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decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/log.txt

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+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 22:56:30
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_probe
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (nsd_cococlip patch attn)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: false
+  norm: false
+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: decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn
+model: flat_mae
+representation: patch
+classifier: attn
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, no_embed_class=True, mask_drop_scale=False
+    (patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
+    (patch_embed): Linear(in_features=1024, out_features=768, bias=True)
+    (pos_embed): SeparablePosEmbed(768, (4, 14, 35))
+    (blocks): ModuleList(
+      (0-11): 12 x Block(
+        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (attn): Attention(
+          num_heads=12
+          (q): Linear(in_features=768, out_features=768, bias=True)
+          (k): Linear(in_features=768, out_features=768, bias=True)
+          (v): Linear(in_features=768, out_features=768, bias=True)
+          (proj): Linear(in_features=768, out_features=768, bias=True)
+        )
+        (drop_path1): Identity()
+        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+        (mlp): Mlp(
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (act): GELU(approximate='none')
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+        )
+        (drop_path2): Identity()
+      )
+    )
+    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
+  )
+)
+creating dataset: nsd_cococlip (flat)
+train (n=32539):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 32539
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[1286 1180 1639 1868  834  824 1026 1042  913 1853 1503 2092 1001 1410
+  794 1241 1904 1872 2267 1428  889  904 1447 1322]
+)
+
+validation (n=5418):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5418
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[197 161 276 345 126 142 143 185 112 295 285 387 169 250 159 193 316 334
+ 343 215 172 141 226 246]
+)
+
+test (n=5390):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5390
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[202 172 274 298 144 180 134 182 186 293 218 343 165 185 140 177 346 333
+ 345 271 165 140 251 246]
+)
+
+testid (n=5187):
+HFDataset(
+    dataset=Dataset({
+    features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
+    num_rows: 5187
+}),
+    labels=[ 3  4  5  6 10 11 17 18 19 20 22 23 25 30 31 33 36 37 38 53 55 59 61 74],
+    counts=[197 159 267 273 123 153 175 184 139 310 215 386 153 230 118 192 330 306
+ 349 223 143 127 249 186]
+)
+
+running backbone on example batch to get embedding dim
+embedding feature dim (patch): 768
+initializing sweep of classifier heads
+classifiers:
+ModuleList(
+  (0-48): 49 x AttnPoolClassifier(
+    (kv): Linear(in_features=768, out_features=1536, bias=True)
+    (linear): Linear(in_features=768, out_features=24, bias=True)
+  )
+)
+classifier params (train): 58.8M (58.8M)
+setting up optimizer
+total batch size: 128 = 64 bs per gpu x 2 accum
+lr: 3.00e-04
+full schedule: epochs = 20 (steps = 4000) (decay = True)
+warmup: epochs = 5 (steps = 1000)
+start training for 20 epochs
+train: [0]  [  0/400]  eta: 0:24:55  lr: nan  time: 3.7392  data: 3.1189  max mem: 21740
+train: [0]  [ 20/400]  eta: 0:04:00  lr: 0.000003  loss: 3.1832 (3.1836)  grad: 0.0656 (0.0658)  time: 0.4766  data: 0.0028  max mem: 22448
+train: [0]  [ 40/400]  eta: 0:03:16  lr: 0.000006  loss: 3.1826 (3.1819)  grad: 0.0628 (0.0629)  time: 0.4565  data: 0.0049  max mem: 22448
+train: [0]  [ 60/400]  eta: 0:02:54  lr: 0.000009  loss: 3.1702 (3.1756)  grad: 0.0585 (0.0611)  time: 0.4441  data: 0.0044  max mem: 22448
+train: [0]  [ 80/400]  eta: 0:02:39  lr: 0.000012  loss: 3.1592 (3.1711)  grad: 0.0600 (0.0619)  time: 0.4536  data: 0.0049  max mem: 22448
+train: [0]  [100/400]  eta: 0:02:26  lr: 0.000015  loss: 3.1544 (3.1684)  grad: 0.0620 (0.0620)  time: 0.4503  data: 0.0047  max mem: 22448
+train: [0]  [120/400]  eta: 0:02:15  lr: 0.000018  loss: 3.1576 (3.1672)  grad: 0.0605 (0.0619)  time: 0.4580  data: 0.0046  max mem: 22448
+train: [0]  [140/400]  eta: 0:02:04  lr: 0.000021  loss: 3.1577 (3.1662)  grad: 0.0584 (0.0614)  time: 0.4571  data: 0.0046  max mem: 22448
+train: [0]  [160/400]  eta: 0:01:54  lr: 0.000024  loss: 3.1545 (3.1645)  grad: 0.0589 (0.0615)  time: 0.4588  data: 0.0046  max mem: 22448
+train: [0]  [180/400]  eta: 0:01:44  lr: 0.000027  loss: 3.1545 (3.1635)  grad: 0.0584 (0.0614)  time: 0.4646  data: 0.0048  max mem: 22448
+train: [0]  [200/400]  eta: 0:01:34  lr: 0.000030  loss: 3.1546 (3.1622)  grad: 0.0571 (0.0610)  time: 0.4618  data: 0.0049  max mem: 22448
+train: [0]  [220/400]  eta: 0:01:25  lr: 0.000033  loss: 3.1326 (3.1588)  grad: 0.0602 (0.0615)  time: 0.4582  data: 0.0047  max mem: 22448
+train: [0]  [240/400]  eta: 0:01:15  lr: 0.000036  loss: 3.1312 (3.1562)  grad: 0.0665 (0.0619)  time: 0.4478  data: 0.0048  max mem: 22448
+train: [0]  [260/400]  eta: 0:01:05  lr: 0.000039  loss: 3.1334 (3.1538)  grad: 0.0672 (0.0625)  time: 0.4589  data: 0.0048  max mem: 22448
+train: [0]  [280/400]  eta: 0:00:56  lr: 0.000042  loss: 3.1168 (3.1511)  grad: 0.0700 (0.0631)  time: 0.4450  data: 0.0045  max mem: 22448
+train: [0]  [300/400]  eta: 0:00:46  lr: 0.000045  loss: 3.1093 (3.1473)  grad: 0.0741 (0.0642)  time: 0.4558  data: 0.0049  max mem: 22448
+train: [0]  [320/400]  eta: 0:00:37  lr: 0.000048  loss: 3.0842 (3.1431)  grad: 0.0807 (0.0651)  time: 0.4593  data: 0.0047  max mem: 22448
+train: [0]  [340/400]  eta: 0:00:27  lr: 0.000051  loss: 3.0729 (3.1389)  grad: 0.0807 (0.0660)  time: 0.4520  data: 0.0045  max mem: 22448
+train: [0]  [360/400]  eta: 0:00:18  lr: 0.000054  loss: 3.0650 (3.1339)  grad: 0.0853 (0.0673)  time: 0.4535  data: 0.0044  max mem: 22448
+train: [0]  [380/400]  eta: 0:00:09  lr: 0.000057  loss: 3.0628 (3.1300)  grad: 0.0916 (0.0686)  time: 0.4671  data: 0.0047  max mem: 22448
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.0485 (3.1249)  grad: 0.0957 (0.0701)  time: 0.4557  data: 0.0048  max mem: 22448
+train: [0] Total time: 0:03:06 (0.4656 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.0485 (3.1249)  grad: 0.0957 (0.0701)
+eval (validation): [0]  [ 0/85]  eta: 0:04:34    time: 3.2325  data: 2.9472  max mem: 22448
+eval (validation): [0]  [20/85]  eta: 0:00:33    time: 0.3834  data: 0.0063  max mem: 22448
+eval (validation): [0]  [40/85]  eta: 0:00:19    time: 0.3266  data: 0.0035  max mem: 22448
+eval (validation): [0]  [60/85]  eta: 0:00:09    time: 0.3336  data: 0.0040  max mem: 22448
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3260  data: 0.0041  max mem: 22448
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3193  data: 0.0041  max mem: 22448
+eval (validation): [0] Total time: 0:00:32 (0.3777 s / it)
+cv: [0]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 2.541  acc: 0.237  f1: 0.159
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:24  lr: nan  time: 3.3621  data: 2.9400  max mem: 22448
+train: [1]  [ 20/400]  eta: 0:03:53  lr: 0.000063  loss: 3.0199 (3.0311)  grad: 0.0985 (0.0994)  time: 0.4784  data: 0.0029  max mem: 22448
+train: [1]  [ 40/400]  eta: 0:03:12  lr: 0.000066  loss: 3.0101 (3.0162)  grad: 0.0985 (0.0975)  time: 0.4517  data: 0.0048  max mem: 22448
+train: [1]  [ 60/400]  eta: 0:02:52  lr: 0.000069  loss: 3.0071 (3.0169)  grad: 0.0924 (0.0960)  time: 0.4458  data: 0.0048  max mem: 22448
+train: [1]  [ 80/400]  eta: 0:02:38  lr: 0.000072  loss: 3.0141 (3.0173)  grad: 0.0931 (0.0962)  time: 0.4588  data: 0.0049  max mem: 22448
+train: [1]  [100/400]  eta: 0:02:26  lr: 0.000075  loss: 2.9947 (3.0068)  grad: 0.0965 (0.0967)  time: 0.4564  data: 0.0047  max mem: 22448
+train: [1]  [120/400]  eta: 0:02:15  lr: 0.000078  loss: 2.9657 (3.0015)  grad: 0.1062 (0.0999)  time: 0.4593  data: 0.0048  max mem: 22448
+train: [1]  [140/400]  eta: 0:02:04  lr: 0.000081  loss: 2.9748 (2.9973)  grad: 0.1029 (0.0993)  time: 0.4554  data: 0.0047  max mem: 22448
+train: [1]  [160/400]  eta: 0:01:54  lr: 0.000084  loss: 2.9728 (2.9922)  grad: 0.0999 (0.1004)  time: 0.4562  data: 0.0049  max mem: 22448
+train: [1]  [180/400]  eta: 0:01:44  lr: 0.000087  loss: 2.9694 (2.9877)  grad: 0.1066 (0.1013)  time: 0.4577  data: 0.0047  max mem: 22448
+train: [1]  [200/400]  eta: 0:01:34  lr: 0.000090  loss: 2.9567 (2.9838)  grad: 0.1066 (0.1016)  time: 0.4596  data: 0.0047  max mem: 22448
+train: [1]  [220/400]  eta: 0:01:25  lr: 0.000093  loss: 2.9334 (2.9796)  grad: 0.1072 (0.1026)  time: 0.4718  data: 0.0047  max mem: 22448
+train: [1]  [240/400]  eta: 0:01:15  lr: 0.000096  loss: 2.9427 (2.9765)  grad: 0.1162 (0.1038)  time: 0.4508  data: 0.0047  max mem: 22448
+train: [1]  [260/400]  eta: 0:01:05  lr: 0.000099  loss: 2.9250 (2.9718)  grad: 0.1162 (0.1046)  time: 0.4531  data: 0.0048  max mem: 22448
+train: [1]  [280/400]  eta: 0:00:56  lr: 0.000102  loss: 2.8963 (2.9668)  grad: 0.1146 (0.1055)  time: 0.4612  data: 0.0046  max mem: 22448
+train: [1]  [300/400]  eta: 0:00:46  lr: 0.000105  loss: 2.9149 (2.9639)  grad: 0.1115 (0.1059)  time: 0.4579  data: 0.0046  max mem: 22448
+train: [1]  [320/400]  eta: 0:00:37  lr: 0.000108  loss: 2.9239 (2.9611)  grad: 0.1120 (0.1068)  time: 0.4726  data: 0.0049  max mem: 22448
+train: [1]  [340/400]  eta: 0:00:28  lr: 0.000111  loss: 2.8879 (2.9562)  grad: 0.1230 (0.1078)  time: 0.4618  data: 0.0048  max mem: 22448
+train: [1]  [360/400]  eta: 0:00:18  lr: 0.000114  loss: 2.8760 (2.9528)  grad: 0.1195 (0.1085)  time: 0.4631  data: 0.0048  max mem: 22448
+train: [1]  [380/400]  eta: 0:00:09  lr: 0.000117  loss: 2.8755 (2.9500)  grad: 0.1187 (0.1092)  time: 0.4601  data: 0.0048  max mem: 22448
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 2.8940 (2.9479)  grad: 0.1224 (0.1099)  time: 0.4708  data: 0.0048  max mem: 22448
+train: [1] Total time: 0:03:07 (0.4679 s / it)
+train: [1] Summary: lr: 0.000120  loss: 2.8940 (2.9479)  grad: 0.1224 (0.1099)
+eval (validation): [1]  [ 0/85]  eta: 0:04:32    time: 3.2008  data: 2.9220  max mem: 22448
+eval (validation): [1]  [20/85]  eta: 0:00:32    time: 0.3654  data: 0.0049  max mem: 22448
+eval (validation): [1]  [40/85]  eta: 0:00:18    time: 0.3245  data: 0.0040  max mem: 22448
+eval (validation): [1]  [60/85]  eta: 0:00:09    time: 0.3496  data: 0.0040  max mem: 22448
+eval (validation): [1]  [80/85]  eta: 0:00:01    time: 0.3447  data: 0.0043  max mem: 22448
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.3389  data: 0.0042  max mem: 22448
+eval (validation): [1] Total time: 0:00:32 (0.3823 s / it)
+cv: [1]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 2.490  acc: 0.254  f1: 0.183
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:22:34  lr: nan  time: 3.3865  data: 2.9748  max mem: 22448
+train: [2]  [ 20/400]  eta: 0:03:46  lr: 0.000123  loss: 2.8588 (2.8603)  grad: 0.1228 (0.1260)  time: 0.4567  data: 0.0038  max mem: 22448
+train: [2]  [ 40/400]  eta: 0:03:10  lr: 0.000126  loss: 2.8441 (2.8389)  grad: 0.1259 (0.1291)  time: 0.4601  data: 0.0048  max mem: 22448
+train: [2]  [ 60/400]  eta: 0:02:51  lr: 0.000129  loss: 2.8232 (2.8379)  grad: 0.1322 (0.1310)  time: 0.4550  data: 0.0051  max mem: 22448
+train: [2]  [ 80/400]  eta: 0:02:37  lr: 0.000132  loss: 2.8416 (2.8483)  grad: 0.1330 (0.1317)  time: 0.4554  data: 0.0048  max mem: 22448
+train: [2]  [100/400]  eta: 0:02:25  lr: 0.000135  loss: 2.8670 (2.8513)  grad: 0.1382 (0.1352)  time: 0.4562  data: 0.0047  max mem: 22448
+train: [2]  [120/400]  eta: 0:02:14  lr: 0.000138  loss: 2.8486 (2.8485)  grad: 0.1388 (0.1358)  time: 0.4620  data: 0.0049  max mem: 22448
+train: [2]  [140/400]  eta: 0:02:04  lr: 0.000141  loss: 2.8444 (2.8490)  grad: 0.1379 (0.1371)  time: 0.4662  data: 0.0049  max mem: 22448
+train: [2]  [160/400]  eta: 0:01:54  lr: 0.000144  loss: 2.8340 (2.8465)  grad: 0.1486 (0.1390)  time: 0.4594  data: 0.0046  max mem: 22448
+train: [2]  [180/400]  eta: 0:01:44  lr: 0.000147  loss: 2.8234 (2.8443)  grad: 0.1532 (0.1412)  time: 0.4768  data: 0.0048  max mem: 22448
+train: [2]  [200/400]  eta: 0:01:35  lr: 0.000150  loss: 2.8362 (2.8474)  grad: 0.1556 (0.1428)  time: 0.4615  data: 0.0048  max mem: 22448
+train: [2]  [220/400]  eta: 0:01:25  lr: 0.000153  loss: 2.8389 (2.8454)  grad: 0.1490 (0.1432)  time: 0.4593  data: 0.0048  max mem: 22448
+train: [2]  [240/400]  eta: 0:01:15  lr: 0.000156  loss: 2.8300 (2.8418)  grad: 0.1446 (0.1432)  time: 0.4589  data: 0.0046  max mem: 22448
+train: [2]  [260/400]  eta: 0:01:06  lr: 0.000159  loss: 2.7906 (2.8373)  grad: 0.1416 (0.1432)  time: 0.4612  data: 0.0047  max mem: 22448
+train: [2]  [280/400]  eta: 0:00:56  lr: 0.000162  loss: 2.7605 (2.8346)  grad: 0.1420 (0.1432)  time: 0.4633  data: 0.0045  max mem: 22448
+train: [2]  [300/400]  eta: 0:00:47  lr: 0.000165  loss: 2.8248 (2.8359)  grad: 0.1464 (0.1437)  time: 0.4593  data: 0.0047  max mem: 22448
+train: [2]  [320/400]  eta: 0:00:37  lr: 0.000168  loss: 2.8130 (2.8322)  grad: 0.1442 (0.1436)  time: 0.4607  data: 0.0047  max mem: 22448
+train: [2]  [340/400]  eta: 0:00:28  lr: 0.000171  loss: 2.7704 (2.8296)  grad: 0.1409 (0.1438)  time: 0.4582  data: 0.0047  max mem: 22448
+train: [2]  [360/400]  eta: 0:00:18  lr: 0.000174  loss: 2.7586 (2.8267)  grad: 0.1418 (0.1441)  time: 0.4606  data: 0.0048  max mem: 22448
+train: [2]  [380/400]  eta: 0:00:09  lr: 0.000177  loss: 2.7900 (2.8255)  grad: 0.1483 (0.1444)  time: 0.4683  data: 0.0046  max mem: 22448
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 2.7997 (2.8243)  grad: 0.1486 (0.1446)  time: 0.4559  data: 0.0050  max mem: 22448
+train: [2] Total time: 0:03:07 (0.4686 s / it)
+train: [2] Summary: lr: 0.000180  loss: 2.7997 (2.8243)  grad: 0.1486 (0.1446)
+eval (validation): [2]  [ 0/85]  eta: 0:04:31    time: 3.1920  data: 2.9057  max mem: 22448
+eval (validation): [2]  [20/85]  eta: 0:00:31    time: 0.3483  data: 0.0048  max mem: 22448
+eval (validation): [2]  [40/85]  eta: 0:00:18    time: 0.3473  data: 0.0039  max mem: 22448
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3511  data: 0.0044  max mem: 22448
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3253  data: 0.0039  max mem: 22448
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3169  data: 0.0039  max mem: 22448
+eval (validation): [2] Total time: 0:00:32 (0.3783 s / it)
+cv: [2]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 2.473  acc: 0.250  f1: 0.176
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [3]  [  0/400]  eta: 0:21:53  lr: nan  time: 3.2826  data: 2.8723  max mem: 22448
+train: [3]  [ 20/400]  eta: 0:03:44  lr: 0.000183  loss: 2.7278 (2.7504)  grad: 0.1505 (0.1579)  time: 0.4562  data: 0.0048  max mem: 22448
+train: [3]  [ 40/400]  eta: 0:03:09  lr: 0.000186  loss: 2.7490 (2.7629)  grad: 0.1670 (0.1661)  time: 0.4593  data: 0.0045  max mem: 22448
+train: [3]  [ 60/400]  eta: 0:02:51  lr: 0.000189  loss: 2.7355 (2.7536)  grad: 0.1647 (0.1630)  time: 0.4575  data: 0.0049  max mem: 22448
+train: [3]  [ 80/400]  eta: 0:02:36  lr: 0.000192  loss: 2.7086 (2.7445)  grad: 0.1600 (0.1651)  time: 0.4476  data: 0.0051  max mem: 22448
+train: [3]  [100/400]  eta: 0:02:24  lr: 0.000195  loss: 2.7523 (2.7530)  grad: 0.1733 (0.1699)  time: 0.4541  data: 0.0049  max mem: 22448
+train: [3]  [120/400]  eta: 0:02:14  lr: 0.000198  loss: 2.8161 (2.7771)  grad: 0.2253 (0.2089)  time: 0.4609  data: 0.0048  max mem: 22448
+WARNING: classifier 48 (50, 1.0) diverged (loss=84.15 > 63.56) at step 665. Freezing.
+train: [3]  [140/400]  eta: 0:02:03  lr: 0.000201  loss: 2.8529 (2.8387)  grad: 0.3532 (0.2663)  time: 0.4558  data: 0.0048  max mem: 22448
+train: [3]  [160/400]  eta: 0:01:53  lr: 0.000204  loss: 2.8340 (2.8302)  grad: 0.1568 (0.2522)  time: 0.4510  data: 0.0047  max mem: 22448
+train: [3]  [180/400]  eta: 0:01:43  lr: 0.000207  loss: 2.7693 (2.8247)  grad: 0.1559 (0.2417)  time: 0.4491  data: 0.0048  max mem: 22448
+train: [3]  [200/400]  eta: 0:01:33  lr: 0.000210  loss: 2.8063 (2.8236)  grad: 0.1562 (0.2337)  time: 0.4580  data: 0.0052  max mem: 22448
+train: [3]  [220/400]  eta: 0:01:24  lr: 0.000213  loss: 2.7817 (2.8158)  grad: 0.1620 (0.2275)  time: 0.4598  data: 0.0048  max mem: 22448
+train: [3]  [240/400]  eta: 0:01:14  lr: 0.000216  loss: 2.7265 (2.8076)  grad: 0.1628 (0.2219)  time: 0.4469  data: 0.0048  max mem: 22448
+train: [3]  [260/400]  eta: 0:01:05  lr: 0.000219  loss: 2.7440 (2.8039)  grad: 0.1629 (0.2174)  time: 0.4526  data: 0.0048  max mem: 22448
+train: [3]  [280/400]  eta: 0:00:55  lr: 0.000222  loss: 2.7631 (2.8002)  grad: 0.1622 (0.2138)  time: 0.4554  data: 0.0049  max mem: 22448
+train: [3]  [300/400]  eta: 0:00:46  lr: 0.000225  loss: 2.7559 (2.7964)  grad: 0.1599 (0.2103)  time: 0.4531  data: 0.0046  max mem: 22448
+train: [3]  [320/400]  eta: 0:00:37  lr: 0.000228  loss: 2.7241 (2.7925)  grad: 0.1599 (0.2073)  time: 0.4531  data: 0.0045  max mem: 22448
+train: [3]  [340/400]  eta: 0:00:27  lr: 0.000231  loss: 2.7235 (2.7876)  grad: 0.1627 (0.2050)  time: 0.4658  data: 0.0048  max mem: 22448
+train: [3]  [360/400]  eta: 0:00:18  lr: 0.000234  loss: 2.7110 (2.7840)  grad: 0.1561 (0.2022)  time: 0.4575  data: 0.0049  max mem: 22448
+train: [3]  [380/400]  eta: 0:00:09  lr: 0.000237  loss: 2.7266 (2.7815)  grad: 0.1561 (0.2001)  time: 0.4767  data: 0.0052  max mem: 22448
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 2.7305 (2.7776)  grad: 0.1711 (0.1991)  time: 0.4610  data: 0.0051  max mem: 22448
+train: [3] Total time: 0:03:05 (0.4642 s / it)
+train: [3] Summary: lr: 0.000240  loss: 2.7305 (2.7776)  grad: 0.1711 (0.1991)
+eval (validation): [3]  [ 0/85]  eta: 0:04:20    time: 3.0661  data: 2.8188  max mem: 22448
+eval (validation): [3]  [20/85]  eta: 0:00:32    time: 0.3652  data: 0.0230  max mem: 22448
+eval (validation): [3]  [40/85]  eta: 0:00:19    time: 0.3613  data: 0.0043  max mem: 22448
+eval (validation): [3]  [60/85]  eta: 0:00:10    time: 0.3598  data: 0.0043  max mem: 22448
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3231  data: 0.0041  max mem: 22448
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3165  data: 0.0041  max mem: 22448
+eval (validation): [3] Total time: 0:00:32 (0.3852 s / it)
+cv: [3]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 2.433  acc: 0.267  f1: 0.194
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:20:18  lr: nan  time: 3.0474  data: 2.7182  max mem: 22448
+train: [4]  [ 20/400]  eta: 0:03:31  lr: 0.000243  loss: 2.6835 (2.7030)  grad: 0.1792 (0.1822)  time: 0.4313  data: 0.0051  max mem: 22448
+train: [4]  [ 40/400]  eta: 0:03:01  lr: 0.000246  loss: 2.6855 (2.7080)  grad: 0.1792 (0.1798)  time: 0.4523  data: 0.0048  max mem: 22448
+train: [4]  [ 60/400]  eta: 0:02:46  lr: 0.000249  loss: 2.7212 (2.7087)  grad: 0.1809 (0.1789)  time: 0.4590  data: 0.0049  max mem: 22448
+train: [4]  [ 80/400]  eta: 0:02:33  lr: 0.000252  loss: 2.6987 (2.7019)  grad: 0.1732 (0.1773)  time: 0.4502  data: 0.0050  max mem: 22448
+train: [4]  [100/400]  eta: 0:02:22  lr: 0.000255  loss: 2.6568 (2.6969)  grad: 0.1832 (0.1833)  time: 0.4568  data: 0.0049  max mem: 22448
+train: [4]  [120/400]  eta: 0:02:12  lr: 0.000258  loss: 2.7616 (2.7478)  grad: 0.2340 (0.2602)  time: 0.4553  data: 0.0048  max mem: 22448
+WARNING: classifier 47 (43, 1.0) diverged (loss=63.92 > 63.56) at step 863. Freezing.
+train: [4]  [140/400]  eta: 0:02:01  lr: 0.000261  loss: 2.8308 (2.8031)  grad: 0.3600 (0.2988)  time: 0.4474  data: 0.0048  max mem: 22448
+train: [4]  [160/400]  eta: 0:01:52  lr: 0.000264  loss: 2.8825 (2.8260)  grad: 0.3292 (0.3436)  time: 0.4552  data: 0.0049  max mem: 22448
+WARNING: classifier 46 (36, 1.0) diverged (loss=121.06 > 63.56) at step 886. Freezing.
