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data_scaling/n100_1/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json

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

data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic/log.txt

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+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: 58906bf7243fb545e1349221e6921a1797e2e666, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-26 17:21:07
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null
+
+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: 0:10:55    time: 4.3443  data: 3.2775  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:49    time: 0.1785  data: 0.0587  max mem: 2851
+extract (train)  [ 40/151]  eta: 0:00:31    time: 0.1801  data: 0.0577  max mem: 2851
+extract (train)  [ 60/151]  eta: 0:00:22    time: 0.1635  data: 0.0539  max mem: 2851
+extract (train)  [ 80/151]  eta: 0:00:15    time: 0.1632  data: 0.0512  max mem: 2851
+extract (train)  [100/151]  eta: 0:00:10    time: 0.1721  data: 0.0563  max mem: 2851
+extract (train)  [120/151]  eta: 0:00:06    time: 0.1678  data: 0.0540  max mem: 2851
+extract (train)  [140/151]  eta: 0:00:02    time: 0.1508  data: 0.0464  max mem: 2851
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1396  data: 0.0413  max mem: 2851
+extract (train) Total time: 0:00:29 (0.1955 s / it)
+extract (validation)  [ 0/32]  eta: 0:01:41    time: 3.1854  data: 3.0519  max mem: 2851
+extract (validation)  [20/32]  eta: 0:00:04    time: 0.2043  data: 0.0751  max mem: 2851
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1554  data: 0.0482  max mem: 2851
+extract (validation) Total time: 0:00:09 (0.2875 s / it)
+extract (test)  [ 0/33]  eta: 0:01:58    time: 3.5812  data: 3.3825  max mem: 2851
+extract (test)  [20/33]  eta: 0:00:04    time: 0.1732  data: 0.0563  max mem: 2851
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1371  data: 0.0376  max mem: 2851
+extract (test) Total time: 0:00:08 (0.2725 s / it)
+feature extraction time: 0:00:47
+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   | 0.73699 |  0.022243 | 0.72603 | 0.023651 | 0.72322 |   0.023123 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | test    | 0.6     |  0.057698 | 0.57063 | 0.063737 | 0.57481 |   0.059405 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.059122930803078115, "f1": 0.6515594541910331, "f1_std": 0.061600951342251554, "bacc": 0.6505791505791505, "bacc_std": 0.06073906918317193}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05720041378589937, "f1": 0.6289401836684041, "f1_std": 0.06114509713123287, "bacc": 0.6283783783783784, "bacc_std": 0.0590464400633895}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05825007098047837, "f1": 0.5321419707123356, "f1_std": 0.062020678710289406, "bacc": 0.5342664092664092, "bacc_std": 0.0594503654731437}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.000774263682681127, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05740152359687505, "f1": 0.61, "f1_std": 0.06252870715084341, "bacc": 0.6105212355212355, "bacc_std": 0.05948094634923437}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.059869491198200106, "f1": 0.5294401544401545, "f1_std": 0.06137770864406378, "bacc": 0.5294401544401545, "bacc_std": 0.06129658833442081}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05803295533854401, "f1": 0.6407113674597452, "f1_std": 0.059151268573164356, "bacc": 0.6414092664092663, "bacc_std": 0.058949435053899574}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05758279657622111, "f1": 0.5921814671814671, "f1_std": 0.05832330670934808, "bacc": 0.5921814671814671, "bacc_std": 0.057999724558730946}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05972668320265686, "f1": 0.6018132810585641, "f1_std": 0.06274203372096271, "bacc": 0.6013513513513513, "bacc_std": 0.0613568925163966}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.0589670372266569, "f1": 0.6474358974358974, "f1_std": 0.061831955306588915, "bacc": 0.6462355212355213, "bacc_std": 0.060170577715491}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.052824335435490186, "f1": 0.5834401435529352, "f1_std": 0.05850498151483573, "bacc": 0.5883204633204633, "bacc_std": 0.054252070047106755}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 88, "C": 0.3593813663804626, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.058685917587415495, "f1": 0.6018132810585641, "f1_std": 0.06142977436334669, "bacc": 0.6013513513513513, "bacc_std": 0.060250911405957834}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 89, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05982715933489438, "f1": 0.5512820512820513, "f1_std": 0.062375614829810154, "bacc": 0.5521235521235521, "bacc_std": 0.060586015306990676}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 90, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.05589826769552833, "f1": 0.5501153550371699, "f1_std": 0.06170522424712748, "bacc": 0.556949806949807, "bacc_std": 0.05692418449684527}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 91, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05823473413998753, "f1": 0.5250692869740489, "f1_std": 0.06245076740475483, "bacc": 0.5299227799227799, "bacc_std": 0.059145661797415386}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 92, "C": 0.005994842503189409, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.051588393722463446, "f1": 0.677124183006536, "f1_std": 0.06112912497719481, "bacc": 0.678088803088803, "bacc_std": 0.05489723214261481}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.046770232782788385, "f1": 0.5902987119758838, "f1_std": 0.062023130297110325, "bacc": 0.6066602316602316, "bacc_std": 0.050252041913286805}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05382860373334661, "f1": 0.5289855072463768, "f1_std": 0.06044560551466636, "bacc": 0.5390926640926641, "bacc_std": 0.05500088311792442}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.055887460883669325, "f1": 0.570630081300813, "f1_std": 0.06216203424157956, "bacc": 0.5748069498069498, "bacc_std": 0.05778176669267205}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.0573820663488301, "f1": 0.6575670498084292, "f1_std": 0.05796864738982916, "bacc": 0.6592664092664093, "bacc_std": 0.057926941380571306}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.057039581398830815, "f1": 0.5626293995859213, "f1_std": 0.06574931455018576, "bacc": 0.5704633204633205, "bacc_std": 0.05935925275804624}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.000774263682681127, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.0558516632162702, "f1": 0.6366869918699187, "f1_std": 0.06191037576540796, "bacc": 0.6375482625482626, "bacc_std": 0.05794528698701383}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.49230769230769234, "acc_std": 0.06096095170792607, "f1": 0.48000000000000004, "f1_std": 0.0613940233805348, "bacc": 0.48021235521235517, "bacc_std": 0.061038339700273066}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 21.54434690031882, "split": "test", "acc": 0.4307692307692308, "acc_std": 0.055858519504041915, "f1": 0.4106836559666748, "f1_std": 0.056675403332023526, "bacc": 0.41312741312741313, "bacc_std": 0.05592474733507681}
+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 | 13.41 |  129.14 | 0.79447 |  0.084271 | 0.78581 | 0.088926 | 0.78314 |   0.089147 |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 13.41 |  129.14 | 0.61292 |  0.058254 | 0.59506 | 0.061189 | 0.59693 |   0.059548 |
+
+
+done! total time: 0:04:35

data_scaling/n100_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 n100_2; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: data_scaling/n100_2/eval_v2/adni_ad_vs_cn__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

data_scaling/n100_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,79,0.3593813663804626,test,0.8536585365853658,0.052540435124026244,0.8136363636363637,0.06416458096030163,0.8354838709677419,0.06759610909379467
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+flat_mae,patch,logistic,adni_ad_vs_cn,81,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,81,21.54434690031882,test,0.7317073170731707,0.056171827827861384,0.5918552036199095,0.085537811098284,0.5854838709677419,0.07580193974218905
+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,0.7317073170731707,0.06228355262843503,0.6232247284878863,0.08737745092691357,0.6193548387096774,0.0853039774218989
+flat_mae,patch,logistic,adni_ad_vs_cn,83,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,83,166.81005372000556,test,0.6585365853658537,0.06684785214282848,0.5370967741935484,0.08324913335607285,0.5370967741935484,0.08357371212888083
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+flat_mae,patch,logistic,adni_ad_vs_cn,84,166.81005372000556,test,0.7317073170731707,0.06245045627054907,0.6232247284878863,0.08751784621291649,0.6193548387096774,0.08658236357123594
+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,0.8292682926829268,0.05182232952118019,0.7402714932126697,0.08557110964689793,0.717741935483871,0.08196531556246789
+flat_mae,patch,logistic,adni_ad_vs_cn,86,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,86,166.81005372000556,test,0.6341463414634146,0.07579883517992969,0.5684210526315789,0.08220750848301907,0.5887096774193548,0.09361762598966995
+flat_mae,patch,logistic,adni_ad_vs_cn,87,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,87,21.54434690031882,test,0.7317073170731707,0.06450145606590792,0.6479313036690086,0.08251565719106585,0.6532258064516129,0.0863588411112917
+flat_mae,patch,logistic,adni_ad_vs_cn,88,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,88,166.81005372000556,test,0.6585365853658537,0.06919480815286877,0.5370967741935484,0.08318589604135711,0.5370967741935484,0.08422599912259748
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,train,0.8943089430894309,0.01321679411073135,0.82903881107666,0.024957699022891567,0.7894444900977895,0.026357555694422732
+flat_mae,patch,logistic,adni_ad_vs_cn,89,0.046415888336127774,test,0.7804878048780488,0.05132588257874008,0.6328358208955224,0.09692641307591987,0.6177419354838709,0.08008538783344239
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.3593813663804626,train,0.991869918699187,0.004545984635638291,0.9884880564885973,0.006544694461539688,0.9825581395348837,0.009752722852038011
+flat_mae,patch,logistic,adni_ad_vs_cn,90,0.3593813663804626,test,0.8292682926829268,0.054470552108417586,0.7402714932126697,0.08880699242711297,0.717741935483871,0.08498103669227908
+flat_mae,patch,logistic,adni_ad_vs_cn,91,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,91,166.81005372000556,test,0.7560975609756098,0.06273208663542718,0.6693548387096775,0.08656911693631089,0.6693548387096775,0.08935651908283723
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.046415888336127774,train,0.8997289972899729,0.012965171840296118,0.8395369336545807,0.023865418408029887,0.8010723970745337,0.025992400164343843
+flat_mae,patch,logistic,adni_ad_vs_cn,92,0.046415888336127774,test,0.7073170731707317,0.046769677532204436,0.4831932773109243,0.07460994949827565,0.5016129032258064,0.057441860304977645
+flat_mae,patch,logistic,adni_ad_vs_cn,93,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,93,21.54434690031882,test,0.6829268292682927,0.07027226730828623,0.5839188134270101,0.08378475287482881,0.5870967741935484,0.08655682205539217
+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,0.7073170731707317,0.06046929299733757,0.5729166666666666,0.08540378759933065,0.5693548387096774,0.07933732431949606
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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,0.6829268292682927,0.0643633351016978,0.5547201336675021,0.08578025354950224,0.5532258064516129,0.08154134431358231
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+flat_mae,patch,logistic,adni_ad_vs_cn,98,0.046415888336127774,test,0.7073170731707317,0.05792822077917882,0.5729166666666666,0.08562176077449665,0.5693548387096774,0.08009604347944656
+flat_mae,patch,logistic,adni_ad_vs_cn,99,0.3593813663804626,train,0.994579945799458,0.003980973543170335,0.9923570836785418,0.00569244660437376,0.9883720930232558,0.0085405769618015
+flat_mae,patch,logistic,adni_ad_vs_cn,99,0.3593813663804626,test,0.7560975609756098,0.060292840792278916,0.6440972222222222,0.08925101816367763,0.635483870967742,0.08413436308066136
+flat_mae,patch,logistic,adni_ad_vs_cn,100,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,adni_ad_vs_cn,100,2.782559402207126,test,0.7804878048780488,0.054971497671216285,0.6660633484162897,0.09086355370732972,0.6516129032258065,0.08404206487262263

data_scaling/n100_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 17:21:07
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: data_scaling/n100_2/eval_v2/adni_ad_vs_cn__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adni_ad_vs_cn
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_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: 0:10:36    time: 3.8806  data: 2.8381  max mem: 2698
+extract (train)  [ 20/164]  eta: 0:00:53    time: 0.1978  data: 0.0715  max mem: 2851
+extract (train)  [ 40/164]  eta: 0:00:33    time: 0.1691  data: 0.0529  max mem: 2851
+extract (train)  [ 60/164]  eta: 0:00:24    time: 0.1505  data: 0.0434  max mem: 2851
+extract (train)  [ 80/164]  eta: 0:00:17    time: 0.1511  data: 0.0442  max mem: 2851
+extract (train)  [100/164]  eta: 0:00:13    time: 0.1779  data: 0.0615  max mem: 2851
+extract (train)  [120/164]  eta: 0:00:08    time: 0.1679  data: 0.0527  max mem: 2851
+extract (train)  [140/164]  eta: 0:00:04    time: 0.1578  data: 0.0450  max mem: 2851
+extract (train)  [160/164]  eta: 0:00:00    time: 0.1379  data: 0.0365  max mem: 2851
+extract (train)  [163/164]  eta: 0:00:00    time: 0.1397  data: 0.0370  max mem: 2851
+extract (train) Total time: 0:00:30 (0.1887 s / it)
+extract (validation)  [ 0/21]  eta: 0:01:09    time: 3.3103  data: 3.2071  max mem: 2851
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.1334  data: 0.0344  max mem: 2851
+extract (validation) Total time: 0:00:06 (0.2961 s / it)
+extract (test)  [ 0/21]  eta: 0:01:06    time: 3.1682  data: 3.0586  max mem: 2851
+extract (test)  [20/21]  eta: 0:00:00    time: 0.1391  data: 0.0361  max mem: 2851
+extract (test) Total time: 0:00:06 (0.2961 s / it)
+feature extraction time: 0:00:43
+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 |         | 2.7826 | train   | 1       |  0        | 1       | 0        | 1       |   0        |
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | test    | 0.78049 |  0.060727 | 0.66606 | 0.091923 | 0.65972 |   0.090194 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06334575997148711, "f1": 0.6232247284878863, "f1_std": 0.08793672358275198, "bacc": 0.6193548387096774, "bacc_std": 0.08598658124385597}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05729319689518083, "f1": 0.6440972222222222, "f1_std": 0.08939344958909794, "bacc": 0.635483870967742, "bacc_std": 0.08318923577609934}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 166.81005372000556, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0642529208256111, "f1": 0.603225806451613, "f1_std": 0.08545209402104924, "bacc": 0.603225806451613, "bacc_std": 0.08584654768981771}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 166.81005372000556, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06269004420065305, "f1": 0.6917293233082706, "f1_std": 0.0874938582730628, "bacc": 0.685483870967742, "bacc_std": 0.08655773722232406}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 2.782559402207126, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06275453819643223, "f1": 0.5547201336675021, "f1_std": 0.08491336852780806, "bacc": 0.5532258064516129, "bacc_std": 0.08101260002717708}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 2.782559402207126, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.057780050240804244, "f1": 0.5918552036199095, "f1_std": 0.0876345941865284, "bacc": 0.5854838709677419, "bacc_std": 0.0778780013259887}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.04773242393868619, "f1": 0.4831932773109243, "f1_std": 0.07144347037645617, "bacc": 0.5016129032258064, "bacc_std": 0.05543197272708444}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 1291.5496650148827, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06260260584176712, "f1": 0.5547201336675021, "f1_std": 0.0806557428489314, "bacc": 0.5532258064516129, "bacc_std": 0.07738504959037022}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06424477285185493, "f1": 0.6232247284878863, "f1_std": 0.0876379328977669, "bacc": 0.6193548387096774, "bacc_std": 0.08531574916245467}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05224595199914958, "f1": 0.6117424242424243, "f1_std": 0.08828437133556405, "bacc": 0.6016129032258064, "bacc_std": 0.07641454552603223}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 11, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06143119558173057, "f1": 0.6693548387096775, "f1_std": 0.08116957425182983, "bacc": 0.6693548387096775, "bacc_std": 0.0828933280774168}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.053071646319570696, "f1": 0.6117424242424243, "f1_std": 0.08833637303289316, "bacc": 0.6016129032258064, "bacc_std": 0.07667909391958687}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 166.81005372000556, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.05110116408508405, "f1": 0.4696517412935323, "f1_std": 0.06685915160968459, "bacc": 0.4854838709677419, "bacc_std": 0.05516540410994869}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.05792822077917882, "f1": 0.5729166666666666, "f1_std": 0.08562176077449665, "bacc": 0.5693548387096774, "bacc_std": 0.08009604347944656}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.060292840792278916, "f1": 0.6440972222222222, "f1_std": 0.08925101816367763, "bacc": 0.635483870967742, "bacc_std": 0.08413436308066136}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 2.782559402207126, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.054971497671216285, "f1": 0.6660633484162897, "f1_std": 0.09086355370732972, "bacc": 0.6516129032258065, "bacc_std": 0.08404206487262263}
+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 | 80.009 |  189.07 | 0.98244 |  0.038253 | 0.97149 | 0.063429 | 0.96443 |   0.077347 |
+| flat_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 80.009 |  189.07 | 0.7322  |  0.050947 | 0.60704 | 0.070466 | 0.60647 |   0.067244 |
+
+
+done! total time: 0:04:32

data_scaling/n100_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 n100_2; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_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/n100_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/n100_2/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 17, "eval/id_best": 43, "eval/lr_best": 0.006599999999999999, "eval/wd_best": 0.05, "eval/train/loss": 0.00010308645869372413, "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": 0.2797021269798279, "eval/validation/acc": 0.9635416666666666, "eval/validation/acc_std": 0.0029880390387508927, "eval/validation/f1": 0.9565002903672909, "eval/validation/f1_std": 0.0038571716148288354, "eval/test/loss": 0.30021265149116516, "eval/test/acc": 0.9632936507936508, "eval/test/acc_std": 0.002535844570267519, "eval/test/f1": 0.9545481066774228, "eval/test/f1_std": 0.003449931567784212}

data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 17, "eval/best/id_best": 43, "eval/best/lr_best": 0.006599999999999999, "eval/best/wd_best": 0.05, "eval/best/train/loss": 0.00010308645869372413, "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": 0.2797021269798279, "eval/best/validation/acc": 0.9635416666666666, "eval/best/validation/acc_std": 0.0029880390387508927, "eval/best/validation/f1": 0.9565002903672909, "eval/best/validation/f1_std": 0.0038571716148288354, "eval/best/test/loss": 0.30021265149116516, "eval/best/test/acc": 0.9632936507936508, "eval/best/test/acc_std": 0.002535844570267519, "eval/best/test/f1": 0.9545481066774228, "eval/best/test/f1_std": 0.003449931567784212}

data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 43, "eval/last/lr_best": 0.006599999999999999, "eval/last/wd_best": 0.05, "eval/last/train/loss": 0.0001044704404193908, "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": 0.2780441343784332, "eval/last/validation/acc": 0.9635416666666666, "eval/last/validation/acc_std": 0.0029650230298895195, "eval/last/validation/f1": 0.9561333052343192, "eval/last/validation/f1_std": 0.003821224564275038, "eval/last/test/loss": 0.29874613881111145, "eval/last/test/acc": 0.9636904761904762, "eval/last/test/acc_std": 0.0025437139072971285, "eval/last/test/f1": 0.9550425705771689, "eval/last/test/f1_std": 0.0034556023368705023}

data_scaling/n100_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,17,0.006599999999999999,0.05,43,"[22, 1.0]",train,0.00010308645869372413,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,17,0.006599999999999999,0.05,43,"[22, 1.0]",validation,0.2797021269798279,0.9635416666666666,0.0029880390387508927,0.9565002903672909,0.0038571716148288354
+flat_mae,patch,attn,hcpya_task21,best,17,0.006599999999999999,0.05,43,"[22, 1.0]",test,0.30021265149116516,0.9632936507936508,0.002535844570267519,0.9545481066774228,0.003449931567784212

data_scaling/n100_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,17,0.006599999999999999,0.05,43,"[22, 1.0]",train,0.00010308645869372413,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,17,0.006599999999999999,0.05,43,"[22, 1.0]",validation,0.2797021269798279,0.9635416666666666,0.0029880390387508927,0.9565002903672909,0.0038571716148288354
+flat_mae,patch,attn,hcpya_task21,best,17,0.006599999999999999,0.05,43,"[22, 1.0]",test,0.30021265149116516,0.9632936507936508,0.002535844570267519,0.9545481066774228,0.003449931567784212

data_scaling/n100_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,19,0.006599999999999999,0.05,43,"[22, 1.0]",train,0.0001044704404193908,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,last,19,0.006599999999999999,0.05,43,"[22, 1.0]",validation,0.2780441343784332,0.9635416666666666,0.0029650230298895195,0.9561333052343192,0.003821224564275038
+flat_mae,patch,attn,hcpya_task21,last,19,0.006599999999999999,0.05,43,"[22, 1.0]",test,0.29874613881111145,0.9636904761904762,0.0025437139072971285,0.9550425705771689,0.0034556023368705023

