 (2.8555)  grad: 0.2340 (0.3303)  time: 0.4404  data: 0.0043  max mem: 22448
+train: [4]  [320/400]  eta: 0:00:36  lr: 0.000288  loss: 2.7943 (2.8512)  grad: 0.2190 (0.3226)  time: 0.4395  data: 0.0042  max mem: 22448
+train: [4]  [340/400]  eta: 0:00:27  lr: 0.000291  loss: 2.7853 (2.8488)  grad: 0.2102 (0.3166)  time: 0.4378  data: 0.0042  max mem: 22448
+train: [4]  [360/400]  eta: 0:00:18  lr: 0.000294  loss: 2.7948 (2.8472)  grad: 0.2225 (0.3117)  time: 0.4397  data: 0.0041  max mem: 22448
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.7948 (2.8439)  grad: 0.2281 (0.3075)  time: 0.4410  data: 0.0042  max mem: 22448
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.7651 (2.8398)  grad: 0.2257 (0.3032)  time: 0.4372  data: 0.0042  max mem: 22448
+train: [4] Total time: 0:03:00 (0.4511 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.7651 (2.8398)  grad: 0.2257 (0.3032)
+eval (validation): [4]  [ 0/85]  eta: 0:05:04    time: 3.5810  data: 3.3474  max mem: 22448
+eval (validation): [4]  [20/85]  eta: 0:00:31    time: 0.3314  data: 0.0037  max mem: 22448
+eval (validation): [4]  [40/85]  eta: 0:00:18    time: 0.3361  data: 0.0039  max mem: 22448
+eval (validation): [4]  [60/85]  eta: 0:00:09    time: 0.3489  data: 0.0042  max mem: 22448
+eval (validation): [4]  [80/85]  eta: 0:00:01    time: 0.3336  data: 0.0041  max mem: 22448
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3294  data: 0.0040  max mem: 22448
+eval (validation): [4] Total time: 0:00:32 (0.3799 s / it)
+cv: [4]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.464  acc: 0.265  f1: 0.192
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:22:48  lr: nan  time: 3.4220  data: 3.0436  max mem: 22448
+train: [5]  [ 20/400]  eta: 0:03:42  lr: 0.000300  loss: 2.6838 (2.6975)  grad: 0.2245 (0.2285)  time: 0.4426  data: 0.0040  max mem: 22448
+train: [5]  [ 40/400]  eta: 0:03:07  lr: 0.000300  loss: 2.7209 (2.7394)  grad: 0.2254 (0.2322)  time: 0.4557  data: 0.0042  max mem: 22448
+train: [5]  [ 60/400]  eta: 0:02:49  lr: 0.000300  loss: 2.7488 (2.7424)  grad: 0.2342 (0.2344)  time: 0.4505  data: 0.0044  max mem: 22448
+train: [5]  [ 80/400]  eta: 0:02:34  lr: 0.000300  loss: 2.7410 (2.7367)  grad: 0.2342 (0.2343)  time: 0.4360  data: 0.0040  max mem: 22448
+train: [5]  [100/400]  eta: 0:02:23  lr: 0.000300  loss: 2.7410 (2.7421)  grad: 0.2386 (0.2374)  time: 0.4652  data: 0.0044  max mem: 22448
+train: [5]  [120/400]  eta: 0:02:12  lr: 0.000300  loss: 2.7200 (2.7349)  grad: 0.2354 (0.2371)  time: 0.4400  data: 0.0043  max mem: 22448
+train: [5]  [140/400]  eta: 0:02:01  lr: 0.000300  loss: 2.6939 (2.7296)  grad: 0.2302 (0.2358)  time: 0.4326  data: 0.0042  max mem: 22448
+train: [5]  [160/400]  eta: 0:01:51  lr: 0.000299  loss: 2.6841 (2.7264)  grad: 0.2296 (0.2348)  time: 0.4425  data: 0.0042  max mem: 22448
+train: [5]  [180/400]  eta: 0:01:41  lr: 0.000299  loss: 2.7240 (2.7299)  grad: 0.2322 (0.2346)  time: 0.4427  data: 0.0043  max mem: 22448
+train: [5]  [200/400]  eta: 0:01:32  lr: 0.000299  loss: 2.7350 (2.7282)  grad: 0.2340 (0.2350)  time: 0.4448  data: 0.0046  max mem: 22448
+train: [5]  [220/400]  eta: 0:01:22  lr: 0.000299  loss: 2.6858 (2.7279)  grad: 0.2349 (0.2347)  time: 0.4521  data: 0.0043  max mem: 22448
+train: [5]  [240/400]  eta: 0:01:13  lr: 0.000299  loss: 2.6913 (2.7275)  grad: 0.2381 (0.2355)  time: 0.4471  data: 0.0043  max mem: 22448
+train: [5]  [260/400]  eta: 0:01:03  lr: 0.000299  loss: 2.6913 (2.7248)  grad: 0.2368 (0.2353)  time: 0.4364  data: 0.0041  max mem: 22448
+train: [5]  [280/400]  eta: 0:00:54  lr: 0.000298  loss: 2.7222 (2.7263)  grad: 0.2380 (0.2365)  time: 0.4544  data: 0.0043  max mem: 22448
+train: [5]  [300/400]  eta: 0:00:45  lr: 0.000298  loss: 2.7523 (2.7237)  grad: 0.2439 (0.2369)  time: 0.4482  data: 0.0043  max mem: 22448
+train: [5]  [320/400]  eta: 0:00:36  lr: 0.000298  loss: 2.7024 (2.7245)  grad: 0.2433 (0.2376)  time: 0.4424  data: 0.0043  max mem: 22448
+train: [5]  [340/400]  eta: 0:00:27  lr: 0.000298  loss: 2.7185 (2.7230)  grad: 0.2433 (0.2379)  time: 0.4349  data: 0.0043  max mem: 22448
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 2.6809 (2.7220)  grad: 0.2446 (0.2385)  time: 0.4555  data: 0.0045  max mem: 22448
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.6586 (2.7216)  grad: 0.2450 (0.2389)  time: 0.4360  data: 0.0043  max mem: 22448
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.6441 (2.7185)  grad: 0.2360 (0.2383)  time: 0.4335  data: 0.0044  max mem: 22448
+train: [5] Total time: 0:03:00 (0.4524 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.6441 (2.7185)  grad: 0.2360 (0.2383)
+eval (validation): [5]  [ 0/85]  eta: 0:04:34    time: 3.2266  data: 3.0004  max mem: 22448
+eval (validation): [5]  [20/85]  eta: 0:00:32    time: 0.3644  data: 0.0054  max mem: 22448
+eval (validation): [5]  [40/85]  eta: 0:00:18    time: 0.3334  data: 0.0035  max mem: 22448
+eval (validation): [5]  [60/85]  eta: 0:00:09    time: 0.3487  data: 0.0041  max mem: 22448
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3431  data: 0.0042  max mem: 22448
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3388  data: 0.0042  max mem: 22448
+eval (validation): [5] Total time: 0:00:32 (0.3828 s / it)
+cv: [5]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.388  acc: 0.280  f1: 0.210
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:22:54  lr: nan  time: 3.4360  data: 3.0482  max mem: 22448
+train: [6]  [ 20/400]  eta: 0:03:44  lr: 0.000296  loss: 2.6672 (2.6592)  grad: 0.2417 (0.2430)  time: 0.4495  data: 0.0035  max mem: 22448
+train: [6]  [ 40/400]  eta: 0:03:08  lr: 0.000296  loss: 2.6862 (2.6774)  grad: 0.2419 (0.2445)  time: 0.4538  data: 0.0043  max mem: 22448
+train: [6]  [ 60/400]  eta: 0:02:50  lr: 0.000296  loss: 2.6578 (2.6733)  grad: 0.2459 (0.2444)  time: 0.4500  data: 0.0044  max mem: 22448
+train: [6]  [ 80/400]  eta: 0:02:34  lr: 0.000295  loss: 2.6286 (2.6606)  grad: 0.2393 (0.2437)  time: 0.4300  data: 0.0041  max mem: 22448
+train: [6]  [100/400]  eta: 0:02:23  lr: 0.000295  loss: 2.6469 (2.6602)  grad: 0.2418 (0.2436)  time: 0.4673  data: 0.0044  max mem: 22448
+train: [6]  [120/400]  eta: 0:02:12  lr: 0.000295  loss: 2.6585 (2.6633)  grad: 0.2487 (0.2453)  time: 0.4462  data: 0.0044  max mem: 22448
+train: [6]  [140/400]  eta: 0:02:02  lr: 0.000294  loss: 2.6678 (2.6682)  grad: 0.2483 (0.2453)  time: 0.4488  data: 0.0042  max mem: 22448
+train: [6]  [160/400]  eta: 0:01:52  lr: 0.000294  loss: 2.6690 (2.6705)  grad: 0.2436 (0.2449)  time: 0.4522  data: 0.0042  max mem: 22448
+train: [6]  [180/400]  eta: 0:01:42  lr: 0.000293  loss: 2.6593 (2.6676)  grad: 0.2509 (0.2457)  time: 0.4549  data: 0.0042  max mem: 22448
+train: [6]  [200/400]  eta: 0:01:32  lr: 0.000293  loss: 2.6625 (2.6694)  grad: 0.2577 (0.2464)  time: 0.4480  data: 0.0042  max mem: 22448
+train: [6]  [220/400]  eta: 0:01:23  lr: 0.000292  loss: 2.6814 (2.6673)  grad: 0.2536 (0.2469)  time: 0.4623  data: 0.0043  max mem: 22448
+train: [6]  [240/400]  eta: 0:01:14  lr: 0.000292  loss: 2.6635 (2.6682)  grad: 0.2474 (0.2472)  time: 0.4543  data: 0.0043  max mem: 22448
+train: [6]  [260/400]  eta: 0:01:04  lr: 0.000291  loss: 2.6619 (2.6640)  grad: 0.2459 (0.2469)  time: 0.4456  data: 0.0042  max mem: 22448
+train: [6]  [280/400]  eta: 0:00:55  lr: 0.000291  loss: 2.6484 (2.6644)  grad: 0.2423 (0.2470)  time: 0.4492  data: 0.0042  max mem: 22448
+train: [6]  [300/400]  eta: 0:00:46  lr: 0.000290  loss: 2.6972 (2.6660)  grad: 0.2485 (0.2474)  time: 0.4554  data: 0.0041  max mem: 22448
+train: [6]  [320/400]  eta: 0:00:36  lr: 0.000290  loss: 2.6951 (2.6659)  grad: 0.2485 (0.2475)  time: 0.4511  data: 0.0042  max mem: 22448
+train: [6]  [340/400]  eta: 0:00:27  lr: 0.000289  loss: 2.6811 (2.6673)  grad: 0.2471 (0.2478)  time: 0.4371  data: 0.0041  max mem: 22448
+train: [6]  [360/400]  eta: 0:00:18  lr: 0.000288  loss: 2.6535 (2.6642)  grad: 0.2471 (0.2476)  time: 0.4622  data: 0.0042  max mem: 22448
+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 2.6291 (2.6663)  grad: 0.2446 (0.2479)  time: 0.4474  data: 0.0042  max mem: 22448
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.6593 (2.6661)  grad: 0.2398 (0.2472)  time: 0.4490  data: 0.0043  max mem: 22448
+train: [6] Total time: 0:03:03 (0.4585 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.6593 (2.6661)  grad: 0.2398 (0.2472)
+eval (validation): [6]  [ 0/85]  eta: 0:04:36    time: 3.2513  data: 2.9658  max mem: 22448
+eval (validation): [6]  [20/85]  eta: 0:00:31    time: 0.3492  data: 0.0037  max mem: 22448
+eval (validation): [6]  [40/85]  eta: 0:00:18    time: 0.3460  data: 0.0039  max mem: 22448
+eval (validation): [6]  [60/85]  eta: 0:00:09    time: 0.3592  data: 0.0041  max mem: 22448
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3442  data: 0.0041  max mem: 22448
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3344  data: 0.0040  max mem: 22448
+eval (validation): [6] Total time: 0:00:32 (0.3861 s / it)
+cv: [6]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 2.390  acc: 0.279  f1: 0.221
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [7]  [  0/400]  eta: 0:22:01  lr: nan  time: 3.3033  data: 2.9314  max mem: 22448
+train: [7]  [ 20/400]  eta: 0:03:41  lr: 0.000286  loss: 2.5746 (2.5929)  grad: 0.2358 (0.2465)  time: 0.4475  data: 0.0048  max mem: 22448
+train: [7]  [ 40/400]  eta: 0:03:09  lr: 0.000286  loss: 2.5984 (2.6048)  grad: 0.2473 (0.2527)  time: 0.4657  data: 0.0042  max mem: 22448
+train: [7]  [ 60/400]  eta: 0:02:50  lr: 0.000285  loss: 2.5939 (2.5931)  grad: 0.2634 (0.2569)  time: 0.4541  data: 0.0043  max mem: 22448
+train: [7]  [ 80/400]  eta: 0:02:36  lr: 0.000284  loss: 2.5816 (2.6023)  grad: 0.2484 (0.2535)  time: 0.4456  data: 0.0042  max mem: 22448
+train: [7]  [100/400]  eta: 0:02:24  lr: 0.000284  loss: 2.5816 (2.5930)  grad: 0.2430 (0.2513)  time: 0.4560  data: 0.0041  max mem: 22448
+train: [7]  [120/400]  eta: 0:02:13  lr: 0.000283  loss: 2.5563 (2.5906)  grad: 0.2435 (0.2506)  time: 0.4529  data: 0.0042  max mem: 22448
+train: [7]  [140/400]  eta: 0:02:03  lr: 0.000282  loss: 2.5782 (2.5928)  grad: 0.2413 (0.2496)  time: 0.4534  data: 0.0046  max mem: 22448
+train: [7]  [160/400]  eta: 0:01:52  lr: 0.000282  loss: 2.5883 (2.5935)  grad: 0.2422 (0.2488)  time: 0.4451  data: 0.0043  max mem: 22448
+train: [7]  [180/400]  eta: 0:01:42  lr: 0.000281  loss: 2.6262 (2.5977)  grad: 0.2501 (0.2496)  time: 0.4343  data: 0.0041  max mem: 22448
+train: [7]  [200/400]  eta: 0:01:33  lr: 0.000280  loss: 2.6032 (2.5970)  grad: 0.2542 (0.2496)  time: 0.4622  data: 0.0043  max mem: 22448
+train: [7]  [220/400]  eta: 0:01:23  lr: 0.000279  loss: 2.5819 (2.5943)  grad: 0.2461 (0.2500)  time: 0.4688  data: 0.0043  max mem: 22448
+train: [7]  [240/400]  eta: 0:01:14  lr: 0.000278  loss: 2.5983 (2.5974)  grad: 0.2502 (0.2503)  time: 0.4433  data: 0.0041  max mem: 22448
+train: [7]  [260/400]  eta: 0:01:04  lr: 0.000278  loss: 2.6029 (2.5963)  grad: 0.2481 (0.2499)  time: 0.4627  data: 0.0042  max mem: 22448
+train: [7]  [280/400]  eta: 0:00:55  lr: 0.000277  loss: 2.5618 (2.5939)  grad: 0.2419 (0.2492)  time: 0.4597  data: 0.0042  max mem: 22448
+train: [7]  [300/400]  eta: 0:00:46  lr: 0.000276  loss: 2.5347 (2.5928)  grad: 0.2428 (0.2492)  time: 0.4573  data: 0.0042  max mem: 22448
+train: [7]  [320/400]  eta: 0:00:36  lr: 0.000275  loss: 2.5394 (2.5924)  grad: 0.2494 (0.2487)  time: 0.4372  data: 0.0042  max mem: 22448
+train: [7]  [340/400]  eta: 0:00:27  lr: 0.000274  loss: 2.5468 (2.5903)  grad: 0.2422 (0.2482)  time: 0.4489  data: 0.0041  max mem: 22448
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 2.5709 (2.5918)  grad: 0.2443 (0.2483)  time: 0.4452  data: 0.0042  max mem: 22448
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 2.5984 (2.5919)  grad: 0.2447 (0.2487)  time: 0.4481  data: 0.0041  max mem: 22448
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.6107 (2.5939)  grad: 0.2487 (0.2491)  time: 0.4466  data: 0.0040  max mem: 22448
+train: [7] Total time: 0:03:03 (0.4591 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.6107 (2.5939)  grad: 0.2487 (0.2491)
+eval (validation): [7]  [ 0/85]  eta: 0:04:37    time: 3.2595  data: 3.0252  max mem: 22448
+eval (validation): [7]  [20/85]  eta: 0:00:30    time: 0.3322  data: 0.0035  max mem: 22448
+eval (validation): [7]  [40/85]  eta: 0:00:18    time: 0.3307  data: 0.0037  max mem: 22448
+eval (validation): [7]  [60/85]  eta: 0:00:09    time: 0.3468  data: 0.0045  max mem: 22448
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3439  data: 0.0041  max mem: 22448
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3346  data: 0.0040  max mem: 22448
+eval (validation): [7] Total time: 0:00:31 (0.3757 s / it)
+cv: [7]  best hparam: (1, 1.0) (024) ('024_lr1.0e+00_wd1.0e+00')  loss: 2.426  acc: 0.275  f1: 0.223
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:23:14  lr: nan  time: 3.4862  data: 3.1100  max mem: 22448
+train: [8]  [ 20/400]  eta: 0:03:40  lr: 0.000270  loss: 2.4558 (2.4894)  grad: 0.2353 (0.2363)  time: 0.4347  data: 0.0030  max mem: 22448
+train: [8]  [ 40/400]  eta: 0:03:06  lr: 0.000270  loss: 2.5018 (2.5068)  grad: 0.2404 (0.2409)  time: 0.4513  data: 0.0043  max mem: 22448
+train: [8]  [ 60/400]  eta: 0:02:48  lr: 0.000269  loss: 2.5435 (2.5166)  grad: 0.2432 (0.2406)  time: 0.4545  data: 0.0043  max mem: 22448
+train: [8]  [ 80/400]  eta: 0:02:34  lr: 0.000268  loss: 2.5552 (2.5260)  grad: 0.2406 (0.2431)  time: 0.4429  data: 0.0042  max mem: 22448
+train: [8]  [100/400]  eta: 0:02:22  lr: 0.000267  loss: 2.5423 (2.5217)  grad: 0.2507 (0.2461)  time: 0.4383  data: 0.0040  max mem: 22448
+train: [8]  [120/400]  eta: 0:02:12  lr: 0.000266  loss: 2.5233 (2.5222)  grad: 0.2600 (0.2494)  time: 0.4597  data: 0.0042  max mem: 22448
+train: [8]  [140/400]  eta: 0:02:01  lr: 0.000265  loss: 2.5305 (2.5265)  grad: 0.2603 (0.2512)  time: 0.4493  data: 0.0041  max mem: 22448
+train: [8]  [160/400]  eta: 0:01:51  lr: 0.000264  loss: 2.5351 (2.5280)  grad: 0.2603 (0.2534)  time: 0.4450  data: 0.0042  max mem: 22448
+train: [8]  [180/400]  eta: 0:01:41  lr: 0.000263  loss: 2.5118 (2.5225)  grad: 0.2541 (0.2530)  time: 0.4390  data: 0.0042  max mem: 22448
+train: [8]  [200/400]  eta: 0:01:32  lr: 0.000262  loss: 2.5016 (2.5268)  grad: 0.2538 (0.2540)  time: 0.4548  data: 0.0043  max mem: 22448
+train: [8]  [220/400]  eta: 0:01:23  lr: 0.000260  loss: 2.5667 (2.5291)  grad: 0.2553 (0.2541)  time: 0.4527  data: 0.0041  max mem: 22448
+train: [8]  [240/400]  eta: 0:01:13  lr: 0.000259  loss: 2.5644 (2.5292)  grad: 0.2530 (0.2544)  time: 0.4664  data: 0.0043  max mem: 22448
+train: [8]  [260/400]  eta: 0:01:04  lr: 0.000258  loss: 2.5633 (2.5313)  grad: 0.2484 (0.2544)  time: 0.4496  data: 0.0041  max mem: 22448
+train: [8]  [280/400]  eta: 0:00:55  lr: 0.000257  loss: 2.5462 (2.5301)  grad: 0.2483 (0.2545)  time: 0.4546  data: 0.0041  max mem: 22448
+train: [8]  [300/400]  eta: 0:00:46  lr: 0.000256  loss: 2.5225 (2.5312)  grad: 0.2505 (0.2547)  time: 0.4703  data: 0.0042  max mem: 22448
+train: [8]  [320/400]  eta: 0:00:36  lr: 0.000255  loss: 2.5073 (2.5301)  grad: 0.2489 (0.2539)  time: 0.4564  data: 0.0043  max mem: 22448
+train: [8]  [340/400]  eta: 0:00:27  lr: 0.000254  loss: 2.4955 (2.5301)  grad: 0.2530 (0.2548)  time: 0.4314  data: 0.0041  max mem: 22448
+train: [8]  [360/400]  eta: 0:00:18  lr: 0.000253  loss: 2.4955 (2.5301)  grad: 0.2588 (0.2546)  time: 0.4577  data: 0.0042  max mem: 22448
+train: [8]  [380/400]  eta: 0:00:09  lr: 0.000252  loss: 2.5085 (2.5307)  grad: 0.2469 (0.2543)  time: 0.4618  data: 0.0044  max mem: 22448
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.5032 (2.5302)  grad: 0.2497 (0.2546)  time: 0.4475  data: 0.0042  max mem: 22448
+train: [8] Total time: 0:03:03 (0.4588 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.5032 (2.5302)  grad: 0.2497 (0.2546)
+eval (validation): [8]  [ 0/85]  eta: 0:04:37    time: 3.2684  data: 2.9886  max mem: 22448
+eval (validation): [8]  [20/85]  eta: 0:00:33    time: 0.3777  data: 0.0042  max mem: 22448
+eval (validation): [8]  [40/85]  eta: 0:00:19    time: 0.3469  data: 0.0039  max mem: 22448
+eval (validation): [8]  [60/85]  eta: 0:00:10    time: 0.3474  data: 0.0043  max mem: 22448
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3396  data: 0.0040  max mem: 22448
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3342  data: 0.0039  max mem: 22448
+eval (validation): [8] Total time: 0:00:33 (0.3899 s / it)
+cv: [8]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 2.456  acc: 0.265  f1: 0.207
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:22:02  lr: nan  time: 3.3060  data: 2.9328  max mem: 22448
+train: [9]  [ 20/400]  eta: 0:03:43  lr: 0.000249  loss: 2.4660 (2.4937)  grad: 0.2561 (0.2656)  time: 0.4535  data: 0.0049  max mem: 22448
+train: [9]  [ 40/400]  eta: 0:03:07  lr: 0.000248  loss: 2.5265 (2.5023)  grad: 0.2535 (0.2558)  time: 0.4469  data: 0.0044  max mem: 22448
+train: [9]  [ 60/400]  eta: 0:02:48  lr: 0.000247  loss: 2.5249 (2.5005)  grad: 0.2449 (0.2529)  time: 0.4442  data: 0.0043  max mem: 22448
+train: [9]  [ 80/400]  eta: 0:02:35  lr: 0.000246  loss: 2.4850 (2.5053)  grad: 0.2579 (0.2555)  time: 0.4538  data: 0.0044  max mem: 22448
+train: [9]  [100/400]  eta: 0:02:22  lr: 0.000244  loss: 2.4825 (2.5006)  grad: 0.2583 (0.2561)  time: 0.4401  data: 0.0043  max mem: 22448
+train: [9]  [120/400]  eta: 0:02:13  lr: 0.000243  loss: 2.4611 (2.4970)  grad: 0.2531 (0.2557)  time: 0.4722  data: 0.0044  max mem: 22448
+train: [9]  [140/400]  eta: 0:02:02  lr: 0.000242  loss: 2.4768 (2.4968)  grad: 0.2525 (0.2566)  time: 0.4486  data: 0.0041  max mem: 22448
+train: [9]  [160/400]  eta: 0:01:52  lr: 0.000241  loss: 2.4629 (2.4920)  grad: 0.2519 (0.2571)  time: 0.4502  data: 0.0044  max mem: 22448
+train: [9]  [180/400]  eta: 0:01:42  lr: 0.000240  loss: 2.4698 (2.4953)  grad: 0.2622 (0.2585)  time: 0.4608  data: 0.0043  max mem: 22448
+train: [9]  [200/400]  eta: 0:01:33  lr: 0.000238  loss: 2.4730 (2.4911)  grad: 0.2623 (0.2594)  time: 0.4525  data: 0.0042  max mem: 22448
+train: [9]  [220/400]  eta: 0:01:23  lr: 0.000237  loss: 2.4630 (2.4883)  grad: 0.2652 (0.2603)  time: 0.4502  data: 0.0043  max mem: 22448
+train: [9]  [240/400]  eta: 0:01:14  lr: 0.000236  loss: 2.4954 (2.4933)  grad: 0.2635 (0.2602)  time: 0.4710  data: 0.0045  max mem: 22448
+train: [9]  [260/400]  eta: 0:01:05  lr: 0.000234  loss: 2.4927 (2.4915)  grad: 0.2521 (0.2595)  time: 0.4699  data: 0.0044  max mem: 22448
+train: [9]  [280/400]  eta: 0:00:55  lr: 0.000233  loss: 2.4806 (2.4904)  grad: 0.2527 (0.2595)  time: 0.4505  data: 0.0043  max mem: 22448
+train: [9]  [300/400]  eta: 0:00:46  lr: 0.000232  loss: 2.4910 (2.4899)  grad: 0.2585 (0.2597)  time: 0.4698  data: 0.0044  max mem: 22448
+train: [9]  [320/400]  eta: 0:00:37  lr: 0.000230  loss: 2.4930 (2.4909)  grad: 0.2562 (0.2596)  time: 0.4442  data: 0.0044  max mem: 22448
+train: [9]  [340/400]  eta: 0:00:27  lr: 0.000229  loss: 2.4930 (2.4882)  grad: 0.2568 (0.2598)  time: 0.4400  data: 0.0043  max mem: 22448
+train: [9]  [360/400]  eta: 0:00:18  lr: 0.000228  loss: 2.4639 (2.4893)  grad: 0.2627 (0.2604)  time: 0.4542  data: 0.0044  max mem: 22448
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 2.4639 (2.4874)  grad: 0.2582 (0.2604)  time: 0.4448  data: 0.0045  max mem: 22448
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.4986 (2.4890)  grad: 0.2654 (0.2608)  time: 0.4611  data: 0.0044  max mem: 22448
+train: [9] Total time: 0:03:04 (0.4613 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.4986 (2.4890)  grad: 0.2654 (0.2608)
+eval (validation): [9]  [ 0/85]  eta: 0:04:44    time: 3.3470  data: 3.1062  max mem: 22448
+eval (validation): [9]  [20/85]  eta: 0:00:31    time: 0.3481  data: 0.0047  max mem: 22448
+eval (validation): [9]  [40/85]  eta: 0:00:18    time: 0.3317  data: 0.0035  max mem: 22448
+eval (validation): [9]  [60/85]  eta: 0:00:09    time: 0.3546  data: 0.0043  max mem: 22448
+eval (validation): [9]  [80/85]  eta: 0:00:01    time: 0.3571  data: 0.0043  max mem: 22448
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3414  data: 0.0041  max mem: 22448
+eval (validation): [9] Total time: 0:00:32 (0.3858 s / it)
+cv: [9]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.408  acc: 0.281  f1: 0.217
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [10]  [  0/400]  eta: 0:23:13  lr: nan  time: 3.4835  data: 3.1020  max mem: 22448
+train: [10]  [ 20/400]  eta: 0:03:49  lr: 0.000224  loss: 2.3985 (2.4204)  grad: 0.2709 (0.2652)  time: 0.4592  data: 0.0042  max mem: 22448
+train: [10]  [ 40/400]  eta: 0:03:10  lr: 0.000222  loss: 2.3985 (2.4138)  grad: 0.2604 (0.2613)  time: 0.4489  data: 0.0047  max mem: 22448
+train: [10]  [ 60/400]  eta: 0:02:51  lr: 0.000221  loss: 2.4241 (2.4306)  grad: 0.2496 (0.2570)  time: 0.4577  data: 0.0043  max mem: 22448
+train: [10]  [ 80/400]  eta: 0:02:37  lr: 0.000220  loss: 2.4163 (2.4217)  grad: 0.2453 (0.2557)  time: 0.4558  data: 0.0043  max mem: 22448
+train: [10]  [100/400]  eta: 0:02:25  lr: 0.000218  loss: 2.4099 (2.4211)  grad: 0.2536 (0.2566)  time: 0.4464  data: 0.0042  max mem: 22448
+train: [10]  [120/400]  eta: 0:02:14  lr: 0.000217  loss: 2.4502 (2.4246)  grad: 0.2591 (0.2572)  time: 0.4682  data: 0.0043  max mem: 22448
+train: [10]  [140/400]  eta: 0:02:04  lr: 0.000215  loss: 2.4446 (2.4280)  grad: 0.2522 (0.2569)  time: 0.4553  data: 0.0043  max mem: 22448
+train: [10]  [160/400]  eta: 0:01:53  lr: 0.000214  loss: 2.4390 (2.4279)  grad: 0.2510 (0.2572)  time: 0.4487  data: 0.0043  max mem: 22448
+train: [10]  [180/400]  eta: 0:01:43  lr: 0.000213  loss: 2.4314 (2.4292)  grad: 0.2507 (0.2574)  time: 0.4538  data: 0.0043  max mem: 22448
+train: [10]  [200/400]  eta: 0:01:33  lr: 0.000211  loss: 2.4529 (2.4311)  grad: 0.2577 (0.2582)  time: 0.4520  data: 0.0043  max mem: 22448
+train: [10]  [220/400]  eta: 0:01:24  lr: 0.000210  loss: 2.4560 (2.4314)  grad: 0.2596 (0.2585)  time: 0.4511  data: 0.0043  max mem: 22448
+train: [10]  [240/400]  eta: 0:01:14  lr: 0.000208  loss: 2.4339 (2.4313)  grad: 0.2572 (0.2581)  time: 0.4758  data: 0.0043  max mem: 22448
+train: [10]  [260/400]  eta: 0:01:05  lr: 0.000207  loss: 2.4273 (2.4300)  grad: 0.2531 (0.2580)  time: 0.4592  data: 0.0042  max mem: 22448
+train: [10]  [280/400]  eta: 0:00:56  lr: 0.000205  loss: 2.4273 (2.4299)  grad: 0.2552 (0.2581)  time: 0.4510  data: 0.0043  max mem: 22448