+train: [4]  [180/400]  eta: 0:01:42  lr: 0.000267  loss: 2.9433 (2.8849)  grad: 0.5791 (0.4251)  time: 0.4468  data: 0.0047  max mem: 22448
+train: [4]  [200/400]  eta: 0:01:32  lr: 0.000270  loss: 2.7390 (2.8664)  grad: 0.1689 (0.3999)  time: 0.4481  data: 0.0047  max mem: 22448
+train: [4]  [220/400]  eta: 0:01:22  lr: 0.000273  loss: 2.6810 (2.8513)  grad: 0.1689 (0.3791)  time: 0.4389  data: 0.0047  max mem: 22448
+train: [4]  [240/400]  eta: 0:01:13  lr: 0.000276  loss: 2.6943 (2.8404)  grad: 0.1702 (0.3618)  time: 0.4485  data: 0.0048  max mem: 22448
+train: [4]  [260/400]  eta: 0:01:04  lr: 0.000279  loss: 2.6938 (2.8285)  grad: 0.1679 (0.3472)  time: 0.4477  data: 0.0048  max mem: 22448
+train: [4]  [280/400]  eta: 0:00:54  lr: 0.000282  loss: 2.6569 (2.8163)  grad: 0.1699 (0.3346)  time: 0.4448  data: 0.0047  max mem: 22448
+train: [4]  [300/400]  eta: 0:00:45  lr: 0.000285  loss: 2.6634 (2.8088)  grad: 0.1675 (0.3235)  time: 0.4414  data: 0.0047  max mem: 22448
+train: [4]  [320/400]  eta: 0:00:36  lr: 0.000288  loss: 2.6876 (2.8027)  grad: 0.1659 (0.3138)  time: 0.4524  data: 0.0047  max mem: 22448
+train: [4]  [340/400]  eta: 0:00:27  lr: 0.000291  loss: 2.6703 (2.7945)  grad: 0.1638 (0.3048)  time: 0.4479  data: 0.0047  max mem: 22448
+train: [4]  [360/400]  eta: 0:00:18  lr: 0.000294  loss: 2.6777 (2.7903)  grad: 0.1638 (0.2975)  time: 0.4672  data: 0.0050  max mem: 22448
+train: [4]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.7067 (2.7872)  grad: 0.1694 (0.2907)  time: 0.4608  data: 0.0048  max mem: 22448
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 2.7062 (2.7821)  grad: 0.1653 (0.2844)  time: 0.4412  data: 0.0046  max mem: 22448
+train: [4] Total time: 0:03:02 (0.4567 s / it)
+train: [4] Summary: lr: 0.000300  loss: 2.7062 (2.7821)  grad: 0.1653 (0.2844)
+eval (validation): [4]  [ 0/85]  eta: 0:04:40    time: 3.3032  data: 3.0073  max mem: 22448
+eval (validation): [4]  [20/85]  eta: 0:00:32    time: 0.3560  data: 0.0055  max mem: 22448
+eval (validation): [4]  [40/85]  eta: 0:00:19    time: 0.3456  data: 0.0038  max mem: 22448
+eval (validation): [4]  [60/85]  eta: 0:00:10    time: 0.3594  data: 0.0046  max mem: 22448
+eval (validation): [4]  [80/85]  eta: 0:00:01    time: 0.3143  data: 0.0043  max mem: 22448
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3087  data: 0.0042  max mem: 22448
+eval (validation): [4] Total time: 0:00:32 (0.3805 s / it)
+cv: [4]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.390  acc: 0.293  f1: 0.215
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [5]  [  0/400]  eta: 0:20:16  lr: nan  time: 3.0404  data: 2.6629  max mem: 22448
+train: [5]  [ 20/400]  eta: 0:03:35  lr: 0.000300  loss: 2.6227 (2.6423)  grad: 0.1650 (0.1733)  time: 0.4442  data: 0.0052  max mem: 22448
+train: [5]  [ 40/400]  eta: 0:03:02  lr: 0.000300  loss: 2.6351 (2.6429)  grad: 0.1665 (0.1694)  time: 0.4420  data: 0.0041  max mem: 22448
+train: [5]  [ 60/400]  eta: 0:02:45  lr: 0.000300  loss: 2.6386 (2.6503)  grad: 0.1676 (0.1704)  time: 0.4472  data: 0.0050  max mem: 22448
+train: [5]  [ 80/400]  eta: 0:02:32  lr: 0.000300  loss: 2.6047 (2.6370)  grad: 0.1668 (0.1684)  time: 0.4470  data: 0.0050  max mem: 22448
+train: [5]  [100/400]  eta: 0:02:21  lr: 0.000300  loss: 2.6441 (2.6487)  grad: 0.1668 (0.1691)  time: 0.4542  data: 0.0048  max mem: 22448
+train: [5]  [120/400]  eta: 0:02:10  lr: 0.000300  loss: 2.6440 (2.6401)  grad: 0.1774 (0.1729)  time: 0.4412  data: 0.0048  max mem: 22448
+train: [5]  [140/400]  eta: 0:02:00  lr: 0.000300  loss: 2.6157 (2.6468)  grad: 0.1951 (0.1762)  time: 0.4506  data: 0.0046  max mem: 22448
+train: [5]  [160/400]  eta: 0:01:51  lr: 0.000299  loss: 2.6991 (2.6586)  grad: 0.1953 (0.1787)  time: 0.4507  data: 0.0048  max mem: 22448
+train: [5]  [180/400]  eta: 0:01:41  lr: 0.000299  loss: 2.6098 (2.6516)  grad: 0.1866 (0.1786)  time: 0.4561  data: 0.0050  max mem: 22448
+train: [5]  [200/400]  eta: 0:01:32  lr: 0.000299  loss: 2.6098 (2.6523)  grad: 0.1767 (0.1790)  time: 0.4460  data: 0.0048  max mem: 22448
+train: [5]  [220/400]  eta: 0:01:22  lr: 0.000299  loss: 2.6210 (2.6483)  grad: 0.1786 (0.1792)  time: 0.4391  data: 0.0047  max mem: 22448
+train: [5]  [240/400]  eta: 0:01:13  lr: 0.000299  loss: 2.6375 (2.6532)  grad: 0.1775 (0.1796)  time: 0.4534  data: 0.0049  max mem: 22448
+train: [5]  [260/400]  eta: 0:01:03  lr: 0.000299  loss: 2.6665 (2.6538)  grad: 0.1785 (0.1809)  time: 0.4415  data: 0.0048  max mem: 22448
+train: [5]  [280/400]  eta: 0:00:54  lr: 0.000298  loss: 2.6591 (2.6546)  grad: 0.2080 (0.1838)  time: 0.4480  data: 0.0049  max mem: 22448
+train: [5]  [300/400]  eta: 0:00:45  lr: 0.000298  loss: 2.7308 (2.6673)  grad: 0.2485 (0.2010)  time: 0.4538  data: 0.0051  max mem: 22448
+train: [5]  [320/400]  eta: 0:00:36  lr: 0.000298  loss: 2.9922 (2.7073)  grad: 0.8482 (0.2627)  time: 0.4526  data: 0.0051  max mem: 22448
+WARNING: classifier 44 (26, 1.0) diverged (loss=84.31 > 63.56) at step 1161. Freezing.
+train: [5]  [340/400]  eta: 0:00:27  lr: 0.000298  loss: 2.9380 (2.7124)  grad: 0.8607 (0.2677)  time: 0.4617  data: 0.0049  max mem: 22448
+train: [5]  [360/400]  eta: 0:00:18  lr: 0.000297  loss: 2.5744 (2.7036)  grad: 0.1693 (0.2620)  time: 0.4459  data: 0.0050  max mem: 22448
+train: [5]  [380/400]  eta: 0:00:09  lr: 0.000297  loss: 2.5953 (2.6988)  grad: 0.1696 (0.2572)  time: 0.4522  data: 0.0050  max mem: 22448
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 2.6457 (2.6974)  grad: 0.1714 (0.2530)  time: 0.4411  data: 0.0046  max mem: 22448
+train: [5] Total time: 0:03:02 (0.4554 s / it)
+train: [5] Summary: lr: 0.000297  loss: 2.6457 (2.6974)  grad: 0.1714 (0.2530)
+eval (validation): [5]  [ 0/85]  eta: 0:04:34    time: 3.2257  data: 2.9213  max mem: 22448
+eval (validation): [5]  [20/85]  eta: 0:00:33    time: 0.3779  data: 0.0061  max mem: 22448
+eval (validation): [5]  [40/85]  eta: 0:00:19    time: 0.3275  data: 0.0033  max mem: 22448
+eval (validation): [5]  [60/85]  eta: 0:00:09    time: 0.3436  data: 0.0040  max mem: 22448
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3310  data: 0.0039  max mem: 22448
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3252  data: 0.0037  max mem: 22448
+eval (validation): [5] Total time: 0:00:32 (0.3809 s / it)
+cv: [5]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.385  acc: 0.287  f1: 0.221
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [6]  [  0/400]  eta: 0:20:52  lr: nan  time: 3.1321  data: 2.7438  max mem: 22448
+train: [6]  [ 20/400]  eta: 0:03:38  lr: 0.000296  loss: 2.5801 (2.6110)  grad: 0.1612 (0.1624)  time: 0.4481  data: 0.0042  max mem: 22448
+train: [6]  [ 40/400]  eta: 0:03:03  lr: 0.000296  loss: 2.5941 (2.6121)  grad: 0.1580 (0.1614)  time: 0.4397  data: 0.0040  max mem: 22448
+train: [6]  [ 60/400]  eta: 0:02:46  lr: 0.000296  loss: 2.5889 (2.6021)  grad: 0.1607 (0.1627)  time: 0.4469  data: 0.0051  max mem: 22448
+train: [6]  [ 80/400]  eta: 0:02:32  lr: 0.000295  loss: 2.5691 (2.5969)  grad: 0.1688 (0.1653)  time: 0.4429  data: 0.0042  max mem: 22448
+train: [6]  [100/400]  eta: 0:02:22  lr: 0.000295  loss: 2.5517 (2.5895)  grad: 0.1722 (0.1671)  time: 0.4577  data: 0.0048  max mem: 22448
+train: [6]  [120/400]  eta: 0:02:11  lr: 0.000295  loss: 2.5508 (2.5870)  grad: 0.1734 (0.1683)  time: 0.4478  data: 0.0050  max mem: 22448
+train: [6]  [140/400]  eta: 0:02:01  lr: 0.000294  loss: 2.5508 (2.5842)  grad: 0.1712 (0.1690)  time: 0.4449  data: 0.0047  max mem: 22448
+train: [6]  [160/400]  eta: 0:01:51  lr: 0.000294  loss: 2.5511 (2.5810)  grad: 0.1706 (0.1687)  time: 0.4478  data: 0.0048  max mem: 22448
+train: [6]  [180/400]  eta: 0:01:41  lr: 0.000293  loss: 2.5347 (2.5771)  grad: 0.1708 (0.1687)  time: 0.4518  data: 0.0050  max mem: 22448
+train: [6]  [200/400]  eta: 0:01:32  lr: 0.000293  loss: 2.5536 (2.5771)  grad: 0.1708 (0.1690)  time: 0.4505  data: 0.0049  max mem: 22448
+train: [6]  [220/400]  eta: 0:01:22  lr: 0.000292  loss: 2.5536 (2.5739)  grad: 0.1664 (0.1689)  time: 0.4369  data: 0.0048  max mem: 22448
+train: [6]  [240/400]  eta: 0:01:13  lr: 0.000292  loss: 2.5488 (2.5727)  grad: 0.1664 (0.1689)  time: 0.4439  data: 0.0049  max mem: 22448
+train: [6]  [260/400]  eta: 0:01:03  lr: 0.000291  loss: 2.6025 (2.5766)  grad: 0.1753 (0.1701)  time: 0.4495  data: 0.0047  max mem: 22448
+train: [6]  [280/400]  eta: 0:00:54  lr: 0.000291  loss: 2.6025 (2.5774)  grad: 0.1790 (0.1707)  time: 0.4515  data: 0.0048  max mem: 22448
+train: [6]  [300/400]  eta: 0:00:45  lr: 0.000290  loss: 2.5403 (2.5750)  grad: 0.1787 (0.1712)  time: 0.4446  data: 0.0047  max mem: 22448
+train: [6]  [320/400]  eta: 0:00:36  lr: 0.000290  loss: 2.5288 (2.5719)  grad: 0.1770 (0.1717)  time: 0.4497  data: 0.0047  max mem: 22448
+train: [6]  [340/400]  eta: 0:00:27  lr: 0.000289  loss: 2.5457 (2.5710)  grad: 0.1732 (0.1716)  time: 0.4458  data: 0.0046  max mem: 22448
+train: [6]  [360/400]  eta: 0:00:18  lr: 0.000288  loss: 2.5423 (2.5691)  grad: 0.1699 (0.1716)  time: 0.4594  data: 0.0048  max mem: 22448
+train: [6]  [380/400]  eta: 0:00:09  lr: 0.000288  loss: 2.5170 (2.5695)  grad: 0.1739 (0.1718)  time: 0.4513  data: 0.0049  max mem: 22448
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 2.5154 (2.5676)  grad: 0.1789 (0.1722)  time: 0.4487  data: 0.0047  max mem: 22448
+train: [6] Total time: 0:03:02 (0.4552 s / it)
+train: [6] Summary: lr: 0.000287  loss: 2.5154 (2.5676)  grad: 0.1789 (0.1722)
+eval (validation): [6]  [ 0/85]  eta: 0:04:39    time: 3.2910  data: 3.0137  max mem: 22448
+eval (validation): [6]  [20/85]  eta: 0:00:33    time: 0.3838  data: 0.0057  max mem: 22448
+eval (validation): [6]  [40/85]  eta: 0:00:19    time: 0.3293  data: 0.0034  max mem: 22448
+eval (validation): [6]  [60/85]  eta: 0:00:09    time: 0.3350  data: 0.0040  max mem: 22448
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3317  data: 0.0039  max mem: 22448
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3220  data: 0.0037  max mem: 22448
+eval (validation): [6] Total time: 0:00:32 (0.3809 s / it)
+cv: [6]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.393  acc: 0.281  f1: 0.209
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [7]  [  0/400]  eta: 0:20:57  lr: nan  time: 3.1439  data: 2.8093  max mem: 22448
+train: [7]  [ 20/400]  eta: 0:03:39  lr: 0.000286  loss: 2.4979 (2.5160)  grad: 0.1839 (0.1817)  time: 0.4500  data: 0.0044  max mem: 22448
+train: [7]  [ 40/400]  eta: 0:03:04  lr: 0.000286  loss: 2.5119 (2.5207)  grad: 0.1845 (0.1821)  time: 0.4436  data: 0.0045  max mem: 22448
+train: [7]  [ 60/400]  eta: 0:02:46  lr: 0.000285  loss: 2.5484 (2.5311)  grad: 0.1834 (0.1813)  time: 0.4425  data: 0.0044  max mem: 22448
+train: [7]  [ 80/400]  eta: 0:02:33  lr: 0.000284  loss: 2.5347 (2.5196)  grad: 0.1775 (0.1787)  time: 0.4446  data: 0.0046  max mem: 22448
+train: [7]  [100/400]  eta: 0:02:21  lr: 0.000284  loss: 2.5050 (2.5266)  grad: 0.1723 (0.1781)  time: 0.4524  data: 0.0047  max mem: 22448
+train: [7]  [120/400]  eta: 0:02:11  lr: 0.000283  loss: 2.5189 (2.5255)  grad: 0.1723 (0.1774)  time: 0.4491  data: 0.0050  max mem: 22448
+train: [7]  [140/400]  eta: 0:02:01  lr: 0.000282  loss: 2.4987 (2.5228)  grad: 0.1766 (0.1774)  time: 0.4490  data: 0.0048  max mem: 22448
+train: [7]  [160/400]  eta: 0:01:50  lr: 0.000282  loss: 2.5224 (2.5271)  grad: 0.1710 (0.1765)  time: 0.4341  data: 0.0046  max mem: 22448
+train: [7]  [180/400]  eta: 0:01:41  lr: 0.000281  loss: 2.5617 (2.5299)  grad: 0.1713 (0.1767)  time: 0.4488  data: 0.0046  max mem: 22448
+train: [7]  [200/400]  eta: 0:01:31  lr: 0.000280  loss: 2.5282 (2.5282)  grad: 0.1744 (0.1768)  time: 0.4492  data: 0.0045  max mem: 22448
+train: [7]  [220/400]  eta: 0:01:22  lr: 0.000279  loss: 2.5315 (2.5329)  grad: 0.1750 (0.1769)  time: 0.4369  data: 0.0045  max mem: 22448
+train: [7]  [240/400]  eta: 0:01:13  lr: 0.000278  loss: 2.5273 (2.5313)  grad: 0.1721 (0.1767)  time: 0.4495  data: 0.0048  max mem: 22448
+train: [7]  [260/400]  eta: 0:01:03  lr: 0.000278  loss: 2.5236 (2.5353)  grad: 0.1709 (0.1768)  time: 0.4412  data: 0.0045  max mem: 22448
+train: [7]  [280/400]  eta: 0:00:54  lr: 0.000277  loss: 2.5236 (2.5334)  grad: 0.1725 (0.1764)  time: 0.4347  data: 0.0047  max mem: 22448
+train: [7]  [300/400]  eta: 0:00:45  lr: 0.000276  loss: 2.4931 (2.5299)  grad: 0.1725 (0.1766)  time: 0.4484  data: 0.0049  max mem: 22448
+train: [7]  [320/400]  eta: 0:00:36  lr: 0.000275  loss: 2.4472 (2.5258)  grad: 0.1755 (0.1768)  time: 0.4438  data: 0.0047  max mem: 22448
+train: [7]  [340/400]  eta: 0:00:27  lr: 0.000274  loss: 2.4987 (2.5254)  grad: 0.1743 (0.1768)  time: 0.4510  data: 0.0049  max mem: 22448
+train: [7]  [360/400]  eta: 0:00:18  lr: 0.000273  loss: 2.4953 (2.5212)  grad: 0.1756 (0.1771)  time: 0.4498  data: 0.0049  max mem: 22448
+train: [7]  [380/400]  eta: 0:00:09  lr: 0.000272  loss: 2.4953 (2.5211)  grad: 0.1756 (0.1772)  time: 0.4523  data: 0.0049  max mem: 22448
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 2.5028 (2.5214)  grad: 0.1786 (0.1774)  time: 0.4613  data: 0.0048  max mem: 22448
+train: [7] Total time: 0:03:01 (0.4539 s / it)
+train: [7] Summary: lr: 0.000271  loss: 2.5028 (2.5214)  grad: 0.1786 (0.1774)
+eval (validation): [7]  [ 0/85]  eta: 0:04:42    time: 3.3205  data: 3.0543  max mem: 22448
+eval (validation): [7]  [20/85]  eta: 0:00:33    time: 0.3774  data: 0.0038  max mem: 22448
+eval (validation): [7]  [40/85]  eta: 0:00:19    time: 0.3612  data: 0.0038  max mem: 22448
+eval (validation): [7]  [60/85]  eta: 0:00:10    time: 0.3315  data: 0.0040  max mem: 22448
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3302  data: 0.0043  max mem: 22448
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3262  data: 0.0042  max mem: 22448
+eval (validation): [7] Total time: 0:00:32 (0.3877 s / it)
+cv: [7]  best hparam: (0.72, 1.0) (022) ('022_lr7.2e-01_wd1.0e+00')  loss: 2.358  acc: 0.292  f1: 0.221
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:21:03  lr: nan  time: 3.1580  data: 2.7863  max mem: 22448
+train: [8]  [ 20/400]  eta: 0:03:44  lr: 0.000270  loss: 2.4076 (2.4419)  grad: 0.1663 (0.1701)  time: 0.4627  data: 0.0038  max mem: 22448
+train: [8]  [ 40/400]  eta: 0:03:04  lr: 0.000270  loss: 2.4472 (2.4576)  grad: 0.1696 (0.1722)  time: 0.4312  data: 0.0048  max mem: 22448
+train: [8]  [ 60/400]  eta: 0:02:47  lr: 0.000269  loss: 2.4815 (2.4668)  grad: 0.1753 (0.1731)  time: 0.4518  data: 0.0049  max mem: 22448
+train: [8]  [ 80/400]  eta: 0:02:33  lr: 0.000268  loss: 2.4815 (2.4729)  grad: 0.1788 (0.1761)  time: 0.4438  data: 0.0047  max mem: 22448
+train: [8]  [100/400]  eta: 0:02:22  lr: 0.000267  loss: 2.4938 (2.4853)  grad: 0.1818 (0.1768)  time: 0.4439  data: 0.0048  max mem: 22448
+train: [8]  [120/400]  eta: 0:02:11  lr: 0.000266  loss: 2.4625 (2.4723)  grad: 0.1737 (0.1759)  time: 0.4475  data: 0.0050  max mem: 22448
+train: [8]  [140/400]  eta: 0:02:01  lr: 0.000265  loss: 2.4240 (2.4659)  grad: 0.1760 (0.1773)  time: 0.4456  data: 0.0048  max mem: 22448
+train: [8]  [160/400]  eta: 0:01:50  lr: 0.000264  loss: 2.4165 (2.4647)  grad: 0.1862 (0.1788)  time: 0.4379  data: 0.0049  max mem: 22448
+train: [8]  [180/400]  eta: 0:01:41  lr: 0.000263  loss: 2.3907 (2.4569)  grad: 0.1835 (0.1791)  time: 0.4347  data: 0.0047  max mem: 22448
+train: [8]  [200/400]  eta: 0:01:31  lr: 0.000262  loss: 2.4180 (2.4593)  grad: 0.1815 (0.1800)  time: 0.4313  data: 0.0049  max mem: 22448
+train: [8]  [220/400]  eta: 0:01:21  lr: 0.000260  loss: 2.4712 (2.4588)  grad: 0.1815 (0.1798)  time: 0.4360  data: 0.0048  max mem: 22448
+train: [8]  [240/400]  eta: 0:01:12  lr: 0.000259  loss: 2.4520 (2.4584)  grad: 0.1831 (0.1809)  time: 0.4338  data: 0.0047  max mem: 22448
+train: [8]  [260/400]  eta: 0:01:03  lr: 0.000258  loss: 2.4694 (2.4611)  grad: 0.1876 (0.1813)  time: 0.4360  data: 0.0047  max mem: 22448
+train: [8]  [280/400]  eta: 0:00:54  lr: 0.000257  loss: 2.5119 (2.4641)  grad: 0.1849 (0.1813)  time: 0.4367  data: 0.0049  max mem: 22448
+train: [8]  [300/400]  eta: 0:00:45  lr: 0.000256  loss: 2.5070 (2.4677)  grad: 0.1856 (0.1819)  time: 0.4442  data: 0.0046  max mem: 22448
+train: [8]  [320/400]  eta: 0:00:35  lr: 0.000255  loss: 2.5047 (2.4679)  grad: 0.1858 (0.1822)  time: 0.4275  data: 0.0049  max mem: 22448
+train: [8]  [340/400]  eta: 0:00:26  lr: 0.000254  loss: 2.5040 (2.4687)  grad: 0.1858 (0.1823)  time: 0.4333  data: 0.0047  max mem: 22448
+train: [8]  [360/400]  eta: 0:00:17  lr: 0.000253  loss: 2.5040 (2.4695)  grad: 0.1818 (0.1823)  time: 0.4355  data: 0.0047  max mem: 22448
+train: [8]  [380/400]  eta: 0:00:08  lr: 0.000252  loss: 2.4632 (2.4677)  grad: 0.1842 (0.1824)  time: 0.4372  data: 0.0048  max mem: 22448
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 2.4368 (2.4689)  grad: 0.1842 (0.1828)  time: 0.4390  data: 0.0048  max mem: 22448
+train: [8] Total time: 0:02:58 (0.4469 s / it)
+train: [8] Summary: lr: 0.000250  loss: 2.4368 (2.4689)  grad: 0.1842 (0.1828)
+eval (validation): [8]  [ 0/85]  eta: 0:04:57    time: 3.5030  data: 3.2275  max mem: 22448
+eval (validation): [8]  [20/85]  eta: 0:00:32    time: 0.3491  data: 0.0032  max mem: 22448
+eval (validation): [8]  [40/85]  eta: 0:00:19    time: 0.3429  data: 0.0039  max mem: 22448
+eval (validation): [8]  [60/85]  eta: 0:00:09    time: 0.3387  data: 0.0043  max mem: 22448
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3141  data: 0.0040  max mem: 22448
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3091  data: 0.0038  max mem: 22448
+eval (validation): [8] Total time: 0:00:31 (0.3743 s / it)
+cv: [8]  best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00')  loss: 2.433  acc: 0.289  f1: 0.215
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:20:56  lr: nan  time: 3.1406  data: 2.7704  max mem: 22448
+train: [9]  [ 20/400]  eta: 0:03:33  lr: 0.000249  loss: 2.4165 (2.4408)  grad: 0.1789 (0.1807)  time: 0.4324  data: 0.0043  max mem: 22448
+train: [9]  [ 40/400]  eta: 0:03:00  lr: 0.000248  loss: 2.4165 (2.4186)  grad: 0.1794 (0.1861)  time: 0.4395  data: 0.0049  max mem: 22448
+train: [9]  [ 60/400]  eta: 0:02:44  lr: 0.000247  loss: 2.4212 (2.4367)  grad: 0.1839 (0.1867)  time: 0.4489  data: 0.0049  max mem: 22448
+train: [9]  [ 80/400]  eta: 0:02:31  lr: 0.000246  loss: 2.4322 (2.4241)  grad: 0.1839 (0.1855)  time: 0.4417  data: 0.0046  max mem: 22448
+train: [9]  [100/400]  eta: 0:02:20  lr: 0.000244  loss: 2.3675 (2.4138)  grad: 0.1824 (0.1863)  time: 0.4451  data: 0.0048  max mem: 22448
+train: [9]  [120/400]  eta: 0:02:10  lr: 0.000243  loss: 2.3614 (2.4113)  grad: 0.1823 (0.1858)  time: 0.4603  data: 0.0050  max mem: 22448
+train: [9]  [140/400]  eta: 0:02:00  lr: 0.000242  loss: 2.3982 (2.4146)  grad: 0.1839 (0.1865)  time: 0.4474  data: 0.0049  max mem: 22448
+train: [9]  [160/400]  eta: 0:01:50  lr: 0.000241  loss: 2.4057 (2.4156)  grad: 0.1852 (0.1867)  time: 0.4442  data: 0.0046  max mem: 22448
+train: [9]  [180/400]  eta: 0:01:41  lr: 0.000240  loss: 2.3722 (2.4196)  grad: 0.1829 (0.1858)  time: 0.4409  data: 0.0046  max mem: 22448
+train: [9]  [200/400]  eta: 0:01:31  lr: 0.000238  loss: 2.4353 (2.4233)  grad: 0.1809 (0.1854)  time: 0.4496  data: 0.0049  max mem: 22448
+train: [9]  [220/400]  eta: 0:01:22  lr: 0.000237  loss: 2.4089 (2.4164)  grad: 0.1812 (0.1853)  time: 0.4495  data: 0.0048  max mem: 22448
+train: [9]  [240/400]  eta: 0:01:13  lr: 0.000236  loss: 2.3601 (2.4143)  grad: 0.1830 (0.1854)  time: 0.4423  data: 0.0048  max mem: 22448
+train: [9]  [260/400]  eta: 0:01:03  lr: 0.000234  loss: 2.4051 (2.4164)  grad: 0.1810 (0.1849)  time: 0.4454  data: 0.0047  max mem: 22448
+train: [9]  [280/400]  eta: 0:00:54  lr: 0.000233  loss: 2.4371 (2.4223)  grad: 0.1827 (0.1856)  time: 0.4460  data: 0.0051  max mem: 22448
+train: [9]  [300/400]  eta: 0:00:45  lr: 0.000232  loss: 2.4332 (2.4219)  grad: 0.1905 (0.1858)  time: 0.4496  data: 0.0047  max mem: 22448
+train: [9]  [320/400]  eta: 0:00:36  lr: 0.000230  loss: 2.4122 (2.4225)  grad: 0.1893 (0.1863)  time: 0.4578  data: 0.0047  max mem: 22448
+train: [9]  [340/400]  eta: 0:00:27  lr: 0.000229  loss: 2.4122 (2.4210)  grad: 0.1882 (0.1864)  time: 0.4515  data: 0.0049  max mem: 22448
+train: [9]  [360/400]  eta: 0:00:18  lr: 0.000228  loss: 2.4077 (2.4229)  grad: 0.1961 (0.1871)  time: 0.4542  data: 0.0048  max mem: 22448
+train: [9]  [380/400]  eta: 0:00:09  lr: 0.000226  loss: 2.4246 (2.4243)  grad: 0.1963 (0.1874)  time: 0.4520  data: 0.0049  max mem: 22448
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 2.3966 (2.4236)  grad: 0.1854 (0.1872)  time: 0.4539  data: 0.0049  max mem: 22448
+train: [9] Total time: 0:03:01 (0.4549 s / it)
+train: [9] Summary: lr: 0.000225  loss: 2.3966 (2.4236)  grad: 0.1854 (0.1872)
+eval (validation): [9]  [ 0/85]  eta: 0:05:34    time: 3.9300  data: 3.6460  max mem: 22448
+eval (validation): [9]  [20/85]  eta: 0:00:34    time: 0.3668  data: 0.0027  max mem: 22448
+eval (validation): [9]  [40/85]  eta: 0:00:19    time: 0.3257  data: 0.0042  max mem: 22448
+eval (validation): [9]  [60/85]  eta: 0:00:10    time: 0.3350  data: 0.0044  max mem: 22448
+eval (validation): [9]  [80/85]  eta: 0:00:01    time: 0.3221  data: 0.0042  max mem: 22448
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3120  data: 0.0040  max mem: 22448
+eval (validation): [9] Total time: 0:00:32 (0.3813 s / it)
+cv: [9]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.369  acc: 0.286  f1: 0.216
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:25:43  lr: nan  time: 3.8576  data: 3.5163  max mem: 22448
+train: [10]  [ 20/400]  eta: 0:03:44  lr: 0.000224  loss: 2.3208 (2.3385)  grad: 0.1738 (0.1763)  time: 0.4286  data: 0.0029  max mem: 22448