data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/log.txt

@@ -0,0 +1,896 @@
+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 19:52:22
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_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/n100_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/n100_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: 0:23:15  lr: nan  time: 3.4895  data: 2.9038  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:52  lr: 0.000003  loss: 3.0515 (3.0471)  grad: 0.2677 (0.2754)  time: 0.4678  data: 0.0035  max mem: 22446
+train: [0]  [ 40/400]  eta: 0:03:12  lr: 0.000006  loss: 3.0028 (3.0091)  grad: 0.2713 (0.2735)  time: 0.4547  data: 0.0035  max mem: 22446
+train: [0]  [ 60/400]  eta: 0:02:52  lr: 0.000009  loss: 2.9208 (2.9675)  grad: 0.2648 (0.2680)  time: 0.4504  data: 0.0034  max mem: 22446
+train: [0]  [ 80/400]  eta: 0:02:38  lr: 0.000012  loss: 2.8568 (2.9296)  grad: 0.2406 (0.2610)  time: 0.4627  data: 0.0033  max mem: 22446
+train: [0]  [100/400]  eta: 0:02:26  lr: 0.000015  loss: 2.7573 (2.8831)  grad: 0.2327 (0.2575)  time: 0.4544  data: 0.0034  max mem: 22446
+train: [0]  [120/400]  eta: 0:02:15  lr: 0.000018  loss: 2.6612 (2.8365)  grad: 0.2367 (0.2533)  time: 0.4659  data: 0.0034  max mem: 22446
+train: [0]  [140/400]  eta: 0:02:04  lr: 0.000021  loss: 2.5433 (2.7904)  grad: 0.2340 (0.2524)  time: 0.4582  data: 0.0034  max mem: 22446
+train: [0]  [160/400]  eta: 0:01:54  lr: 0.000024  loss: 2.4872 (2.7517)  grad: 0.2254 (0.2483)  time: 0.4510  data: 0.0035  max mem: 22446
+train: [0]  [180/400]  eta: 0:01:44  lr: 0.000027  loss: 2.4056 (2.7097)  grad: 0.2113 (0.2447)  time: 0.4539  data: 0.0034  max mem: 22446
+train: [0]  [200/400]  eta: 0:01:34  lr: 0.000030  loss: 2.3815 (2.6700)  grad: 0.2229 (0.2428)  time: 0.4629  data: 0.0036  max mem: 22446
+train: [0]  [220/400]  eta: 0:01:24  lr: 0.000033  loss: 2.2589 (2.6306)  grad: 0.2186 (0.2405)  time: 0.4600  data: 0.0035  max mem: 22446
+train: [0]  [240/400]  eta: 0:01:15  lr: 0.000036  loss: 2.1653 (2.5891)  grad: 0.2186 (0.2397)  time: 0.4504  data: 0.0035  max mem: 22446
+train: [0]  [260/400]  eta: 0:01:05  lr: 0.000039  loss: 2.1107 (2.5525)  grad: 0.2275 (0.2385)  time: 0.4743  data: 0.0035  max mem: 22446
+train: [0]  [280/400]  eta: 0:00:56  lr: 0.000042  loss: 2.1107 (2.5205)  grad: 0.2085 (0.2364)  time: 0.4505  data: 0.0034  max mem: 22446
+train: [0]  [300/400]  eta: 0:00:48  lr: 0.000045  loss: 2.0499 (2.4884)  grad: 0.2031 (0.2341)  time: 0.6454  data: 0.1803  max mem: 22446
+train: [0]  [320/400]  eta: 0:00:38  lr: 0.000048  loss: 2.0026 (2.4566)  grad: 0.2031 (0.2324)  time: 0.4684  data: 0.0029  max mem: 22446
+train: [0]  [340/400]  eta: 0:00:28  lr: 0.000051  loss: 1.9344 (2.4250)  grad: 0.2096 (0.2314)  time: 0.4529  data: 0.0036  max mem: 22446
+train: [0]  [360/400]  eta: 0:00:19  lr: 0.000054  loss: 1.9118 (2.3975)  grad: 0.2104 (0.2304)  time: 0.4489  data: 0.0034  max mem: 22446
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 1.9077 (2.3705)  grad: 0.2081 (0.2290)  time: 0.4616  data: 0.0034  max mem: 22446
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 1.8568 (2.3429)  grad: 0.2036 (0.2281)  time: 0.4484  data: 0.0036  max mem: 22446
+train: [0] Total time: 0:03:09 (0.4750 s / it)
+train: [0] Summary: lr: 0.000060  loss: 1.8568 (2.3429)  grad: 0.2036 (0.2281)
+eval (validation): [0]  [ 0/63]  eta: 0:03:26    time: 3.2793  data: 2.9889  max mem: 22446
+eval (validation): [0]  [20/63]  eta: 0:00:21    time: 0.3627  data: 0.0039  max mem: 22446
+eval (validation): [0]  [40/63]  eta: 0:00:09    time: 0.3467  data: 0.0032  max mem: 22446
+eval (validation): [0]  [60/63]  eta: 0:00:01    time: 0.3223  data: 0.0033  max mem: 22446
+eval (validation): [0]  [62/63]  eta: 0:00:00    time: 0.3205  data: 0.0032  max mem: 22446
+eval (validation): [0] Total time: 0:00:24 (0.3943 s / it)
+cv: [0]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 0.432  acc: 0.865  f1: 0.839
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:33  lr: nan  time: 3.3848  data: 3.0435  max mem: 22446
+train: [1]  [ 20/400]  eta: 0:03:38  lr: 0.000063  loss: 1.7689 (1.7831)  grad: 0.1944 (0.2038)  time: 0.4355  data: 0.0030  max mem: 22446
+train: [1]  [ 40/400]  eta: 0:03:03  lr: 0.000066  loss: 1.7740 (1.7676)  grad: 0.1997 (0.2042)  time: 0.4424  data: 0.0034  max mem: 22446
+train: [1]  [ 60/400]  eta: 0:02:47  lr: 0.000069  loss: 1.7173 (1.7483)  grad: 0.2005 (0.2023)  time: 0.4548  data: 0.0033  max mem: 22446
+train: [1]  [ 80/400]  eta: 0:02:36  lr: 0.000072  loss: 1.7131 (1.7400)  grad: 0.2041 (0.2049)  time: 0.4752  data: 0.0036  max mem: 22446
+train: [1]  [100/400]  eta: 0:02:24  lr: 0.000075  loss: 1.6917 (1.7305)  grad: 0.2083 (0.2048)  time: 0.4584  data: 0.0034  max mem: 22446
+train: [1]  [120/400]  eta: 0:02:13  lr: 0.000078  loss: 1.6634 (1.7130)  grad: 0.2036 (0.2047)  time: 0.4545  data: 0.0033  max mem: 22446
+train: [1]  [140/400]  eta: 0:02:03  lr: 0.000081  loss: 1.6266 (1.7009)  grad: 0.2036 (0.2036)  time: 0.4692  data: 0.0034  max mem: 22446
+train: [1]  [160/400]  eta: 0:01:53  lr: 0.000084  loss: 1.5843 (1.6824)  grad: 0.1884 (0.2021)  time: 0.4591  data: 0.0036  max mem: 22446
+train: [1]  [180/400]  eta: 0:01:43  lr: 0.000087  loss: 1.5581 (1.6698)  grad: 0.1945 (0.2017)  time: 0.4511  data: 0.0035  max mem: 22446
+train: [1]  [200/400]  eta: 0:01:34  lr: 0.000090  loss: 1.4995 (1.6542)  grad: 0.1950 (0.2006)  time: 0.4607  data: 0.0036  max mem: 22446
+train: [1]  [220/400]  eta: 0:01:24  lr: 0.000093  loss: 1.4913 (1.6386)  grad: 0.1947 (0.2006)  time: 0.4510  data: 0.0034  max mem: 22446
+train: [1]  [240/400]  eta: 0:01:14  lr: 0.000096  loss: 1.4664 (1.6254)  grad: 0.1963 (0.2002)  time: 0.4531  data: 0.0034  max mem: 22446
+train: [1]  [260/400]  eta: 0:01:05  lr: 0.000099  loss: 1.4664 (1.6149)  grad: 0.1886 (0.1993)  time: 0.4504  data: 0.0035  max mem: 22446
+train: [1]  [280/400]  eta: 0:00:55  lr: 0.000102  loss: 1.4426 (1.6014)  grad: 0.1920 (0.1997)  time: 0.4534  data: 0.0035  max mem: 22446
+train: [1]  [300/400]  eta: 0:00:47  lr: 0.000105  loss: 1.4057 (1.5884)  grad: 0.1880 (0.1983)  time: 0.6148  data: 0.1795  max mem: 22446
+train: [1]  [320/400]  eta: 0:00:37  lr: 0.000108  loss: 1.4082 (1.5774)  grad: 0.1783 (0.1973)  time: 0.4613  data: 0.0034  max mem: 22446
+train: [1]  [340/400]  eta: 0:00:28  lr: 0.000111  loss: 1.4048 (1.5645)  grad: 0.1738 (0.1957)  time: 0.4508  data: 0.0035  max mem: 22446
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 1.3491 (1.5554)  grad: 0.1718 (0.1947)  time: 0.4570  data: 0.0035  max mem: 22446
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 1.3564 (1.5452)  grad: 0.1854 (0.1944)  time: 0.4668  data: 0.0035  max mem: 22446
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 1.3497 (1.5344)  grad: 0.1854 (0.1938)  time: 0.4495  data: 0.0036  max mem: 22446
+train: [1] Total time: 0:03:08 (0.4711 s / it)
+train: [1] Summary: lr: 0.000120  loss: 1.3497 (1.5344)  grad: 0.1854 (0.1938)
+eval (validation): [1]  [ 0/63]  eta: 0:03:23    time: 3.2252  data: 2.9938  max mem: 22446
+eval (validation): [1]  [20/63]  eta: 0:00:20    time: 0.3411  data: 0.0036  max mem: 22446
+eval (validation): [1]  [40/63]  eta: 0:00:09    time: 0.3514  data: 0.0030  max mem: 22446
+eval (validation): [1]  [60/63]  eta: 0:00:01    time: 0.3184  data: 0.0032  max mem: 22446
+eval (validation): [1]  [62/63]  eta: 0:00:00    time: 0.3213  data: 0.0032  max mem: 22446
+eval (validation): [1] Total time: 0:00:24 (0.3885 s / it)
+cv: [1]  best hparam: (36, 1.0) (046) ('046_lr3.6e+01_wd1.0e+00')  loss: 0.373  acc: 0.899  f1: 0.877
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:22:56  lr: nan  time: 3.4423  data: 3.0504  max mem: 22446
+train: [2]  [ 20/400]  eta: 0:03:48  lr: 0.000123  loss: 1.3165 (1.2960)  grad: 0.1940 (0.1952)  time: 0.4589  data: 0.0030  max mem: 22446
+train: [2]  [ 40/400]  eta: 0:03:09  lr: 0.000126  loss: 1.3016 (1.2889)  grad: 0.1912 (0.1935)  time: 0.4459  data: 0.0036  max mem: 22446
+train: [2]  [ 60/400]  eta: 0:02:52  lr: 0.000129  loss: 1.2466 (1.2790)  grad: 0.1902 (0.1941)  time: 0.4719  data: 0.0035  max mem: 22446
+train: [2]  [ 80/400]  eta: 0:02:39  lr: 0.000132  loss: 1.2597 (1.2842)  grad: 0.1944 (0.1954)  time: 0.4744  data: 0.0035  max mem: 22446
+train: [2]  [100/400]  eta: 0:02:27  lr: 0.000135  loss: 1.2749 (1.2791)  grad: 0.1979 (0.1969)  time: 0.4627  data: 0.0033  max mem: 22446
+train: [2]  [120/400]  eta: 0:02:16  lr: 0.000138  loss: 1.2601 (1.2767)  grad: 0.1962 (0.1972)  time: 0.4613  data: 0.0034  max mem: 22446
+train: [2]  [140/400]  eta: 0:02:05  lr: 0.000141  loss: 1.2057 (1.2652)  grad: 0.1918 (0.1971)  time: 0.4468  data: 0.0034  max mem: 22446
+train: [2]  [160/400]  eta: 0:01:55  lr: 0.000144  loss: 1.2169 (1.2650)  grad: 0.1940 (0.1984)  time: 0.4684  data: 0.0035  max mem: 22446
+train: [2]  [180/400]  eta: 0:01:44  lr: 0.000147  loss: 1.2258 (1.2599)  grad: 0.2016 (0.1984)  time: 0.4426  data: 0.0033  max mem: 22446
+train: [2]  [200/400]  eta: 0:01:35  lr: 0.000150  loss: 1.2102 (1.2502)  grad: 0.1972 (0.1988)  time: 0.4725  data: 0.0038  max mem: 22446
+train: [2]  [220/400]  eta: 0:01:25  lr: 0.000153  loss: 1.2247 (1.2512)  grad: 0.1974 (0.1993)  time: 0.4486  data: 0.0036  max mem: 22446
+train: [2]  [240/400]  eta: 0:01:15  lr: 0.000156  loss: 1.2041 (1.2426)  grad: 0.2104 (0.2006)  time: 0.4532  data: 0.0035  max mem: 22446
+train: [2]  [260/400]  eta: 0:01:05  lr: 0.000159  loss: 1.2041 (1.2411)  grad: 0.2262 (0.2030)  time: 0.4489  data: 0.0035  max mem: 22446
+train: [2]  [280/400]  eta: 0:00:56  lr: 0.000162  loss: 1.2256 (1.2398)  grad: 0.2306 (0.2050)  time: 0.4477  data: 0.0035  max mem: 22446
+train: [2]  [300/400]  eta: 0:00:47  lr: 0.000165  loss: 1.1597 (1.2349)  grad: 0.2312 (0.2074)  time: 0.6209  data: 0.1804  max mem: 22446
+train: [2]  [320/400]  eta: 0:00:38  lr: 0.000168  loss: 1.1244 (1.2308)  grad: 0.2435 (0.2105)  time: 0.4617  data: 0.0027  max mem: 22446
+train: [2]  [340/400]  eta: 0:00:28  lr: 0.000171  loss: 1.1276 (1.2246)  grad: 0.2348 (0.2109)  time: 0.4501  data: 0.0034  max mem: 22446
+train: [2]  [360/400]  eta: 0:00:18  lr: 0.000174  loss: 1.1123 (1.2188)  grad: 0.2197 (0.2116)  time: 0.4615  data: 0.0035  max mem: 22446
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 1.1123 (1.2140)  grad: 0.2298 (0.2157)  time: 0.4611  data: 0.0035  max mem: 22446
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 1.0732 (1.2064)  grad: 0.2728 (0.2186)  time: 0.4552  data: 0.0035  max mem: 22446
+train: [2] Total time: 0:03:09 (0.4735 s / it)
+train: [2] Summary: lr: 0.000180  loss: 1.0732 (1.2064)  grad: 0.2728 (0.2186)
+eval (validation): [2]  [ 0/63]  eta: 0:03:36    time: 3.4410  data: 3.1613  max mem: 22446
+eval (validation): [2]  [20/63]  eta: 0:00:21    time: 0.3600  data: 0.0029  max mem: 22446
+eval (validation): [2]  [40/63]  eta: 0:00:09    time: 0.3333  data: 0.0035  max mem: 22446
+eval (validation): [2]  [60/63]  eta: 0:00:01    time: 0.3457  data: 0.0034  max mem: 22446
+eval (validation): [2]  [62/63]  eta: 0:00:00    time: 0.3464  data: 0.0034  max mem: 22446
+eval (validation): [2] Total time: 0:00:25 (0.4003 s / it)
+cv: [2]  best hparam: (19, 1.0) (042) ('042_lr1.9e+01_wd1.0e+00')  loss: 0.308  acc: 0.922  f1: 0.895
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:22:41  lr: nan  time: 3.4037  data: 3.0673  max mem: 22446
+train: [3]  [ 20/400]  eta: 0:03:48  lr: 0.000183  loss: 1.0854 (1.0700)  grad: 0.2782 (0.2676)  time: 0.4604  data: 0.0032  max mem: 22446
+train: [3]  [ 40/400]  eta: 0:03:08  lr: 0.000186  loss: 1.1078 (1.1061)  grad: 0.2849 (0.2864)  time: 0.4437  data: 0.0035  max mem: 22446
+train: [3]  [ 60/400]  eta: 0:02:53  lr: 0.000189  loss: 1.1430 (1.1222)  grad: 0.3115 (0.3045)  time: 0.4816  data: 0.0036  max mem: 22446
+train: [3]  [ 80/400]  eta: 0:02:39  lr: 0.000192  loss: 1.1189 (1.1260)  grad: 0.2962 (0.3010)  time: 0.4658  data: 0.0036  max mem: 22446
+train: [3]  [100/400]  eta: 0:02:28  lr: 0.000195  loss: 1.0850 (1.1173)  grad: 0.2816 (0.2965)  time: 0.4806  data: 0.0035  max mem: 22446
+train: [3]  [120/400]  eta: 0:02:17  lr: 0.000198  loss: 1.0701 (1.1225)  grad: 0.2808 (0.3026)  time: 0.4629  data: 0.0034  max mem: 22446
+train: [3]  [140/400]  eta: 0:02:06  lr: 0.000201  loss: 1.1380 (1.1237)  grad: 0.3201 (0.3040)  time: 0.4548  data: 0.0034  max mem: 22446
+train: [3]  [160/400]  eta: 0:01:55  lr: 0.000204  loss: 1.1370 (1.1248)  grad: 0.3255 (0.3094)  time: 0.4630  data: 0.0034  max mem: 22446
+train: [3]  [180/400]  eta: 0:01:45  lr: 0.000207  loss: 1.1191 (1.1226)  grad: 0.3237 (0.3114)  time: 0.4661  data: 0.0034  max mem: 22446
+train: [3]  [200/400]  eta: 0:01:35  lr: 0.000210  loss: 1.1080 (1.1202)  grad: 0.3454 (0.3160)  time: 0.4716  data: 0.0035  max mem: 22446
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+train: [3]  [240/400]  eta: 0:01:16  lr: 0.000216  loss: 1.1254 (1.1281)  grad: 0.3769 (0.3315)  time: 0.4542  data: 0.0034  max mem: 22446
+train: [3]  [260/400]  eta: 0:01:06  lr: 0.000219  loss: 1.1435 (1.1269)  grad: 0.3841 (0.3417)  time: 0.4562  data: 0.0034  max mem: 22446
+train: [3]  [280/400]  eta: 0:00:56  lr: 0.000222  loss: 1.1686 (1.1335)  grad: 0.4210 (0.3499)  time: 0.4531  data: 0.0037  max mem: 22446
+train: [3]  [300/400]  eta: 0:00:48  lr: 0.000225  loss: 1.1841 (1.1358)  grad: 0.4285 (0.3590)  time: 0.6547  data: 0.1872  max mem: 22446
+train: [3]  [320/400]  eta: 0:00:38  lr: 0.000228  loss: 1.1599 (1.1384)  grad: 0.4305 (0.3712)  time: 0.4638  data: 0.0027  max mem: 22446
+train: [3]  [340/400]  eta: 0:00:28  lr: 0.000231  loss: 1.0359 (1.1335)  grad: 0.4264 (0.3766)  time: 0.4520  data: 0.0036  max mem: 22446
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+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 1.1013 (1.1345)  grad: 0.5282 (0.3947)  time: 0.4542  data: 0.0035  max mem: 22446
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 1.1013 (1.1338)  grad: 0.5282 (0.4004)  time: 0.4506  data: 0.0035  max mem: 22446
+train: [3] Total time: 0:03:11 (0.4786 s / it)
+train: [3] Summary: lr: 0.000240  loss: 1.1013 (1.1338)  grad: 0.5282 (0.4004)
+eval (validation): [3]  [ 0/63]  eta: 0:03:23    time: 3.2295  data: 2.9485  max mem: 22446
+eval (validation): [3]  [20/63]  eta: 0:00:21    time: 0.3624  data: 0.0051  max mem: 22446
+eval (validation): [3]  [40/63]  eta: 0:00:09    time: 0.3445  data: 0.0028  max mem: 22446
+eval (validation): [3]  [60/63]  eta: 0:00:01    time: 0.3432  data: 0.0033  max mem: 22446
+eval (validation): [3]  [62/63]  eta: 0:00:00    time: 0.3397  data: 0.0033  max mem: 22446
+eval (validation): [3] Total time: 0:00:25 (0.3997 s / it)
+cv: [3]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 0.238  acc: 0.927  f1: 0.914
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:22:17  lr: nan  time: 3.3430  data: 3.0040  max mem: 22446
+train: [4]  [ 20/400]  eta: 0:03:43  lr: 0.000243  loss: 1.2110 (1.2238)  grad: 0.5054 (0.5320)  time: 0.4513  data: 0.0037  max mem: 22446
+train: [4]  [ 40/400]  eta: 0:03:08  lr: 0.000246  loss: 1.2635 (1.2825)  grad: 0.5195 (0.5606)  time: 0.4551  data: 0.0034  max mem: 22446
+train: [4]  [ 60/400]  eta: 0:02:51  lr: 0.000249  loss: 1.2465 (1.2628)  grad: 0.6204 (0.6037)  time: 0.4604  data: 0.0036  max mem: 22446
+train: [4]  [ 80/400]  eta: 0:02:37  lr: 0.000252  loss: 1.2465 (1.2712)  grad: 0.7194 (0.6369)  time: 0.4601  data: 0.0037  max mem: 22446
+train: [4]  [100/400]  eta: 0:02:27  lr: 0.000255  loss: 1.2683 (1.2761)  grad: 0.6728 (0.6446)  time: 0.4829  data: 0.0039  max mem: 22446
+train: [4]  [120/400]  eta: 0:02:15  lr: 0.000258  loss: 1.3739 (1.3268)  grad: 0.7808 (0.6914)  time: 0.4556  data: 0.0037  max mem: 22446
+train: [4]  [140/400]  eta: 0:02:04  lr: 0.000261  loss: 1.5892 (1.3399)  grad: 0.8359 (0.7271)  time: 0.4468  data: 0.0036  max mem: 22446
+train: [4]  [160/400]  eta: 0:01:54  lr: 0.000264  loss: 1.4695 (1.3760)  grad: 0.8034 (0.7366)  time: 0.4665  data: 0.0035  max mem: 22446
+train: [4]  [180/400]  eta: 0:01:44  lr: 0.000267  loss: 1.4695 (1.3906)  grad: 0.9041 (0.8447)  time: 0.4634  data: 0.0037  max mem: 22446
+train: [4]  [200/400]  eta: 0:01:34  lr: 0.000270  loss: 1.3969 (1.3993)  grad: 1.0012 (0.8610)  time: 0.4573  data: 0.0036  max mem: 22446
+train: [4]  [220/400]  eta: 0:01:25  lr: 0.000273  loss: 1.4889 (1.4304)  grad: 1.0010 (0.8745)  time: 0.4649  data: 0.0035  max mem: 22446
+train: [4]  [240/400]  eta: 0:01:15  lr: 0.000276  loss: 1.7027 (1.4549)  grad: 0.9527 (0.8778)  time: 0.4654  data: 0.0035  max mem: 22446
+train: [4]  [260/400]  eta: 0:01:06  lr: 0.000279  loss: 1.7359 (1.4831)  grad: 0.9103 (0.8847)  time: 0.4622  data: 0.0035  max mem: 22446
+WARNING: classifier 48 (50, 1.0) diverged (loss=63.83 > 60.89) at step 937. Freezing.
+train: [4]  [280/400]  eta: 0:00:56  lr: 0.000282  loss: 1.5376 (1.4922)  grad: 1.0027 (0.8966)  time: 0.4630  data: 0.0034  max mem: 22446
+train: [4]  [300/400]  eta: 0:00:48  lr: 0.000285  loss: 1.2707 (1.4746)  grad: 0.7992 (0.8858)  time: 0.6401  data: 0.1853  max mem: 22446
+train: [4]  [320/400]  eta: 0:00:38  lr: 0.000288  loss: 1.1047 (1.4509)  grad: 0.6937 (0.8728)  time: 0.4608  data: 0.0035  max mem: 22446
+train: [4]  [340/400]  eta: 0:00:28  lr: 0.000291  loss: 1.0521 (1.4288)  grad: 0.6110 (0.8573)  time: 0.4536  data: 0.0031  max mem: 22446
+train: [4]  [360/400]  eta: 0:00:19  lr: 0.000294  loss: 1.0968 (1.4192)  grad: 0.6110 (0.8507)  time: 0.4747  data: 0.0036  max mem: 22446
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 1.2922 (1.4125)  grad: 0.7048 (0.8447)  time: 0.4545  data: 0.0035  max mem: 22446
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 1.2996 (1.4080)  grad: 0.7782 (0.8635)  time: 0.4557  data: 0.0036  max mem: 22446
+train: [4] Total time: 0:03:10 (0.4772 s / it)
+train: [4] Summary: lr: 0.000300  loss: 1.2996 (1.4080)  grad: 0.7782 (0.8635)
+eval (validation): [4]  [ 0/63]  eta: 0:03:29    time: 3.3229  data: 3.0534  max mem: 22446
+eval (validation): [4]  [20/63]  eta: 0:00:22    time: 0.3747  data: 0.0025  max mem: 22446
+eval (validation): [4]  [40/63]  eta: 0:00:09    time: 0.3481  data: 0.0037  max mem: 22446
+eval (validation): [4]  [60/63]  eta: 0:00:01    time: 0.3424  data: 0.0035  max mem: 22446
+eval (validation): [4]  [62/63]  eta: 0:00:00    time: 0.3420  data: 0.0034  max mem: 22446
+eval (validation): [4] Total time: 0:00:25 (0.4067 s / it)
+cv: [4]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.298  acc: 0.925  f1: 0.909
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:22:51  lr: nan  time: 3.4283  data: 3.0468  max mem: 22446
+train: [5]  [ 20/400]  eta: 0:03:40  lr: 0.000300  loss: 0.9793 (1.0898)  grad: 0.6019 (0.6255)  time: 0.4373  data: 0.0029  max mem: 22446
+train: [5]  [ 40/400]  eta: 0:03:07  lr: 0.000300  loss: 1.1380 (1.1444)  grad: 0.6493 (0.6709)  time: 0.4575  data: 0.0032  max mem: 22446
+train: [5]  [ 60/400]  eta: 0:02:49  lr: 0.000300  loss: 1.1920 (1.1876)  grad: 0.6638 (0.6632)  time: 0.4542  data: 0.0035  max mem: 22446
+train: [5]  [ 80/400]  eta: 0:02:37  lr: 0.000300  loss: 1.2797 (1.1973)  grad: 0.6685 (0.6677)  time: 0.4712  data: 0.0038  max mem: 22446
+train: [5]  [100/400]  eta: 0:02:25  lr: 0.000300  loss: 1.3081 (1.2414)  grad: 0.6866 (0.6984)  time: 0.4629  data: 0.0037  max mem: 22446
+train: [5]  [120/400]  eta: 0:02:14  lr: 0.000300  loss: 1.3590 (1.2838)  grad: 0.6880 (0.7013)  time: 0.4500  data: 0.0035  max mem: 22446
+train: [5]  [140/400]  eta: 0:02:03  lr: 0.000300  loss: 1.3115 (1.2737)  grad: 0.7069 (0.7050)  time: 0.4454  data: 0.0033  max mem: 22446
+train: [5]  [160/400]  eta: 0:01:53  lr: 0.000299  loss: 1.2305 (1.2884)  grad: 0.7069 (0.7054)  time: 0.4604  data: 0.0033  max mem: 22446
+train: [5]  [180/400]  eta: 0:01:43  lr: 0.000299  loss: 1.2305 (1.2846)  grad: 0.6889 (0.7098)  time: 0.4521  data: 0.0032  max mem: 22446
+train: [5]  [200/400]  eta: 0:01:33  lr: 0.000299  loss: 1.2111 (1.2787)  grad: 0.6810 (0.7055)  time: 0.4533  data: 0.0035  max mem: 22446
+train: [5]  [220/400]  eta: 0:01:24  lr: 0.000299  loss: 1.1648 (1.2722)  grad: 0.6839 (0.7198)  time: 0.4463  data: 0.0034  max mem: 22446
+train: [5]  [240/400]  eta: 0:01:14  lr: 0.000299  loss: 1.1341 (1.2570)  grad: 0.6889 (0.7182)  time: 0.4506  data: 0.0034  max mem: 22446
+train: [5]  [260/400]  eta: 0:01:04  lr: 0.000299  loss: 1.1443 (1.2622)  grad: 0.7131 (0.7197)  time: 0.4382  data: 0.0033  max mem: 22446
+train: [5]  [280/400]  eta: 0:00:55  lr: 0.000298  loss: 1.2137 (1.2659)  grad: 0.7191 (0.7292)  time: 0.4479  data: 0.0035  max mem: 22446
+train: [5]  [300/400]  eta: 0:00:47  lr: 0.000298  loss: 1.1761 (1.2568)  grad: 0.7762 (0.7337)  time: 0.6459  data: 0.1853  max mem: 22446
+train: [5]  [320/400]  eta: 0:00:37  lr: 0.000298  loss: 1.1882 (1.2533)  grad: 0.7327 (0.7315)  time: 0.4485  data: 0.0032  max mem: 22446
+train: [5]  [340/400]  eta: 0:00:28  lr: 0.000298  loss: 1.1882 (1.2482)  grad: 0.6913 (0.7288)  time: 0.4574  data: 0.0035  max mem: 22446
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 1.0544 (1.2385)  grad: 0.6764 (0.7288)  time: 0.4679  data: 0.0035  max mem: 22446
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 1.0342 (1.2337)  grad: 0.6764 (0.7323)  time: 0.4577  data: 0.0035  max mem: 22446
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 1.1123 (1.2262)  grad: 0.6679 (0.7306)  time: 0.4604  data: 0.0035  max mem: 22446
+train: [5] Total time: 0:03:08 (0.4710 s / it)
+train: [5] Summary: lr: 0.000297  loss: 1.1123 (1.2262)  grad: 0.6679 (0.7306)
+eval (validation): [5]  [ 0/63]  eta: 0:03:24    time: 3.2430  data: 3.0129  max mem: 22446
+eval (validation): [5]  [20/63]  eta: 0:00:21    time: 0.3662  data: 0.0043  max mem: 22446
+eval (validation): [5]  [40/63]  eta: 0:00:09    time: 0.3507  data: 0.0033  max mem: 22446
+eval (validation): [5]  [60/63]  eta: 0:00:01    time: 0.3279  data: 0.0031  max mem: 22446
+eval (validation): [5]  [62/63]  eta: 0:00:00    time: 0.3222  data: 0.0031  max mem: 22446
+eval (validation): [5] Total time: 0:00:25 (0.3974 s / it)
+cv: [5]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.322  acc: 0.928  f1: 0.907
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:22:24  lr: nan  time: 3.3617  data: 3.0224  max mem: 22446
+train: [6]  [ 20/400]  eta: 0:03:42  lr: 0.000296  loss: 0.9415 (0.9634)  grad: 0.5855 (0.6209)  time: 0.4476  data: 0.0033  max mem: 22446
+train: [6]  [ 40/400]  eta: 0:03:09  lr: 0.000296  loss: 0.8831 (0.9333)  grad: 0.5736 (0.6209)  time: 0.4659  data: 0.0035  max mem: 22446
+train: [6]  [ 60/400]  eta: 0:02:50  lr: 0.000296  loss: 0.9409 (0.9716)  grad: 0.6482 (0.6418)  time: 0.4506  data: 0.0037  max mem: 22446
+train: [6]  [ 80/400]  eta: 0:02:39  lr: 0.000295  loss: 1.0271 (0.9912)  grad: 0.6653 (0.6333)  time: 0.4840  data: 0.0038  max mem: 22446
+train: [6]  [100/400]  eta: 0:02:26  lr: 0.000295  loss: 1.0171 (1.0003)  grad: 0.6099 (0.6368)  time: 0.4583  data: 0.0036  max mem: 22446
+train: [6]  [120/400]  eta: 0:02:15  lr: 0.000295  loss: 0.9381 (0.9893)  grad: 0.6121 (0.6365)  time: 0.4574  data: 0.0035  max mem: 22446
+train: [6]  [140/400]  eta: 0:02:04  lr: 0.000294  loss: 0.8828 (0.9908)  grad: 0.6706 (0.6495)  time: 0.4435  data: 0.0034  max mem: 22446
+train: [6]  [160/400]  eta: 0:01:54  lr: 0.000294  loss: 0.8828 (0.9863)  grad: 0.6314 (0.6431)  time: 0.4641  data: 0.0035  max mem: 22446
+train: [6]  [180/400]  eta: 0:01:44  lr: 0.000293  loss: 0.9114 (0.9878)  grad: 0.5833 (0.6403)  time: 0.4561  data: 0.0035  max mem: 22446
+train: [6]  [200/400]  eta: 0:01:34  lr: 0.000293  loss: 0.9619 (0.9949)  grad: 0.5833 (0.6345)  time: 0.4596  data: 0.0034  max mem: 22446
+train: [6]  [220/400]  eta: 0:01:25  lr: 0.000292  loss: 0.9034 (0.9864)  grad: 0.5568 (0.6296)  time: 0.4692  data: 0.0034  max mem: 22446
+train: [6]  [240/400]  eta: 0:01:15  lr: 0.000292  loss: 0.8862 (0.9805)  grad: 0.5687 (0.6232)  time: 0.4503  data: 0.0036  max mem: 22446
+train: [6]  [260/400]  eta: 0:01:05  lr: 0.000291  loss: 0.8015 (0.9676)  grad: 0.5687 (0.6210)  time: 0.4543  data: 0.0036  max mem: 22446
+train: [6]  [280/400]  eta: 0:00:56  lr: 0.000291  loss: 0.8680 (0.9683)  grad: 0.6385 (0.6197)  time: 0.4519  data: 0.0036  max mem: 22446
+train: [6]  [300/400]  eta: 0:00:48  lr: 0.000290  loss: 0.9739 (0.9666)  grad: 0.5912 (0.6177)  time: 0.6563  data: 0.1895  max mem: 22446
+train: [6]  [320/400]  eta: 0:00:38  lr: 0.000290  loss: 0.8435 (0.9610)  grad: 0.5496 (0.6141)  time: 0.4563  data: 0.0032  max mem: 22446
+train: [6]  [340/400]  eta: 0:00:28  lr: 0.000289  loss: 0.8271 (0.9554)  grad: 0.4891 (0.6079)  time: 0.4533  data: 0.0034  max mem: 22446
+train: [6]  [360/400]  eta: 0:00:19  lr: 0.000288  loss: 0.7490 (0.9460)  grad: 0.4492 (0.6006)  time: 0.4651  data: 0.0036  max mem: 22446
+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 0.7886 (0.9410)  grad: 0.4586 (0.5943)  time: 0.4497  data: 0.0036  max mem: 22446
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 0.7863 (0.9293)  grad: 0.4468 (0.5865)  time: 0.4542  data: 0.0038  max mem: 22446
+train: [6] Total time: 0:03:09 (0.4750 s / it)
+train: [6] Summary: lr: 0.000287  loss: 0.7863 (0.9293)  grad: 0.4468 (0.5865)
+eval (validation): [6]  [ 0/63]  eta: 0:03:25    time: 3.2622  data: 3.0201  max mem: 22446
+eval (validation): [6]  [20/63]  eta: 0:00:20    time: 0.3488  data: 0.0103  max mem: 22446
+eval (validation): [6]  [40/63]  eta: 0:00:09    time: 0.3489  data: 0.0034  max mem: 22446
+eval (validation): [6]  [60/63]  eta: 0:00:01    time: 0.3266  data: 0.0025  max mem: 22446
+eval (validation): [6]  [62/63]  eta: 0:00:00    time: 0.3255  data: 0.0028  max mem: 22446
+eval (validation): [6] Total time: 0:00:24 (0.3927 s / it)
+cv: [6]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 0.456  acc: 0.937  f1: 0.926
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:21:49  lr: nan  time: 3.2729  data: 2.9364  max mem: 22446
+train: [7]  [ 20/400]  eta: 0:03:44  lr: 0.000286  loss: 0.7467 (0.7339)  grad: 0.4447 (0.4294)  time: 0.4556  data: 0.0031  max mem: 22446
+train: [7]  [ 40/400]  eta: 0:03:08  lr: 0.000286  loss: 0.7467 (0.7492)  grad: 0.4351 (0.4349)  time: 0.4553  data: 0.0037  max mem: 22446
+train: [7]  [ 60/400]  eta: 0:02:50  lr: 0.000285  loss: 0.7520 (0.7710)  grad: 0.4337 (0.4331)  time: 0.4543  data: 0.0035  max mem: 22446
+train: [7]  [ 80/400]  eta: 0:02:37  lr: 0.000284  loss: 0.7346 (0.7565)  grad: 0.4109 (0.4331)  time: 0.4643  data: 0.0035  max mem: 22446
+train: [7]  [100/400]  eta: 0:02:25  lr: 0.000284  loss: 0.6738 (0.7520)  grad: 0.3841 (0.4304)  time: 0.4583  data: 0.0036  max mem: 22446
+train: [7]  [120/400]  eta: 0:02:14  lr: 0.000283  loss: 0.6587 (0.7430)  grad: 0.3973 (0.4289)  time: 0.4593  data: 0.0036  max mem: 22446
+train: [7]  [140/400]  eta: 0:02:04  lr: 0.000282  loss: 0.7005 (0.7444)  grad: 0.4135 (0.4355)  time: 0.4607  data: 0.0036  max mem: 22446
+train: [7]  [160/400]  eta: 0:01:54  lr: 0.000282  loss: 0.7027 (0.7404)  grad: 0.4270 (0.4325)  time: 0.4546  data: 0.0034  max mem: 22446
+train: [7]  [180/400]  eta: 0:01:44  lr: 0.000281  loss: 0.6994 (0.7414)  grad: 0.4021 (0.4288)  time: 0.4579  data: 0.0035  max mem: 22446
+train: [7]  [200/400]  eta: 0:01:34  lr: 0.000280  loss: 0.6771 (0.7340)  grad: 0.4312 (0.4282)  time: 0.4529  data: 0.0035  max mem: 22446
+train: [7]  [220/400]  eta: 0:01:24  lr: 0.000279  loss: 0.6484 (0.7307)  grad: 0.4424 (0.4273)  time: 0.4586  data: 0.0034  max mem: 22446
+train: [7]  [240/400]  eta: 0:01:14  lr: 0.000278  loss: 0.6827 (0.7317)  grad: 0.4251 (0.4267)  time: 0.4513  data: 0.0034  max mem: 22446
+train: [7]  [260/400]  eta: 0:01:05  lr: 0.000278  loss: 0.6718 (0.7279)  grad: 0.4285 (0.4259)  time: 0.4510  data: 0.0034  max mem: 22446
+train: [7]  [280/400]  eta: 0:00:55  lr: 0.000277  loss: 0.6653 (0.7302)  grad: 0.3817 (0.4219)  time: 0.4481  data: 0.0035  max mem: 22446
+train: [7]  [300/400]  eta: 0:00:47  lr: 0.000276  loss: 0.6962 (0.7305)  grad: 0.3649 (0.4185)  time: 0.6246  data: 0.1819  max mem: 22446
+train: [7]  [320/400]  eta: 0:00:37  lr: 0.000275  loss: 0.6665 (0.7269)  grad: 0.3528 (0.4145)  time: 0.4409  data: 0.0030  max mem: 22446
+train: [7]  [340/400]  eta: 0:00:28  lr: 0.000274  loss: 0.6324 (0.7205)  grad: 0.3528 (0.4128)  time: 0.4550  data: 0.0036  max mem: 22446
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 0.5845 (0.7137)  grad: 0.3431 (0.4094)  time: 0.4656  data: 0.0038  max mem: 22446
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 0.5991 (0.7080)  grad: 0.3322 (0.4058)  time: 0.4499  data: 0.0038  max mem: 22446
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 0.6135 (0.7026)  grad: 0.3369 (0.4029)  time: 0.4525  data: 0.0034  max mem: 22446
+train: [7] Total time: 0:03:08 (0.4708 s / it)
+train: [7] Summary: lr: 0.000271  loss: 0.6135 (0.7026)  grad: 0.3369 (0.4029)
+eval (validation): [7]  [ 0/63]  eta: 0:04:24    time: 4.2033  data: 3.8918  max mem: 22446
+eval (validation): [7]  [20/63]  eta: 0:00:23    time: 0.3605  data: 0.0026  max mem: 22446
+eval (validation): [7]  [40/63]  eta: 0:00:10    time: 0.3492  data: 0.0033  max mem: 22446
+eval (validation): [7]  [60/63]  eta: 0:00:01    time: 0.3471  data: 0.0035  max mem: 22446
+eval (validation): [7]  [62/63]  eta: 0:00:00    time: 0.3427  data: 0.0035  max mem: 22446
+eval (validation): [7] Total time: 0:00:26 (0.4178 s / it)
+cv: [7]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 0.300  acc: 0.943  f1: 0.929
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [8]  [  0/400]  eta: 0:22:01  lr: nan  time: 3.3047  data: 2.9574  max mem: 22446
+train: [8]  [ 20/400]  eta: 0:03:48  lr: 0.000270  loss: 0.5605 (0.5891)  grad: 0.3333 (0.3335)  time: 0.4669  data: 0.0034  max mem: 22446
+train: [8]  [ 40/400]  eta: 0:03:12  lr: 0.000270  loss: 0.5699 (0.5970)  grad: 0.3270 (0.3327)  time: 0.4616  data: 0.0034  max mem: 22446
+train: [8]  [ 60/400]  eta: 0:02:53  lr: 0.000269  loss: 0.5444 (0.5897)  grad: 0.2942 (0.3204)  time: 0.4628  data: 0.0037  max mem: 22446
+train: [8]  [ 80/400]  eta: 0:02:39  lr: 0.000268  loss: 0.5262 (0.5808)  grad: 0.2803 (0.3120)  time: 0.4679  data: 0.0037  max mem: 22446
+train: [8]  [100/400]  eta: 0:02:27  lr: 0.000267  loss: 0.5230 (0.5716)  grad: 0.2607 (0.3005)  time: 0.4608  data: 0.0037  max mem: 22446
+train: [8]  [120/400]  eta: 0:02:15  lr: 0.000266  loss: 0.5641 (0.5849)  grad: 0.2801 (0.3059)  time: 0.4486  data: 0.0035  max mem: 22446
+train: [8]  [140/400]  eta: 0:02:05  lr: 0.000265  loss: 0.5899 (0.5854)  grad: 0.3360 (0.3110)  time: 0.4621  data: 0.0035  max mem: 22446
+train: [8]  [160/400]  eta: 0:01:54  lr: 0.000264  loss: 0.5550 (0.5837)  grad: 0.2936 (0.3099)  time: 0.4450  data: 0.0034  max mem: 22446
+train: [8]  [180/400]  eta: 0:01:44  lr: 0.000263  loss: 0.5436 (0.5811)  grad: 0.2813 (0.3073)  time: 0.4661  data: 0.0036  max mem: 22446
+train: [8]  [200/400]  eta: 0:01:34  lr: 0.000262  loss: 0.5331 (0.5815)  grad: 0.2917 (0.3094)  time: 0.4519  data: 0.0035  max mem: 22446
+train: [8]  [220/400]  eta: 0:01:24  lr: 0.000260  loss: 0.5769 (0.5830)  grad: 0.3058 (0.3093)  time: 0.4499  data: 0.0035  max mem: 22446
+train: [8]  [240/400]  eta: 0:01:15  lr: 0.000259  loss: 0.6026 (0.5871)  grad: 0.3036 (0.3093)  time: 0.4502  data: 0.0036  max mem: 22446
+train: [8]  [260/400]  eta: 0:01:05  lr: 0.000258  loss: 0.5892 (0.5849)  grad: 0.2983 (0.3095)  time: 0.4437  data: 0.0034  max mem: 22446
+train: [8]  [280/400]  eta: 0:00:55  lr: 0.000257  loss: 0.5792 (0.5884)  grad: 0.3205 (0.3114)  time: 0.4539  data: 0.0036  max mem: 22446
+train: [8]  [300/400]  eta: 0:00:47  lr: 0.000256  loss: 0.5698 (0.5871)  grad: 0.3240 (0.3110)  time: 0.6282  data: 0.1863  max mem: 22446
+train: [8]  [320/400]  eta: 0:00:38  lr: 0.000255  loss: 0.5313 (0.5823)  grad: 0.2796 (0.3078)  time: 0.4455  data: 0.0035  max mem: 22446
+train: [8]  [340/400]  eta: 0:00:28  lr: 0.000254  loss: 0.5189 (0.5798)  grad: 0.2442 (0.3037)  time: 0.4696  data: 0.0033  max mem: 22446
+train: [8]  [360/400]  eta: 0:00:18  lr: 0.000253  loss: 0.5117 (0.5737)  grad: 0.2166 (0.2986)  time: 0.4640  data: 0.0035  max mem: 22446
+train: [8]  [380/400]  eta: 0:00:09  lr: 0.000252  loss: 0.4781 (0.5701)  grad: 0.2303 (0.2952)  time: 0.4604  data: 0.0037  max mem: 22446
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 0.4811 (0.5665)  grad: 0.2302 (0.2914)  time: 0.4579  data: 0.0036  max mem: 22446
+train: [8] Total time: 0:03:09 (0.4733 s / it)
+train: [8] Summary: lr: 0.000250  loss: 0.4811 (0.5665)  grad: 0.2302 (0.2914)
+eval (validation): [8]  [ 0/63]  eta: 0:03:38    time: 3.4705  data: 3.1741  max mem: 22446
+eval (validation): [8]  [20/63]  eta: 0:00:22    time: 0.3768  data: 0.0037  max mem: 22446
+eval (validation): [8]  [40/63]  eta: 0:00:10    time: 0.3679  data: 0.0035  max mem: 22446
+eval (validation): [8]  [60/63]  eta: 0:00:01    time: 0.3456  data: 0.0032  max mem: 22446
+eval (validation): [8]  [62/63]  eta: 0:00:00    time: 0.3440  data: 0.0032  max mem: 22446
+eval (validation): [8] Total time: 0:00:26 (0.4175 s / it)
+cv: [8]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 0.723  acc: 0.949  f1: 0.940
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [9]  [  0/400]  eta: 0:22:43  lr: nan  time: 3.4097  data: 3.0668  max mem: 22446
+train: [9]  [ 20/400]  eta: 0:03:44  lr: 0.000249  loss: 0.4806 (0.5352)  grad: 0.2084 (0.2138)  time: 0.4488  data: 0.0037  max mem: 22446
+train: [9]  [ 40/400]  eta: 0:03:08  lr: 0.000248  loss: 0.4799 (0.5103)  grad: 0.2106 (0.2163)  time: 0.4533  data: 0.0030  max mem: 22446
+train: [9]  [ 60/400]  eta: 0:02:51  lr: 0.000247  loss: 0.4799 (0.5085)  grad: 0.2145 (0.2132)  time: 0.4661  data: 0.0036  max mem: 22446
+train: [9]  [ 80/400]  eta: 0:02:37  lr: 0.000246  loss: 0.5009 (0.5061)  grad: 0.2145 (0.2126)  time: 0.4546  data: 0.0034  max mem: 22446
+train: [9]  [100/400]  eta: 0:02:25  lr: 0.000244  loss: 0.4530 (0.5002)  grad: 0.2087 (0.2121)  time: 0.4578  data: 0.0035  max mem: 22446
+train: [9]  [120/400]  eta: 0:02:15  lr: 0.000243  loss: 0.5037 (0.5043)  grad: 0.2047 (0.2119)  time: 0.4708  data: 0.0035  max mem: 22446
+train: [9]  [140/400]  eta: 0:02:04  lr: 0.000242  loss: 0.5037 (0.5041)  grad: 0.2047 (0.2125)  time: 0.4542  data: 0.0035  max mem: 22446
+train: [9]  [160/400]  eta: 0:01:54  lr: 0.000241  loss: 0.4789 (0.5034)  grad: 0.2171 (0.2136)  time: 0.4576  data: 0.0035  max mem: 22446
+train: [9]  [180/400]  eta: 0:01:44  lr: 0.000240  loss: 0.4802 (0.5016)  grad: 0.2137 (0.2136)  time: 0.4615  data: 0.0035  max mem: 22446
+train: [9]  [200/400]  eta: 0:01:34  lr: 0.000238  loss: 0.4800 (0.4995)  grad: 0.2114 (0.2136)  time: 0.4598  data: 0.0034  max mem: 22446
+train: [9]  [220/400]  eta: 0:01:24  lr: 0.000237  loss: 0.4776 (0.5011)  grad: 0.2127 (0.2136)  time: 0.4529  data: 0.0033  max mem: 22446
+train: [9]  [240/400]  eta: 0:01:15  lr: 0.000236  loss: 0.4828 (0.4991)  grad: 0.2180 (0.2141)  time: 0.4690  data: 0.0033  max mem: 22446
+train: [9]  [260/400]  eta: 0:01:05  lr: 0.000234  loss: 0.4868 (0.5004)  grad: 0.2107 (0.2145)  time: 0.4597  data: 0.0033  max mem: 22446
+train: [9]  [280/400]  eta: 0:00:56  lr: 0.000233  loss: 0.4868 (0.4988)  grad: 0.2100 (0.2146)  time: 0.4573  data: 0.0035  max mem: 22446
+train: [9]  [300/400]  eta: 0:00:47  lr: 0.000232  loss: 0.4844 (0.4987)  grad: 0.2085 (0.2140)  time: 0.6251  data: 0.1884  max mem: 22446
+train: [9]  [320/400]  eta: 0:00:38  lr: 0.000230  loss: 0.4715 (0.4957)  grad: 0.1857 (0.2121)  time: 0.4437  data: 0.0030  max mem: 22446
+train: [9]  [340/400]  eta: 0:00:28  lr: 0.000229  loss: 0.4569 (0.4939)  grad: 0.1852 (0.2112)  time: 0.4529  data: 0.0035  max mem: 22446
+train: [9]  [360/400]  eta: 0:00:18  lr: 0.000228  loss: 0.4673 (0.4921)  grad: 0.1961 (0.2100)  time: 0.4532  data: 0.0036  max mem: 22446
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 0.4580 (0.4906)  grad: 0.1950 (0.2095)  time: 0.4537  data: 0.0035  max mem: 22446
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 0.4633 (0.4900)  grad: 0.1845 (0.2079)  time: 0.4699  data: 0.0035  max mem: 22446
+train: [9] Total time: 0:03:09 (0.4738 s / it)
+train: [9] Summary: lr: 0.000225  loss: 0.4633 (0.4900)  grad: 0.1845 (0.2079)
+eval (validation): [9]  [ 0/63]  eta: 0:03:33    time: 3.3827  data: 3.0987  max mem: 22446
+eval (validation): [9]  [20/63]  eta: 0:00:22    time: 0.3904  data: 0.0029  max mem: 22446
+eval (validation): [9]  [40/63]  eta: 0:00:10    time: 0.3751  data: 0.0034  max mem: 22446
+eval (validation): [9]  [60/63]  eta: 0:00:01    time: 0.3264  data: 0.0031  max mem: 22446
+eval (validation): [9]  [62/63]  eta: 0:00:00    time: 0.3309  data: 0.0031  max mem: 22446
+eval (validation): [9] Total time: 0:00:26 (0.4165 s / it)
+cv: [9]  best hparam: (36, 1.0) (046) ('046_lr3.6e+01_wd1.0e+00')  loss: 1.762  acc: 0.950  f1: 0.942
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [10]  [  0/400]  eta: 0:23:20  lr: nan  time: 3.5003  data: 3.1161  max mem: 22446
+train: [10]  [ 20/400]  eta: 0:03:51  lr: 0.000224  loss: 0.4989 (0.5118)  grad: 0.1798 (0.1901)  time: 0.4652  data: 0.0026  max mem: 22446
+train: [10]  [ 40/400]  eta: 0:03:13  lr: 0.000222  loss: 0.4632 (0.4835)  grad: 0.1798 (0.1856)  time: 0.4622  data: 0.0033  max mem: 22446
+train: [10]  [ 60/400]  eta: 0:02:55  lr: 0.000221  loss: 0.4590 (0.4738)  grad: 0.1724 (0.1820)  time: 0.4699  data: 0.0034  max mem: 22446
+train: [10]  [ 80/400]  eta: 0:02:40  lr: 0.000220  loss: 0.4354 (0.4660)  grad: 0.1620 (0.1776)  time: 0.4606  data: 0.0033  max mem: 22446
+train: [10]  [100/400]  eta: 0:02:27  lr: 0.000218  loss: 0.4354 (0.4639)  grad: 0.1650 (0.1763)  time: 0.4445  data: 0.0033  max mem: 22446
+train: [10]  [120/400]  eta: 0:02:16  lr: 0.000217  loss: 0.4463 (0.4574)  grad: 0.1760 (0.1764)  time: 0.4701  data: 0.0035  max mem: 22446
+train: [10]  [140/400]  eta: 0:02:05  lr: 0.000215  loss: 0.4418 (0.4564)  grad: 0.1807 (0.1754)  time: 0.4533  data: 0.0034  max mem: 22446
+train: [10]  [160/400]  eta: 0:01:54  lr: 0.000214  loss: 0.4385 (0.4528)  grad: 0.1673 (0.1754)  time: 0.4530  data: 0.0033  max mem: 22446
+train: [10]  [180/400]  eta: 0:01:44  lr: 0.000213  loss: 0.4279 (0.4518)  grad: 0.1649 (0.1753)  time: 0.4568  data: 0.0035  max mem: 22446
+train: [10]  [200/400]  eta: 0:01:34  lr: 0.000211  loss: 0.4655 (0.4525)  grad: 0.1695 (0.1756)  time: 0.4551  data: 0.0034  max mem: 22446
+train: [10]  [220/400]  eta: 0:01:25  lr: 0.000210  loss: 0.4522 (0.4517)  grad: 0.1614 (0.1744)  time: 0.4559  data: 0.0033  max mem: 22446
+train: [10]  [240/400]  eta: 0:01:15  lr: 0.000208  loss: 0.4388 (0.4500)  grad: 0.1627 (0.1741)  time: 0.4737  data: 0.0034  max mem: 22446
+train: [10]  [260/400]  eta: 0:01:05  lr: 0.000207  loss: 0.4372 (0.4496)  grad: 0.1627 (0.1738)  time: 0.4566  data: 0.0034  max mem: 22446
+train: [10]  [280/400]  eta: 0:00:56  lr: 0.000205  loss: 0.4430 (0.4483)  grad: 0.1551 (0.1723)  time: 0.4472  data: 0.0035  max mem: 22446
+train: [10]  [300/400]  eta: 0:00:47  lr: 0.000204  loss: 0.4374 (0.4466)  grad: 0.1487 (0.1707)  time: 0.6112  data: 0.1793  max mem: 22446
+train: [10]  [320/400]  eta: 0:00:38  lr: 0.000202  loss: 0.4259 (0.4456)  grad: 0.1487 (0.1696)  time: 0.4415  data: 0.0031  max mem: 22446
+train: [10]  [340/400]  eta: 0:00:28  lr: 0.000201  loss: 0.4259 (0.4448)  grad: 0.1532 (0.1686)  time: 0.4517  data: 0.0035  max mem: 22446
+train: [10]  [360/400]  eta: 0:00:18  lr: 0.000199  loss: 0.4212 (0.4431)  grad: 0.1468 (0.1672)  time: 0.4457  data: 0.0034  max mem: 22446
+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 0.4198 (0.4416)  grad: 0.1331 (0.1655)  time: 0.4525  data: 0.0034  max mem: 22446
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 0.4191 (0.4411)  grad: 0.1334 (0.1646)  time: 0.4694  data: 0.0035  max mem: 22446
+train: [10] Total time: 0:03:09 (0.4727 s / it)