+train: [10]  [300/400]  eta: 0:00:46  lr: 0.000204  loss: 2.4089 (2.4280)  grad: 0.2555 (0.2580)  time: 0.4637  data: 0.0042  max mem: 22448
+train: [10]  [320/400]  eta: 0:00:37  lr: 0.000202  loss: 2.4089 (2.4269)  grad: 0.2613 (0.2587)  time: 0.4398  data: 0.0043  max mem: 22448
+train: [10]  [340/400]  eta: 0:00:27  lr: 0.000201  loss: 2.4244 (2.4265)  grad: 0.2597 (0.2588)  time: 0.4545  data: 0.0044  max mem: 22448
+train: [10]  [360/400]  eta: 0:00:18  lr: 0.000199  loss: 2.4007 (2.4249)  grad: 0.2546 (0.2585)  time: 0.4484  data: 0.0044  max mem: 22448
+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 2.3929 (2.4246)  grad: 0.2546 (0.2588)  time: 0.4478  data: 0.0042  max mem: 22448
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.4164 (2.4268)  grad: 0.2668 (0.2593)  time: 0.4442  data: 0.0043  max mem: 22448
+train: [10] Total time: 0:03:04 (0.4620 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.4164 (2.4268)  grad: 0.2668 (0.2593)
+eval (validation): [10]  [ 0/85]  eta: 0:04:43    time: 3.3318  data: 3.0952  max mem: 22448
+eval (validation): [10]  [20/85]  eta: 0:00:32    time: 0.3614  data: 0.0108  max mem: 22448
+eval (validation): [10]  [40/85]  eta: 0:00:19    time: 0.3521  data: 0.0040  max mem: 22448
+eval (validation): [10]  [60/85]  eta: 0:00:10    time: 0.3523  data: 0.0043  max mem: 22448
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.3377  data: 0.0043  max mem: 22448
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.3263  data: 0.0041  max mem: 22448
+eval (validation): [10] Total time: 0:00:32 (0.3872 s / it)
+cv: [10]  best hparam: (0.61, 1.0) (021) ('021_lr6.1e-01_wd1.0e+00')  loss: 2.415  acc: 0.278  f1: 0.221
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:26:01  lr: nan  time: 3.9027  data: 3.5082  max mem: 22448
+train: [11]  [ 20/400]  eta: 0:03:54  lr: 0.000195  loss: 2.3661 (2.3731)  grad: 0.2523 (0.2546)  time: 0.4532  data: 0.0037  max mem: 22448
+train: [11]  [ 40/400]  eta: 0:03:15  lr: 0.000193  loss: 2.3970 (2.3906)  grad: 0.2523 (0.2551)  time: 0.4631  data: 0.0039  max mem: 22448
+train: [11]  [ 60/400]  eta: 0:02:56  lr: 0.000192  loss: 2.3599 (2.3673)  grad: 0.2567 (0.2568)  time: 0.4701  data: 0.0047  max mem: 22448
+train: [11]  [ 80/400]  eta: 0:02:40  lr: 0.000190  loss: 2.3576 (2.3810)  grad: 0.2584 (0.2591)  time: 0.4459  data: 0.0045  max mem: 22448
+train: [11]  [100/400]  eta: 0:02:26  lr: 0.000189  loss: 2.3659 (2.3770)  grad: 0.2584 (0.2594)  time: 0.4428  data: 0.0043  max mem: 22448
+train: [11]  [120/400]  eta: 0:02:15  lr: 0.000187  loss: 2.3152 (2.3665)  grad: 0.2591 (0.2597)  time: 0.4520  data: 0.0044  max mem: 22448
+train: [11]  [140/400]  eta: 0:02:04  lr: 0.000186  loss: 2.3599 (2.3674)  grad: 0.2603 (0.2601)  time: 0.4436  data: 0.0045  max mem: 22448
+train: [11]  [160/400]  eta: 0:01:54  lr: 0.000184  loss: 2.3835 (2.3721)  grad: 0.2623 (0.2609)  time: 0.4603  data: 0.0044  max mem: 22448
+train: [11]  [180/400]  eta: 0:01:43  lr: 0.000183  loss: 2.3706 (2.3752)  grad: 0.2700 (0.2626)  time: 0.4505  data: 0.0043  max mem: 22448
+train: [11]  [200/400]  eta: 0:01:33  lr: 0.000181  loss: 2.3898 (2.3792)  grad: 0.2696 (0.2627)  time: 0.4433  data: 0.0042  max mem: 22448
+train: [11]  [220/400]  eta: 0:01:24  lr: 0.000180  loss: 2.4305 (2.3844)  grad: 0.2574 (0.2619)  time: 0.4575  data: 0.0043  max mem: 22448
+train: [11]  [240/400]  eta: 0:01:15  lr: 0.000178  loss: 2.4301 (2.3852)  grad: 0.2539 (0.2622)  time: 0.4729  data: 0.0043  max mem: 22448
+train: [11]  [260/400]  eta: 0:01:05  lr: 0.000177  loss: 2.3789 (2.3851)  grad: 0.2619 (0.2626)  time: 0.4487  data: 0.0042  max mem: 22448
+train: [11]  [280/400]  eta: 0:00:55  lr: 0.000175  loss: 2.3888 (2.3845)  grad: 0.2611 (0.2627)  time: 0.4398  data: 0.0043  max mem: 22448
+train: [11]  [300/400]  eta: 0:00:46  lr: 0.000174  loss: 2.4024 (2.3876)  grad: 0.2546 (0.2625)  time: 0.4750  data: 0.0045  max mem: 22448
+train: [11]  [320/400]  eta: 0:00:37  lr: 0.000172  loss: 2.4080 (2.3892)  grad: 0.2660 (0.2632)  time: 0.4558  data: 0.0044  max mem: 22448
+train: [11]  [340/400]  eta: 0:00:27  lr: 0.000170  loss: 2.4005 (2.3897)  grad: 0.2695 (0.2639)  time: 0.4496  data: 0.0043  max mem: 22448
+train: [11]  [360/400]  eta: 0:00:18  lr: 0.000169  loss: 2.3961 (2.3887)  grad: 0.2629 (0.2639)  time: 0.4546  data: 0.0041  max mem: 22448
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 2.3466 (2.3868)  grad: 0.2593 (0.2637)  time: 0.4499  data: 0.0042  max mem: 22448
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.3890 (2.3892)  grad: 0.2553 (0.2631)  time: 0.4615  data: 0.0043  max mem: 22448
+train: [11] Total time: 0:03:05 (0.4634 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.3890 (2.3892)  grad: 0.2553 (0.2631)
+eval (validation): [11]  [ 0/85]  eta: 0:04:48    time: 3.3938  data: 3.1511  max mem: 22448
+eval (validation): [11]  [20/85]  eta: 0:00:32    time: 0.3488  data: 0.0037  max mem: 22448
+eval (validation): [11]  [40/85]  eta: 0:00:19    time: 0.3847  data: 0.0044  max mem: 22448
+eval (validation): [11]  [60/85]  eta: 0:00:10    time: 0.3656  data: 0.0048  max mem: 22448
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.3434  data: 0.0040  max mem: 22448
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.3403  data: 0.0041  max mem: 22448
+eval (validation): [11] Total time: 0:00:33 (0.3979 s / it)
+cv: [11]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.436  acc: 0.273  f1: 0.219
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:22:52  lr: nan  time: 3.4315  data: 3.0950  max mem: 22448
+train: [12]  [ 20/400]  eta: 0:03:49  lr: 0.000164  loss: 2.2776 (2.2827)  grad: 0.2435 (0.2470)  time: 0.4634  data: 0.0041  max mem: 22448
+train: [12]  [ 40/400]  eta: 0:03:12  lr: 0.000163  loss: 2.3123 (2.3054)  grad: 0.2542 (0.2547)  time: 0.4585  data: 0.0037  max mem: 22448
+train: [12]  [ 60/400]  eta: 0:02:51  lr: 0.000161  loss: 2.3144 (2.3065)  grad: 0.2527 (0.2527)  time: 0.4454  data: 0.0043  max mem: 22448
+train: [12]  [ 80/400]  eta: 0:02:37  lr: 0.000160  loss: 2.3178 (2.3138)  grad: 0.2515 (0.2539)  time: 0.4528  data: 0.0046  max mem: 22448
+train: [12]  [100/400]  eta: 0:02:24  lr: 0.000158  loss: 2.3384 (2.3203)  grad: 0.2605 (0.2549)  time: 0.4454  data: 0.0041  max mem: 22448
+train: [12]  [120/400]  eta: 0:02:14  lr: 0.000156  loss: 2.3317 (2.3235)  grad: 0.2510 (0.2538)  time: 0.4681  data: 0.0044  max mem: 22448
+train: [12]  [140/400]  eta: 0:02:03  lr: 0.000155  loss: 2.3114 (2.3217)  grad: 0.2549 (0.2561)  time: 0.4538  data: 0.0043  max mem: 22448
+train: [12]  [160/400]  eta: 0:01:53  lr: 0.000153  loss: 2.3176 (2.3239)  grad: 0.2693 (0.2583)  time: 0.4471  data: 0.0045  max mem: 22448
+train: [12]  [180/400]  eta: 0:01:43  lr: 0.000152  loss: 2.3531 (2.3246)  grad: 0.2693 (0.2590)  time: 0.4504  data: 0.0045  max mem: 22448
+train: [12]  [200/400]  eta: 0:01:33  lr: 0.000150  loss: 2.3479 (2.3296)  grad: 0.2664 (0.2598)  time: 0.4484  data: 0.0043  max mem: 22448
+train: [12]  [220/400]  eta: 0:01:24  lr: 0.000149  loss: 2.3479 (2.3333)  grad: 0.2599 (0.2597)  time: 0.4579  data: 0.0042  max mem: 22448
+train: [12]  [240/400]  eta: 0:01:14  lr: 0.000147  loss: 2.3346 (2.3316)  grad: 0.2623 (0.2609)  time: 0.4684  data: 0.0044  max mem: 22448
+train: [12]  [260/400]  eta: 0:01:05  lr: 0.000145  loss: 2.2961 (2.3313)  grad: 0.2643 (0.2607)  time: 0.4521  data: 0.0040  max mem: 22448
+train: [12]  [280/400]  eta: 0:00:55  lr: 0.000144  loss: 2.2922 (2.3273)  grad: 0.2555 (0.2607)  time: 0.4559  data: 0.0042  max mem: 22448
+train: [12]  [300/400]  eta: 0:00:46  lr: 0.000142  loss: 2.2985 (2.3297)  grad: 0.2637 (0.2618)  time: 0.4706  data: 0.0043  max mem: 22448
+train: [12]  [320/400]  eta: 0:00:37  lr: 0.000141  loss: 2.3087 (2.3311)  grad: 0.2685 (0.2619)  time: 0.4504  data: 0.0044  max mem: 22448
+train: [12]  [340/400]  eta: 0:00:27  lr: 0.000139  loss: 2.2947 (2.3295)  grad: 0.2647 (0.2616)  time: 0.4588  data: 0.0046  max mem: 22448
+train: [12]  [360/400]  eta: 0:00:18  lr: 0.000138  loss: 2.3243 (2.3305)  grad: 0.2576 (0.2615)  time: 0.4530  data: 0.0044  max mem: 22448
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 2.3313 (2.3313)  grad: 0.2625 (0.2616)  time: 0.4574  data: 0.0044  max mem: 22448
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.3041 (2.3302)  grad: 0.2677 (0.2621)  time: 0.4688  data: 0.0047  max mem: 22448
+train: [12] Total time: 0:03:05 (0.4638 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.3041 (2.3302)  grad: 0.2677 (0.2621)
+eval (validation): [12]  [ 0/85]  eta: 0:04:46    time: 3.3692  data: 3.1283  max mem: 22448
+eval (validation): [12]  [20/85]  eta: 0:00:33    time: 0.3702  data: 0.0140  max mem: 22448
+eval (validation): [12]  [40/85]  eta: 0:00:19    time: 0.3613  data: 0.0041  max mem: 22448
+eval (validation): [12]  [60/85]  eta: 0:00:10    time: 0.3378  data: 0.0034  max mem: 22448
+eval (validation): [12]  [80/85]  eta: 0:00:01    time: 0.3367  data: 0.0042  max mem: 22448
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.3327  data: 0.0041  max mem: 22448
+eval (validation): [12] Total time: 0:00:33 (0.3889 s / it)
+cv: [12]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.451  acc: 0.268  f1: 0.212
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:23:07  lr: nan  time: 3.4694  data: 3.0774  max mem: 22448
+train: [13]  [ 20/400]  eta: 0:03:48  lr: 0.000133  loss: 2.2976 (2.2960)  grad: 0.2548 (0.2605)  time: 0.4575  data: 0.0041  max mem: 22448
+train: [13]  [ 40/400]  eta: 0:03:08  lr: 0.000131  loss: 2.2976 (2.2866)  grad: 0.2580 (0.2607)  time: 0.4399  data: 0.0039  max mem: 22448
+train: [13]  [ 60/400]  eta: 0:02:49  lr: 0.000130  loss: 2.2894 (2.2904)  grad: 0.2592 (0.2614)  time: 0.4527  data: 0.0042  max mem: 22448
+train: [13]  [ 80/400]  eta: 0:02:36  lr: 0.000128  loss: 2.2598 (2.2839)  grad: 0.2592 (0.2616)  time: 0.4521  data: 0.0045  max mem: 22448
+train: [13]  [100/400]  eta: 0:02:24  lr: 0.000127  loss: 2.2356 (2.2781)  grad: 0.2527 (0.2597)  time: 0.4513  data: 0.0042  max mem: 22448
+train: [13]  [120/400]  eta: 0:02:14  lr: 0.000125  loss: 2.2466 (2.2733)  grad: 0.2526 (0.2595)  time: 0.4721  data: 0.0044  max mem: 22448
+train: [13]  [140/400]  eta: 0:02:03  lr: 0.000124  loss: 2.3087 (2.2823)  grad: 0.2681 (0.2610)  time: 0.4552  data: 0.0042  max mem: 22448
+train: [13]  [160/400]  eta: 0:01:53  lr: 0.000122  loss: 2.3174 (2.2821)  grad: 0.2731 (0.2621)  time: 0.4608  data: 0.0042  max mem: 22448
+train: [13]  [180/400]  eta: 0:01:43  lr: 0.000120  loss: 2.2870 (2.2873)  grad: 0.2682 (0.2624)  time: 0.4562  data: 0.0044  max mem: 22448
+train: [13]  [200/400]  eta: 0:01:34  lr: 0.000119  loss: 2.2559 (2.2809)  grad: 0.2591 (0.2620)  time: 0.4557  data: 0.0045  max mem: 22448
+train: [13]  [220/400]  eta: 0:01:24  lr: 0.000117  loss: 2.2559 (2.2823)  grad: 0.2570 (0.2626)  time: 0.4545  data: 0.0043  max mem: 22448
+train: [13]  [240/400]  eta: 0:01:15  lr: 0.000116  loss: 2.3244 (2.2843)  grad: 0.2631 (0.2628)  time: 0.4772  data: 0.0044  max mem: 22448
+train: [13]  [260/400]  eta: 0:01:05  lr: 0.000114  loss: 2.2891 (2.2852)  grad: 0.2496 (0.2619)  time: 0.4549  data: 0.0043  max mem: 22448
+train: [13]  [280/400]  eta: 0:00:56  lr: 0.000113  loss: 2.2613 (2.2827)  grad: 0.2474 (0.2611)  time: 0.4490  data: 0.0042  max mem: 22448
+train: [13]  [300/400]  eta: 0:00:47  lr: 0.000111  loss: 2.2617 (2.2827)  grad: 0.2490 (0.2601)  time: 0.5128  data: 0.0045  max mem: 22448
+train: [13]  [320/400]  eta: 0:00:37  lr: 0.000110  loss: 2.2914 (2.2841)  grad: 0.2580 (0.2605)  time: 0.4545  data: 0.0042  max mem: 22448
+train: [13]  [340/400]  eta: 0:00:28  lr: 0.000108  loss: 2.2895 (2.2845)  grad: 0.2573 (0.2600)  time: 0.4502  data: 0.0042  max mem: 22448
+train: [13]  [360/400]  eta: 0:00:18  lr: 0.000107  loss: 2.2773 (2.2848)  grad: 0.2573 (0.2602)  time: 0.4528  data: 0.0043  max mem: 22448
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 2.2773 (2.2845)  grad: 0.2618 (0.2605)  time: 0.4658  data: 0.0044  max mem: 22448
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.2835 (2.2854)  grad: 0.2618 (0.2608)  time: 0.4414  data: 0.0045  max mem: 22448
+train: [13] Total time: 0:03:06 (0.4660 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.2835 (2.2854)  grad: 0.2618 (0.2608)
+eval (validation): [13]  [ 0/85]  eta: 0:04:41    time: 3.3162  data: 3.0760  max mem: 22448
+eval (validation): [13]  [20/85]  eta: 0:00:31    time: 0.3463  data: 0.0043  max mem: 22448
+eval (validation): [13]  [40/85]  eta: 0:00:19    time: 0.3707  data: 0.0037  max mem: 22448
+eval (validation): [13]  [60/85]  eta: 0:00:10    time: 0.3798  data: 0.0042  max mem: 22448
+eval (validation): [13]  [80/85]  eta: 0:00:01    time: 0.3460  data: 0.0044  max mem: 22448
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3336  data: 0.0040  max mem: 22448
+eval (validation): [13] Total time: 0:00:33 (0.3971 s / it)
+cv: [13]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 2.475  acc: 0.274  f1: 0.216
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:23:16  lr: nan  time: 3.4905  data: 3.1083  max mem: 22448
+train: [14]  [ 20/400]  eta: 0:03:53  lr: 0.000102  loss: 2.1924 (2.2019)  grad: 0.2479 (0.2458)  time: 0.4715  data: 0.0035  max mem: 22448
+train: [14]  [ 40/400]  eta: 0:03:14  lr: 0.000101  loss: 2.2041 (2.2161)  grad: 0.2469 (0.2475)  time: 0.4610  data: 0.0043  max mem: 22448
+train: [14]  [ 60/400]  eta: 0:02:55  lr: 0.000099  loss: 2.2447 (2.2232)  grad: 0.2498 (0.2505)  time: 0.4679  data: 0.0045  max mem: 22448
+train: [14]  [ 80/400]  eta: 0:02:41  lr: 0.000098  loss: 2.2298 (2.2298)  grad: 0.2498 (0.2504)  time: 0.4697  data: 0.0045  max mem: 22448
+train: [14]  [100/400]  eta: 0:02:27  lr: 0.000096  loss: 2.2229 (2.2324)  grad: 0.2515 (0.2520)  time: 0.4422  data: 0.0042  max mem: 22448
+train: [14]  [120/400]  eta: 0:02:17  lr: 0.000095  loss: 2.2177 (2.2296)  grad: 0.2590 (0.2537)  time: 0.4820  data: 0.0044  max mem: 22448
+train: [14]  [140/400]  eta: 0:02:06  lr: 0.000093  loss: 2.2177 (2.2287)  grad: 0.2655 (0.2551)  time: 0.4489  data: 0.0044  max mem: 22448
+train: [14]  [160/400]  eta: 0:01:55  lr: 0.000092  loss: 2.1856 (2.2275)  grad: 0.2578 (0.2552)  time: 0.4459  data: 0.0044  max mem: 22448
+train: [14]  [180/400]  eta: 0:01:45  lr: 0.000090  loss: 2.1775 (2.2232)  grad: 0.2551 (0.2559)  time: 0.4638  data: 0.0045  max mem: 22448
+train: [14]  [200/400]  eta: 0:01:35  lr: 0.000089  loss: 2.1763 (2.2236)  grad: 0.2597 (0.2571)  time: 0.4501  data: 0.0045  max mem: 22448
+train: [14]  [220/400]  eta: 0:01:25  lr: 0.000088  loss: 2.2275 (2.2238)  grad: 0.2638 (0.2580)  time: 0.4426  data: 0.0044  max mem: 22448
+train: [14]  [240/400]  eta: 0:01:15  lr: 0.000086  loss: 2.2339 (2.2278)  grad: 0.2591 (0.2581)  time: 0.4598  data: 0.0043  max mem: 22448
+train: [14]  [260/400]  eta: 0:01:05  lr: 0.000085  loss: 2.2135 (2.2290)  grad: 0.2585 (0.2580)  time: 0.4635  data: 0.0045  max mem: 22448
+train: [14]  [280/400]  eta: 0:00:56  lr: 0.000083  loss: 2.2468 (2.2299)  grad: 0.2584 (0.2579)  time: 0.4494  data: 0.0044  max mem: 22448
+train: [14]  [300/400]  eta: 0:00:46  lr: 0.000082  loss: 2.2579 (2.2344)  grad: 0.2584 (0.2582)  time: 0.4805  data: 0.0044  max mem: 22448
+train: [14]  [320/400]  eta: 0:00:37  lr: 0.000081  loss: 2.2579 (2.2353)  grad: 0.2633 (0.2588)  time: 0.4693  data: 0.0044  max mem: 22448
+train: [14]  [340/400]  eta: 0:00:28  lr: 0.000079  loss: 2.2377 (2.2353)  grad: 0.2702 (0.2597)  time: 0.4545  data: 0.0042  max mem: 22448
+train: [14]  [360/400]  eta: 0:00:18  lr: 0.000078  loss: 2.2241 (2.2354)  grad: 0.2623 (0.2595)  time: 0.4580  data: 0.0044  max mem: 22448
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 2.2144 (2.2342)  grad: 0.2535 (0.2593)  time: 0.4546  data: 0.0043  max mem: 22448
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.2115 (2.2338)  grad: 0.2613 (0.2597)  time: 0.4410  data: 0.0042  max mem: 22448
+train: [14] Total time: 0:03:06 (0.4667 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.2115 (2.2338)  grad: 0.2613 (0.2597)
+eval (validation): [14]  [ 0/85]  eta: 0:04:46    time: 3.3736  data: 3.1378  max mem: 22448
+eval (validation): [14]  [20/85]  eta: 0:00:33    time: 0.3779  data: 0.0083  max mem: 22448
+eval (validation): [14]  [40/85]  eta: 0:00:19    time: 0.3639  data: 0.0046  max mem: 22448
+eval (validation): [14]  [60/85]  eta: 0:00:10    time: 0.3472  data: 0.0037  max mem: 22448
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3308  data: 0.0043  max mem: 22448
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.3289  data: 0.0041  max mem: 22448
+eval (validation): [14] Total time: 0:00:33 (0.3926 s / it)
+cv: [14]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.442  acc: 0.278  f1: 0.219
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:23:03  lr: nan  time: 3.4579  data: 3.0768  max mem: 22448
+train: [15]  [ 20/400]  eta: 0:03:49  lr: 0.000074  loss: 2.2014 (2.1833)  grad: 0.2457 (0.2462)  time: 0.4600  data: 0.0039  max mem: 22448
+train: [15]  [ 40/400]  eta: 0:03:10  lr: 0.000072  loss: 2.2014 (2.1806)  grad: 0.2447 (0.2473)  time: 0.4528  data: 0.0040  max mem: 22448
+train: [15]  [ 60/400]  eta: 0:02:51  lr: 0.000071  loss: 2.1700 (2.1821)  grad: 0.2453 (0.2492)  time: 0.4542  data: 0.0044  max mem: 22448
+train: [15]  [ 80/400]  eta: 0:02:36  lr: 0.000070  loss: 2.1543 (2.1739)  grad: 0.2459 (0.2488)  time: 0.4463  data: 0.0046  max mem: 22448
+train: [15]  [100/400]  eta: 0:02:24  lr: 0.000068  loss: 2.1071 (2.1649)  grad: 0.2503 (0.2495)  time: 0.4448  data: 0.0042  max mem: 22448
+train: [15]  [120/400]  eta: 0:02:13  lr: 0.000067  loss: 2.1850 (2.1729)  grad: 0.2580 (0.2521)  time: 0.4605  data: 0.0041  max mem: 22448
+train: [15]  [140/400]  eta: 0:02:03  lr: 0.000066  loss: 2.2167 (2.1815)  grad: 0.2606 (0.2542)  time: 0.4670  data: 0.0043  max mem: 22448
+train: [15]  [160/400]  eta: 0:01:53  lr: 0.000064  loss: 2.1749 (2.1801)  grad: 0.2572 (0.2545)  time: 0.4494  data: 0.0045  max mem: 22448
+train: [15]  [180/400]  eta: 0:01:43  lr: 0.000063  loss: 2.1963 (2.1882)  grad: 0.2589 (0.2559)  time: 0.4470  data: 0.0043  max mem: 22448
+train: [15]  [200/400]  eta: 0:01:33  lr: 0.000062  loss: 2.1963 (2.1864)  grad: 0.2607 (0.2557)  time: 0.4458  data: 0.0044  max mem: 22448
+train: [15]  [220/400]  eta: 0:01:23  lr: 0.000061  loss: 2.1751 (2.1865)  grad: 0.2472 (0.2557)  time: 0.4471  data: 0.0041  max mem: 22448
+train: [15]  [240/400]  eta: 0:01:14  lr: 0.000059  loss: 2.1945 (2.1854)  grad: 0.2534 (0.2560)  time: 0.4746  data: 0.0043  max mem: 22448
+train: [15]  [260/400]  eta: 0:01:05  lr: 0.000058  loss: 2.1983 (2.1897)  grad: 0.2548 (0.2557)  time: 0.4585  data: 0.0042  max mem: 22448
+train: [15]  [280/400]  eta: 0:00:55  lr: 0.000057  loss: 2.2082 (2.1876)  grad: 0.2484 (0.2551)  time: 0.4401  data: 0.0042  max mem: 22448
+train: [15]  [300/400]  eta: 0:00:46  lr: 0.000056  loss: 2.1466 (2.1873)  grad: 0.2519 (0.2552)  time: 0.4607  data: 0.0044  max mem: 22448
+train: [15]  [320/400]  eta: 0:00:37  lr: 0.000054  loss: 2.2049 (2.1878)  grad: 0.2530 (0.2551)  time: 0.4650  data: 0.0043  max mem: 22448
+train: [15]  [340/400]  eta: 0:00:27  lr: 0.000053  loss: 2.2081 (2.1895)  grad: 0.2486 (0.2550)  time: 0.4591  data: 0.0043  max mem: 22448
+train: [15]  [360/400]  eta: 0:00:18  lr: 0.000052  loss: 2.2081 (2.1904)  grad: 0.2544 (0.2553)  time: 0.4548  data: 0.0043  max mem: 22448
+train: [15]  [380/400]  eta: 0:00:09  lr: 0.000051  loss: 2.1948 (2.1890)  grad: 0.2459 (0.2548)  time: 0.4593  data: 0.0042  max mem: 22448
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.1773 (2.1900)  grad: 0.2459 (0.2546)  time: 0.4798  data: 0.0045  max mem: 22448
+train: [15] Total time: 0:03:05 (0.4641 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.1773 (2.1900)  grad: 0.2459 (0.2546)
+eval (validation): [15]  [ 0/85]  eta: 0:04:46    time: 3.3752  data: 3.0931  max mem: 22448
+eval (validation): [15]  [20/85]  eta: 0:00:35    time: 0.4127  data: 0.0044  max mem: 22448
+eval (validation): [15]  [40/85]  eta: 0:00:20    time: 0.3546  data: 0.0046  max mem: 22448
+eval (validation): [15]  [60/85]  eta: 0:00:10    time: 0.3659  data: 0.0050  max mem: 22448
+eval (validation): [15]  [80/85]  eta: 0:00:02    time: 0.3418  data: 0.0043  max mem: 22448
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.3341  data: 0.0041  max mem: 22448
+eval (validation): [15] Total time: 0:00:34 (0.4053 s / it)
+cv: [15]  best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00')  loss: 2.460  acc: 0.274  f1: 0.220
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:23:45  lr: nan  time: 3.5648  data: 3.1770  max mem: 22448
+train: [16]  [ 20/400]  eta: 0:03:49  lr: 0.000048  loss: 2.1129 (2.1489)  grad: 0.2389 (0.2399)  time: 0.4570  data: 0.0028  max mem: 22448
+train: [16]  [ 40/400]  eta: 0:03:12  lr: 0.000047  loss: 2.1365 (2.1390)  grad: 0.2389 (0.2387)  time: 0.4637  data: 0.0042  max mem: 22448
+train: [16]  [ 60/400]  eta: 0:02:54  lr: 0.000046  loss: 2.1103 (2.1303)  grad: 0.2393 (0.2410)  time: 0.4702  data: 0.0044  max mem: 22448
+train: [16]  [ 80/400]  eta: 0:02:40  lr: 0.000045  loss: 2.1252 (2.1399)  grad: 0.2545 (0.2451)  time: 0.4571  data: 0.0043  max mem: 22448
+train: [16]  [100/400]  eta: 0:02:26  lr: 0.000044  loss: 2.1301 (2.1420)  grad: 0.2569 (0.2467)  time: 0.4444  data: 0.0042  max mem: 22448
+train: [16]  [120/400]  eta: 0:02:15  lr: 0.000043  loss: 2.1301 (2.1453)  grad: 0.2546 (0.2475)  time: 0.4670  data: 0.0042  max mem: 22448
+train: [16]  [140/400]  eta: 0:02:05  lr: 0.000042  loss: 2.1512 (2.1466)  grad: 0.2436 (0.2471)  time: 0.4574  data: 0.0043  max mem: 22448
+train: [16]  [160/400]  eta: 0:01:54  lr: 0.000041  loss: 2.1585 (2.1538)  grad: 0.2519 (0.2486)  time: 0.4429  data: 0.0041  max mem: 22448
+train: [16]  [180/400]  eta: 0:01:44  lr: 0.000040  loss: 2.1964 (2.1557)  grad: 0.2546 (0.2485)  time: 0.4582  data: 0.0043  max mem: 22448
+train: [16]  [200/400]  eta: 0:01:34  lr: 0.000039  loss: 2.1394 (2.1530)  grad: 0.2440 (0.2476)  time: 0.4548  data: 0.0042  max mem: 22448
+train: [16]  [220/400]  eta: 0:01:24  lr: 0.000038  loss: 2.1302 (2.1530)  grad: 0.2408 (0.2474)  time: 0.4541  data: 0.0043  max mem: 22448
+train: [16]  [240/400]  eta: 0:01:15  lr: 0.000036  loss: 2.1344 (2.1520)  grad: 0.2432 (0.2477)  time: 0.4658  data: 0.0042  max mem: 22448
+train: [16]  [260/400]  eta: 0:01:05  lr: 0.000035  loss: 2.1497 (2.1559)  grad: 0.2567 (0.2487)  time: 0.4642  data: 0.0043  max mem: 22448