+train: [10]  [ 40/400]  eta: 0:03:06  lr: 0.000222  loss: 2.3534 (2.3696)  grad: 0.1792 (0.1843)  time: 0.4394  data: 0.0046  max mem: 22448
+train: [10]  [ 60/400]  eta: 0:02:48  lr: 0.000221  loss: 2.3466 (2.3644)  grad: 0.1892 (0.1853)  time: 0.4462  data: 0.0048  max mem: 22448
+train: [10]  [ 80/400]  eta: 0:02:33  lr: 0.000220  loss: 2.3667 (2.3755)  grad: 0.1807 (0.1849)  time: 0.4314  data: 0.0045  max mem: 22448
+train: [10]  [100/400]  eta: 0:02:21  lr: 0.000218  loss: 2.3816 (2.3755)  grad: 0.1863 (0.1856)  time: 0.4448  data: 0.0049  max mem: 22448
+train: [10]  [120/400]  eta: 0:02:11  lr: 0.000217  loss: 2.3511 (2.3765)  grad: 0.1885 (0.1865)  time: 0.4516  data: 0.0048  max mem: 22448
+train: [10]  [140/400]  eta: 0:02:01  lr: 0.000215  loss: 2.3344 (2.3716)  grad: 0.1859 (0.1866)  time: 0.4477  data: 0.0048  max mem: 22448
+train: [10]  [160/400]  eta: 0:01:50  lr: 0.000214  loss: 2.3403 (2.3715)  grad: 0.1770 (0.1855)  time: 0.4363  data: 0.0044  max mem: 22448
+train: [10]  [180/400]  eta: 0:01:41  lr: 0.000213  loss: 2.3660 (2.3690)  grad: 0.1753 (0.1853)  time: 0.4513  data: 0.0048  max mem: 22448
+train: [10]  [200/400]  eta: 0:01:31  lr: 0.000211  loss: 2.3221 (2.3626)  grad: 0.1805 (0.1850)  time: 0.4464  data: 0.0050  max mem: 22448
+train: [10]  [220/400]  eta: 0:01:22  lr: 0.000210  loss: 2.3091 (2.3598)  grad: 0.1847 (0.1856)  time: 0.4551  data: 0.0049  max mem: 22448
+train: [10]  [240/400]  eta: 0:01:13  lr: 0.000208  loss: 2.3479 (2.3589)  grad: 0.1862 (0.1855)  time: 0.4432  data: 0.0046  max mem: 22448
+train: [10]  [260/400]  eta: 0:01:04  lr: 0.000207  loss: 2.3924 (2.3645)  grad: 0.1832 (0.1850)  time: 0.4530  data: 0.0049  max mem: 22448
+train: [10]  [280/400]  eta: 0:00:54  lr: 0.000205  loss: 2.4086 (2.3687)  grad: 0.1832 (0.1853)  time: 0.4454  data: 0.0049  max mem: 22448
+train: [10]  [300/400]  eta: 0:00:45  lr: 0.000204  loss: 2.4216 (2.3696)  grad: 0.1894 (0.1863)  time: 0.4498  data: 0.0048  max mem: 22448
+train: [10]  [320/400]  eta: 0:00:36  lr: 0.000202  loss: 2.4147 (2.3723)  grad: 0.2011 (0.1872)  time: 0.4451  data: 0.0048  max mem: 22448
+train: [10]  [340/400]  eta: 0:00:27  lr: 0.000201  loss: 2.4067 (2.3722)  grad: 0.1997 (0.1874)  time: 0.4452  data: 0.0048  max mem: 22448
+train: [10]  [360/400]  eta: 0:00:18  lr: 0.000199  loss: 2.3912 (2.3746)  grad: 0.1893 (0.1879)  time: 0.4487  data: 0.0047  max mem: 22448
+train: [10]  [380/400]  eta: 0:00:09  lr: 0.000198  loss: 2.3861 (2.3735)  grad: 0.1977 (0.1885)  time: 0.4530  data: 0.0047  max mem: 22448
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 2.3861 (2.3750)  grad: 0.1942 (0.1888)  time: 0.4663  data: 0.0047  max mem: 22448
+train: [10] Total time: 0:03:02 (0.4555 s / it)
+train: [10] Summary: lr: 0.000196  loss: 2.3861 (2.3750)  grad: 0.1942 (0.1888)
+eval (validation): [10]  [ 0/85]  eta: 0:04:28    time: 3.1595  data: 2.9120  max mem: 22448
+eval (validation): [10]  [20/85]  eta: 0:00:32    time: 0.3598  data: 0.0054  max mem: 22448
+eval (validation): [10]  [40/85]  eta: 0:00:18    time: 0.3407  data: 0.0033  max mem: 22448
+eval (validation): [10]  [60/85]  eta: 0:00:09    time: 0.3345  data: 0.0040  max mem: 22448
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.3390  data: 0.0040  max mem: 22448
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.3303  data: 0.0039  max mem: 22448
+eval (validation): [10] Total time: 0:00:32 (0.3792 s / it)
+cv: [10]  best hparam: (0.61, 1.0) (021) ('021_lr6.1e-01_wd1.0e+00')  loss: 2.367  acc: 0.290  f1: 0.223
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:20:36  lr: nan  time: 3.0912  data: 2.7608  max mem: 22448
+train: [11]  [ 20/400]  eta: 0:03:41  lr: 0.000195  loss: 2.3424 (2.3481)  grad: 0.1948 (0.1991)  time: 0.4573  data: 0.0047  max mem: 22448
+train: [11]  [ 40/400]  eta: 0:03:07  lr: 0.000193  loss: 2.3147 (2.3202)  grad: 0.1924 (0.1943)  time: 0.4545  data: 0.0046  max mem: 22448
+train: [11]  [ 60/400]  eta: 0:02:49  lr: 0.000192  loss: 2.2893 (2.3198)  grad: 0.1870 (0.1907)  time: 0.4531  data: 0.0049  max mem: 22448
+train: [11]  [ 80/400]  eta: 0:02:34  lr: 0.000190  loss: 2.3331 (2.3325)  grad: 0.1877 (0.1922)  time: 0.4357  data: 0.0048  max mem: 22448
+train: [11]  [100/400]  eta: 0:02:23  lr: 0.000189  loss: 2.3190 (2.3241)  grad: 0.1894 (0.1905)  time: 0.4586  data: 0.0050  max mem: 22448
+train: [11]  [120/400]  eta: 0:02:12  lr: 0.000187  loss: 2.3053 (2.3247)  grad: 0.1876 (0.1894)  time: 0.4570  data: 0.0050  max mem: 22448
+train: [11]  [140/400]  eta: 0:02:03  lr: 0.000186  loss: 2.3324 (2.3280)  grad: 0.1876 (0.1896)  time: 0.4720  data: 0.0050  max mem: 22448
+train: [11]  [160/400]  eta: 0:01:53  lr: 0.000184  loss: 2.3298 (2.3258)  grad: 0.1864 (0.1891)  time: 0.4497  data: 0.0047  max mem: 22448
+train: [11]  [180/400]  eta: 0:01:43  lr: 0.000183  loss: 2.2959 (2.3272)  grad: 0.1828 (0.1894)  time: 0.4581  data: 0.0051  max mem: 22448
+train: [11]  [200/400]  eta: 0:01:33  lr: 0.000181  loss: 2.3219 (2.3296)  grad: 0.1841 (0.1897)  time: 0.4584  data: 0.0049  max mem: 22448
+train: [11]  [220/400]  eta: 0:01:23  lr: 0.000180  loss: 2.3294 (2.3308)  grad: 0.1909 (0.1901)  time: 0.4438  data: 0.0048  max mem: 22448
+train: [11]  [240/400]  eta: 0:01:14  lr: 0.000178  loss: 2.3559 (2.3335)  grad: 0.1961 (0.1911)  time: 0.4507  data: 0.0048  max mem: 22448
+train: [11]  [260/400]  eta: 0:01:05  lr: 0.000177  loss: 2.3315 (2.3356)  grad: 0.1992 (0.1915)  time: 0.4885  data: 0.0050  max mem: 22448
+train: [11]  [280/400]  eta: 0:00:55  lr: 0.000175  loss: 2.2970 (2.3331)  grad: 0.1930 (0.1911)  time: 0.4436  data: 0.0046  max mem: 22448
+train: [11]  [300/400]  eta: 0:00:46  lr: 0.000174  loss: 2.3326 (2.3341)  grad: 0.1811 (0.1908)  time: 0.4647  data: 0.0051  max mem: 22448
+train: [11]  [320/400]  eta: 0:00:37  lr: 0.000172  loss: 2.3326 (2.3346)  grad: 0.1868 (0.1909)  time: 0.4396  data: 0.0045  max mem: 22448
+train: [11]  [340/400]  eta: 0:00:27  lr: 0.000170  loss: 2.3040 (2.3330)  grad: 0.1947 (0.1911)  time: 0.4343  data: 0.0043  max mem: 22448
+train: [11]  [360/400]  eta: 0:00:18  lr: 0.000169  loss: 2.3288 (2.3329)  grad: 0.1976 (0.1915)  time: 0.4472  data: 0.0044  max mem: 22448
+train: [11]  [380/400]  eta: 0:00:09  lr: 0.000167  loss: 2.3405 (2.3347)  grad: 0.1910 (0.1912)  time: 0.4450  data: 0.0048  max mem: 22448
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 2.3179 (2.3330)  grad: 0.1885 (0.1913)  time: 0.4310  data: 0.0046  max mem: 22448
+train: [11] Total time: 0:03:03 (0.4593 s / it)
+train: [11] Summary: lr: 0.000166  loss: 2.3179 (2.3330)  grad: 0.1885 (0.1913)
+eval (validation): [11]  [ 0/85]  eta: 0:04:13    time: 2.9825  data: 2.7078  max mem: 22448
+eval (validation): [11]  [20/85]  eta: 0:00:30    time: 0.3494  data: 0.0042  max mem: 22448
+eval (validation): [11]  [40/85]  eta: 0:00:18    time: 0.3238  data: 0.0038  max mem: 22448
+eval (validation): [11]  [60/85]  eta: 0:00:09    time: 0.3328  data: 0.0040  max mem: 22448
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.3220  data: 0.0042  max mem: 22448
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.3147  data: 0.0040  max mem: 22448
+eval (validation): [11] Total time: 0:00:30 (0.3646 s / it)
+cv: [11]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.358  acc: 0.295  f1: 0.224
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [12]  [  0/400]  eta: 0:20:45  lr: nan  time: 3.1144  data: 2.7874  max mem: 22448
+train: [12]  [ 20/400]  eta: 0:03:31  lr: 0.000164  loss: 2.2653 (2.2877)  grad: 0.1840 (0.1881)  time: 0.4276  data: 0.0031  max mem: 22448
+train: [12]  [ 40/400]  eta: 0:02:59  lr: 0.000163  loss: 2.2653 (2.2825)  grad: 0.1827 (0.1886)  time: 0.4366  data: 0.0041  max mem: 22448
+train: [12]  [ 60/400]  eta: 0:02:45  lr: 0.000161  loss: 2.2963 (2.2954)  grad: 0.1842 (0.1888)  time: 0.4612  data: 0.0050  max mem: 22448
+train: [12]  [ 80/400]  eta: 0:02:32  lr: 0.000160  loss: 2.2942 (2.2809)  grad: 0.1923 (0.1900)  time: 0.4534  data: 0.0050  max mem: 22448
+train: [12]  [100/400]  eta: 0:02:20  lr: 0.000158  loss: 2.2405 (2.2787)  grad: 0.1933 (0.1914)  time: 0.4387  data: 0.0048  max mem: 22448
+train: [12]  [120/400]  eta: 0:02:10  lr: 0.000156  loss: 2.2875 (2.2853)  grad: 0.1927 (0.1914)  time: 0.4501  data: 0.0048  max mem: 22448
+train: [12]  [140/400]  eta: 0:02:00  lr: 0.000155  loss: 2.3368 (2.2941)  grad: 0.1927 (0.1918)  time: 0.4553  data: 0.0049  max mem: 22448
+train: [12]  [160/400]  eta: 0:01:51  lr: 0.000153  loss: 2.3152 (2.2958)  grad: 0.1918 (0.1922)  time: 0.4468  data: 0.0050  max mem: 22448
+train: [12]  [180/400]  eta: 0:01:41  lr: 0.000152  loss: 2.2634 (2.2903)  grad: 0.1934 (0.1923)  time: 0.4524  data: 0.0051  max mem: 22448
+train: [12]  [200/400]  eta: 0:01:32  lr: 0.000150  loss: 2.2329 (2.2938)  grad: 0.1866 (0.1922)  time: 0.4683  data: 0.0050  max mem: 22448
+train: [12]  [220/400]  eta: 0:01:23  lr: 0.000149  loss: 2.3382 (2.2949)  grad: 0.1905 (0.1927)  time: 0.4617  data: 0.0048  max mem: 22448
+train: [12]  [240/400]  eta: 0:01:13  lr: 0.000147  loss: 2.2869 (2.2920)  grad: 0.1929 (0.1925)  time: 0.4502  data: 0.0047  max mem: 22448
+train: [12]  [260/400]  eta: 0:01:04  lr: 0.000145  loss: 2.2787 (2.2900)  grad: 0.1903 (0.1925)  time: 0.4356  data: 0.0048  max mem: 22448
+train: [12]  [280/400]  eta: 0:00:54  lr: 0.000144  loss: 2.2787 (2.2930)  grad: 0.1903 (0.1924)  time: 0.4296  data: 0.0047  max mem: 22448
+train: [12]  [300/400]  eta: 0:00:45  lr: 0.000142  loss: 2.2994 (2.2942)  grad: 0.1872 (0.1919)  time: 0.4404  data: 0.0048  max mem: 22448
+train: [12]  [320/400]  eta: 0:00:36  lr: 0.000141  loss: 2.3048 (2.2974)  grad: 0.1898 (0.1924)  time: 0.4374  data: 0.0048  max mem: 22448
+train: [12]  [340/400]  eta: 0:00:27  lr: 0.000139  loss: 2.3203 (2.2979)  grad: 0.1984 (0.1925)  time: 0.4341  data: 0.0049  max mem: 22448
+train: [12]  [360/400]  eta: 0:00:18  lr: 0.000138  loss: 2.2919 (2.2961)  grad: 0.1911 (0.1925)  time: 0.4312  data: 0.0047  max mem: 22448
+train: [12]  [380/400]  eta: 0:00:09  lr: 0.000136  loss: 2.2744 (2.2970)  grad: 0.1911 (0.1927)  time: 0.4500  data: 0.0045  max mem: 22448
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 2.3195 (2.2996)  grad: 0.1926 (0.1928)  time: 0.4469  data: 0.0048  max mem: 22448
+train: [12] Total time: 0:03:01 (0.4526 s / it)
+train: [12] Summary: lr: 0.000134  loss: 2.3195 (2.2996)  grad: 0.1926 (0.1928)
+eval (validation): [12]  [ 0/85]  eta: 0:04:11    time: 2.9554  data: 2.6765  max mem: 22448
+eval (validation): [12]  [20/85]  eta: 0:00:31    time: 0.3532  data: 0.0048  max mem: 22448
+eval (validation): [12]  [40/85]  eta: 0:00:18    time: 0.3309  data: 0.0033  max mem: 22448
+eval (validation): [12]  [60/85]  eta: 0:00:09    time: 0.3346  data: 0.0041  max mem: 22448
+eval (validation): [12]  [80/85]  eta: 0:00:01    time: 0.3202  data: 0.0038  max mem: 22448
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.3152  data: 0.0038  max mem: 22448
+eval (validation): [12] Total time: 0:00:31 (0.3672 s / it)
+cv: [12]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.367  acc: 0.293  f1: 0.226
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [13]  [  0/400]  eta: 0:23:20  lr: nan  time: 3.5016  data: 3.0952  max mem: 22448
+train: [13]  [ 20/400]  eta: 0:03:51  lr: 0.000133  loss: 2.2357 (2.2953)  grad: 0.1766 (0.1875)  time: 0.4639  data: 0.0040  max mem: 22448
+train: [13]  [ 40/400]  eta: 0:03:12  lr: 0.000131  loss: 2.2303 (2.2481)  grad: 0.1795 (0.1860)  time: 0.4585  data: 0.0045  max mem: 22448
+train: [13]  [ 60/400]  eta: 0:02:51  lr: 0.000130  loss: 2.1989 (2.2420)  grad: 0.1865 (0.1867)  time: 0.4407  data: 0.0046  max mem: 22448
+train: [13]  [ 80/400]  eta: 0:02:37  lr: 0.000128  loss: 2.2437 (2.2479)  grad: 0.1906 (0.1883)  time: 0.4501  data: 0.0049  max mem: 22448
+train: [13]  [100/400]  eta: 0:02:25  lr: 0.000127  loss: 2.2666 (2.2544)  grad: 0.1874 (0.1881)  time: 0.4606  data: 0.0048  max mem: 22448
+train: [13]  [120/400]  eta: 0:02:14  lr: 0.000125  loss: 2.2666 (2.2507)  grad: 0.1892 (0.1895)  time: 0.4624  data: 0.0049  max mem: 22448
+train: [13]  [140/400]  eta: 0:02:04  lr: 0.000124  loss: 2.2360 (2.2491)  grad: 0.1934 (0.1903)  time: 0.4684  data: 0.0048  max mem: 22448
+train: [13]  [160/400]  eta: 0:01:54  lr: 0.000122  loss: 2.2360 (2.2516)  grad: 0.1927 (0.1908)  time: 0.4595  data: 0.0052  max mem: 22448
+train: [13]  [180/400]  eta: 0:01:44  lr: 0.000120  loss: 2.2988 (2.2592)  grad: 0.1917 (0.1912)  time: 0.4547  data: 0.0049  max mem: 22448
+train: [13]  [200/400]  eta: 0:01:34  lr: 0.000119  loss: 2.2969 (2.2585)  grad: 0.1877 (0.1917)  time: 0.4637  data: 0.0048  max mem: 22448
+train: [13]  [220/400]  eta: 0:01:24  lr: 0.000117  loss: 2.2675 (2.2580)  grad: 0.1864 (0.1913)  time: 0.4538  data: 0.0047  max mem: 22448
+train: [13]  [240/400]  eta: 0:01:15  lr: 0.000116  loss: 2.2446 (2.2594)  grad: 0.1897 (0.1917)  time: 0.4566  data: 0.0048  max mem: 22448
+train: [13]  [260/400]  eta: 0:01:05  lr: 0.000114  loss: 2.2808 (2.2620)  grad: 0.1893 (0.1909)  time: 0.4552  data: 0.0047  max mem: 22448
+train: [13]  [280/400]  eta: 0:00:56  lr: 0.000113  loss: 2.2808 (2.2614)  grad: 0.1861 (0.1910)  time: 0.4596  data: 0.0048  max mem: 22448
+train: [13]  [300/400]  eta: 0:00:46  lr: 0.000111  loss: 2.2311 (2.2564)  grad: 0.1885 (0.1909)  time: 0.4483  data: 0.0049  max mem: 22448
+train: [13]  [320/400]  eta: 0:00:37  lr: 0.000110  loss: 2.2442 (2.2564)  grad: 0.1907 (0.1913)  time: 0.4304  data: 0.0047  max mem: 22448
+train: [13]  [340/400]  eta: 0:00:27  lr: 0.000108  loss: 2.2550 (2.2568)  grad: 0.1992 (0.1920)  time: 0.4462  data: 0.0044  max mem: 22448
+train: [13]  [360/400]  eta: 0:00:18  lr: 0.000107  loss: 2.2501 (2.2554)  grad: 0.1977 (0.1918)  time: 0.4406  data: 0.0047  max mem: 22448
+train: [13]  [380/400]  eta: 0:00:09  lr: 0.000105  loss: 2.2398 (2.2538)  grad: 0.1874 (0.1919)  time: 0.4324  data: 0.0047  max mem: 22448
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 2.2627 (2.2566)  grad: 0.1881 (0.1917)  time: 0.4460  data: 0.0047  max mem: 22448
+train: [13] Total time: 0:03:04 (0.4608 s / it)
+train: [13] Summary: lr: 0.000104  loss: 2.2627 (2.2566)  grad: 0.1881 (0.1917)
+eval (validation): [13]  [ 0/85]  eta: 0:04:12    time: 2.9714  data: 2.7326  max mem: 22448
+eval (validation): [13]  [20/85]  eta: 0:00:29    time: 0.3354  data: 0.0040  max mem: 22448
+eval (validation): [13]  [40/85]  eta: 0:00:17    time: 0.3326  data: 0.0038  max mem: 22448
+eval (validation): [13]  [60/85]  eta: 0:00:09    time: 0.3128  data: 0.0040  max mem: 22448
+eval (validation): [13]  [80/85]  eta: 0:00:01    time: 0.3227  data: 0.0040  max mem: 22448
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3141  data: 0.0039  max mem: 22448
+eval (validation): [13] Total time: 0:00:30 (0.3593 s / it)
+cv: [13]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.358  acc: 0.294  f1: 0.228
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:20:02  lr: nan  time: 3.0069  data: 2.6903  max mem: 22448
+train: [14]  [ 20/400]  eta: 0:03:31  lr: 0.000102  loss: 2.1862 (2.1856)  grad: 0.1793 (0.1847)  time: 0.4341  data: 0.0047  max mem: 22448
+train: [14]  [ 40/400]  eta: 0:02:59  lr: 0.000101  loss: 2.2392 (2.2231)  grad: 0.1902 (0.1884)  time: 0.4365  data: 0.0045  max mem: 22448
+train: [14]  [ 60/400]  eta: 0:02:42  lr: 0.000099  loss: 2.2392 (2.2211)  grad: 0.1907 (0.1894)  time: 0.4329  data: 0.0047  max mem: 22448
+train: [14]  [ 80/400]  eta: 0:02:29  lr: 0.000098  loss: 2.2134 (2.2242)  grad: 0.1957 (0.1913)  time: 0.4391  data: 0.0048  max mem: 22448
+train: [14]  [100/400]  eta: 0:02:19  lr: 0.000096  loss: 2.2278 (2.2274)  grad: 0.1893 (0.1910)  time: 0.4583  data: 0.0049  max mem: 22448
+train: [14]  [120/400]  eta: 0:02:10  lr: 0.000095  loss: 2.1896 (2.2185)  grad: 0.1867 (0.1902)  time: 0.4608  data: 0.0047  max mem: 22448
+train: [14]  [140/400]  eta: 0:02:00  lr: 0.000093  loss: 2.1691 (2.2135)  grad: 0.1883 (0.1914)  time: 0.4579  data: 0.0048  max mem: 22448
+train: [14]  [160/400]  eta: 0:01:51  lr: 0.000092  loss: 2.2178 (2.2141)  grad: 0.1928 (0.1914)  time: 0.4593  data: 0.0048  max mem: 22448
+train: [14]  [180/400]  eta: 0:01:41  lr: 0.000090  loss: 2.1901 (2.2107)  grad: 0.1879 (0.1916)  time: 0.4595  data: 0.0048  max mem: 22448
+train: [14]  [200/400]  eta: 0:01:32  lr: 0.000089  loss: 2.1879 (2.2080)  grad: 0.1929 (0.1914)  time: 0.4631  data: 0.0049  max mem: 22448
+train: [14]  [220/400]  eta: 0:01:23  lr: 0.000088  loss: 2.2003 (2.2116)  grad: 0.1883 (0.1910)  time: 0.4626  data: 0.0049  max mem: 22448
+train: [14]  [240/400]  eta: 0:01:13  lr: 0.000086  loss: 2.2433 (2.2120)  grad: 0.1883 (0.1909)  time: 0.4558  data: 0.0048  max mem: 22448
+train: [14]  [260/400]  eta: 0:01:04  lr: 0.000085  loss: 2.1416 (2.2074)  grad: 0.1858 (0.1906)  time: 0.4640  data: 0.0051  max mem: 22448
+train: [14]  [280/400]  eta: 0:00:55  lr: 0.000083  loss: 2.1954 (2.2087)  grad: 0.1897 (0.1909)  time: 0.4413  data: 0.0048  max mem: 22448
+train: [14]  [300/400]  eta: 0:00:45  lr: 0.000082  loss: 2.2419 (2.2085)  grad: 0.1926 (0.1910)  time: 0.4347  data: 0.0049  max mem: 22448
+train: [14]  [320/400]  eta: 0:00:36  lr: 0.000081  loss: 2.2578 (2.2121)  grad: 0.2020 (0.1918)  time: 0.4719  data: 0.0051  max mem: 22448
+train: [14]  [340/400]  eta: 0:00:27  lr: 0.000079  loss: 2.2246 (2.2119)  grad: 0.1998 (0.1921)  time: 0.4613  data: 0.0049  max mem: 22448
+train: [14]  [360/400]  eta: 0:00:18  lr: 0.000078  loss: 2.1906 (2.2105)  grad: 0.1938 (0.1921)  time: 0.4609  data: 0.0052  max mem: 22448
+train: [14]  [380/400]  eta: 0:00:09  lr: 0.000076  loss: 2.1906 (2.2129)  grad: 0.1870 (0.1920)  time: 0.4614  data: 0.0050  max mem: 22448
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 2.2398 (2.2143)  grad: 0.1864 (0.1919)  time: 0.4583  data: 0.0046  max mem: 22448
+train: [14] Total time: 0:03:04 (0.4607 s / it)
+train: [14] Summary: lr: 0.000075  loss: 2.2398 (2.2143)  grad: 0.1864 (0.1919)
+eval (validation): [14]  [ 0/85]  eta: 0:04:59    time: 3.5247  data: 3.1948  max mem: 22448
+eval (validation): [14]  [20/85]  eta: 0:00:34    time: 0.3833  data: 0.0042  max mem: 22448
+eval (validation): [14]  [40/85]  eta: 0:00:20    time: 0.3516  data: 0.0041  max mem: 22448
+eval (validation): [14]  [60/85]  eta: 0:00:10    time: 0.3311  data: 0.0036  max mem: 22448
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3253  data: 0.0039  max mem: 22448
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.3208  data: 0.0038  max mem: 22448
+eval (validation): [14] Total time: 0:00:32 (0.3864 s / it)
+cv: [14]  best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00')  loss: 2.357  acc: 0.296  f1: 0.225
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+train: [15]  [  0/400]  eta: 0:23:22  lr: nan  time: 3.5071  data: 3.1495  max mem: 22448
+train: [15]  [ 20/400]  eta: 0:03:52  lr: 0.000074  loss: 2.1619 (2.1872)  grad: 0.1829 (0.1823)  time: 0.4670  data: 0.0051  max mem: 22448
+train: [15]  [ 40/400]  eta: 0:03:09  lr: 0.000072  loss: 2.1825 (2.1897)  grad: 0.1870 (0.1863)  time: 0.4382  data: 0.0042  max mem: 22448
+train: [15]  [ 60/400]  eta: 0:02:50  lr: 0.000071  loss: 2.1808 (2.1840)  grad: 0.1883 (0.1859)  time: 0.4446  data: 0.0049  max mem: 22448
+train: [15]  [ 80/400]  eta: 0:02:36  lr: 0.000070  loss: 2.1808 (2.1955)  grad: 0.1837 (0.1878)  time: 0.4550  data: 0.0050  max mem: 22448
+train: [15]  [100/400]  eta: 0:02:25  lr: 0.000068  loss: 2.1727 (2.1916)  grad: 0.1906 (0.1890)  time: 0.4630  data: 0.0050  max mem: 22448
+train: [15]  [120/400]  eta: 0:02:14  lr: 0.000067  loss: 2.1618 (2.1891)  grad: 0.1926 (0.1896)  time: 0.4576  data: 0.0048  max mem: 22448
+train: [15]  [140/400]  eta: 0:02:03  lr: 0.000066  loss: 2.1681 (2.1889)  grad: 0.1896 (0.1896)  time: 0.4497  data: 0.0048  max mem: 22448
+train: [15]  [160/400]  eta: 0:01:53  lr: 0.000064  loss: 2.1956 (2.1911)  grad: 0.1953 (0.1904)  time: 0.4462  data: 0.0046  max mem: 22448
+train: [15]  [180/400]  eta: 0:01:43  lr: 0.000063  loss: 2.1956 (2.1888)  grad: 0.1960 (0.1909)  time: 0.4406  data: 0.0049  max mem: 22448
+train: [15]  [200/400]  eta: 0:01:33  lr: 0.000062  loss: 2.1694 (2.1883)  grad: 0.1945 (0.1905)  time: 0.4455  data: 0.0049  max mem: 22448
+train: [15]  [220/400]  eta: 0:01:23  lr: 0.000061  loss: 2.1703 (2.1926)  grad: 0.1945 (0.1906)  time: 0.4581  data: 0.0051  max mem: 22448
+train: [15]  [240/400]  eta: 0:01:14  lr: 0.000059  loss: 2.1671 (2.1892)  grad: 0.1919 (0.1905)  time: 0.4586  data: 0.0051  max mem: 22448
+train: [15]  [260/400]  eta: 0:01:05  lr: 0.000058  loss: 2.1554 (2.1918)  grad: 0.1905 (0.1907)  time: 0.4648  data: 0.0050  max mem: 22448
+train: [15]  [280/400]  eta: 0:00:55  lr: 0.000057  loss: 2.2009 (2.1936)  grad: 0.1950 (0.1910)  time: 0.4621  data: 0.0051  max mem: 22448
+train: [15]  [300/400]  eta: 0:00:46  lr: 0.000056  loss: 2.2070 (2.1931)  grad: 0.1875 (0.1907)  time: 0.4577  data: 0.0048  max mem: 22448
+train: [15]  [320/400]  eta: 0:00:37  lr: 0.000054  loss: 2.2070 (2.1945)  grad: 0.1898 (0.1909)  time: 0.4825  data: 0.0049  max mem: 22448
+train: [15]  [340/400]  eta: 0:00:27  lr: 0.000053  loss: 2.1873 (2.1964)  grad: 0.1950 (0.1914)  time: 0.4732  data: 0.0051  max mem: 22448
+train: [15]  [360/400]  eta: 0:00:18  lr: 0.000052  loss: 2.1913 (2.1980)  grad: 0.1927 (0.1912)  time: 0.4604  data: 0.0051  max mem: 22448
+train: [15]  [380/400]  eta: 0:00:09  lr: 0.000051  loss: 2.2187 (2.1979)  grad: 0.1896 (0.1912)  time: 0.4466  data: 0.0046  max mem: 22448
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 2.1508 (2.1942)  grad: 0.1877 (0.1910)  time: 0.4689  data: 0.0049  max mem: 22448