+train: [10] Summary: lr: 0.000196  loss: 0.4191 (0.4411)  grad: 0.1334 (0.1646)
+eval (validation): [10]  [ 0/63]  eta: 0:03:26    time: 3.2825  data: 3.0523  max mem: 22446
+eval (validation): [10]  [20/63]  eta: 0:00:21    time: 0.3695  data: 0.0050  max mem: 22446
+eval (validation): [10]  [40/63]  eta: 0:00:09    time: 0.3229  data: 0.0029  max mem: 22446
+eval (validation): [10]  [60/63]  eta: 0:00:01    time: 0.3316  data: 0.0032  max mem: 22446
+eval (validation): [10]  [62/63]  eta: 0:00:00    time: 0.3340  data: 0.0032  max mem: 22446
+eval (validation): [10] Total time: 0:00:24 (0.3930 s / it)
+cv: [10]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 0.509  acc: 0.957  f1: 0.950
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [11]  [  0/400]  eta: 0:22:59  lr: nan  time: 3.4498  data: 3.0546  max mem: 22446
+train: [11]  [ 20/400]  eta: 0:03:59  lr: 0.000195  loss: 0.4001 (0.4039)  grad: 0.1312 (0.1390)  time: 0.4879  data: 0.0031  max mem: 22446
+train: [11]  [ 40/400]  eta: 0:03:16  lr: 0.000193  loss: 0.4133 (0.4116)  grad: 0.1314 (0.1376)  time: 0.4610  data: 0.0035  max mem: 22446
+train: [11]  [ 60/400]  eta: 0:02:57  lr: 0.000192  loss: 0.4240 (0.4169)  grad: 0.1355 (0.1387)  time: 0.4742  data: 0.0036  max mem: 22446
+train: [11]  [ 80/400]  eta: 0:02:42  lr: 0.000190  loss: 0.4153 (0.4151)  grad: 0.1477 (0.1417)  time: 0.4661  data: 0.0036  max mem: 22446
+train: [11]  [100/400]  eta: 0:02:29  lr: 0.000189  loss: 0.4052 (0.4119)  grad: 0.1481 (0.1427)  time: 0.4518  data: 0.0034  max mem: 22446
+train: [11]  [120/400]  eta: 0:02:17  lr: 0.000187  loss: 0.4118 (0.4130)  grad: 0.1442 (0.1449)  time: 0.4649  data: 0.0035  max mem: 22446
+train: [11]  [140/400]  eta: 0:02:06  lr: 0.000186  loss: 0.4133 (0.4153)  grad: 0.1474 (0.1448)  time: 0.4526  data: 0.0035  max mem: 22446
+train: [11]  [160/400]  eta: 0:01:55  lr: 0.000184  loss: 0.4332 (0.4162)  grad: 0.1363 (0.1452)  time: 0.4565  data: 0.0035  max mem: 22446
+train: [11]  [180/400]  eta: 0:01:46  lr: 0.000183  loss: 0.4003 (0.4141)  grad: 0.1361 (0.1451)  time: 0.4929  data: 0.0036  max mem: 22446
+train: [11]  [200/400]  eta: 0:01:36  lr: 0.000181  loss: 0.3943 (0.4117)  grad: 0.1360 (0.1439)  time: 0.4571  data: 0.0036  max mem: 22446
+train: [11]  [220/400]  eta: 0:01:26  lr: 0.000180  loss: 0.3962 (0.4108)  grad: 0.1285 (0.1425)  time: 0.4536  data: 0.0035  max mem: 22446
+train: [11]  [240/400]  eta: 0:01:16  lr: 0.000178  loss: 0.3962 (0.4090)  grad: 0.1287 (0.1422)  time: 0.4647  data: 0.0035  max mem: 22446
+train: [11]  [260/400]  eta: 0:01:06  lr: 0.000177  loss: 0.4052 (0.4101)  grad: 0.1418 (0.1418)  time: 0.4640  data: 0.0035  max mem: 22446
+train: [11]  [280/400]  eta: 0:00:57  lr: 0.000175  loss: 0.4150 (0.4095)  grad: 0.1434 (0.1421)  time: 0.4668  data: 0.0038  max mem: 22446
+train: [11]  [300/400]  eta: 0:00:48  lr: 0.000174  loss: 0.4249 (0.4104)  grad: 0.1430 (0.1415)  time: 0.6227  data: 0.1828  max mem: 22446
+train: [11]  [320/400]  eta: 0:00:38  lr: 0.000172  loss: 0.4114 (0.4096)  grad: 0.1334 (0.1413)  time: 0.4515  data: 0.0031  max mem: 22446
+train: [11]  [340/400]  eta: 0:00:28  lr: 0.000170  loss: 0.3944 (0.4086)  grad: 0.1293 (0.1404)  time: 0.4525  data: 0.0036  max mem: 22446
+train: [11]  [360/400]  eta: 0:00:19  lr: 0.000169  loss: 0.3905 (0.4076)  grad: 0.1215 (0.1395)  time: 0.4462  data: 0.0035  max mem: 22446
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 0.3905 (0.4074)  grad: 0.1215 (0.1388)  time: 0.4591  data: 0.0034  max mem: 22446
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 0.3944 (0.4074)  grad: 0.1247 (0.1386)  time: 0.4684  data: 0.0035  max mem: 22446
+train: [11] Total time: 0:03:11 (0.4785 s / it)
+train: [11] Summary: lr: 0.000166  loss: 0.3944 (0.4074)  grad: 0.1247 (0.1386)
+eval (validation): [11]  [ 0/63]  eta: 0:03:30    time: 3.3467  data: 3.0603  max mem: 22446
+eval (validation): [11]  [20/63]  eta: 0:00:22    time: 0.3836  data: 0.0171  max mem: 22446
+eval (validation): [11]  [40/63]  eta: 0:00:10    time: 0.3676  data: 0.0032  max mem: 22446
+eval (validation): [11]  [60/63]  eta: 0:00:01    time: 0.3353  data: 0.0035  max mem: 22446
+eval (validation): [11]  [62/63]  eta: 0:00:00    time: 0.3381  data: 0.0035  max mem: 22446
+eval (validation): [11] Total time: 0:00:26 (0.4145 s / it)
+cv: [11]  best hparam: (16, 1.0) (041) ('041_lr1.6e+01_wd1.0e+00')  loss: 0.380  acc: 0.957  f1: 0.950
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [12]  [  0/400]  eta: 0:22:28  lr: nan  time: 3.3710  data: 3.0372  max mem: 22446
+train: [12]  [ 20/400]  eta: 0:03:46  lr: 0.000164  loss: 0.3691 (0.3722)  grad: 0.1366 (0.1328)  time: 0.4578  data: 0.0033  max mem: 22446
+train: [12]  [ 40/400]  eta: 0:03:08  lr: 0.000163  loss: 0.3753 (0.3822)  grad: 0.1259 (0.1276)  time: 0.4469  data: 0.0031  max mem: 22446
+train: [12]  [ 60/400]  eta: 0:02:52  lr: 0.000161  loss: 0.3827 (0.3822)  grad: 0.1204 (0.1267)  time: 0.4708  data: 0.0035  max mem: 22446
+train: [12]  [ 80/400]  eta: 0:02:38  lr: 0.000160  loss: 0.3769 (0.3834)  grad: 0.1211 (0.1253)  time: 0.4559  data: 0.0034  max mem: 22446
+train: [12]  [100/400]  eta: 0:02:26  lr: 0.000158  loss: 0.3813 (0.3882)  grad: 0.1171 (0.1232)  time: 0.4631  data: 0.0034  max mem: 22446
+train: [12]  [120/400]  eta: 0:02:14  lr: 0.000156  loss: 0.3957 (0.3895)  grad: 0.1171 (0.1228)  time: 0.4510  data: 0.0033  max mem: 22446
+train: [12]  [140/400]  eta: 0:02:04  lr: 0.000155  loss: 0.3895 (0.3895)  grad: 0.1126 (0.1216)  time: 0.4532  data: 0.0035  max mem: 22446
+train: [12]  [160/400]  eta: 0:01:54  lr: 0.000153  loss: 0.3922 (0.3894)  grad: 0.1126 (0.1212)  time: 0.4631  data: 0.0034  max mem: 22446
+train: [12]  [180/400]  eta: 0:01:44  lr: 0.000152  loss: 0.3922 (0.3895)  grad: 0.1180 (0.1210)  time: 0.4795  data: 0.0035  max mem: 22446
+train: [12]  [200/400]  eta: 0:01:34  lr: 0.000150  loss: 0.3713 (0.3887)  grad: 0.1170 (0.1210)  time: 0.4528  data: 0.0034  max mem: 22446
+train: [12]  [220/400]  eta: 0:01:25  lr: 0.000149  loss: 0.3700 (0.3883)  grad: 0.1191 (0.1211)  time: 0.4616  data: 0.0034  max mem: 22446
+train: [12]  [240/400]  eta: 0:01:15  lr: 0.000147  loss: 0.3954 (0.3897)  grad: 0.1210 (0.1217)  time: 0.4767  data: 0.0037  max mem: 22446
+train: [12]  [260/400]  eta: 0:01:06  lr: 0.000145  loss: 0.4010 (0.3894)  grad: 0.1137 (0.1209)  time: 0.4633  data: 0.0036  max mem: 22446
+train: [12]  [280/400]  eta: 0:00:56  lr: 0.000144  loss: 0.3689 (0.3885)  grad: 0.1122 (0.1202)  time: 0.4611  data: 0.0037  max mem: 22446
+train: [12]  [300/400]  eta: 0:00:48  lr: 0.000142  loss: 0.3815 (0.3891)  grad: 0.1134 (0.1204)  time: 0.6258  data: 0.1834  max mem: 22446
+train: [12]  [320/400]  eta: 0:00:38  lr: 0.000141  loss: 0.3953 (0.3890)  grad: 0.1168 (0.1199)  time: 0.4422  data: 0.0033  max mem: 22446
+train: [12]  [340/400]  eta: 0:00:28  lr: 0.000139  loss: 0.3979 (0.3899)  grad: 0.1148 (0.1196)  time: 0.4490  data: 0.0031  max mem: 22446
+train: [12]  [360/400]  eta: 0:00:19  lr: 0.000138  loss: 0.3954 (0.3895)  grad: 0.1148 (0.1195)  time: 0.4551  data: 0.0034  max mem: 22446
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 0.3891 (0.3900)  grad: 0.1149 (0.1197)  time: 0.4579  data: 0.0034  max mem: 22446
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 0.3749 (0.3892)  grad: 0.1183 (0.1197)  time: 0.4548  data: 0.0035  max mem: 22446
+train: [12] Total time: 0:03:09 (0.4747 s / it)
+train: [12] Summary: lr: 0.000134  loss: 0.3749 (0.3892)  grad: 0.1183 (0.1197)
+eval (validation): [12]  [ 0/63]  eta: 0:03:32    time: 3.3688  data: 3.0841  max mem: 22446
+eval (validation): [12]  [20/63]  eta: 0:00:22    time: 0.3732  data: 0.0045  max mem: 22446
+eval (validation): [12]  [40/63]  eta: 0:00:10    time: 0.3690  data: 0.0031  max mem: 22446
+eval (validation): [12]  [60/63]  eta: 0:00:01    time: 0.3306  data: 0.0033  max mem: 22446
+eval (validation): [12]  [62/63]  eta: 0:00:00    time: 0.3307  data: 0.0032  max mem: 22446
+eval (validation): [12] Total time: 0:00:25 (0.4089 s / it)
+cv: [12]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 0.409  acc: 0.960  f1: 0.953
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:22:48  lr: nan  time: 3.4205  data: 3.0727  max mem: 22446
+train: [13]  [ 20/400]  eta: 0:03:48  lr: 0.000133  loss: 0.3647 (0.3822)  grad: 0.1045 (0.1066)  time: 0.4590  data: 0.0031  max mem: 22446
+train: [13]  [ 40/400]  eta: 0:03:09  lr: 0.000131  loss: 0.3647 (0.3694)  grad: 0.1040 (0.1072)  time: 0.4492  data: 0.0036  max mem: 22446
+train: [13]  [ 60/400]  eta: 0:02:54  lr: 0.000130  loss: 0.3637 (0.3699)  grad: 0.1095 (0.1123)  time: 0.4819  data: 0.0028  max mem: 22446
+train: [13]  [ 80/400]  eta: 0:02:40  lr: 0.000128  loss: 0.3591 (0.3713)  grad: 0.1092 (0.1101)  time: 0.4704  data: 0.0034  max mem: 22446
+train: [13]  [100/400]  eta: 0:02:29  lr: 0.000127  loss: 0.3848 (0.3742)  grad: 0.1043 (0.1104)  time: 0.4887  data: 0.0037  max mem: 22446
+train: [13]  [120/400]  eta: 0:02:17  lr: 0.000125  loss: 0.3774 (0.3721)  grad: 0.1077 (0.1103)  time: 0.4541  data: 0.0033  max mem: 22446
+train: [13]  [140/400]  eta: 0:02:06  lr: 0.000124  loss: 0.3528 (0.3730)  grad: 0.1073 (0.1106)  time: 0.4685  data: 0.0034  max mem: 22446
+train: [13]  [160/400]  eta: 0:01:56  lr: 0.000122  loss: 0.3528 (0.3735)  grad: 0.1074 (0.1105)  time: 0.4693  data: 0.0034  max mem: 22446
+train: [13]  [180/400]  eta: 0:01:46  lr: 0.000120  loss: 0.3736 (0.3763)  grad: 0.1075 (0.1109)  time: 0.4586  data: 0.0032  max mem: 22446
+train: [13]  [200/400]  eta: 0:01:36  lr: 0.000119  loss: 0.3814 (0.3768)  grad: 0.1157 (0.1116)  time: 0.4666  data: 0.0035  max mem: 22446
+train: [13]  [220/400]  eta: 0:01:26  lr: 0.000117  loss: 0.3727 (0.3760)  grad: 0.1137 (0.1121)  time: 0.4635  data: 0.0034  max mem: 22446
+train: [13]  [240/400]  eta: 0:01:16  lr: 0.000116  loss: 0.3727 (0.3765)  grad: 0.1113 (0.1123)  time: 0.4708  data: 0.0035  max mem: 22446
+train: [13]  [260/400]  eta: 0:01:06  lr: 0.000114  loss: 0.3669 (0.3763)  grad: 0.1128 (0.1125)  time: 0.4511  data: 0.0034  max mem: 22446
+train: [13]  [280/400]  eta: 0:00:57  lr: 0.000113  loss: 0.3669 (0.3755)  grad: 0.1103 (0.1127)  time: 0.4542  data: 0.0034  max mem: 22446
+train: [13]  [300/400]  eta: 0:00:48  lr: 0.000111  loss: 0.3669 (0.3763)  grad: 0.1108 (0.1131)  time: 0.6372  data: 0.1799  max mem: 22446
+train: [13]  [320/400]  eta: 0:00:38  lr: 0.000110  loss: 0.3669 (0.3753)  grad: 0.1126 (0.1128)  time: 0.4518  data: 0.0029  max mem: 22446
+train: [13]  [340/400]  eta: 0:00:28  lr: 0.000108  loss: 0.3660 (0.3743)  grad: 0.1084 (0.1127)  time: 0.4543  data: 0.0033  max mem: 22446
+train: [13]  [360/400]  eta: 0:00:19  lr: 0.000107  loss: 0.3298 (0.3728)  grad: 0.1090 (0.1124)  time: 0.4547  data: 0.0033  max mem: 22446
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 0.3592 (0.3731)  grad: 0.1087 (0.1123)  time: 0.4539  data: 0.0034  max mem: 22446
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 0.3762 (0.3727)  grad: 0.1051 (0.1120)  time: 0.4637  data: 0.0035  max mem: 22446
+train: [13] Total time: 0:03:11 (0.4788 s / it)
+train: [13] Summary: lr: 0.000104  loss: 0.3762 (0.3727)  grad: 0.1051 (0.1120)
+eval (validation): [13]  [ 0/63]  eta: 0:03:30    time: 3.3370  data: 3.0355  max mem: 22446
+eval (validation): [13]  [20/63]  eta: 0:00:21    time: 0.3568  data: 0.0037  max mem: 22446
+eval (validation): [13]  [40/63]  eta: 0:00:09    time: 0.3380  data: 0.0034  max mem: 22446
+eval (validation): [13]  [60/63]  eta: 0:00:01    time: 0.3298  data: 0.0032  max mem: 22446
+eval (validation): [13]  [62/63]  eta: 0:00:00    time: 0.3299  data: 0.0032  max mem: 22446
+eval (validation): [13] Total time: 0:00:24 (0.3934 s / it)
+cv: [13]  best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00')  loss: 0.299  acc: 0.962  f1: 0.955
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [14]  [  0/400]  eta: 0:23:08  lr: nan  time: 3.4702  data: 3.1314  max mem: 22446
+train: [14]  [ 20/400]  eta: 0:03:44  lr: 0.000102  loss: 0.3753 (0.3688)  grad: 0.0948 (0.1011)  time: 0.4477  data: 0.0027  max mem: 22446
+train: [14]  [ 40/400]  eta: 0:03:08  lr: 0.000101  loss: 0.3656 (0.3649)  grad: 0.0948 (0.0994)  time: 0.4505  data: 0.0034  max mem: 22446
+train: [14]  [ 60/400]  eta: 0:02:49  lr: 0.000099  loss: 0.3433 (0.3578)  grad: 0.0985 (0.1001)  time: 0.4496  data: 0.0036  max mem: 22446
+train: [14]  [ 80/400]  eta: 0:02:37  lr: 0.000098  loss: 0.3431 (0.3561)  grad: 0.1028 (0.1030)  time: 0.4658  data: 0.0036  max mem: 22446
+train: [14]  [100/400]  eta: 0:02:24  lr: 0.000096  loss: 0.3570 (0.3589)  grad: 0.1077 (0.1043)  time: 0.4537  data: 0.0035  max mem: 22446
+train: [14]  [120/400]  eta: 0:02:14  lr: 0.000095  loss: 0.3745 (0.3641)  grad: 0.1108 (0.1056)  time: 0.4555  data: 0.0035  max mem: 22446
+train: [14]  [140/400]  eta: 0:02:03  lr: 0.000093  loss: 0.3945 (0.3695)  grad: 0.1142 (0.1074)  time: 0.4484  data: 0.0034  max mem: 22446
+train: [14]  [160/400]  eta: 0:01:53  lr: 0.000092  loss: 0.3713 (0.3685)  grad: 0.1037 (0.1071)  time: 0.4603  data: 0.0034  max mem: 22446
+train: [14]  [180/400]  eta: 0:01:43  lr: 0.000090  loss: 0.3550 (0.3677)  grad: 0.1016 (0.1068)  time: 0.4573  data: 0.0034  max mem: 22446
+train: [14]  [200/400]  eta: 0:01:33  lr: 0.000089  loss: 0.3622 (0.3673)  grad: 0.1037 (0.1076)  time: 0.4496  data: 0.0034  max mem: 22446
+train: [14]  [220/400]  eta: 0:01:24  lr: 0.000088  loss: 0.3603 (0.3674)  grad: 0.1029 (0.1072)  time: 0.4540  data: 0.0035  max mem: 22446
+train: [14]  [240/400]  eta: 0:01:14  lr: 0.000086  loss: 0.3450 (0.3654)  grad: 0.1029 (0.1073)  time: 0.4604  data: 0.0035  max mem: 22446
+train: [14]  [260/400]  eta: 0:01:05  lr: 0.000085  loss: 0.3445 (0.3647)  grad: 0.1058 (0.1072)  time: 0.4537  data: 0.0034  max mem: 22446
+train: [14]  [280/400]  eta: 0:00:55  lr: 0.000083  loss: 0.3700 (0.3663)  grad: 0.1077 (0.1074)  time: 0.4505  data: 0.0036  max mem: 22446
+train: [14]  [300/400]  eta: 0:00:47  lr: 0.000082  loss: 0.3674 (0.3658)  grad: 0.1061 (0.1074)  time: 0.6143  data: 0.1786  max mem: 22446
+train: [14]  [320/400]  eta: 0:00:37  lr: 0.000081  loss: 0.3610 (0.3652)  grad: 0.1004 (0.1068)  time: 0.4679  data: 0.0058  max mem: 22446
+train: [14]  [340/400]  eta: 0:00:28  lr: 0.000079  loss: 0.3711 (0.3662)  grad: 0.0990 (0.1066)  time: 0.4756  data: 0.0032  max mem: 22446
+train: [14]  [360/400]  eta: 0:00:18  lr: 0.000078  loss: 0.3668 (0.3660)  grad: 0.1049 (0.1067)  time: 0.4654  data: 0.0036  max mem: 22446
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 0.3554 (0.3659)  grad: 0.1057 (0.1067)  time: 0.4641  data: 0.0036  max mem: 22446
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 0.3585 (0.3658)  grad: 0.1049 (0.1067)  time: 0.4565  data: 0.0035  max mem: 22446
+train: [14] Total time: 0:03:09 (0.4729 s / it)
+train: [14] Summary: lr: 0.000075  loss: 0.3585 (0.3658)  grad: 0.1049 (0.1067)
+eval (validation): [14]  [ 0/63]  eta: 0:03:28    time: 3.3106  data: 3.0790  max mem: 22446
+eval (validation): [14]  [20/63]  eta: 0:00:22    time: 0.3719  data: 0.0193  max mem: 22446
+eval (validation): [14]  [40/63]  eta: 0:00:09    time: 0.3466  data: 0.0031  max mem: 22446
+eval (validation): [14]  [60/63]  eta: 0:00:01    time: 0.3258  data: 0.0033  max mem: 22446
+eval (validation): [14]  [62/63]  eta: 0:00:00    time: 0.3269  data: 0.0032  max mem: 22446
+eval (validation): [14] Total time: 0:00:25 (0.3997 s / it)
+cv: [14]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.311  acc: 0.963  f1: 0.955
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:22:39  lr: nan  time: 3.3998  data: 3.0107  max mem: 22446
+train: [15]  [ 20/400]  eta: 0:03:46  lr: 0.000074  loss: 0.3739 (0.3731)  grad: 0.0973 (0.1006)  time: 0.4568  data: 0.0038  max mem: 22446
+train: [15]  [ 40/400]  eta: 0:03:10  lr: 0.000072  loss: 0.3739 (0.3733)  grad: 0.0993 (0.1045)  time: 0.4574  data: 0.0034  max mem: 22446
+train: [15]  [ 60/400]  eta: 0:02:52  lr: 0.000071  loss: 0.3642 (0.3701)  grad: 0.0999 (0.1033)  time: 0.4597  data: 0.0036  max mem: 22446
+train: [15]  [ 80/400]  eta: 0:02:38  lr: 0.000070  loss: 0.3545 (0.3664)  grad: 0.0997 (0.1027)  time: 0.4561  data: 0.0035  max mem: 22446
+train: [15]  [100/400]  eta: 0:02:25  lr: 0.000068  loss: 0.3452 (0.3619)  grad: 0.1002 (0.1026)  time: 0.4480  data: 0.0036  max mem: 22446
+train: [15]  [120/400]  eta: 0:02:14  lr: 0.000067  loss: 0.3361 (0.3595)  grad: 0.1002 (0.1034)  time: 0.4537  data: 0.0035  max mem: 22446
+train: [15]  [140/400]  eta: 0:02:03  lr: 0.000066  loss: 0.3435 (0.3601)  grad: 0.1053 (0.1043)  time: 0.4552  data: 0.0034  max mem: 22446
+train: [15]  [160/400]  eta: 0:01:53  lr: 0.000064  loss: 0.3528 (0.3579)  grad: 0.1067 (0.1050)  time: 0.4539  data: 0.0034  max mem: 22446
+train: [15]  [180/400]  eta: 0:01:43  lr: 0.000063  loss: 0.3503 (0.3578)  grad: 0.1067 (0.1054)  time: 0.4542  data: 0.0036  max mem: 22446
+train: [15]  [200/400]  eta: 0:01:33  lr: 0.000062  loss: 0.3439 (0.3560)  grad: 0.1021 (0.1048)  time: 0.4500  data: 0.0035  max mem: 22446
+train: [15]  [220/400]  eta: 0:01:24  lr: 0.000061  loss: 0.3439 (0.3557)  grad: 0.1024 (0.1050)  time: 0.4545  data: 0.0035  max mem: 22446
+train: [15]  [240/400]  eta: 0:01:14  lr: 0.000059  loss: 0.3467 (0.3548)  grad: 0.1048 (0.1049)  time: 0.4572  data: 0.0034  max mem: 22446
+train: [15]  [260/400]  eta: 0:01:05  lr: 0.000058  loss: 0.3446 (0.3548)  grad: 0.1039 (0.1045)  time: 0.4573  data: 0.0034  max mem: 22446
+train: [15]  [280/400]  eta: 0:00:55  lr: 0.000057  loss: 0.3557 (0.3556)  grad: 0.1020 (0.1046)  time: 0.4566  data: 0.0034  max mem: 22446
+train: [15]  [300/400]  eta: 0:00:47  lr: 0.000056  loss: 0.3454 (0.3562)  grad: 0.1004 (0.1047)  time: 0.6178  data: 0.1798  max mem: 22446
+train: [15]  [320/400]  eta: 0:00:37  lr: 0.000054  loss: 0.3422 (0.3562)  grad: 0.1020 (0.1048)  time: 0.4474  data: 0.0030  max mem: 22446
+train: [15]  [340/400]  eta: 0:00:28  lr: 0.000053  loss: 0.3509 (0.3566)  grad: 0.1100 (0.1049)  time: 0.4601  data: 0.0034  max mem: 22446
+train: [15]  [360/400]  eta: 0:00:18  lr: 0.000052  loss: 0.3510 (0.3558)  grad: 0.1021 (0.1047)  time: 0.4615  data: 0.0035  max mem: 22446
+train: [15]  [380/400]  eta: 0:00:09  lr: 0.000051  loss: 0.3504 (0.3554)  grad: 0.1027 (0.1045)  time: 0.4552  data: 0.0035  max mem: 22446
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 0.3561 (0.3569)  grad: 0.1027 (0.1046)  time: 0.4460  data: 0.0035  max mem: 22446
+train: [15] Total time: 0:03:08 (0.4706 s / it)
+train: [15] Summary: lr: 0.000050  loss: 0.3561 (0.3569)  grad: 0.1027 (0.1046)
+eval (validation): [15]  [ 0/63]  eta: 0:03:22    time: 3.2187  data: 2.9736  max mem: 22446
+eval (validation): [15]  [20/63]  eta: 0:00:20    time: 0.3499  data: 0.0069  max mem: 22446
+eval (validation): [15]  [40/63]  eta: 0:00:09    time: 0.3338  data: 0.0034  max mem: 22446
+eval (validation): [15]  [60/63]  eta: 0:00:01    time: 0.3446  data: 0.0030  max mem: 22446
+eval (validation): [15]  [62/63]  eta: 0:00:00    time: 0.3422  data: 0.0033  max mem: 22446
+eval (validation): [15] Total time: 0:00:24 (0.3937 s / it)
+cv: [15]  best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00')  loss: 0.278  acc: 0.962  f1: 0.955
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:22:15  lr: nan  time: 3.3383  data: 3.0007  max mem: 22446
+train: [16]  [ 20/400]  eta: 0:03:40  lr: 0.000048  loss: 0.3383 (0.3520)  grad: 0.0948 (0.1009)  time: 0.4430  data: 0.0034  max mem: 22446
+train: [16]  [ 40/400]  eta: 0:03:04  lr: 0.000047  loss: 0.3596 (0.3556)  grad: 0.0996 (0.1039)  time: 0.4433  data: 0.0031  max mem: 22446
+train: [16]  [ 60/400]  eta: 0:02:48  lr: 0.000046  loss: 0.3549 (0.3524)  grad: 0.1045 (0.1038)  time: 0.4549  data: 0.0034  max mem: 22446
+train: [16]  [ 80/400]  eta: 0:02:35  lr: 0.000045  loss: 0.3549 (0.3544)  grad: 0.1045 (0.1036)  time: 0.4627  data: 0.0035  max mem: 22446
+train: [16]  [100/400]  eta: 0:02:23  lr: 0.000044  loss: 0.3604 (0.3576)  grad: 0.1032 (0.1035)  time: 0.4470  data: 0.0036  max mem: 22446
+train: [16]  [120/400]  eta: 0:02:12  lr: 0.000043  loss: 0.3547 (0.3576)  grad: 0.0958 (0.1037)  time: 0.4543  data: 0.0035  max mem: 22446
+train: [16]  [140/400]  eta: 0:02:02  lr: 0.000042  loss: 0.3547 (0.3574)  grad: 0.0995 (0.1041)  time: 0.4551  data: 0.0034  max mem: 22446
+train: [16]  [160/400]  eta: 0:01:52  lr: 0.000041  loss: 0.3498 (0.3547)  grad: 0.1001 (0.1037)  time: 0.4559  data: 0.0035  max mem: 22446
+train: [16]  [180/400]  eta: 0:01:43  lr: 0.000040  loss: 0.3482 (0.3551)  grad: 0.0967 (0.1034)  time: 0.4659  data: 0.0036  max mem: 22446
+train: [16]  [200/400]  eta: 0:01:33  lr: 0.000039  loss: 0.3482 (0.3555)  grad: 0.1013 (0.1038)  time: 0.4579  data: 0.0035  max mem: 22446
+train: [16]  [220/400]  eta: 0:01:24  lr: 0.000038  loss: 0.3454 (0.3541)  grad: 0.0992 (0.1030)  time: 0.4592  data: 0.0034  max mem: 22446
+train: [16]  [240/400]  eta: 0:01:14  lr: 0.000036  loss: 0.3489 (0.3544)  grad: 0.0975 (0.1029)  time: 0.4703  data: 0.0034  max mem: 22446
+train: [16]  [260/400]  eta: 0:01:05  lr: 0.000035  loss: 0.3597 (0.3559)  grad: 0.0997 (0.1031)  time: 0.4538  data: 0.0034  max mem: 22446
+train: [16]  [280/400]  eta: 0:00:55  lr: 0.000034  loss: 0.3663 (0.3565)  grad: 0.1067 (0.1037)  time: 0.4490  data: 0.0036  max mem: 22446
+train: [16]  [300/400]  eta: 0:00:47  lr: 0.000033  loss: 0.3663 (0.3573)  grad: 0.1076 (0.1042)  time: 0.6126  data: 0.1789  max mem: 22446
+train: [16]  [320/400]  eta: 0:00:37  lr: 0.000032  loss: 0.3483 (0.3572)  grad: 0.1076 (0.1045)  time: 0.4467  data: 0.0032  max mem: 22446
+train: [16]  [340/400]  eta: 0:00:28  lr: 0.000031  loss: 0.3516 (0.3580)  grad: 0.1092 (0.1048)  time: 0.4619  data: 0.0035  max mem: 22446
+train: [16]  [360/400]  eta: 0:00:18  lr: 0.000031  loss: 0.3519 (0.3580)  grad: 0.1090 (0.1048)  time: 0.4697  data: 0.0035  max mem: 22446
+train: [16]  [380/400]  eta: 0:00:09  lr: 0.000030  loss: 0.3424 (0.3563)  grad: 0.0987 (0.1046)  time: 0.4637  data: 0.0036  max mem: 22446
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 0.3253 (0.3566)  grad: 0.1031 (0.1050)  time: 0.4584  data: 0.0036  max mem: 22446
+train: [16] Total time: 0:03:08 (0.4717 s / it)
+train: [16] Summary: lr: 0.000029  loss: 0.3253 (0.3566)  grad: 0.1031 (0.1050)
+eval (validation): [16]  [ 0/63]  eta: 0:03:27    time: 3.2881  data: 2.9983  max mem: 22446
+eval (validation): [16]  [20/63]  eta: 0:00:21    time: 0.3539  data: 0.0035  max mem: 22446
+eval (validation): [16]  [40/63]  eta: 0:00:09    time: 0.3445  data: 0.0028  max mem: 22446
+eval (validation): [16]  [60/63]  eta: 0:00:01    time: 0.3333  data: 0.0035  max mem: 22446
+eval (validation): [16]  [62/63]  eta: 0:00:00    time: 0.3356  data: 0.0034  max mem: 22446
+eval (validation): [16] Total time: 0:00:24 (0.3953 s / it)
+cv: [16]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.284  acc: 0.963  f1: 0.956
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [17]  [  0/400]  eta: 0:22:21  lr: nan  time: 3.3526  data: 3.0109  max mem: 22446
+train: [17]  [ 20/400]  eta: 0:03:41  lr: 0.000028  loss: 0.3398 (0.3491)  grad: 0.1012 (0.1026)  time: 0.4445  data: 0.0038  max mem: 22446
+train: [17]  [ 40/400]  eta: 0:03:06  lr: 0.000027  loss: 0.3424 (0.3567)  grad: 0.1028 (0.1056)  time: 0.4510  data: 0.0031  max mem: 22446
+train: [17]  [ 60/400]  eta: 0:02:50  lr: 0.000026  loss: 0.3591 (0.3570)  grad: 0.0961 (0.1018)  time: 0.4708  data: 0.0036  max mem: 22446
+train: [17]  [ 80/400]  eta: 0:02:35  lr: 0.000025  loss: 0.3591 (0.3595)  grad: 0.0969 (0.1023)  time: 0.4391  data: 0.0037  max mem: 22446
+train: [17]  [100/400]  eta: 0:02:23  lr: 0.000024  loss: 0.3513 (0.3564)  grad: 0.1025 (0.1024)  time: 0.4471  data: 0.0035  max mem: 22446
+train: [17]  [120/400]  eta: 0:02:13  lr: 0.000023  loss: 0.3319 (0.3527)  grad: 0.0972 (0.1012)  time: 0.4561  data: 0.0034  max mem: 22446
+train: [17]  [140/400]  eta: 0:02:02  lr: 0.000023  loss: 0.3319 (0.3506)  grad: 0.0961 (0.1012)  time: 0.4450  data: 0.0035  max mem: 22446
+train: [17]  [160/400]  eta: 0:01:52  lr: 0.000022  loss: 0.3496 (0.3510)  grad: 0.0977 (0.1018)  time: 0.4552  data: 0.0034  max mem: 22446
+train: [17]  [180/400]  eta: 0:01:42  lr: 0.000021  loss: 0.3535 (0.3519)  grad: 0.0989 (0.1017)  time: 0.4578  data: 0.0035  max mem: 22446
+train: [17]  [200/400]  eta: 0:01:33  lr: 0.000020  loss: 0.3612 (0.3522)  grad: 0.0999 (0.1020)  time: 0.4486  data: 0.0035  max mem: 22446
+train: [17]  [220/400]  eta: 0:01:23  lr: 0.000019  loss: 0.3576 (0.3526)  grad: 0.0999 (0.1020)  time: 0.4596  data: 0.0033  max mem: 22446
+train: [17]  [240/400]  eta: 0:01:14  lr: 0.000019  loss: 0.3559 (0.3522)  grad: 0.0986 (0.1022)  time: 0.4479  data: 0.0035  max mem: 22446
+train: [17]  [260/400]  eta: 0:01:04  lr: 0.000018  loss: 0.3533 (0.3525)  grad: 0.1024 (0.1020)  time: 0.4568  data: 0.0033  max mem: 22446
+train: [17]  [280/400]  eta: 0:00:55  lr: 0.000017  loss: 0.3463 (0.3513)  grad: 0.1012 (0.1018)  time: 0.4576  data: 0.0035  max mem: 22446
+train: [17]  [300/400]  eta: 0:00:47  lr: 0.000016  loss: 0.3419 (0.3514)  grad: 0.1032 (0.1023)  time: 0.6189  data: 0.1783  max mem: 22446
+train: [17]  [320/400]  eta: 0:00:37  lr: 0.000016  loss: 0.3546 (0.3520)  grad: 0.1034 (0.1024)  time: 0.4516  data: 0.0028  max mem: 22446
+train: [17]  [340/400]  eta: 0:00:28  lr: 0.000015  loss: 0.3420 (0.3517)  grad: 0.0964 (0.1020)  time: 0.4511  data: 0.0035  max mem: 22446
+train: [17]  [360/400]  eta: 0:00:18  lr: 0.000014  loss: 0.3364 (0.3505)  grad: 0.0928 (0.1015)  time: 0.4574  data: 0.0034  max mem: 22446
+train: [17]  [380/400]  eta: 0:00:09  lr: 0.000014  loss: 0.3476 (0.3514)  grad: 0.0977 (0.1017)  time: 0.4525  data: 0.0035  max mem: 22446
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 0.3621 (0.3517)  grad: 0.1047 (0.1017)  time: 0.4577  data: 0.0036  max mem: 22446
+train: [17] Total time: 0:03:07 (0.4689 s / it)
+train: [17] Summary: lr: 0.000013  loss: 0.3621 (0.3517)  grad: 0.1047 (0.1017)
+eval (validation): [17]  [ 0/63]  eta: 0:03:17    time: 3.1420  data: 2.9136  max mem: 22446
+eval (validation): [17]  [20/63]  eta: 0:00:21    time: 0.3606  data: 0.0038  max mem: 22446
+eval (validation): [17]  [40/63]  eta: 0:00:09    time: 0.3531  data: 0.0029  max mem: 22446
+eval (validation): [17]  [60/63]  eta: 0:00:01    time: 0.3539  data: 0.0035  max mem: 22446
+eval (validation): [17]  [62/63]  eta: 0:00:00    time: 0.3516  data: 0.0035  max mem: 22446
+eval (validation): [17] Total time: 0:00:25 (0.4044 s / it)
+cv: [17]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.280  acc: 0.964  f1: 0.957
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+train: [18]  [  0/400]  eta: 0:23:00  lr: nan  time: 3.4519  data: 3.0687  max mem: 22446
+train: [18]  [ 20/400]  eta: 0:03:51  lr: 0.000012  loss: 0.3475 (0.3582)  grad: 0.0995 (0.1021)  time: 0.4658  data: 0.0036  max mem: 22446
+train: [18]  [ 40/400]  eta: 0:03:08  lr: 0.000012  loss: 0.3408 (0.3537)  grad: 0.0996 (0.0998)  time: 0.4378  data: 0.0028  max mem: 22446
+train: [18]  [ 60/400]  eta: 0:02:51  lr: 0.000011  loss: 0.3346 (0.3503)  grad: 0.0996 (0.0998)  time: 0.4628  data: 0.0036  max mem: 22446
+train: [18]  [ 80/400]  eta: 0:02:36  lr: 0.000011  loss: 0.3344 (0.3473)  grad: 0.1005 (0.1006)  time: 0.4480  data: 0.0035  max mem: 22446
+train: [18]  [100/400]  eta: 0:02:24  lr: 0.000010  loss: 0.3430 (0.3544)  grad: 0.1075 (0.1031)  time: 0.4418  data: 0.0036  max mem: 22446
+train: [18]  [120/400]  eta: 0:02:13  lr: 0.000009  loss: 0.3665 (0.3543)  grad: 0.1075 (0.1033)  time: 0.4483  data: 0.0035  max mem: 22446
+train: [18]  [140/400]  eta: 0:02:02  lr: 0.000009  loss: 0.3590 (0.3540)  grad: 0.1024 (0.1027)  time: 0.4484  data: 0.0036  max mem: 22446
+train: [18]  [160/400]  eta: 0:01:52  lr: 0.000008  loss: 0.3439 (0.3529)  grad: 0.0963 (0.1026)  time: 0.4574  data: 0.0035  max mem: 22446
+train: [18]  [180/400]  eta: 0:01:43  lr: 0.000008  loss: 0.3456 (0.3528)  grad: 0.1038 (0.1034)  time: 0.4600  data: 0.0035  max mem: 22446
+train: [18]  [200/400]  eta: 0:01:33  lr: 0.000007  loss: 0.3481 (0.3518)  grad: 0.1093 (0.1038)  time: 0.4511  data: 0.0035  max mem: 22446
+train: [18]  [220/400]  eta: 0:01:23  lr: 0.000007  loss: 0.3358 (0.3509)  grad: 0.1012 (0.1033)  time: 0.4550  data: 0.0033  max mem: 22446
+train: [18]  [240/400]  eta: 0:01:14  lr: 0.000006  loss: 0.3424 (0.3499)  grad: 0.0974 (0.1031)  time: 0.4610  data: 0.0034  max mem: 22446
+train: [18]  [260/400]  eta: 0:01:05  lr: 0.000006  loss: 0.3431 (0.3509)  grad: 0.0996 (0.1032)  time: 0.4560  data: 0.0035  max mem: 22446
+train: [18]  [280/400]  eta: 0:00:55  lr: 0.000006  loss: 0.3452 (0.3504)  grad: 0.1016 (0.1032)  time: 0.4561  data: 0.0035  max mem: 22446
+train: [18]  [300/400]  eta: 0:00:47  lr: 0.000005  loss: 0.3452 (0.3505)  grad: 0.1063 (0.1034)  time: 0.6217  data: 0.1770  max mem: 22446
+train: [18]  [320/400]  eta: 0:00:37  lr: 0.000005  loss: 0.3635 (0.3505)  grad: 0.1058 (0.1036)  time: 0.4401  data: 0.0036  max mem: 22446
+train: [18]  [340/400]  eta: 0:00:28  lr: 0.000004  loss: 0.3461 (0.3509)  grad: 0.1002 (0.1033)  time: 0.4571  data: 0.0037  max mem: 22446
+train: [18]  [360/400]  eta: 0:00:18  lr: 0.000004  loss: 0.3485 (0.3506)  grad: 0.1002 (0.1037)  time: 0.4631  data: 0.0036  max mem: 22446
+train: [18]  [380/400]  eta: 0:00:09  lr: 0.000004  loss: 0.3419 (0.3498)  grad: 0.1036 (0.1037)  time: 0.4631  data: 0.0036  max mem: 22446
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 0.3333 (0.3489)  grad: 0.1018 (0.1036)  time: 0.4557  data: 0.0038  max mem: 22446
+train: [18] Total time: 0:03:08 (0.4703 s / it)
+train: [18] Summary: lr: 0.000003  loss: 0.3333 (0.3489)  grad: 0.1018 (0.1036)
+eval (validation): [18]  [ 0/63]  eta: 0:03:23    time: 3.2346  data: 2.9790  max mem: 22446
+eval (validation): [18]  [20/63]  eta: 0:00:20    time: 0.3348  data: 0.0032  max mem: 22446
+eval (validation): [18]  [40/63]  eta: 0:00:09    time: 0.3471  data: 0.0031  max mem: 22446
+eval (validation): [18]  [60/63]  eta: 0:00:01    time: 0.3456  data: 0.0033  max mem: 22446
+eval (validation): [18]  [62/63]  eta: 0:00:00    time: 0.3441  data: 0.0033  max mem: 22446
+eval (validation): [18] Total time: 0:00:24 (0.3938 s / it)
+cv: [18]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.278  acc: 0.963  f1: 0.956
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:22:37  lr: nan  time: 3.3943  data: 3.0111  max mem: 22446
+train: [19]  [ 20/400]  eta: 0:03:53  lr: 0.000003  loss: 0.3454 (0.3368)  grad: 0.0937 (0.0960)  time: 0.4756  data: 0.0039  max mem: 22446
+train: [19]  [ 40/400]  eta: 0:03:11  lr: 0.000003  loss: 0.3420 (0.3418)  grad: 0.0968 (0.1008)  time: 0.4435  data: 0.0034  max mem: 22446
+train: [19]  [ 60/400]  eta: 0:02:54  lr: 0.000002  loss: 0.3395 (0.3449)  grad: 0.1001 (0.1010)  time: 0.4752  data: 0.0037  max mem: 22446
+train: [19]  [ 80/400]  eta: 0:02:39  lr: 0.000002  loss: 0.3404 (0.3438)  grad: 0.1001 (0.1001)  time: 0.4529  data: 0.0035  max mem: 22446
+train: [19]  [100/400]  eta: 0:02:26  lr: 0.000002  loss: 0.3422 (0.3470)  grad: 0.0989 (0.1001)  time: 0.4462  data: 0.0036  max mem: 22446
+train: [19]  [120/400]  eta: 0:02:14  lr: 0.000002  loss: 0.3421 (0.3475)  grad: 0.0960 (0.0994)  time: 0.4526  data: 0.0035  max mem: 22446
+train: [19]  [140/400]  eta: 0:02:03  lr: 0.000001  loss: 0.3265 (0.3455)  grad: 0.0947 (0.0991)  time: 0.4419  data: 0.0036  max mem: 22446
+train: [19]  [160/400]  eta: 0:01:53  lr: 0.000001  loss: 0.3353 (0.3463)  grad: 0.1000 (0.0995)  time: 0.4411  data: 0.0036  max mem: 22446
+train: [19]  [180/400]  eta: 0:01:43  lr: 0.000001  loss: 0.3505 (0.3465)  grad: 0.1011 (0.0992)  time: 0.4611  data: 0.0035  max mem: 22446
+train: [19]  [200/400]  eta: 0:01:33  lr: 0.000001  loss: 0.3336 (0.3456)  grad: 0.0956 (0.0994)  time: 0.4540  data: 0.0036  max mem: 22446
+train: [19]  [220/400]  eta: 0:01:24  lr: 0.000001  loss: 0.3500 (0.3469)  grad: 0.0998 (0.1003)  time: 0.4499  data: 0.0034  max mem: 22446
+train: [19]  [240/400]  eta: 0:01:14  lr: 0.000001  loss: 0.3429 (0.3465)  grad: 0.0998 (0.1000)  time: 0.4662  data: 0.0035  max mem: 22446
+train: [19]  [260/400]  eta: 0:01:05  lr: 0.000000  loss: 0.3400 (0.3470)  grad: 0.1042 (0.1005)  time: 0.4573  data: 0.0034  max mem: 22446
+train: [19]  [280/400]  eta: 0:00:55  lr: 0.000000  loss: 0.3544 (0.3484)  grad: 0.1054 (0.1007)  time: 0.4612  data: 0.0035  max mem: 22446
+train: [19]  [300/400]  eta: 0:00:47  lr: 0.000000  loss: 0.3656 (0.3493)  grad: 0.1002 (0.1007)  time: 0.6555  data: 0.2067  max mem: 22446
+train: [19]  [320/400]  eta: 0:00:38  lr: 0.000000  loss: 0.3711 (0.3495)  grad: 0.1002 (0.1011)  time: 0.4343  data: 0.0026  max mem: 22446
+train: [19]  [340/400]  eta: 0:00:28  lr: 0.000000  loss: 0.3478 (0.3485)  grad: 0.1009 (0.1009)  time: 0.4485  data: 0.0036  max mem: 22446
+train: [19]  [360/400]  eta: 0:00:18  lr: 0.000000  loss: 0.3433 (0.3478)  grad: 0.1009 (0.1013)  time: 0.4581  data: 0.0036  max mem: 22446
+train: [19]  [380/400]  eta: 0:00:09  lr: 0.000000  loss: 0.3437 (0.3478)  grad: 0.1016 (0.1014)  time: 0.4615  data: 0.0037  max mem: 22446
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 0.3378 (0.3474)  grad: 0.0996 (0.1012)  time: 0.4536  data: 0.0035  max mem: 22446
+train: [19] Total time: 0:03:08 (0.4722 s / it)
+train: [19] Summary: lr: 0.000000  loss: 0.3378 (0.3474)  grad: 0.0996 (0.1012)
+eval (validation): [19]  [ 0/63]  eta: 0:03:24    time: 3.2423  data: 3.0148  max mem: 22446
+eval (validation): [19]  [20/63]  eta: 0:00:20    time: 0.3360  data: 0.0044  max mem: 22446
+eval (validation): [19]  [40/63]  eta: 0:00:09    time: 0.3415  data: 0.0030  max mem: 22446
+eval (validation): [19]  [60/63]  eta: 0:00:01    time: 0.3516  data: 0.0033  max mem: 22446
+eval (validation): [19]  [62/63]  eta: 0:00:00    time: 0.3445  data: 0.0032  max mem: 22446
+eval (validation): [19] Total time: 0:00:24 (0.3939 s / it)
+cv: [19]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 0.278  acc: 0.964  f1: 0.956
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.9635416666666666, "hparam": [22, 1.0], "hparam_id": 43, "epoch": 19, "is_best": false, "best_score": 0.9635416666666666}
+eval (train): [20]  [  0/297]  eta: 0:14:56    time: 3.0182  data: 2.7710  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:23    time: 0.3933  data: 0.0042  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:01:52    time: 0.3546  data: 0.0031  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:37    time: 0.3523  data: 0.0033  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:26    time: 0.3624  data: 0.0035  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:17    time: 0.3732  data: 0.0035  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:08    time: 0.3458  data: 0.0036  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:00:59    time: 0.3478  data: 0.0036  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:51    time: 0.3535  data: 0.0035  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:43    time: 0.3677  data: 0.0038  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:36    time: 0.3785  data: 0.0035  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:28    time: 0.3599  data: 0.0034  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:21    time: 0.3597  data: 0.0033  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:13    time: 0.3651  data: 0.0035  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:06    time: 0.3691  data: 0.0036  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3554  data: 0.0031  max mem: 22446
+eval (train): [20] Total time: 0:01:50 (0.3729 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:03:14    time: 3.0798  data: 2.8475  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:20    time: 0.3580  data: 0.0309  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:09    time: 0.3517  data: 0.0058  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3284  data: 0.0021  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3255  data: 0.0024  max mem: 22446
+eval (validation): [20] Total time: 0:00:24 (0.3932 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:04:09    time: 3.1555  data: 2.8764  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:30    time: 0.3763  data: 0.0229  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:17    time: 0.3630  data: 0.0030  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:07    time: 0.3596  data: 0.0035  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3361  data: 0.0032  max mem: 22446
+eval (test): [20] Total time: 0:00:31 (0.3993 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.9635416666666666, "hparam": [22, 1.0], "hparam_id": 43, "epoch": 17, "is_best": true, "best_score": 0.9635416666666666}
+eval (train): [20]  [  0/297]  eta: 0:15:41    time: 3.1711  data: 2.8793  max mem: 22446
+eval (train): [20]  [ 20/297]  eta: 0:02:26    time: 0.3969  data: 0.0150  max mem: 22446
+eval (train): [20]  [ 40/297]  eta: 0:01:54    time: 0.3588  data: 0.0033  max mem: 22446
+eval (train): [20]  [ 60/297]  eta: 0:01:39    time: 0.3604  data: 0.0035  max mem: 22446
+eval (train): [20]  [ 80/297]  eta: 0:01:26    time: 0.3354  data: 0.0032  max mem: 22446
+eval (train): [20]  [100/297]  eta: 0:01:16    time: 0.3620  data: 0.0035  max mem: 22446
+eval (train): [20]  [120/297]  eta: 0:01:08    time: 0.3808  data: 0.0036  max mem: 22446
+eval (train): [20]  [140/297]  eta: 0:01:00    time: 0.3824  data: 0.0036  max mem: 22446
+eval (train): [20]  [160/297]  eta: 0:00:52    time: 0.3489  data: 0.0035  max mem: 22446
+eval (train): [20]  [180/297]  eta: 0:00:44    time: 0.3556  data: 0.0032  max mem: 22446
+eval (train): [20]  [200/297]  eta: 0:00:36    time: 0.3446  data: 0.0030  max mem: 22446
+eval (train): [20]  [220/297]  eta: 0:00:29    time: 0.3804  data: 0.0037  max mem: 22446
+eval (train): [20]  [240/297]  eta: 0:00:21    time: 0.3799  data: 0.0037  max mem: 22446
+eval (train): [20]  [260/297]  eta: 0:00:13    time: 0.3679  data: 0.0035  max mem: 22446
+eval (train): [20]  [280/297]  eta: 0:00:06    time: 0.3738  data: 0.0036  max mem: 22446
+eval (train): [20]  [296/297]  eta: 0:00:00    time: 0.3193  data: 0.0033  max mem: 22446
+eval (train): [20] Total time: 0:01:51 (0.3749 s / it)
+eval (validation): [20]  [ 0/63]  eta: 0:03:58    time: 3.7810  data: 3.5348  max mem: 22446
+eval (validation): [20]  [20/63]  eta: 0:00:21    time: 0.3446  data: 0.0028  max mem: 22446
+eval (validation): [20]  [40/63]  eta: 0:00:10    time: 0.3726  data: 0.0034  max mem: 22446
+eval (validation): [20]  [60/63]  eta: 0:00:01    time: 0.3339  data: 0.0035  max mem: 22446
+eval (validation): [20]  [62/63]  eta: 0:00:00    time: 0.3323  data: 0.0035  max mem: 22446
+eval (validation): [20] Total time: 0:00:25 (0.4084 s / it)
+eval (test): [20]  [ 0/79]  eta: 0:03:57    time: 3.0031  data: 2.7628  max mem: 22446
+eval (test): [20]  [20/79]  eta: 0:00:28    time: 0.3570  data: 0.0035  max mem: 22446
+eval (test): [20]  [40/79]  eta: 0:00:16    time: 0.3655  data: 0.0035  max mem: 22446
+eval (test): [20]  [60/79]  eta: 0:00:07    time: 0.3856  data: 0.0034  max mem: 22446
+eval (test): [20]  [78/79]  eta: 0:00:00    time: 0.3324  data: 0.0031  max mem: 22446
+eval (test): [20] Total time: 0:00:31 (0.3978 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   |      17 | 0.0066 | 0.05 |          43 | [22, 1.0] | train      | 0.00010309 | 1       | 0         | 1       | 0         |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      17 | 0.0066 | 0.05 |          43 | [22, 1.0] | validation | 0.2797     | 0.96354 | 0.002988  | 0.9565  | 0.0038572 |
+| flat_mae | patch  | attn  | hcpya_task21 | best   |      17 | 0.0066 | 0.05 |          43 | [22, 1.0] | test       | 0.30021    | 0.96329 | 0.0025358 | 0.95455 | 0.0034499 |
+
+
+done! total time: 1:19:51