+train: [16]  [280/400]  eta: 0:00:56  lr: 0.000034  loss: 2.1562 (2.1565)  grad: 0.2530 (0.2488)  time: 0.4526  data: 0.0043  max mem: 22448
+train: [16]  [300/400]  eta: 0:00:46  lr: 0.000033  loss: 2.1562 (2.1587)  grad: 0.2503 (0.2489)  time: 0.4553  data: 0.0044  max mem: 22448
+train: [16]  [320/400]  eta: 0:00:37  lr: 0.000032  loss: 2.1863 (2.1616)  grad: 0.2522 (0.2494)  time: 0.4776  data: 0.0043  max mem: 22448
+train: [16]  [340/400]  eta: 0:00:28  lr: 0.000031  loss: 2.1630 (2.1608)  grad: 0.2501 (0.2494)  time: 0.4576  data: 0.0045  max mem: 22448
+train: [16]  [360/400]  eta: 0:00:18  lr: 0.000031  loss: 2.1630 (2.1612)  grad: 0.2509 (0.2497)  time: 0.4554  data: 0.0043  max mem: 22448
+train: [16]  [380/400]  eta: 0:00:09  lr: 0.000030  loss: 2.1786 (2.1610)  grad: 0.2542 (0.2501)  time: 0.4579  data: 0.0043  max mem: 22448
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.1558 (2.1607)  grad: 0.2611 (0.2506)  time: 0.4631  data: 0.0045  max mem: 22448
+train: [16] Total time: 0:03:06 (0.4669 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.1558 (2.1607)  grad: 0.2611 (0.2506)
+eval (validation): [16]  [ 0/85]  eta: 0:04:48    time: 3.3993  data: 3.1072  max mem: 22448
+eval (validation): [16]  [20/85]  eta: 0:00:33    time: 0.3661  data: 0.0050  max mem: 22448
+eval (validation): [16]  [40/85]  eta: 0:00:19    time: 0.3570  data: 0.0038  max mem: 22448
+eval (validation): [16]  [60/85]  eta: 0:00:10    time: 0.3664  data: 0.0046  max mem: 22448
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3564  data: 0.0040  max mem: 22448
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.3494  data: 0.0040  max mem: 22448
+eval (validation): [16] Total time: 0:00:33 (0.3988 s / it)
+cv: [16]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.434  acc: 0.276  f1: 0.219
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:23:08  lr: nan  time: 3.4708  data: 3.1293  max mem: 22448
+train: [17]  [ 20/400]  eta: 0:03:46  lr: 0.000028  loss: 2.1034 (2.1025)  grad: 0.2288 (0.2370)  time: 0.4526  data: 0.0049  max mem: 22448
+train: [17]  [ 40/400]  eta: 0:03:12  lr: 0.000027  loss: 2.1038 (2.1209)  grad: 0.2435 (0.2406)  time: 0.4683  data: 0.0041  max mem: 22448
+train: [17]  [ 60/400]  eta: 0:02:51  lr: 0.000026  loss: 2.1241 (2.1291)  grad: 0.2374 (0.2390)  time: 0.4396  data: 0.0041  max mem: 22448
+train: [17]  [ 80/400]  eta: 0:02:39  lr: 0.000025  loss: 2.1232 (2.1214)  grad: 0.2364 (0.2393)  time: 0.4806  data: 0.0047  max mem: 22448
+train: [17]  [100/400]  eta: 0:02:27  lr: 0.000024  loss: 2.1270 (2.1295)  grad: 0.2345 (0.2389)  time: 0.4741  data: 0.0043  max mem: 22448
+train: [17]  [120/400]  eta: 0:02:16  lr: 0.000023  loss: 2.1231 (2.1270)  grad: 0.2363 (0.2393)  time: 0.4558  data: 0.0041  max mem: 22448
+train: [17]  [140/400]  eta: 0:02:06  lr: 0.000023  loss: 2.0914 (2.1248)  grad: 0.2360 (0.2392)  time: 0.4740  data: 0.0043  max mem: 22448
+train: [17]  [160/400]  eta: 0:01:55  lr: 0.000022  loss: 2.1168 (2.1235)  grad: 0.2401 (0.2401)  time: 0.4615  data: 0.0042  max mem: 22448
+train: [17]  [180/400]  eta: 0:01:45  lr: 0.000021  loss: 2.1138 (2.1225)  grad: 0.2401 (0.2398)  time: 0.4550  data: 0.0042  max mem: 22448
+train: [17]  [200/400]  eta: 0:01:35  lr: 0.000020  loss: 2.0956 (2.1209)  grad: 0.2359 (0.2396)  time: 0.4584  data: 0.0043  max mem: 22448
+train: [17]  [220/400]  eta: 0:01:25  lr: 0.000019  loss: 2.1267 (2.1239)  grad: 0.2385 (0.2403)  time: 0.4613  data: 0.0042  max mem: 22448
+train: [17]  [240/400]  eta: 0:01:15  lr: 0.000019  loss: 2.1435 (2.1227)  grad: 0.2404 (0.2409)  time: 0.4571  data: 0.0043  max mem: 22448
+train: [17]  [260/400]  eta: 0:01:06  lr: 0.000018  loss: 2.0956 (2.1219)  grad: 0.2456 (0.2412)  time: 0.4630  data: 0.0043  max mem: 22448
+train: [17]  [280/400]  eta: 0:00:56  lr: 0.000017  loss: 2.1389 (2.1243)  grad: 0.2462 (0.2416)  time: 0.4565  data: 0.0042  max mem: 22448
+train: [17]  [300/400]  eta: 0:00:47  lr: 0.000016  loss: 2.1502 (2.1262)  grad: 0.2448 (0.2414)  time: 0.4597  data: 0.0044  max mem: 22448
+train: [17]  [320/400]  eta: 0:00:37  lr: 0.000016  loss: 2.1502 (2.1268)  grad: 0.2302 (0.2406)  time: 0.4699  data: 0.0046  max mem: 22448
+train: [17]  [340/400]  eta: 0:00:28  lr: 0.000015  loss: 2.1171 (2.1272)  grad: 0.2328 (0.2408)  time: 0.4614  data: 0.0045  max mem: 22448
+train: [17]  [360/400]  eta: 0:00:18  lr: 0.000014  loss: 2.1278 (2.1282)  grad: 0.2409 (0.2409)  time: 0.4651  data: 0.0043  max mem: 22448
+train: [17]  [380/400]  eta: 0:00:09  lr: 0.000014  loss: 2.1247 (2.1278)  grad: 0.2455 (0.2417)  time: 0.4644  data: 0.0043  max mem: 22448
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.1025 (2.1260)  grad: 0.2397 (0.2413)  time: 0.4669  data: 0.0042  max mem: 22448
+train: [17] Total time: 0:03:08 (0.4701 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.1025 (2.1260)  grad: 0.2397 (0.2413)
+eval (validation): [17]  [ 0/85]  eta: 0:04:48    time: 3.3972  data: 3.1008  max mem: 22448
+eval (validation): [17]  [20/85]  eta: 0:00:33    time: 0.3790  data: 0.0145  max mem: 22448
+eval (validation): [17]  [40/85]  eta: 0:00:19    time: 0.3429  data: 0.0033  max mem: 22448
+eval (validation): [17]  [60/85]  eta: 0:00:10    time: 0.3633  data: 0.0043  max mem: 22448
+eval (validation): [17]  [80/85]  eta: 0:00:02    time: 0.3741  data: 0.0043  max mem: 22448
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3668  data: 0.0042  max mem: 22448
+eval (validation): [17] Total time: 0:00:34 (0.4039 s / it)
+cv: [17]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.420  acc: 0.277  f1: 0.220
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:23:36  lr: nan  time: 3.5400  data: 3.1581  max mem: 22448
+train: [18]  [ 20/400]  eta: 0:03:53  lr: 0.000012  loss: 2.1492 (2.1479)  grad: 0.2396 (0.2381)  time: 0.4678  data: 0.0040  max mem: 22448
+train: [18]  [ 40/400]  eta: 0:03:15  lr: 0.000012  loss: 2.1183 (2.1091)  grad: 0.2384 (0.2378)  time: 0.4667  data: 0.0042  max mem: 22448
+train: [18]  [ 60/400]  eta: 0:02:54  lr: 0.000011  loss: 2.0438 (2.1022)  grad: 0.2431 (0.2403)  time: 0.4502  data: 0.0043  max mem: 22448
+train: [18]  [ 80/400]  eta: 0:02:40  lr: 0.000011  loss: 2.0728 (2.1015)  grad: 0.2382 (0.2383)  time: 0.4712  data: 0.0043  max mem: 22448
+train: [18]  [100/400]  eta: 0:02:29  lr: 0.000010  loss: 2.1177 (2.1058)  grad: 0.2289 (0.2379)  time: 0.4901  data: 0.0048  max mem: 22448
+train: [18]  [120/400]  eta: 0:02:17  lr: 0.000009  loss: 2.0785 (2.1010)  grad: 0.2329 (0.2372)  time: 0.4455  data: 0.0040  max mem: 22448
+train: [18]  [140/400]  eta: 0:02:06  lr: 0.000009  loss: 2.1064 (2.1050)  grad: 0.2346 (0.2376)  time: 0.4557  data: 0.0042  max mem: 22448
+train: [18]  [160/400]  eta: 0:01:55  lr: 0.000008  loss: 2.1109 (2.1087)  grad: 0.2315 (0.2370)  time: 0.4556  data: 0.0044  max mem: 22448
+train: [18]  [180/400]  eta: 0:01:45  lr: 0.000008  loss: 2.0868 (2.1064)  grad: 0.2358 (0.2375)  time: 0.4506  data: 0.0043  max mem: 22448
+train: [18]  [200/400]  eta: 0:01:35  lr: 0.000007  loss: 2.1056 (2.1086)  grad: 0.2376 (0.2376)  time: 0.4456  data: 0.0042  max mem: 22448
+train: [18]  [220/400]  eta: 0:01:25  lr: 0.000007  loss: 2.1142 (2.1084)  grad: 0.2347 (0.2373)  time: 0.4479  data: 0.0041  max mem: 22448
+train: [18]  [240/400]  eta: 0:01:15  lr: 0.000006  loss: 2.1223 (2.1123)  grad: 0.2355 (0.2372)  time: 0.4619  data: 0.0043  max mem: 22448
+train: [18]  [260/400]  eta: 0:01:06  lr: 0.000006  loss: 2.1342 (2.1132)  grad: 0.2378 (0.2374)  time: 0.4721  data: 0.0042  max mem: 22448
+train: [18]  [280/400]  eta: 0:00:56  lr: 0.000006  loss: 2.1191 (2.1124)  grad: 0.2386 (0.2375)  time: 0.4663  data: 0.0043  max mem: 22448
+train: [18]  [300/400]  eta: 0:00:47  lr: 0.000005  loss: 2.0946 (2.1112)  grad: 0.2334 (0.2372)  time: 0.4597  data: 0.0042  max mem: 22448
+train: [18]  [320/400]  eta: 0:00:37  lr: 0.000005  loss: 2.1300 (2.1141)  grad: 0.2332 (0.2371)  time: 0.4519  data: 0.0041  max mem: 22448
+train: [18]  [340/400]  eta: 0:00:28  lr: 0.000004  loss: 2.0749 (2.1104)  grad: 0.2327 (0.2368)  time: 0.4615  data: 0.0042  max mem: 22448
+train: [18]  [360/400]  eta: 0:00:18  lr: 0.000004  loss: 2.0731 (2.1106)  grad: 0.2342 (0.2367)  time: 0.4719  data: 0.0044  max mem: 22448
+train: [18]  [380/400]  eta: 0:00:09  lr: 0.000004  loss: 2.1073 (2.1110)  grad: 0.2367 (0.2367)  time: 0.4694  data: 0.0043  max mem: 22448
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.1036 (2.1095)  grad: 0.2373 (0.2369)  time: 0.4660  data: 0.0042  max mem: 22448
+train: [18] Total time: 0:03:07 (0.4694 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.1036 (2.1095)  grad: 0.2373 (0.2369)
+eval (validation): [18]  [ 0/85]  eta: 0:04:50    time: 3.4219  data: 3.1704  max mem: 22448
+eval (validation): [18]  [20/85]  eta: 0:00:35    time: 0.3991  data: 0.0048  max mem: 22448
+eval (validation): [18]  [40/85]  eta: 0:00:20    time: 0.3695  data: 0.0041  max mem: 22448
+eval (validation): [18]  [60/85]  eta: 0:00:10    time: 0.3597  data: 0.0044  max mem: 22448
+eval (validation): [18]  [80/85]  eta: 0:00:02    time: 0.3528  data: 0.0041  max mem: 22448
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3478  data: 0.0041  max mem: 22448
+eval (validation): [18] Total time: 0:00:34 (0.4083 s / it)
+cv: [18]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.417  acc: 0.277  f1: 0.219
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:22:49  lr: nan  time: 3.4246  data: 3.1013  max mem: 22448
+train: [19]  [ 20/400]  eta: 0:03:50  lr: 0.000003  loss: 2.1159 (2.1173)  grad: 0.2264 (0.2303)  time: 0.4657  data: 0.0040  max mem: 22448
+train: [19]  [ 40/400]  eta: 0:03:14  lr: 0.000003  loss: 2.0866 (2.0865)  grad: 0.2266 (0.2332)  time: 0.4725  data: 0.0044  max mem: 22448
+train: [19]  [ 60/400]  eta: 0:02:56  lr: 0.000002  loss: 2.0944 (2.1037)  grad: 0.2292 (0.2339)  time: 0.4717  data: 0.0044  max mem: 22448
+train: [19]  [ 80/400]  eta: 0:02:41  lr: 0.000002  loss: 2.1374 (2.1088)  grad: 0.2292 (0.2327)  time: 0.4680  data: 0.0044  max mem: 22448
+train: [19]  [100/400]  eta: 0:02:29  lr: 0.000002  loss: 2.1140 (2.1085)  grad: 0.2358 (0.2337)  time: 0.4652  data: 0.0041  max mem: 22448
+train: [19]  [120/400]  eta: 0:02:18  lr: 0.000002  loss: 2.1004 (2.1103)  grad: 0.2389 (0.2348)  time: 0.4717  data: 0.0043  max mem: 22448
+train: [19]  [140/400]  eta: 0:02:06  lr: 0.000001  loss: 2.1210 (2.1075)  grad: 0.2287 (0.2338)  time: 0.4570  data: 0.0043  max mem: 22448
+train: [19]  [160/400]  eta: 0:01:56  lr: 0.000001  loss: 2.0956 (2.1032)  grad: 0.2257 (0.2331)  time: 0.4608  data: 0.0044  max mem: 22448
+train: [19]  [180/400]  eta: 0:01:46  lr: 0.000001  loss: 2.0692 (2.1010)  grad: 0.2290 (0.2343)  time: 0.4610  data: 0.0042  max mem: 22448
+train: [19]  [200/400]  eta: 0:01:35  lr: 0.000001  loss: 2.0893 (2.1016)  grad: 0.2352 (0.2337)  time: 0.4559  data: 0.0043  max mem: 22448
+train: [19]  [220/400]  eta: 0:01:26  lr: 0.000001  loss: 2.0907 (2.0999)  grad: 0.2285 (0.2333)  time: 0.4701  data: 0.0042  max mem: 22448
+train: [19]  [240/400]  eta: 0:01:16  lr: 0.000001  loss: 2.0980 (2.0983)  grad: 0.2307 (0.2336)  time: 0.4573  data: 0.0042  max mem: 22448
+train: [19]  [260/400]  eta: 0:01:06  lr: 0.000000  loss: 2.0980 (2.0980)  grad: 0.2316 (0.2337)  time: 0.4559  data: 0.0042  max mem: 22448
+train: [19]  [280/400]  eta: 0:00:56  lr: 0.000000  loss: 2.1179 (2.1006)  grad: 0.2331 (0.2337)  time: 0.4593  data: 0.0043  max mem: 22448
+train: [19]  [300/400]  eta: 0:00:47  lr: 0.000000  loss: 2.1179 (2.1010)  grad: 0.2299 (0.2334)  time: 0.4708  data: 0.0043  max mem: 22448
+train: [19]  [320/400]  eta: 0:00:37  lr: 0.000000  loss: 2.1152 (2.1032)  grad: 0.2341 (0.2338)  time: 0.4578  data: 0.0042  max mem: 22448
+train: [19]  [340/400]  eta: 0:00:28  lr: 0.000000  loss: 2.1378 (2.1048)  grad: 0.2326 (0.2334)  time: 0.4646  data: 0.0044  max mem: 22448
+train: [19]  [360/400]  eta: 0:00:18  lr: 0.000000  loss: 2.1110 (2.1050)  grad: 0.2251 (0.2330)  time: 0.4644  data: 0.0043  max mem: 22448
+train: [19]  [380/400]  eta: 0:00:09  lr: 0.000000  loss: 2.0920 (2.1041)  grad: 0.2251 (0.2328)  time: 0.4521  data: 0.0043  max mem: 22448
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.0977 (2.1062)  grad: 0.2274 (0.2328)  time: 0.4651  data: 0.0044  max mem: 22448
+train: [19] Total time: 0:03:08 (0.4712 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.0977 (2.1062)  grad: 0.2274 (0.2328)
+eval (validation): [19]  [ 0/85]  eta: 0:04:50    time: 3.4144  data: 3.1190  max mem: 22448
+eval (validation): [19]  [20/85]  eta: 0:00:34    time: 0.3851  data: 0.0049  max mem: 22448
+eval (validation): [19]  [40/85]  eta: 0:00:20    time: 0.3877  data: 0.0046  max mem: 22448
+eval (validation): [19]  [60/85]  eta: 0:00:10    time: 0.3564  data: 0.0045  max mem: 22448
+eval (validation): [19]  [80/85]  eta: 0:00:02    time: 0.3399  data: 0.0045  max mem: 22448
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.3344  data: 0.0041  max mem: 22448
+eval (validation): [19] Total time: 0:00:34 (0.4045 s / it)
+cv: [19]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.417  acc: 0.276  f1: 0.218
+saving checkpoint experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.27593207825765964, "hparam": [0.44, 1.0], "hparam_id": 19, "epoch": 19, "is_best": false, "best_score": 0.2809154669619786}
+eval (train): [20]  [  0/509]  eta: 0:26:54    time: 3.1724  data: 2.9258  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:07    time: 0.3720  data: 0.0049  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:27    time: 0.3743  data: 0.0043  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:07    time: 0.3689  data: 0.0036  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:56    time: 0.3921  data: 0.0046  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:45    time: 0.3715  data: 0.0045  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:35    time: 0.3768  data: 0.0044  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:25    time: 0.3645  data: 0.0045  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:17    time: 0.3933  data: 0.0044  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:08    time: 0.3705  data: 0.0042  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:02:00    time: 0.3690  data: 0.0045  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:51    time: 0.3654  data: 0.0047  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:43    time: 0.3581  data: 0.0045  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:35    time: 0.3649  data: 0.0043  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:27    time: 0.3933  data: 0.0047  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:20    time: 0.4182  data: 0.0047  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:12    time: 0.3636  data: 0.0044  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:04    time: 0.3720  data: 0.0044  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:57    time: 0.3695  data: 0.0044  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:49    time: 0.3650  data: 0.0045  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:41    time: 0.3570  data: 0.0045  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:33    time: 0.3604  data: 0.0043  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:26    time: 0.3645  data: 0.0040  max mem: 22448
+eval (train): [20]  [460/509]  eta: 0:00:18    time: 0.3736  data: 0.0042  max mem: 22448
+eval (train): [20]  [480/509]  eta: 0:00:10    time: 0.3756  data: 0.0044  max mem: 22448
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3609  data: 0.0040  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3407  data: 0.0039  max mem: 22448
+eval (train): [20] Total time: 0:03:13 (0.3792 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:27    time: 3.1420  data: 2.8935  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:32    time: 0.3719  data: 0.0080  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:19    time: 0.3673  data: 0.0049  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3623  data: 0.0039  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:01    time: 0.3544  data: 0.0041  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3432  data: 0.0041  max mem: 22448
+eval (validation): [20] Total time: 0:00:33 (0.3986 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:37    time: 3.2639  data: 3.0151  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:37    time: 0.4423  data: 0.0315  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:21    time: 0.3649  data: 0.0038  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3706  data: 0.0043  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:02    time: 0.3732  data: 0.0044  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3507  data: 0.0040  max mem: 22448
+eval (test): [20] Total time: 0:00:35 (0.4202 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:31    time: 3.3147  data: 3.0034  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:32    time: 0.3922  data: 0.0218  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:19    time: 0.3796  data: 0.0039  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3477  data: 0.0046  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3334  data: 0.0040  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3253  data: 0.0040  max mem: 22448
+eval (testid): [20] Total time: 0:00:32 (0.4007 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.2809154669619786, "hparam": [0.52, 1.0], "hparam_id": 20, "epoch": 9, "is_best": true, "best_score": 0.2809154669619786}
+eval (train): [20]  [  0/509]  eta: 0:27:16    time: 3.2157  data: 2.9207  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:22    time: 0.4030  data: 0.0206  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:43    time: 0.4131  data: 0.0049  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:21    time: 0.3931  data: 0.0049  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:03:03    time: 0.3636  data: 0.0043  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:49    time: 0.3578  data: 0.0042  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:39    time: 0.3940  data: 0.0045  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:31    time: 0.4121  data: 0.0051  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:22    time: 0.3890  data: 0.0046  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:13    time: 0.3991  data: 0.0046  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:02:04    time: 0.3799  data: 0.0046  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:55    time: 0.3623  data: 0.0046  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:47    time: 0.3888  data: 0.0045  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:39    time: 0.3808  data: 0.0043  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:31    time: 0.4015  data: 0.0048  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:23    time: 0.3852  data: 0.0044  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:14    time: 0.3511  data: 0.0046  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:06    time: 0.3595  data: 0.0040  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:58    time: 0.3592  data: 0.0042  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:50    time: 0.3666  data: 0.0045  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:42    time: 0.3588  data: 0.0043  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:34    time: 0.3550  data: 0.0045  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:26    time: 0.3641  data: 0.0046  max mem: 22448
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+eval (train): [20]  [480/509]  eta: 0:00:11    time: 0.3517  data: 0.0043  max mem: 22448
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3663  data: 0.0043  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3506  data: 0.0040  max mem: 22448
+eval (train): [20] Total time: 0:03:14 (0.3826 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:40    time: 3.2992  data: 2.9955  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:32    time: 0.3551  data: 0.0045  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:19    time: 0.3794  data: 0.0042  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3991  data: 0.0043  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:02    time: 0.3401  data: 0.0039  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3397  data: 0.0039  max mem: 22448
+eval (validation): [20] Total time: 0:00:34 (0.4058 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:37    time: 3.2675  data: 2.9792  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:33    time: 0.3728  data: 0.0053  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:20    time: 0.3757  data: 0.0041  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3690  data: 0.0038  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:02    time: 0.3544  data: 0.0043  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3320  data: 0.0037  max mem: 22448
+eval (test): [20] Total time: 0:00:34 (0.4018 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:27    time: 3.2613  data: 2.9659  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:36    time: 0.4469  data: 0.0247  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:20    time: 0.4025  data: 0.0043  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:10    time: 0.3921  data: 0.0047  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3592  data: 0.0045  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3460  data: 0.0043  max mem: 22448
+eval (testid): [20] Total time: 0:00:35 (0.4352 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   |       9 | 0.000156 | 0.05 |          20 | [0.52, 1.0] | train      | 2.1536 | 0.3521  | 0.0023937 | 0.29117 | 0.0025241 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       9 | 0.000156 | 0.05 |          20 | [0.52, 1.0] | validation | 2.4081 | 0.28092 | 0.0051957 | 0.21711 | 0.0048963 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       9 | 0.000156 | 0.05 |          20 | [0.52, 1.0] | test       | 2.2584 | 0.31447 | 0.0054437 | 0.24012 | 0.0051975 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |       9 | 0.000156 | 0.05 |          20 | [0.52, 1.0] | testid     | 2.303  | 0.29439 | 0.0053613 | 0.232   | 0.0052603 |
+
+
+done! total time: 1:25:18