+train: [15] Total time: 0:03:06 (0.4652 s / it)
+train: [15] Summary: lr: 0.000050  loss: 2.1508 (2.1942)  grad: 0.1877 (0.1910)
+eval (validation): [15]  [ 0/85]  eta: 0:04:53    time: 3.4518  data: 3.1409  max mem: 22448
+eval (validation): [15]  [20/85]  eta: 0:00:32    time: 0.3481  data: 0.0040  max mem: 22448
+eval (validation): [15]  [40/85]  eta: 0:00:19    time: 0.3498  data: 0.0040  max mem: 22448
+eval (validation): [15]  [60/85]  eta: 0:00:10    time: 0.3802  data: 0.0045  max mem: 22448
+eval (validation): [15]  [80/85]  eta: 0:00:01    time: 0.3504  data: 0.0041  max mem: 22448
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.3423  data: 0.0041  max mem: 22448
+eval (validation): [15] Total time: 0:00:33 (0.3961 s / it)
+cv: [15]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.375  acc: 0.293  f1: 0.227
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:23:12  lr: nan  time: 3.4818  data: 3.1196  max mem: 22448
+train: [16]  [ 20/400]  eta: 0:03:55  lr: 0.000048  loss: 2.1542 (2.1401)  grad: 0.1951 (0.1908)  time: 0.4759  data: 0.0033  max mem: 22448
+train: [16]  [ 40/400]  eta: 0:03:14  lr: 0.000047  loss: 2.1626 (2.1613)  grad: 0.1978 (0.1939)  time: 0.4563  data: 0.0045  max mem: 22448
+train: [16]  [ 60/400]  eta: 0:02:54  lr: 0.000046  loss: 2.1520 (2.1592)  grad: 0.1924 (0.1912)  time: 0.4619  data: 0.0051  max mem: 22448
+train: [16]  [ 80/400]  eta: 0:02:40  lr: 0.000045  loss: 2.1457 (2.1549)  grad: 0.1850 (0.1911)  time: 0.4623  data: 0.0050  max mem: 22448
+train: [16]  [100/400]  eta: 0:02:26  lr: 0.000044  loss: 2.1583 (2.1537)  grad: 0.1850 (0.1895)  time: 0.4397  data: 0.0049  max mem: 22448
+train: [16]  [120/400]  eta: 0:02:15  lr: 0.000043  loss: 2.1550 (2.1534)  grad: 0.1863 (0.1906)  time: 0.4664  data: 0.0049  max mem: 22448
+train: [16]  [140/400]  eta: 0:02:05  lr: 0.000042  loss: 2.1319 (2.1532)  grad: 0.1920 (0.1903)  time: 0.4699  data: 0.0051  max mem: 22448
+train: [16]  [160/400]  eta: 0:01:55  lr: 0.000041  loss: 2.1266 (2.1512)  grad: 0.1825 (0.1891)  time: 0.4593  data: 0.0050  max mem: 22448
+train: [16]  [180/400]  eta: 0:01:45  lr: 0.000040  loss: 2.1111 (2.1493)  grad: 0.1814 (0.1886)  time: 0.4660  data: 0.0050  max mem: 22448
+train: [16]  [200/400]  eta: 0:01:35  lr: 0.000039  loss: 2.1483 (2.1501)  grad: 0.1856 (0.1884)  time: 0.4825  data: 0.0049  max mem: 22448
+train: [16]  [220/400]  eta: 0:01:26  lr: 0.000038  loss: 2.1361 (2.1508)  grad: 0.1855 (0.1882)  time: 0.4720  data: 0.0049  max mem: 22448
+train: [16]  [240/400]  eta: 0:01:16  lr: 0.000036  loss: 2.1568 (2.1557)  grad: 0.1855 (0.1881)  time: 0.4626  data: 0.0051  max mem: 22448
+train: [16]  [260/400]  eta: 0:01:06  lr: 0.000035  loss: 2.1613 (2.1540)  grad: 0.1882 (0.1882)  time: 0.4622  data: 0.0050  max mem: 22448
+train: [16]  [280/400]  eta: 0:00:56  lr: 0.000034  loss: 2.1641 (2.1573)  grad: 0.1885 (0.1883)  time: 0.4483  data: 0.0048  max mem: 22448
+train: [16]  [300/400]  eta: 0:00:47  lr: 0.000033  loss: 2.1641 (2.1545)  grad: 0.1943 (0.1887)  time: 0.4466  data: 0.0049  max mem: 22448
+train: [16]  [320/400]  eta: 0:00:37  lr: 0.000032  loss: 2.1333 (2.1557)  grad: 0.1867 (0.1888)  time: 0.4580  data: 0.0050  max mem: 22448
+train: [16]  [340/400]  eta: 0:00:28  lr: 0.000031  loss: 2.1853 (2.1594)  grad: 0.1817 (0.1885)  time: 0.4456  data: 0.0046  max mem: 22448
+train: [16]  [360/400]  eta: 0:00:18  lr: 0.000031  loss: 2.1966 (2.1605)  grad: 0.1861 (0.1885)  time: 0.4551  data: 0.0050  max mem: 22448
+train: [16]  [380/400]  eta: 0:00:09  lr: 0.000030  loss: 2.1648 (2.1589)  grad: 0.1873 (0.1884)  time: 0.4723  data: 0.0049  max mem: 22448
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 2.1512 (2.1581)  grad: 0.1828 (0.1883)  time: 0.4642  data: 0.0049  max mem: 22448
+train: [16] Total time: 0:03:07 (0.4695 s / it)
+train: [16] Summary: lr: 0.000029  loss: 2.1512 (2.1581)  grad: 0.1828 (0.1883)
+eval (validation): [16]  [ 0/85]  eta: 0:04:25    time: 3.1214  data: 2.8234  max mem: 22448
+eval (validation): [16]  [20/85]  eta: 0:00:31    time: 0.3577  data: 0.0056  max mem: 22448
+eval (validation): [16]  [40/85]  eta: 0:00:19    time: 0.3664  data: 0.0036  max mem: 22448
+eval (validation): [16]  [60/85]  eta: 0:00:10    time: 0.3561  data: 0.0042  max mem: 22448
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3373  data: 0.0039  max mem: 22448
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.3260  data: 0.0037  max mem: 22448
+eval (validation): [16] Total time: 0:00:33 (0.3883 s / it)
+cv: [16]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.372  acc: 0.295  f1: 0.228
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:20:58  lr: nan  time: 3.1460  data: 2.7746  max mem: 22448
+train: [17]  [ 20/400]  eta: 0:03:42  lr: 0.000028  loss: 2.0478 (2.0527)  grad: 0.1863 (0.1850)  time: 0.4563  data: 0.0041  max mem: 22448
+train: [17]  [ 40/400]  eta: 0:03:07  lr: 0.000027  loss: 2.0896 (2.1009)  grad: 0.1901 (0.1872)  time: 0.4567  data: 0.0049  max mem: 22448
+train: [17]  [ 60/400]  eta: 0:02:51  lr: 0.000026  loss: 2.1421 (2.1204)  grad: 0.1901 (0.1869)  time: 0.4650  data: 0.0050  max mem: 22448
+train: [17]  [ 80/400]  eta: 0:02:37  lr: 0.000025  loss: 2.1357 (2.1191)  grad: 0.1877 (0.1871)  time: 0.4562  data: 0.0048  max mem: 22448
+train: [17]  [100/400]  eta: 0:02:26  lr: 0.000024  loss: 2.0951 (2.1226)  grad: 0.1845 (0.1865)  time: 0.4698  data: 0.0049  max mem: 22448
+train: [17]  [120/400]  eta: 0:02:15  lr: 0.000023  loss: 2.1286 (2.1219)  grad: 0.1807 (0.1854)  time: 0.4648  data: 0.0050  max mem: 22448
+train: [17]  [140/400]  eta: 0:02:05  lr: 0.000023  loss: 2.1373 (2.1296)  grad: 0.1829 (0.1861)  time: 0.4636  data: 0.0051  max mem: 22448
+train: [17]  [160/400]  eta: 0:01:54  lr: 0.000022  loss: 2.1455 (2.1319)  grad: 0.1907 (0.1860)  time: 0.4626  data: 0.0050  max mem: 22448
+train: [17]  [180/400]  eta: 0:01:44  lr: 0.000021  loss: 2.1439 (2.1376)  grad: 0.1851 (0.1857)  time: 0.4654  data: 0.0049  max mem: 22448
+train: [17]  [200/400]  eta: 0:01:34  lr: 0.000020  loss: 2.1439 (2.1378)  grad: 0.1857 (0.1856)  time: 0.4529  data: 0.0053  max mem: 22448
+train: [17]  [220/400]  eta: 0:01:24  lr: 0.000019  loss: 2.1171 (2.1366)  grad: 0.1882 (0.1858)  time: 0.4370  data: 0.0049  max mem: 22448
+train: [17]  [240/400]  eta: 0:01:15  lr: 0.000019  loss: 2.1134 (2.1361)  grad: 0.1878 (0.1862)  time: 0.4489  data: 0.0049  max mem: 22448
+train: [17]  [260/400]  eta: 0:01:05  lr: 0.000018  loss: 2.1180 (2.1343)  grad: 0.1854 (0.1861)  time: 0.4595  data: 0.0050  max mem: 22448
+train: [17]  [280/400]  eta: 0:00:56  lr: 0.000017  loss: 2.1037 (2.1327)  grad: 0.1804 (0.1859)  time: 0.4687  data: 0.0050  max mem: 22448
+train: [17]  [300/400]  eta: 0:00:46  lr: 0.000016  loss: 2.1037 (2.1336)  grad: 0.1812 (0.1860)  time: 0.4652  data: 0.0049  max mem: 22448
+train: [17]  [320/400]  eta: 0:00:37  lr: 0.000016  loss: 2.1296 (2.1331)  grad: 0.1812 (0.1856)  time: 0.4747  data: 0.0048  max mem: 22448
+train: [17]  [340/400]  eta: 0:00:28  lr: 0.000015  loss: 2.1347 (2.1354)  grad: 0.1813 (0.1856)  time: 0.4512  data: 0.0048  max mem: 22448
+train: [17]  [360/400]  eta: 0:00:18  lr: 0.000014  loss: 2.1347 (2.1349)  grad: 0.1813 (0.1852)  time: 0.4782  data: 0.0052  max mem: 22448
+train: [17]  [380/400]  eta: 0:00:09  lr: 0.000014  loss: 2.1149 (2.1344)  grad: 0.1771 (0.1852)  time: 0.4659  data: 0.0048  max mem: 22448
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 2.1435 (2.1349)  grad: 0.1836 (0.1851)  time: 0.4479  data: 0.0045  max mem: 22448
+train: [17] Total time: 0:03:07 (0.4679 s / it)
+train: [17] Summary: lr: 0.000013  loss: 2.1435 (2.1349)  grad: 0.1836 (0.1851)
+eval (validation): [17]  [ 0/85]  eta: 0:04:19    time: 3.0487  data: 2.8214  max mem: 22448
+eval (validation): [17]  [20/85]  eta: 0:00:31    time: 0.3568  data: 0.0041  max mem: 22448
+eval (validation): [17]  [40/85]  eta: 0:00:18    time: 0.3490  data: 0.0036  max mem: 22448
+eval (validation): [17]  [60/85]  eta: 0:00:09    time: 0.3495  data: 0.0037  max mem: 22448
+eval (validation): [17]  [80/85]  eta: 0:00:01    time: 0.3459  data: 0.0042  max mem: 22448
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3353  data: 0.0040  max mem: 22448
+eval (validation): [17] Total time: 0:00:32 (0.3839 s / it)
+cv: [17]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.371  acc: 0.292  f1: 0.226
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:23:05  lr: nan  time: 3.4632  data: 3.0970  max mem: 22448
+train: [18]  [ 20/400]  eta: 0:03:54  lr: 0.000012  loss: 2.1493 (2.1543)  grad: 0.1835 (0.1837)  time: 0.4758  data: 0.0037  max mem: 22448
+train: [18]  [ 40/400]  eta: 0:03:16  lr: 0.000012  loss: 2.1385 (2.1405)  grad: 0.1846 (0.1841)  time: 0.4693  data: 0.0043  max mem: 22448
+train: [18]  [ 60/400]  eta: 0:02:56  lr: 0.000011  loss: 2.1299 (2.1428)  grad: 0.1838 (0.1840)  time: 0.4631  data: 0.0050  max mem: 22448
+train: [18]  [ 80/400]  eta: 0:02:41  lr: 0.000011  loss: 2.1303 (2.1374)  grad: 0.1833 (0.1841)  time: 0.4582  data: 0.0050  max mem: 22448
+train: [18]  [100/400]  eta: 0:02:28  lr: 0.000010  loss: 2.1303 (2.1320)  grad: 0.1769 (0.1822)  time: 0.4558  data: 0.0050  max mem: 22448
+train: [18]  [120/400]  eta: 0:02:16  lr: 0.000009  loss: 2.1193 (2.1267)  grad: 0.1766 (0.1810)  time: 0.4501  data: 0.0050  max mem: 22448
+train: [18]  [140/400]  eta: 0:02:04  lr: 0.000009  loss: 2.1057 (2.1197)  grad: 0.1809 (0.1817)  time: 0.4381  data: 0.0049  max mem: 22448
+train: [18]  [160/400]  eta: 0:01:54  lr: 0.000008  loss: 2.1057 (2.1204)  grad: 0.1826 (0.1823)  time: 0.4480  data: 0.0051  max mem: 22448
+train: [18]  [180/400]  eta: 0:01:44  lr: 0.000008  loss: 2.1195 (2.1255)  grad: 0.1826 (0.1823)  time: 0.4600  data: 0.0051  max mem: 22448
+train: [18]  [200/400]  eta: 0:01:34  lr: 0.000007  loss: 2.0736 (2.1187)  grad: 0.1780 (0.1817)  time: 0.4649  data: 0.0048  max mem: 22448
+train: [18]  [220/400]  eta: 0:01:25  lr: 0.000007  loss: 2.0559 (2.1138)  grad: 0.1743 (0.1811)  time: 0.4724  data: 0.0049  max mem: 22448
+train: [18]  [240/400]  eta: 0:01:15  lr: 0.000006  loss: 2.0600 (2.1123)  grad: 0.1759 (0.1810)  time: 0.4541  data: 0.0049  max mem: 22448
+train: [18]  [260/400]  eta: 0:01:05  lr: 0.000006  loss: 2.0975 (2.1123)  grad: 0.1793 (0.1812)  time: 0.4559  data: 0.0050  max mem: 22448
+train: [18]  [280/400]  eta: 0:00:56  lr: 0.000006  loss: 2.1267 (2.1133)  grad: 0.1800 (0.1816)  time: 0.4651  data: 0.0050  max mem: 22448
+train: [18]  [300/400]  eta: 0:00:46  lr: 0.000005  loss: 2.1042 (2.1125)  grad: 0.1816 (0.1816)  time: 0.4615  data: 0.0050  max mem: 22448
+train: [18]  [320/400]  eta: 0:00:37  lr: 0.000005  loss: 2.0736 (2.1101)  grad: 0.1816 (0.1816)  time: 0.4553  data: 0.0048  max mem: 22448
+train: [18]  [340/400]  eta: 0:00:28  lr: 0.000004  loss: 2.0888 (2.1103)  grad: 0.1839 (0.1818)  time: 0.4627  data: 0.0048  max mem: 22448
+train: [18]  [360/400]  eta: 0:00:18  lr: 0.000004  loss: 2.0926 (2.1089)  grad: 0.1800 (0.1815)  time: 0.4596  data: 0.0050  max mem: 22448
+train: [18]  [380/400]  eta: 0:00:09  lr: 0.000004  loss: 2.1050 (2.1105)  grad: 0.1768 (0.1812)  time: 0.4880  data: 0.0049  max mem: 22448
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 2.1188 (2.1090)  grad: 0.1786 (0.1814)  time: 0.4925  data: 0.0050  max mem: 22448
+train: [18] Total time: 0:03:08 (0.4707 s / it)
+train: [18] Summary: lr: 0.000003  loss: 2.1188 (2.1090)  grad: 0.1786 (0.1814)
+eval (validation): [18]  [ 0/85]  eta: 0:04:45    time: 3.3553  data: 3.0856  max mem: 22448
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+eval (validation): [18]  [40/85]  eta: 0:00:20    time: 0.3779  data: 0.0041  max mem: 22448
+eval (validation): [18]  [60/85]  eta: 0:00:10    time: 0.3665  data: 0.0046  max mem: 22448
+eval (validation): [18]  [80/85]  eta: 0:00:02    time: 0.3675  data: 0.0044  max mem: 22448
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3603  data: 0.0045  max mem: 22448
+eval (validation): [18] Total time: 0:00:34 (0.4097 s / it)
+cv: [18]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.368  acc: 0.295  f1: 0.228
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:23:07  lr: nan  time: 3.4691  data: 3.0582  max mem: 22448
+train: [19]  [ 20/400]  eta: 0:03:55  lr: 0.000003  loss: 2.0923 (2.0941)  grad: 0.1806 (0.1819)  time: 0.4764  data: 0.0046  max mem: 22448
+train: [19]  [ 40/400]  eta: 0:03:11  lr: 0.000003  loss: 2.0923 (2.1013)  grad: 0.1813 (0.1818)  time: 0.4431  data: 0.0044  max mem: 22448
+train: [19]  [ 60/400]  eta: 0:02:51  lr: 0.000002  loss: 2.1057 (2.1048)  grad: 0.1813 (0.1821)  time: 0.4446  data: 0.0050  max mem: 22448
+train: [19]  [ 80/400]  eta: 0:02:38  lr: 0.000002  loss: 2.1133 (2.1023)  grad: 0.1824 (0.1829)  time: 0.4633  data: 0.0048  max mem: 22448
+train: [19]  [100/400]  eta: 0:02:26  lr: 0.000002  loss: 2.1280 (2.1021)  grad: 0.1822 (0.1827)  time: 0.4698  data: 0.0053  max mem: 22448
+train: [19]  [120/400]  eta: 0:02:15  lr: 0.000002  loss: 2.1146 (2.1130)  grad: 0.1816 (0.1827)  time: 0.4668  data: 0.0049  max mem: 22448
+train: [19]  [140/400]  eta: 0:02:05  lr: 0.000001  loss: 2.0972 (2.1115)  grad: 0.1833 (0.1834)  time: 0.4536  data: 0.0048  max mem: 22448
+train: [19]  [160/400]  eta: 0:01:54  lr: 0.000001  loss: 2.0938 (2.1114)  grad: 0.1833 (0.1832)  time: 0.4541  data: 0.0051  max mem: 22448
+train: [19]  [180/400]  eta: 0:01:44  lr: 0.000001  loss: 2.1294 (2.1164)  grad: 0.1825 (0.1825)  time: 0.4714  data: 0.0049  max mem: 22448
+train: [19]  [200/400]  eta: 0:01:35  lr: 0.000001  loss: 2.0929 (2.1092)  grad: 0.1789 (0.1827)  time: 0.4683  data: 0.0051  max mem: 22448
+train: [19]  [220/400]  eta: 0:01:25  lr: 0.000001  loss: 2.0640 (2.1077)  grad: 0.1857 (0.1834)  time: 0.4460  data: 0.0048  max mem: 22448
+train: [19]  [240/400]  eta: 0:01:15  lr: 0.000001  loss: 2.1480 (2.1142)  grad: 0.1886 (0.1833)  time: 0.4531  data: 0.0049  max mem: 22448
+train: [19]  [260/400]  eta: 0:01:06  lr: 0.000000  loss: 2.1490 (2.1125)  grad: 0.1843 (0.1833)  time: 0.4774  data: 0.0051  max mem: 22448
+train: [19]  [280/400]  eta: 0:00:56  lr: 0.000000  loss: 2.1101 (2.1127)  grad: 0.1771 (0.1827)  time: 0.4804  data: 0.0050  max mem: 22448
+train: [19]  [300/400]  eta: 0:00:47  lr: 0.000000  loss: 2.1000 (2.1120)  grad: 0.1767 (0.1828)  time: 0.4618  data: 0.0050  max mem: 22448
+train: [19]  [320/400]  eta: 0:00:37  lr: 0.000000  loss: 2.0767 (2.1104)  grad: 0.1767 (0.1825)  time: 0.4604  data: 0.0048  max mem: 22448
+train: [19]  [340/400]  eta: 0:00:28  lr: 0.000000  loss: 2.0527 (2.1064)  grad: 0.1756 (0.1820)  time: 0.4697  data: 0.0050  max mem: 22448
+train: [19]  [360/400]  eta: 0:00:18  lr: 0.000000  loss: 2.0797 (2.1090)  grad: 0.1842 (0.1823)  time: 0.4678  data: 0.0048  max mem: 22448
+train: [19]  [380/400]  eta: 0:00:09  lr: 0.000000  loss: 2.1346 (2.1088)  grad: 0.1850 (0.1824)  time: 0.4605  data: 0.0050  max mem: 22448
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 2.0873 (2.1092)  grad: 0.1737 (0.1822)  time: 0.4630  data: 0.0051  max mem: 22448
+train: [19] Total time: 0:03:08 (0.4707 s / it)
+train: [19] Summary: lr: 0.000000  loss: 2.0873 (2.1092)  grad: 0.1737 (0.1822)
+eval (validation): [19]  [ 0/85]  eta: 0:04:41    time: 3.3092  data: 2.9864  max mem: 22448
+eval (validation): [19]  [20/85]  eta: 0:00:32    time: 0.3639  data: 0.0052  max mem: 22448
+eval (validation): [19]  [40/85]  eta: 0:00:19    time: 0.3450  data: 0.0036  max mem: 22448
+eval (validation): [19]  [60/85]  eta: 0:00:09    time: 0.3302  data: 0.0041  max mem: 22448
+eval (validation): [19]  [80/85]  eta: 0:00:01    time: 0.3372  data: 0.0044  max mem: 22448
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.3318  data: 0.0044  max mem: 22448
+eval (validation): [19] Total time: 0:00:32 (0.3811 s / it)
+cv: [19]  best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00')  loss: 2.367  acc: 0.295  f1: 0.228
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+evaluating last checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
+eval model info:
+{"score": 0.29494278331487633, "hparam": [0.52, 1.0], "hparam_id": 20, "epoch": 19, "is_best": false, "best_score": 0.2956810631229236}
+eval (train): [20]  [  0/509]  eta: 0:27:27    time: 3.2374  data: 2.9738  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:01    time: 0.3562  data: 0.0032  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:16    time: 0.3425  data: 0.0054  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:02:56    time: 0.3367  data: 0.0034  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:02:44    time: 0.3525  data: 0.0038  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:35    time: 0.3702  data: 0.0044  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:26    time: 0.3513  data: 0.0042  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:17    time: 0.3490  data: 0.0041  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:08    time: 0.3474  data: 0.0037  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:01    time: 0.3644  data: 0.0042  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:01:53    time: 0.3631  data: 0.0043  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:45    time: 0.3574  data: 0.0042  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:38    time: 0.3780  data: 0.0042  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:31    time: 0.3632  data: 0.0040  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:24    time: 0.3631  data: 0.0043  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:16    time: 0.3599  data: 0.0044  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:09    time: 0.3652  data: 0.0044  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:01    time: 0.3575  data: 0.0042  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:54    time: 0.3616  data: 0.0041  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:47    time: 0.3588  data: 0.0044  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:39    time: 0.3571  data: 0.0042  max mem: 22448
+eval (train): [20]  [420/509]  eta: 0:00:32    time: 0.3672  data: 0.0039  max mem: 22448
+eval (train): [20]  [440/509]  eta: 0:00:25    time: 0.3587  data: 0.0041  max mem: 22448
+eval (train): [20]  [460/509]  eta: 0:00:17    time: 0.3261  data: 0.0037  max mem: 22448
+eval (train): [20]  [480/509]  eta: 0:00:10    time: 0.3671  data: 0.0045  max mem: 22448
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3450  data: 0.0041  max mem: 22448
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3321  data: 0.0039  max mem: 22448
+eval (train): [20] Total time: 0:03:05 (0.3635 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:41    time: 3.3093  data: 2.9952  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:33    time: 0.3728  data: 0.0046  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:19    time: 0.3434  data: 0.0038  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3994  data: 0.0049  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:02    time: 0.3509  data: 0.0041  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3407  data: 0.0040  max mem: 22448
+eval (validation): [20] Total time: 0:00:34 (0.4025 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:25    time: 3.1229  data: 2.8156  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:33    time: 0.3881  data: 0.0044  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:19    time: 0.3426  data: 0.0042  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3973  data: 0.0047  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:02    time: 0.3470  data: 0.0043  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3327  data: 0.0040  max mem: 22448
+eval (test): [20] Total time: 0:00:34 (0.4006 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:23    time: 3.2187  data: 2.9194  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:31    time: 0.3692  data: 0.0043  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:18    time: 0.3789  data: 0.0038  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3637  data: 0.0043  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3534  data: 0.0044  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3378  data: 0.0042  max mem: 22448
+eval (testid): [20] Total time: 0:00:32 (0.4019 s / it)
+evaluating best checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
+eval model info:
+{"score": 0.2956810631229236, "hparam": [0.38, 1.0], "hparam_id": 18, "epoch": 14, "is_best": true, "best_score": 0.2956810631229236}
+eval (train): [20]  [  0/509]  eta: 0:27:24    time: 3.2308  data: 2.9194  max mem: 22448
+eval (train): [20]  [ 20/509]  eta: 0:04:22    time: 0.4021  data: 0.0054  max mem: 22448
+eval (train): [20]  [ 40/509]  eta: 0:03:26    time: 0.3374  data: 0.0034  max mem: 22448
+eval (train): [20]  [ 60/509]  eta: 0:03:08    time: 0.3768  data: 0.0046  max mem: 22448
+eval (train): [20]  [ 80/509]  eta: 0:03:00    time: 0.4227  data: 0.0046  max mem: 22448
+eval (train): [20]  [100/509]  eta: 0:02:48    time: 0.3821  data: 0.0045  max mem: 22448
+eval (train): [20]  [120/509]  eta: 0:02:37    time: 0.3659  data: 0.0040  max mem: 22448
+eval (train): [20]  [140/509]  eta: 0:02:25    time: 0.3394  data: 0.0038  max mem: 22448
+eval (train): [20]  [160/509]  eta: 0:02:16    time: 0.3503  data: 0.0043  max mem: 22448
+eval (train): [20]  [180/509]  eta: 0:02:07    time: 0.3771  data: 0.0044  max mem: 22448
+eval (train): [20]  [200/509]  eta: 0:02:00    time: 0.4001  data: 0.0049  max mem: 22448
+eval (train): [20]  [220/509]  eta: 0:01:51    time: 0.3516  data: 0.0040  max mem: 22448