data_scaling/n100_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 n100_2; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_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/n100_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/n100_2/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null

data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 17, "eval/id_best": 17, "eval/lr_best": 9.599999999999999e-05, "eval/wd_best": 0.05, "eval/train/loss": 2.2801589965820312, "eval/train/acc": 0.3174037309075263, "eval/train/acc_std": 0.0021720362405662453, "eval/train/f1": 0.24996921159420885, "eval/train/f1_std": 0.002174505316110855, "eval/validation/loss": 2.5121238231658936, "eval/validation/acc": 0.25212255444813586, "eval/validation/acc_std": 0.005558349328010697, "eval/validation/f1": 0.178891054967223, "eval/validation/f1_std": 0.0046179386179950035, "eval/test/loss": 2.4678409099578857, "eval/test/acc": 0.25918367346938775, "eval/test/acc_std": 0.005123598094173278, "eval/test/f1": 0.18567873372894725, "eval/test/f1_std": 0.004625389002219636, "eval/testid/loss": 2.4389657974243164, "eval/testid/acc": 0.2614227877385772, "eval/testid/acc_std": 0.005253431050437182, "eval/testid/f1": 0.1981972157232437, "eval/testid/f1_std": 0.00478330422356263}

data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 17, "eval/best/id_best": 17, "eval/best/lr_best": 9.599999999999999e-05, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.2801589965820312, "eval/best/train/acc": 0.3174037309075263, "eval/best/train/acc_std": 0.0021720362405662453, "eval/best/train/f1": 0.24996921159420885, "eval/best/train/f1_std": 0.002174505316110855, "eval/best/validation/loss": 2.5121238231658936, "eval/best/validation/acc": 0.25212255444813586, "eval/best/validation/acc_std": 0.005558349328010697, "eval/best/validation/f1": 0.178891054967223, "eval/best/validation/f1_std": 0.0046179386179950035, "eval/best/test/loss": 2.4678409099578857, "eval/best/test/acc": 0.25918367346938775, "eval/best/test/acc_std": 0.005123598094173278, "eval/best/test/f1": 0.18567873372894725, "eval/best/test/f1_std": 0.004625389002219636, "eval/best/testid/loss": 2.4389657974243164, "eval/best/testid/acc": 0.2614227877385772, "eval/best/testid/acc_std": 0.005253431050437182, "eval/best/testid/f1": 0.1981972157232437, "eval/best/testid/f1_std": 0.00478330422356263}

data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 17, "eval/last/lr_best": 9.599999999999999e-05, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.278407096862793, "eval/last/train/acc": 0.31801837794646426, "eval/last/train/acc_std": 0.002194595491474176, "eval/last/train/f1": 0.2495535751701682, "eval/last/train/f1_std": 0.0021728134707087207, "eval/last/validation/loss": 2.5161631107330322, "eval/last/validation/acc": 0.2502768549280177, "eval/last/validation/acc_std": 0.0054280929764676635, "eval/last/validation/f1": 0.1774014094554318, "eval/last/validation/f1_std": 0.004530486232532907, "eval/last/test/loss": 2.4701080322265625, "eval/last/test/acc": 0.2560296846011132, "eval/last/test/acc_std": 0.005052262139712686, "eval/last/test/f1": 0.1818746620569582, "eval/last/test/f1_std": 0.00453373808990997, "eval/last/testid/loss": 2.4380226135253906, "eval/last/testid/acc": 0.26045883940620784, "eval/last/testid/acc_std": 0.005337878665213572, "eval/last/testid/f1": 0.19665621106494888, "eval/last/testid/f1_std": 0.004862487025304217}

data_scaling/n100_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,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",train,2.2801589965820312,0.3174037309075263,0.0021720362405662453,0.24996921159420885,0.002174505316110855
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",validation,2.5121238231658936,0.25212255444813586,0.005558349328010697,0.178891054967223,0.0046179386179950035
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",test,2.4678409099578857,0.25918367346938775,0.005123598094173278,0.18567873372894725,0.004625389002219636
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",testid,2.4389657974243164,0.2614227877385772,0.005253431050437182,0.1981972157232437,0.00478330422356263

data_scaling/n100_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,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",train,2.2801589965820312,0.3174037309075263,0.0021720362405662453,0.24996921159420885,0.002174505316110855
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",validation,2.5121238231658936,0.25212255444813586,0.005558349328010697,0.178891054967223,0.0046179386179950035
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",test,2.4678409099578857,0.25918367346938775,0.005123598094173278,0.18567873372894725,0.004625389002219636
+flat_mae,patch,attn,nsd_cococlip,best,17,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",testid,2.4389657974243164,0.2614227877385772,0.005253431050437182,0.1981972157232437,0.00478330422356263

data_scaling/n100_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,19,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",train,2.278407096862793,0.31801837794646426,0.002194595491474176,0.2495535751701682,0.0021728134707087207
+flat_mae,patch,attn,nsd_cococlip,last,19,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",validation,2.5161631107330322,0.2502768549280177,0.0054280929764676635,0.1774014094554318,0.004530486232532907
+flat_mae,patch,attn,nsd_cococlip,last,19,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",test,2.4701080322265625,0.2560296846011132,0.005052262139712686,0.1818746620569582,0.00453373808990997
+flat_mae,patch,attn,nsd_cococlip,last,19,9.599999999999999e-05,0.05,17,"[0.32, 1.0]",testid,2.4380226135253906,0.26045883940620784,0.005337878665213572,0.19665621106494888,0.004862487025304217