data_scaling/n1600_2/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 n1600_2; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_2/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n1600_2/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

data_scaling/n1600_2/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
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+flat_mae,patch,logistic,ppmi_dx,93,0.005994842503189409,test,0.6,0.04299514391184195,0.554367201426025,0.048838026298816464,0.5551782682512734,0.04597629673000883
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,train,0.7170818505338078,0.017034209960037846,0.670658219170509,0.02120093499868722,0.665863840719332,0.0190772340312857
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,test,0.67,0.04243572080217325,0.6239316239316239,0.04989518625406,0.6218166383701189,0.045969326044563885
+flat_mae,patch,logistic,ppmi_dx,95,0.005994842503189409,train,0.7277580071174378,0.016707499445872142,0.6859750419072452,0.02080287364708624,0.6797527296082209,0.019040110909972884
+flat_mae,patch,logistic,ppmi_dx,95,0.005994842503189409,test,0.59,0.03643323208281143,0.48589341692789967,0.046729922896647375,0.5114601018675722,0.03810114201071901
+flat_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,train,0.7419928825622776,0.016726149389603262,0.702394647559154,0.021042823855342608,0.6947923356882895,0.01939269367975713
+flat_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,test,0.56,0.04099140885600298,0.48574100046750823,0.046075441278664905,0.49745331069609505,0.04168240788763774
+flat_mae,patch,logistic,ppmi_dx,97,0.005994842503189409,train,0.7188612099644128,0.01642007074184314,0.6701338841255925,0.02076328542669974,0.6655694712053094,0.018562104678165804
+flat_mae,patch,logistic,ppmi_dx,97,0.005994842503189409,test,0.69,0.04169206639158102,0.6343908479773559,0.05311016640407874,0.6328522920203735,0.04679064491037331
+flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,train,0.9092526690391459,0.011811929362191661,0.9027810283597733,0.012816101309080064,0.8975995504174694,0.013401791456099918
+flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,test,0.62,0.046904345214489454,0.5824175824175825,0.05258840931482419,0.5814940577249575,0.05027702145373579
+flat_mae,patch,logistic,ppmi_dx,99,0.005994842503189409,train,0.7402135231316725,0.015613392232174663,0.6921818727490996,0.02052825006875797,0.6855196959965746,0.018235970459112225
+flat_mae,patch,logistic,ppmi_dx,99,0.005994842503189409,test,0.65,0.039627722619398655,0.5792763553311696,0.05079452933492927,0.5853140916808149,0.04339808130107774
+flat_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,train,0.7722419928825622,0.0170135422472702,0.744896945969332,0.02005680169020756,0.7358836437593663,0.019340650338508545
+flat_mae,patch,logistic,ppmi_dx,100,0.046415888336127774,test,0.69,0.04364446814889602,0.6656239887822242,0.048604387223645525,0.6634125636672326,0.04826104137813689