+eval (train): [20]  [240/509]  eta: 0:01:43    time: 0.3504  data: 0.0042  max mem: 22448
+eval (train): [20]  [260/509]  eta: 0:01:34    time: 0.3530  data: 0.0041  max mem: 22448
+eval (train): [20]  [280/509]  eta: 0:01:27    time: 0.3858  data: 0.0045  max mem: 22448
+eval (train): [20]  [300/509]  eta: 0:01:19    time: 0.3875  data: 0.0042  max mem: 22448
+eval (train): [20]  [320/509]  eta: 0:01:11    time: 0.3360  data: 0.0038  max mem: 22448
+eval (train): [20]  [340/509]  eta: 0:01:03    time: 0.3544  data: 0.0041  max mem: 22448
+eval (train): [20]  [360/509]  eta: 0:00:56    time: 0.3801  data: 0.0042  max mem: 22448
+eval (train): [20]  [380/509]  eta: 0:00:48    time: 0.3632  data: 0.0041  max mem: 22448
+eval (train): [20]  [400/509]  eta: 0:00:41    time: 0.3653  data: 0.0038  max mem: 22448
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+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.3412  data: 0.0040  max mem: 22448
+eval (train): [20] Total time: 0:03:10 (0.3752 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:12    time: 2.9657  data: 2.6766  max mem: 22448
+eval (validation): [20]  [20/85]  eta: 0:00:34    time: 0.4097  data: 0.0056  max mem: 22448
+eval (validation): [20]  [40/85]  eta: 0:00:20    time: 0.3932  data: 0.0043  max mem: 22448
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3662  data: 0.0043  max mem: 22448
+eval (validation): [20]  [80/85]  eta: 0:00:02    time: 0.3644  data: 0.0042  max mem: 22448
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3471  data: 0.0040  max mem: 22448
+eval (validation): [20] Total time: 0:00:35 (0.4137 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:08    time: 2.9203  data: 2.6670  max mem: 22448
+eval (test): [20]  [20/85]  eta: 0:00:29    time: 0.3364  data: 0.0050  max mem: 22448
+eval (test): [20]  [40/85]  eta: 0:00:18    time: 0.3723  data: 0.0034  max mem: 22448
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3841  data: 0.0044  max mem: 22448
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3405  data: 0.0041  max mem: 22448
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.3263  data: 0.0039  max mem: 22448
+eval (test): [20] Total time: 0:00:33 (0.3885 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:03:55    time: 2.8663  data: 2.5948  max mem: 22448
+eval (testid): [20]  [20/82]  eta: 0:00:33    time: 0.4178  data: 0.0046  max mem: 22448
+eval (testid): [20]  [40/82]  eta: 0:00:18    time: 0.3569  data: 0.0043  max mem: 22448
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3728  data: 0.0048  max mem: 22448
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3323  data: 0.0040  max mem: 22448
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3202  data: 0.0040  max mem: 22448
+eval (testid): [20] Total time: 0:00:32 (0.4006 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   |      14 | 0.000114 | 0.05 |          18 | [0.38, 1.0] | train      | 2.1286 | 0.35831 | 0.0023368 | 0.29595 | 0.0023133 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.000114 | 0.05 |          18 | [0.38, 1.0] | validation | 2.3574 | 0.29568 | 0.0052654 | 0.2247  | 0.0049165 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.000114 | 0.05 |          18 | [0.38, 1.0] | test       | 2.2639 | 0.3128  | 0.00559   | 0.24265 | 0.0055982 |
+| flat_mae | patch  | attn  | nsd_cococlip | best   |      14 | 0.000114 | 0.05 |          18 | [0.38, 1.0] | testid     | 2.2681 | 0.31001 | 0.0056353 | 0.24743 | 0.0053609 |
+
+
+done! total time: 1:24:59

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 12, "eval/id_best": 46, "eval/lr_best": 0.010799999999999999, "eval/wd_best": 0.05, "eval/train/loss": 2.9561731815338135, "eval/train/acc": 0.13442330741571654, "eval/train/acc_std": 0.0016399786261972085, "eval/train/f1": 0.07360312856380823, "eval/train/f1_std": 0.0011894892872814158, "eval/validation/loss": 3.050022840499878, "eval/validation/acc": 0.11517165005537099, "eval/validation/acc_std": 0.0037073851707885345, "eval/validation/f1": 0.0568927381373181, "eval/validation/f1_std": 0.0025098501705171287, "eval/test/loss": 3.0346412658691406, "eval/test/acc": 0.11706864564007421, "eval/test/acc_std": 0.0036309939339685808, "eval/test/f1": 0.05047890849878861, "eval/test/f1_std": 0.0020235796960419747, "eval/testid/loss": 3.0452094078063965, "eval/testid/acc": 0.10776942355889724, "eval/testid/acc_std": 0.0037217302058737472, "eval/testid/f1": 0.05534874186236579, "eval/testid/f1_std": 0.0025537881427831616}

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 48, "eval/last/lr_best": 0.015, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.909435749053955, "eval/last/train/acc": 0.15160269215403055, "eval/last/train/acc_std": 0.0017969187787028554, "eval/last/train/f1": 0.09924394846514734, "eval/last/train/f1_std": 0.0015441521680767947, "eval/last/validation/loss": 3.0152688026428223, "eval/last/validation/acc": 0.11351052048726468, "eval/last/validation/acc_std": 0.0035745682190531473, "eval/last/validation/f1": 0.06564991315905983, "eval/last/validation/f1_std": 0.002770352672686336, "eval/last/test/loss": 3.0052855014801025, "eval/last/test/acc": 0.12393320964749537, "eval/last/test/acc_std": 0.0037463069176298263, "eval/last/test/f1": 0.05954316439826204, "eval/last/test/f1_std": 0.0025967099322259223, "eval/last/testid/loss": 3.0140411853790283, "eval/last/testid/acc": 0.11490264121843069, "eval/last/testid/acc_std": 0.003935210204753251, "eval/last/testid/f1": 0.07020355932941731, "eval/last/testid/f1_std": 0.0029887924236395194}

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/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,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",train,2.9561731815338135,0.13442330741571654,0.0016399786261972085,0.07360312856380823,0.0011894892872814158
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",validation,3.050022840499878,0.11517165005537099,0.0037073851707885345,0.0568927381373181,0.0025098501705171287
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",test,3.0346412658691406,0.11706864564007421,0.0036309939339685808,0.05047890849878861,0.0020235796960419747
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",testid,3.0452094078063965,0.10776942355889724,0.0037217302058737472,0.05534874186236579,0.0025537881427831616

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/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,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",train,2.9561731815338135,0.13442330741571654,0.0016399786261972085,0.07360312856380823,0.0011894892872814158
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",validation,3.050022840499878,0.11517165005537099,0.0037073851707885345,0.0568927381373181,0.0025098501705171287
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",test,3.0346412658691406,0.11706864564007421,0.0036309939339685808,0.05047890849878861,0.0020235796960419747
+flat_mae,patch,linear,nsd_cococlip,best,12,0.010799999999999999,0.05,46,"[36, 1.0]",testid,3.0452094078063965,0.10776942355889724,0.0037217302058737472,0.05534874186236579,0.0025537881427831616

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/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,linear,nsd_cococlip,last,19,0.015,0.05,48,"[50, 1.0]",train,2.909435749053955,0.15160269215403055,0.0017969187787028554,0.09924394846514734,0.0015441521680767947
+flat_mae,patch,linear,nsd_cococlip,last,19,0.015,0.05,48,"[50, 1.0]",validation,3.0152688026428223,0.11351052048726468,0.0035745682190531473,0.06564991315905983,0.002770352672686336
+flat_mae,patch,linear,nsd_cococlip,last,19,0.015,0.05,48,"[50, 1.0]",test,3.0052855014801025,0.12393320964749537,0.0037463069176298263,0.05954316439826204,0.0025967099322259223
+flat_mae,patch,linear,nsd_cococlip,last,19,0.015,0.05,48,"[50, 1.0]",testid,3.0140411853790283,0.11490264121843069,0.003935210204753251,0.07020355932941731,0.0029887924236395194

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/log.txt

@@ -0,0 +1,956 @@
+fMRI foundation model probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 23:07:30
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_probe
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (nsd_cococlip patch linear)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: false
+  norm: false
+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: decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear
+model: flat_mae
+representation: patch
+classifier: linear
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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 LinearClassifier(
+    (linear): Linear(in_features=768, out_features=24, bias=True)
+  )
+)
+classifier params (train): 0.9M (0.9M)
+setting up optimizer
+total batch size: 128 = 64 bs per gpu x 2 accum
+lr: 3.00e-04
+full schedule: epochs = 20 (steps = 4000) (decay = True)
+warmup: epochs = 5 (steps = 1000)
+start training for 20 epochs
+train: [0]  [  0/400]  eta: 0:24:18  lr: nan  time: 3.6457  data: 3.2831  max mem: 3910
+train: [0]  [ 20/400]  eta: 0:03:08  lr: 0.000003  loss: 3.1782 (3.1830)  grad: 0.0810 (0.0840)  time: 0.3384  data: 0.0038  max mem: 3953
+train: [0]  [ 40/400]  eta: 0:02:32  lr: 0.000006  loss: 3.1785 (3.1807)  grad: 0.0810 (0.0804)  time: 0.3462  data: 0.0040  max mem: 3953
+train: [0]  [ 60/400]  eta: 0:02:16  lr: 0.000009  loss: 3.1779 (3.1803)  grad: 0.0757 (0.0780)  time: 0.3562  data: 0.0043  max mem: 3953
+train: [0]  [ 80/400]  eta: 0:02:04  lr: 0.000012  loss: 3.1742 (3.1780)  grad: 0.0705 (0.0761)  time: 0.3488  data: 0.0037  max mem: 3953
+train: [0]  [100/400]  eta: 0:01:54  lr: 0.000015  loss: 3.1697 (3.1763)  grad: 0.0694 (0.0752)  time: 0.3625  data: 0.0042  max mem: 3953
+train: [0]  [120/400]  eta: 0:01:45  lr: 0.000018  loss: 3.1685 (3.1761)  grad: 0.0710 (0.0749)  time: 0.3526  data: 0.0042  max mem: 3953
+train: [0]  [140/400]  eta: 0:01:37  lr: 0.000021  loss: 3.1655 (3.1742)  grad: 0.0749 (0.0755)  time: 0.3591  data: 0.0045  max mem: 3953
+train: [0]  [160/400]  eta: 0:01:29  lr: 0.000024  loss: 3.1645 (3.1732)  grad: 0.0744 (0.0753)  time: 0.3451  data: 0.0042  max mem: 3953
+train: [0]  [180/400]  eta: 0:01:21  lr: 0.000027  loss: 3.1656 (3.1722)  grad: 0.0741 (0.0750)  time: 0.3533  data: 0.0043  max mem: 3953
+train: [0]  [200/400]  eta: 0:01:13  lr: 0.000030  loss: 3.1652 (3.1713)  grad: 0.0722 (0.0744)  time: 0.3402  data: 0.0042  max mem: 3953
+train: [0]  [220/400]  eta: 0:01:05  lr: 0.000033  loss: 3.1624 (3.1702)  grad: 0.0720 (0.0741)  time: 0.3415  data: 0.0042  max mem: 3953
+train: [0]  [240/400]  eta: 0:00:58  lr: 0.000036  loss: 3.1596 (3.1695)  grad: 0.0709 (0.0739)  time: 0.3472  data: 0.0042  max mem: 3953
+train: [0]  [260/400]  eta: 0:00:50  lr: 0.000039  loss: 3.1591 (3.1686)  grad: 0.0694 (0.0736)  time: 0.3532  data: 0.0041  max mem: 3953
+train: [0]  [280/400]  eta: 0:00:43  lr: 0.000042  loss: 3.1518 (3.1674)  grad: 0.0681 (0.0734)  time: 0.3482  data: 0.0041  max mem: 3953
+train: [0]  [300/400]  eta: 0:00:36  lr: 0.000045  loss: 3.1467 (3.1663)  grad: 0.0737 (0.0736)  time: 0.3480  data: 0.0040  max mem: 3953
+train: [0]  [320/400]  eta: 0:00:28  lr: 0.000048  loss: 3.1467 (3.1651)  grad: 0.0745 (0.0735)  time: 0.3622  data: 0.0044  max mem: 3953
+train: [0]  [340/400]  eta: 0:00:21  lr: 0.000051  loss: 3.1551 (3.1652)  grad: 0.0732 (0.0733)  time: 0.3684  data: 0.0041  max mem: 3953
+train: [0]  [360/400]  eta: 0:00:14  lr: 0.000054  loss: 3.1552 (3.1647)  grad: 0.0694 (0.0732)  time: 0.3442  data: 0.0041  max mem: 3953
+train: [0]  [380/400]  eta: 0:00:07  lr: 0.000057  loss: 3.1495 (3.1637)  grad: 0.0698 (0.0732)  time: 0.3429  data: 0.0042  max mem: 3953
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.1498 (3.1630)  grad: 0.0710 (0.0734)  time: 0.3518  data: 0.0040  max mem: 3953
+train: [0] Total time: 0:02:23 (0.3590 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.1498 (3.1630)  grad: 0.0710 (0.0734)
+eval (validation): [0]  [ 0/85]  eta: 0:04:53    time: 3.4578  data: 3.1789  max mem: 3953
+eval (validation): [0]  [20/85]  eta: 0:00:32    time: 0.3464  data: 0.0139  max mem: 3953
+eval (validation): [0]  [40/85]  eta: 0:00:18    time: 0.3369  data: 0.0083  max mem: 3953
+eval (validation): [0]  [60/85]  eta: 0:00:09    time: 0.3387  data: 0.0031  max mem: 3953
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3192  data: 0.0041  max mem: 3953
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3135  data: 0.0039  max mem: 3953
+eval (validation): [0] Total time: 0:00:31 (0.3744 s / it)
+cv: [0]  best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00')  loss: 3.123  acc: 0.069  f1: 0.010
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [1]  [  0/400]  eta: 0:22:01  lr: nan  time: 3.3034  data: 3.0143  max mem: 3953
+train: [1]  [ 20/400]  eta: 0:03:09  lr: 0.000063  loss: 3.1469 (3.1488)  grad: 0.0683 (0.0684)  time: 0.3591  data: 0.0037  max mem: 3953
+train: [1]  [ 40/400]  eta: 0:02:32  lr: 0.000066  loss: 3.1515 (3.1520)  grad: 0.0683 (0.0678)  time: 0.3424  data: 0.0041  max mem: 3953
+train: [1]  [ 60/400]  eta: 0:02:16  lr: 0.000069  loss: 3.1531 (3.1524)  grad: 0.0712 (0.0690)  time: 0.3566  data: 0.0042  max mem: 3953
+train: [1]  [ 80/400]  eta: 0:02:04  lr: 0.000072  loss: 3.1431 (3.1475)  grad: 0.0714 (0.0704)  time: 0.3520  data: 0.0044  max mem: 3953
+train: [1]  [100/400]  eta: 0:01:54  lr: 0.000075  loss: 3.1420 (3.1490)  grad: 0.0697 (0.0701)  time: 0.3598  data: 0.0041  max mem: 3953
+train: [1]  [120/400]  eta: 0:01:45  lr: 0.000078  loss: 3.1429 (3.1465)  grad: 0.0676 (0.0694)  time: 0.3500  data: 0.0040  max mem: 3953
+train: [1]  [140/400]  eta: 0:01:37  lr: 0.000081  loss: 3.1322 (3.1445)  grad: 0.0690 (0.0696)  time: 0.3527  data: 0.0041  max mem: 3953
+train: [1]  [160/400]  eta: 0:01:28  lr: 0.000084  loss: 3.1409 (3.1466)  grad: 0.0683 (0.0691)  time: 0.3460  data: 0.0038  max mem: 3953
+train: [1]  [180/400]  eta: 0:01:20  lr: 0.000087  loss: 3.1506 (3.1461)  grad: 0.0667 (0.0689)  time: 0.3461  data: 0.0041  max mem: 3953
+train: [1]  [200/400]  eta: 0:01:13  lr: 0.000090  loss: 3.1320 (3.1454)  grad: 0.0655 (0.0684)  time: 0.3442  data: 0.0038  max mem: 3953
+train: [1]  [220/400]  eta: 0:01:05  lr: 0.000093  loss: 3.1317 (3.1450)  grad: 0.0712 (0.0690)  time: 0.3490  data: 0.0040  max mem: 3953
+train: [1]  [240/400]  eta: 0:00:58  lr: 0.000096  loss: 3.1326 (3.1435)  grad: 0.0719 (0.0691)  time: 0.3488  data: 0.0038  max mem: 3953
+train: [1]  [260/400]  eta: 0:00:50  lr: 0.000099  loss: 3.1347 (3.1440)  grad: 0.0684 (0.0688)  time: 0.3682  data: 0.0042  max mem: 3953
+train: [1]  [280/400]  eta: 0:00:43  lr: 0.000102  loss: 3.1339 (3.1421)  grad: 0.0701 (0.0690)  time: 0.3425  data: 0.0040  max mem: 3953
+train: [1]  [300/400]  eta: 0:00:36  lr: 0.000105  loss: 3.1339 (3.1422)  grad: 0.0692 (0.0689)  time: 0.3841  data: 0.0039  max mem: 3953
+train: [1]  [320/400]  eta: 0:00:28  lr: 0.000108  loss: 3.1401 (3.1430)  grad: 0.0664 (0.0685)  time: 0.3366  data: 0.0039  max mem: 3953
+train: [1]  [340/400]  eta: 0:00:21  lr: 0.000111  loss: 3.1345 (3.1423)  grad: 0.0670 (0.0686)  time: 0.3555  data: 0.0042  max mem: 3953
+train: [1]  [360/400]  eta: 0:00:14  lr: 0.000114  loss: 3.1293 (3.1421)  grad: 0.0670 (0.0684)  time: 0.3550  data: 0.0041  max mem: 3953
+train: [1]  [380/400]  eta: 0:00:07  lr: 0.000117  loss: 3.1274 (3.1414)  grad: 0.0682 (0.0687)  time: 0.3483  data: 0.0042  max mem: 3953
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 3.1279 (3.1410)  grad: 0.0730 (0.0688)  time: 0.3660  data: 0.0040  max mem: 3953
+train: [1] Total time: 0:02:24 (0.3606 s / it)
+train: [1] Summary: lr: 0.000120  loss: 3.1279 (3.1410)  grad: 0.0730 (0.0688)
+eval (validation): [1]  [ 0/85]  eta: 0:04:59    time: 3.5271  data: 3.2414  max mem: 3953
+eval (validation): [1]  [20/85]  eta: 0:00:33    time: 0.3706  data: 0.0113  max mem: 3953
+eval (validation): [1]  [40/85]  eta: 0:00:19    time: 0.3493  data: 0.0040  max mem: 3953
+eval (validation): [1]  [60/85]  eta: 0:00:10    time: 0.3384  data: 0.0040  max mem: 3953
+eval (validation): [1]  [80/85]  eta: 0:00:01    time: 0.3310  data: 0.0044  max mem: 3953
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.3175  data: 0.0041  max mem: 3953
+eval (validation): [1] Total time: 0:00:32 (0.3853 s / it)
+cv: [1]  best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00')  loss: 3.148  acc: 0.079  f1: 0.023
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:21:10  lr: nan  time: 3.1756  data: 2.9628  max mem: 3953
+train: [2]  [ 20/400]  eta: 0:03:08  lr: 0.000123  loss: 3.1116 (3.1236)  grad: 0.0620 (0.0640)  time: 0.3610  data: 0.0312  max mem: 3953
+train: [2]  [ 40/400]  eta: 0:02:34  lr: 0.000126  loss: 3.1290 (3.1303)  grad: 0.0645 (0.0663)  time: 0.3611  data: 0.0043  max mem: 3953
+train: [2]  [ 60/400]  eta: 0:02:17  lr: 0.000129  loss: 3.1307 (3.1308)  grad: 0.0662 (0.0658)  time: 0.3516  data: 0.0032  max mem: 3953
+train: [2]  [ 80/400]  eta: 0:02:04  lr: 0.000132  loss: 3.1307 (3.1303)  grad: 0.0638 (0.0663)  time: 0.3457  data: 0.0034  max mem: 3953
+train: [2]  [100/400]  eta: 0:01:54  lr: 0.000135  loss: 3.1319 (3.1297)  grad: 0.0662 (0.0672)  time: 0.3541  data: 0.0038  max mem: 3953
+train: [2]  [120/400]  eta: 0:01:46  lr: 0.000138  loss: 3.1365 (3.1327)  grad: 0.0679 (0.0672)  time: 0.3677  data: 0.0040  max mem: 3953
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+train: [2]  [160/400]  eta: 0:01:30  lr: 0.000144  loss: 3.1306 (3.1342)  grad: 0.0657 (0.0670)  time: 0.3494  data: 0.0043  max mem: 3953
+train: [2]  [180/400]  eta: 0:01:22  lr: 0.000147  loss: 3.1309 (3.1334)  grad: 0.0680 (0.0672)  time: 0.3540  data: 0.0040  max mem: 3953
+train: [2]  [200/400]  eta: 0:01:14  lr: 0.000150  loss: 3.1181 (3.1319)  grad: 0.0685 (0.0675)  time: 0.3488  data: 0.0042  max mem: 3953
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+train: [2]  [260/400]  eta: 0:00:51  lr: 0.000159  loss: 3.1368 (3.1306)  grad: 0.0649 (0.0676)  time: 0.3788  data: 0.0044  max mem: 3953
+train: [2]  [280/400]  eta: 0:00:44  lr: 0.000162  loss: 3.1247 (3.1297)  grad: 0.0666 (0.0677)  time: 0.3541  data: 0.0043  max mem: 3953
+train: [2]  [300/400]  eta: 0:00:36  lr: 0.000165  loss: 3.1247 (3.1300)  grad: 0.0666 (0.0675)  time: 0.3534  data: 0.0040  max mem: 3953
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+train: [2]  [340/400]  eta: 0:00:21  lr: 0.000171  loss: 3.1200 (3.1300)  grad: 0.0678 (0.0674)  time: 0.3527  data: 0.0037  max mem: 3953
+train: [2]  [360/400]  eta: 0:00:14  lr: 0.000174  loss: 3.1204 (3.1293)  grad: 0.0626 (0.0671)  time: 0.3547  data: 0.0042  max mem: 3953
+train: [2]  [380/400]  eta: 0:00:07  lr: 0.000177  loss: 3.1306 (3.1298)  grad: 0.0615 (0.0670)  time: 0.3465  data: 0.0042  max mem: 3953
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 3.1453 (3.1308)  grad: 0.0662 (0.0671)  time: 0.3442  data: 0.0041  max mem: 3953
+train: [2] Total time: 0:02:24 (0.3623 s / it)
+train: [2] Summary: lr: 0.000180  loss: 3.1453 (3.1308)  grad: 0.0662 (0.0671)
+eval (validation): [2]  [ 0/85]  eta: 0:04:59    time: 3.5238  data: 3.2100  max mem: 3953
+eval (validation): [2]  [20/85]  eta: 0:00:33    time: 0.3590  data: 0.0053  max mem: 3953
+eval (validation): [2]  [40/85]  eta: 0:00:19    time: 0.3343  data: 0.0044  max mem: 3953
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3444  data: 0.0043  max mem: 3953
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3222  data: 0.0041  max mem: 3953
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3160  data: 0.0040  max mem: 3953
+eval (validation): [2] Total time: 0:00:32 (0.3784 s / it)
+cv: [2]  best hparam: (19, 1.0) (042) ('042_lr1.9e+01_wd1.0e+00')  loss: 3.106  acc: 0.089  f1: 0.033
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [3]  [  0/400]  eta: 0:22:57  lr: nan  time: 3.4435  data: 3.1913  max mem: 3953
+train: [3]  [ 20/400]  eta: 0:03:45  lr: 0.000183  loss: 3.1199 (3.1282)  grad: 0.0681 (0.0694)  time: 0.4506  data: 0.0296  max mem: 3953
+train: [3]  [ 40/400]  eta: 0:02:53  lr: 0.000186  loss: 3.1199 (3.1228)  grad: 0.0660 (0.0677)  time: 0.3646  data: 0.0040  max mem: 3953
+train: [3]  [ 60/400]  eta: 0:02:32  lr: 0.000189  loss: 3.1292 (3.1290)  grad: 0.0640 (0.0671)  time: 0.3784  data: 0.0044  max mem: 3953
+train: [3]  [ 80/400]  eta: 0:02:16  lr: 0.000192  loss: 3.1395 (3.1317)  grad: 0.0678 (0.0688)  time: 0.3637  data: 0.0043  max mem: 3953
+train: [3]  [100/400]  eta: 0:02:04  lr: 0.000195  loss: 3.1370 (3.1321)  grad: 0.0675 (0.0680)  time: 0.3676  data: 0.0045  max mem: 3953
+train: [3]  [120/400]  eta: 0:01:54  lr: 0.000198  loss: 3.1288 (3.1315)  grad: 0.0649 (0.0674)  time: 0.3693  data: 0.0042  max mem: 3953
+train: [3]  [140/400]  eta: 0:01:44  lr: 0.000201  loss: 3.1154 (3.1302)  grad: 0.0645 (0.0675)  time: 0.3745  data: 0.0043  max mem: 3953
+train: [3]  [160/400]  eta: 0:01:35  lr: 0.000204  loss: 3.1096 (3.1269)  grad: 0.0660 (0.0676)  time: 0.3774  data: 0.0044  max mem: 3953
+train: [3]  [180/400]  eta: 0:01:27  lr: 0.000207  loss: 3.1132 (3.1269)  grad: 0.0708 (0.0681)  time: 0.3636  data: 0.0042  max mem: 3953
+train: [3]  [200/400]  eta: 0:01:18  lr: 0.000210  loss: 3.1178 (3.1260)  grad: 0.0683 (0.0680)  time: 0.3619  data: 0.0043  max mem: 3953
+train: [3]  [220/400]  eta: 0:01:10  lr: 0.000213  loss: 3.1152 (3.1263)  grad: 0.0660 (0.0679)  time: 0.3712  data: 0.0041  max mem: 3953
+train: [3]  [240/400]  eta: 0:01:02  lr: 0.000216  loss: 3.1252 (3.1275)  grad: 0.0654 (0.0681)  time: 0.4137  data: 0.0042  max mem: 3953
+train: [3]  [260/400]  eta: 0:00:54  lr: 0.000219  loss: 3.1252 (3.1275)  grad: 0.0689 (0.0682)  time: 0.3696  data: 0.0042  max mem: 3953