data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/log.txt

@@ -0,0 +1,967 @@
+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: clean, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 19:52:41
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_probe
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_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/n100_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/n100_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: 0:23:59  lr: nan  time: 3.5979  data: 3.0040  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:03:50  lr: 0.000003  loss: 3.1987 (3.2031)  grad: 0.1707 (0.1773)  time: 0.4574  data: 0.0042  max mem: 22448
+train: [0]  [ 40/400]  eta: 0:03:11  lr: 0.000006  loss: 3.1899 (3.1878)  grad: 0.1707 (0.1749)  time: 0.4515  data: 0.0043  max mem: 22448
+train: [0]  [ 60/400]  eta: 0:02:51  lr: 0.000009  loss: 3.1775 (3.1879)  grad: 0.1686 (0.1740)  time: 0.4535  data: 0.0042  max mem: 22448
+train: [0]  [ 80/400]  eta: 0:02:37  lr: 0.000012  loss: 3.1726 (3.1833)  grad: 0.1663 (0.1718)  time: 0.4541  data: 0.0041  max mem: 22448
+train: [0]  [100/400]  eta: 0:02:25  lr: 0.000015  loss: 3.1695 (3.1806)  grad: 0.1597 (0.1694)  time: 0.4595  data: 0.0041  max mem: 22448
+train: [0]  [120/400]  eta: 0:02:14  lr: 0.000018  loss: 3.1566 (3.1774)  grad: 0.1526 (0.1666)  time: 0.4447  data: 0.0042  max mem: 22448
+train: [0]  [140/400]  eta: 0:02:08  lr: 0.000021  loss: 3.1592 (3.1761)  grad: 0.1530 (0.1660)  time: 0.5738  data: 0.1236  max mem: 22448
+train: [0]  [160/400]  eta: 0:01:57  lr: 0.000024  loss: 3.1560 (3.1717)  grad: 0.1716 (0.1668)  time: 0.4584  data: 0.0058  max mem: 22448
+train: [0]  [180/400]  eta: 0:01:46  lr: 0.000027  loss: 3.1333 (3.1684)  grad: 0.1640 (0.1661)  time: 0.4455  data: 0.0041  max mem: 22448
+train: [0]  [200/400]  eta: 0:01:36  lr: 0.000030  loss: 3.1582 (3.1678)  grad: 0.1523 (0.1646)  time: 0.4840  data: 0.0044  max mem: 22448
+train: [0]  [220/400]  eta: 0:01:26  lr: 0.000033  loss: 3.1699 (3.1678)  grad: 0.1521 (0.1636)  time: 0.4611  data: 0.0043  max mem: 22448
+train: [0]  [240/400]  eta: 0:01:16  lr: 0.000036  loss: 3.1600 (3.1669)  grad: 0.1539 (0.1627)  time: 0.4457  data: 0.0042  max mem: 22448
+train: [0]  [260/400]  eta: 0:01:06  lr: 0.000039  loss: 3.1523 (3.1657)  grad: 0.1473 (0.1615)  time: 0.4608  data: 0.0045  max mem: 22448
+train: [0]  [280/400]  eta: 0:00:57  lr: 0.000042  loss: 3.1507 (3.1645)  grad: 0.1453 (0.1606)  time: 0.4524  data: 0.0044  max mem: 22448
+train: [0]  [300/400]  eta: 0:00:47  lr: 0.000045  loss: 3.1404 (3.1621)  grad: 0.1466 (0.1603)  time: 0.4484  data: 0.0044  max mem: 22448
+train: [0]  [320/400]  eta: 0:00:37  lr: 0.000048  loss: 3.1289 (3.1605)  grad: 0.1577 (0.1604)  time: 0.4622  data: 0.0042  max mem: 22448
+train: [0]  [340/400]  eta: 0:00:28  lr: 0.000051  loss: 3.1386 (3.1589)  grad: 0.1580 (0.1604)  time: 0.4576  data: 0.0044  max mem: 22448
+train: [0]  [360/400]  eta: 0:00:18  lr: 0.000054  loss: 3.1250 (3.1568)  grad: 0.1586 (0.1608)  time: 0.4520  data: 0.0040  max mem: 22448
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 3.1169 (3.1544)  grad: 0.1686 (0.1612)  time: 0.4790  data: 0.0045  max mem: 22448
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.1137 (3.1531)  grad: 0.1686 (0.1617)  time: 0.4581  data: 0.0043  max mem: 22448
+train: [0] Total time: 0:03:08 (0.4711 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.1137 (3.1531)  grad: 0.1686 (0.1617)
+eval (validation): [0]  [ 0/85]  eta: 0:04:32    time: 3.2020  data: 2.9252  max mem: 22448
+eval (validation): [0]  [20/85]  eta: 0:00:32    time: 0.3634  data: 0.0057  max mem: 22448
+eval (validation): [0]  [40/85]  eta: 0:00:18    time: 0.3360  data: 0.0035  max mem: 22448
+eval (validation): [0]  [60/85]  eta: 0:00:09    time: 0.3409  data: 0.0042  max mem: 22448
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3910  data: 0.0471  max mem: 22448
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3882  data: 0.0466  max mem: 22448
+eval (validation): [0] Total time: 0:00:33 (0.3933 s / it)
+cv: [0]  best hparam: (36, 1.0) (046) ('046_lr3.6e+01_wd1.0e+00')  loss: 2.719  acc: 0.203  f1: 0.122
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:54  lr: nan  time: 3.4369  data: 3.0610  max mem: 22448
+train: [1]  [ 20/400]  eta: 0:03:53  lr: 0.000063  loss: 3.0796 (3.0783)  grad: 0.1695 (0.1722)  time: 0.4725  data: 0.0188  max mem: 22448
+train: [1]  [ 40/400]  eta: 0:03:12  lr: 0.000066  loss: 3.0888 (3.0804)  grad: 0.1626 (0.1666)  time: 0.4491  data: 0.0039  max mem: 22448
+train: [1]  [ 60/400]  eta: 0:02:52  lr: 0.000069  loss: 3.0604 (3.0659)  grad: 0.1609 (0.1667)  time: 0.4524  data: 0.0040  max mem: 22448
+train: [1]  [ 80/400]  eta: 0:02:38  lr: 0.000072  loss: 3.0533 (3.0651)  grad: 0.1674 (0.1689)  time: 0.4584  data: 0.0043  max mem: 22448
+train: [1]  [100/400]  eta: 0:02:25  lr: 0.000075  loss: 3.0541 (3.0613)  grad: 0.1739 (0.1714)  time: 0.4496  data: 0.0041  max mem: 22448
+train: [1]  [120/400]  eta: 0:02:14  lr: 0.000078  loss: 3.0541 (3.0589)  grad: 0.1822 (0.1731)  time: 0.4484  data: 0.0041  max mem: 22448
+train: [1]  [140/400]  eta: 0:02:03  lr: 0.000081  loss: 3.0510 (3.0572)  grad: 0.1789 (0.1747)  time: 0.4502  data: 0.0042  max mem: 22448
+train: [1]  [160/400]  eta: 0:01:53  lr: 0.000084  loss: 3.0480 (3.0577)  grad: 0.1778 (0.1750)  time: 0.4410  data: 0.0041  max mem: 22448
+train: [1]  [180/400]  eta: 0:01:43  lr: 0.000087  loss: 3.0480 (3.0568)  grad: 0.1772 (0.1757)  time: 0.4641  data: 0.0042  max mem: 22448
+train: [1]  [200/400]  eta: 0:01:34  lr: 0.000090  loss: 3.0251 (3.0544)  grad: 0.1778 (0.1766)  time: 0.4679  data: 0.0042  max mem: 22448
+train: [1]  [220/400]  eta: 0:01:24  lr: 0.000093  loss: 2.9974 (3.0484)  grad: 0.1917 (0.1783)  time: 0.4447  data: 0.0043  max mem: 22448
+train: [1]  [240/400]  eta: 0:01:14  lr: 0.000096  loss: 2.9974 (3.0449)  grad: 0.1917 (0.1787)  time: 0.4575  data: 0.0041  max mem: 22448
+train: [1]  [260/400]  eta: 0:01:05  lr: 0.000099  loss: 3.0142 (3.0436)  grad: 0.1875 (0.1797)  time: 0.4552  data: 0.0042  max mem: 22448
+train: [1]  [280/400]  eta: 0:00:55  lr: 0.000102  loss: 3.0136 (3.0404)  grad: 0.1912 (0.1804)  time: 0.4425  data: 0.0043  max mem: 22448
+train: [1]  [300/400]  eta: 0:00:46  lr: 0.000105  loss: 2.9956 (3.0387)  grad: 0.1912 (0.1811)  time: 0.4564  data: 0.0043  max mem: 22448
+train: [1]  [320/400]  eta: 0:00:37  lr: 0.000108  loss: 2.9807 (3.0352)  grad: 0.1932 (0.1821)  time: 0.4507  data: 0.0042  max mem: 22448
+train: [1]  [340/400]  eta: 0:00:27  lr: 0.000111  loss: 2.9755 (3.0317)  grad: 0.1932 (0.1826)  time: 0.4563  data: 0.0041  max mem: 22448
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 3.0016 (3.0294)  grad: 0.1877 (0.1829)  time: 0.4655  data: 0.0042  max mem: 22448
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 2.9677 (3.0253)  grad: 0.1877 (0.1837)  time: 0.4582  data: 0.0041  max mem: 22448
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 2.9632 (3.0230)  grad: 0.1986 (0.1848)  time: 0.4408  data: 0.0043  max mem: 22448
+train: [1] Total time: 0:03:04 (0.4618 s / it)
+train: [1] Summary: lr: 0.000120  loss: 2.9632 (3.0230)  grad: 0.1986 (0.1848)
+eval (validation): [1]  [ 0/85]  eta: 0:04:40    time: 3.3015  data: 3.0642  max mem: 22448
+eval (validation): [1]  [20/85]  eta: 0:00:36    time: 0.4280  data: 0.0925  max mem: 22448
+eval (validation): [1]  [40/85]  eta: 0:00:23    time: 0.4744  data: 0.1348  max mem: 22448
+eval (validation): [1]  [60/85]  eta: 0:00:11    time: 0.3246  data: 0.0042  max mem: 22448
+eval (validation): [1]  [80/85]  eta: 0:00:02    time: 0.3223  data: 0.0034  max mem: 22448
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.3093  data: 0.0039  max mem: 22448
+eval (validation): [1] Total time: 0:00:35 (0.4215 s / it)
+cv: [1]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 2.597  acc: 0.226  f1: 0.154
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:36:27  lr: nan  time: 5.4688  data: 5.0898  max mem: 22448
+train: [2]  [ 20/400]  eta: 0:04:31  lr: 0.000123  loss: 2.9553 (2.9600)  grad: 0.2186 (0.2173)  time: 0.4776  data: 0.0436  max mem: 22448
+train: [2]  [ 40/400]  eta: 0:03:33  lr: 0.000126  loss: 2.9635 (2.9628)  grad: 0.2138 (0.2164)  time: 0.4619  data: 0.0051  max mem: 22448
+train: [2]  [ 60/400]  eta: 0:03:04  lr: 0.000129  loss: 2.9602 (2.9610)  grad: 0.2080 (0.2148)  time: 0.4455  data: 0.0038  max mem: 22448
+train: [2]  [ 80/400]  eta: 0:02:46  lr: 0.000132  loss: 2.9472 (2.9610)  grad: 0.2254 (0.2195)  time: 0.4479  data: 0.0044  max mem: 22448
+train: [2]  [100/400]  eta: 0:02:31  lr: 0.000135  loss: 2.9469 (2.9604)  grad: 0.2607 (0.2368)  time: 0.4439  data: 0.0042  max mem: 22448
+train: [2]  [120/400]  eta: 0:02:18  lr: 0.000138  loss: 3.0107 (3.0015)  grad: 0.3478 (0.3268)  time: 0.4464  data: 0.0043  max mem: 22448
+WARNING: classifier 48 (50, 1.0) diverged (loss=84.11 > 63.56) at step 468. Freezing.
+train: [2]  [140/400]  eta: 0:02:06  lr: 0.000141  loss: 3.3472 (3.0999)  grad: 0.9542 (0.4773)  time: 0.4457  data: 0.0043  max mem: 22448
+train: [2]  [160/400]  eta: 0:01:56  lr: 0.000144  loss: 2.9770 (3.0786)  grad: 0.2315 (0.4453)  time: 0.4703  data: 0.0042  max mem: 22448
+train: [2]  [180/400]  eta: 0:01:46  lr: 0.000147  loss: 2.9154 (3.0601)  grad: 0.2209 (0.4207)  time: 0.4568  data: 0.0042  max mem: 22448
+train: [2]  [200/400]  eta: 0:01:35  lr: 0.000150  loss: 2.9036 (3.0450)  grad: 0.2232 (0.4018)  time: 0.4438  data: 0.0043  max mem: 22448
+train: [2]  [220/400]  eta: 0:01:26  lr: 0.000153  loss: 2.9191 (3.0368)  grad: 0.2449 (0.3886)  time: 0.4678  data: 0.0043  max mem: 22448
+train: [2]  [240/400]  eta: 0:01:16  lr: 0.000156  loss: 2.9829 (3.0334)  grad: 0.2603 (0.3808)  time: 0.4551  data: 0.0041  max mem: 22448
+train: [2]  [260/400]  eta: 0:01:06  lr: 0.000159  loss: 3.0398 (3.0427)  grad: 0.3467 (0.4113)  time: 0.4401  data: 0.0042  max mem: 22448
+WARNING: classifier 47 (43, 1.0) diverged (loss=75.94 > 63.56) at step 535. Freezing.
+train: [2]  [280/400]  eta: 0:00:56  lr: 0.000162  loss: 3.1504 (3.0663)  grad: 0.7249 (0.4424)  time: 0.4525  data: 0.0042  max mem: 22448
+train: [2]  [300/400]  eta: 0:00:47  lr: 0.000165  loss: 2.9308 (3.0547)  grad: 0.2201 (0.4270)  time: 0.4511  data: 0.0041  max mem: 22448
+train: [2]  [320/400]  eta: 0:00:37  lr: 0.000168  loss: 2.8853 (3.0443)  grad: 0.2058 (0.4130)  time: 0.4572  data: 0.0042  max mem: 22448
+train: [2]  [340/400]  eta: 0:00:28  lr: 0.000171  loss: 2.8867 (3.0369)  grad: 0.2110 (0.4016)  time: 0.4614  data: 0.0042  max mem: 22448
+train: [2]  [360/400]  eta: 0:00:18  lr: 0.000174  loss: 2.9032 (3.0303)  grad: 0.2168 (0.3914)  time: 0.4611  data: 0.0044  max mem: 22448
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 2.9286 (3.0261)  grad: 0.2368 (0.3860)  time: 0.4902  data: 0.0042  max mem: 22448
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 3.0245 (3.0328)  grad: 0.3758 (0.4053)  time: 0.4575  data: 0.0043  max mem: 22448
+train: [2] Total time: 0:03:07 (0.4695 s / it)
+train: [2] Summary: lr: 0.000180  loss: 3.0245 (3.0328)  grad: 0.3758 (0.4053)
+eval (validation): [2]  [ 0/85]  eta: 0:04:39    time: 3.2832  data: 3.0413  max mem: 22448
+eval (validation): [2]  [20/85]  eta: 0:00:31    time: 0.3383  data: 0.0037  max mem: 22448
+eval (validation): [2]  [40/85]  eta: 0:00:18    time: 0.3387  data: 0.0040  max mem: 22448
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3437  data: 0.0042  max mem: 22448
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3348  data: 0.0041  max mem: 22448
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3292  data: 0.0040  max mem: 22448
+eval (validation): [2] Total time: 0:00:31 (0.3761 s / it)
+cv: [2]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 2.556  acc: 0.240  f1: 0.168
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:21:55  lr: nan  time: 3.2887  data: 2.9609  max mem: 22448
+WARNING: classifier 46 (36, 1.0) diverged (loss=65.80 > 63.56) at step 603. Freezing.
+train: [3]  [ 20/400]  eta: 0:03:40  lr: 0.000183  loss: 2.8718 (3.1773)  grad: 0.2107 (0.6778)  time: 0.4444  data: 0.0044  max mem: 22448
+train: [3]  [ 40/400]  eta: 0:03:06  lr: 0.000186  loss: 2.9062 (3.0506)  grad: 0.2161 (0.4519)  time: 0.4541  data: 0.0038  max mem: 22448
+train: [3]  [ 60/400]  eta: 0:02:48  lr: 0.000189  loss: 2.8881 (2.9936)  grad: 0.2224 (0.3733)  time: 0.4513  data: 0.0042  max mem: 22448
+train: [3]  [ 80/400]  eta: 0:02:34  lr: 0.000192  loss: 2.8632 (2.9644)  grad: 0.2123 (0.3329)  time: 0.4434  data: 0.0041  max mem: 22448
+train: [3]  [100/400]  eta: 0:02:23  lr: 0.000195  loss: 2.8581 (2.9440)  grad: 0.2089 (0.3093)  time: 0.4512  data: 0.0042  max mem: 22448
+train: [3]  [120/400]  eta: 0:02:12  lr: 0.000198  loss: 2.8576 (2.9289)  grad: 0.2185 (0.2953)  time: 0.4495  data: 0.0042  max mem: 22448
+train: [3]  [140/400]  eta: 0:02:01  lr: 0.000201  loss: 2.8576 (2.9226)  grad: 0.2346 (0.2882)  time: 0.4452  data: 0.0044  max mem: 22448
+train: [3]  [160/400]  eta: 0:01:52  lr: 0.000204  loss: 2.8888 (2.9191)  grad: 0.2453 (0.2827)  time: 0.4764  data: 0.0046  max mem: 22448
+train: [3]  [180/400]  eta: 0:01:42  lr: 0.000207  loss: 2.8888 (2.9120)  grad: 0.2438 (0.2783)  time: 0.4424  data: 0.0043  max mem: 22448
+train: [3]  [200/400]  eta: 0:01:32  lr: 0.000210  loss: 2.8705 (2.9121)  grad: 0.2457 (0.2768)  time: 0.4430  data: 0.0043  max mem: 22448
+train: [3]  [220/400]  eta: 0:01:23  lr: 0.000213  loss: 2.9120 (2.9156)  grad: 0.2759 (0.2920)  time: 0.4623  data: 0.0042  max mem: 22448
+WARNING: classifier 45 (31, 1.0) diverged (loss=66.38 > 63.56) at step 719. Freezing.
+train: [3]  [240/400]  eta: 0:01:13  lr: 0.000216  loss: 3.0322 (2.9624)  grad: 0.7472 (0.3847)  time: 0.4437  data: 0.0042  max mem: 22448
+train: [3]  [260/400]  eta: 0:01:04  lr: 0.000219  loss: 2.9531 (2.9553)  grad: 0.2600 (0.3729)  time: 0.4401  data: 0.0042  max mem: 22448
+train: [3]  [280/400]  eta: 0:00:55  lr: 0.000222  loss: 2.8608 (2.9472)  grad: 0.2272 (0.3627)  time: 0.4570  data: 0.0043  max mem: 22448
+train: [3]  [300/400]  eta: 0:00:45  lr: 0.000225  loss: 2.8784 (2.9435)  grad: 0.2291 (0.3548)  time: 0.4523  data: 0.0042  max mem: 22448
+train: [3]  [320/400]  eta: 0:00:36  lr: 0.000228  loss: 2.8912 (2.9405)  grad: 0.2731 (0.3537)  time: 0.4536  data: 0.0041  max mem: 22448
+train: [3]  [340/400]  eta: 0:00:27  lr: 0.000231  loss: 2.9424 (2.9578)  grad: 0.4246 (0.3854)  time: 0.4559  data: 0.0041  max mem: 22448
+WARNING: classifier 44 (26, 1.0) diverged (loss=69.34 > 63.56) at step 778. Freezing.
+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 3.3448 (2.9989)  grad: 1.1328 (0.4434)  time: 0.4427  data: 0.0041  max mem: 22448
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 2.9113 (2.9906)  grad: 0.2289 (0.4316)  time: 0.4449  data: 0.0041  max mem: 22448
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.8200 (2.9825)  grad: 0.2158 (0.4208)  time: 0.4399  data: 0.0044  max mem: 22448
+train: [3] Total time: 0:03:02 (0.4570 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.8200 (2.9825)  grad: 0.2158 (0.4208)
+eval (validation): [3]  [ 0/85]  eta: 0:04:42    time: 3.3229  data: 3.0421  max mem: 22448
+eval (validation): [3]  [20/85]  eta: 0:00:31    time: 0.3409  data: 0.0032  max mem: 22448
+eval (validation): [3]  [40/85]  eta: 0:00:18    time: 0.3391  data: 0.0041  max mem: 22448
+eval (validation): [3]  [60/85]  eta: 0:00:09    time: 0.3310  data: 0.0043  max mem: 22448
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3211  data: 0.0039  max mem: 22448
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3130  data: 0.0038  max mem: 22448
+eval (validation): [3] Total time: 0:00:31 (0.3706 s / it)
+cv: [3]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 2.580  acc: 0.230  f1: 0.157
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [4]  [  0/400]  eta: 0:22:32  lr: nan  time: 3.3825  data: 3.0539  max mem: 22448
+train: [4]  [ 20/400]  eta: 0:03:34  lr: 0.000243  loss: 2.8024 (2.8050)  grad: 0.2216 (0.2214)  time: 0.4223  data: 0.0036  max mem: 22448
+train: [4]  [ 40/400]  eta: 0:03:03  lr: 0.000246  loss: 2.8228 (2.8143)  grad: 0.2226 (0.2213)  time: 0.4549  data: 0.0040  max mem: 22448
+train: [4]  [ 60/400]  eta: 0:02:45  lr: 0.000249  loss: 2.8300 (2.8193)  grad: 0.2160 (0.2208)  time: 0.4412  data: 0.0043  max mem: 22448
+train: [4]  [ 80/400]  eta: 0:02:32  lr: 0.000252  loss: 2.8335 (2.8219)  grad: 0.2139 (0.2189)  time: 0.4451  data: 0.0043  max mem: 22448
+train: [4]  [100/400]  eta: 0:02:21  lr: 0.000255  loss: 2.8445 (2.8298)  grad: 0.2183 (0.2213)  time: 0.4414  data: 0.0043  max mem: 22448
+train: [4]  [120/400]  eta: 0:02:10  lr: 0.000258  loss: 2.8445 (2.8274)  grad: 0.2288 (0.2224)  time: 0.4426  data: 0.0042  max mem: 22448
+train: [4]  [140/400]  eta: 0:02:00  lr: 0.000261  loss: 2.8262 (2.8279)  grad: 0.2326 (0.2250)  time: 0.4406  data: 0.0042  max mem: 22448
+train: [4]  [160/400]  eta: 0:01:50  lr: 0.000264  loss: 2.8262 (2.8296)  grad: 0.2362 (0.2264)  time: 0.4473  data: 0.0043  max mem: 22448
+train: [4]  [180/400]  eta: 0:01:41  lr: 0.000267  loss: 2.8246 (2.8331)  grad: 0.2414 (0.2286)  time: 0.4602  data: 0.0043  max mem: 22448
+train: [4]  [200/400]  eta: 0:01:31  lr: 0.000270  loss: 2.8224 (2.8292)  grad: 0.2448 (0.2300)  time: 0.4473  data: 0.0042  max mem: 22448
+train: [4]  [220/400]  eta: 0:01:22  lr: 0.000273  loss: 2.8378 (2.8319)  grad: 0.2479 (0.2322)  time: 0.4540  data: 0.0044  max mem: 22448
+train: [4]  [240/400]  eta: 0:01:13  lr: 0.000276  loss: 2.8443 (2.8320)  grad: 0.2557 (0.2353)  time: 0.4522  data: 0.0043  max mem: 22448
+train: [4]  [260/400]  eta: 0:01:03  lr: 0.000279  loss: 2.8560 (2.8364)  grad: 0.2832 (0.2420)  time: 0.4466  data: 0.0042  max mem: 22448
+train: [4]  [280/400]  eta: 0:00:54  lr: 0.000282  loss: 2.9925 (2.8581)  grad: 0.4488 (0.2887)  time: 0.4428  data: 0.0042  max mem: 22448
+WARNING: classifier 43 (22, 1.0) diverged (loss=77.93 > 63.56) at step 944. Freezing.
+train: [4]  [300/400]  eta: 0:00:45  lr: 0.000285  loss: 3.0891 (2.8863)  grad: 0.7468 (0.3320)  time: 0.4575  data: 0.0043  max mem: 22448
+train: [4]  [320/400]  eta: 0:00:36  lr: 0.000288  loss: 3.1947 (2.9080)  grad: 0.9362 (0.3753)  time: 0.4604  data: 0.0042  max mem: 22448
+WARNING: classifier 42 (19, 1.0) diverged (loss=75.21 > 63.56) at step 968. Freezing.
+train: [4]  [340/400]  eta: 0:00:27  lr: 0.000291  loss: 3.3012 (2.9449)  grad: 1.1647 (0.4348)  time: 0.4420  data: 0.0041  max mem: 22448
+train: [4]  [360/400]  eta: 0:00:18  lr: 0.000294  loss: 2.9188 (2.9402)  grad: 0.2197 (0.4224)  time: 0.4516  data: 0.0041  max mem: 22448
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.8279 (2.9342)  grad: 0.2094 (0.4113)  time: 0.4516  data: 0.0042  max mem: 22448
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.8118 (2.9279)  grad: 0.2102 (0.4015)  time: 0.4421  data: 0.0043  max mem: 22448
+train: [4] Total time: 0:03:01 (0.4548 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.8118 (2.9279)  grad: 0.2102 (0.4015)
+eval (validation): [4]  [ 0/85]  eta: 0:04:38    time: 3.2743  data: 3.0249  max mem: 22448
+eval (validation): [4]  [20/85]  eta: 0:00:30    time: 0.3324  data: 0.0043  max mem: 22448
+eval (validation): [4]  [40/85]  eta: 0:00:18    time: 0.3362  data: 0.0038  max mem: 22448
+eval (validation): [4]  [60/85]  eta: 0:00:09    time: 0.3450  data: 0.0044  max mem: 22448
+eval (validation): [4]  [80/85]  eta: 0:00:01    time: 0.3395  data: 0.0041  max mem: 22448
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3273  data: 0.0040  max mem: 22448
+eval (validation): [4] Total time: 0:00:31 (0.3754 s / it)
+cv: [4]  best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00')  loss: 2.531  acc: 0.242  f1: 0.179
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:22:51  lr: nan  time: 3.4298  data: 3.1002  max mem: 22448
+train: [5]  [ 20/400]  eta: 0:03:35  lr: 0.000300  loss: 2.7570 (2.7577)  grad: 0.2189 (0.2203)  time: 0.4229  data: 0.0027  max mem: 22448
+train: [5]  [ 40/400]  eta: 0:03:04  lr: 0.000300  loss: 2.7822 (2.7994)  grad: 0.2246 (0.2237)  time: 0.4544  data: 0.0043  max mem: 22448
+train: [5]  [ 60/400]  eta: 0:02:45  lr: 0.000300  loss: 2.8088 (2.8034)  grad: 0.2263 (0.2266)  time: 0.4339  data: 0.0045  max mem: 22448
+train: [5]  [ 80/400]  eta: 0:02:31  lr: 0.000300  loss: 2.8056 (2.7963)  grad: 0.2256 (0.2259)  time: 0.4392  data: 0.0044  max mem: 22448
+train: [5]  [100/400]  eta: 0:02:20  lr: 0.000300  loss: 2.8080 (2.7994)  grad: 0.2290 (0.2289)  time: 0.4380  data: 0.0044  max mem: 22448
+train: [5]  [120/400]  eta: 0:02:09  lr: 0.000300  loss: 2.7802 (2.7934)  grad: 0.2310 (0.2285)  time: 0.4404  data: 0.0041  max mem: 22448
+train: [5]  [140/400]  eta: 0:01:59  lr: 0.000300  loss: 2.7469 (2.7862)  grad: 0.2207 (0.2274)  time: 0.4306  data: 0.0042  max mem: 22448
+train: [5]  [160/400]  eta: 0:01:49  lr: 0.000299  loss: 2.7415 (2.7842)  grad: 0.2176 (0.2274)  time: 0.4456  data: 0.0040  max mem: 22448
+train: [5]  [180/400]  eta: 0:01:40  lr: 0.000299  loss: 2.7647 (2.7860)  grad: 0.2180 (0.2268)  time: 0.4404  data: 0.0043  max mem: 22448
+train: [5]  [200/400]  eta: 0:01:30  lr: 0.000299  loss: 2.7757 (2.7851)  grad: 0.2228 (0.2271)  time: 0.4341  data: 0.0041  max mem: 22448
+train: [5]  [220/400]  eta: 0:01:21  lr: 0.000299  loss: 2.7648 (2.7837)  grad: 0.2248 (0.2267)  time: 0.4442  data: 0.0042  max mem: 22448
+train: [5]  [240/400]  eta: 0:01:12  lr: 0.000299  loss: 2.7697 (2.7831)  grad: 0.2248 (0.2270)  time: 0.4430  data: 0.0042  max mem: 22448
+train: [5]  [260/400]  eta: 0:01:03  lr: 0.000299  loss: 2.7770 (2.7806)  grad: 0.2210 (0.2263)  time: 0.4421  data: 0.0043  max mem: 22448
+train: [5]  [280/400]  eta: 0:00:54  lr: 0.000298  loss: 2.7782 (2.7805)  grad: 0.2216 (0.2266)  time: 0.4488  data: 0.0041  max mem: 22448
+train: [5]  [300/400]  eta: 0:00:45  lr: 0.000298  loss: 2.7574 (2.7777)  grad: 0.2250 (0.2265)  time: 0.4602  data: 0.0044  max mem: 22448
+train: [5]  [320/400]  eta: 0:00:36  lr: 0.000298  loss: 2.7477 (2.7786)  grad: 0.2287 (0.2270)  time: 0.4462  data: 0.0043  max mem: 22448
+train: [5]  [340/400]  eta: 0:00:27  lr: 0.000298  loss: 2.7481 (2.7765)  grad: 0.2318 (0.2272)  time: 0.4405  data: 0.0042  max mem: 22448
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 2.7475 (2.7760)  grad: 0.2309 (0.2276)  time: 0.4530  data: 0.0042  max mem: 22448
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.7481 (2.7753)  grad: 0.2320 (0.2279)  time: 0.4473  data: 0.0045  max mem: 22448
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.7041 (2.7725)  grad: 0.2205 (0.2272)  time: 0.4393  data: 0.0042  max mem: 22448
+train: [5] Total time: 0:02:59 (0.4500 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.7041 (2.7725)  grad: 0.2205 (0.2272)
+eval (validation): [5]  [ 0/85]  eta: 0:04:43    time: 3.3358  data: 3.0564  max mem: 22448
+eval (validation): [5]  [20/85]  eta: 0:00:32    time: 0.3591  data: 0.0050  max mem: 22448
+eval (validation): [5]  [40/85]  eta: 0:00:18    time: 0.3302  data: 0.0037  max mem: 22448
+eval (validation): [5]  [60/85]  eta: 0:00:09    time: 0.3422  data: 0.0045  max mem: 22448
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3273  data: 0.0042  max mem: 22448
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3176  data: 0.0040  max mem: 22448
+eval (validation): [5] Total time: 0:00:31 (0.3764 s / it)
+cv: [5]  best hparam: (1.2, 1.0) (025) ('025_lr1.2e+00_wd1.0e+00')  loss: 2.535  acc: 0.244  f1: 0.169
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [6]  [  0/400]  eta: 0:22:32  lr: nan  time: 3.3802  data: 3.0174  max mem: 22448
+train: [6]  [ 20/400]  eta: 0:03:37  lr: 0.000296  loss: 2.7077 (2.7023)  grad: 0.2227 (0.2208)  time: 0.4306  data: 0.0047  max mem: 22448
+train: [6]  [ 40/400]  eta: 0:03:04  lr: 0.000296  loss: 2.7202 (2.7172)  grad: 0.2237 (0.2250)  time: 0.4499  data: 0.0036  max mem: 22448
+train: [6]  [ 60/400]  eta: 0:02:46  lr: 0.000296  loss: 2.7099 (2.7125)  grad: 0.2294 (0.2269)  time: 0.4432  data: 0.0043  max mem: 22448
+train: [6]  [ 80/400]  eta: 0:02:32  lr: 0.000295  loss: 2.7037 (2.7063)  grad: 0.2312 (0.2298)  time: 0.4398  data: 0.0042  max mem: 22448
+train: [6]  [100/400]  eta: 0:02:20  lr: 0.000295  loss: 2.7200 (2.7108)  grad: 0.2371 (0.2311)  time: 0.4347  data: 0.0044  max mem: 22448
+train: [6]  [120/400]  eta: 0:02:09  lr: 0.000295  loss: 2.7251 (2.7124)  grad: 0.2425 (0.2326)  time: 0.4411  data: 0.0042  max mem: 22448
+train: [6]  [140/400]  eta: 0:01:59  lr: 0.000294  loss: 2.7362 (2.7197)  grad: 0.2425 (0.2342)  time: 0.4384  data: 0.0042  max mem: 22448
+train: [6]  [160/400]  eta: 0:01:50  lr: 0.000294  loss: 2.7377 (2.7213)  grad: 0.2354 (0.2338)  time: 0.4441  data: 0.0041  max mem: 22448
+train: [6]  [180/400]  eta: 0:01:40  lr: 0.000293  loss: 2.7031 (2.7189)  grad: 0.2339 (0.2347)  time: 0.4552  data: 0.0041  max mem: 22448
+train: [6]  [200/400]  eta: 0:01:31  lr: 0.000293  loss: 2.7157 (2.7196)  grad: 0.2444 (0.2352)  time: 0.4365  data: 0.0043  max mem: 22448
+train: [6]  [220/400]  eta: 0:01:21  lr: 0.000292  loss: 2.7421 (2.7168)  grad: 0.2412 (0.2359)  time: 0.4485  data: 0.0043  max mem: 22448
+train: [6]  [240/400]  eta: 0:01:12  lr: 0.000292  loss: 2.7423 (2.7188)  grad: 0.2365 (0.2358)  time: 0.4327  data: 0.0042  max mem: 22448
+train: [6]  [260/400]  eta: 0:01:03  lr: 0.000291  loss: 2.7364 (2.7157)  grad: 0.2350 (0.2356)  time: 0.4392  data: 0.0041  max mem: 22448
+train: [6]  [280/400]  eta: 0:00:54  lr: 0.000291  loss: 2.7224 (2.7168)  grad: 0.2241 (0.2353)  time: 0.4272  data: 0.0043  max mem: 22448
+train: [6]  [300/400]  eta: 0:00:45  lr: 0.000290  loss: 2.7373 (2.7179)  grad: 0.2279 (0.2352)  time: 0.4636  data: 0.0045  max mem: 22448
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+train: [6]  [340/400]  eta: 0:00:27  lr: 0.000289  loss: 2.7255 (2.7195)  grad: 0.2350 (0.2354)  time: 0.4377  data: 0.0041  max mem: 22448
+train: [6]  [360/400]  eta: 0:00:18  lr: 0.000288  loss: 2.7034 (2.7180)  grad: 0.2325 (0.2351)  time: 0.4416  data: 0.0042  max mem: 22448
+train: [6]  [380/400]  eta: 0:00:08  lr: 0.000288  loss: 2.6940 (2.7191)  grad: 0.2325 (0.2351)  time: 0.4446  data: 0.0043  max mem: 22448
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.7143 (2.7184)  grad: 0.2264 (0.2345)  time: 0.4388  data: 0.0042  max mem: 22448
+train: [6] Total time: 0:02:59 (0.4497 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.7143 (2.7184)  grad: 0.2264 (0.2345)
+eval (validation): [6]  [ 0/85]  eta: 0:04:37    time: 3.2682  data: 3.0412  max mem: 22448
+eval (validation): [6]  [20/85]  eta: 0:00:31    time: 0.3482  data: 0.0038  max mem: 22448
+eval (validation): [6]  [40/85]  eta: 0:00:18    time: 0.3344  data: 0.0040  max mem: 22448
+eval (validation): [6]  [60/85]  eta: 0:00:09    time: 0.3284  data: 0.0043  max mem: 22448
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3309  data: 0.0042  max mem: 22448
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3247  data: 0.0041  max mem: 22448
+eval (validation): [6] Total time: 0:00:31 (0.3721 s / it)
+cv: [6]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.521  acc: 0.245  f1: 0.179
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:22:16  lr: nan  time: 3.3416  data: 3.0142  max mem: 22448
+train: [7]  [ 20/400]  eta: 0:03:37  lr: 0.000286  loss: 2.6445 (2.6481)  grad: 0.2274 (0.2337)  time: 0.4339  data: 0.0043  max mem: 22448
+train: [7]  [ 40/400]  eta: 0:03:03  lr: 0.000286  loss: 2.6464 (2.6529)  grad: 0.2344 (0.2378)  time: 0.4426  data: 0.0039  max mem: 22448
+train: [7]  [ 60/400]  eta: 0:02:44  lr: 0.000285  loss: 2.6596 (2.6514)  grad: 0.2408 (0.2412)  time: 0.4359  data: 0.0044  max mem: 22448
+train: [7]  [ 80/400]  eta: 0:02:31  lr: 0.000284  loss: 2.6596 (2.6570)  grad: 0.2363 (0.2378)  time: 0.4378  data: 0.0044  max mem: 22448
+train: [7]  [100/400]  eta: 0:02:19  lr: 0.000284  loss: 2.6220 (2.6515)  grad: 0.2243 (0.2366)  time: 0.4302  data: 0.0043  max mem: 22448
+train: [7]  [120/400]  eta: 0:02:08  lr: 0.000283  loss: 2.6381 (2.6510)  grad: 0.2328 (0.2370)  time: 0.4328  data: 0.0042  max mem: 22448
+train: [7]  [140/400]  eta: 0:01:58  lr: 0.000282  loss: 2.6491 (2.6540)  grad: 0.2323 (0.2366)  time: 0.4278  data: 0.0042  max mem: 22448
+train: [7]  [160/400]  eta: 0:01:48  lr: 0.000282  loss: 2.6550 (2.6528)  grad: 0.2303 (0.2365)  time: 0.4472  data: 0.0041  max mem: 22448
+train: [7]  [180/400]  eta: 0:01:39  lr: 0.000281  loss: 2.6810 (2.6577)  grad: 0.2399 (0.2376)  time: 0.4512  data: 0.0042  max mem: 22448
+train: [7]  [200/400]  eta: 0:01:30  lr: 0.000280  loss: 2.6681 (2.6570)  grad: 0.2347 (0.2370)  time: 0.4298  data: 0.0043  max mem: 22448
+train: [7]  [220/400]  eta: 0:01:21  lr: 0.000279  loss: 2.6274 (2.6526)  grad: 0.2347 (0.2373)  time: 0.4445  data: 0.0042  max mem: 22448
+train: [7]  [240/400]  eta: 0:01:12  lr: 0.000278  loss: 2.6316 (2.6533)  grad: 0.2382 (0.2374)  time: 0.4476  data: 0.0042  max mem: 22448
+train: [7]  [260/400]  eta: 0:01:02  lr: 0.000278  loss: 2.6599 (2.6532)  grad: 0.2371 (0.2371)  time: 0.4335  data: 0.0042  max mem: 22448
+train: [7]  [280/400]  eta: 0:00:53  lr: 0.000277  loss: 2.6094 (2.6494)  grad: 0.2368 (0.2371)  time: 0.4346  data: 0.0042  max mem: 22448
+train: [7]  [300/400]  eta: 0:00:44  lr: 0.000276  loss: 2.5884 (2.6492)  grad: 0.2383 (0.2373)  time: 0.4699  data: 0.0046  max mem: 22448
+train: [7]  [320/400]  eta: 0:00:35  lr: 0.000275  loss: 2.6572 (2.6489)  grad: 0.2383 (0.2372)  time: 0.4452  data: 0.0044  max mem: 22448
+train: [7]  [340/400]  eta: 0:00:26  lr: 0.000274  loss: 2.6376 (2.6468)  grad: 0.2285 (0.2367)  time: 0.4322  data: 0.0042  max mem: 22448
+train: [7]  [360/400]  eta: 0:00:17  lr: 0.000273  loss: 2.6376 (2.6480)  grad: 0.2307 (0.2368)  time: 0.4344  data: 0.0041  max mem: 22448
+train: [7]  [380/400]  eta: 0:00:08  lr: 0.000272  loss: 2.6681 (2.6484)  grad: 0.2351 (0.2371)  time: 0.4547  data: 0.0042  max mem: 22448
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.6724 (2.6495)  grad: 0.2432 (0.2378)  time: 0.4365  data: 0.0041  max mem: 22448
+train: [7] Total time: 0:02:59 (0.4477 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.6724 (2.6495)  grad: 0.2432 (0.2378)
+eval (validation): [7]  [ 0/85]  eta: 0:04:41    time: 3.3111  data: 3.0697  max mem: 22448
+eval (validation): [7]  [20/85]  eta: 0:00:31    time: 0.3401  data: 0.0040  max mem: 22448
+eval (validation): [7]  [40/85]  eta: 0:00:18    time: 0.3396  data: 0.0036  max mem: 22448
+eval (validation): [7]  [60/85]  eta: 0:00:09    time: 0.3343  data: 0.0043  max mem: 22448
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3334  data: 0.0044  max mem: 22448
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3218  data: 0.0041  max mem: 22448
+eval (validation): [7] Total time: 0:00:31 (0.3739 s / it)
+cv: [7]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.541  acc: 0.242  f1: 0.176
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:22:29  lr: nan  time: 3.3729  data: 3.0507  max mem: 22448
+train: [8]  [ 20/400]  eta: 0:03:34  lr: 0.000270  loss: 2.5072 (2.5522)  grad: 0.2250 (0.2327)  time: 0.4246  data: 0.0033  max mem: 22448
+train: [8]  [ 40/400]  eta: 0:03:00  lr: 0.000270  loss: 2.5637 (2.5710)  grad: 0.2330 (0.2354)  time: 0.4335  data: 0.0037  max mem: 22448
+train: [8]  [ 60/400]  eta: 0:02:43  lr: 0.000269  loss: 2.6047 (2.5833)  grad: 0.2388 (0.2365)  time: 0.4427  data: 0.0042  max mem: 22448
+train: [8]  [ 80/400]  eta: 0:02:30  lr: 0.000268  loss: 2.5883 (2.5887)  grad: 0.2369 (0.2385)  time: 0.4342  data: 0.0043  max mem: 22448
+train: [8]  [100/400]  eta: 0:02:18  lr: 0.000267  loss: 2.5780 (2.5909)  grad: 0.2511 (0.2425)  time: 0.4335  data: 0.0043  max mem: 22448
+train: [8]  [120/400]  eta: 0:02:08  lr: 0.000266  loss: 2.5571 (2.5870)  grad: 0.2606 (0.2454)  time: 0.4339  data: 0.0043  max mem: 22448
+train: [8]  [140/400]  eta: 0:01:58  lr: 0.000265  loss: 2.5686 (2.5908)  grad: 0.2505 (0.2463)  time: 0.4358  data: 0.0045  max mem: 22448
+train: [8]  [160/400]  eta: 0:01:48  lr: 0.000264  loss: 2.5965 (2.5923)  grad: 0.2500 (0.2484)  time: 0.4299  data: 0.0042  max mem: 22448
+train: [8]  [180/400]  eta: 0:01:39  lr: 0.000263  loss: 2.5734 (2.5891)  grad: 0.2502 (0.2484)  time: 0.4411  data: 0.0042  max mem: 22448
+train: [8]  [200/400]  eta: 0:01:30  lr: 0.000262  loss: 2.5734 (2.5907)  grad: 0.2492 (0.2484)  time: 0.4548  data: 0.0043  max mem: 22448
+train: [8]  [220/400]  eta: 0:01:20  lr: 0.000260  loss: 2.6069 (2.5921)  grad: 0.2471 (0.2483)  time: 0.4371  data: 0.0042  max mem: 22448
+train: [8]  [240/400]  eta: 0:01:11  lr: 0.000259  loss: 2.5905 (2.5906)  grad: 0.2471 (0.2485)  time: 0.4459  data: 0.0043  max mem: 22448
+train: [8]  [260/400]  eta: 0:01:02  lr: 0.000258  loss: 2.5982 (2.5927)  grad: 0.2448 (0.2486)  time: 0.4463  data: 0.0041  max mem: 22448
+train: [8]  [280/400]  eta: 0:00:53  lr: 0.000257  loss: 2.5917 (2.5910)  grad: 0.2431 (0.2488)  time: 0.4384  data: 0.0042  max mem: 22448
+train: [8]  [300/400]  eta: 0:00:44  lr: 0.000256  loss: 2.5502 (2.5921)  grad: 0.2475 (0.2487)  time: 0.4507  data: 0.0042  max mem: 22448
+train: [8]  [320/400]  eta: 0:00:35  lr: 0.000255  loss: 2.5879 (2.5921)  grad: 0.2364 (0.2478)  time: 0.4703  data: 0.0044  max mem: 22448
+train: [8]  [340/400]  eta: 0:00:26  lr: 0.000254  loss: 2.5879 (2.5913)  grad: 0.2390 (0.2480)  time: 0.4496  data: 0.0043  max mem: 22448
+train: [8]  [360/400]  eta: 0:00:17  lr: 0.000253  loss: 2.6100 (2.5922)  grad: 0.2477 (0.2477)  time: 0.4340  data: 0.0041  max mem: 22448
+train: [8]  [380/400]  eta: 0:00:08  lr: 0.000252  loss: 2.6041 (2.5933)  grad: 0.2435 (0.2474)  time: 0.4620  data: 0.0041  max mem: 22448
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.6024 (2.5942)  grad: 0.2467 (0.2483)  time: 0.4405  data: 0.0042  max mem: 22448
+train: [8] Total time: 0:02:59 (0.4495 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.6024 (2.5942)  grad: 0.2467 (0.2483)
+eval (validation): [8]  [ 0/85]  eta: 0:04:46    time: 3.3745  data: 3.0984  max mem: 22448
+eval (validation): [8]  [20/85]  eta: 0:00:31    time: 0.3333  data: 0.0040  max mem: 22448
+eval (validation): [8]  [40/85]  eta: 0:00:18    time: 0.3423  data: 0.0036  max mem: 22448
+eval (validation): [8]  [60/85]  eta: 0:00:09    time: 0.3242  data: 0.0044  max mem: 22448
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3272  data: 0.0041  max mem: 22448
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3205  data: 0.0039  max mem: 22448
+eval (validation): [8] Total time: 0:00:31 (0.3707 s / it)
+cv: [8]  best hparam: (0.61, 1.0) (021) ('021_lr6.1e-01_wd1.0e+00')  loss: 2.568  acc: 0.243  f1: 0.178
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:22:58  lr: nan  time: 3.4452  data: 3.0709  max mem: 22448
+train: [9]  [ 20/400]  eta: 0:03:42  lr: 0.000249  loss: 2.5495 (2.5383)  grad: 0.2504 (0.2550)  time: 0.4438  data: 0.0039  max mem: 22448
+train: [9]  [ 40/400]  eta: 0:03:02  lr: 0.000248  loss: 2.5535 (2.5566)  grad: 0.2444 (0.2476)  time: 0.4245  data: 0.0040  max mem: 22448
+train: [9]  [ 60/400]  eta: 0:02:45  lr: 0.000247  loss: 2.5487 (2.5487)  grad: 0.2367 (0.2431)  time: 0.4426  data: 0.0044  max mem: 22448
+train: [9]  [ 80/400]  eta: 0:02:31  lr: 0.000246  loss: 2.5368 (2.5559)  grad: 0.2432 (0.2453)  time: 0.4405  data: 0.0044  max mem: 22448
+train: [9]  [100/400]  eta: 0:02:20  lr: 0.000244  loss: 2.5368 (2.5554)  grad: 0.2499 (0.2474)  time: 0.4379  data: 0.0043  max mem: 22448
+train: [9]  [120/400]  eta: 0:02:09  lr: 0.000243  loss: 2.5209 (2.5537)  grad: 0.2500 (0.2477)  time: 0.4453  data: 0.0042  max mem: 22448
+train: [9]  [140/400]  eta: 0:02:00  lr: 0.000242  loss: 2.5309 (2.5545)  grad: 0.2481 (0.2487)  time: 0.4485  data: 0.0044  max mem: 22448
+train: [9]  [160/400]  eta: 0:01:50  lr: 0.000241  loss: 2.5538 (2.5505)  grad: 0.2516 (0.2490)  time: 0.4395  data: 0.0044  max mem: 22448
+train: [9]  [180/400]  eta: 0:01:40  lr: 0.000240  loss: 2.5327 (2.5541)  grad: 0.2516 (0.2499)  time: 0.4352  data: 0.0042  max mem: 22448
+train: [9]  [200/400]  eta: 0:01:30  lr: 0.000238  loss: 2.5327 (2.5520)  grad: 0.2521 (0.2505)  time: 0.4360  data: 0.0042  max mem: 22448
+train: [9]  [220/400]  eta: 0:01:21  lr: 0.000237  loss: 2.5341 (2.5488)  grad: 0.2536 (0.2510)  time: 0.4491  data: 0.0043  max mem: 22448
+train: [9]  [240/400]  eta: 0:01:12  lr: 0.000236  loss: 2.5571 (2.5513)  grad: 0.2536 (0.2511)  time: 0.4433  data: 0.0042  max mem: 22448
+train: [9]  [260/400]  eta: 0:01:03  lr: 0.000234  loss: 2.5586 (2.5507)  grad: 0.2451 (0.2506)  time: 0.4417  data: 0.0042  max mem: 22448
+train: [9]  [280/400]  eta: 0:00:54  lr: 0.000233  loss: 2.5435 (2.5513)  grad: 0.2528 (0.2511)  time: 0.4457  data: 0.0044  max mem: 22448
+train: [9]  [300/400]  eta: 0:00:45  lr: 0.000232  loss: 2.5561 (2.5523)  grad: 0.2600 (0.2517)  time: 0.4471  data: 0.0045  max mem: 22448
+train: [9]  [320/400]  eta: 0:00:36  lr: 0.000230  loss: 2.5490 (2.5530)  grad: 0.2479 (0.2512)  time: 0.4562  data: 0.0043  max mem: 22448
+train: [9]  [340/400]  eta: 0:00:27  lr: 0.000229  loss: 2.5295 (2.5507)  grad: 0.2474 (0.2512)  time: 0.4609  data: 0.0043  max mem: 22448
+train: [9]  [360/400]  eta: 0:00:18  lr: 0.000228  loss: 2.5412 (2.5520)  grad: 0.2554 (0.2518)  time: 0.4340  data: 0.0039  max mem: 22448
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 2.5333 (2.5515)  grad: 0.2519 (0.2516)  time: 0.4588  data: 0.0042  max mem: 22448
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.5330 (2.5528)  grad: 0.2563 (0.2523)  time: 0.4732  data: 0.0043  max mem: 22448
+train: [9] Total time: 0:03:01 (0.4527 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.5330 (2.5528)  grad: 0.2563 (0.2523)
+eval (validation): [9]  [ 0/85]  eta: 0:04:28    time: 3.1595  data: 2.9309  max mem: 22448
+eval (validation): [9]  [20/85]  eta: 0:00:30    time: 0.3318  data: 0.0047  max mem: 22448
+eval (validation): [9]  [40/85]  eta: 0:00:18    time: 0.3564  data: 0.0033  max mem: 22448
+eval (validation): [9]  [60/85]  eta: 0:00:10    time: 0.3840  data: 0.0043  max mem: 22448
+eval (validation): [9]  [80/85]  eta: 0:00:01    time: 0.3564  data: 0.0045  max mem: 22448
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3321  data: 0.0042  max mem: 22448
+eval (validation): [9] Total time: 0:00:33 (0.3910 s / it)
+cv: [9]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.518  acc: 0.249  f1: 0.173
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [10]  [  0/400]  eta: 0:22:02  lr: nan  time: 3.3051  data: 2.9504  max mem: 22448
+train: [10]  [ 20/400]  eta: 0:03:36  lr: 0.000224  loss: 2.5131 (2.5087)  grad: 0.2548 (0.2627)  time: 0.4337  data: 0.0045  max mem: 22448
+train: [10]  [ 40/400]  eta: 0:03:03  lr: 0.000222  loss: 2.4904 (2.4906)  grad: 0.2523 (0.2565)  time: 0.4443  data: 0.0038  max mem: 22448
+train: [10]  [ 60/400]  eta: 0:02:45  lr: 0.000221  loss: 2.5005 (2.5072)  grad: 0.2486 (0.2541)  time: 0.4410  data: 0.0042  max mem: 22448
+train: [10]  [ 80/400]  eta: 0:02:32  lr: 0.000220  loss: 2.5114 (2.4988)  grad: 0.2463 (0.2518)  time: 0.4411  data: 0.0041  max mem: 22448
+train: [10]  [100/400]  eta: 0:02:20  lr: 0.000218  loss: 2.5012 (2.4976)  grad: 0.2442 (0.2508)  time: 0.4398  data: 0.0041  max mem: 22448
+train: [10]  [120/400]  eta: 0:02:09  lr: 0.000217  loss: 2.5090 (2.4993)  grad: 0.2509 (0.2516)  time: 0.4427  data: 0.0041  max mem: 22448
+train: [10]  [140/400]  eta: 0:02:00  lr: 0.000215  loss: 2.4965 (2.4995)  grad: 0.2509 (0.2510)  time: 0.4486  data: 0.0044  max mem: 22448
+train: [10]  [160/400]  eta: 0:01:50  lr: 0.000214  loss: 2.4763 (2.4984)  grad: 0.2448 (0.2510)  time: 0.4395  data: 0.0043  max mem: 22448
+train: [10]  [180/400]  eta: 0:01:40  lr: 0.000213  loss: 2.5002 (2.5018)  grad: 0.2458 (0.2513)  time: 0.4525  data: 0.0043  max mem: 22448
+train: [10]  [200/400]  eta: 0:01:31  lr: 0.000211  loss: 2.5289 (2.5039)  grad: 0.2535 (0.2517)  time: 0.4266  data: 0.0043  max mem: 22448
+train: [10]  [220/400]  eta: 0:01:21  lr: 0.000210  loss: 2.5141 (2.5040)  grad: 0.2531 (0.2518)  time: 0.4484  data: 0.0043  max mem: 22448
+train: [10]  [240/400]  eta: 0:01:12  lr: 0.000208  loss: 2.4902 (2.5040)  grad: 0.2474 (0.2514)  time: 0.4440  data: 0.0043  max mem: 22448
+train: [10]  [260/400]  eta: 0:01:03  lr: 0.000207  loss: 2.4902 (2.5030)  grad: 0.2458 (0.2513)  time: 0.4351  data: 0.0042  max mem: 22448
+train: [10]  [280/400]  eta: 0:00:54  lr: 0.000205  loss: 2.4993 (2.5045)  grad: 0.2501 (0.2514)  time: 0.4415  data: 0.0043  max mem: 22448
+train: [10]  [300/400]  eta: 0:00:45  lr: 0.000204  loss: 2.4705 (2.5019)  grad: 0.2512 (0.2514)  time: 0.4513  data: 0.0043  max mem: 22448
+train: [10]  [320/400]  eta: 0:00:36  lr: 0.000202  loss: 2.4782 (2.5019)  grad: 0.2559 (0.2522)  time: 0.4350  data: 0.0041  max mem: 22448
+train: [10]  [340/400]  eta: 0:00:27  lr: 0.000201  loss: 2.5026 (2.5031)  grad: 0.2593 (0.2522)  time: 0.4473  data: 0.0043  max mem: 22448
+train: [10]  [360/400]  eta: 0:00:17  lr: 0.000199  loss: 2.4942 (2.5019)  grad: 0.2432 (0.2518)  time: 0.4322  data: 0.0043  max mem: 22448
+train: [10]  [380/400]  eta: 0:00:08  lr: 0.000198  loss: 2.4712 (2.5004)  grad: 0.2453 (0.2517)  time: 0.4436  data: 0.0042  max mem: 22448
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.4581 (2.5010)  grad: 0.2548 (0.2520)  time: 0.4598  data: 0.0043  max mem: 22448
+train: [10] Total time: 0:02:59 (0.4499 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.4581 (2.5010)  grad: 0.2548 (0.2520)
+eval (validation): [10]  [ 0/85]  eta: 0:04:42    time: 3.3179  data: 3.0455  max mem: 22448
+eval (validation): [10]  [20/85]  eta: 0:00:32    time: 0.3579  data: 0.0042  max mem: 22448
+eval (validation): [10]  [40/85]  eta: 0:00:19    time: 0.3614  data: 0.0043  max mem: 22448
+eval (validation): [10]  [60/85]  eta: 0:00:10    time: 0.3559  data: 0.0045  max mem: 22448
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.3344  data: 0.0041  max mem: 22448
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.3292  data: 0.0041  max mem: 22448
+eval (validation): [10] Total time: 0:00:33 (0.3892 s / it)
+cv: [10]  best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00')  loss: 2.515  acc: 0.248  f1: 0.176
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:22:20  lr: nan  time: 3.3509  data: 3.0262  max mem: 22448
+train: [11]  [ 20/400]  eta: 0:03:34  lr: 0.000195  loss: 2.4486 (2.4473)  grad: 0.2432 (0.2480)  time: 0.4244  data: 0.0035  max mem: 22448