data_scaling/n1600_2/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 17:27:35
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_2; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_2/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n1600_2/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: 0:17:04    time: 4.4141  data: 3.4733  max mem: 2698
+extract (train)  [ 20/232]  eta: 0:01:21    time: 0.1833  data: 0.0579  max mem: 2851
+extract (train)  [ 40/232]  eta: 0:00:53    time: 0.1680  data: 0.0514  max mem: 2851
+extract (train)  [ 60/232]  eta: 0:00:41    time: 0.1601  data: 0.0514  max mem: 2851
+extract (train)  [ 80/232]  eta: 0:00:34    time: 0.1771  data: 0.0578  max mem: 2851
+extract (train)  [100/232]  eta: 0:00:28    time: 0.1726  data: 0.0561  max mem: 2851
+extract (train)  [120/232]  eta: 0:00:23    time: 0.1655  data: 0.0530  max mem: 2851
+extract (train)  [140/232]  eta: 0:00:18    time: 0.1468  data: 0.0424  max mem: 2851
+extract (train)  [160/232]  eta: 0:00:14    time: 0.1766  data: 0.0600  max mem: 2851
+extract (train)  [180/232]  eta: 0:00:10    time: 0.1949  data: 0.0675  max mem: 2851
+extract (train)  [200/232]  eta: 0:00:06    time: 0.1772  data: 0.0580  max mem: 2851
+extract (train)  [220/232]  eta: 0:00:02    time: 0.1401  data: 0.0401  max mem: 2851
+extract (train)  [231/232]  eta: 0:00:00    time: 0.1380  data: 0.0400  max mem: 2851
+extract (train) Total time: 0:00:43 (0.1874 s / it)
+extract (validation)  [ 0/50]  eta: 0:02:35    time: 3.1083  data: 2.9695  max mem: 2851
+extract (validation)  [20/50]  eta: 0:00:10    time: 0.1963  data: 0.0693  max mem: 2851
+extract (validation)  [40/50]  eta: 0:00:02    time: 0.1477  data: 0.0453  max mem: 2851
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.1469  data: 0.0463  max mem: 2851
+extract (validation) Total time: 0:00:11 (0.2304 s / it)
+extract (test)  [ 0/50]  eta: 0:02:30    time: 3.0098  data: 2.8459  max mem: 2851
+extract (test)  [20/50]  eta: 0:00:10    time: 0.2032  data: 0.0730  max mem: 2851
+extract (test)  [40/50]  eta: 0:00:02    time: 0.1448  data: 0.0424  max mem: 2851
+extract (test)  [49/50]  eta: 0:00:00    time: 0.1493  data: 0.0464  max mem: 2851
+extract (test) Total time: 0:00:11 (0.2307 s / it)
+feature extraction time: 0:01:06
+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   |         | 1291.5 | train   |   1   |  0        | 1       |  0       | 1       |   0        |
+| flat_mae | patch  | logistic | ppmi_dx   |         | 1291.5 | test    |   0.6 |  0.048694 | 0.58333 |  0.04891 | 0.58773 |   0.049742 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.040429073697031456, "f1": 0.5552350042072365, "f1_std": 0.051637656648449576, "bacc": 0.5640916808149405, "bacc_std": 0.043971221488107816}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.66, "acc_std": 0.046820828698347486, "f1": 0.6263736263736264, "f1_std": 0.05147980260544329, "bacc": 0.6239388794567062, "bacc_std": 0.04995305323507526}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.57, "acc_std": 0.05138771837705971, "f1": 0.5361881134721174, "f1_std": 0.05409505915376182, "bacc": 0.5360780984719864, "bacc_std": 0.0528845561622563}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.04390704271526381, "f1": 0.5847828526540231, "f1_std": 0.04937889117923564, "bacc": 0.5844651952461799, "bacc_std": 0.04627010872655931}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04215563544770735, "f1": 0.5481404240528328, "f1_std": 0.04944228140894859, "bacc": 0.5530560271646858, "bacc_std": 0.04436183537151056}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04723904740783837, "f1": 0.5555555555555556, "f1_std": 0.05504323312341346, "bacc": 0.5581494057724957, "bacc_std": 0.050665388896024274}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.0469717319246374, "f1": 0.6043956043956044, "f1_std": 0.05181346642499735, "bacc": 0.6027164685908319, "bacc_std": 0.04986930878419167}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.045169440997205185, "f1": 0.5906626839252129, "f1_std": 0.04999388637579124, "bacc": 0.5895585738539898, "bacc_std": 0.04744115142272761}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 1291.5496650148827, "split": "test", "acc": 0.55, "acc_std": 0.0488961798098788, "f1": 0.5331465919701214, "f1_std": 0.04948299283281709, "bacc": 0.5352292020373515, "bacc_std": 0.05013425620223031}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 1291.5496650148827, "split": "test", "acc": 0.52, "acc_std": 0.050159310202593496, "f1": 0.5, "f1_std": 0.05106733050086067, "bacc": 0.5008488964346349, "bacc_std": 0.05180919087564185}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.04375511398682445, "f1": 0.5555555555555556, "f1_std": 0.050426227916429776, "bacc": 0.5581494057724957, "bacc_std": 0.04628535296942758}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.04517087114502, "f1": 0.5404411764705883, "f1_std": 0.05208567371761849, "bacc": 0.5449915110356536, "bacc_std": 0.0475872271671216}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.59, "acc_std": 0.045775294646785175, "f1": 0.5464100011063171, "f1_std": 0.05084082978592787, "bacc": 0.5471137521222411, "bacc_std": 0.04818313486811179}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.59, "acc_std": 0.04136616975258888, "f1": 0.5071523019593701, "f1_std": 0.05100424828546607, "bacc": 0.5216468590831919, "bacc_std": 0.043527726675725895}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.68, "acc_std": 0.04502081296467223, "f1": 0.6381727725011307, "f1_std": 0.05225141557565537, "bacc": 0.634974533106961, "bacc_std": 0.048819454589090455}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.55, "acc_std": 0.04828972561529005, "f1": 0.529239460194581, "f1_std": 0.04967495040696181, "bacc": 0.5301358234295416, "bacc_std": 0.05043605258250788}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04262742779009778, "f1": 0.5792426367461431, "f1_std": 0.05111855259715643, "bacc": 0.5823429541595926, "bacc_std": 0.045750350889944856}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.56, "acc_std": 0.043838115835423394, "f1": 0.5024875621890548, "f1_std": 0.04892075497756002, "bacc": 0.5076400679117148, "bacc_std": 0.04541456522462666}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 19, "C": 1291.5496650148827, "split": "test", "acc": 0.55, "acc_std": 0.049460998776814036, "f1": 0.5331465919701214, "f1_std": 0.050819585653223254, "bacc": 0.5352292020373515, "bacc_std": 0.05178693166345413}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04291503699171189, "f1": 0.5863970588235294, "f1_std": 0.05053796835688112, "bacc": 0.5874363327674024, "bacc_std": 0.04588832447355167}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 21, "C": 0.000774263682681127, "split": "test", "acc": 0.66, "acc_std": 0.032773959174930326, "f1": 0.5582120582120582, "f1_std": 0.0502627408930332, "bacc": 0.5780984719864176, "bacc_std": 0.03760463163987164}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.6, "acc_std": 0.0474968798975259, "f1": 0.5755517826825127, "f1_std": 0.049401388660944005, "bacc": 0.5755517826825127, "bacc_std": 0.04950875849670929}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 23, "C": 1291.5496650148827, "split": "test", "acc": 0.53, "acc_std": 0.047204961603628075, "f1": 0.5037482842360892, "f1_std": 0.04839700884090082, "bacc": 0.5038200339558574, "bacc_std": 0.04841371843096276}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04260645491002508, "f1": 0.5311936530833032, "f1_std": 0.05151230649124757, "bacc": 0.5428692699490663, "bacc_std": 0.04462887080388074}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.03839439542433245, "f1": 0.5311936530833032, "f1_std": 0.04830015076294016, "bacc": 0.5428692699490663, "bacc_std": 0.04111604196852534}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.04630826708051167, "f1": 0.5793334052421529, "f1_std": 0.04873201275399989, "bacc": 0.5785229202037352, "bacc_std": 0.047714744481380585}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.62, "acc_std": 0.046904345214489454, "f1": 0.5824175824175825, "f1_std": 0.05258840931482419, "bacc": 0.5814940577249575, "bacc_std": 0.05027702145373579}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.039627722619398655, "f1": 0.5792763553311696, "f1_std": 0.05079452933492927, "bacc": 0.5853140916808149, "bacc_std": 0.04339808130107774}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.69, "acc_std": 0.04364446814889602, "f1": 0.6656239887822242, "f1_std": 0.048604387223645525, "bacc": 0.6634125636672326, "bacc_std": 0.04826104137813689}
+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 | 59.099 |  254.96 | 0.80719 |  0.11168  | 0.77649 | 0.13286  | 0.77193 |   0.13331  |
+| flat_mae | patch  | logistic | ppmi_dx   | test    |        100 | 59.099 |  254.96 | 0.6248  |  0.050121 | 0.57879 | 0.050196 | 0.58114 |   0.047695 |
+
+
+done! total time: 0:05:15