+train: [3]  [280/400]  eta: 0:00:46  lr: 0.000222  loss: 3.1224 (3.1275)  grad: 0.0693 (0.0683)  time: 0.3592  data: 0.0043  max mem: 3953
+train: [3]  [300/400]  eta: 0:00:38  lr: 0.000225  loss: 3.1191 (3.1261)  grad: 0.0683 (0.0681)  time: 0.3540  data: 0.0041  max mem: 3953
+train: [3]  [320/400]  eta: 0:00:30  lr: 0.000228  loss: 3.1186 (3.1266)  grad: 0.0663 (0.0681)  time: 0.3575  data: 0.0043  max mem: 3953
+train: [3]  [340/400]  eta: 0:00:23  lr: 0.000231  loss: 3.1193 (3.1259)  grad: 0.0663 (0.0681)  time: 0.3674  data: 0.0040  max mem: 3953
+train: [3]  [360/400]  eta: 0:00:15  lr: 0.000234  loss: 3.1206 (3.1260)  grad: 0.0654 (0.0679)  time: 0.3574  data: 0.0042  max mem: 3953
+train: [3]  [380/400]  eta: 0:00:07  lr: 0.000237  loss: 3.1238 (3.1257)  grad: 0.0661 (0.0679)  time: 0.3595  data: 0.0039  max mem: 3953
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 3.1247 (3.1256)  grad: 0.0688 (0.0680)  time: 0.3524  data: 0.0044  max mem: 3953
+train: [3] Total time: 0:02:31 (0.3797 s / it)
+train: [3] Summary: lr: 0.000240  loss: 3.1247 (3.1256)  grad: 0.0688 (0.0680)
+eval (validation): [3]  [ 0/85]  eta: 0:04:44    time: 3.3444  data: 3.1186  max mem: 3953
+eval (validation): [3]  [20/85]  eta: 0:00:34    time: 0.3910  data: 0.0053  max mem: 3953
+eval (validation): [3]  [40/85]  eta: 0:00:19    time: 0.3181  data: 0.0041  max mem: 3953
+eval (validation): [3]  [60/85]  eta: 0:00:09    time: 0.3199  data: 0.0042  max mem: 3953
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3193  data: 0.0044  max mem: 3953
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3125  data: 0.0042  max mem: 3953
+eval (validation): [3] Total time: 0:00:31 (0.3726 s / it)
+cv: [3]  best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00')  loss: 3.107  acc: 0.104  f1: 0.047
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [4]  [  0/400]  eta: 0:22:22  lr: nan  time: 3.3571  data: 3.1324  max mem: 3953
+train: [4]  [ 20/400]  eta: 0:03:15  lr: 0.000243  loss: 3.1151 (3.1134)  grad: 0.0632 (0.0645)  time: 0.3734  data: 0.0073  max mem: 3953
+train: [4]  [ 40/400]  eta: 0:02:34  lr: 0.000246  loss: 3.0830 (3.1058)  grad: 0.0611 (0.0630)  time: 0.3359  data: 0.0039  max mem: 3953
+train: [4]  [ 60/400]  eta: 0:02:14  lr: 0.000249  loss: 3.1096 (3.1093)  grad: 0.0618 (0.0638)  time: 0.3301  data: 0.0043  max mem: 3953
+train: [4]  [ 80/400]  eta: 0:02:01  lr: 0.000252  loss: 3.1142 (3.1091)  grad: 0.0665 (0.0648)  time: 0.3316  data: 0.0041  max mem: 3953
+train: [4]  [100/400]  eta: 0:01:53  lr: 0.000255  loss: 3.1057 (3.1106)  grad: 0.0671 (0.0661)  time: 0.3675  data: 0.0040  max mem: 3953
+train: [4]  [120/400]  eta: 0:01:44  lr: 0.000258  loss: 3.0981 (3.1095)  grad: 0.0676 (0.0670)  time: 0.3554  data: 0.0041  max mem: 3953
+train: [4]  [140/400]  eta: 0:01:37  lr: 0.000261  loss: 3.0887 (3.1084)  grad: 0.0682 (0.0671)  time: 0.3814  data: 0.0046  max mem: 3953
+train: [4]  [160/400]  eta: 0:01:29  lr: 0.000264  loss: 3.1189 (3.1107)  grad: 0.0679 (0.0669)  time: 0.3487  data: 0.0040  max mem: 3953
+train: [4]  [180/400]  eta: 0:01:21  lr: 0.000267  loss: 3.1189 (3.1097)  grad: 0.0649 (0.0669)  time: 0.3438  data: 0.0038  max mem: 3953
+train: [4]  [200/400]  eta: 0:01:13  lr: 0.000270  loss: 3.0828 (3.1093)  grad: 0.0673 (0.0670)  time: 0.3672  data: 0.0041  max mem: 3953
+train: [4]  [220/400]  eta: 0:01:06  lr: 0.000273  loss: 3.1164 (3.1104)  grad: 0.0667 (0.0671)  time: 0.3607  data: 0.0042  max mem: 3953
+train: [4]  [240/400]  eta: 0:00:58  lr: 0.000276  loss: 3.1134 (3.1104)  grad: 0.0698 (0.0674)  time: 0.3723  data: 0.0041  max mem: 3953
+train: [4]  [260/400]  eta: 0:00:51  lr: 0.000279  loss: 3.0940 (3.1107)  grad: 0.0684 (0.0671)  time: 0.3717  data: 0.0043  max mem: 3953
+train: [4]  [280/400]  eta: 0:00:44  lr: 0.000282  loss: 3.1087 (3.1116)  grad: 0.0679 (0.0672)  time: 0.3482  data: 0.0042  max mem: 3953
+train: [4]  [300/400]  eta: 0:00:36  lr: 0.000285  loss: 3.1233 (3.1117)  grad: 0.0679 (0.0672)  time: 0.3381  data: 0.0044  max mem: 3953
+train: [4]  [320/400]  eta: 0:00:29  lr: 0.000288  loss: 3.1228 (3.1124)  grad: 0.0667 (0.0672)  time: 0.3364  data: 0.0044  max mem: 3953
+train: [4]  [340/400]  eta: 0:00:21  lr: 0.000291  loss: 3.1239 (3.1127)  grad: 0.0660 (0.0672)  time: 0.3698  data: 0.0037  max mem: 3953
+train: [4]  [360/400]  eta: 0:00:14  lr: 0.000294  loss: 3.1203 (3.1130)  grad: 0.0682 (0.0673)  time: 0.3413  data: 0.0041  max mem: 3953
+train: [4]  [380/400]  eta: 0:00:07  lr: 0.000297  loss: 3.1143 (3.1126)  grad: 0.0669 (0.0673)  time: 0.3568  data: 0.0044  max mem: 3953
+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 3.1090 (3.1117)  grad: 0.0658 (0.0673)  time: 0.3548  data: 0.0043  max mem: 3953
+train: [4] Total time: 0:02:24 (0.3618 s / it)
+train: [4] Summary: lr: 0.000300  loss: 3.1090 (3.1117)  grad: 0.0658 (0.0673)
+eval (validation): [4]  [ 0/85]  eta: 0:05:01    time: 3.5453  data: 3.2452  max mem: 3953
+eval (validation): [4]  [20/85]  eta: 0:00:35    time: 0.3932  data: 0.0042  max mem: 3953
+eval (validation): [4]  [40/85]  eta: 0:00:19    time: 0.3104  data: 0.0038  max mem: 3953
+eval (validation): [4]  [60/85]  eta: 0:00:10    time: 0.3605  data: 0.0049  max mem: 3953
+eval (validation): [4]  [80/85]  eta: 0:00:01    time: 0.3280  data: 0.0045  max mem: 3953
+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3049  data: 0.0042  max mem: 3953
+eval (validation): [4] Total time: 0:00:32 (0.3861 s / it)
+cv: [4]  best hparam: (16, 1.0) (041) ('041_lr1.6e+01_wd1.0e+00')  loss: 3.080  acc: 0.092  f1: 0.034
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:23:31  lr: nan  time: 3.5299  data: 3.2921  max mem: 3953
+train: [5]  [ 20/400]  eta: 0:03:16  lr: 0.000300  loss: 3.0858 (3.0825)  grad: 0.0676 (0.0670)  time: 0.3657  data: 0.0041  max mem: 3953
+train: [5]  [ 40/400]  eta: 0:02:37  lr: 0.000300  loss: 3.1016 (3.0979)  grad: 0.0654 (0.0658)  time: 0.3568  data: 0.0036  max mem: 3953
+train: [5]  [ 60/400]  eta: 0:02:19  lr: 0.000300  loss: 3.1221 (3.0990)  grad: 0.0652 (0.0661)  time: 0.3488  data: 0.0043  max mem: 3953
+train: [5]  [ 80/400]  eta: 0:02:07  lr: 0.000300  loss: 3.0935 (3.0999)  grad: 0.0657 (0.0661)  time: 0.3701  data: 0.0043  max mem: 3953
+train: [5]  [100/400]  eta: 0:01:57  lr: 0.000300  loss: 3.0935 (3.0971)  grad: 0.0666 (0.0664)  time: 0.3567  data: 0.0042  max mem: 3953
+train: [5]  [120/400]  eta: 0:01:47  lr: 0.000300  loss: 3.0989 (3.1008)  grad: 0.0666 (0.0663)  time: 0.3563  data: 0.0042  max mem: 3953
+train: [5]  [140/400]  eta: 0:01:39  lr: 0.000300  loss: 3.1055 (3.1019)  grad: 0.0661 (0.0666)  time: 0.3585  data: 0.0041  max mem: 3953
+train: [5]  [160/400]  eta: 0:01:30  lr: 0.000299  loss: 3.0932 (3.1005)  grad: 0.0672 (0.0672)  time: 0.3603  data: 0.0040  max mem: 3953
+train: [5]  [180/400]  eta: 0:01:22  lr: 0.000299  loss: 3.0932 (3.0998)  grad: 0.0707 (0.0675)  time: 0.3571  data: 0.0044  max mem: 3953
+train: [5]  [200/400]  eta: 0:01:14  lr: 0.000299  loss: 3.0870 (3.0974)  grad: 0.0685 (0.0675)  time: 0.3562  data: 0.0038  max mem: 3953
+train: [5]  [220/400]  eta: 0:01:07  lr: 0.000299  loss: 3.0784 (3.0970)  grad: 0.0652 (0.0672)  time: 0.3736  data: 0.0042  max mem: 3953
+train: [5]  [240/400]  eta: 0:00:59  lr: 0.000299  loss: 3.1041 (3.0987)  grad: 0.0662 (0.0674)  time: 0.3716  data: 0.0043  max mem: 3953
+train: [5]  [260/400]  eta: 0:00:52  lr: 0.000299  loss: 3.1165 (3.1004)  grad: 0.0697 (0.0675)  time: 0.3562  data: 0.0043  max mem: 3953
+train: [5]  [280/400]  eta: 0:00:44  lr: 0.000298  loss: 3.1191 (3.1015)  grad: 0.0671 (0.0675)  time: 0.3590  data: 0.0040  max mem: 3953
+train: [5]  [300/400]  eta: 0:00:36  lr: 0.000298  loss: 3.1216 (3.1026)  grad: 0.0662 (0.0673)  time: 0.3427  data: 0.0042  max mem: 3953
+train: [5]  [320/400]  eta: 0:00:29  lr: 0.000298  loss: 3.1141 (3.1035)  grad: 0.0633 (0.0672)  time: 0.3391  data: 0.0039  max mem: 3953
+train: [5]  [340/400]  eta: 0:00:22  lr: 0.000298  loss: 3.0993 (3.1024)  grad: 0.0641 (0.0672)  time: 0.3582  data: 0.0041  max mem: 3953
+train: [5]  [360/400]  eta: 0:00:14  lr: 0.000297  loss: 3.0834 (3.1015)  grad: 0.0691 (0.0674)  time: 0.3467  data: 0.0040  max mem: 3953
+train: [5]  [380/400]  eta: 0:00:07  lr: 0.000297  loss: 3.0864 (3.1019)  grad: 0.0658 (0.0671)  time: 0.3488  data: 0.0040  max mem: 3953
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 3.0864 (3.1014)  grad: 0.0647 (0.0672)  time: 0.3475  data: 0.0040  max mem: 3953
+train: [5] Total time: 0:02:25 (0.3647 s / it)
+train: [5] Summary: lr: 0.000297  loss: 3.0864 (3.1014)  grad: 0.0647 (0.0672)
+eval (validation): [5]  [ 0/85]  eta: 0:04:56    time: 3.4833  data: 3.2424  max mem: 3953
+eval (validation): [5]  [20/85]  eta: 0:00:32    time: 0.3481  data: 0.0036  max mem: 3953
+eval (validation): [5]  [40/85]  eta: 0:00:18    time: 0.3384  data: 0.0038  max mem: 3953
+eval (validation): [5]  [60/85]  eta: 0:00:09    time: 0.3552  data: 0.0048  max mem: 3953
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3382  data: 0.0043  max mem: 3953
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3252  data: 0.0040  max mem: 3953
+eval (validation): [5] Total time: 0:00:32 (0.3839 s / it)
+cv: [5]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 3.099  acc: 0.097  f1: 0.046
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [6]  [  0/400]  eta: 0:22:55  lr: nan  time: 3.4378  data: 3.1840  max mem: 3953
+train: [6]  [ 20/400]  eta: 0:03:15  lr: 0.000296  loss: 3.0766 (3.0917)  grad: 0.0641 (0.0670)  time: 0.3672  data: 0.0047  max mem: 3953
+train: [6]  [ 40/400]  eta: 0:02:39  lr: 0.000296  loss: 3.0927 (3.0954)  grad: 0.0641 (0.0661)  time: 0.3707  data: 0.0037  max mem: 3953
+train: [6]  [ 60/400]  eta: 0:02:22  lr: 0.000296  loss: 3.0986 (3.1012)  grad: 0.0644 (0.0663)  time: 0.3692  data: 0.0041  max mem: 3953
+train: [6]  [ 80/400]  eta: 0:02:09  lr: 0.000295  loss: 3.0932 (3.0973)  grad: 0.0646 (0.0668)  time: 0.3552  data: 0.0042  max mem: 3953
+train: [6]  [100/400]  eta: 0:01:59  lr: 0.000295  loss: 3.0833 (3.0979)  grad: 0.0646 (0.0665)  time: 0.3835  data: 0.0044  max mem: 3953
+train: [6]  [120/400]  eta: 0:01:50  lr: 0.000295  loss: 3.1089 (3.0983)  grad: 0.0638 (0.0659)  time: 0.3700  data: 0.0039  max mem: 3953
+train: [6]  [140/400]  eta: 0:01:41  lr: 0.000294  loss: 3.1089 (3.0997)  grad: 0.0671 (0.0664)  time: 0.3681  data: 0.0042  max mem: 3953
+train: [6]  [160/400]  eta: 0:01:32  lr: 0.000294  loss: 3.0985 (3.0990)  grad: 0.0676 (0.0667)  time: 0.3554  data: 0.0043  max mem: 3953
+train: [6]  [180/400]  eta: 0:01:24  lr: 0.000293  loss: 3.0908 (3.0999)  grad: 0.0660 (0.0669)  time: 0.3519  data: 0.0041  max mem: 3953
+train: [6]  [200/400]  eta: 0:01:16  lr: 0.000293  loss: 3.0752 (3.0980)  grad: 0.0658 (0.0669)  time: 0.3746  data: 0.0041  max mem: 3953
+train: [6]  [220/400]  eta: 0:01:08  lr: 0.000292  loss: 3.0789 (3.0970)  grad: 0.0664 (0.0671)  time: 0.3712  data: 0.0043  max mem: 3953
+train: [6]  [240/400]  eta: 0:01:00  lr: 0.000292  loss: 3.1058 (3.0982)  grad: 0.0692 (0.0672)  time: 0.3649  data: 0.0038  max mem: 3953
+train: [6]  [260/400]  eta: 0:00:52  lr: 0.000291  loss: 3.1003 (3.0981)  grad: 0.0686 (0.0672)  time: 0.3518  data: 0.0041  max mem: 3953
+train: [6]  [280/400]  eta: 0:00:45  lr: 0.000291  loss: 3.0846 (3.0962)  grad: 0.0664 (0.0672)  time: 0.3629  data: 0.0044  max mem: 3953
+train: [6]  [300/400]  eta: 0:00:37  lr: 0.000290  loss: 3.0890 (3.0965)  grad: 0.0686 (0.0672)  time: 0.3576  data: 0.0040  max mem: 3953
+train: [6]  [320/400]  eta: 0:00:29  lr: 0.000290  loss: 3.1034 (3.0971)  grad: 0.0689 (0.0672)  time: 0.3662  data: 0.0041  max mem: 3953
+train: [6]  [340/400]  eta: 0:00:22  lr: 0.000289  loss: 3.1028 (3.0972)  grad: 0.0651 (0.0670)  time: 0.3692  data: 0.0043  max mem: 3953
+train: [6]  [360/400]  eta: 0:00:14  lr: 0.000288  loss: 3.0950 (3.0971)  grad: 0.0647 (0.0669)  time: 0.3631  data: 0.0042  max mem: 3953
+train: [6]  [380/400]  eta: 0:00:07  lr: 0.000288  loss: 3.0950 (3.0961)  grad: 0.0674 (0.0669)  time: 0.3436  data: 0.0041  max mem: 3953
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 3.0999 (3.0970)  grad: 0.0627 (0.0666)  time: 0.3537  data: 0.0043  max mem: 3953
+train: [6] Total time: 0:02:28 (0.3715 s / it)
+train: [6] Summary: lr: 0.000287  loss: 3.0999 (3.0970)  grad: 0.0627 (0.0666)
+eval (validation): [6]  [ 0/85]  eta: 0:04:54    time: 3.4632  data: 3.2228  max mem: 3953
+eval (validation): [6]  [20/85]  eta: 0:00:34    time: 0.3847  data: 0.0057  max mem: 3953
+eval (validation): [6]  [40/85]  eta: 0:00:19    time: 0.3174  data: 0.0040  max mem: 3953
+eval (validation): [6]  [60/85]  eta: 0:00:09    time: 0.3295  data: 0.0041  max mem: 3953
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3467  data: 0.0045  max mem: 3953
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3327  data: 0.0033  max mem: 3953
+eval (validation): [6] Total time: 0:00:32 (0.3830 s / it)
+cv: [6]  best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00')  loss: 3.071  acc: 0.097  f1: 0.043
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [7]  [  0/400]  eta: 0:23:37  lr: nan  time: 3.5425  data: 3.2392  max mem: 3953
+train: [7]  [ 20/400]  eta: 0:03:21  lr: 0.000286  loss: 3.0695 (3.0677)  grad: 0.0639 (0.0653)  time: 0.3795  data: 0.0045  max mem: 3953
+train: [7]  [ 40/400]  eta: 0:02:44  lr: 0.000286  loss: 3.0695 (3.0607)  grad: 0.0643 (0.0672)  time: 0.3774  data: 0.0042  max mem: 3953
+train: [7]  [ 60/400]  eta: 0:02:26  lr: 0.000285  loss: 3.0711 (3.0663)  grad: 0.0646 (0.0668)  time: 0.3792  data: 0.0036  max mem: 3953
+train: [7]  [ 80/400]  eta: 0:02:14  lr: 0.000284  loss: 3.0855 (3.0745)  grad: 0.0646 (0.0667)  time: 0.3886  data: 0.0044  max mem: 3953
+train: [7]  [100/400]  eta: 0:02:02  lr: 0.000284  loss: 3.0975 (3.0780)  grad: 0.0678 (0.0667)  time: 0.3560  data: 0.0044  max mem: 3953
+train: [7]  [120/400]  eta: 0:01:52  lr: 0.000283  loss: 3.0704 (3.0761)  grad: 0.0692 (0.0674)  time: 0.3787  data: 0.0042  max mem: 3953
+train: [7]  [140/400]  eta: 0:01:43  lr: 0.000282  loss: 3.0822 (3.0803)  grad: 0.0687 (0.0674)  time: 0.3631  data: 0.0044  max mem: 3953
+train: [7]  [160/400]  eta: 0:01:34  lr: 0.000282  loss: 3.1046 (3.0832)  grad: 0.0666 (0.0674)  time: 0.3655  data: 0.0044  max mem: 3953
+train: [7]  [180/400]  eta: 0:01:25  lr: 0.000281  loss: 3.0673 (3.0807)  grad: 0.0665 (0.0674)  time: 0.3711  data: 0.0041  max mem: 3953
+train: [7]  [200/400]  eta: 0:01:17  lr: 0.000280  loss: 3.0776 (3.0828)  grad: 0.0659 (0.0675)  time: 0.3658  data: 0.0041  max mem: 3953
+train: [7]  [220/400]  eta: 0:01:09  lr: 0.000279  loss: 3.0979 (3.0842)  grad: 0.0666 (0.0674)  time: 0.3722  data: 0.0041  max mem: 3953
+train: [7]  [240/400]  eta: 0:01:01  lr: 0.000278  loss: 3.0979 (3.0860)  grad: 0.0675 (0.0679)  time: 0.3507  data: 0.0041  max mem: 3953
+train: [7]  [260/400]  eta: 0:00:53  lr: 0.000278  loss: 3.1240 (3.0894)  grad: 0.0670 (0.0679)  time: 0.3628  data: 0.0040  max mem: 3953
+train: [7]  [280/400]  eta: 0:00:45  lr: 0.000277  loss: 3.1244 (3.0918)  grad: 0.0672 (0.0682)  time: 0.3665  data: 0.0041  max mem: 3953
+train: [7]  [300/400]  eta: 0:00:38  lr: 0.000276  loss: 3.0899 (3.0916)  grad: 0.0695 (0.0683)  time: 0.3657  data: 0.0042  max mem: 3953
+train: [7]  [320/400]  eta: 0:00:30  lr: 0.000275  loss: 3.0899 (3.0928)  grad: 0.0677 (0.0683)  time: 0.3644  data: 0.0040  max mem: 3953
+train: [7]  [340/400]  eta: 0:00:22  lr: 0.000274  loss: 3.0831 (3.0913)  grad: 0.0670 (0.0683)  time: 0.3525  data: 0.0042  max mem: 3953
+train: [7]  [360/400]  eta: 0:00:15  lr: 0.000273  loss: 3.0714 (3.0922)  grad: 0.0670 (0.0683)  time: 0.3419  data: 0.0044  max mem: 3953
+train: [7]  [380/400]  eta: 0:00:07  lr: 0.000272  loss: 3.0833 (3.0918)  grad: 0.0673 (0.0683)  time: 0.3564  data: 0.0041  max mem: 3953
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 3.0651 (3.0899)  grad: 0.0666 (0.0685)  time: 0.3545  data: 0.0042  max mem: 3953
+train: [7] Total time: 0:02:29 (0.3738 s / it)
+train: [7] Summary: lr: 0.000271  loss: 3.0651 (3.0899)  grad: 0.0666 (0.0685)
+eval (validation): [7]  [ 0/85]  eta: 0:04:52    time: 3.4375  data: 3.1450  max mem: 3953
+eval (validation): [7]  [20/85]  eta: 0:00:32    time: 0.3527  data: 0.0029  max mem: 3953
+eval (validation): [7]  [40/85]  eta: 0:00:19    time: 0.3479  data: 0.0043  max mem: 3953
+eval (validation): [7]  [60/85]  eta: 0:00:10    time: 0.3757  data: 0.0049  max mem: 3953
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3453  data: 0.0041  max mem: 3953
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3387  data: 0.0039  max mem: 3953
+eval (validation): [7] Total time: 0:00:33 (0.3934 s / it)
+cv: [7]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 3.116  acc: 0.111  f1: 0.049
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [8]  [  0/400]  eta: 0:23:21  lr: nan  time: 3.5050  data: 3.2585  max mem: 3953
+train: [8]  [ 20/400]  eta: 0:03:41  lr: 0.000270  loss: 3.0791 (3.0851)  grad: 0.0690 (0.0666)  time: 0.4357  data: 0.0087  max mem: 3953
+train: [8]  [ 40/400]  eta: 0:02:53  lr: 0.000270  loss: 3.0865 (3.0872)  grad: 0.0657 (0.0658)  time: 0.3757  data: 0.0040  max mem: 3953
+train: [8]  [ 60/400]  eta: 0:02:31  lr: 0.000269  loss: 3.0917 (3.0930)  grad: 0.0644 (0.0664)  time: 0.3722  data: 0.0040  max mem: 3953
+train: [8]  [ 80/400]  eta: 0:02:17  lr: 0.000268  loss: 3.0807 (3.0874)  grad: 0.0644 (0.0657)  time: 0.3771  data: 0.0042  max mem: 3953
+train: [8]  [100/400]  eta: 0:02:04  lr: 0.000267  loss: 3.0807 (3.0875)  grad: 0.0645 (0.0665)  time: 0.3548  data: 0.0043  max mem: 3953
+train: [8]  [120/400]  eta: 0:01:53  lr: 0.000266  loss: 3.0689 (3.0835)  grad: 0.0652 (0.0658)  time: 0.3561  data: 0.0041  max mem: 3953
+train: [8]  [140/400]  eta: 0:01:43  lr: 0.000265  loss: 3.0714 (3.0822)  grad: 0.0624 (0.0658)  time: 0.3601  data: 0.0037  max mem: 3953
+train: [8]  [160/400]  eta: 0:01:34  lr: 0.000264  loss: 3.0767 (3.0830)  grad: 0.0625 (0.0656)  time: 0.3496  data: 0.0042  max mem: 3953
+train: [8]  [180/400]  eta: 0:01:25  lr: 0.000263  loss: 3.0813 (3.0826)  grad: 0.0632 (0.0655)  time: 0.3642  data: 0.0040  max mem: 3953
+train: [8]  [200/400]  eta: 0:01:17  lr: 0.000262  loss: 3.0747 (3.0828)  grad: 0.0632 (0.0653)  time: 0.3664  data: 0.0043  max mem: 3953
+train: [8]  [220/400]  eta: 0:01:09  lr: 0.000260  loss: 3.0808 (3.0826)  grad: 0.0665 (0.0655)  time: 0.3514  data: 0.0039  max mem: 3953
+train: [8]  [240/400]  eta: 0:01:01  lr: 0.000259  loss: 3.0817 (3.0841)  grad: 0.0678 (0.0656)  time: 0.3591  data: 0.0042  max mem: 3953
+train: [8]  [260/400]  eta: 0:00:53  lr: 0.000258  loss: 3.0688 (3.0825)  grad: 0.0679 (0.0659)  time: 0.3556  data: 0.0044  max mem: 3953
+train: [8]  [280/400]  eta: 0:00:45  lr: 0.000257  loss: 3.0767 (3.0841)  grad: 0.0631 (0.0659)  time: 0.3851  data: 0.0042  max mem: 3953
+train: [8]  [300/400]  eta: 0:00:37  lr: 0.000256  loss: 3.1019 (3.0846)  grad: 0.0620 (0.0656)  time: 0.3577  data: 0.0045  max mem: 3953
+train: [8]  [320/400]  eta: 0:00:30  lr: 0.000255  loss: 3.0993 (3.0855)  grad: 0.0622 (0.0657)  time: 0.3589  data: 0.0041  max mem: 3953
+train: [8]  [340/400]  eta: 0:00:22  lr: 0.000254  loss: 3.0902 (3.0848)  grad: 0.0656 (0.0656)  time: 0.3600  data: 0.0041  max mem: 3953
+train: [8]  [360/400]  eta: 0:00:14  lr: 0.000253  loss: 3.0572 (3.0837)  grad: 0.0641 (0.0656)  time: 0.3320  data: 0.0041  max mem: 3953
+train: [8]  [380/400]  eta: 0:00:07  lr: 0.000252  loss: 3.0660 (3.0825)  grad: 0.0687 (0.0656)  time: 0.3481  data: 0.0045  max mem: 3953
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 3.0554 (3.0813)  grad: 0.0628 (0.0654)  time: 0.4154  data: 0.0040  max mem: 3953
+train: [8] Total time: 0:02:29 (0.3748 s / it)
+train: [8] Summary: lr: 0.000250  loss: 3.0554 (3.0813)  grad: 0.0628 (0.0654)
+eval (validation): [8]  [ 0/85]  eta: 0:04:46    time: 3.3692  data: 3.0933  max mem: 3953
+eval (validation): [8]  [20/85]  eta: 0:00:33    time: 0.3742  data: 0.0050  max mem: 3953
+eval (validation): [8]  [40/85]  eta: 0:00:19    time: 0.3544  data: 0.0042  max mem: 3953
+eval (validation): [8]  [60/85]  eta: 0:00:10    time: 0.3637  data: 0.0043  max mem: 3953
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3283  data: 0.0042  max mem: 3953
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3253  data: 0.0041  max mem: 3953
+eval (validation): [8] Total time: 0:00:33 (0.3932 s / it)
+cv: [8]  best hparam: (9.8, 1.0) (038) ('038_lr9.8e+00_wd1.0e+00')  loss: 3.060  acc: 0.101  f1: 0.046
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:27:17  lr: nan  time: 4.0937  data: 3.8438  max mem: 3953
+train: [9]  [ 20/400]  eta: 0:03:27  lr: 0.000249  loss: 3.0696 (3.0898)  grad: 0.0593 (0.0636)  time: 0.3675  data: 0.0038  max mem: 3953
+train: [9]  [ 40/400]  eta: 0:02:43  lr: 0.000248  loss: 3.0687 (3.0813)  grad: 0.0601 (0.0639)  time: 0.3570  data: 0.0042  max mem: 3953
+train: [9]  [ 60/400]  eta: 0:02:22  lr: 0.000247  loss: 3.0574 (3.0806)  grad: 0.0635 (0.0639)  time: 0.3519  data: 0.0041  max mem: 3953
+train: [9]  [ 80/400]  eta: 0:02:09  lr: 0.000246  loss: 3.0555 (3.0783)  grad: 0.0640 (0.0649)  time: 0.3531  data: 0.0042  max mem: 3953
+train: [9]  [100/400]  eta: 0:01:58  lr: 0.000244  loss: 3.0554 (3.0775)  grad: 0.0640 (0.0651)  time: 0.3528  data: 0.0041  max mem: 3953
+train: [9]  [120/400]  eta: 0:01:47  lr: 0.000243  loss: 3.0561 (3.0746)  grad: 0.0630 (0.0649)  time: 0.3345  data: 0.0043  max mem: 3953
+train: [9]  [140/400]  eta: 0:01:37  lr: 0.000242  loss: 3.0668 (3.0757)  grad: 0.0638 (0.0651)  time: 0.3182  data: 0.0041  max mem: 3953