+train: [11]  [ 40/400]  eta: 0:03:02  lr: 0.000193  loss: 2.4486 (2.4563)  grad: 0.2492 (0.2500)  time: 0.4480  data: 0.0042  max mem: 22448
+train: [11]  [ 60/400]  eta: 0:02:45  lr: 0.000192  loss: 2.4442 (2.4482)  grad: 0.2519 (0.2546)  time: 0.4441  data: 0.0042  max mem: 22448
+train: [11]  [ 80/400]  eta: 0:02:34  lr: 0.000190  loss: 2.4584 (2.4552)  grad: 0.2558 (0.2559)  time: 0.4728  data: 0.0045  max mem: 22448
+train: [11]  [100/400]  eta: 0:02:22  lr: 0.000189  loss: 2.4560 (2.4498)  grad: 0.2527 (0.2545)  time: 0.4470  data: 0.0043  max mem: 22448
+train: [11]  [120/400]  eta: 0:02:11  lr: 0.000187  loss: 2.3900 (2.4383)  grad: 0.2487 (0.2537)  time: 0.4335  data: 0.0044  max mem: 22448
+train: [11]  [140/400]  eta: 0:02:01  lr: 0.000186  loss: 2.4301 (2.4442)  grad: 0.2492 (0.2541)  time: 0.4506  data: 0.0044  max mem: 22448
+train: [11]  [160/400]  eta: 0:01:51  lr: 0.000184  loss: 2.4669 (2.4501)  grad: 0.2552 (0.2543)  time: 0.4392  data: 0.0043  max mem: 22448
+train: [11]  [180/400]  eta: 0:01:41  lr: 0.000183  loss: 2.4542 (2.4508)  grad: 0.2658 (0.2563)  time: 0.4403  data: 0.0044  max mem: 22448
+train: [11]  [200/400]  eta: 0:01:31  lr: 0.000181  loss: 2.4542 (2.4546)  grad: 0.2661 (0.2565)  time: 0.4314  data: 0.0042  max mem: 22448
+train: [11]  [220/400]  eta: 0:01:22  lr: 0.000180  loss: 2.4927 (2.4604)  grad: 0.2526 (0.2561)  time: 0.4528  data: 0.0044  max mem: 22448
+train: [11]  [240/400]  eta: 0:01:12  lr: 0.000178  loss: 2.5046 (2.4635)  grad: 0.2490 (0.2560)  time: 0.4406  data: 0.0042  max mem: 22448
+train: [11]  [260/400]  eta: 0:01:03  lr: 0.000177  loss: 2.4816 (2.4637)  grad: 0.2545 (0.2564)  time: 0.4383  data: 0.0042  max mem: 22448
+train: [11]  [280/400]  eta: 0:00:54  lr: 0.000175  loss: 2.4656 (2.4643)  grad: 0.2589 (0.2565)  time: 0.4310  data: 0.0040  max mem: 22448
+train: [11]  [300/400]  eta: 0:00:45  lr: 0.000174  loss: 2.4680 (2.4674)  grad: 0.2527 (0.2568)  time: 0.4424  data: 0.0041  max mem: 22448
+train: [11]  [320/400]  eta: 0:00:36  lr: 0.000172  loss: 2.4946 (2.4681)  grad: 0.2597 (0.2576)  time: 0.4526  data: 0.0043  max mem: 22448
+train: [11]  [340/400]  eta: 0:00:27  lr: 0.000170  loss: 2.4797 (2.4680)  grad: 0.2603 (0.2584)  time: 0.4525  data: 0.0043  max mem: 22448
+train: [11]  [360/400]  eta: 0:00:18  lr: 0.000169  loss: 2.4581 (2.4673)  grad: 0.2640 (0.2590)  time: 0.4337  data: 0.0041  max mem: 22448
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 2.4114 (2.4638)  grad: 0.2588 (0.2586)  time: 0.4396  data: 0.0041  max mem: 22448
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.4509 (2.4670)  grad: 0.2483 (0.2581)  time: 0.4495  data: 0.0043  max mem: 22448
+train: [11] Total time: 0:03:00 (0.4508 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.4509 (2.4670)  grad: 0.2483 (0.2581)
+eval (validation): [11]  [ 0/85]  eta: 0:04:49    time: 3.4085  data: 3.1249  max mem: 22448
+eval (validation): [11]  [20/85]  eta: 0:00:33    time: 0.3698  data: 0.0045  max mem: 22448
+eval (validation): [11]  [40/85]  eta: 0:00:20    time: 0.3760  data: 0.0045  max mem: 22448
+eval (validation): [11]  [60/85]  eta: 0:00:10    time: 0.3637  data: 0.0044  max mem: 22448
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.3278  data: 0.0040  max mem: 22448
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.3240  data: 0.0037  max mem: 22448
+eval (validation): [11] Total time: 0:00:33 (0.3972 s / it)
+cv: [11]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.519  acc: 0.245  f1: 0.173
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:23:15  lr: nan  time: 3.4886  data: 3.1168  max mem: 22448
+train: [12]  [ 20/400]  eta: 0:03:42  lr: 0.000164  loss: 2.3647 (2.3702)  grad: 0.2376 (0.2406)  time: 0.4414  data: 0.0034  max mem: 22448
+train: [12]  [ 40/400]  eta: 0:03:06  lr: 0.000163  loss: 2.3730 (2.3849)  grad: 0.2440 (0.2471)  time: 0.4454  data: 0.0043  max mem: 22448
+train: [12]  [ 60/400]  eta: 0:02:47  lr: 0.000161  loss: 2.4109 (2.3933)  grad: 0.2488 (0.2477)  time: 0.4430  data: 0.0041  max mem: 22448
+train: [12]  [ 80/400]  eta: 0:02:34  lr: 0.000160  loss: 2.3956 (2.3975)  grad: 0.2471 (0.2462)  time: 0.4550  data: 0.0042  max mem: 22448
+train: [12]  [100/400]  eta: 0:02:22  lr: 0.000158  loss: 2.3674 (2.4004)  grad: 0.2443 (0.2464)  time: 0.4475  data: 0.0042  max mem: 22448
+train: [12]  [120/400]  eta: 0:02:11  lr: 0.000156  loss: 2.4234 (2.4040)  grad: 0.2417 (0.2453)  time: 0.4338  data: 0.0042  max mem: 22448
+train: [12]  [140/400]  eta: 0:02:01  lr: 0.000155  loss: 2.4234 (2.4048)  grad: 0.2468 (0.2484)  time: 0.4512  data: 0.0043  max mem: 22448
+train: [12]  [160/400]  eta: 0:01:51  lr: 0.000153  loss: 2.4171 (2.4044)  grad: 0.2623 (0.2503)  time: 0.4410  data: 0.0042  max mem: 22448
+train: [12]  [180/400]  eta: 0:01:41  lr: 0.000152  loss: 2.3974 (2.4064)  grad: 0.2586 (0.2512)  time: 0.4388  data: 0.0043  max mem: 22448
+train: [12]  [200/400]  eta: 0:01:31  lr: 0.000150  loss: 2.4395 (2.4121)  grad: 0.2509 (0.2518)  time: 0.4364  data: 0.0044  max mem: 22448
+train: [12]  [220/400]  eta: 0:01:22  lr: 0.000149  loss: 2.4469 (2.4152)  grad: 0.2504 (0.2514)  time: 0.4404  data: 0.0043  max mem: 22448
+train: [12]  [240/400]  eta: 0:01:13  lr: 0.000147  loss: 2.4172 (2.4135)  grad: 0.2553 (0.2532)  time: 0.4526  data: 0.0044  max mem: 22448
+train: [12]  [260/400]  eta: 0:01:03  lr: 0.000145  loss: 2.3890 (2.4141)  grad: 0.2591 (0.2533)  time: 0.4355  data: 0.0043  max mem: 22448
+train: [12]  [280/400]  eta: 0:00:54  lr: 0.000144  loss: 2.4010 (2.4115)  grad: 0.2519 (0.2535)  time: 0.4416  data: 0.0043  max mem: 22448
+train: [12]  [300/400]  eta: 0:00:45  lr: 0.000142  loss: 2.4209 (2.4154)  grad: 0.2553 (0.2544)  time: 0.4450  data: 0.0044  max mem: 22448
+train: [12]  [320/400]  eta: 0:00:36  lr: 0.000141  loss: 2.4363 (2.4163)  grad: 0.2553 (0.2543)  time: 0.4319  data: 0.0041  max mem: 22448
+train: [12]  [340/400]  eta: 0:00:27  lr: 0.000139  loss: 2.4190 (2.4158)  grad: 0.2523 (0.2542)  time: 0.4373  data: 0.0041  max mem: 22448
+train: [12]  [360/400]  eta: 0:00:18  lr: 0.000138  loss: 2.4140 (2.4164)  grad: 0.2493 (0.2543)  time: 0.4475  data: 0.0042  max mem: 22448
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 2.4140 (2.4174)  grad: 0.2545 (0.2542)  time: 0.4442  data: 0.0042  max mem: 22448
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.4102 (2.4172)  grad: 0.2575 (0.2548)  time: 0.4359  data: 0.0041  max mem: 22448
+train: [12] Total time: 0:03:00 (0.4502 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.4102 (2.4172)  grad: 0.2575 (0.2548)
+eval (validation): [12]  [ 0/85]  eta: 0:05:37    time: 3.9660  data: 3.6795  max mem: 22448
+eval (validation): [12]  [20/85]  eta: 0:00:33    time: 0.3506  data: 0.0045  max mem: 22448
+eval (validation): [12]  [40/85]  eta: 0:00:19    time: 0.3274  data: 0.0035  max mem: 22448
+eval (validation): [12]  [60/85]  eta: 0:00:10    time: 0.3558  data: 0.0045  max mem: 22448
+eval (validation): [12]  [80/85]  eta: 0:00:01    time: 0.3451  data: 0.0042  max mem: 22448
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.3323  data: 0.0039  max mem: 22448
+eval (validation): [12] Total time: 0:00:33 (0.3900 s / it)
+cv: [12]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.544  acc: 0.240  f1: 0.172
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:22:22  lr: nan  time: 3.3560  data: 3.0291  max mem: 22448
+train: [13]  [ 20/400]  eta: 0:03:33  lr: 0.000133  loss: 2.3628 (2.3745)  grad: 0.2518 (0.2562)  time: 0.4229  data: 0.0044  max mem: 22448
+train: [13]  [ 40/400]  eta: 0:02:58  lr: 0.000131  loss: 2.3724 (2.3729)  grad: 0.2539 (0.2544)  time: 0.4282  data: 0.0039  max mem: 22448
+train: [13]  [ 60/400]  eta: 0:02:43  lr: 0.000130  loss: 2.3724 (2.3760)  grad: 0.2508 (0.2531)  time: 0.4463  data: 0.0044  max mem: 22448
+train: [13]  [ 80/400]  eta: 0:02:31  lr: 0.000128  loss: 2.3718 (2.3766)  grad: 0.2502 (0.2540)  time: 0.4492  data: 0.0043  max mem: 22448
+train: [13]  [100/400]  eta: 0:02:20  lr: 0.000127  loss: 2.3537 (2.3763)  grad: 0.2494 (0.2529)  time: 0.4492  data: 0.0042  max mem: 22448
+train: [13]  [120/400]  eta: 0:02:10  lr: 0.000125  loss: 2.3402 (2.3699)  grad: 0.2500 (0.2531)  time: 0.4460  data: 0.0042  max mem: 22448
+train: [13]  [140/400]  eta: 0:02:00  lr: 0.000124  loss: 2.3979 (2.3794)  grad: 0.2590 (0.2553)  time: 0.4478  data: 0.0042  max mem: 22448
+train: [13]  [160/400]  eta: 0:01:50  lr: 0.000122  loss: 2.3882 (2.3783)  grad: 0.2657 (0.2565)  time: 0.4530  data: 0.0043  max mem: 22448
+train: [13]  [180/400]  eta: 0:01:40  lr: 0.000120  loss: 2.3882 (2.3853)  grad: 0.2657 (0.2574)  time: 0.4377  data: 0.0042  max mem: 22448
+train: [13]  [200/400]  eta: 0:01:31  lr: 0.000119  loss: 2.3689 (2.3803)  grad: 0.2576 (0.2575)  time: 0.4375  data: 0.0041  max mem: 22448
+train: [13]  [220/400]  eta: 0:01:21  lr: 0.000117  loss: 2.3689 (2.3804)  grad: 0.2576 (0.2583)  time: 0.4421  data: 0.0039  max mem: 22448
+train: [13]  [240/400]  eta: 0:01:12  lr: 0.000116  loss: 2.3931 (2.3804)  grad: 0.2574 (0.2584)  time: 0.4558  data: 0.0039  max mem: 22448
+train: [13]  [260/400]  eta: 0:01:03  lr: 0.000114  loss: 2.3876 (2.3829)  grad: 0.2529 (0.2577)  time: 0.4491  data: 0.0041  max mem: 22448
+train: [13]  [280/400]  eta: 0:00:54  lr: 0.000113  loss: 2.3876 (2.3823)  grad: 0.2451 (0.2570)  time: 0.4431  data: 0.0040  max mem: 22448
+train: [13]  [300/400]  eta: 0:00:45  lr: 0.000111  loss: 2.3546 (2.3816)  grad: 0.2438 (0.2560)  time: 0.4399  data: 0.0040  max mem: 22448
+train: [13]  [320/400]  eta: 0:00:36  lr: 0.000110  loss: 2.3567 (2.3811)  grad: 0.2534 (0.2565)  time: 0.4410  data: 0.0040  max mem: 22448
+train: [13]  [340/400]  eta: 0:00:27  lr: 0.000108  loss: 2.3688 (2.3812)  grad: 0.2581 (0.2562)  time: 0.4498  data: 0.0041  max mem: 22448
+train: [13]  [360/400]  eta: 0:00:18  lr: 0.000107  loss: 2.3760 (2.3811)  grad: 0.2582 (0.2566)  time: 0.4458  data: 0.0043  max mem: 22448
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 2.3857 (2.3818)  grad: 0.2640 (0.2571)  time: 0.4440  data: 0.0042  max mem: 22448
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.4062 (2.3825)  grad: 0.2579 (0.2573)  time: 0.4312  data: 0.0042  max mem: 22448
+train: [13] Total time: 0:03:00 (0.4506 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.4062 (2.3825)  grad: 0.2579 (0.2573)
+eval (validation): [13]  [ 0/85]  eta: 0:04:35    time: 3.2362  data: 2.9606  max mem: 22448
+eval (validation): [13]  [20/85]  eta: 0:00:32    time: 0.3685  data: 0.0052  max mem: 22448
+eval (validation): [13]  [40/85]  eta: 0:00:18    time: 0.3284  data: 0.0041  max mem: 22448
+eval (validation): [13]  [60/85]  eta: 0:00:09    time: 0.3341  data: 0.0043  max mem: 22448
+eval (validation): [13]  [80/85]  eta: 0:00:01    time: 0.3409  data: 0.0044  max mem: 22448
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3326  data: 0.0039  max mem: 22448
+eval (validation): [13] Total time: 0:00:32 (0.3798 s / it)
+cv: [13]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.518  acc: 0.244  f1: 0.177
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:22:42  lr: nan  time: 3.4074  data: 3.0365  max mem: 22448
+train: [14]  [ 20/400]  eta: 0:03:37  lr: 0.000102  loss: 2.3012 (2.3091)  grad: 0.2486 (0.2489)  time: 0.4318  data: 0.0034  max mem: 22448
+train: [14]  [ 40/400]  eta: 0:03:01  lr: 0.000101  loss: 2.3012 (2.3134)  grad: 0.2417 (0.2485)  time: 0.4294  data: 0.0044  max mem: 22448
+train: [14]  [ 60/400]  eta: 0:02:42  lr: 0.000099  loss: 2.3324 (2.3203)  grad: 0.2500 (0.2503)  time: 0.4302  data: 0.0042  max mem: 22448
+train: [14]  [ 80/400]  eta: 0:02:30  lr: 0.000098  loss: 2.3371 (2.3308)  grad: 0.2445 (0.2491)  time: 0.4374  data: 0.0044  max mem: 22448
+train: [14]  [100/400]  eta: 0:02:19  lr: 0.000096  loss: 2.3423 (2.3356)  grad: 0.2425 (0.2494)  time: 0.4481  data: 0.0043  max mem: 22448
+train: [14]  [120/400]  eta: 0:02:09  lr: 0.000095  loss: 2.3073 (2.3322)  grad: 0.2556 (0.2503)  time: 0.4417  data: 0.0042  max mem: 22448
+train: [14]  [140/400]  eta: 0:01:58  lr: 0.000093  loss: 2.2938 (2.3332)  grad: 0.2614 (0.2520)  time: 0.4339  data: 0.0042  max mem: 22448
+train: [14]  [160/400]  eta: 0:01:49  lr: 0.000092  loss: 2.2866 (2.3319)  grad: 0.2543 (0.2523)  time: 0.4496  data: 0.0041  max mem: 22448
+train: [14]  [180/400]  eta: 0:01:40  lr: 0.000090  loss: 2.2980 (2.3310)  grad: 0.2543 (0.2532)  time: 0.4495  data: 0.0043  max mem: 22448
+train: [14]  [200/400]  eta: 0:01:30  lr: 0.000089  loss: 2.2836 (2.3289)  grad: 0.2573 (0.2538)  time: 0.4479  data: 0.0044  max mem: 22448
+train: [14]  [220/400]  eta: 0:01:21  lr: 0.000088  loss: 2.3129 (2.3309)  grad: 0.2573 (0.2545)  time: 0.4399  data: 0.0043  max mem: 22448
+train: [14]  [240/400]  eta: 0:01:12  lr: 0.000086  loss: 2.3369 (2.3346)  grad: 0.2552 (0.2544)  time: 0.4431  data: 0.0041  max mem: 22448
+train: [14]  [260/400]  eta: 0:01:03  lr: 0.000085  loss: 2.3506 (2.3350)  grad: 0.2513 (0.2542)  time: 0.4564  data: 0.0042  max mem: 22448
+train: [14]  [280/400]  eta: 0:00:54  lr: 0.000083  loss: 2.3541 (2.3355)  grad: 0.2511 (0.2536)  time: 0.4525  data: 0.0042  max mem: 22448
+train: [14]  [300/400]  eta: 0:00:45  lr: 0.000082  loss: 2.3629 (2.3398)  grad: 0.2507 (0.2539)  time: 0.4520  data: 0.0042  max mem: 22448
+train: [14]  [320/400]  eta: 0:00:36  lr: 0.000081  loss: 2.3656 (2.3410)  grad: 0.2578 (0.2544)  time: 0.4437  data: 0.0043  max mem: 22448
+train: [14]  [340/400]  eta: 0:00:27  lr: 0.000079  loss: 2.3340 (2.3418)  grad: 0.2628 (0.2551)  time: 0.4356  data: 0.0042  max mem: 22448
+train: [14]  [360/400]  eta: 0:00:18  lr: 0.000078  loss: 2.3501 (2.3422)  grad: 0.2577 (0.2553)  time: 0.4494  data: 0.0043  max mem: 22448
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 2.3331 (2.3405)  grad: 0.2577 (0.2555)  time: 0.4532  data: 0.0044  max mem: 22448
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.3331 (2.3423)  grad: 0.2643 (0.2562)  time: 0.4382  data: 0.0043  max mem: 22448
+train: [14] Total time: 0:03:00 (0.4508 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.3331 (2.3423)  grad: 0.2643 (0.2562)
+eval (validation): [14]  [ 0/85]  eta: 0:04:37    time: 3.2664  data: 2.9797  max mem: 22448
+eval (validation): [14]  [20/85]  eta: 0:00:32    time: 0.3630  data: 0.0038  max mem: 22448
+eval (validation): [14]  [40/85]  eta: 0:00:18    time: 0.3369  data: 0.0038  max mem: 22448
+eval (validation): [14]  [60/85]  eta: 0:00:09    time: 0.3272  data: 0.0040  max mem: 22448
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3497  data: 0.0042  max mem: 22448
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.3426  data: 0.0041  max mem: 22448
+eval (validation): [14] Total time: 0:00:32 (0.3804 s / it)
+cv: [14]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.520  acc: 0.248  f1: 0.173
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:22:27  lr: nan  time: 3.3697  data: 3.0137  max mem: 22448
+train: [15]  [ 20/400]  eta: 0:03:34  lr: 0.000074  loss: 2.2950 (2.3233)  grad: 0.2413 (0.2493)  time: 0.4231  data: 0.0040  max mem: 22448
+train: [15]  [ 40/400]  eta: 0:02:58  lr: 0.000072  loss: 2.3146 (2.3224)  grad: 0.2413 (0.2515)  time: 0.4268  data: 0.0039  max mem: 22448
+train: [15]  [ 60/400]  eta: 0:02:41  lr: 0.000071  loss: 2.3072 (2.3132)  grad: 0.2566 (0.2529)  time: 0.4282  data: 0.0041  max mem: 22448
+train: [15]  [ 80/400]  eta: 0:02:28  lr: 0.000070  loss: 2.2743 (2.3047)  grad: 0.2499 (0.2518)  time: 0.4309  data: 0.0042  max mem: 22448
+train: [15]  [100/400]  eta: 0:02:17  lr: 0.000068  loss: 2.2686 (2.2978)  grad: 0.2486 (0.2522)  time: 0.4380  data: 0.0041  max mem: 22448
+train: [15]  [120/400]  eta: 0:02:07  lr: 0.000067  loss: 2.2940 (2.3029)  grad: 0.2498 (0.2533)  time: 0.4446  data: 0.0041  max mem: 22448
+train: [15]  [140/400]  eta: 0:01:58  lr: 0.000066  loss: 2.3310 (2.3083)  grad: 0.2571 (0.2548)  time: 0.4395  data: 0.0042  max mem: 22448
+train: [15]  [160/400]  eta: 0:01:48  lr: 0.000064  loss: 2.3132 (2.3069)  grad: 0.2548 (0.2545)  time: 0.4279  data: 0.0042  max mem: 22448
+train: [15]  [180/400]  eta: 0:01:39  lr: 0.000063  loss: 2.3132 (2.3141)  grad: 0.2538 (0.2550)  time: 0.4496  data: 0.0043  max mem: 22448
+train: [15]  [200/400]  eta: 0:01:29  lr: 0.000062  loss: 2.3099 (2.3116)  grad: 0.2538 (0.2546)  time: 0.4335  data: 0.0043  max mem: 22448
+train: [15]  [220/400]  eta: 0:01:20  lr: 0.000061  loss: 2.3099 (2.3112)  grad: 0.2490 (0.2546)  time: 0.4407  data: 0.0043  max mem: 22448
+train: [15]  [240/400]  eta: 0:01:11  lr: 0.000059  loss: 2.3120 (2.3105)  grad: 0.2573 (0.2551)  time: 0.4374  data: 0.0040  max mem: 22448
+train: [15]  [260/400]  eta: 0:01:02  lr: 0.000058  loss: 2.3501 (2.3148)  grad: 0.2585 (0.2551)  time: 0.4391  data: 0.0042  max mem: 22448
+train: [15]  [280/400]  eta: 0:00:53  lr: 0.000057  loss: 2.3227 (2.3123)  grad: 0.2480 (0.2544)  time: 0.4409  data: 0.0043  max mem: 22448
+train: [15]  [300/400]  eta: 0:00:44  lr: 0.000056  loss: 2.3009 (2.3122)  grad: 0.2446 (0.2543)  time: 0.4450  data: 0.0043  max mem: 22448
+train: [15]  [320/400]  eta: 0:00:35  lr: 0.000054  loss: 2.3098 (2.3135)  grad: 0.2494 (0.2544)  time: 0.4467  data: 0.0042  max mem: 22448
+train: [15]  [340/400]  eta: 0:00:26  lr: 0.000053  loss: 2.3227 (2.3137)  grad: 0.2492 (0.2543)  time: 0.4488  data: 0.0043  max mem: 22448
+train: [15]  [360/400]  eta: 0:00:17  lr: 0.000052  loss: 2.3070 (2.3145)  grad: 0.2506 (0.2548)  time: 0.4269  data: 0.0042  max mem: 22448
+train: [15]  [380/400]  eta: 0:00:08  lr: 0.000051  loss: 2.2806 (2.3123)  grad: 0.2500 (0.2545)  time: 0.4495  data: 0.0041  max mem: 22448
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.2755 (2.3132)  grad: 0.2457 (0.2544)  time: 0.4560  data: 0.0044  max mem: 22448
+train: [15] Total time: 0:02:58 (0.4462 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.2755 (2.3132)  grad: 0.2457 (0.2544)
+eval (validation): [15]  [ 0/85]  eta: 0:04:37    time: 3.2679  data: 3.0439  max mem: 22448
+eval (validation): [15]  [20/85]  eta: 0:00:30    time: 0.3352  data: 0.0038  max mem: 22448
+eval (validation): [15]  [40/85]  eta: 0:00:18    time: 0.3662  data: 0.0038  max mem: 22448
+eval (validation): [15]  [60/85]  eta: 0:00:09    time: 0.3428  data: 0.0044  max mem: 22448
+eval (validation): [15]  [80/85]  eta: 0:00:01    time: 0.3263  data: 0.0040  max mem: 22448
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.3230  data: 0.0040  max mem: 22448
+eval (validation): [15] Total time: 0:00:32 (0.3790 s / it)
+cv: [15]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.518  acc: 0.250  f1: 0.174
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [16]  [  0/400]  eta: 0:22:05  lr: nan  time: 3.3132  data: 2.9959  max mem: 22448
+train: [16]  [ 20/400]  eta: 0:03:30  lr: 0.000048  loss: 2.2646 (2.2892)  grad: 0.2353 (0.2428)  time: 0.4172  data: 0.0037  max mem: 22448
+train: [16]  [ 40/400]  eta: 0:02:58  lr: 0.000047  loss: 2.2646 (2.2813)  grad: 0.2370 (0.2424)  time: 0.4323  data: 0.0044  max mem: 22448
+train: [16]  [ 60/400]  eta: 0:02:42  lr: 0.000046  loss: 2.2630 (2.2739)  grad: 0.2422 (0.2442)  time: 0.4381  data: 0.0043  max mem: 22448
+train: [16]  [ 80/400]  eta: 0:02:29  lr: 0.000045  loss: 2.3090 (2.2849)  grad: 0.2464 (0.2462)  time: 0.4415  data: 0.0042  max mem: 22448
+train: [16]  [100/400]  eta: 0:02:18  lr: 0.000044  loss: 2.3037 (2.2812)  grad: 0.2512 (0.2474)  time: 0.4374  data: 0.0042  max mem: 22448
+train: [16]  [120/400]  eta: 0:02:08  lr: 0.000043  loss: 2.2629 (2.2830)  grad: 0.2512 (0.2481)  time: 0.4533  data: 0.0042  max mem: 22448
+train: [16]  [140/400]  eta: 0:01:59  lr: 0.000042  loss: 2.2629 (2.2812)  grad: 0.2468 (0.2473)  time: 0.4502  data: 0.0041  max mem: 22448
+train: [16]  [160/400]  eta: 0:01:49  lr: 0.000041  loss: 2.3127 (2.2865)  grad: 0.2479 (0.2487)  time: 0.4360  data: 0.0041  max mem: 22448
+train: [16]  [180/400]  eta: 0:01:40  lr: 0.000040  loss: 2.3187 (2.2871)  grad: 0.2513 (0.2486)  time: 0.4460  data: 0.0042  max mem: 22448
+train: [16]  [200/400]  eta: 0:01:30  lr: 0.000039  loss: 2.2565 (2.2831)  grad: 0.2449 (0.2480)  time: 0.4366  data: 0.0042  max mem: 22448
+train: [16]  [220/400]  eta: 0:01:21  lr: 0.000038  loss: 2.2492 (2.2818)  grad: 0.2366 (0.2475)  time: 0.4425  data: 0.0044  max mem: 22448
+train: [16]  [240/400]  eta: 0:01:11  lr: 0.000036  loss: 2.2774 (2.2819)  grad: 0.2436 (0.2479)  time: 0.4224  data: 0.0043  max mem: 22448
+train: [16]  [260/400]  eta: 0:01:02  lr: 0.000035  loss: 2.2892 (2.2846)  grad: 0.2554 (0.2486)  time: 0.4508  data: 0.0043  max mem: 22448
+train: [16]  [280/400]  eta: 0:00:53  lr: 0.000034  loss: 2.3000 (2.2855)  grad: 0.2514 (0.2486)  time: 0.4408  data: 0.0043  max mem: 22448
+train: [16]  [300/400]  eta: 0:00:44  lr: 0.000033  loss: 2.3000 (2.2874)  grad: 0.2479 (0.2487)  time: 0.4367  data: 0.0045  max mem: 22448
+train: [16]  [320/400]  eta: 0:00:35  lr: 0.000032  loss: 2.3293 (2.2912)  grad: 0.2501 (0.2491)  time: 0.4518  data: 0.0044  max mem: 22448
+train: [16]  [340/400]  eta: 0:00:26  lr: 0.000031  loss: 2.2988 (2.2910)  grad: 0.2508 (0.2493)  time: 0.4445  data: 0.0043  max mem: 22448
+train: [16]  [360/400]  eta: 0:00:17  lr: 0.000031  loss: 2.2721 (2.2906)  grad: 0.2452 (0.2494)  time: 0.4354  data: 0.0043  max mem: 22448
+train: [16]  [380/400]  eta: 0:00:08  lr: 0.000030  loss: 2.2763 (2.2900)  grad: 0.2527 (0.2499)  time: 0.4434  data: 0.0041  max mem: 22448
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.2848 (2.2903)  grad: 0.2554 (0.2503)  time: 0.4464  data: 0.0042  max mem: 22448
+train: [16] Total time: 0:02:59 (0.4476 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.2848 (2.2903)  grad: 0.2554 (0.2503)
+eval (validation): [16]  [ 0/85]  eta: 0:04:37    time: 3.2626  data: 2.9978  max mem: 22448
+eval (validation): [16]  [20/85]  eta: 0:00:31    time: 0.3535  data: 0.0038  max mem: 22448
+eval (validation): [16]  [40/85]  eta: 0:00:19    time: 0.3514  data: 0.0041  max mem: 22448
+eval (validation): [16]  [60/85]  eta: 0:00:09    time: 0.3406  data: 0.0044  max mem: 22448
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3254  data: 0.0042  max mem: 22448
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.3235  data: 0.0042  max mem: 22448
+eval (validation): [16] Total time: 0:00:32 (0.3795 s / it)
+cv: [16]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.523  acc: 0.246  f1: 0.179
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:22:39  lr: nan  time: 3.3994  data: 3.0276  max mem: 22448
+train: [17]  [ 20/400]  eta: 0:03:43  lr: 0.000028  loss: 2.2005 (2.2366)  grad: 0.2286 (0.2373)  time: 0.4464  data: 0.0051  max mem: 22448
+train: [17]  [ 40/400]  eta: 0:03:04  lr: 0.000027  loss: 2.2514 (2.2609)  grad: 0.2387 (0.2410)  time: 0.4341  data: 0.0045  max mem: 22448
+train: [17]  [ 60/400]  eta: 0:02:44  lr: 0.000026  loss: 2.2800 (2.2742)  grad: 0.2381 (0.2405)  time: 0.4278  data: 0.0044  max mem: 22448
+train: [17]  [ 80/400]  eta: 0:02:31  lr: 0.000025  loss: 2.2593 (2.2636)  grad: 0.2381 (0.2411)  time: 0.4410  data: 0.0042  max mem: 22448
+train: [17]  [100/400]  eta: 0:02:20  lr: 0.000024  loss: 2.2574 (2.2697)  grad: 0.2424 (0.2412)  time: 0.4401  data: 0.0042  max mem: 22448
+train: [17]  [120/400]  eta: 0:02:10  lr: 0.000023  loss: 2.2884 (2.2686)  grad: 0.2424 (0.2417)  time: 0.4670  data: 0.0043  max mem: 22448
+train: [17]  [140/400]  eta: 0:02:00  lr: 0.000023  loss: 2.2779 (2.2678)  grad: 0.2429 (0.2421)  time: 0.4485  data: 0.0044  max mem: 22448
+train: [17]  [160/400]  eta: 0:01:50  lr: 0.000022  loss: 2.2616 (2.2670)  grad: 0.2425 (0.2430)  time: 0.4344  data: 0.0040  max mem: 22448
+train: [17]  [180/400]  eta: 0:01:41  lr: 0.000021  loss: 2.2438 (2.2652)  grad: 0.2405 (0.2425)  time: 0.4510  data: 0.0042  max mem: 22448
+train: [17]  [200/400]  eta: 0:01:31  lr: 0.000020  loss: 2.2336 (2.2656)  grad: 0.2405 (0.2424)  time: 0.4464  data: 0.0042  max mem: 22448
+train: [17]  [220/400]  eta: 0:01:22  lr: 0.000019  loss: 2.2623 (2.2653)  grad: 0.2459 (0.2432)  time: 0.4369  data: 0.0040  max mem: 22448
+train: [17]  [240/400]  eta: 0:01:12  lr: 0.000019  loss: 2.2701 (2.2648)  grad: 0.2505 (0.2439)  time: 0.4353  data: 0.0041  max mem: 22448
+train: [17]  [260/400]  eta: 0:01:03  lr: 0.000018  loss: 2.2852 (2.2658)  grad: 0.2505 (0.2442)  time: 0.4465  data: 0.0040  max mem: 22448
+train: [17]  [280/400]  eta: 0:00:54  lr: 0.000017  loss: 2.2756 (2.2670)  grad: 0.2464 (0.2442)  time: 0.4455  data: 0.0042  max mem: 22448
+train: [17]  [300/400]  eta: 0:00:45  lr: 0.000016  loss: 2.2756 (2.2680)  grad: 0.2449 (0.2440)  time: 0.4299  data: 0.0041  max mem: 22448
+train: [17]  [320/400]  eta: 0:00:36  lr: 0.000016  loss: 2.2645 (2.2683)  grad: 0.2356 (0.2434)  time: 0.4400  data: 0.0041  max mem: 22448
+train: [17]  [340/400]  eta: 0:00:27  lr: 0.000015  loss: 2.2480 (2.2684)  grad: 0.2376 (0.2437)  time: 0.4444  data: 0.0042  max mem: 22448
+train: [17]  [360/400]  eta: 0:00:17  lr: 0.000014  loss: 2.2588 (2.2689)  grad: 0.2414 (0.2437)  time: 0.4358  data: 0.0043  max mem: 22448
+train: [17]  [380/400]  eta: 0:00:08  lr: 0.000014  loss: 2.2588 (2.2672)  grad: 0.2434 (0.2445)  time: 0.4403  data: 0.0042  max mem: 22448
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.2332 (2.2662)  grad: 0.2490 (0.2442)  time: 0.4559  data: 0.0042  max mem: 22448
+train: [17] Total time: 0:03:00 (0.4500 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.2332 (2.2662)  grad: 0.2490 (0.2442)
+eval (validation): [17]  [ 0/85]  eta: 0:04:40    time: 3.2942  data: 3.0585  max mem: 22448
+eval (validation): [17]  [20/85]  eta: 0:00:32    time: 0.3662  data: 0.0392  max mem: 22448
+eval (validation): [17]  [40/85]  eta: 0:00:19    time: 0.3521  data: 0.0039  max mem: 22448
+eval (validation): [17]  [60/85]  eta: 0:00:10    time: 0.3767  data: 0.0046  max mem: 22448
+eval (validation): [17]  [80/85]  eta: 0:00:01    time: 0.3316  data: 0.0042  max mem: 22448
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3291  data: 0.0040  max mem: 22448
+eval (validation): [17] Total time: 0:00:33 (0.3935 s / it)
+cv: [17]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.512  acc: 0.252  f1: 0.179
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [18]  [  0/400]  eta: 0:22:06  lr: nan  time: 3.3165  data: 2.9946  max mem: 22448
+train: [18]  [ 20/400]  eta: 0:03:34  lr: 0.000012  loss: 2.2785 (2.3164)  grad: 0.2457 (0.2459)  time: 0.4261  data: 0.0045  max mem: 22448
+train: [18]  [ 40/400]  eta: 0:02:58  lr: 0.000012  loss: 2.2618 (2.2597)  grad: 0.2402 (0.2439)  time: 0.4244  data: 0.0041  max mem: 22448
+train: [18]  [ 60/400]  eta: 0:02:41  lr: 0.000011  loss: 2.2174 (2.2522)  grad: 0.2470 (0.2472)  time: 0.4316  data: 0.0042  max mem: 22448
+train: [18]  [ 80/400]  eta: 0:02:28  lr: 0.000011  loss: 2.2242 (2.2498)  grad: 0.2388 (0.2441)  time: 0.4297  data: 0.0044  max mem: 22448
+train: [18]  [100/400]  eta: 0:02:17  lr: 0.000010  loss: 2.2357 (2.2491)  grad: 0.2336 (0.2429)  time: 0.4335  data: 0.0042  max mem: 22448
+train: [18]  [120/400]  eta: 0:02:07  lr: 0.000009  loss: 2.2357 (2.2448)  grad: 0.2421 (0.2425)  time: 0.4520  data: 0.0043  max mem: 22448
+train: [18]  [140/400]  eta: 0:01:58  lr: 0.000009  loss: 2.2461 (2.2487)  grad: 0.2424 (0.2428)  time: 0.4440  data: 0.0044  max mem: 22448
+train: [18]  [160/400]  eta: 0:01:48  lr: 0.000008  loss: 2.2586 (2.2498)  grad: 0.2382 (0.2420)  time: 0.4287  data: 0.0043  max mem: 22448
+train: [18]  [180/400]  eta: 0:01:39  lr: 0.000008  loss: 2.2495 (2.2494)  grad: 0.2403 (0.2426)  time: 0.4432  data: 0.0043  max mem: 22448
+train: [18]  [200/400]  eta: 0:01:29  lr: 0.000007  loss: 2.2659 (2.2531)  grad: 0.2427 (0.2425)  time: 0.4365  data: 0.0043  max mem: 22448
+train: [18]  [220/400]  eta: 0:01:20  lr: 0.000007  loss: 2.2573 (2.2530)  grad: 0.2434 (0.2426)  time: 0.4436  data: 0.0042  max mem: 22448
+train: [18]  [240/400]  eta: 0:01:11  lr: 0.000006  loss: 2.2465 (2.2549)  grad: 0.2423 (0.2424)  time: 0.4387  data: 0.0044  max mem: 22448
+train: [18]  [260/400]  eta: 0:01:02  lr: 0.000006  loss: 2.2465 (2.2553)  grad: 0.2397 (0.2423)  time: 0.4375  data: 0.0043  max mem: 22448
+train: [18]  [280/400]  eta: 0:00:53  lr: 0.000006  loss: 2.2433 (2.2548)  grad: 0.2407 (0.2423)  time: 0.4483  data: 0.0043  max mem: 22448
+train: [18]  [300/400]  eta: 0:00:44  lr: 0.000005  loss: 2.2580 (2.2552)  grad: 0.2389 (0.2420)  time: 0.4335  data: 0.0042  max mem: 22448
+train: [18]  [320/400]  eta: 0:00:35  lr: 0.000005  loss: 2.2788 (2.2563)  grad: 0.2389 (0.2419)  time: 0.4528  data: 0.0044  max mem: 22448
+train: [18]  [340/400]  eta: 0:00:26  lr: 0.000004  loss: 2.2310 (2.2533)  grad: 0.2404 (0.2417)  time: 0.4623  data: 0.0043  max mem: 22448
+train: [18]  [360/400]  eta: 0:00:17  lr: 0.000004  loss: 2.2025 (2.2524)  grad: 0.2404 (0.2416)  time: 0.4429  data: 0.0045  max mem: 22448
+train: [18]  [380/400]  eta: 0:00:08  lr: 0.000004  loss: 2.2396 (2.2524)  grad: 0.2410 (0.2418)  time: 0.4391  data: 0.0043  max mem: 22448
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.2320 (2.2503)  grad: 0.2416 (0.2420)  time: 0.4560  data: 0.0042  max mem: 22448
+train: [18] Total time: 0:02:59 (0.4476 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.2320 (2.2503)  grad: 0.2416 (0.2420)
+eval (validation): [18]  [ 0/85]  eta: 0:04:51    time: 3.4250  data: 3.1459  max mem: 22448
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+eval (validation): [18]  [40/85]  eta: 0:00:18    time: 0.3465  data: 0.0038  max mem: 22448
+eval (validation): [18]  [60/85]  eta: 0:00:09    time: 0.3535  data: 0.0043  max mem: 22448
+eval (validation): [18]  [80/85]  eta: 0:00:01    time: 0.3263  data: 0.0041  max mem: 22448
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3192  data: 0.0039  max mem: 22448
+eval (validation): [18] Total time: 0:00:32 (0.3789 s / it)
+cv: [18]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.517  acc: 0.250  f1: 0.177
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:23:04  lr: nan  time: 3.4622  data: 3.0876  max mem: 22448
+train: [19]  [ 20/400]  eta: 0:03:44  lr: 0.000003  loss: 2.2759 (2.2952)  grad: 0.2303 (0.2376)  time: 0.4464  data: 0.0040  max mem: 22448
+train: [19]  [ 40/400]  eta: 0:03:03  lr: 0.000003  loss: 2.2416 (2.2590)  grad: 0.2358 (0.2393)  time: 0.4278  data: 0.0042  max mem: 22448
+train: [19]  [ 60/400]  eta: 0:02:44  lr: 0.000002  loss: 2.2300 (2.2675)  grad: 0.2382 (0.2383)  time: 0.4302  data: 0.0044  max mem: 22448
+train: [19]  [ 80/400]  eta: 0:02:31  lr: 0.000002  loss: 2.2657 (2.2680)  grad: 0.2337 (0.2376)  time: 0.4422  data: 0.0043  max mem: 22448
+train: [19]  [100/400]  eta: 0:02:20  lr: 0.000002  loss: 2.2541 (2.2630)  grad: 0.2352 (0.2375)  time: 0.4387  data: 0.0042  max mem: 22448
+train: [19]  [120/400]  eta: 0:02:09  lr: 0.000002  loss: 2.2562 (2.2655)  grad: 0.2377 (0.2387)  time: 0.4440  data: 0.0042  max mem: 22448
+train: [19]  [140/400]  eta: 0:01:59  lr: 0.000001  loss: 2.2641 (2.2599)  grad: 0.2377 (0.2377)  time: 0.4459  data: 0.0043  max mem: 22448
+train: [19]  [160/400]  eta: 0:01:49  lr: 0.000001  loss: 2.2132 (2.2528)  grad: 0.2272 (0.2369)  time: 0.4366  data: 0.0043  max mem: 22448
+train: [19]  [180/400]  eta: 0:01:40  lr: 0.000001  loss: 2.2048 (2.2493)  grad: 0.2389 (0.2382)  time: 0.4411  data: 0.0041  max mem: 22448
+train: [19]  [200/400]  eta: 0:01:31  lr: 0.000001  loss: 2.2399 (2.2505)  grad: 0.2389 (0.2381)  time: 0.4503  data: 0.0043  max mem: 22448
+train: [19]  [220/400]  eta: 0:01:21  lr: 0.000001  loss: 2.2399 (2.2485)  grad: 0.2322 (0.2376)  time: 0.4436  data: 0.0042  max mem: 22448
+train: [19]  [240/400]  eta: 0:01:12  lr: 0.000001  loss: 2.2329 (2.2477)  grad: 0.2321 (0.2379)  time: 0.4424  data: 0.0043  max mem: 22448
+train: [19]  [260/400]  eta: 0:01:03  lr: 0.000000  loss: 2.2202 (2.2458)  grad: 0.2343 (0.2378)  time: 0.4340  data: 0.0042  max mem: 22448
+train: [19]  [280/400]  eta: 0:00:54  lr: 0.000000  loss: 2.2404 (2.2473)  grad: 0.2379 (0.2378)  time: 0.4408  data: 0.0042  max mem: 22448
+train: [19]  [300/400]  eta: 0:00:44  lr: 0.000000  loss: 2.2712 (2.2472)  grad: 0.2403 (0.2375)  time: 0.4352  data: 0.0041  max mem: 22448
+train: [19]  [320/400]  eta: 0:00:35  lr: 0.000000  loss: 2.2486 (2.2485)  grad: 0.2393 (0.2378)  time: 0.4357  data: 0.0041  max mem: 22448
+train: [19]  [340/400]  eta: 0:00:26  lr: 0.000000  loss: 2.2667 (2.2512)  grad: 0.2350 (0.2377)  time: 0.4498  data: 0.0041  max mem: 22448
+train: [19]  [360/400]  eta: 0:00:17  lr: 0.000000  loss: 2.2667 (2.2509)  grad: 0.2307 (0.2374)  time: 0.4438  data: 0.0041  max mem: 22448
+train: [19]  [380/400]  eta: 0:00:08  lr: 0.000000  loss: 2.2255 (2.2501)  grad: 0.2293 (0.2373)  time: 0.4314  data: 0.0043  max mem: 22448
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.2499 (2.2521)  grad: 0.2394 (0.2375)  time: 0.4426  data: 0.0043  max mem: 22448
+train: [19] Total time: 0:02:59 (0.4480 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.2499 (2.2521)  grad: 0.2394 (0.2375)
+eval (validation): [19]  [ 0/85]  eta: 0:04:54    time: 3.4664  data: 3.2052  max mem: 22448
+eval (validation): [19]  [20/85]  eta: 0:00:32    time: 0.3439  data: 0.0036  max mem: 22448
+eval (validation): [19]  [40/85]  eta: 0:00:18    time: 0.3452  data: 0.0036  max mem: 22448
+eval (validation): [19]  [60/85]  eta: 0:00:09    time: 0.3564  data: 0.0042  max mem: 22448
+eval (validation): [19]  [80/85]  eta: 0:00:01    time: 0.3346  data: 0.0041  max mem: 22448
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.3243  data: 0.0039  max mem: 22448
+eval (validation): [19] Total time: 0:00:32 (0.3831 s / it)
+cv: [19]  best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00')  loss: 2.516  acc: 0.250  f1: 0.177
+saving checkpoint experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.2502768549280177, "hparam": [0.32, 1.0], "hparam_id": 17, "epoch": 19, "is_best": false, "best_score": 0.25212255444813586}
+eval (train): [20]  [  0/509]  eta: 0:25:45    time: 3.0362  data: 2.7978  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:11    time: 0.3891  data: 0.0047  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:30    time: 0.3796  data: 0.0042  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:11    time: 0.3790  data: 0.0050  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:54    time: 0.3507  data: 0.0040  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:41    time: 0.3396  data: 0.0045  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:30    time: 0.3440  data: 0.0044  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:19    time: 0.3327  data: 0.0040  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:11    time: 0.3557  data: 0.0043  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:03    time: 0.3716  data: 0.0045  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:01:55    time: 0.3622  data: 0.0044  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:46    time: 0.3343  data: 0.0040  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:38    time: 0.3393  data: 0.0041  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:30    time: 0.3397  data: 0.0038  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:23    time: 0.3821  data: 0.0046  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:16    time: 0.3489  data: 0.0039  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:08    time: 0.3348  data: 0.0042  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:01    time: 0.3509  data: 0.0042  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:54    time: 0.3763  data: 0.0045  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:46    time: 0.3617  data: 0.0047  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:39    time: 0.3556  data: 0.0043  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:32    time: 0.3490  data: 0.0043  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:25    time: 0.4014  data: 0.0045  max mem: 22448
+eval (train): [20]  [460/509]  eta: 0:00:17    time: 0.4183  data: 0.0046  max mem: 22448
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+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3367  data: 0.0039  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3272  data: 0.0038  max mem: 22448
+eval (train): [20] Total time: 0:03:06 (0.3659 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:19    time: 3.0579  data: 2.7758  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:31    time: 0.3604  data: 0.0046  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:19    time: 0.3746  data: 0.0040  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3909  data: 0.0044  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:02    time: 0.3549  data: 0.0045  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3476  data: 0.0042  max mem: 22448
+eval (validation): [20] Total time: 0:00:34 (0.4037 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:23    time: 3.0986  data: 2.8337  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:30    time: 0.3458  data: 0.0081  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:19    time: 0.3789  data: 0.0043  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3468  data: 0.0037  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3495  data: 0.0043  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3333  data: 0.0040  max mem: 22448
+eval (test): [20] Total time: 0:00:32 (0.3877 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:03:59    time: 2.9223  data: 2.6627  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:31    time: 0.3876  data: 0.0051  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:18    time: 0.3508  data: 0.0039  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3685  data: 0.0046  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3287  data: 0.0042  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3188  data: 0.0040  max mem: 22448
+eval (testid): [20] Total time: 0:00:32 (0.3914 s / it)
+evaluating best checkpoint: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.25212255444813586, "hparam": [0.32, 1.0], "hparam_id": 17, "epoch": 17, "is_best": true, "best_score": 0.25212255444813586}
+eval (train): [20]  [  0/509]  eta: 0:25:41    time: 3.0279  data: 2.7896  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:11    time: 0.3884  data: 0.0054  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:25    time: 0.3586  data: 0.0035  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:05    time: 0.3613  data: 0.0041  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:49    time: 0.3430  data: 0.0041  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:37    time: 0.3414  data: 0.0042  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:26    time: 0.3380  data: 0.0040  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:17    time: 0.3400  data: 0.0040  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:08    time: 0.3462  data: 0.0041  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:00    time: 0.3356  data: 0.0042  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:01:52    time: 0.3422  data: 0.0041  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:45    time: 0.3907  data: 0.0046  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:37    time: 0.3402  data: 0.0041  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:30    time: 0.3415  data: 0.0044  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:22    time: 0.3653  data: 0.0044  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:15    time: 0.3534  data: 0.0044  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:08    time: 0.3644  data: 0.0044  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:00    time: 0.3454  data: 0.0043  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:53    time: 0.3471  data: 0.0044  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:46    time: 0.3562  data: 0.0041  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:39    time: 0.3817  data: 0.0044  max mem: 22448
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+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3394  data: 0.0042  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3270  data: 0.0041  max mem: 22448
+eval (train): [20] Total time: 0:03:03 (0.3598 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:27    time: 3.1484  data: 2.8737  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:34    time: 0.4056  data: 0.0051  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:20    time: 0.3593  data: 0.0038  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3673  data: 0.0044  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:02    time: 0.3508  data: 0.0041  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3343  data: 0.0039  max mem: 22448
+eval (validation): [20] Total time: 0:00:34 (0.4038 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:17    time: 3.0316  data: 2.7612  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:35    time: 0.4219  data: 0.0057  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:21    time: 0.3868  data: 0.0040  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3378  data: 0.0043  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:02    time: 0.3469  data: 0.0044  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3285  data: 0.0042  max mem: 22448
+eval (test): [20] Total time: 0:00:34 (0.4040 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:11    time: 3.0726  data: 2.7813  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:31    time: 0.3824  data: 0.0045  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:18    time: 0.3601  data: 0.0043  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3679  data: 0.0049  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3379  data: 0.0042  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3280  data: 0.0041  max mem: 22448
+eval (testid): [20] Total time: 0:00:32 (0.3962 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   |      17 | 9.6e-05 | 0.05 |          17 | [0.32, 1.0] | train      | 2.2802 | 0.3174  | 0.002172  | 0.24997 | 0.0021745 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      17 | 9.6e-05 | 0.05 |          17 | [0.32, 1.0] | validation | 2.5121 | 0.25212 | 0.0055583 | 0.17889 | 0.0046179 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      17 | 9.6e-05 | 0.05 |          17 | [0.32, 1.0] | test       | 2.4678 | 0.25918 | 0.0051236 | 0.18568 | 0.0046254 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      17 | 9.6e-05 | 0.05 |          17 | [0.32, 1.0] | testid     | 2.439  | 0.26142 | 0.0052534 | 0.1982  | 0.0047833 |
+
+
+done! total time: 1:23:26