data_scaling/n1600_2/pretrain/config.yaml

@@ -0,0 +1,109 @@
+name: data_scaling/n1600_2/pretrain
+notes: data scaling experiment n1600_2 (seed=3472)
+output_dir: experiments/data_scaling/output/data_scaling/n1600_2/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..01599}.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: 3472
+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/n1600_2/pretrain/log.json

@@ -0,0 +1,100 @@
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data_scaling/n200_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 n200_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/aabc_age__patch__logistic
+remote_dir: null

data_scaling/n200_1/eval_v2/aabc_age__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,train,0.5295275590551181,0.01995789595962517,0.5181940322364641,0.02051263410108343,0.5299463308585336,0.019917509239800586
+flat_mae,patch,logistic,aabc_age,88,0.000774263682681127,test,0.4423076923076923,0.06214456328241539,0.4377320954907162,0.06227833438814349,0.4416208791208791,0.062423911894021605
+flat_mae,patch,logistic,aabc_age,89,0.005994842503189409,train,0.6062992125984252,0.02060869971485363,0.5991144578788364,0.021359297242644983,0.6087224461966018,0.020612953081786546
+flat_mae,patch,logistic,aabc_age,89,0.005994842503189409,test,0.5,0.06385328968463798,0.49599462365591396,0.06491230478651001,0.5041208791208791,0.06421586804131663
+flat_mae,patch,logistic,aabc_age,90,0.3593813663804626,train,0.9448818897637795,0.010155722243020839,0.9456000047073481,0.010042453366968154,0.9461768228712153,0.009993833742652385
+flat_mae,patch,logistic,aabc_age,90,0.3593813663804626,test,0.5576923076923077,0.06473391143705769,0.5544650751547303,0.06479893145571873,0.5604395604395604,0.06490436175671059
+flat_mae,patch,logistic,aabc_age,91,0.005994842503189409,train,0.6240157480314961,0.02116454142842758,0.619185134982426,0.021804597858218866,0.6262472693133151,0.021093216407927166
+flat_mae,patch,logistic,aabc_age,91,0.005994842503189409,test,0.5384615384615384,0.06798215916324694,0.533219653909309,0.07034789474331184,0.5382326007326007,0.06812227867564317
+flat_mae,patch,logistic,aabc_age,92,0.046415888336127774,train,0.7440944881889764,0.01911987921351916,0.7413916641247644,0.019578168257505656,0.7445917299751241,0.01909237729207365
+flat_mae,patch,logistic,aabc_age,92,0.046415888336127774,test,0.4230769230769231,0.05956214744520234,0.4062968515742129,0.05962622929010991,0.4267399267399267,0.06015573623882396
+flat_mae,patch,logistic,aabc_age,93,0.3593813663804626,train,0.9468503937007874,0.009845523950947628,0.9474367517597551,0.0097395003886213,0.9479253973520729,0.009679688213680968
+flat_mae,patch,logistic,aabc_age,93,0.3593813663804626,test,0.40384615384615385,0.058746408677844975,0.3928571428571429,0.05766718327000729,0.4015567765567766,0.05853243577775881
+flat_mae,patch,logistic,aabc_age,94,0.3593813663804626,train,0.9468503937007874,0.009992534723557387,0.9474466795113512,0.00988598837191807,0.9476078561304516,0.009829310589794863
+flat_mae,patch,logistic,aabc_age,94,0.3593813663804626,test,0.46153846153846156,0.06873939939791299,0.46016483516483514,0.0685140773443774,0.4597069597069597,0.06869828200008239
+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,0.4230769230769231,0.061226372087959405,0.3980815182428086,0.057753373124122744,0.4191849816849817,0.0603176273419392
+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,0.5,0.0700726060478929,0.4956667585699843,0.07071223369597181,0.49679487179487175,0.06983658930805174
+flat_mae,patch,logistic,aabc_age,97,0.3593813663804626,train,0.9507874015748031,0.00970430195052444,0.9516338201458279,0.009525239613592392,0.951690100865189,0.00956820780557337
+flat_mae,patch,logistic,aabc_age,97,0.3593813663804626,test,0.5384615384615384,0.07193969367899213,0.5374625374625375,0.07272528511678485,0.5384615384615385,0.07195594867451986
+flat_mae,patch,logistic,aabc_age,98,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,98,2.782559402207126,test,0.5384615384615384,0.06284877830882031,0.5383602841436425,0.06397926813270578,0.5414377289377289,0.06310083630638856
+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,0.46153846153846156,0.06530651444076899,0.4596164021164021,0.06568385806912216,0.4626831501831502,0.06551568030153448
+flat_mae,patch,logistic,aabc_age,100,0.005994842503189409,train,0.6200787401574803,0.02174939415414351,0.6128998111810897,0.022326097548752453,0.6217974966867348,0.021601562967469512
+flat_mae,patch,logistic,aabc_age,100,0.005994842503189409,test,0.4807692307692308,0.06605192282496959,0.4790993788819876,0.06546119095953593,0.4832875457875458,0.06621330980406112

data_scaling/n200_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 17:14:52
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n200_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_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: 0:20:28    time: 5.3890  data: 4.6243  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:51    time: 0.2950  data: 0.0751  max mem: 3393
+extract (train)  [ 40/228]  eta: 0:01:18    time: 0.2885  data: 0.0755  max mem: 3393
+extract (train)  [ 60/228]  eta: 0:01:02    time: 0.2763  data: 0.0778  max mem: 3393
+extract (train)  [ 80/228]  eta: 0:00:49    time: 0.2346  data: 0.0582  max mem: 3393
+extract (train)  [100/228]  eta: 0:00:40    time: 0.2502  data: 0.0645  max mem: 3393
+extract (train)  [120/228]  eta: 0:00:32    time: 0.2047  data: 0.0540  max mem: 3393
+extract (train)  [140/228]  eta: 0:00:25    time: 0.2143  data: 0.0553  max mem: 3393
+extract (train)  [160/228]  eta: 0:00:19    time: 0.2309  data: 0.0647  max mem: 3393
+extract (train)  [180/228]  eta: 0:00:13    time: 0.2044  data: 0.0525  max mem: 3393
+extract (train)  [200/228]  eta: 0:00:07    time: 0.2005  data: 0.0543  max mem: 3393
+extract (train)  [220/228]  eta: 0:00:02    time: 0.1677  data: 0.0425  max mem: 3393
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1637  data: 0.0422  max mem: 3393
+extract (train) Total time: 0:00:58 (0.2552 s / it)
+extract (validation)  [ 0/27]  eta: 0:01:54    time: 4.2337  data: 4.0576  max mem: 3393
+extract (validation)  [20/27]  eta: 0:00:02    time: 0.1634  data: 0.0389  max mem: 3393
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1491  data: 0.0347  max mem: 3393
+extract (validation) Total time: 0:00:08 (0.3193 s / it)
+extract (test)  [ 0/26]  eta: 0:01:38    time: 3.7694  data: 3.6285  max mem: 3393
+extract (test)  [20/26]  eta: 0:00:01    time: 0.1531  data: 0.0361  max mem: 3393
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1492  data: 0.0340  max mem: 3393
+extract (test) Total time: 0:00:07 (0.3002 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  |         | 0.00077426 | train   | 0.53937 |  0.022059 | 0.5338  | 0.022508 | 0.54011 |   0.022068 |
+| flat_mae | patch  | logistic | aabc_age  |         | 0.00077426 | test    | 0.38462 |  0.063958 | 0.36673 | 0.066177 | 0.37569 |   0.063652 |
+
+
+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": 0.06396896205589767, "f1": 0.4669047619047619, "f1_std": 0.06279221697137408, "bacc": 0.4624542124542124, "bacc_std": 0.06409288727980507}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 0.000774263682681127, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06617480660784585, "f1": 0.536163204947234, "f1_std": 0.06985378790914647, "bacc": 0.5528846153846154, "bacc_std": 0.06572905825157893}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.0641131927085989, "f1": 0.47806794654620743, "f1_std": 0.06856255669212304, "bacc": 0.49793956043956045, "bacc_std": 0.06413696122365242}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.0661074412375963, "f1": 0.5563556067588326, "f1_std": 0.06803194653615789, "bacc": 0.5560897435897436, "bacc_std": 0.066389090836557}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06377729140852788, "f1": 0.47761155264661576, "f1_std": 0.06425566165041992, "bacc": 0.4789377289377289, "bacc_std": 0.06393883245921134}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06639909816169486, "f1": 0.5443007662835249, "f1_std": 0.06605558079270425, "bacc": 0.5414377289377289, "bacc_std": 0.06662756325587842}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.5, "acc_std": 0.0608228098757648, "f1": 0.4697020328599276, "f1_std": 0.05819924773328102, "bacc": 0.4919871794871795, "bacc_std": 0.060052782324222495}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06498092574024451, "f1": 0.5071816770186335, "f1_std": 0.0672882040103936, "bacc": 0.5247252747252747, "bacc_std": 0.06579880011621912}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.6346153846153846, "acc_std": 0.057377809454104124, "f1": 0.6005973715651134, "f1_std": 0.06798608035528797, "bacc": 0.6298076923076923, "bacc_std": 0.057205245488500346}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.5, "acc_std": 0.0655594382284382, "f1": 0.4964607139245627, "f1_std": 0.0673214641532656, "bacc": 0.4981684981684982, "bacc_std": 0.06556544048324738}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.5961538461538461, "acc_std": 0.07058089487880903, "f1": 0.5941964285714286, "f1_std": 0.07358466242483123, "bacc": 0.5934065934065934, "bacc_std": 0.07084845898082233}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06340878926626038, "f1": 0.45216120931131076, "f1_std": 0.06588660531386337, "bacc": 0.4567307692307692, "bacc_std": 0.06314896229872773}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06386348235649404, "f1": 0.4301242236024845, "f1_std": 0.06584836077820744, "bacc": 0.44024725274725274, "bacc_std": 0.06383431123604838}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06655042133511746, "f1": 0.5795520890348477, "f1_std": 0.06756813768531444, "bacc": 0.5801282051282051, "bacc_std": 0.06619642880262602}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06747951694477808, "f1": 0.5273613193403298, "f1_std": 0.06661289977831557, "bacc": 0.5222069597069597, "bacc_std": 0.06749041936009575}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06919310941511839, "f1": 0.46235431235431235, "f1_std": 0.07128658654310129, "bacc": 0.4585622710622711, "bacc_std": 0.06918736871129098}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06486785611839233, "f1": 0.5412614760440847, "f1_std": 0.06984316386201626, "bacc": 0.5544871794871795, "bacc_std": 0.06474363211648468}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06387037307087706, "f1": 0.5467816091954023, "f1_std": 0.06647431405763161, "bacc": 0.5558608058608059, "bacc_std": 0.06365486619812823}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06698797693059881, "f1": 0.37920548654244307, "f1_std": 0.06865394793279432, "bacc": 0.38118131868131866, "bacc_std": 0.0668742260792529}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 20, "C": 21.54434690031882, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06709062801065366, "f1": 0.48400357823569606, "f1_std": 0.0650703654840754, "bacc": 0.4787087912087912, "bacc_std": 0.0670586418984146}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 76, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.06910264854188267, "f1": 0.5053872053872054, "f1_std": 0.06933484325181331, "bacc": 0.5043498168498168, "bacc_std": 0.0693173167325263}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 77, "C": 0.3593813663804626, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06615486358788264, "f1": 0.4555569132182036, "f1_std": 0.06844981323287663, "bacc": 0.4626831501831502, "bacc_std": 0.06629027264149517}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 81, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.07026167278624879, "f1": 0.5353317659352143, "f1_std": 0.0723696191735435, "bacc": 0.5428113553113553, "bacc_std": 0.07032613495787049}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 82, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06359166146818325, "f1": 0.5354830987867412, "f1_std": 0.06920977040455437, "bacc": 0.551510989010989, "bacc_std": 0.06333838573551276}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 94, "C": 0.3593813663804626, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06873939939791299, "f1": 0.46016483516483514, "f1_std": 0.0685140773443774, "bacc": 0.4597069597069597, "bacc_std": 0.06869828200008239}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 95, "C": 2.782559402207126, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.061226372087959405, "f1": 0.3980815182428086, "f1_std": 0.057753373124122744, "bacc": 0.4191849816849817, "bacc_std": 0.0603176273419392}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 96, "C": 2.782559402207126, "split": "test", "acc": 0.5, "acc_std": 0.0700726060478929, "f1": 0.4956667585699843, "f1_std": 0.07071223369597181, "bacc": 0.49679487179487175, "bacc_std": 0.06983658930805174}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.07193969367899213, "f1": 0.5374625374625375, "f1_std": 0.07272528511678485, "bacc": 0.5384615384615385, "bacc_std": 0.07195594867451986}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 2.782559402207126, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06284877830882031, "f1": 0.5383602841436425, "f1_std": 0.06397926813270578, "bacc": 0.5414377289377289, "bacc_std": 0.06310083630638856}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 166.81005372000556, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06530651444076899, "f1": 0.4596164021164021, "f1_std": 0.06568385806912216, "bacc": 0.4626831501831502, "bacc_std": 0.06551568030153448}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06605192282496959, "f1": 0.4790993788819876, "f1_std": 0.06546119095953593, "bacc": 0.4832875457875458, "bacc_std": 0.06621330980406112}
+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 | 22.374 |   133.3 | 0.78051 |  0.16288  | 0.77786 | 0.16576  | 0.78143 |   0.16239  |
+| flat_mae | patch  | logistic | aabc_age  | test    |        100 | 22.374 |   133.3 | 0.49135 |  0.058328 | 0.48306 | 0.056512 | 0.49031 |   0.058299 |
+
+
+done! total time: 0:05:58

data_scaling/n200_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 n200_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

data_scaling/n200_1/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n200_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 17:14:52
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n200_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_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: 0:16:39    time: 4.2341  data: 3.5593  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:01:31    time: 0.2315  data: 0.0785  max mem: 3393
+extract (train)  [ 40/236]  eta: 0:01:01    time: 0.1997  data: 0.0536  max mem: 3393
+extract (train)  [ 60/236]  eta: 0:00:47    time: 0.1851  data: 0.0521  max mem: 3393
+extract (train)  [ 80/236]  eta: 0:00:39    time: 0.1852  data: 0.0522  max mem: 3393
+extract (train)  [100/236]  eta: 0:00:32    time: 0.1883  data: 0.0547  max mem: 3393
+extract (train)  [120/236]  eta: 0:00:26    time: 0.1931  data: 0.0571  max mem: 3393
+extract (train)  [140/236]  eta: 0:00:21    time: 0.1861  data: 0.0520  max mem: 3393
+extract (train)  [160/236]  eta: 0:00:16    time: 0.1817  data: 0.0516  max mem: 3393
+extract (train)  [180/236]  eta: 0:00:11    time: 0.1717  data: 0.0466  max mem: 3393
+extract (train)  [200/236]  eta: 0:00:07    time: 0.1930  data: 0.0560  max mem: 3393
+extract (train)  [220/236]  eta: 0:00:03    time: 0.1521  data: 0.0361  max mem: 3393
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1456  data: 0.0337  max mem: 3393
+extract (train) Total time: 0:00:48 (0.2039 s / it)
+extract (validation)  [ 0/29]  eta: 0:01:40    time: 3.4769  data: 3.3482  max mem: 3393
+extract (validation)  [20/29]  eta: 0:00:02    time: 0.1685  data: 0.0407  max mem: 3393
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1477  data: 0.0335  max mem: 3393
+extract (validation) Total time: 0:00:08 (0.2850 s / it)
+extract (test)  [ 0/28]  eta: 0:01:36    time: 3.4332  data: 3.2992  max mem: 3393
+extract (test)  [20/28]  eta: 0:00:02    time: 0.1484  data: 0.0338  max mem: 3393
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1428  data: 0.0320  max mem: 3393
+extract (test) Total time: 0:00:07 (0.2707 s / it)
+feature extraction time: 0:01:04
+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  |         | 0.0001 | train   | 0.82231 |  0.016575 | 0.8125  | 0.018018 | 0.80618 |   0.017921 |
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.0001 | test    | 0.87273 |  0.047047 | 0.86637 | 0.050206 | 0.86364 |   0.051247 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05963875826677904, "f1": 0.7585275244849713, "f1_std": 0.061043747028189106, "bacc": 0.7601902173913043, "bacc_std": 0.060942367949982194}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05096614984234206, "f1": 0.8074229691876751, "f1_std": 0.056010939062922475, "bacc": 0.8009510869565217, "bacc_std": 0.05510995879587451}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04892016096417026, "f1": 0.8281846581048247, "f1_std": 0.052511582534858915, "bacc": 0.8226902173913043, "bacc_std": 0.052278243880216874}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.047335496788333646, "f1": 0.8533333333333333, "f1_std": 0.047276725464629986, "bacc": 0.8627717391304348, "bacc_std": 0.045007834791071}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.056522942025398946, "f1": 0.7758152173913043, "f1_std": 0.058312520721893704, "bacc": 0.7758152173913043, "bacc_std": 0.05820996688529159}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 1291.5496650148827, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.046749142388385956, "f1": 0.84593837535014, "f1_std": 0.05172714714288341, "bacc": 0.8383152173913043, "bacc_std": 0.0520769404768327}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.000774263682681127, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04747876776444129, "f1": 0.8484848484848485, "f1_std": 0.05033052039079987, "bacc": 0.8444293478260869, "bacc_std": 0.05086820185835605}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.000774263682681127, "split": "test", "acc": 0.8, "acc_std": 0.05062386331751144, "f1": 0.7931623931623932, "f1_std": 0.05291327392953862, "bacc": 0.7914402173913043, "bacc_std": 0.053140379511720584}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.0532391951556975, "f1": 0.8151881720430108, "f1_std": 0.05390843846619316, "bacc": 0.8192934782608696, "bacc_std": 0.05393268558466024}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04532156547958885, "f1": 0.8711943793911007, "f1_std": 0.04543596047743937, "bacc": 0.8783967391304348, "bacc_std": 0.04373788759805453}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.03823841692494107, "f1": 0.8821428571428571, "f1_std": 0.044853582200982456, "bacc": 0.8695652173913043, "bacc_std": 0.045719846323299104}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05511291489687105, "f1": 0.7727272727272727, "f1_std": 0.0585360205050311, "bacc": 0.7697010869565217, "bacc_std": 0.05820739067623639}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 9.999999999999999e-05, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.041837845179409014, "f1": 0.8863636363636364, "f1_std": 0.04450551839411857, "bacc": 0.8817934782608696, "bacc_std": 0.045627809455768574}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05417937761434404, "f1": 0.790003471017008, "f1_std": 0.05805186646140256, "bacc": 0.7853260869565217, "bacc_std": 0.057594988243102045}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05449708158081902, "f1": 0.7975911676145868, "f1_std": 0.054963903154801146, "bacc": 0.8036684782608696, "bacc_std": 0.054599590023029415}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.000774263682681127, "split": "test", "acc": 0.8, "acc_std": 0.05544022467844707, "f1": 0.7931623931623932, "f1_std": 0.057687005094238214, "bacc": 0.7914402173913043, "bacc_std": 0.05751685760313325}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05262876063789636, "f1": 0.8106060606060606, "f1_std": 0.05561245603839103, "bacc": 0.8070652173913043, "bacc_std": 0.0554625404049746}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05667236270189268, "f1": 0.7585275244849713, "f1_std": 0.0579640681689636, "bacc": 0.7601902173913043, "bacc_std": 0.05805032540321126}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04918703047493461, "f1": 0.8343927735028438, "f1_std": 0.049377339078782355, "bacc": 0.8410326086956521, "bacc_std": 0.048256322215106104}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04611463975789031, "f1": 0.84593837535014, "f1_std": 0.050835627373937875, "bacc": 0.8383152173913043, "bacc_std": 0.05095297851561257}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.3593813663804626, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.053245900785067204, "f1": 0.8151881720430108, "f1_std": 0.054106815769444615, "bacc": 0.8192934782608696, "bacc_std": 0.05388143075391746}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03790995856866222, "f1": 0.9079959852793577, "f1_std": 0.03803304522669623, "bacc": 0.9157608695652174, "bacc_std": 0.03585892144864134}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.0497720290528029, "f1": 0.8505434782608696, "f1_std": 0.05150109050171396, "bacc": 0.8505434782608696, "bacc_std": 0.051486921252089875}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04622416097509222, "f1": 0.8683760683760684, "f1_std": 0.048187831480914364, "bacc": 0.8661684782608696, "bacc_std": 0.04859333032781537}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04214098336938571, "f1": 0.89, "f1_std": 0.04211953352778616, "bacc": 0.9001358695652174, "bacc_std": 0.039320137870911646}
+{"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": 0.3593813663804626, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.03499791729340524, "f1": 0.9260752688172043, "f1_std": 0.03528238942593053, "bacc": 0.9313858695652174, "bacc_std": 0.033331541677589935}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05455053309690955, "f1": 0.76890756302521, "f1_std": 0.05959466852272445, "bacc": 0.7635869565217391, "bacc_std": 0.058443552942586664}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 0.3593813663804626, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04651491896102164, "f1": 0.8281846581048247, "f1_std": 0.04980723381199522, "bacc": 0.8226902173913043, "bacc_std": 0.04974395160732304}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06105838961312983, "f1": 0.741263440860215, "f1_std": 0.06204672921554356, "bacc": 0.7445652173913043, "bacc_std": 0.06214308914016745}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.052616862457543316, "f1": 0.795677136102668, "f1_std": 0.05337598306017483, "bacc": 0.7975543478260869, "bacc_std": 0.05280562085375003}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04828291281003544, "f1": 0.8307692307692308, "f1_std": 0.05052228051792292, "bacc": 0.8288043478260869, "bacc_std": 0.05068179332819235}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.0419347623997163, "f1": 0.8711943793911007, "f1_std": 0.042148685193013825, "bacc": 0.8783967391304348, "bacc_std": 0.04083431505800604}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 0.3593813663804626, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04847657353627811, "f1": 0.8281846581048247, "f1_std": 0.05193491914503974, "bacc": 0.8226902173913043, "bacc_std": 0.05184961577085621}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 35, "C": 0.000774263682681127, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04860689336549524, "f1": 0.8250265111346766, "f1_std": 0.05417576295187562, "bacc": 0.8165760869565217, "bacc_std": 0.0535672711797696}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 36, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04269989064572676, "f1": 0.8683760683760684, "f1_std": 0.044493220207708566, "bacc": 0.8661684782608696, "bacc_std": 0.04501132440277631}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 37, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05681088244021882, "f1": 0.7518222839291913, "f1_std": 0.06128367398974725, "bacc": 0.7479619565217391, "bacc_std": 0.060426977546705556}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 38, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05413962970073058, "f1": 0.790003471017008, "f1_std": 0.05814803632702101, "bacc": 0.7853260869565217, "bacc_std": 0.05772370204947282}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.05392710108475104, "f1": 0.7931623931623932, "f1_std": 0.05613271478559031, "bacc": 0.7914402173913043, "bacc_std": 0.055938920985531354}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 40, "C": 0.3593813663804626, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.04939208622662318, "f1": 0.8106060606060606, "f1_std": 0.052118592532249854, "bacc": 0.8070652173913043, "bacc_std": 0.05216803937937907}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 41, "C": 9.999999999999999e-05, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03766983140853046, "f1": 0.905982905982906, "f1_std": 0.03950602611861526, "bacc": 0.9035326086956521, "bacc_std": 0.04020209983511469}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 42, "C": 2.782559402207126, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05828421670877436, "f1": 0.7433333333333334, "f1_std": 0.05840780874356886, "bacc": 0.7506793478260869, "bacc_std": 0.0576633953006058}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05546993527135454, "f1": 0.7727272727272727, "f1_std": 0.05910887385191523, "bacc": 0.7697010869565217, "bacc_std": 0.05848077950054914}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.046178367802387287, "f1": 0.8250265111346766, "f1_std": 0.05130275545967982, "bacc": 0.8165760869565217, "bacc_std": 0.05097952429239414}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.043108611630325255, "f1": 0.8639095086603039, "f1_std": 0.04863199169717293, "bacc": 0.8539402173913043, "bacc_std": 0.04925282316330472}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.03997519892290243, "f1": 0.89, "f1_std": 0.03993673829927572, "bacc": 0.9001358695652174, "bacc_std": 0.03710348543578273}
+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 | 27.943 |   182.2 | 0.91907 |  0.046628 | 0.91635 | 0.048564 | 0.91443 |   0.049674 |
+| flat_mae | patch  | logistic | aabc_sex  | test    |        100 | 27.943 |   182.2 | 0.84018 |  0.046466 | 0.83412 | 0.048361 | 0.83276 |   0.048938 |
+
+
+done! total time: 0:04:53