+train: [9]  [160/400]  eta: 0:01:28  lr: 0.000241  loss: 3.0891 (3.0788)  grad: 0.0638 (0.0648)  time: 0.3132  data: 0.0048  max mem: 3953
+train: [9]  [180/400]  eta: 0:01:19  lr: 0.000240  loss: 3.1027 (3.0806)  grad: 0.0606 (0.0643)  time: 0.3377  data: 0.0048  max mem: 3953
+train: [9]  [200/400]  eta: 0:01:11  lr: 0.000238  loss: 3.0914 (3.0788)  grad: 0.0636 (0.0644)  time: 0.3163  data: 0.0038  max mem: 3953
+train: [9]  [220/400]  eta: 0:01:03  lr: 0.000237  loss: 3.0687 (3.0788)  grad: 0.0696 (0.0649)  time: 0.3200  data: 0.0043  max mem: 3953
+train: [9]  [240/400]  eta: 0:00:56  lr: 0.000236  loss: 3.0687 (3.0786)  grad: 0.0711 (0.0656)  time: 0.3241  data: 0.0040  max mem: 3953
+train: [9]  [260/400]  eta: 0:00:49  lr: 0.000234  loss: 3.0679 (3.0781)  grad: 0.0697 (0.0656)  time: 0.3241  data: 0.0043  max mem: 3953
+train: [9]  [280/400]  eta: 0:00:41  lr: 0.000233  loss: 3.0493 (3.0756)  grad: 0.0637 (0.0655)  time: 0.3422  data: 0.0041  max mem: 3953
+train: [9]  [300/400]  eta: 0:00:35  lr: 0.000232  loss: 3.0721 (3.0767)  grad: 0.0637 (0.0654)  time: 0.3524  data: 0.0045  max mem: 3953
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+train: [9]  [380/400]  eta: 0:00:07  lr: 0.000226  loss: 3.0944 (3.0781)  grad: 0.0672 (0.0657)  time: 0.3297  data: 0.0042  max mem: 3953
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 3.0762 (3.0779)  grad: 0.0650 (0.0655)  time: 0.3849  data: 0.0041  max mem: 3953
+train: [9] Total time: 0:02:21 (0.3534 s / it)
+train: [9] Summary: lr: 0.000225  loss: 3.0762 (3.0779)  grad: 0.0650 (0.0655)
+eval (validation): [9]  [ 0/85]  eta: 0:05:15    time: 3.7093  data: 3.4020  max mem: 3953
+eval (validation): [9]  [20/85]  eta: 0:00:36    time: 0.4023  data: 0.0034  max mem: 3953
+eval (validation): [9]  [40/85]  eta: 0:00:20    time: 0.3492  data: 0.0042  max mem: 3953
+eval (validation): [9]  [60/85]  eta: 0:00:10    time: 0.3467  data: 0.0041  max mem: 3953
+eval (validation): [9]  [80/85]  eta: 0:00:02    time: 0.3467  data: 0.0044  max mem: 3953
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3423  data: 0.0042  max mem: 3953
+eval (validation): [9] Total time: 0:00:34 (0.4018 s / it)
+cv: [9]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 3.105  acc: 0.099  f1: 0.055
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:24:32  lr: nan  time: 3.6813  data: 3.3634  max mem: 3953
+train: [10]  [ 20/400]  eta: 0:03:23  lr: 0.000224  loss: 3.1046 (3.1094)  grad: 0.0659 (0.0639)  time: 0.3781  data: 0.0046  max mem: 3953
+train: [10]  [ 40/400]  eta: 0:02:44  lr: 0.000222  loss: 3.0902 (3.0939)  grad: 0.0659 (0.0653)  time: 0.3750  data: 0.0035  max mem: 3953
+train: [10]  [ 60/400]  eta: 0:02:25  lr: 0.000221  loss: 3.0660 (3.0812)  grad: 0.0684 (0.0663)  time: 0.3686  data: 0.0037  max mem: 3953
+train: [10]  [ 80/400]  eta: 0:02:13  lr: 0.000220  loss: 3.0660 (3.0796)  grad: 0.0658 (0.0657)  time: 0.3862  data: 0.0041  max mem: 3953
+train: [10]  [100/400]  eta: 0:02:02  lr: 0.000218  loss: 3.0721 (3.0780)  grad: 0.0631 (0.0662)  time: 0.3748  data: 0.0043  max mem: 3953
+train: [10]  [120/400]  eta: 0:01:53  lr: 0.000217  loss: 3.0874 (3.0799)  grad: 0.0672 (0.0663)  time: 0.3777  data: 0.0042  max mem: 3953
+train: [10]  [140/400]  eta: 0:01:43  lr: 0.000215  loss: 3.0776 (3.0789)  grad: 0.0674 (0.0666)  time: 0.3594  data: 0.0043  max mem: 3953
+train: [10]  [160/400]  eta: 0:01:34  lr: 0.000214  loss: 3.0624 (3.0781)  grad: 0.0665 (0.0665)  time: 0.3791  data: 0.0043  max mem: 3953
+train: [10]  [180/400]  eta: 0:01:26  lr: 0.000213  loss: 3.0693 (3.0791)  grad: 0.0661 (0.0665)  time: 0.3823  data: 0.0043  max mem: 3953
+train: [10]  [200/400]  eta: 0:01:17  lr: 0.000211  loss: 3.0745 (3.0789)  grad: 0.0652 (0.0666)  time: 0.3513  data: 0.0039  max mem: 3953
+train: [10]  [220/400]  eta: 0:01:09  lr: 0.000210  loss: 3.0664 (3.0776)  grad: 0.0649 (0.0663)  time: 0.3697  data: 0.0042  max mem: 3953
+train: [10]  [240/400]  eta: 0:01:02  lr: 0.000208  loss: 3.0405 (3.0748)  grad: 0.0641 (0.0662)  time: 0.3953  data: 0.0041  max mem: 3953
+train: [10]  [260/400]  eta: 0:00:54  lr: 0.000207  loss: 3.0586 (3.0752)  grad: 0.0660 (0.0661)  time: 0.3622  data: 0.0041  max mem: 3953
+train: [10]  [280/400]  eta: 0:00:46  lr: 0.000205  loss: 3.0913 (3.0760)  grad: 0.0628 (0.0658)  time: 0.3666  data: 0.0040  max mem: 3953
+train: [10]  [300/400]  eta: 0:00:38  lr: 0.000204  loss: 3.1030 (3.0762)  grad: 0.0628 (0.0658)  time: 0.3755  data: 0.0040  max mem: 3953
+train: [10]  [320/400]  eta: 0:00:30  lr: 0.000202  loss: 3.0933 (3.0763)  grad: 0.0653 (0.0659)  time: 0.3751  data: 0.0040  max mem: 3953
+train: [10]  [340/400]  eta: 0:00:22  lr: 0.000201  loss: 3.0700 (3.0753)  grad: 0.0644 (0.0657)  time: 0.3523  data: 0.0042  max mem: 3953
+train: [10]  [360/400]  eta: 0:00:15  lr: 0.000199  loss: 3.0667 (3.0748)  grad: 0.0633 (0.0655)  time: 0.3654  data: 0.0043  max mem: 3953
+train: [10]  [380/400]  eta: 0:00:07  lr: 0.000198  loss: 3.0612 (3.0742)  grad: 0.0641 (0.0656)  time: 0.3582  data: 0.0040  max mem: 3953
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 3.0770 (3.0749)  grad: 0.0651 (0.0658)  time: 0.3302  data: 0.0042  max mem: 3953
+train: [10] Total time: 0:02:31 (0.3777 s / it)
+train: [10] Summary: lr: 0.000196  loss: 3.0770 (3.0749)  grad: 0.0651 (0.0658)
+eval (validation): [10]  [ 0/85]  eta: 0:04:59    time: 3.5223  data: 3.2774  max mem: 3953
+eval (validation): [10]  [20/85]  eta: 0:00:35    time: 0.3961  data: 0.0061  max mem: 3953
+eval (validation): [10]  [40/85]  eta: 0:00:20    time: 0.3534  data: 0.0036  max mem: 3953
+eval (validation): [10]  [60/85]  eta: 0:00:10    time: 0.3472  data: 0.0043  max mem: 3953
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.3409  data: 0.0042  max mem: 3953
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.3395  data: 0.0040  max mem: 3953
+eval (validation): [10] Total time: 0:00:33 (0.3979 s / it)
+cv: [10]  best hparam: (43, 1.0) (047) ('047_lr4.3e+01_wd1.0e+00')  loss: 3.065  acc: 0.104  f1: 0.068
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:26:06  lr: nan  time: 3.9174  data: 3.6104  max mem: 3953
+train: [11]  [ 20/400]  eta: 0:03:29  lr: 0.000195  loss: 3.0621 (3.0725)  grad: 0.0650 (0.0662)  time: 0.3821  data: 0.0186  max mem: 3953
+train: [11]  [ 40/400]  eta: 0:02:46  lr: 0.000193  loss: 3.0830 (3.0785)  grad: 0.0654 (0.0666)  time: 0.3728  data: 0.0038  max mem: 3953
+train: [11]  [ 60/400]  eta: 0:02:29  lr: 0.000192  loss: 3.0742 (3.0702)  grad: 0.0671 (0.0681)  time: 0.3869  data: 0.0026  max mem: 3953
+train: [11]  [ 80/400]  eta: 0:02:15  lr: 0.000190  loss: 3.0550 (3.0663)  grad: 0.0654 (0.0673)  time: 0.3762  data: 0.0038  max mem: 3953
+train: [11]  [100/400]  eta: 0:02:04  lr: 0.000189  loss: 3.0486 (3.0672)  grad: 0.0654 (0.0672)  time: 0.3758  data: 0.0037  max mem: 3953
+train: [11]  [120/400]  eta: 0:01:54  lr: 0.000187  loss: 3.0486 (3.0622)  grad: 0.0657 (0.0666)  time: 0.3820  data: 0.0039  max mem: 3953
+train: [11]  [140/400]  eta: 0:01:44  lr: 0.000186  loss: 3.0531 (3.0632)  grad: 0.0657 (0.0663)  time: 0.3707  data: 0.0037  max mem: 3953
+train: [11]  [160/400]  eta: 0:01:36  lr: 0.000184  loss: 3.0604 (3.0634)  grad: 0.0663 (0.0665)  time: 0.3832  data: 0.0041  max mem: 3953
+train: [11]  [180/400]  eta: 0:01:27  lr: 0.000183  loss: 3.0633 (3.0640)  grad: 0.0657 (0.0662)  time: 0.3836  data: 0.0040  max mem: 3953
+train: [11]  [200/400]  eta: 0:01:18  lr: 0.000181  loss: 3.0661 (3.0631)  grad: 0.0639 (0.0663)  time: 0.3517  data: 0.0038  max mem: 3953
+train: [11]  [220/400]  eta: 0:01:10  lr: 0.000180  loss: 3.0784 (3.0648)  grad: 0.0632 (0.0661)  time: 0.3778  data: 0.0046  max mem: 3953
+train: [11]  [240/400]  eta: 0:01:02  lr: 0.000178  loss: 3.0851 (3.0658)  grad: 0.0640 (0.0658)  time: 0.3653  data: 0.0043  max mem: 3953
+train: [11]  [260/400]  eta: 0:00:54  lr: 0.000177  loss: 3.0736 (3.0661)  grad: 0.0617 (0.0655)  time: 0.3697  data: 0.0040  max mem: 3953
+train: [11]  [280/400]  eta: 0:00:46  lr: 0.000175  loss: 3.0623 (3.0653)  grad: 0.0609 (0.0653)  time: 0.3655  data: 0.0041  max mem: 3953
+train: [11]  [300/400]  eta: 0:00:38  lr: 0.000174  loss: 3.0700 (3.0655)  grad: 0.0628 (0.0654)  time: 0.3662  data: 0.0040  max mem: 3953
+train: [11]  [320/400]  eta: 0:00:30  lr: 0.000172  loss: 3.0781 (3.0664)  grad: 0.0665 (0.0654)  time: 0.3728  data: 0.0042  max mem: 3953
+train: [11]  [340/400]  eta: 0:00:22  lr: 0.000170  loss: 3.0863 (3.0683)  grad: 0.0639 (0.0652)  time: 0.3556  data: 0.0042  max mem: 3953
+train: [11]  [360/400]  eta: 0:00:15  lr: 0.000169  loss: 3.0748 (3.0668)  grad: 0.0611 (0.0651)  time: 0.3696  data: 0.0041  max mem: 3953
+train: [11]  [380/400]  eta: 0:00:07  lr: 0.000167  loss: 3.0633 (3.0671)  grad: 0.0666 (0.0653)  time: 0.3499  data: 0.0039  max mem: 3953
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 3.0600 (3.0669)  grad: 0.0676 (0.0656)  time: 0.3779  data: 0.0043  max mem: 3953
+train: [11] Total time: 0:02:32 (0.3810 s / it)
+train: [11] Summary: lr: 0.000166  loss: 3.0600 (3.0669)  grad: 0.0676 (0.0656)
+eval (validation): [11]  [ 0/85]  eta: 0:04:56    time: 3.4917  data: 3.2334  max mem: 3953
+eval (validation): [11]  [20/85]  eta: 0:00:34    time: 0.3817  data: 0.0058  max mem: 3953
+eval (validation): [11]  [40/85]  eta: 0:00:20    time: 0.3712  data: 0.0038  max mem: 3953
+eval (validation): [11]  [60/85]  eta: 0:00:10    time: 0.3321  data: 0.0042  max mem: 3953
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.3322  data: 0.0044  max mem: 3953
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.3351  data: 0.0042  max mem: 3953
+eval (validation): [11] Total time: 0:00:33 (0.3939 s / it)
+cv: [11]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 3.059  acc: 0.099  f1: 0.050
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:23:46  lr: nan  time: 3.5670  data: 3.3165  max mem: 3953
+train: [12]  [ 20/400]  eta: 0:03:28  lr: 0.000164  loss: 3.0487 (3.0477)  grad: 0.0653 (0.0647)  time: 0.3981  data: 0.0042  max mem: 3953
+train: [12]  [ 40/400]  eta: 0:02:49  lr: 0.000163  loss: 3.0538 (3.0471)  grad: 0.0622 (0.0627)  time: 0.3901  data: 0.0038  max mem: 3953
+train: [12]  [ 60/400]  eta: 0:02:30  lr: 0.000161  loss: 3.0817 (3.0597)  grad: 0.0618 (0.0633)  time: 0.3870  data: 0.0042  max mem: 3953
+train: [12]  [ 80/400]  eta: 0:02:16  lr: 0.000160  loss: 3.0576 (3.0500)  grad: 0.0647 (0.0641)  time: 0.3744  data: 0.0041  max mem: 3953
+train: [12]  [100/400]  eta: 0:02:04  lr: 0.000158  loss: 3.0404 (3.0516)  grad: 0.0648 (0.0648)  time: 0.3750  data: 0.0044  max mem: 3953
+train: [12]  [120/400]  eta: 0:01:54  lr: 0.000156  loss: 3.0627 (3.0547)  grad: 0.0648 (0.0650)  time: 0.3744  data: 0.0042  max mem: 3953
+train: [12]  [140/400]  eta: 0:01:45  lr: 0.000155  loss: 3.0627 (3.0552)  grad: 0.0591 (0.0644)  time: 0.3842  data: 0.0042  max mem: 3953
+train: [12]  [160/400]  eta: 0:01:36  lr: 0.000153  loss: 3.0501 (3.0563)  grad: 0.0658 (0.0651)  time: 0.3735  data: 0.0044  max mem: 3953
+train: [12]  [180/400]  eta: 0:01:27  lr: 0.000152  loss: 3.0581 (3.0598)  grad: 0.0662 (0.0647)  time: 0.3702  data: 0.0042  max mem: 3953
+train: [12]  [200/400]  eta: 0:01:18  lr: 0.000150  loss: 3.0708 (3.0586)  grad: 0.0628 (0.0650)  time: 0.3596  data: 0.0040  max mem: 3953
+train: [12]  [220/400]  eta: 0:01:10  lr: 0.000149  loss: 3.0564 (3.0564)  grad: 0.0599 (0.0645)  time: 0.3788  data: 0.0042  max mem: 3953
+train: [12]  [240/400]  eta: 0:01:03  lr: 0.000147  loss: 3.0740 (3.0588)  grad: 0.0599 (0.0646)  time: 0.4135  data: 0.0043  max mem: 3953
+train: [12]  [260/400]  eta: 0:00:54  lr: 0.000145  loss: 3.0769 (3.0597)  grad: 0.0634 (0.0646)  time: 0.3690  data: 0.0041  max mem: 3953
+train: [12]  [280/400]  eta: 0:00:47  lr: 0.000144  loss: 3.0731 (3.0610)  grad: 0.0634 (0.0645)  time: 0.3780  data: 0.0041  max mem: 3953
+train: [12]  [300/400]  eta: 0:00:39  lr: 0.000142  loss: 3.0616 (3.0595)  grad: 0.0626 (0.0643)  time: 0.3861  data: 0.0041  max mem: 3953
+train: [12]  [320/400]  eta: 0:00:31  lr: 0.000141  loss: 3.0448 (3.0595)  grad: 0.0631 (0.0642)  time: 0.3682  data: 0.0041  max mem: 3953
+train: [12]  [340/400]  eta: 0:00:23  lr: 0.000139  loss: 3.0737 (3.0602)  grad: 0.0634 (0.0642)  time: 0.3674  data: 0.0041  max mem: 3953
+train: [12]  [360/400]  eta: 0:00:15  lr: 0.000138  loss: 3.0800 (3.0623)  grad: 0.0626 (0.0640)  time: 0.3754  data: 0.0042  max mem: 3953
+train: [12]  [380/400]  eta: 0:00:07  lr: 0.000136  loss: 3.0896 (3.0633)  grad: 0.0626 (0.0641)  time: 0.3553  data: 0.0041  max mem: 3953
+train: [12]  [399/400]  eta: 0:00:00  lr: 0.000134  loss: 3.0754 (3.0632)  grad: 0.0694 (0.0644)  time: 0.3572  data: 0.0043  max mem: 3953
+train: [12] Total time: 0:02:34 (0.3851 s / it)
+train: [12] Summary: lr: 0.000134  loss: 3.0754 (3.0632)  grad: 0.0694 (0.0644)
+eval (validation): [12]  [ 0/85]  eta: 0:04:57    time: 3.5047  data: 3.2709  max mem: 3953
+eval (validation): [12]  [20/85]  eta: 0:00:37    time: 0.4279  data: 0.0349  max mem: 3953
+eval (validation): [12]  [40/85]  eta: 0:00:21    time: 0.3774  data: 0.0040  max mem: 3953
+eval (validation): [12]  [60/85]  eta: 0:00:10    time: 0.3186  data: 0.0038  max mem: 3953
+eval (validation): [12]  [80/85]  eta: 0:00:02    time: 0.3384  data: 0.0041  max mem: 3953
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.3246  data: 0.0036  max mem: 3953
+eval (validation): [12] Total time: 0:00:34 (0.4033 s / it)
+cv: [12]  best hparam: (36, 1.0) (046) ('046_lr3.6e+01_wd1.0e+00')  loss: 3.050  acc: 0.115  f1: 0.057
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:23:32  lr: nan  time: 3.5309  data: 3.2413  max mem: 3953
+train: [13]  [ 20/400]  eta: 0:03:28  lr: 0.000133  loss: 3.0321 (3.0440)  grad: 0.0615 (0.0625)  time: 0.3986  data: 0.0043  max mem: 3953
+train: [13]  [ 40/400]  eta: 0:02:48  lr: 0.000131  loss: 3.0472 (3.0381)  grad: 0.0611 (0.0623)  time: 0.3843  data: 0.0040  max mem: 3953
+train: [13]  [ 60/400]  eta: 0:02:30  lr: 0.000130  loss: 3.0425 (3.0440)  grad: 0.0619 (0.0630)  time: 0.3919  data: 0.0041  max mem: 3953
+train: [13]  [ 80/400]  eta: 0:02:16  lr: 0.000128  loss: 3.0554 (3.0477)  grad: 0.0630 (0.0636)  time: 0.3702  data: 0.0043  max mem: 3953
+train: [13]  [100/400]  eta: 0:02:04  lr: 0.000127  loss: 3.0639 (3.0510)  grad: 0.0624 (0.0640)  time: 0.3806  data: 0.0042  max mem: 3953
+train: [13]  [120/400]  eta: 0:01:55  lr: 0.000125  loss: 3.0553 (3.0523)  grad: 0.0622 (0.0641)  time: 0.3884  data: 0.0041  max mem: 3953
+train: [13]  [140/400]  eta: 0:01:46  lr: 0.000124  loss: 3.0587 (3.0538)  grad: 0.0659 (0.0641)  time: 0.3856  data: 0.0040  max mem: 3953
+train: [13]  [160/400]  eta: 0:01:36  lr: 0.000122  loss: 3.0509 (3.0521)  grad: 0.0652 (0.0637)  time: 0.3748  data: 0.0041  max mem: 3953
+train: [13]  [180/400]  eta: 0:01:27  lr: 0.000120  loss: 3.0369 (3.0522)  grad: 0.0648 (0.0637)  time: 0.3602  data: 0.0038  max mem: 3953
+train: [13]  [200/400]  eta: 0:01:19  lr: 0.000119  loss: 3.0447 (3.0535)  grad: 0.0672 (0.0640)  time: 0.3684  data: 0.0042  max mem: 3953
+train: [13]  [220/400]  eta: 0:01:11  lr: 0.000117  loss: 3.0611 (3.0523)  grad: 0.0691 (0.0641)  time: 0.3849  data: 0.0039  max mem: 3953
+train: [13]  [240/400]  eta: 0:01:03  lr: 0.000116  loss: 3.0395 (3.0528)  grad: 0.0689 (0.0643)  time: 0.3844  data: 0.0038  max mem: 3953
+train: [13]  [260/400]  eta: 0:00:54  lr: 0.000114  loss: 3.0628 (3.0550)  grad: 0.0589 (0.0638)  time: 0.3762  data: 0.0039  max mem: 3953
+train: [13]  [280/400]  eta: 0:00:46  lr: 0.000113  loss: 3.0707 (3.0554)  grad: 0.0591 (0.0640)  time: 0.3772  data: 0.0040  max mem: 3953
+train: [13]  [300/400]  eta: 0:00:38  lr: 0.000111  loss: 3.0445 (3.0555)  grad: 0.0628 (0.0639)  time: 0.3658  data: 0.0041  max mem: 3953
+train: [13]  [320/400]  eta: 0:00:31  lr: 0.000110  loss: 3.0464 (3.0562)  grad: 0.0598 (0.0638)  time: 0.3571  data: 0.0041  max mem: 3953
+train: [13]  [340/400]  eta: 0:00:23  lr: 0.000108  loss: 3.0610 (3.0556)  grad: 0.0603 (0.0636)  time: 0.3735  data: 0.0040  max mem: 3953
+train: [13]  [360/400]  eta: 0:00:15  lr: 0.000107  loss: 3.0344 (3.0560)  grad: 0.0611 (0.0635)  time: 0.3870  data: 0.0041  max mem: 3953
+train: [13]  [380/400]  eta: 0:00:07  lr: 0.000105  loss: 3.0742 (3.0570)  grad: 0.0647 (0.0636)  time: 0.3668  data: 0.0038  max mem: 3953
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 3.0583 (3.0570)  grad: 0.0649 (0.0637)  time: 0.3568  data: 0.0039  max mem: 3953
+train: [13] Total time: 0:02:33 (0.3848 s / it)
+train: [13] Summary: lr: 0.000104  loss: 3.0583 (3.0570)  grad: 0.0649 (0.0637)
+eval (validation): [13]  [ 0/85]  eta: 0:04:57    time: 3.4988  data: 3.2268  max mem: 3953
+eval (validation): [13]  [20/85]  eta: 0:00:34    time: 0.3893  data: 0.0429  max mem: 3953
+eval (validation): [13]  [40/85]  eta: 0:00:20    time: 0.3837  data: 0.0046  max mem: 3953
+eval (validation): [13]  [60/85]  eta: 0:00:10    time: 0.3334  data: 0.0037  max mem: 3953
+eval (validation): [13]  [80/85]  eta: 0:00:02    time: 0.3495  data: 0.0039  max mem: 3953
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3396  data: 0.0038  max mem: 3953
+eval (validation): [13] Total time: 0:00:34 (0.4023 s / it)
+cv: [13]  best hparam: (43, 1.0) (047) ('047_lr4.3e+01_wd1.0e+00')  loss: 3.037  acc: 0.111  f1: 0.065
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [14]  [  0/400]  eta: 0:23:47  lr: nan  time: 3.5695  data: 3.3147  max mem: 3953
+train: [14]  [ 20/400]  eta: 0:03:24  lr: 0.000102  loss: 3.0730 (3.0773)  grad: 0.0632 (0.0671)  time: 0.3855  data: 0.0143  max mem: 3953
+train: [14]  [ 40/400]  eta: 0:02:53  lr: 0.000101  loss: 3.0607 (3.0710)  grad: 0.0640 (0.0657)  time: 0.4237  data: 0.0038  max mem: 3953
+train: [14]  [ 60/400]  eta: 0:02:30  lr: 0.000099  loss: 3.0601 (3.0699)  grad: 0.0648 (0.0649)  time: 0.3607  data: 0.0041  max mem: 3953
+train: [14]  [ 80/400]  eta: 0:02:14  lr: 0.000098  loss: 3.0710 (3.0714)  grad: 0.0596 (0.0633)  time: 0.3583  data: 0.0041  max mem: 3953
+train: [14]  [100/400]  eta: 0:02:04  lr: 0.000096  loss: 3.0515 (3.0641)  grad: 0.0581 (0.0634)  time: 0.3823  data: 0.0042  max mem: 3953
+train: [14]  [120/400]  eta: 0:01:53  lr: 0.000095  loss: 3.0281 (3.0605)  grad: 0.0604 (0.0629)  time: 0.3716  data: 0.0042  max mem: 3953
+train: [14]  [140/400]  eta: 0:01:45  lr: 0.000093  loss: 3.0417 (3.0612)  grad: 0.0615 (0.0635)  time: 0.3902  data: 0.0041  max mem: 3953
+train: [14]  [160/400]  eta: 0:01:36  lr: 0.000092  loss: 3.0546 (3.0608)  grad: 0.0659 (0.0637)  time: 0.3745  data: 0.0043  max mem: 3953
+train: [14]  [180/400]  eta: 0:01:26  lr: 0.000090  loss: 3.0499 (3.0598)  grad: 0.0652 (0.0639)  time: 0.3498  data: 0.0040  max mem: 3953
+train: [14]  [200/400]  eta: 0:01:18  lr: 0.000089  loss: 3.0478 (3.0596)  grad: 0.0623 (0.0634)  time: 0.3685  data: 0.0048  max mem: 3953
+train: [14]  [220/400]  eta: 0:01:10  lr: 0.000088  loss: 3.0460 (3.0583)  grad: 0.0629 (0.0637)  time: 0.3572  data: 0.0038  max mem: 3953
+train: [14]  [240/400]  eta: 0:01:01  lr: 0.000086  loss: 3.0471 (3.0591)  grad: 0.0659 (0.0639)  time: 0.3507  data: 0.0043  max mem: 3953
+train: [14]  [260/400]  eta: 0:00:53  lr: 0.000085  loss: 3.0584 (3.0589)  grad: 0.0656 (0.0639)  time: 0.3549  data: 0.0043  max mem: 3953
+train: [14]  [280/400]  eta: 0:00:45  lr: 0.000083  loss: 3.0426 (3.0591)  grad: 0.0600 (0.0637)  time: 0.3395  data: 0.0044  max mem: 3953
+train: [14]  [300/400]  eta: 0:00:37  lr: 0.000082  loss: 3.0521 (3.0599)  grad: 0.0611 (0.0636)  time: 0.3422  data: 0.0040  max mem: 3953
+train: [14]  [320/400]  eta: 0:00:30  lr: 0.000081  loss: 3.0521 (3.0586)  grad: 0.0624 (0.0635)  time: 0.3476  data: 0.0042  max mem: 3953
+train: [14]  [340/400]  eta: 0:00:22  lr: 0.000079  loss: 3.0561 (3.0599)  grad: 0.0626 (0.0637)  time: 0.3442  data: 0.0042  max mem: 3953
+train: [14]  [360/400]  eta: 0:00:14  lr: 0.000078  loss: 3.0748 (3.0598)  grad: 0.0617 (0.0637)  time: 0.3245  data: 0.0043  max mem: 3953
+train: [14]  [380/400]  eta: 0:00:07  lr: 0.000076  loss: 3.0519 (3.0590)  grad: 0.0632 (0.0636)  time: 0.3245  data: 0.0042  max mem: 3953
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 3.0519 (3.0585)  grad: 0.0609 (0.0635)  time: 0.3252  data: 0.0041  max mem: 3953
+train: [14] Total time: 0:02:26 (0.3671 s / it)
+train: [14] Summary: lr: 0.000075  loss: 3.0519 (3.0585)  grad: 0.0609 (0.0635)
+eval (validation): [14]  [ 0/85]  eta: 0:04:52    time: 3.4358  data: 3.1605  max mem: 3953
+eval (validation): [14]  [20/85]  eta: 0:00:32    time: 0.3541  data: 0.0047  max mem: 3953
+eval (validation): [14]  [40/85]  eta: 0:00:19    time: 0.3563  data: 0.0043  max mem: 3953
+eval (validation): [14]  [60/85]  eta: 0:00:09    time: 0.3093  data: 0.0047  max mem: 3953
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3000  data: 0.0040  max mem: 3953
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.2955  data: 0.0040  max mem: 3953
+eval (validation): [14] Total time: 0:00:31 (0.3673 s / it)
+cv: [14]  best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00')  loss: 3.038  acc: 0.107  f1: 0.057
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:23:50  lr: nan  time: 3.5770  data: 3.2700  max mem: 3953
+train: [15]  [ 20/400]  eta: 0:03:32  lr: 0.000074  loss: 3.0622 (3.0524)  grad: 0.0653 (0.0673)  time: 0.4090  data: 0.0517  max mem: 3953
+train: [15]  [ 40/400]  eta: 0:02:50  lr: 0.000072  loss: 3.0594 (3.0472)  grad: 0.0633 (0.0635)  time: 0.3839  data: 0.0034  max mem: 3953
+train: [15]  [ 60/400]  eta: 0:02:29  lr: 0.000071  loss: 3.0351 (3.0397)  grad: 0.0597 (0.0630)  time: 0.3665  data: 0.0039  max mem: 3953
+train: [15]  [ 80/400]  eta: 0:02:17  lr: 0.000070  loss: 3.0234 (3.0347)  grad: 0.0605 (0.0619)  time: 0.4037  data: 0.0044  max mem: 3953