data_scaling/n100_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 n100_2; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: data_scaling/n100_2/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_2/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

data_scaling/n100_2/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,ppmi_dx,83,0.005994842503189409,test,0.55,0.04488474128253386,0.4950061721467849,0.049063062388602766,0.49957555178268254,0.046268002758962444
+flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,train,0.7206405693950177,0.016366046223926088,0.6727456151087274,0.020882336121732615,0.6678842860201242,0.01867328477500557
+flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,test,0.62,0.043030221937610316,0.5558672276764843,0.05216053561327231,0.5611205432937181,0.046595878313170074
+flat_mae,patch,logistic,ppmi_dx,85,0.005994842503189409,train,0.7135231316725978,0.016993959832447652,0.6599891024557052,0.022319674804665807,0.6568855705416399,0.01947134647211207
+flat_mae,patch,logistic,ppmi_dx,85,0.005994842503189409,test,0.67,0.039275086250700954,0.6108031607500884,0.04879808525631727,0.6116298811544991,0.04315210083848125
+flat_mae,patch,logistic,ppmi_dx,86,0.005994842503189409,train,0.7117437722419929,0.015953334180295767,0.6617828432173797,0.020383113586638035,0.6580496681652751,0.01808131697880107
+flat_mae,patch,logistic,ppmi_dx,86,0.005994842503189409,test,0.6,0.033981753927659474,0.4802494802494802,0.04660085655962064,0.5144312393887945,0.035912207151033125
+flat_mae,patch,logistic,ppmi_dx,87,0.005994842503189409,train,0.7330960854092526,0.015537177036549062,0.681608049311095,0.020634915023924547,0.6762604367373153,0.018047021378966827
+flat_mae,patch,logistic,ppmi_dx,87,0.005994842503189409,test,0.64,0.04130024213004084,0.5714285714285714,0.052669706395553774,0.5772495755517827,0.04549350974532303
+flat_mae,patch,logistic,ppmi_dx,88,0.046415888336127774,train,0.797153024911032,0.01577350861147377,0.7727988425039363,0.01859601822617641,0.7622029543994862,0.018166409367622836
+flat_mae,patch,logistic,ppmi_dx,88,0.046415888336127774,test,0.67,0.04419579618017985,0.6239316239316239,0.053049282059780815,0.6218166383701189,0.04895413199911891
+flat_mae,patch,logistic,ppmi_dx,89,0.000774263682681127,train,0.6637010676156584,0.01202930096729991,0.5450562158689367,0.020377925356645125,0.5764156497538,0.014065314337778339
+flat_mae,patch,logistic,ppmi_dx,89,0.000774263682681127,test,0.69,0.027810789273229896,0.5689055764149632,0.05245936438260108,0.5971986417657046,0.034939224103906354
+flat_mae,patch,logistic,ppmi_dx,90,0.005994842503189409,train,0.7046263345195729,0.01625139495694541,0.6523106738223017,0.020509931953599615,0.6496601370156283,0.01822096203518249
+flat_mae,patch,logistic,ppmi_dx,90,0.005994842503189409,test,0.6,0.04426409380073199,0.554367201426025,0.048642700141135586,0.5551782682512734,0.04605045177256103
+flat_mae,patch,logistic,ppmi_dx,91,0.005994842503189409,train,0.7277580071174378,0.016149848487215075,0.6768840538864777,0.021186083712887542,0.6719251766217085,0.01859548411006172
+flat_mae,patch,logistic,ppmi_dx,91,0.005994842503189409,test,0.66,0.04199055131812394,0.6026180458158018,0.05130547396716849,0.6035653650254669,0.045415148621044066
+flat_mae,patch,logistic,ppmi_dx,92,0.005994842503189409,train,0.7135231316725978,0.016226360603503288,0.6611199125103463,0.02119540639993342,0.6577552986512524,0.018643524156222183
+flat_mae,patch,logistic,ppmi_dx,92,0.005994842503189409,test,0.71,0.03867410503166169,0.6579785352046232,0.048854507627825276,0.6540747028862479,0.0435394465880211
+flat_mae,patch,logistic,ppmi_dx,93,0.005994842503189409,train,0.7241992882562278,0.015637505924705294,0.6737489840938117,0.020497665761945463,0.6690350032113038,0.01807448521474058
+flat_mae,patch,logistic,ppmi_dx,93,0.005994842503189409,test,0.61,0.04276084190003747,0.5555555555555556,0.05006736013708233,0.5581494057724957,0.04596553901604652
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,train,0.7224199288256228,0.01676074942666491,0.6753466050479915,0.021199399363619587,0.670199100834939,0.018997352287090805
+flat_mae,patch,logistic,ppmi_dx,94,0.005994842503189409,test,0.62,0.04355685939091569,0.5703301673450927,0.04963352692359453,0.5713073005093379,0.04604799234924177
+flat_mae,patch,logistic,ppmi_dx,95,0.000774263682681127,train,0.693950177935943,0.014771212749126711,0.612227214377407,0.021799483768878202,0.6209858702633269,0.017155381305588854
+flat_mae,patch,logistic,ppmi_dx,95,0.000774263682681127,test,0.6,0.02591872682058284,0.435347261434218,0.03782882002620203,0.499151103565365,0.02658462348272596
+flat_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,train,0.7206405693950177,0.01626279035318215,0.6737813106571772,0.02104805905053743,0.6687540141297367,0.018881722342296948
+flat_mae,patch,logistic,ppmi_dx,96,0.005994842503189409,test,0.58,0.03895564657402056,0.4900437105390966,0.04700482821225266,0.5084889643463497,0.040280001252098706
+flat_mae,patch,logistic,ppmi_dx,97,0.000774263682681127,train,0.6725978647686833,0.01289734116203946,0.5665325285043594,0.02094714353615797,0.5897291800470991,0.015115434323279974
+flat_mae,patch,logistic,ppmi_dx,97,0.000774263682681127,test,0.66,0.031188562005966217,0.5466666666666666,0.050217985971710136,0.5730050933786077,0.03609859343964925
+flat_mae,patch,logistic,ppmi_dx,98,0.046415888336127774,train,0.7811387900355872,0.015376154734548881,0.7532298311813025,0.018465402657404217,0.7431090772853779,0.017830720393493024
+flat_mae,patch,logistic,ppmi_dx,98,0.046415888336127774,test,0.67,0.04562281885197362,0.6239316239316239,0.0542794661392299,0.6218166383701189,0.050046180575418465
+flat_mae,patch,logistic,ppmi_dx,99,0.005994842503189409,train,0.7206405693950177,0.015854933656108412,0.6673164441461585,0.020674144792773037,0.6635356454720617,0.0181203580187174
+flat_mae,patch,logistic,ppmi_dx,99,0.005994842503189409,test,0.66,0.04129570922020834,0.5952380952380952,0.051478102868304,0.5984719864176571,0.04498487328906596
+flat_mae,patch,logistic,ppmi_dx,100,0.005994842503189409,train,0.7099644128113879,0.01701832010092387,0.6569102219825245,0.02192360850433153,0.6539953971312353,0.01931226359302563
+flat_mae,patch,logistic,ppmi_dx,100,0.005994842503189409,test,0.64,0.043991503725151294,0.5863970588235294,0.05331733670580259,0.5874363327674024,0.04829926095997664

data_scaling/n100_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 17:21:08
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n100_2; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n100_2/pretrain/checkpoint-best.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: data_scaling/n100_2/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/data_scaling/output/data_scaling/n100_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: 0:14:11    time: 3.6686  data: 2.9641  max mem: 2698
+extract (train)  [ 20/232]  eta: 0:01:13    time: 0.1820  data: 0.0567  max mem: 2851
+extract (train)  [ 40/232]  eta: 0:00:49    time: 0.1645  data: 0.0451  max mem: 2851
+extract (train)  [ 60/232]  eta: 0:00:39    time: 0.1773  data: 0.0526  max mem: 2851
+extract (train)  [ 80/232]  eta: 0:00:33    time: 0.1798  data: 0.0517  max mem: 2851
+extract (train)  [100/232]  eta: 0:00:27    time: 0.1647  data: 0.0489  max mem: 2851
+extract (train)  [120/232]  eta: 0:00:22    time: 0.1620  data: 0.0463  max mem: 2851
+extract (train)  [140/232]  eta: 0:00:18    time: 0.1739  data: 0.0476  max mem: 2851
+extract (train)  [160/232]  eta: 0:00:13    time: 0.1698  data: 0.0488  max mem: 2851
+extract (train)  [180/232]  eta: 0:00:09    time: 0.1679  data: 0.0498  max mem: 2851
+extract (train)  [200/232]  eta: 0:00:06    time: 0.1709  data: 0.0519  max mem: 2851
+extract (train)  [220/232]  eta: 0:00:02    time: 0.1433  data: 0.0391  max mem: 2851
+extract (train)  [231/232]  eta: 0:00:00    time: 0.1395  data: 0.0398  max mem: 2851
+extract (train) Total time: 0:00:42 (0.1839 s / it)
+extract (validation)  [ 0/50]  eta: 0:02:54    time: 3.4954  data: 3.3335  max mem: 2851
+extract (validation)  [20/50]  eta: 0:00:11    time: 0.2114  data: 0.0625  max mem: 2851
+extract (validation)  [40/50]  eta: 0:00:02    time: 0.1345  data: 0.0326  max mem: 2851
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.1309  data: 0.0320  max mem: 2851
+extract (validation) Total time: 0:00:11 (0.2374 s / it)
+extract (test)  [ 0/50]  eta: 0:02:53    time: 3.4723  data: 3.3099  max mem: 2851
+extract (test)  [20/50]  eta: 0:00:10    time: 0.2066  data: 0.0609  max mem: 2851
+extract (test)  [40/50]  eta: 0:00:02    time: 0.1301  data: 0.0309  max mem: 2851
+extract (test)  [49/50]  eta: 0:00:00    time: 0.1335  data: 0.0343  max mem: 2851
+extract (test) Total time: 0:00:11 (0.2339 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   |         | 0.0059948 | train   | 0.7153 |  0.01652  | 0.6691  | 0.020825 | 0.66468 |   0.018751 |
+| flat_mae | patch  | logistic | ppmi_dx   |         | 0.0059948 | test    | 0.6    |  0.040802 | 0.51433 | 0.049654 | 0.52638 |   0.042939 |
+
+
+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": 0.042910436958856525, "f1": 0.5636277862955537, "f1_std": 0.052661966233036685, "bacc": 0.5691850594227504, "bacc_std": 0.046042239444618674}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.04592576183363755, "f1": 0.6043956043956044, "f1_std": 0.04998183011352542, "bacc": 0.6027164685908319, "bacc_std": 0.04832999056978765}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.04314486759743272, "f1": 0.612789025334661, "f1_std": 0.049220156160455775, "bacc": 0.6107809847198642, "bacc_std": 0.04704041778046909}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04272147469364792, "f1": 0.6239316239316239, "f1_std": 0.05001481661872014, "bacc": 0.6218166383701189, "bacc_std": 0.04634679800420373}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.04216384707305537, "f1": 0.5324918186068257, "f1_std": 0.04959622890638098, "bacc": 0.5398981324278438, "bacc_std": 0.04406331995021132}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04257212233375265, "f1": 0.5400400990682863, "f1_std": 0.053588468882064436, "bacc": 0.547962648556876, "bacc_std": 0.0465224406717436}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.62, "acc_std": 0.045790549243266344, "f1": 0.5824175824175825, "f1_std": 0.05140039161643029, "bacc": 0.5814940577249575, "bacc_std": 0.0493052508620957}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.03796197571254689, "f1": 0.5460679671205987, "f1_std": 0.0493629908011042, "bacc": 0.5589983022071308, "bacc_std": 0.04099909192315337}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.04008483004828634, "f1": 0.6493688639551192, "f1_std": 0.04955815575951578, "bacc": 0.6460101867572157, "bacc_std": 0.04458778549670705}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.04026114752462974, "f1": 0.6026180458158018, "f1_std": 0.04940179498687207, "bacc": 0.6035653650254669, "bacc_std": 0.044058183457382445}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.71, "acc_std": 0.040367194601557334, "f1": 0.6579785352046232, "f1_std": 0.05249896150907498, "bacc": 0.6540747028862479, "bacc_std": 0.04637017230555599}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.59, "acc_std": 0.03768212308243791, "f1": 0.48589341692789967, "f1_std": 0.048101291326795116, "bacc": 0.5114601018675722, "bacc_std": 0.03938125257107833}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04076264957040943, "f1": 0.5481404240528328, "f1_std": 0.0476285931785262, "bacc": 0.5530560271646858, "bacc_std": 0.04269974502653194}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.04125929713410058, "f1": 0.5636277862955537, "f1_std": 0.05111540045144083, "bacc": 0.5691850594227504, "bacc_std": 0.04462291644387462}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 97, "C": 0.000774263682681127, "split": "test", "acc": 0.66, "acc_std": 0.031188562005966217, "f1": 0.5466666666666666, "f1_std": 0.050217985971710136, "bacc": 0.5730050933786077, "bacc_std": 0.03609859343964925}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04562281885197362, "f1": 0.6239316239316239, "f1_std": 0.0542794661392299, "bacc": 0.6218166383701189, "bacc_std": 0.050046180575418465}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.04129570922020834, "f1": 0.5952380952380952, "f1_std": 0.051478102868304, "bacc": 0.5984719864176571, "bacc_std": 0.04498487328906596}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.043991503725151294, "f1": 0.5863970588235294, "f1_std": 0.05331733670580259, "bacc": 0.5874363327674024, "bacc_std": 0.04829926095997664}
+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 | 12.953 |  129.15 | 0.72593 |  0.048752 | 0.67361 | 0.065176 | 0.6716  |   0.06115  |
+| flat_mae | patch  | logistic | ppmi_dx   | test    |        100 | 12.953 |  129.15 | 0.637   |  0.039505 | 0.56903 | 0.0497   | 0.57651 |   0.041909 |
+
+
+done! total time: 0:05:01

data_scaling/n100_2/pretrain/config.yaml

@@ -0,0 +1,109 @@
+name: data_scaling/n100_2/pretrain
+notes: data scaling experiment n100_2 (seed=3472)
+output_dir: experiments/data_scaling/output/data_scaling/n100_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-{00800..00899}.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/n100_2/pretrain/log.json

@@ -0,0 +1,100 @@
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data_scaling/n1600_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 n1600_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/aabc_age__patch__logistic
+remote_dir: null

data_scaling/n1600_1/eval_v2/aabc_age__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,aabc_age,76,0.005994842503189409,test,0.5,0.06637935624056053,0.49935861562306405,0.06807193242922643,0.4981684981684982,0.06654080635386729
+flat_mae,patch,logistic,aabc_age,77,0.046415888336127774,train,0.844488188976378,0.016130863923223156,0.8435251895597595,0.01637904549419637,0.8441802809418687,0.016137279747212442
+flat_mae,patch,logistic,aabc_age,77,0.046415888336127774,test,0.5,0.0691320235953681,0.5014688759516346,0.06988077873142089,0.49977106227106227,0.06937151922362128
+flat_mae,patch,logistic,aabc_age,78,0.046415888336127774,train,0.8503937007874016,0.015340633401853614,0.8492545365446951,0.015498925322833687,0.8498435189465063,0.015305063236706551
+flat_mae,patch,logistic,aabc_age,78,0.046415888336127774,test,0.5,0.07239899105184715,0.5039010989010989,0.07217295931440829,0.5027472527472527,0.07270306661751606
+flat_mae,patch,logistic,aabc_age,79,0.046415888336127774,train,0.8503937007874016,0.015472843656477046,0.8508077513818949,0.015445067113803883,0.851766387476529,0.015348116286398884
+flat_mae,patch,logistic,aabc_age,79,0.046415888336127774,test,0.38461538461538464,0.062416287132512814,0.3760262725779967,0.0638259470453883,0.3825549450549451,0.062347247343594175
+flat_mae,patch,logistic,aabc_age,80,0.005994842503189409,train,0.6712598425196851,0.021808189360748093,0.6701400210016727,0.02221177086932435,0.6725734048857954,0.021808745096314388
+flat_mae,patch,logistic,aabc_age,80,0.005994842503189409,test,0.46153846153846156,0.0642487192790182,0.43763736263736264,0.06490923467663018,0.4592490842490842,0.06400773653712143
+flat_mae,patch,logistic,aabc_age,81,0.046415888336127774,train,0.8484251968503937,0.01667637904951902,0.8484208393260741,0.01675376054162948,0.84868004023867,0.01670853495706794
+flat_mae,patch,logistic,aabc_age,81,0.046415888336127774,test,0.5,0.06556987329577257,0.5017717789456919,0.06624935497303235,0.5027472527472527,0.06589529603938821
+flat_mae,patch,logistic,aabc_age,82,0.046415888336127774,train,0.8543307086614174,0.015163410231805376,0.8535321736278225,0.015319894535313993,0.8541433315624227,0.015154810839918001
+flat_mae,patch,logistic,aabc_age,82,0.046415888336127774,test,0.4807692307692308,0.0670881694876757,0.4732175925925926,0.06904602513776205,0.48031135531135527,0.0670650395332144
+flat_mae,patch,logistic,aabc_age,83,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,83,2.782559402207126,test,0.36538461538461536,0.06419804555634415,0.37195616883116883,0.06390664280665741,0.36652930402930406,0.06453481998065173
+flat_mae,patch,logistic,aabc_age,84,0.046415888336127774,train,0.8484251968503937,0.015679427339315804,0.848849875918747,0.015677321456707364,0.8487800135791128,0.015658480435040274
+flat_mae,patch,logistic,aabc_age,84,0.046415888336127774,test,0.5384615384615384,0.06249112363003548,0.5356276289780126,0.06429364092482233,0.5338827838827839,0.06252349907763052
+flat_mae,patch,logistic,aabc_age,85,0.046415888336127774,train,0.8562992125984252,0.016233491354372392,0.8563125149363312,0.01627732092190166,0.8566269618269661,0.016191621939164465
+flat_mae,patch,logistic,aabc_age,85,0.046415888336127774,test,0.4423076923076923,0.06004066669187422,0.4293912427950248,0.06037655230998462,0.44024725274725274,0.05987147390114897
+flat_mae,patch,logistic,aabc_age,86,0.046415888336127774,train,0.8385826771653543,0.015731260262455114,0.8385684128408564,0.015727804729087445,0.8387169896181161,0.015659363998697294
+flat_mae,patch,logistic,aabc_age,86,0.046415888336127774,test,0.5961538461538461,0.06318296667699697,0.5872947454844006,0.06707402349579383,0.594551282051282,0.06347956881139816
+flat_mae,patch,logistic,aabc_age,87,0.046415888336127774,train,0.8503937007874016,0.015890007645834738,0.8499580424525646,0.01602025971473414,0.8506961692709283,0.015913279711858734
+flat_mae,patch,logistic,aabc_age,87,0.046415888336127774,test,0.5,0.0685825529420372,0.4977193942711184,0.06966483313801954,0.5011446886446886,0.0687655165752896
+flat_mae,patch,logistic,aabc_age,88,9.999999999999999e-05,train,0.4822834645669291,0.01804677171606985,0.44360224402520426,0.018566553435947545,0.478836578830777,0.017798067171176955
+flat_mae,patch,logistic,aabc_age,88,9.999999999999999e-05,test,0.36538461538461536,0.062133886265750414,0.3672577996715928,0.06443395991380554,0.3630952380952381,0.0619940751230195
+flat_mae,patch,logistic,aabc_age,89,0.005994842503189409,train,0.687007874015748,0.019277920369594515,0.6844603754218843,0.019530098437732797,0.6866943395430283,0.019186290180985176
+flat_mae,patch,logistic,aabc_age,89,0.005994842503189409,test,0.40384615384615385,0.06488743097260712,0.3979793833242109,0.06548820994966015,0.4017857142857143,0.06472372130519154
+flat_mae,patch,logistic,aabc_age,90,9.999999999999999e-05,train,0.4763779527559055,0.01957050776064709,0.44710707836965213,0.01997272839153692,0.4725530026525301,0.019466152527572465
+flat_mae,patch,logistic,aabc_age,90,9.999999999999999e-05,test,0.5,0.060633673051303,0.4821001877453491,0.0614229528228039,0.49633699633699635,0.060272415046333176
+flat_mae,patch,logistic,aabc_age,91,0.000774263682681127,train,0.562992125984252,0.02171711891035519,0.5524947478991596,0.02195739632842267,0.5611069661999006,0.021572197868785826
+flat_mae,patch,logistic,aabc_age,91,0.000774263682681127,test,0.46153846153846156,0.06624006010059304,0.4420980262131378,0.06994111915377038,0.4608516483516484,0.06621558216525715
+flat_mae,patch,logistic,aabc_age,92,0.3593813663804626,train,0.9940944881889764,0.0032925510143024853,0.994122290094622,0.0032784463833620448,0.9943367619953625,0.003166697486148637
+flat_mae,patch,logistic,aabc_age,92,0.3593813663804626,test,0.5192307692307693,0.06869849873039913,0.509158615136876,0.07182624442984537,0.5203754578754579,0.06902399008228371
+flat_mae,patch,logistic,aabc_age,93,0.046415888336127774,train,0.8523622047244095,0.016214938185156336,0.8521219560039508,0.016316268024657375,0.8517920401082493,0.016321178026465023
+flat_mae,patch,logistic,aabc_age,93,0.046415888336127774,test,0.34615384615384615,0.052826205506033466,0.3232976314872867,0.05099056347859926,0.34226190476190477,0.05200557331540366
+flat_mae,patch,logistic,aabc_age,94,9.999999999999999e-05,train,0.4763779527559055,0.019576502355601957,0.4339208152937611,0.019206173111804368,0.472638231723339,0.019362741013550667
+flat_mae,patch,logistic,aabc_age,94,9.999999999999999e-05,test,0.40384615384615385,0.05859260358839051,0.36620670995670995,0.0603907605172631,0.39720695970695974,0.057529726545082385
+flat_mae,patch,logistic,aabc_age,95,0.005994842503189409,train,0.6771653543307087,0.01988621610370313,0.6758810608908616,0.02012455969116714,0.6780690616794754,0.01979989456448332
+flat_mae,patch,logistic,aabc_age,95,0.005994842503189409,test,0.5,0.06301939215021236,0.4875899962106859,0.06286901645579311,0.4965659340659341,0.06263604936950677
+flat_mae,patch,logistic,aabc_age,96,0.046415888336127774,train,0.8503937007874016,0.015308886919826596,0.8503615616540265,0.015440399447607424,0.8506961692709282,0.01534308410435802
+flat_mae,patch,logistic,aabc_age,96,0.046415888336127774,test,0.38461538461538464,0.062227346107240476,0.3859447004608295,0.0619538480792982,0.38713369963369965,0.06253333821332652
+flat_mae,patch,logistic,aabc_age,97,0.005994842503189409,train,0.6850393700787402,0.019758690564197445,0.684256590300752,0.019827751341816597,0.6847281971809915,0.0198135470972096
+flat_mae,patch,logistic,aabc_age,97,0.005994842503189409,test,0.4230769230769231,0.06486426428369545,0.41858974358974355,0.06362208792538525,0.4223901098901099,0.06471972336270713
+flat_mae,patch,logistic,aabc_age,98,0.046415888336127774,train,0.844488188976378,0.015520979344152228,0.8448581975887655,0.015551471623894527,0.8457856082502699,0.0154451885111312
+flat_mae,patch,logistic,aabc_age,98,0.046415888336127774,test,0.46153846153846156,0.06736383372904602,0.4535035366931919,0.06810922398770386,0.4610805860805861,0.06746827878366458
+flat_mae,patch,logistic,aabc_age,99,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,99,21.54434690031882,test,0.4807692307692308,0.06696532706995313,0.47916666666666674,0.06720532961517768,0.48328754578754574,0.06733063586148494
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,train,0.860236220472441,0.01572130803460872,0.8604067297874014,0.015740529324844096,0.8597889483667664,0.01579739667270168
+flat_mae,patch,logistic,aabc_age,100,0.046415888336127774,test,0.36538461538461536,0.06512140460154485,0.3701539855072464,0.06596356107803629,0.36652930402930406,0.0650708943151534

data_scaling/n1600_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 17:20:38
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:21:23    time: 5.6288  data: 4.5380  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:46    time: 0.2558  data: 0.0857  max mem: 3393
+extract (train)  [ 40/228]  eta: 0:01:10    time: 0.2321  data: 0.0749  max mem: 3393
+extract (train)  [ 60/228]  eta: 0:00:54    time: 0.2197  data: 0.0709  max mem: 3393
+extract (train)  [ 80/228]  eta: 0:00:44    time: 0.2266  data: 0.0778  max mem: 3393
+extract (train)  [100/228]  eta: 0:00:37    time: 0.2518  data: 0.0900  max mem: 3393
+extract (train)  [120/228]  eta: 0:00:29    time: 0.2006  data: 0.0659  max mem: 3393
+extract (train)  [140/228]  eta: 0:00:23    time: 0.2376  data: 0.0837  max mem: 3393
+extract (train)  [160/228]  eta: 0:00:17    time: 0.2173  data: 0.0760  max mem: 3393
+extract (train)  [180/228]  eta: 0:00:12    time: 0.2098  data: 0.0737  max mem: 3393
+extract (train)  [200/228]  eta: 0:00:07    time: 0.2030  data: 0.0656  max mem: 3393
+extract (train)  [220/228]  eta: 0:00:01    time: 0.1831  data: 0.0588  max mem: 3393
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1831  data: 0.0600  max mem: 3393
+extract (train) Total time: 0:00:56 (0.2462 s / it)
+extract (validation)  [ 0/27]  eta: 0:01:54    time: 4.2247  data: 4.0818  max mem: 3393
+extract (validation)  [20/27]  eta: 0:00:02    time: 0.2007  data: 0.0632  max mem: 3393
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1752  data: 0.0520  max mem: 3393
+extract (validation) Total time: 0:00:09 (0.3542 s / it)
+extract (test)  [ 0/26]  eta: 0:01:58    time: 4.5586  data: 4.4067  max mem: 3393
+extract (test)  [20/26]  eta: 0:00:02    time: 0.1852  data: 0.0483  max mem: 3393
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1680  data: 0.0417  max mem: 3393
+extract (test) Total time: 0:00:09 (0.3595 s / it)
+feature extraction time: 0:01:15
+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  |         | 0.0001 | train   | 0.49016 |  0.021038 | 0.46514 | 0.021461 | 0.48726 |   0.02089  |
+| flat_mae | patch  | logistic | aabc_age  |         | 0.0001 | test    | 0.28846 |  0.05737  | 0.25203 | 0.050677 | 0.27747 |   0.055733 |
+
+
+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": 0.06124413992012797, "f1": 0.448951048951049, "f1_std": 0.061843570504675774, "bacc": 0.45947802197802196, "bacc_std": 0.06077154372766225}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 0.000774263682681127, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06527588244062554, "f1": 0.5339433551198256, "f1_std": 0.06807932188148856, "bacc": 0.5338827838827839, "bacc_std": 0.06522589118218011}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.05889930982808587, "f1": 0.38554376657824935, "f1_std": 0.05951691557202573, "bacc": 0.4015567765567766, "bacc_std": 0.05885096166091861}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06714040750194049, "f1": 0.5225378382237453, "f1_std": 0.06678976575968712, "bacc": 0.5203754578754579, "bacc_std": 0.06730941528500405}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06080421339729596, "f1": 0.41138716356107663, "f1_std": 0.06071568495113502, "bacc": 0.4207875457875458, "bacc_std": 0.06058450169798242}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.062227346107240476, "f1": 0.3859447004608295, "f1_std": 0.0619538480792982, "bacc": 0.38713369963369965, "bacc_std": 0.06253333821332652}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 0.005994842503189409, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06486426428369545, "f1": 0.41858974358974355, "f1_std": 0.06362208792538525, "bacc": 0.4223901098901099, "bacc_std": 0.06471972336270713}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06736383372904602, "f1": 0.4535035366931919, "f1_std": 0.06810922398770386, "bacc": 0.4610805860805861, "bacc_std": 0.06746827878366458}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 21.54434690031882, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06696532706995313, "f1": 0.47916666666666674, "f1_std": 0.06720532961517768, "bacc": 0.48328754578754574, "bacc_std": 0.06733063586148494}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.06512140460154485, "f1": 0.3701539855072464, "f1_std": 0.06596356107803629, "bacc": 0.36652930402930406, "bacc_std": 0.0650708943151534}
+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 | 2.3044 |  16.898 | 0.76906 |  0.15034  | 0.76481 | 0.15781  | 0.76876 |   0.15111  |
+| flat_mae | patch  | logistic | aabc_age  | test    |        100 | 2.3044 |  16.898 | 0.45173 |  0.064015 | 0.44272 | 0.064637 | 0.45048 |   0.064005 |
+
+
+done! total time: 0:05:31

data_scaling/n1600_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 n1600_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

data_scaling/n1600_1/eval_v2/aabc_sex__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
+flat_mae,patch,logistic,aabc_sex,,0.3593813663804626,train,0.9905482041587902,0.004188331238116485,0.9903381466044704,0.00427237663297526,0.9912104800936768,0.003908802732771462
+flat_mae,patch,logistic,aabc_sex,,0.3593813663804626,test,0.9636363636363636,0.025035406332790652,0.9626358695652174,0.025353011679456864,0.9696969696969697,0.020862838610658855
+flat_mae,patch,logistic,aabc_sex,1,0.046415888336127774,train,0.947069943289225,0.010150154304871008,0.9453818696716718,0.010550057936558768,0.9426932207860723,0.011107771566807152
+flat_mae,patch,logistic,aabc_sex,1,0.046415888336127774,test,0.8181818181818182,0.05199937698291747,0.8151881720430108,0.052766511852328744,0.8192934782608696,0.052299282119432
+flat_mae,patch,logistic,aabc_sex,2,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_sex,2,2.782559402207126,test,0.9090909090909091,0.03942971988129456,0.9071259709557582,0.0402703007695785,0.9096467391304348,0.040006773036680716
+flat_mae,patch,logistic,aabc_sex,3,0.3593813663804626,train,0.994328922495274,0.0033052107034787204,0.9941893034853195,0.00338405446495056,0.9944898736774231,0.0032516989990078143
+flat_mae,patch,logistic,aabc_sex,3,0.3593813663804626,test,0.7818181818181819,0.05799300043258283,0.7758152173913043,0.059816896096389016,0.7758152173913043,0.0598727041432624
+flat_mae,patch,logistic,aabc_sex,4,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_sex,4,21.54434690031882,test,0.8181818181818182,0.051276324825432866,0.8176392572944298,0.051163831855481824,0.8315217391304348,0.047817871758453624
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+flat_mae,patch,logistic,aabc_sex,95,0.046415888336127774,train,0.9508506616257089,0.00923252346692333,0.9493518927677125,0.009575922812089506,0.9471775257188078,0.010072320425744633
+flat_mae,patch,logistic,aabc_sex,95,0.046415888336127774,test,0.8363636363636363,0.049351979202861426,0.8307692307692308,0.05148845711246185,0.8288043478260869,0.051683076424542534
+flat_mae,patch,logistic,aabc_sex,96,0.005994842503189409,train,0.9017013232514177,0.01272452079300834,0.8982812684889363,0.013307078082030242,0.8949632169758786,0.013690822202203107
+flat_mae,patch,logistic,aabc_sex,96,0.005994842503189409,test,0.9090909090909091,0.03760209243342585,0.9045470322804582,0.04058931587271754,0.8974184782608696,0.04220810073840855
+flat_mae,patch,logistic,aabc_sex,97,0.046415888336127774,train,0.9489603024574669,0.010002511110847382,0.9473684210526316,0.01038508763159377,0.9449353732524399,0.010894337273563053
+flat_mae,patch,logistic,aabc_sex,97,0.046415888336127774,test,0.9090909090909091,0.03705909509048514,0.905982905982906,0.03869099332324131,0.9035326086956521,0.039355652704487015
+flat_mae,patch,logistic,aabc_sex,98,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_sex,98,2.782559402207126,test,0.9090909090909091,0.03698342715727575,0.9027925061859314,0.041888358739349874,0.8913043478260869,0.04421931507935143
+flat_mae,patch,logistic,aabc_sex,99,0.005994842503189409,train,0.9035916824196597,0.013312799919093447,0.9004483312116013,0.013823121058464193,0.8978135349805094,0.014088777843543945
+flat_mae,patch,logistic,aabc_sex,99,0.005994842503189409,test,0.8909090909090909,0.043499725466978285,0.884453781512605,0.04815895374305792,0.8756793478260869,0.049355247684443784
+flat_mae,patch,logistic,aabc_sex,100,0.3593813663804626,train,0.9886578449905482,0.004523703349669597,0.9883855386416862,0.004627137376630234,0.9889797473548463,0.004476861312665524
+flat_mae,patch,logistic,aabc_sex,100,0.3593813663804626,test,0.8727272727272727,0.043879624971087716,0.8711943793911007,0.04400777865124005,0.8783967391304348,0.042285790159173255