data_scaling/n200_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 n200_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/abide_dx__patch__logistic
+remote_dir: null

data_scaling/n200_1/eval_v2/abide_dx__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,abide_dx,80,0.005994842503189409,test,0.5887096774193549,0.045124362782706026,0.5854473942969518,0.045373802744253205,0.585609243697479,0.04528081462630979
+flat_mae,patch,logistic,abide_dx,81,0.005994842503189409,train,0.688034188034188,0.01676538712747326,0.6771315704916174,0.017603844841818626,0.6763012181616832,0.017129841256208353
+flat_mae,patch,logistic,abide_dx,81,0.005994842503189409,test,0.5725806451612904,0.04348720612748299,0.5599598259122867,0.04521772634850149,0.5614495798319328,0.04401366617922187
+flat_mae,patch,logistic,abide_dx,82,0.005994842503189409,train,0.698005698005698,0.01715341058825607,0.6901178528297172,0.01796912143472099,0.6888888888888889,0.017683757286605963
+flat_mae,patch,logistic,abide_dx,82,0.005994842503189409,test,0.5806451612903226,0.04380338722713393,0.5643243243243243,0.04592060163987864,0.5672268907563025,0.0442914929512199
+flat_mae,patch,logistic,abide_dx,83,0.005994842503189409,train,0.6923076923076923,0.016733942432789643,0.6836703188497709,0.017379108821969226,0.6825396825396826,0.017069387972965207
+flat_mae,patch,logistic,abide_dx,83,0.005994842503189409,test,0.6290322580645161,0.046566220065586916,0.6169755573462261,0.0485562403896337,0.6176470588235294,0.0472045008923006
+flat_mae,patch,logistic,abide_dx,84,0.005994842503189409,train,0.6965811965811965,0.016101411505578533,0.6866458917932264,0.016892176363927657,0.685529715762274,0.01650276644719896
+flat_mae,patch,logistic,abide_dx,84,0.005994842503189409,test,0.6693548387096774,0.04035810048446086,0.6575739206573719,0.043118019429154726,0.657563025210084,0.04156049511720293
+flat_mae,patch,logistic,abide_dx,85,0.000774263682681127,train,0.6467236467236467,0.016995576952864737,0.624161305643107,0.01883345084830444,0.6282022886674049,0.01750206805696902
+flat_mae,patch,logistic,abide_dx,85,0.000774263682681127,test,0.6532258064516129,0.035670754204461225,0.6151569830386142,0.04347549718747418,0.6286764705882353,0.037038712932463705
+flat_mae,patch,logistic,abide_dx,86,0.005994842503189409,train,0.6937321937321937,0.016641084903691247,0.6846710387491304,0.017314899024332486,0.6835363602805463,0.01696875456732406
+flat_mae,patch,logistic,abide_dx,86,0.005994842503189409,test,0.5725806451612904,0.04410804950592898,0.5599598259122867,0.045562289651589434,0.5614495798319328,0.04449807034047879
+flat_mae,patch,logistic,abide_dx,87,0.000774263682681127,train,0.6595441595441596,0.01653884802658016,0.6358691995668055,0.018688921703173426,0.6401255075673681,0.01718151678347984
+flat_mae,patch,logistic,abide_dx,87,0.000774263682681127,test,0.6048387096774194,0.043882502634432524,0.5880957223239103,0.04678210257540907,0.5908613445378151,0.044651792778492676
+flat_mae,patch,logistic,abide_dx,88,0.046415888336127774,train,0.7962962962962963,0.014628962165439937,0.7924961604368937,0.015032981231044936,0.790734588409007,0.01504901423741774
+flat_mae,patch,logistic,abide_dx,88,0.046415888336127774,test,0.5725806451612904,0.04720732440657352,0.5691904293674206,0.04786015406258098,0.569327731092437,0.04781060986115456
+flat_mae,patch,logistic,abide_dx,89,0.005994842503189409,train,0.7051282051282052,0.016652927348841745,0.6957889778615167,0.0175831995303564,0.6944629014396456,0.01719206029340763
+flat_mae,patch,logistic,abide_dx,89,0.005994842503189409,test,0.5725806451612904,0.043839086494618276,0.5712141971683957,0.04409822764368797,0.5724789915966386,0.04427009189941174
+flat_mae,patch,logistic,abide_dx,90,0.000774263682681127,train,0.6495726495726496,0.015507838274435262,0.6271922628556625,0.017190147794733462,0.6310815799187892,0.0159667424175435
+flat_mae,patch,logistic,abide_dx,90,0.000774263682681127,test,0.5806451612903226,0.03929561911437057,0.5507246376811594,0.04460675812202254,0.5609243697478992,0.040264457772849045
+flat_mae,patch,logistic,abide_dx,91,0.046415888336127774,train,0.8091168091168092,0.014723530954886972,0.8057810628897055,0.015064644051985283,0.8041343669250646,0.015064106545195077
+flat_mae,patch,logistic,abide_dx,91,0.046415888336127774,test,0.6129032258064516,0.04181900697569087,0.6092436974789917,0.04226285672423103,0.6092436974789917,0.04218163440629113
+flat_mae,patch,logistic,abide_dx,92,2.782559402207126,train,0.9985754985754985,0.001493758031785558,0.998559926150059,0.001511180774532141,0.9984126984126984,0.0016644732354181946
+flat_mae,patch,logistic,abide_dx,92,2.782559402207126,test,0.5725806451612904,0.04467981826563018,0.5662332519305657,0.04544336216107304,0.5661764705882353,0.04507821426011617
+flat_mae,patch,logistic,abide_dx,93,0.000774263682681127,train,0.6538461538461539,0.016213781093509183,0.6303461725987323,0.018092932255197498,0.6346622369878184,0.01671864636869525
+flat_mae,patch,logistic,abide_dx,93,0.000774263682681127,test,0.5645161290322581,0.04064092399827942,0.5244318181818182,0.04614820692047816,0.5414915966386554,0.0412723103263576
+flat_mae,patch,logistic,abide_dx,94,0.005994842503189409,train,0.7022792022792023,0.017175540644622924,0.694069173170635,0.018067350763869,0.692764857881137,0.01778819036093524
+flat_mae,patch,logistic,abide_dx,94,0.005994842503189409,test,0.5887096774193549,0.042816243987761966,0.5712833028269271,0.0453006265384392,0.5745798319327731,0.04336640910956285
+flat_mae,patch,logistic,abide_dx,95,0.005994842503189409,train,0.7051282051282052,0.016339425276611804,0.6951507840772014,0.016978569621650032,0.6938722775932078,0.01657436736490217
+flat_mae,patch,logistic,abide_dx,95,0.005994842503189409,test,0.6209677419354839,0.041832889390471965,0.6021028196900389,0.04564494929031946,0.6055672268907563,0.04297068923810225
+flat_mae,patch,logistic,abide_dx,96,0.3593813663804626,train,0.9031339031339032,0.011429442061753147,0.9020454396217743,0.01155314020571745,0.9018087855297157,0.011574460190811812
+flat_mae,patch,logistic,abide_dx,96,0.3593813663804626,test,0.5887096774193549,0.04403691042419571,0.5808311791608669,0.04540073122154053,0.5808823529411764,0.04460656458554057
+flat_mae,patch,logistic,abide_dx,97,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,97,1291.5496650148827,test,0.5161290322580645,0.043677047098999736,0.5129615082482325,0.04400493914178148,0.5131302521008403,0.044095563638154756
+flat_mae,patch,logistic,abide_dx,98,0.046415888336127774,train,0.801994301994302,0.01516688254125023,0.7979764100844094,0.01558817401014057,0.7959025470653378,0.01554877218197414
+flat_mae,patch,logistic,abide_dx,98,0.046415888336127774,test,0.5806451612903226,0.04509883642430504,0.5643243243243243,0.04739541333500298,0.5672268907563025,0.045696753031141644
+flat_mae,patch,logistic,abide_dx,99,0.005994842503189409,train,0.6965811965811965,0.016345726241854465,0.6859772808891074,0.017151059306063685,0.684939091915836,0.01670980749828492
+flat_mae,patch,logistic,abide_dx,99,0.005994842503189409,test,0.6129032258064516,0.04074645276722062,0.5951020408163266,0.044123583808845415,0.5982142857142857,0.041744355889530814
+flat_mae,patch,logistic,abide_dx,100,0.005994842503189409,train,0.6937321937321937,0.017720572364155508,0.6830287107566106,0.018621236017921393,0.6820598006644518,0.018115676842795702
+flat_mae,patch,logistic,abide_dx,100,0.005994842503189409,test,0.6209677419354839,0.04292742465745089,0.6137071651090342,0.04383770539646089,0.6134453781512605,0.04337296610351456

data_scaling/n200_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 n200_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_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: 0:21:58    time: 4.5624  data: 3.8008  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:45    time: 0.1847  data: 0.0517  max mem: 2851
+extract (train)  [ 40/289]  eta: 0:01:09    time: 0.1581  data: 0.0411  max mem: 2851
+extract (train)  [ 60/289]  eta: 0:00:55    time: 0.1747  data: 0.0570  max mem: 2851
+extract (train)  [ 80/289]  eta: 0:00:48    time: 0.1862  data: 0.0564  max mem: 2851
+extract (train)  [100/289]  eta: 0:00:42    time: 0.1941  data: 0.0622  max mem: 2851
+extract (train)  [120/289]  eta: 0:00:36    time: 0.1775  data: 0.0511  max mem: 2851
+extract (train)  [140/289]  eta: 0:00:31    time: 0.2028  data: 0.0609  max mem: 2851
+extract (train)  [160/289]  eta: 0:00:27    time: 0.1861  data: 0.0565  max mem: 2851
+extract (train)  [180/289]  eta: 0:00:22    time: 0.1770  data: 0.0511  max mem: 2851
+extract (train)  [200/289]  eta: 0:00:18    time: 0.1787  data: 0.0558  max mem: 2851
+extract (train)  [220/289]  eta: 0:00:13    time: 0.1874  data: 0.0581  max mem: 2851
+extract (train)  [240/289]  eta: 0:00:09    time: 0.1797  data: 0.0539  max mem: 2851
+extract (train)  [260/289]  eta: 0:00:05    time: 0.1770  data: 0.0539  max mem: 2851
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1453  data: 0.0412  max mem: 2851
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1474  data: 0.0425  max mem: 2851
+extract (train) Total time: 0:00:56 (0.1951 s / it)
+extract (validation)  [ 0/62]  eta: 0:04:15    time: 4.1139  data: 3.9594  max mem: 2851
+extract (validation)  [20/62]  eta: 0:00:16    time: 0.2111  data: 0.0654  max mem: 2851
+extract (validation)  [40/62]  eta: 0:00:06    time: 0.1604  data: 0.0449  max mem: 2851
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1426  data: 0.0404  max mem: 2851
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1429  data: 0.0408  max mem: 2851
+extract (validation) Total time: 0:00:14 (0.2398 s / it)
+extract (test)  [ 0/62]  eta: 0:04:02    time: 3.9069  data: 3.7442  max mem: 2851
+extract (test)  [20/62]  eta: 0:00:15    time: 0.2033  data: 0.0733  max mem: 2851
+extract (test)  [40/62]  eta: 0:00:06    time: 0.1666  data: 0.0505  max mem: 2851
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1300  data: 0.0327  max mem: 2851
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1301  data: 0.0327  max mem: 2851
+extract (test) Total time: 0:00:14 (0.2319 s / it)
+feature extraction time: 0:01:25
+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  |         | 0.00077426 | train   | 0.65954 |  0.016721 | 0.6353  | 0.018755 | 0.63947 |   0.017271 |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.00077426 | test    | 0.55645 |  0.036764 | 0.50201 | 0.042935 | 0.53325 |   0.036979 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.040349471678378206, "f1": 0.575109649122807, "f1_std": 0.04420664210062104, "bacc": 0.5803571428571428, "bacc_std": 0.04130481701560835}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.532258064516129, "acc_std": 0.04211071021334379, "f1": 0.5262187088274045, "f1_std": 0.04242731480433336, "bacc": 0.5262605042016807, "bacc_std": 0.04207803546879132}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04374648789232836, "f1": 0.5865315462569467, "f1_std": 0.04390322158115644, "bacc": 0.5871848739495797, "bacc_std": 0.04402238609954078}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.03997255822473933, "f1": 0.6145945945945945, "f1_std": 0.042653755554722526, "bacc": 0.6160714285714286, "bacc_std": 0.04105988543758308}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.000774263682681127, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.039953354696556385, "f1": 0.5880957223239103, "f1_std": 0.04315962423588338, "bacc": 0.5908613445378151, "bacc_std": 0.04094939994735334}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 21.54434690031882, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04553850527915155, "f1": 0.5854473942969518, "f1_std": 0.0457291653013008, "bacc": 0.585609243697479, "bacc_std": 0.04558346526568744}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.532258064516129, "acc_std": 0.045958120102217684, "f1": 0.5291961246399581, "f1_std": 0.046178565357979394, "bacc": 0.5294117647058824, "bacc_std": 0.04618725638623132}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.0426352869100977, "f1": 0.5603991596638656, "f1_std": 0.04318817697917029, "bacc": 0.5603991596638656, "bacc_std": 0.043152963440004945}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 96, "C": 0.3593813663804626, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04403691042419571, "f1": 0.5808311791608669, "f1_std": 0.04540073122154053, "bacc": 0.5808823529411764, "bacc_std": 0.04460656458554057}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 97, "C": 1291.5496650148827, "split": "test", "acc": 0.5161290322580645, "acc_std": 0.043677047098999736, "f1": 0.5129615082482325, "f1_std": 0.04400493914178148, "bacc": 0.5131302521008403, "bacc_std": 0.044095563638154756}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04509883642430504, "f1": 0.5643243243243243, "f1_std": 0.04739541333500298, "bacc": 0.5672268907563025, "bacc_std": 0.045696753031141644}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04074645276722062, "f1": 0.5951020408163266, "f1_std": 0.044123583808845415, "bacc": 0.5982142857142857, "bacc_std": 0.041744355889530814}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04292742465745089, "f1": 0.6137071651090342, "f1_std": 0.04383770539646089, "bacc": 0.6134453781512605, "bacc_std": 0.04337296610351456}
+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 | 128.41 |  1013.6 | 0.78195 |  0.12163  | 0.77331 |  0.12848 | 0.77323 |   0.12751  |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 128.41 |  1013.6 | 0.58427 |  0.037273 | 0.57113 |  0.03739 | 0.57403 |   0.036383 |
+
+
+done! total time: 0:05:50

data_scaling/n200_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 n200_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

data_scaling/n200_1/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
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+flat_mae,patch,logistic,adhd200_dx,1,0.046415888336127774,test,0.6153846153846154,0.05902891478813658,0.6094688776736361,0.05971933587274193,0.61003861003861,0.0596014575809186
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+flat_mae,patch,logistic,adhd200_dx,7,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
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+flat_mae,patch,logistic,adhd200_dx,98,0.046415888336127774,train,0.8301369863013699,0.01910393415618752,0.8249210868354273,0.01990720159267238,0.8215332478475912,0.01997177278048096
+flat_mae,patch,logistic,adhd200_dx,98,0.046415888336127774,test,0.7230769230769231,0.05632745344709203,0.7149122807017544,0.05844231123241714,0.7133204633204633,0.05769412152769752
+flat_mae,patch,logistic,adhd200_dx,99,0.005994842503189409,train,0.7506849315068493,0.023145197696138167,0.7422576414808837,0.02433638653522453,0.7396653843805336,0.024032823073633226
+flat_mae,patch,logistic,adhd200_dx,99,0.005994842503189409,test,0.5846153846153846,0.059929394354459375,0.5699583435432491,0.06305121550757367,0.5699806949806949,0.06155920839495504
+flat_mae,patch,logistic,adhd200_dx,100,0.005994842503189409,train,0.7095890410958904,0.021650684541479413,0.6967868338557994,0.023185599015888736,0.6946479819258716,0.022536487168508187
+flat_mae,patch,logistic,adhd200_dx,100,0.005994842503189409,test,0.6153846153846154,0.054473488476006034,0.5834401435529352,0.06011209477234731,0.5883204633204633,0.05589827077089486