data_scaling/n1600_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 17:20:46
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:18:41    time: 4.7528  data: 4.1739  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:01:39    time: 0.2462  data: 0.0779  max mem: 3393
+extract (train)  [ 40/236]  eta: 0:01:05    time: 0.1970  data: 0.0563  max mem: 3393
+extract (train)  [ 60/236]  eta: 0:00:51    time: 0.2199  data: 0.0636  max mem: 3393
+extract (train)  [ 80/236]  eta: 0:00:42    time: 0.2113  data: 0.0647  max mem: 3393
+extract (train)  [100/236]  eta: 0:00:36    time: 0.2372  data: 0.0736  max mem: 3393
+extract (train)  [120/236]  eta: 0:00:29    time: 0.2118  data: 0.0610  max mem: 3393
+extract (train)  [140/236]  eta: 0:00:24    time: 0.2087  data: 0.0623  max mem: 3393
+extract (train)  [160/236]  eta: 0:00:18    time: 0.2107  data: 0.0602  max mem: 3393
+extract (train)  [180/236]  eta: 0:00:13    time: 0.2074  data: 0.0598  max mem: 3393
+extract (train)  [200/236]  eta: 0:00:08    time: 0.1970  data: 0.0560  max mem: 3393
+extract (train)  [220/236]  eta: 0:00:03    time: 0.1939  data: 0.0546  max mem: 3393
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1647  data: 0.0440  max mem: 3393
+extract (train) Total time: 0:00:54 (0.2306 s / it)
+extract (validation)  [ 0/29]  eta: 0:02:15    time: 4.6676  data: 4.4682  max mem: 3393
+extract (validation)  [20/29]  eta: 0:00:03    time: 0.1983  data: 0.0531  max mem: 3393
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1627  data: 0.0387  max mem: 3393
+extract (validation) Total time: 0:00:10 (0.3500 s / it)
+extract (test)  [ 0/28]  eta: 0:01:59    time: 4.2576  data: 4.0889  max mem: 3393
+extract (test)  [20/28]  eta: 0:00:03    time: 0.1864  data: 0.0471  max mem: 3393
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1613  data: 0.0371  max mem: 3393
+extract (test) Total time: 0:00:09 (0.3351 s / it)
+feature extraction time: 0:01:14
+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  |         | 0.35938 | train   | 0.99055 | 0.0041883 | 0.99034 | 0.0042724 | 0.99121 |  0.0039088 |
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.35938 | test    | 0.96364 | 0.025035  | 0.96264 | 0.025353  | 0.9697  |  0.020863  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05199937698291747, "f1": 0.8151881720430108, "f1_std": 0.052766511852328744, "bacc": 0.8192934782608696, "bacc_std": 0.052299282119432}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 2.782559402207126, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03942971988129456, "f1": 0.9071259709557582, "f1_std": 0.0402703007695785, "bacc": 0.9096467391304348, "bacc_std": 0.040006773036680716}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05799300043258283, "f1": 0.7758152173913043, "f1_std": 0.059816896096389016, "bacc": 0.7758152173913043, "bacc_std": 0.0598727041432624}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 21.54434690031882, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.051276324825432866, "f1": 0.8176392572944298, "f1_std": 0.051163831855481824, "bacc": 0.8315217391304348, "bacc_std": 0.047817871758453624}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 21.54434690031882, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05178820185370088, "f1": 0.8106060606060606, "f1_std": 0.05441675829108289, "bacc": 0.8070652173913043, "bacc_std": 0.05414839896590889}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.9454545454545454, "acc_std": 0.030464720145665536, "f1": 0.9435897435897436, "f1_std": 0.031856996515590215, "bacc": 0.9408967391304348, "bacc_std": 0.03345344386828878}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04419335427395696, "f1": 0.8683760683760684, "f1_std": 0.046336155470605274, "bacc": 0.8661684782608696, "bacc_std": 0.047050180221596266}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.0482003532101869, "f1": 0.8328267477203647, "f1_std": 0.04925630976767027, "bacc": 0.8349184782608696, "bacc_std": 0.0494145761527003}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04613563904491454, "f1": 0.8683760683760684, "f1_std": 0.048134896265990525, "bacc": 0.8661684782608696, "bacc_std": 0.04862276659367932}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04786946437798013, "f1": 0.8484848484848485, "f1_std": 0.05079627292231832, "bacc": 0.8444293478260869, "bacc_std": 0.05113769010411776}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.03661195216486434, "f1": 0.9252717391304348, "f1_std": 0.03771518057012766, "bacc": 0.9252717391304348, "bacc_std": 0.03802982215594783}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.055322214688283466, "f1": 0.7727272727272727, "f1_std": 0.058789336337662784, "bacc": 0.7697010869565217, "bacc_std": 0.05838419559668523}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 2.782559402207126, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04483875242475267, "f1": 0.8683760683760684, "f1_std": 0.04644283901059012, "bacc": 0.8661684782608696, "bacc_std": 0.04642564110533098}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.033057145612303836, "f1": 0.9266666666666667, "f1_std": 0.032968321281559826, "bacc": 0.9375, "bacc_std": 0.0284084845105736}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04467781448815525, "f1": 0.8699763593380614, "f1_std": 0.045720592378123606, "bacc": 0.8722826086956521, "bacc_std": 0.04575159534606618}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 166.81005372000556, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04376361747931758, "f1": 0.8639095086603039, "f1_std": 0.049347647260052904, "bacc": 0.8539402173913043, "bacc_std": 0.04981773616200069}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.046291508958478444, "f1": 0.84593837535014, "f1_std": 0.050932976570092874, "bacc": 0.8383152173913043, "bacc_std": 0.05095552319455272}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04425290203865701, "f1": 0.8711943793911007, "f1_std": 0.04443531354761817, "bacc": 0.8783967391304348, "bacc_std": 0.04283113976051379}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.040495623679480265, "f1": 0.8891129032258065, "f1_std": 0.04092174142487251, "bacc": 0.8940217391304348, "bacc_std": 0.0399339461506134}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.3593813663804626, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.0464259260712185, "f1": 0.8484848484848485, "f1_std": 0.04937434624824649, "bacc": 0.8444293478260869, "bacc_std": 0.049726421696210533}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.04873936078013535, "f1": 0.8106060606060606, "f1_std": 0.05176715961049238, "bacc": 0.8070652173913043, "bacc_std": 0.0519198730634737}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 0.005994842503189409, "split": "test", "acc": 0.9454545454545454, "acc_std": 0.030531685208204498, "f1": 0.9442755825734549, "f1_std": 0.031119085664288105, "bacc": 0.9470108695652174, "bacc_std": 0.030249032194588295}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 2.782559402207126, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.045079236025704295, "f1": 0.8711943793911007, "f1_std": 0.045326019952044244, "bacc": 0.8783967391304348, "bacc_std": 0.04402196633395475}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.042659908211282405, "f1": 0.8639095086603039, "f1_std": 0.04810423536346599, "bacc": 0.8539402173913043, "bacc_std": 0.04835060225729434}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.9818181818181818, "acc_std": 0.01828349750435446, "f1": 0.9814251941911516, "f1_std": 0.018512548584299526, "bacc": 0.984375, "bacc_std": 0.015712380667804608}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.04986560450246936, "f1": 0.7931623931623932, "f1_std": 0.052388615428699485, "bacc": 0.7914402173913043, "bacc_std": 0.05247020282758435}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.046415888336127774, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03651966525604011, "f1": 0.905982905982906, "f1_std": 0.03804541247166257, "bacc": 0.9035326086956521, "bacc_std": 0.03859428091845315}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 2.782559402207126, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03698342715727575, "f1": 0.9027925061859314, "f1_std": 0.041888358739349874, "bacc": 0.8913043478260869, "bacc_std": 0.04421931507935143}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.043879624971087716, "f1": 0.8711943793911007, "f1_std": 0.04400777865124005, "bacc": 0.8783967391304348, "bacc_std": 0.042285790159173255}
+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 | 7.9301 |  32.868 | 0.96299 |  0.032515 | 0.96182 | 0.033599 | 0.96056 |   0.034865 |
+| flat_mae | patch  | logistic | aabc_sex  | test    |        100 | 7.9301 |  32.868 | 0.88236 |  0.044345 | 0.87824 | 0.045705 | 0.87665 |   0.045811 |
+
+
+done! total time: 0:04:55

data_scaling/n1600_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 n1600_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/abide_dx__patch__logistic
+remote_dir: null

data_scaling/n1600_1/eval_v2/abide_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,abide_dx,91,2.782559402207126,test,0.5967741935483871,0.042496161912757444,0.58994708994709,0.043359028820421676,0.5898109243697479,0.04300432856990718
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+flat_mae,patch,logistic,abide_dx,92,2.782559402207126,test,0.6048387096774194,0.043205146723902264,0.6004471624909581,0.04339853860387639,0.6003151260504203,0.04313197555109993
+flat_mae,patch,logistic,abide_dx,93,0.005994842503189409,train,0.7293447293447294,0.016044536881460743,0.7217470397289651,0.016774203226238387,0.7199704688076781,0.01651369954497792
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data_scaling/n1600_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 17:20:26
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:18:12    time: 3.7787  data: 2.9693  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:29    time: 0.1613  data: 0.0473  max mem: 2851
+extract (train)  [ 40/289]  eta: 0:00:59    time: 0.1364  data: 0.0340  max mem: 2851
+extract (train)  [ 60/289]  eta: 0:00:47    time: 0.1525  data: 0.0417  max mem: 2851
+extract (train)  [ 80/289]  eta: 0:00:40    time: 0.1450  data: 0.0375  max mem: 2851
+extract (train)  [100/289]  eta: 0:00:34    time: 0.1469  data: 0.0391  max mem: 2851
+extract (train)  [120/289]  eta: 0:00:30    time: 0.1501  data: 0.0415  max mem: 2851
+extract (train)  [140/289]  eta: 0:00:25    time: 0.1395  data: 0.0353  max mem: 2851
+extract (train)  [160/289]  eta: 0:00:21    time: 0.1447  data: 0.0394  max mem: 2851
+extract (train)  [180/289]  eta: 0:00:18    time: 0.1510  data: 0.0409  max mem: 2851
+extract (train)  [200/289]  eta: 0:00:14    time: 0.1626  data: 0.0450  max mem: 2851
+extract (train)  [220/289]  eta: 0:00:11    time: 0.1618  data: 0.0448  max mem: 2851
+extract (train)  [240/289]  eta: 0:00:08    time: 0.1402  data: 0.0355  max mem: 2851
+extract (train)  [260/289]  eta: 0:00:04    time: 0.1695  data: 0.0486  max mem: 2851
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1442  data: 0.0369  max mem: 2851
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1389  data: 0.0348  max mem: 2851
+extract (train) Total time: 0:00:47 (0.1640 s / it)
+extract (validation)  [ 0/62]  eta: 0:03:57    time: 3.8324  data: 3.6282  max mem: 2851
+extract (validation)  [20/62]  eta: 0:00:15    time: 0.2019  data: 0.0561  max mem: 2851
+extract (validation)  [40/62]  eta: 0:00:05    time: 0.1622  data: 0.0424  max mem: 2851
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1376  data: 0.0342  max mem: 2851
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1381  data: 0.0345  max mem: 2851
+extract (validation) Total time: 0:00:14 (0.2306 s / it)
+extract (test)  [ 0/62]  eta: 0:03:51    time: 3.7271  data: 3.5164  max mem: 2851
+extract (test)  [20/62]  eta: 0:00:16    time: 0.2174  data: 0.0644  max mem: 2851
+extract (test)  [40/62]  eta: 0:00:05    time: 0.1467  data: 0.0363  max mem: 2851
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1386  data: 0.0354  max mem: 2851
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1384  data: 0.0354  max mem: 2851
+extract (test) Total time: 0:00:14 (0.2290 s / it)
+feature extraction time: 0:01:16
+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   | 0.80769 |  0.015023 | 0.80469 | 0.015309 | 0.80356 |   0.015319 |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.046416 | test    | 0.64516 |  0.043225 | 0.64284 | 0.043592 | 0.64284 |   0.043448 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04315406600902564, "f1": 0.6418067226890756, "f1_std": 0.043853897294788585, "bacc": 0.6418067226890756, "bacc_std": 0.04374980580424382}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04283227215524843, "f1": 0.6569169960474308, "f1_std": 0.043587852597675246, "bacc": 0.6565126050420168, "bacc_std": 0.04327851883971466}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 10000.0, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04284558339723388, "f1": 0.6035753898349319, "f1_std": 0.042888159933636594, "bacc": 0.6050420168067226, "bacc_std": 0.04271828205266412}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 92, "C": 2.782559402207126, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.043205146723902264, "f1": 0.6004471624909581, "f1_std": 0.04339853860387639, "bacc": 0.6003151260504203, "bacc_std": 0.04313197555109993}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 93, "C": 0.005994842503189409, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04514774854576142, "f1": 0.5376584638329605, "f1_std": 0.04768133916298761, "bacc": 0.542016806722689, "bacc_std": 0.04567978406006964}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 94, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.044076047586684576, "f1": 0.6189604445897352, "f1_std": 0.04447211580082471, "bacc": 0.6197478991596639, "bacc_std": 0.044591352531199974}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04333056636220568, "f1": 0.6448884448884449, "f1_std": 0.04522132117033592, "bacc": 0.6444327731092437, "bacc_std": 0.04435618201376702}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 96, "C": 0.3593813663804626, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04540522828334925, "f1": 0.6351748937561295, "f1_std": 0.04555807068059736, "bacc": 0.6360294117647058, "bacc_std": 0.04550355303189617}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.041961356782870045, "f1": 0.6743697478991597, "f1_std": 0.04252339113435198, "bacc": 0.6743697478991597, "bacc_std": 0.04252518320339834}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04488942073197699, "f1": 0.5643931861867832, "f1_std": 0.04610272171366664, "bacc": 0.5646008403361344, "bacc_std": 0.04552168369616218}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04165320316646794, "f1": 0.6513893429225237, "f1_std": 0.041771605536080954, "bacc": 0.6523109243697479, "bacc_std": 0.04174381092633018}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04270227922962411, "f1": 0.6191239316239316, "f1_std": 0.04398560197563504, "bacc": 0.6192226890756303, "bacc_std": 0.0430599411110248}
+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 | 450.76 |  1971.7 | 0.89679 |  0.092351 | 0.89472 | 0.094941 | 0.8941  |   0.095465 |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 450.76 |  1971.7 | 0.62548 |  0.040982 | 0.61966 | 0.042188 | 0.62034 |   0.041933 |
+
+
+done! total time: 0:05:29

data_scaling/n1600_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 n1600_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

data_scaling/n1600_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,87,0.046415888336127774,test,0.5846153846153846,0.06171822973528003,0.5699583435432491,0.06466889068390361,0.5699806949806949,0.0633015003255363
+flat_mae,patch,logistic,adhd200_dx,88,0.000774263682681127,train,0.6986301369863014,0.023626606843718482,0.6867783810772015,0.024932194098526976,0.684939244061794,0.024450030141254516
+flat_mae,patch,logistic,adhd200_dx,88,0.000774263682681127,test,0.5692307692307692,0.05893200173422218,0.545,0.06342751147989714,0.5477799227799228,0.06035422217292734
+flat_mae,patch,logistic,adhd200_dx,89,0.000774263682681127,train,0.6986301369863014,0.022532127143532404,0.6830196425187898,0.024340032755200403,0.6813518959516395,0.023399732502318138
+flat_mae,patch,logistic,adhd200_dx,89,0.000774263682681127,test,0.6615384615384615,0.05229842451834082,0.6366869918699187,0.05807127810247345,0.6375482625482626,0.05435196774360066
+flat_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,train,0.7726027397260274,0.021425413573178936,0.7639197350477304,0.02271755733742019,0.7605177993527508,0.022477915738060166
+flat_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,test,0.6461538461538462,0.058365354074253833,0.6289401836684041,0.06364232891221606,0.6283783783783784,0.06071089643018875
+flat_mae,patch,logistic,adhd200_dx,91,0.046415888336127774,train,0.8575342465753425,0.018370041779441328,0.8539730411768327,0.01896553080118171,0.8515448494840325,0.019116424454876043
+flat_mae,patch,logistic,adhd200_dx,91,0.046415888336127774,test,0.5384615384615384,0.05467313090178346,0.5125,0.05817411414308972,0.5164092664092664,0.05537289452171042
+flat_mae,patch,logistic,adhd200_dx,92,0.005994842503189409,train,0.7506849315068493,0.020639049176503128,0.74059090447591,0.021915645770071168,0.737512975514441,0.02152060987961763
+flat_mae,patch,logistic,adhd200_dx,92,0.005994842503189409,test,0.6153846153846154,0.05465672108499845,0.5905769715293525,0.05998352883650437,0.5926640926640927,0.056329223481131084
+flat_mae,patch,logistic,adhd200_dx,93,0.005994842503189409,train,0.7561643835616438,0.02157270503557849,0.7462922032786373,0.022825910576414116,0.7430848140685107,0.02245759526190402
+flat_mae,patch,logistic,adhd200_dx,93,0.005994842503189409,test,0.6,0.048062090019488544,0.5427489177489178,0.05885037218273165,0.5617760617760618,0.049888139386200236
+flat_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,train,0.7397260273972602,0.02188914838541792,0.7303675710142223,0.022984216370794272,0.7278042376503633,0.022667099903711435
+flat_mae,patch,logistic,adhd200_dx,94,0.005994842503189409,test,0.676923076923077,0.05151375756955526,0.6431372549019607,0.06135123667299099,0.6467181467181468,0.05472765618837617
+flat_mae,patch,logistic,adhd200_dx,95,0.000774263682681127,train,0.6986301369863014,0.02488720073386717,0.6893761218047905,0.025855162290165698,0.6878091225499176,0.025580141301761308
+flat_mae,patch,logistic,adhd200_dx,95,0.000774263682681127,test,0.5384615384615384,0.06190759290403397,0.5192307692307693,0.06543764537688596,0.5207528957528957,0.06332721930480743
+flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,train,0.7479452054794521,0.022703175113961645,0.7411826365766432,0.023381532899840386,0.7393906087806069,0.023257137914097906
+flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,test,0.6461538461538462,0.0602544427392909,0.6431129147767964,0.060720651457537377,0.6457528957528957,0.060999149259355095
+flat_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,train,0.7780821917808219,0.021283615911043573,0.7685696169833032,0.0226973017780711,0.7646546986627587,0.02229178197844689
+flat_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,test,0.6153846153846154,0.05090052951910661,0.5656241646618552,0.06264535151441536,0.5796332046332047,0.053371860799706405
+flat_mae,patch,logistic,adhd200_dx,98,0.005994842503189409,train,0.7397260273972602,0.022053399768711488,0.729787648548607,0.023354671805246387,0.7270867680283324,0.022997980591105933
+flat_mae,patch,logistic,adhd200_dx,98,0.005994842503189409,test,0.6923076923076923,0.0566086618082072,0.6697154471544715,0.06270357711228097,0.6689189189189189,0.0587243208373348
+flat_mae,patch,logistic,adhd200_dx,99,0.005994842503189409,train,0.7671232876712328,0.023014702522135673,0.7602043576723012,0.02410223543389857,0.7578158392868046,0.024069057153170294
+flat_mae,patch,logistic,adhd200_dx,99,0.005994842503189409,test,0.5692307692307692,0.05758990707102855,0.5608108108108107,0.058365391499428834,0.5608108108108107,0.05808956055244975
+flat_mae,patch,logistic,adhd200_dx,100,0.3593813663804626,train,0.9643835616438357,0.009548248260751279,0.9636456968610415,0.009794317309887304,0.9619893753434695,0.01030868453289804
+flat_mae,patch,logistic,adhd200_dx,100,0.3593813663804626,test,0.6153846153846154,0.06101949635084874,0.6094688776736361,0.06163909468077069,0.61003861003861,0.06159971511889918

data_scaling/n1600_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 17:20:26
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:09:29    time: 3.7683  data: 2.9924  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:45    time: 0.1764  data: 0.0500  max mem: 2851
+extract (train)  [ 40/151]  eta: 0:00:28    time: 0.1602  data: 0.0409  max mem: 2851
+extract (train)  [ 60/151]  eta: 0:00:20    time: 0.1550  data: 0.0403  max mem: 2851
+extract (train)  [ 80/151]  eta: 0:00:14    time: 0.1488  data: 0.0384  max mem: 2851
+extract (train)  [100/151]  eta: 0:00:09    time: 0.1544  data: 0.0420  max mem: 2851
+extract (train)  [120/151]  eta: 0:00:05    time: 0.1550  data: 0.0403  max mem: 2851
+extract (train)  [140/151]  eta: 0:00:01    time: 0.1365  data: 0.0325  max mem: 2851
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1357  data: 0.0327  max mem: 2851
+extract (train) Total time: 0:00:27 (0.1798 s / it)
+extract (validation)  [ 0/32]  eta: 0:01:58    time: 3.7075  data: 3.5771  max mem: 2851
+extract (validation)  [20/32]  eta: 0:00:04    time: 0.1658  data: 0.0451  max mem: 2851
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1288  data: 0.0304  max mem: 2851
+extract (validation) Total time: 0:00:08 (0.2717 s / it)
+extract (test)  [ 0/33]  eta: 0:02:01    time: 3.6960  data: 3.4967  max mem: 2851
+extract (test)  [20/33]  eta: 0:00:04    time: 0.1699  data: 0.0438  max mem: 2851
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1318  data: 0.0317  max mem: 2851
+extract (test) Total time: 0:00:08 (0.2697 s / it)
+feature extraction time: 0:00:44
+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   | 0.7589  |  0.02163  | 0.7505  | 0.02265  | 0.74766 |   0.022401 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.0059948 | test    | 0.63077 |  0.058589 | 0.60366 | 0.065902 | 0.60618 |   0.061262 |
+
+
+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": 0.05687315898574434, "f1": 0.5775, "f1_std": 0.060290791754569605, "bacc": 0.5791505791505791, "bacc_std": 0.057933054764911535}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05578402138085886, "f1": 0.6289401836684041, "f1_std": 0.05968094560415457, "bacc": 0.6283783783783784, "bacc_std": 0.05752966114564144}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.000774263682681127, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05977697405539139, "f1": 0.5512820512820513, "f1_std": 0.062463142528234775, "bacc": 0.5521235521235521, "bacc_std": 0.06089063844054056}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.06090439496000094, "f1": 0.6407113674597452, "f1_std": 0.06188663055524596, "bacc": 0.6414092664092663, "bacc_std": 0.06189718308359479}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.06250698611842893, "f1": 0.5066413662239089, "f1_std": 0.06284448377491744, "bacc": 0.5111003861003861, "bacc_std": 0.06397993758826467}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05822523091231928, "f1": 0.6336682185738789, "f1_std": 0.060916278468128957, "bacc": 0.6327220077220077, "bacc_std": 0.05971745127252575}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05940116150519012, "f1": 0.5921814671814671, "f1_std": 0.06059235440050431, "bacc": 0.5921814671814671, "bacc_std": 0.060227089811499164}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.05772658673928373, "f1": 0.6934723256391164, "f1_std": 0.06203714844494515, "bacc": 0.6911196911196911, "bacc_std": 0.06015470888144337}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06393099830581951, "f1": 0.564176245210728, "f1_std": 0.06450444271486197, "bacc": 0.5651544401544402, "bacc_std": 0.06476597677360901}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05866893576390013, "f1": 0.5833333333333333, "f1_std": 0.06148898240968189, "bacc": 0.5834942084942085, "bacc_std": 0.05980521627926769}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.057128204207521974, "f1": 0.5699583435432491, "f1_std": 0.059513438420802985, "bacc": 0.5699806949806949, "bacc_std": 0.058284035366820204}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.058593300907549176, "f1": 0.5512820512820513, "f1_std": 0.061481823260508384, "bacc": 0.5521235521235521, "bacc_std": 0.05952379381804377}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05620752755500062, "f1": 0.6575670498084292, "f1_std": 0.05687973184477358, "bacc": 0.6592664092664093, "bacc_std": 0.05712165929996483}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 166.81005372000556, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.062257484044302876, "f1": 0.5521501544309813, "f1_std": 0.062437789287548354, "bacc": 0.555984555984556, "bacc_std": 0.06253361281411537}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06079342069665104, "f1": 0.5608108108108107, "f1_std": 0.06159096857721931, "bacc": 0.5608108108108107, "bacc_std": 0.061467094643038345}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.061070528632521444, "f1": 0.6235521235521235, "f1_std": 0.06221801201152397, "bacc": 0.6235521235521235, "bacc_std": 0.06208149045894029}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05686653324715949, "f1": 0.570630081300813, "f1_std": 0.06270939294419887, "bacc": 0.5748069498069498, "bacc_std": 0.058517693114287214}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05622191027763351, "f1": 0.6233308138070043, "f1_std": 0.06167850873617281, "bacc": 0.6240347490347491, "bacc_std": 0.05819478120036686}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.676923076923077, "acc_std": 0.05866280335136926, "f1": 0.6719538572458543, "f1_std": 0.060002484258066535, "bacc": 0.6727799227799228, "bacc_std": 0.060091977733538006}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05090052951910661, "f1": 0.5656241646618552, "f1_std": 0.06264535151441536, "bacc": 0.5796332046332047, "bacc_std": 0.053371860799706405}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.0566086618082072, "f1": 0.6697154471544715, "f1_std": 0.06270357711228097, "bacc": 0.6689189189189189, "bacc_std": 0.0587243208373348}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.06101949635084874, "f1": 0.6094688776736361, "f1_std": 0.06163909468077069, "bacc": 0.61003861003861, "bacc_std": 0.06159971511889918}
+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 | 1.9052 |  16.796 | 0.77334 |  0.065003 | 0.76427 | 0.069092 | 0.76176 |   0.068906 |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 1.9052 |  16.796 | 0.61354 |  0.052127 | 0.59663 | 0.053926 | 0.59856 |   0.052574 |
+
+
+done! total time: 0:04:31

data_scaling/n1600_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 n1600_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/adni_ad_vs_cn__patch__logistic
+remote_dir: null

data_scaling/n1600_1/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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data_scaling/n1600_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 17:20:33
+config:
+output_root: experiments/data_scaling/output
+name_prefix: eval_logistic
+remote_root: null
+notes: data scaling experiment n1600_1; eval v2 (adni_ad_vs_cn patch logistic)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_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: 0:10:43    time: 3.9212  data: 3.1305  max mem: 2698
+extract (train)  [ 20/164]  eta: 0:00:51    time: 0.1772  data: 0.0521  max mem: 2851
+extract (train)  [ 40/164]  eta: 0:00:31    time: 0.1505  data: 0.0389  max mem: 2851
+extract (train)  [ 60/164]  eta: 0:00:23    time: 0.1597  data: 0.0439  max mem: 2851
+extract (train)  [ 80/164]  eta: 0:00:17    time: 0.1488  data: 0.0391  max mem: 2851
+extract (train)  [100/164]  eta: 0:00:12    time: 0.1588  data: 0.0435  max mem: 2851
+extract (train)  [120/164]  eta: 0:00:08    time: 0.1377  data: 0.0336  max mem: 2851
+extract (train)  [140/164]  eta: 0:00:04    time: 0.1613  data: 0.0435  max mem: 2851
+extract (train)  [160/164]  eta: 0:00:00    time: 0.1356  data: 0.0327  max mem: 2851
+extract (train)  [163/164]  eta: 0:00:00    time: 0.1343  data: 0.0323  max mem: 2851
+extract (train) Total time: 0:00:29 (0.1781 s / it)
+extract (validation)  [ 0/21]  eta: 0:01:11    time: 3.4228  data: 3.3173  max mem: 2851
+extract (validation)  [20/21]  eta: 0:00:00    time: 0.1354  data: 0.0322  max mem: 2851
+extract (validation) Total time: 0:00:06 (0.3043 s / it)
+extract (test)  [ 0/21]  eta: 0:01:11    time: 3.3996  data: 3.2866  max mem: 2851
+extract (test)  [20/21]  eta: 0:00:00    time: 0.1294  data: 0.0321  max mem: 2851
+extract (test) Total time: 0:00:06 (0.2965 s / it)
+feature extraction time: 0:00:41
+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 |         | 2.7826 | train   | 1       |   0       | 1       | 0        | 1       |   0        |
+| flat_mae | patch  | logistic | adni_ad_vs_cn |         | 2.7826 | test    | 0.68293 |   0.07119 | 0.60722 | 0.081977 | 0.63715 |   0.094539 |
+
+
+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": 0.8292682926829268, "acc_std": 0.044615456569465285, "f1": 0.7144278606965174, "f1_std": 0.09340480274059657, "bacc": 0.6838709677419355, "bacc_std": 0.08078069230787673}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.06538905667538786, "f1": 0.5370967741935484, "f1_std": 0.08187743070866882, "bacc": 0.5370967741935484, "bacc_std": 0.08222176928546894}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.03283097157905182, "f1": 0.4142857142857143, "f1_std": 0.011405586722440925, "bacc": 0.46774193548387094, "bacc_std": 0.021710803786147183}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.8780487804878049, "acc_std": 0.04911444222889114, "f1": 0.8144796380090498, "f1_std": 0.0856773303969942, "bacc": 0.7838709677419355, "bacc_std": 0.08576489388982769}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.05299983724846774, "f1": 0.6117424242424243, "f1_std": 0.09074299984464605, "bacc": 0.6016129032258064, "bacc_std": 0.07851778565326317}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.055439963294014204, "f1": 0.6660633484162897, "f1_std": 0.09086103084408982, "bacc": 0.6516129032258065, "bacc_std": 0.0833982256877615}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 21.54434690031882, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06298004934191503, "f1": 0.7119437939110069, "f1_std": 0.0836242294036542, "bacc": 0.7193548387096774, "bacc_std": 0.08865864872507646}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.062276847310106964, "f1": 0.6693548387096775, "f1_std": 0.08344388183577806, "bacc": 0.6693548387096775, "bacc_std": 0.08534050516032682}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 9, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06852453674147349, "f1": 0.6479313036690086, "f1_std": 0.08802308060663055, "bacc": 0.6532258064516129, "bacc_std": 0.09222527383659629}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.05752977657993554, "f1": 0.5918552036199095, "f1_std": 0.08764680134574954, "bacc": 0.5854838709677419, "bacc_std": 0.07860463270722981}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 11, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04608377649109437, "f1": 0.6328358208955224, "f1_std": 0.08553723647886512, "bacc": 0.6177419354838709, "bacc_std": 0.07055688187274561}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 12, "C": 2.782559402207126, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06818639813002635, "f1": 0.6835087719298245, "f1_std": 0.07446293809244599, "bacc": 0.7209677419354839, "bacc_std": 0.08189114491191579}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 13, "C": 21.54434690031882, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06129624964145098, "f1": 0.5729166666666666, "f1_std": 0.0851123418248348, "bacc": 0.5693548387096774, "bacc_std": 0.07940571900183079}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 14, "C": 0.046415888336127774, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.055148515075662416, "f1": 0.7152777777777778, "f1_std": 0.08295335683947047, "bacc": 0.7016129032258065, "bacc_std": 0.08160677931888531}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04905471698801797, "f1": 0.6328358208955224, "f1_std": 0.0935793641588938, "bacc": 0.6177419354838709, "bacc_std": 0.07665219800434285}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05054553080260012, "f1": 0.6893939393939394, "f1_std": 0.09268677112506415, "bacc": 0.667741935483871, "bacc_std": 0.08232770714141972}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 17, "C": 10000.0, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.07240804133335124, "f1": 0.5467943994104643, "f1_std": 0.08136393805526873, "bacc": 0.5548387096774194, "bacc_std": 0.08832380498220657}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 18, "C": 2.782559402207126, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06014014962293201, "f1": 0.6440972222222222, "f1_std": 0.0909942791935425, "bacc": 0.635483870967742, "bacc_std": 0.08612247232478888}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 19, "C": 21.54434690031882, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06663950269511851, "f1": 0.6693548387096775, "f1_std": 0.08651149879664517, "bacc": 0.6693548387096775, "bacc_std": 0.08716104462609528}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.061023902827009785, "f1": 0.6917293233082706, "f1_std": 0.0859515648188523, "bacc": 0.685483870967742, "bacc_std": 0.08622919505065721}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 21, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.050141975704424815, "f1": 0.6893939393939394, "f1_std": 0.09304177549058085, "bacc": 0.667741935483871, "bacc_std": 0.08195253467493364}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 23, "C": 0.046415888336127774, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.044049185639494816, "f1": 0.6554621848739496, "f1_std": 0.0971305937716581, "bacc": 0.6338709677419355, "bacc_std": 0.07753342161430543}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 24, "C": 0.046415888336127774, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06429009210516919, "f1": 0.6232247284878863, "f1_std": 0.08753730109380746, "bacc": 0.6193548387096774, "bacc_std": 0.08564302141789398}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06811564159998082, "f1": 0.6272727272727273, "f1_std": 0.08113468788804802, "bacc": 0.6370967741935484, "bacc_std": 0.08670078553438183}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 26, "C": 0.046415888336127774, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06170565889992301, "f1": 0.6440972222222222, "f1_std": 0.08828459934581037, "bacc": 0.635483870967742, "bacc_std": 0.0828625453096422}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 35, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.0470667504783885, "f1": 0.6328358208955224, "f1_std": 0.09430909214066663, "bacc": 0.6177419354838709, "bacc_std": 0.07652525376697483}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.03855522858702796, "f1": 0.5886287625418061, "f1_std": 0.09388485894110113, "bacc": 0.5838709677419355, "bacc_std": 0.0675461314593965}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 41, "C": 166.81005372000556, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.07020790066569005, "f1": 0.6259649122807017, "f1_std": 0.07632025330626781, "bacc": 0.6548387096774193, "bacc_std": 0.08646945386443067}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 42, "C": 0.046415888336127774, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.04919734084503436, "f1": 0.5512437810945273, "f1_std": 0.08384285561479822, "bacc": 0.5516129032258065, "bacc_std": 0.06814751666095509}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 43, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.056199779617275535, "f1": 0.7152777777777778, "f1_std": 0.08306519300058633, "bacc": 0.7016129032258065, "bacc_std": 0.08249847039227791}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 46, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06067822113352215, "f1": 0.5918552036199095, "f1_std": 0.09384681027510482, "bacc": 0.5854838709677419, "bacc_std": 0.08471789508822196}
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+eval results (random splits):
+
+| model    | repr   | clf      | dataset       | split   |   n_trials |      C |   C_std |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:--------------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | adni_ad_vs_cn | train   |        100 | 239.25 |  1408.3 | 0.95084 |  0.054517 | 0.92202 | 0.090557 | 0.90785 |   0.1033   |
+| flat_mae | patch  | logistic | adni_ad_vs_cn | test    |        100 | 239.25 |  1408.3 | 0.73927 |  0.063894 | 0.62514 | 0.080067 | 0.62537 |   0.072669 |
+
+
+done! total time: 0:04:31

data_scaling/n1600_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 n1600_1; eval v2 (hcpya_task21 patch attn)
+model_kwargs:
+  ckpt_path: experiments/data_scaling/output/data_scaling/n1600_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/n1600_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/n1600_1/eval_v2/hcpya_task21__patch__attn
+remote_dir: null

data_scaling/n1600_1/eval_v2/hcpya_task21__patch__attn/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 14, "eval/id_best": 35, "eval/lr_best": 0.0018, "eval/wd_best": 0.05, "eval/train/loss": 4.079829159309156e-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": 0.04726891964673996, "eval/validation/acc": 0.9923115079365079, "eval/validation/acc_std": 0.001394593143699758, "eval/validation/f1": 0.9909580918726532, "eval/validation/f1_std": 0.0018312636287125701, "eval/test/loss": 0.0648389458656311, "eval/test/acc": 0.9900793650793651, "eval/test/acc_std": 0.0013837034151484417, "eval/test/f1": 0.9885227082201382, "eval/test/f1_std": 0.001718861469679251}

data_scaling/n1600_1/eval_v2/hcpya_task21__patch__attn/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 14, "eval/best/id_best": 35, "eval/best/lr_best": 0.0018, "eval/best/wd_best": 0.05, "eval/best/train/loss": 4.079829159309156e-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": 0.04726891964673996, "eval/best/validation/acc": 0.9923115079365079, "eval/best/validation/acc_std": 0.001394593143699758, "eval/best/validation/f1": 0.9909580918726532, "eval/best/validation/f1_std": 0.0018312636287125701, "eval/best/test/loss": 0.0648389458656311, "eval/best/test/acc": 0.9900793650793651, "eval/best/test/acc_std": 0.0013837034151484417, "eval/best/test/f1": 0.9885227082201382, "eval/best/test/f1_std": 0.001718861469679251}

data_scaling/n1600_1/eval_v2/hcpya_task21__patch__attn/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 35, "eval/last/lr_best": 0.0018, "eval/last/wd_best": 0.05, "eval/last/train/loss": 3.980837209383026e-05, "eval/last/train/acc": 1.0, "eval/last/train/acc_std": 0.0, "eval/last/train/f1": 1.0, "eval/last/train/f1_std": 0.0, "eval/last/validation/loss": 0.04662461578845978, "eval/last/validation/acc": 0.9923115079365079, "eval/last/validation/acc_std": 0.001377199247868492, "eval/last/validation/f1": 0.9910555649604573, "eval/last/validation/f1_std": 0.0018081433238288382, "eval/last/test/loss": 0.06386362761259079, "eval/last/test/acc": 0.9902777777777778, "eval/last/test/acc_std": 0.0013742425018964562, "eval/last/test/f1": 0.9886659786745963, "eval/last/test/f1_std": 0.0017185115791900094}

data_scaling/n1600_1/eval_v2/hcpya_task21__patch__attn/eval_table.csv

@@ -0,0 +1,4 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",train,4.079829159309156e-05,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",validation,0.04726891964673996,0.9923115079365079,0.001394593143699758,0.9909580918726532,0.0018312636287125701
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",test,0.0648389458656311,0.9900793650793651,0.0013837034151484417,0.9885227082201382,0.001718861469679251

data_scaling/n1600_1/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv

@@ -0,0 +1,4 @@
+model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",train,4.079829159309156e-05,1.0,0.0,1.0,0.0
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",validation,0.04726891964673996,0.9923115079365079,0.001394593143699758,0.9909580918726532,0.0018312636287125701
+flat_mae,patch,attn,hcpya_task21,best,14,0.0018,0.05,35,"[6, 1.0]",test,0.0648389458656311,0.9900793650793651,0.0013837034151484417,0.9885227082201382,0.001718861469679251