data_scaling/n200_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 17:14:52
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n200_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_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: 0:10:26    time: 4.1487  data: 3.2099  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:50    time: 0.1938  data: 0.0554  max mem: 2851
+extract (train)  [ 40/151]  eta: 0:00:30    time: 0.1646  data: 0.0416  max mem: 2851
+extract (train)  [ 60/151]  eta: 0:00:22    time: 0.1775  data: 0.0469  max mem: 2851
+extract (train)  [ 80/151]  eta: 0:00:16    time: 0.1795  data: 0.0475  max mem: 2851
+extract (train)  [100/151]  eta: 0:00:10    time: 0.1646  data: 0.0426  max mem: 2851
+extract (train)  [120/151]  eta: 0:00:06    time: 0.1622  data: 0.0415  max mem: 2851
+extract (train)  [140/151]  eta: 0:00:02    time: 0.1342  data: 0.0315  max mem: 2851
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1295  data: 0.0314  max mem: 2851
+extract (train) Total time: 0:00:29 (0.1937 s / it)
+extract (validation)  [ 0/32]  eta: 0:01:40    time: 3.1476  data: 2.9940  max mem: 2851
+extract (validation)  [20/32]  eta: 0:00:03    time: 0.1830  data: 0.0502  max mem: 2851
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1386  data: 0.0326  max mem: 2851
+extract (validation) Total time: 0:00:08 (0.2687 s / it)
+extract (test)  [ 0/33]  eta: 0:01:41    time: 3.0653  data: 2.9167  max mem: 2851
+extract (test)  [20/33]  eta: 0:00:03    time: 0.1684  data: 0.0477  max mem: 2851
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1308  data: 0.0325  max mem: 2851
+extract (test) Total time: 0:00:08 (0.2501 s / it)
+feature extraction time: 0:00:46
+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   | 0.71781 |  0.022662 | 0.70502 | 0.024088 | 0.70265 |   0.023434 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | test    | 0.58462 |  0.060742 | 0.56441 | 0.064907 | 0.56564 |   0.062531 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05902891478813658, "f1": 0.6094688776736361, "f1_std": 0.05971933587274193, "bacc": 0.61003861003861, "bacc_std": 0.0596014575809186}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.676923076923077, "acc_std": 0.05112246347927054, "f1": 0.6431372549019607, "f1_std": 0.060208811343929115, "bacc": 0.6467181467181468, "bacc_std": 0.05389196231650927}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.0593660871852972, "f1": 0.6375757575757576, "f1_std": 0.06110128406031661, "bacc": 0.6370656370656371, "bacc_std": 0.0606413860567933}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.055911773273010626, "f1": 0.6425000000000001, "f1_std": 0.06203933158839263, "bacc": 0.6418918918918919, "bacc_std": 0.05891705208385637}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05993488379781834, "f1": 0.5500119360229172, "f1_std": 0.06051761571739301, "bacc": 0.5516409266409266, "bacc_std": 0.06096379188445721}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05790480659857984, "f1": 0.6289401836684041, "f1_std": 0.06211581826727859, "bacc": 0.6283783783783784, "bacc_std": 0.05979374936109197}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 2.782559402207126, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.0612964780786961, "f1": 0.5745454545454545, "f1_std": 0.06319721402782594, "bacc": 0.5743243243243243, "bacc_std": 0.06267852125958226}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.06214984754845227, "f1": 0.6474358974358974, "f1_std": 0.06526503987019404, "bacc": 0.6462355212355213, "bacc_std": 0.06366485571648704}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 2.782559402207126, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.06207955194128553, "f1": 0.48000000000000004, "f1_std": 0.06304689841077235, "bacc": 0.48021235521235517, "bacc_std": 0.06272712565320752}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06090177560785357, "f1": 0.5565302144249512, "f1_std": 0.06339421400075335, "bacc": 0.5564671814671815, "bacc_std": 0.06251030590631775}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05261538461538462, "f1": 0.6167649320687003, "f1_std": 0.060208477130999034, "bacc": 0.6196911196911197, "bacc_std": 0.055012390974836976}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.060160290821404575, "f1": 0.5125, "f1_std": 0.06296119002615906, "bacc": 0.5164092664092664, "bacc_std": 0.0605998954319777}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.059744077471322696, "f1": 0.545, "f1_std": 0.06350769453818378, "bacc": 0.5477799227799228, "bacc_std": 0.060692334977753185}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 0.000774263682681127, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05305331916625211, "f1": 0.5751633986928104, "f1_std": 0.062112836052669644, "bacc": 0.583976833976834, "bacc_std": 0.05538692082652214}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.000774263682681127, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.0546880210372474, "f1": 0.5834401435529352, "f1_std": 0.062230269019208216, "bacc": 0.5883204633204633, "bacc_std": 0.05709126769453092}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.0629311636706866, "f1": 0.5248538011695907, "f1_std": 0.06393048315723239, "bacc": 0.525096525096525, "bacc_std": 0.06339465842180607}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.057454846690980076, "f1": 0.61, "f1_std": 0.06176745780935435, "bacc": 0.6105212355212355, "bacc_std": 0.059107263436545605}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05990569314352282, "f1": 0.5921814671814671, "f1_std": 0.06058074829729388, "bacc": 0.5921814671814671, "bacc_std": 0.06016469351549355}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 88, "C": 0.000774263682681127, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06295399654091369, "f1": 0.5565302144249512, "f1_std": 0.06529791928096015, "bacc": 0.5564671814671815, "bacc_std": 0.06439762259564047}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 89, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.057628888219333624, "f1": 0.5833333333333333, "f1_std": 0.05998277332902277, "bacc": 0.5834942084942085, "bacc_std": 0.058443909047589365}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 90, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05722391191341742, "f1": 0.6153846153846154, "f1_std": 0.0604163271992031, "bacc": 0.6148648648648649, "bacc_std": 0.05846304675869907}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 91, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05702569544361912, "f1": 0.5167905665214048, "f1_std": 0.06319493587974628, "bacc": 0.5255791505791506, "bacc_std": 0.05813159438746824}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 92, "C": 0.005994842503189409, "split": "test", "acc": 0.676923076923077, "acc_std": 0.05073915192887578, "f1": 0.6431372549019607, "f1_std": 0.059891859634364754, "bacc": 0.6467181467181468, "bacc_std": 0.05345232790122109}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 10000.0, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06167685208277281, "f1": 0.578226387887527, "f1_std": 0.06225156143377882, "bacc": 0.5786679536679536, "bacc_std": 0.061960059418901064}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05368569498622372, "f1": 0.6299171842650104, "f1_std": 0.0622317841456796, "bacc": 0.6332046332046332, "bacc_std": 0.0564418189089554}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05501602993942636, "f1": 0.5167905665214048, "f1_std": 0.06147565085945736, "bacc": 0.5255791505791506, "bacc_std": 0.05645038875140719}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06401788951156381, "f1": 0.5846153846153846, "f1_std": 0.06440924202401369, "bacc": 0.596042471042471, "bacc_std": 0.06379295683545831}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.055078564651084266, "f1": 0.5308740978348035, "f1_std": 0.06526184544864624, "bacc": 0.5482625482625483, "bacc_std": 0.05680255568838492}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.7230769230769231, "acc_std": 0.05632745344709203, "f1": 0.7149122807017544, "f1_std": 0.05844231123241714, "bacc": 0.7133204633204633, "bacc_std": 0.05769412152769752}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.059929394354459375, "f1": 0.5699583435432491, "f1_std": 0.06305121550757367, "bacc": 0.5699806949806949, "bacc_std": 0.06155920839495504}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.054473488476006034, "f1": 0.5834401435529352, "f1_std": 0.06011209477234731, "bacc": 0.5883204633204633, "bacc_std": 0.05589827077089486}
+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 | 128.2 |  1013.7 | 0.78932 |  0.10955  | 0.77931 | 0.11622  | 0.77763 |   0.11618  |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 128.2 |  1013.7 | 0.60215 |  0.054493 | 0.58259 | 0.056177 | 0.58526 |   0.054448 |
+
+
+done! total time: 0:04:35

data_scaling/n200_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 n200_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

data_scaling/n200_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,71,0.005994842503189409,train,0.8563685636856369,0.012185278773746944,0.7447699836867863,0.027657039569526535,0.7040019722245049,0.02470408996198703
+flat_mae,patch,logistic,adni_ad_vs_cn,71,0.005994842503189409,test,0.7073170731707317,0.03218695387886215,0.4142857142857143,0.011177423337779723,0.46774193548387094,0.02128492111344111
+flat_mae,patch,logistic,adni_ad_vs_cn,72,0.046415888336127774,train,0.907859078590786,0.012422977055172615,0.8548454276194002,0.02220024662375012,0.81851425753965,0.0249878144459651
+flat_mae,patch,logistic,adni_ad_vs_cn,72,0.046415888336127774,test,0.7804878048780488,0.0225593275601093,0.5275288092189501,0.07957919813043343,0.55,0.04624662149822406
+flat_mae,patch,logistic,adni_ad_vs_cn,73,0.046415888336127774,train,0.8915989159891599,0.014107012165796986,0.8274410774410774,0.025320971236147696,0.7917248746815679,0.026852252562832843
+flat_mae,patch,logistic,adni_ad_vs_cn,73,0.046415888336127774,test,0.7804878048780488,0.047466598043294485,0.6328358208955224,0.09144187902117322,0.6177419354838709,0.07490463836849828
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+flat_mae,patch,logistic,adni_ad_vs_cn,78,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
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+flat_mae,patch,logistic,adni_ad_vs_cn,79,166.81005372000556,test,0.8048780487804879,0.05344638771058278,0.7152777777777778,0.0857487609604647,0.7016129032258065,0.0831472497469319
+flat_mae,patch,logistic,adni_ad_vs_cn,80,0.005994842503189409,train,0.8563685636856369,0.012748394947020682,0.747967060582239,0.027930818351653654,0.7080491412605802,0.025137114983840587
+flat_mae,patch,logistic,adni_ad_vs_cn,80,0.005994842503189409,test,0.7804878048780488,0.03759466338525411,0.5886287625418061,0.09151954698084103,0.5838709677419355,0.06472790170479109
+flat_mae,patch,logistic,adni_ad_vs_cn,81,0.005994842503189409,train,0.8373983739837398,0.013400255084093355,0.7014563106796117,0.03226161707915974,0.6673514668419755,0.026518599098188537
+flat_mae,patch,logistic,adni_ad_vs_cn,81,0.005994842503189409,test,0.8292682926829268,0.035667977796549954,0.6800445930880714,0.09685457699460995,0.65,0.07311935448292743
+flat_mae,patch,logistic,adni_ad_vs_cn,82,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,82,2.782559402207126,test,0.6341463414634146,0.06659806901070622,0.5467943994104643,0.0779676825087148,0.5548387096774194,0.08655177370648029
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+flat_mae,patch,logistic,adni_ad_vs_cn,83,2.782559402207126,test,0.6829268292682927,0.06806755576286863,0.5839188134270101,0.08300250003450266,0.5870967741935484,0.0859435966818584
+flat_mae,patch,logistic,adni_ad_vs_cn,84,0.005994842503189409,train,0.8536585365853658,0.01404921960570382,0.7382841231480508,0.03145650410588466,0.6981880187361328,0.02748481696430801
+flat_mae,patch,logistic,adni_ad_vs_cn,84,0.005994842503189409,test,0.7560975609756098,0.046417780547776726,0.569327731092437,0.09010797752086626,0.567741935483871,0.06807685708332452
+flat_mae,patch,logistic,adni_ad_vs_cn,85,0.3593813663804626,train,0.964769647696477,0.00886738094597462,0.948818351560416,0.013314443910131476,0.9325129427233133,0.017061491230336692
+flat_mae,patch,logistic,adni_ad_vs_cn,85,0.3593813663804626,test,0.7317073170731707,0.06726210198082751,0.6479313036690086,0.08609929276879244,0.6532258064516129,0.08994826186589215
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+flat_mae,patch,logistic,adni_ad_vs_cn,86,0.3593813663804626,test,0.6585365853658537,0.06995972326445087,0.5876436781609196,0.07925740963050203,0.6048387096774194,0.08899417044126326
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.005994842503189409,train,0.8428184281842819,0.01331666064902472,0.7152171598892911,0.03089964006570977,0.6789793738187198,0.026178636023078974
+flat_mae,patch,logistic,adni_ad_vs_cn,87,0.005994842503189409,test,0.8048780487804879,0.04399718773122879,0.6554621848739496,0.09933427553024542,0.6338709677419355,0.07741031795376113
+flat_mae,patch,logistic,adni_ad_vs_cn,88,0.046415888336127774,train,0.8888888888888888,0.014209331358011265,0.8221895751307516,0.02559514840774878,0.7859109211931958,0.026466857628402826
+flat_mae,patch,logistic,adni_ad_vs_cn,88,0.046415888336127774,test,0.7073170731707317,0.06442753908834613,0.603225806451613,0.08388094939097777,0.603225806451613,0.08569346059265824
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,train,0.8997289972899729,0.012799363605187238,0.8395369336545807,0.023442376145635158,0.8010723970745337,0.02508600461435727
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,test,0.7804878048780488,0.05054117598511493,0.6328358208955224,0.09658981395585098,0.6177419354838709,0.07805208345412476
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.005994842503189409,train,0.8401084010840109,0.01322543315058336,0.7083863535904202,0.03212922795930055,0.6731654203303477,0.026657959726769948
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.005994842503189409,test,0.8292682926829268,0.03466010706865614,0.6800445930880714,0.09554125739944969,0.65,0.0710532194907451
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,train,0.9024390243902439,0.012779861426182561,0.8463069233617179,0.022766726978934615,0.8109335195989811,0.02495180265563722
+flat_mae,patch,logistic,adni_ad_vs_cn,91,0.046415888336127774,test,0.6585365853658537,0.05736989781663184,0.4564393939393939,0.06429576084444681,0.4693548387096774,0.05785696049060336
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,train,0.975609756097561,0.007374262107451476,0.9645665510802881,0.01116639948187265,0.9476744186046512,0.015820364637497607
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.3593813663804626,test,0.7073170731707317,0.05996120101868055,0.5729166666666666,0.0864723161681521,0.5693548387096774,0.07968075244432404
+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,0.5365853658536586,0.06987182963002837,0.42593957258658804,0.06996456671814621,0.42258064516129035,0.07677243282068816
+flat_mae,patch,logistic,adni_ad_vs_cn,94,0.046415888336127774,train,0.907859078590786,0.013047708533782296,0.8563215758131013,0.022281506217296874,0.8225614265757252,0.024454033011425718
+flat_mae,patch,logistic,adni_ad_vs_cn,94,0.046415888336127774,test,0.6585365853658537,0.06167991293546346,0.5017361111111112,0.07651141726443857,0.5032258064516129,0.07101827379850527
+flat_mae,patch,logistic,adni_ad_vs_cn,95,0.005994842503189409,train,0.8401084010840109,0.013693979050754108,0.7083863535904202,0.032477401912800685,0.6731654203303477,0.02716578222362547
+flat_mae,patch,logistic,adni_ad_vs_cn,95,0.005994842503189409,test,0.8292682926829268,0.0450229632731091,0.7144278606965174,0.09088634811942033,0.6838709677419355,0.07923824348109176
+flat_mae,patch,logistic,adni_ad_vs_cn,96,0.046415888336127774,train,0.9024390243902439,0.013557288085115863,0.8478699038021071,0.023645582825983235,0.8149806886350563,0.026220030555354802
+flat_mae,patch,logistic,adni_ad_vs_cn,96,0.046415888336127774,test,0.7317073170731707,0.03907448004870622,0.4972129319955407,0.07539703795042978,0.5177419354838709,0.05341259609339359
+flat_mae,patch,logistic,adni_ad_vs_cn,97,0.046415888336127774,train,0.8915989159891599,0.013733475112218042,0.8274410774410774,0.02456708573862555,0.7917248746815679,0.0260436521810757
+flat_mae,patch,logistic,adni_ad_vs_cn,97,0.046415888336127774,test,0.7073170731707317,0.0460881136273502,0.4831932773109243,0.07321723830802376,0.5016129032258064,0.05649029927864652
+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.005994842503189409,train,0.8644986449864499,0.012523844224430395,0.7607376283846872,0.028324857988342774,0.7173966636535458,0.025850743031494424
+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.005994842503189409,test,0.7317073170731707,0.04724558340940896,0.5512437810945273,0.086899679075301,0.5516129032258065,0.06949349051332279
+flat_mae,patch,logistic,adni_ad_vs_cn,99,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,99,2.782559402207126,test,0.6829268292682927,0.06216929162327585,0.5547201336675021,0.08163123125114741,0.5532258064516129,0.0783563509993637
+flat_mae,patch,logistic,adni_ad_vs_cn,100,0.005994842503189409,train,0.8455284552845529,0.013981616821678046,0.7219512195121951,0.03205376661904997,0.6847933273070919,0.02732332800764378
+flat_mae,patch,logistic,adni_ad_vs_cn,100,0.005994842503189409,test,0.7804878048780488,0.050853956833657496,0.6328358208955224,0.09669752236583778,0.6177419354838709,0.07902751769852076

data_scaling/n200_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 17:14:52
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n200_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_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: 0:10:56    time: 4.0052  data: 3.0490  max mem: 2698
+extract (train)  [ 20/164]  eta: 0:00:53    time: 0.1868  data: 0.0541  max mem: 2851
+extract (train)  [ 40/164]  eta: 0:00:33    time: 0.1617  data: 0.0429  max mem: 2851
+extract (train)  [ 60/164]  eta: 0:00:24    time: 0.1707  data: 0.0431  max mem: 2851
+extract (train)  [ 80/164]  eta: 0:00:19    time: 0.2053  data: 0.0513  max mem: 2851
+extract (train)  [100/164]  eta: 0:00:14    time: 0.2248  data: 0.0595  max mem: 2851
+extract (train)  [120/164]  eta: 0:00:09    time: 0.2095  data: 0.0528  max mem: 2851
+extract (train)  [140/164]  eta: 0:00:05    time: 0.2040  data: 0.0515  max mem: 2851
+extract (train)  [160/164]  eta: 0:00:00    time: 0.1550  data: 0.0344  max mem: 2851
+extract (train)  [163/164]  eta: 0:00:00    time: 0.1553  data: 0.0346  max mem: 2851
+extract (train) Total time: 0:00:35 (0.2145 s / it)
+extract (validation)  [ 0/21]  eta: 0:01:33    time: 4.4313  data: 4.2506  max mem: 2851
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.1556  data: 0.0355  max mem: 2851
+extract (validation) Total time: 0:00:07 (0.3720 s / it)
+extract (test)  [ 0/21]  eta: 0:01:08    time: 3.2602  data: 3.1520  max mem: 2851
+extract (test)  [20/21]  eta: 0:00:00    time: 0.1364  data: 0.0321  max mem: 2851
+extract (test) Total time: 0:00:06 (0.2982 s / it)
+feature extraction time: 0:00:49
+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   | 0.84282 |  0.012868 | 0.71522 | 0.03104  | 0.67859 |   0.025888 |
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 0.0059948 | test    | 0.78049 |  0.042364 | 0.58863 | 0.087227 | 0.57986 |   0.067465 |
+
+
+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": 0.8048780487804879, "acc_std": 0.04082652903361707, "f1": 0.6554621848739496, "f1_std": 0.09257579501289646, "bacc": 0.6338709677419355, "bacc_std": 0.07251958004645687}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 93, "C": 166.81005372000556, "split": "test", "acc": 0.5365853658536586, "acc_std": 0.06987182963002837, "f1": 0.42593957258658804, "f1_std": 0.06996456671814621, "bacc": 0.42258064516129035, "bacc_std": 0.07677243282068816}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.0450229632731091, "f1": 0.7144278606965174, "f1_std": 0.09088634811942033, "bacc": 0.6838709677419355, "bacc_std": 0.07923824348109176}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.03907448004870622, "f1": 0.4972129319955407, "f1_std": 0.07539703795042978, "bacc": 0.5177419354838709, "bacc_std": 0.05341259609339359}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0460881136273502, "f1": 0.4831932773109243, "f1_std": 0.07321723830802376, "bacc": 0.5016129032258064, "bacc_std": 0.05649029927864652}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.04724558340940896, "f1": 0.5512437810945273, "f1_std": 0.086899679075301, "bacc": 0.5516129032258065, "bacc_std": 0.06949349051332279}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 2.782559402207126, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06216929162327585, "f1": 0.5547201336675021, "f1_std": 0.08163123125114741, "bacc": 0.5532258064516129, "bacc_std": 0.0783563509993637}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.050853956833657496, "f1": 0.6328358208955224, "f1_std": 0.09669752236583778, "bacc": 0.6177419354838709, "bacc_std": 0.07902751769852076}
+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 | 25.107 |  134.85 | 0.91314 |  0.058148 | 0.85281 | 0.10551  | 0.82674 |    0.11898 |
+| flat_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 25.107 |  134.85 | 0.73488 |  0.068607 | 0.58179 | 0.078135 | 0.58081 |    0.06753 |
+
+
+done! total time: 0:04:35

data_scaling/n200_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 n200_1; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n200_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/n200_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/n200_1/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

data_scaling/n200_1/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 15, "eval/id_best": 43, "eval/lr_best": 0.006599999999999999, "eval/wd_best": 0.05, "eval/train/loss": 1.1493029887787998e-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": 0.2840571701526642, "eval/validation/acc": 0.9759424603174603, "eval/validation/acc_std": 0.0024221558778802325, "eval/validation/f1": 0.9718077067429253, "eval/validation/f1_std": 0.0031730930828658926, "eval/test/loss": 0.4188261032104492, "eval/test/acc": 0.9726190476190476, "eval/test/acc_std": 0.002195754510067255, "eval/test/f1": 0.9677612274146545, "eval/test/f1_std": 0.0028631635113233415}

data_scaling/n200_1/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 15, "eval/best/id_best": 43, "eval/best/lr_best": 0.006599999999999999, "eval/best/wd_best": 0.05, "eval/best/train/loss": 1.1493029887787998e-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": 0.2840571701526642, "eval/best/validation/acc": 0.9759424603174603, "eval/best/validation/acc_std": 0.0024221558778802325, "eval/best/validation/f1": 0.9718077067429253, "eval/best/validation/f1_std": 0.0031730930828658926, "eval/best/test/loss": 0.4188261032104492, "eval/best/test/acc": 0.9726190476190476, "eval/best/test/acc_std": 0.002195754510067255, "eval/best/test/f1": 0.9677612274146545, "eval/best/test/f1_std": 0.0028631635113233415}