+train: [15]  [100/400]  eta: 0:02:04  lr: 0.000068  loss: 3.0349 (3.0406)  grad: 0.0592 (0.0615)  time: 0.3539  data: 0.0040  max mem: 3953
+train: [15]  [120/400]  eta: 0:01:54  lr: 0.000067  loss: 3.0371 (3.0387)  grad: 0.0635 (0.0627)  time: 0.3838  data: 0.0049  max mem: 3953
+train: [15]  [140/400]  eta: 0:01:45  lr: 0.000066  loss: 3.0193 (3.0379)  grad: 0.0653 (0.0630)  time: 0.3907  data: 0.0040  max mem: 3953
+train: [15]  [160/400]  eta: 0:01:36  lr: 0.000064  loss: 3.0370 (3.0390)  grad: 0.0651 (0.0634)  time: 0.3644  data: 0.0044  max mem: 3953
+train: [15]  [180/400]  eta: 0:01:27  lr: 0.000063  loss: 3.0596 (3.0425)  grad: 0.0654 (0.0636)  time: 0.3458  data: 0.0041  max mem: 3953
+train: [15]  [200/400]  eta: 0:01:18  lr: 0.000062  loss: 3.0662 (3.0444)  grad: 0.0621 (0.0635)  time: 0.3487  data: 0.0040  max mem: 3953
+train: [15]  [220/400]  eta: 0:01:10  lr: 0.000061  loss: 3.0483 (3.0440)  grad: 0.0626 (0.0634)  time: 0.3705  data: 0.0044  max mem: 3953
+train: [15]  [240/400]  eta: 0:01:01  lr: 0.000059  loss: 3.0477 (3.0443)  grad: 0.0632 (0.0635)  time: 0.3616  data: 0.0040  max mem: 3953
+train: [15]  [260/400]  eta: 0:00:53  lr: 0.000058  loss: 3.0495 (3.0457)  grad: 0.0625 (0.0632)  time: 0.3713  data: 0.0045  max mem: 3953
+train: [15]  [280/400]  eta: 0:00:46  lr: 0.000057  loss: 3.0502 (3.0477)  grad: 0.0640 (0.0634)  time: 0.3609  data: 0.0041  max mem: 3953
+train: [15]  [300/400]  eta: 0:00:38  lr: 0.000056  loss: 3.0499 (3.0489)  grad: 0.0649 (0.0634)  time: 0.3470  data: 0.0043  max mem: 3953
+train: [15]  [320/400]  eta: 0:00:30  lr: 0.000054  loss: 3.0579 (3.0499)  grad: 0.0649 (0.0635)  time: 0.3516  data: 0.0042  max mem: 3953
+train: [15]  [340/400]  eta: 0:00:22  lr: 0.000053  loss: 3.0528 (3.0484)  grad: 0.0633 (0.0635)  time: 0.3533  data: 0.0042  max mem: 3953
+train: [15]  [360/400]  eta: 0:00:15  lr: 0.000052  loss: 3.0620 (3.0501)  grad: 0.0610 (0.0633)  time: 0.3509  data: 0.0041  max mem: 3953
+train: [15]  [380/400]  eta: 0:00:07  lr: 0.000051  loss: 3.0495 (3.0498)  grad: 0.0610 (0.0633)  time: 0.3408  data: 0.0042  max mem: 3953
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 3.0495 (3.0504)  grad: 0.0630 (0.0633)  time: 0.3491  data: 0.0043  max mem: 3953
+train: [15] Total time: 0:02:29 (0.3738 s / it)
+train: [15] Summary: lr: 0.000050  loss: 3.0495 (3.0504)  grad: 0.0630 (0.0633)
+eval (validation): [15]  [ 0/85]  eta: 0:05:00    time: 3.5367  data: 3.2884  max mem: 3953
+eval (validation): [15]  [20/85]  eta: 0:00:34    time: 0.3817  data: 0.0054  max mem: 3953
+eval (validation): [15]  [40/85]  eta: 0:00:19    time: 0.3430  data: 0.0039  max mem: 3953
+eval (validation): [15]  [60/85]  eta: 0:00:10    time: 0.3536  data: 0.0043  max mem: 3953
+eval (validation): [15]  [80/85]  eta: 0:00:01    time: 0.3562  data: 0.0046  max mem: 3953
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.3664  data: 0.0044  max mem: 3953
+eval (validation): [15] Total time: 0:00:33 (0.3990 s / it)
+cv: [15]  best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00')  loss: 3.033  acc: 0.105  f1: 0.056
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:24:23  lr: nan  time: 3.6593  data: 3.3534  max mem: 3953
+train: [16]  [ 20/400]  eta: 0:03:25  lr: 0.000048  loss: 3.0406 (3.0510)  grad: 0.0574 (0.0592)  time: 0.3861  data: 0.0043  max mem: 3953
+train: [16]  [ 40/400]  eta: 0:02:45  lr: 0.000047  loss: 3.0544 (3.0509)  grad: 0.0591 (0.0620)  time: 0.3732  data: 0.0040  max mem: 3953
+train: [16]  [ 60/400]  eta: 0:02:29  lr: 0.000046  loss: 3.0577 (3.0604)  grad: 0.0649 (0.0634)  time: 0.4012  data: 0.0046  max mem: 3953
+train: [16]  [ 80/400]  eta: 0:02:16  lr: 0.000045  loss: 3.0551 (3.0501)  grad: 0.0619 (0.0629)  time: 0.3831  data: 0.0043  max mem: 3953
+train: [16]  [100/400]  eta: 0:02:05  lr: 0.000044  loss: 3.0551 (3.0567)  grad: 0.0630 (0.0639)  time: 0.3808  data: 0.0044  max mem: 3953
+train: [16]  [120/400]  eta: 0:01:53  lr: 0.000043  loss: 3.0577 (3.0552)  grad: 0.0632 (0.0639)  time: 0.3506  data: 0.0045  max mem: 3953
+train: [16]  [140/400]  eta: 0:01:43  lr: 0.000042  loss: 3.0524 (3.0547)  grad: 0.0621 (0.0639)  time: 0.3352  data: 0.0038  max mem: 3953
+train: [16]  [160/400]  eta: 0:01:33  lr: 0.000041  loss: 3.0604 (3.0565)  grad: 0.0606 (0.0638)  time: 0.3282  data: 0.0044  max mem: 3953
+train: [16]  [180/400]  eta: 0:01:24  lr: 0.000040  loss: 3.0683 (3.0586)  grad: 0.0627 (0.0636)  time: 0.3537  data: 0.0041  max mem: 3953
+train: [16]  [200/400]  eta: 0:01:16  lr: 0.000039  loss: 3.0582 (3.0585)  grad: 0.0627 (0.0637)  time: 0.3763  data: 0.0044  max mem: 3953
+train: [16]  [220/400]  eta: 0:01:08  lr: 0.000038  loss: 3.0404 (3.0572)  grad: 0.0630 (0.0634)  time: 0.3601  data: 0.0039  max mem: 3953
+train: [16]  [240/400]  eta: 0:01:00  lr: 0.000036  loss: 3.0396 (3.0565)  grad: 0.0630 (0.0637)  time: 0.3558  data: 0.0041  max mem: 3953
+train: [16]  [260/400]  eta: 0:00:52  lr: 0.000035  loss: 3.0417 (3.0558)  grad: 0.0648 (0.0639)  time: 0.3609  data: 0.0043  max mem: 3953
+train: [16]  [280/400]  eta: 0:00:45  lr: 0.000034  loss: 3.0228 (3.0533)  grad: 0.0630 (0.0636)  time: 0.3480  data: 0.0047  max mem: 3953
+train: [16]  [300/400]  eta: 0:00:37  lr: 0.000033  loss: 3.0333 (3.0524)  grad: 0.0605 (0.0635)  time: 0.3527  data: 0.0045  max mem: 3953
+train: [16]  [320/400]  eta: 0:00:29  lr: 0.000032  loss: 3.0528 (3.0535)  grad: 0.0622 (0.0635)  time: 0.3623  data: 0.0038  max mem: 3953
+train: [16]  [340/400]  eta: 0:00:22  lr: 0.000031  loss: 3.0525 (3.0517)  grad: 0.0595 (0.0631)  time: 0.3590  data: 0.0041  max mem: 3953
+train: [16]  [360/400]  eta: 0:00:14  lr: 0.000031  loss: 3.0364 (3.0516)  grad: 0.0591 (0.0632)  time: 0.3776  data: 0.0046  max mem: 3953
+train: [16]  [380/400]  eta: 0:00:07  lr: 0.000030  loss: 3.0364 (3.0511)  grad: 0.0609 (0.0631)  time: 0.3475  data: 0.0043  max mem: 3953
+train: [16]  [399/400]  eta: 0:00:00  lr: 0.000029  loss: 3.0408 (3.0511)  grad: 0.0615 (0.0631)  time: 0.3730  data: 0.0046  max mem: 3953
+train: [16] Total time: 0:02:28 (0.3718 s / it)
+train: [16] Summary: lr: 0.000029  loss: 3.0408 (3.0511)  grad: 0.0615 (0.0631)
+eval (validation): [16]  [ 0/85]  eta: 0:04:57    time: 3.4989  data: 3.2529  max mem: 3953
+eval (validation): [16]  [20/85]  eta: 0:00:36    time: 0.4079  data: 0.0041  max mem: 3953
+eval (validation): [16]  [40/85]  eta: 0:00:20    time: 0.3299  data: 0.0038  max mem: 3953
+eval (validation): [16]  [60/85]  eta: 0:00:10    time: 0.3176  data: 0.0043  max mem: 3953
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3379  data: 0.0039  max mem: 3953
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.3350  data: 0.0038  max mem: 3953
+eval (validation): [16] Total time: 0:00:32 (0.3868 s / it)
+cv: [16]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 3.027  acc: 0.110  f1: 0.061
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:23:36  lr: nan  time: 3.5423  data: 3.2834  max mem: 3953
+train: [17]  [ 20/400]  eta: 0:03:18  lr: 0.000028  loss: 3.0577 (3.0473)  grad: 0.0639 (0.0620)  time: 0.3709  data: 0.0039  max mem: 3953
+train: [17]  [ 40/400]  eta: 0:02:41  lr: 0.000027  loss: 3.0629 (3.0576)  grad: 0.0651 (0.0641)  time: 0.3702  data: 0.0035  max mem: 3953
+train: [17]  [ 60/400]  eta: 0:02:24  lr: 0.000026  loss: 3.0700 (3.0643)  grad: 0.0651 (0.0637)  time: 0.3748  data: 0.0042  max mem: 3953
+train: [17]  [ 80/400]  eta: 0:02:14  lr: 0.000025  loss: 3.0569 (3.0584)  grad: 0.0602 (0.0634)  time: 0.4058  data: 0.0042  max mem: 3953
+train: [17]  [100/400]  eta: 0:02:02  lr: 0.000024  loss: 3.0241 (3.0541)  grad: 0.0612 (0.0633)  time: 0.3693  data: 0.0043  max mem: 3953
+train: [17]  [120/400]  eta: 0:01:52  lr: 0.000023  loss: 3.0276 (3.0500)  grad: 0.0619 (0.0632)  time: 0.3584  data: 0.0040  max mem: 3953
+train: [17]  [140/400]  eta: 0:01:42  lr: 0.000023  loss: 3.0347 (3.0515)  grad: 0.0650 (0.0638)  time: 0.3598  data: 0.0038  max mem: 3953
+train: [17]  [160/400]  eta: 0:01:33  lr: 0.000022  loss: 3.0469 (3.0510)  grad: 0.0684 (0.0643)  time: 0.3647  data: 0.0046  max mem: 3953
+train: [17]  [180/400]  eta: 0:01:26  lr: 0.000021  loss: 3.0641 (3.0527)  grad: 0.0668 (0.0645)  time: 0.3978  data: 0.0038  max mem: 3953
+train: [17]  [200/400]  eta: 0:01:18  lr: 0.000020  loss: 3.0555 (3.0516)  grad: 0.0663 (0.0646)  time: 0.3782  data: 0.0041  max mem: 3953
+train: [17]  [220/400]  eta: 0:01:09  lr: 0.000019  loss: 3.0394 (3.0501)  grad: 0.0656 (0.0648)  time: 0.3697  data: 0.0040  max mem: 3953
+train: [17]  [240/400]  eta: 0:01:01  lr: 0.000019  loss: 3.0436 (3.0508)  grad: 0.0606 (0.0646)  time: 0.3675  data: 0.0041  max mem: 3953
+train: [17]  [260/400]  eta: 0:00:53  lr: 0.000018  loss: 3.0458 (3.0512)  grad: 0.0600 (0.0642)  time: 0.3491  data: 0.0037  max mem: 3953
+train: [17]  [280/400]  eta: 0:00:45  lr: 0.000017  loss: 3.0398 (3.0496)  grad: 0.0590 (0.0638)  time: 0.3661  data: 0.0041  max mem: 3953
+train: [17]  [300/400]  eta: 0:00:38  lr: 0.000016  loss: 3.0432 (3.0496)  grad: 0.0627 (0.0639)  time: 0.3659  data: 0.0038  max mem: 3953
+train: [17]  [320/400]  eta: 0:00:30  lr: 0.000016  loss: 3.0506 (3.0491)  grad: 0.0644 (0.0639)  time: 0.3617  data: 0.0040  max mem: 3953
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+train: [17]  [380/400]  eta: 0:00:07  lr: 0.000014  loss: 3.0468 (3.0486)  grad: 0.0638 (0.0638)  time: 0.3397  data: 0.0037  max mem: 3953
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 3.0351 (3.0470)  grad: 0.0619 (0.0636)  time: 0.3593  data: 0.0041  max mem: 3953
+train: [17] Total time: 0:02:30 (0.3756 s / it)
+train: [17] Summary: lr: 0.000013  loss: 3.0351 (3.0470)  grad: 0.0619 (0.0636)
+eval (validation): [17]  [ 0/85]  eta: 0:04:58    time: 3.5093  data: 3.2566  max mem: 3953
+eval (validation): [17]  [20/85]  eta: 0:00:33    time: 0.3691  data: 0.0055  max mem: 3953
+eval (validation): [17]  [40/85]  eta: 0:00:19    time: 0.3610  data: 0.0041  max mem: 3953
+eval (validation): [17]  [60/85]  eta: 0:00:10    time: 0.3287  data: 0.0039  max mem: 3953
+eval (validation): [17]  [80/85]  eta: 0:00:01    time: 0.3465  data: 0.0039  max mem: 3953
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3353  data: 0.0035  max mem: 3953
+eval (validation): [17] Total time: 0:00:33 (0.3895 s / it)
+cv: [17]  best hparam: (43, 1.0) (047) ('047_lr4.3e+01_wd1.0e+00')  loss: 3.023  acc: 0.108  f1: 0.059
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:26:06  lr: nan  time: 3.9169  data: 3.6756  max mem: 3953
+train: [18]  [ 20/400]  eta: 0:03:34  lr: 0.000012  loss: 3.0191 (3.0301)  grad: 0.0612 (0.0623)  time: 0.3979  data: 0.0220  max mem: 3953
+train: [18]  [ 40/400]  eta: 0:02:52  lr: 0.000012  loss: 3.0415 (3.0589)  grad: 0.0622 (0.0632)  time: 0.3892  data: 0.0042  max mem: 3953
+train: [18]  [ 60/400]  eta: 0:02:30  lr: 0.000011  loss: 3.0682 (3.0563)  grad: 0.0620 (0.0620)  time: 0.3685  data: 0.0031  max mem: 3953
+train: [18]  [ 80/400]  eta: 0:02:15  lr: 0.000011  loss: 3.0470 (3.0535)  grad: 0.0618 (0.0626)  time: 0.3667  data: 0.0043  max mem: 3953
+train: [18]  [100/400]  eta: 0:02:04  lr: 0.000010  loss: 3.0584 (3.0548)  grad: 0.0626 (0.0631)  time: 0.3806  data: 0.0044  max mem: 3953
+train: [18]  [120/400]  eta: 0:01:54  lr: 0.000009  loss: 3.0527 (3.0545)  grad: 0.0626 (0.0626)  time: 0.3692  data: 0.0039  max mem: 3953
+train: [18]  [140/400]  eta: 0:01:44  lr: 0.000009  loss: 3.0285 (3.0484)  grad: 0.0611 (0.0625)  time: 0.3520  data: 0.0042  max mem: 3953
+train: [18]  [160/400]  eta: 0:01:34  lr: 0.000008  loss: 3.0269 (3.0475)  grad: 0.0607 (0.0622)  time: 0.3330  data: 0.0041  max mem: 3953
+train: [18]  [180/400]  eta: 0:01:25  lr: 0.000008  loss: 3.0363 (3.0465)  grad: 0.0592 (0.0622)  time: 0.3646  data: 0.0042  max mem: 3953
+train: [18]  [200/400]  eta: 0:01:17  lr: 0.000007  loss: 3.0555 (3.0491)  grad: 0.0647 (0.0628)  time: 0.3744  data: 0.0042  max mem: 3953
+train: [18]  [220/400]  eta: 0:01:09  lr: 0.000007  loss: 3.0666 (3.0496)  grad: 0.0625 (0.0626)  time: 0.3600  data: 0.0038  max mem: 3953
+train: [18]  [240/400]  eta: 0:01:01  lr: 0.000006  loss: 3.0462 (3.0479)  grad: 0.0584 (0.0623)  time: 0.3495  data: 0.0040  max mem: 3953
+train: [18]  [260/400]  eta: 0:00:53  lr: 0.000006  loss: 3.0299 (3.0459)  grad: 0.0592 (0.0623)  time: 0.3745  data: 0.0041  max mem: 3953
+train: [18]  [280/400]  eta: 0:00:45  lr: 0.000006  loss: 3.0315 (3.0450)  grad: 0.0607 (0.0621)  time: 0.3768  data: 0.0044  max mem: 3953
+train: [18]  [300/400]  eta: 0:00:38  lr: 0.000005  loss: 3.0372 (3.0450)  grad: 0.0592 (0.0621)  time: 0.3712  data: 0.0040  max mem: 3953
+train: [18]  [320/400]  eta: 0:00:30  lr: 0.000005  loss: 3.0517 (3.0469)  grad: 0.0613 (0.0621)  time: 0.3603  data: 0.0039  max mem: 3953
+train: [18]  [340/400]  eta: 0:00:22  lr: 0.000004  loss: 3.0517 (3.0475)  grad: 0.0613 (0.0621)  time: 0.3714  data: 0.0042  max mem: 3953
+train: [18]  [360/400]  eta: 0:00:15  lr: 0.000004  loss: 3.0449 (3.0477)  grad: 0.0641 (0.0623)  time: 0.3797  data: 0.0041  max mem: 3953
+train: [18]  [380/400]  eta: 0:00:07  lr: 0.000004  loss: 3.0449 (3.0474)  grad: 0.0641 (0.0622)  time: 0.3617  data: 0.0042  max mem: 3953
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 3.0545 (3.0484)  grad: 0.0588 (0.0620)  time: 0.3664  data: 0.0043  max mem: 3953
+train: [18] Total time: 0:02:30 (0.3775 s / it)
+train: [18] Summary: lr: 0.000003  loss: 3.0545 (3.0484)  grad: 0.0588 (0.0620)
+eval (validation): [18]  [ 0/85]  eta: 0:05:31    time: 3.9052  data: 3.5606  max mem: 3953
+eval (validation): [18]  [20/85]  eta: 0:00:38    time: 0.4257  data: 0.0032  max mem: 3953
+eval (validation): [18]  [40/85]  eta: 0:00:21    time: 0.3655  data: 0.0051  max mem: 3953
+eval (validation): [18]  [60/85]  eta: 0:00:10    time: 0.3495  data: 0.0047  max mem: 3953
+eval (validation): [18]  [80/85]  eta: 0:00:02    time: 0.3479  data: 0.0044  max mem: 3953
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3490  data: 0.0042  max mem: 3953
+eval (validation): [18] Total time: 0:00:35 (0.4162 s / it)
+cv: [18]  best hparam: (43, 1.0) (047) ('047_lr4.3e+01_wd1.0e+00')  loss: 3.020  acc: 0.112  f1: 0.064
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+train: [19]  [  0/400]  eta: 0:24:54  lr: nan  time: 3.7354  data: 3.4869  max mem: 3953
+train: [19]  [ 20/400]  eta: 0:03:26  lr: 0.000003  loss: 3.0570 (3.0583)  grad: 0.0632 (0.0643)  time: 0.3830  data: 0.0033  max mem: 3953
+train: [19]  [ 40/400]  eta: 0:02:45  lr: 0.000003  loss: 3.0545 (3.0495)  grad: 0.0625 (0.0625)  time: 0.3738  data: 0.0038  max mem: 3953
+train: [19]  [ 60/400]  eta: 0:02:26  lr: 0.000002  loss: 3.0545 (3.0513)  grad: 0.0602 (0.0616)  time: 0.3740  data: 0.0040  max mem: 3953
+train: [19]  [ 80/400]  eta: 0:02:15  lr: 0.000002  loss: 3.0481 (3.0459)  grad: 0.0598 (0.0612)  time: 0.3984  data: 0.0040  max mem: 3953
+train: [19]  [100/400]  eta: 0:02:04  lr: 0.000002  loss: 3.0394 (3.0447)  grad: 0.0608 (0.0616)  time: 0.3733  data: 0.0041  max mem: 3953
+train: [19]  [120/400]  eta: 0:01:52  lr: 0.000002  loss: 3.0514 (3.0478)  grad: 0.0630 (0.0614)  time: 0.3439  data: 0.0039  max mem: 3953
+train: [19]  [140/400]  eta: 0:01:43  lr: 0.000001  loss: 3.0689 (3.0494)  grad: 0.0620 (0.0618)  time: 0.3786  data: 0.0041  max mem: 3953
+train: [19]  [160/400]  eta: 0:01:35  lr: 0.000001  loss: 3.0714 (3.0528)  grad: 0.0641 (0.0621)  time: 0.3843  data: 0.0041  max mem: 3953
+train: [19]  [180/400]  eta: 0:01:27  lr: 0.000001  loss: 3.0865 (3.0572)  grad: 0.0639 (0.0620)  time: 0.4142  data: 0.0041  max mem: 3953
+train: [19]  [200/400]  eta: 0:01:19  lr: 0.000001  loss: 3.0776 (3.0563)  grad: 0.0606 (0.0620)  time: 0.3905  data: 0.0043  max mem: 3953
+train: [19]  [220/400]  eta: 0:01:11  lr: 0.000001  loss: 3.0370 (3.0558)  grad: 0.0623 (0.0622)  time: 0.3623  data: 0.0040  max mem: 3953
+train: [19]  [240/400]  eta: 0:01:02  lr: 0.000001  loss: 3.0408 (3.0555)  grad: 0.0628 (0.0623)  time: 0.3719  data: 0.0040  max mem: 3953
+train: [19]  [260/400]  eta: 0:00:54  lr: 0.000000  loss: 3.0338 (3.0538)  grad: 0.0652 (0.0626)  time: 0.3563  data: 0.0041  max mem: 3953
+train: [19]  [280/400]  eta: 0:00:46  lr: 0.000000  loss: 3.0281 (3.0532)  grad: 0.0615 (0.0623)  time: 0.3728  data: 0.0041  max mem: 3953
+train: [19]  [300/400]  eta: 0:00:38  lr: 0.000000  loss: 3.0416 (3.0534)  grad: 0.0622 (0.0625)  time: 0.3552  data: 0.0040  max mem: 3953
+train: [19]  [320/400]  eta: 0:00:30  lr: 0.000000  loss: 3.0337 (3.0523)  grad: 0.0660 (0.0627)  time: 0.3498  data: 0.0041  max mem: 3953
+train: [19]  [340/400]  eta: 0:00:22  lr: 0.000000  loss: 3.0337 (3.0516)  grad: 0.0663 (0.0629)  time: 0.3457  data: 0.0038  max mem: 3953
+train: [19]  [360/400]  eta: 0:00:15  lr: 0.000000  loss: 3.0505 (3.0511)  grad: 0.0638 (0.0630)  time: 0.3414  data: 0.0041  max mem: 3953
+train: [19]  [380/400]  eta: 0:00:07  lr: 0.000000  loss: 3.0464 (3.0513)  grad: 0.0635 (0.0630)  time: 0.3351  data: 0.0038  max mem: 3953
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 3.0430 (3.0511)  grad: 0.0596 (0.0629)  time: 0.3352  data: 0.0038  max mem: 3953
+train: [19] Total time: 0:02:30 (0.3758 s / it)
+train: [19] Summary: lr: 0.000000  loss: 3.0430 (3.0511)  grad: 0.0596 (0.0629)
+eval (validation): [19]  [ 0/85]  eta: 0:05:27    time: 3.8560  data: 3.5815  max mem: 3953
+eval (validation): [19]  [20/85]  eta: 0:00:32    time: 0.3319  data: 0.0038  max mem: 3953
+eval (validation): [19]  [40/85]  eta: 0:00:17    time: 0.2925  data: 0.0041  max mem: 3953
+eval (validation): [19]  [60/85]  eta: 0:00:09    time: 0.3310  data: 0.0047  max mem: 3953
+eval (validation): [19]  [80/85]  eta: 0:00:01    time: 0.2981  data: 0.0043  max mem: 3953
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.2932  data: 0.0040  max mem: 3953
+eval (validation): [19] Total time: 0:00:30 (0.3581 s / it)
+cv: [19]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 3.015  acc: 0.114  f1: 0.066
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+evaluating last checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-last.pth
+eval model info:
+{"score": 0.11351052048726468, "hparam": [50, 1.0], "hparam_id": 48, "epoch": 19, "is_best": false, "best_score": 0.11517165005537099}
+eval (train): [20]  [  0/509]  eta: 0:29:32    time: 3.4825  data: 3.2764  max mem: 3953
+eval (train): [20]  [ 20/509]  eta: 0:03:49    time: 0.3186  data: 0.0040  max mem: 3953
+eval (train): [20]  [ 40/509]  eta: 0:03:06    time: 0.3229  data: 0.0062  max mem: 3953
+eval (train): [20]  [ 60/509]  eta: 0:02:44    time: 0.3004  data: 0.0041  max mem: 3953
+eval (train): [20]  [ 80/509]  eta: 0:02:33    time: 0.3345  data: 0.0240  max mem: 3953
+eval (train): [20]  [100/509]  eta: 0:02:22    time: 0.3144  data: 0.0033  max mem: 3953
+eval (train): [20]  [120/509]  eta: 0:02:17    time: 0.3697  data: 0.0050  max mem: 3953
+eval (train): [20]  [140/509]  eta: 0:02:09    time: 0.3335  data: 0.0044  max mem: 3953
+eval (train): [20]  [160/509]  eta: 0:02:02    time: 0.3460  data: 0.0041  max mem: 3953
+eval (train): [20]  [180/509]  eta: 0:01:54    time: 0.3235  data: 0.0046  max mem: 3953
+eval (train): [20]  [200/509]  eta: 0:01:46    time: 0.3181  data: 0.0039  max mem: 3953
+eval (train): [20]  [220/509]  eta: 0:01:38    time: 0.3195  data: 0.0052  max mem: 3953
+eval (train): [20]  [240/509]  eta: 0:01:31    time: 0.3307  data: 0.0034  max mem: 3953
+eval (train): [20]  [260/509]  eta: 0:01:24    time: 0.3350  data: 0.0043  max mem: 3953
+eval (train): [20]  [280/509]  eta: 0:01:17    time: 0.3411  data: 0.0042  max mem: 3953
+eval (train): [20]  [300/509]  eta: 0:01:11    time: 0.3508  data: 0.0047  max mem: 3953
+eval (train): [20]  [320/509]  eta: 0:01:04    time: 0.3524  data: 0.0043  max mem: 3953
+eval (train): [20]  [340/509]  eta: 0:00:57    time: 0.3256  data: 0.0044  max mem: 3953
+eval (train): [20]  [360/509]  eta: 0:00:50    time: 0.3255  data: 0.0041  max mem: 3953
+eval (train): [20]  [380/509]  eta: 0:00:43    time: 0.3208  data: 0.0040  max mem: 3953
+eval (train): [20]  [400/509]  eta: 0:00:36    time: 0.3363  data: 0.0042  max mem: 3953
+eval (train): [20]  [420/509]  eta: 0:00:30    time: 0.3679  data: 0.0053  max mem: 3953
+eval (train): [20]  [440/509]  eta: 0:00:23    time: 0.3207  data: 0.0043  max mem: 3953
+eval (train): [20]  [460/509]  eta: 0:00:16    time: 0.3226  data: 0.0042  max mem: 3953
+eval (train): [20]  [480/509]  eta: 0:00:09    time: 0.2886  data: 0.0038  max mem: 3953
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.2864  data: 0.0039  max mem: 3953
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.2887  data: 0.0038  max mem: 3953
+eval (train): [20] Total time: 0:02:50 (0.3351 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:49    time: 3.4112  data: 3.1242  max mem: 3953
+eval (validation): [20]  [20/85]  eta: 0:00:36    time: 0.4185  data: 0.0043  max mem: 3953
+eval (validation): [20]  [40/85]  eta: 0:00:20    time: 0.3503  data: 0.0051  max mem: 3953
+eval (validation): [20]  [60/85]  eta: 0:00:10    time: 0.3090  data: 0.0043  max mem: 3953
+eval (validation): [20]  [80/85]  eta: 0:00:01    time: 0.3204  data: 0.0045  max mem: 3953
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.3154  data: 0.0041  max mem: 3953
+eval (validation): [20] Total time: 0:00:32 (0.3868 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:57    time: 3.5011  data: 3.2164  max mem: 3953
+eval (test): [20]  [20/85]  eta: 0:00:30    time: 0.3202  data: 0.0043  max mem: 3953
+eval (test): [20]  [40/85]  eta: 0:00:18    time: 0.3302  data: 0.0050  max mem: 3953
+eval (test): [20]  [60/85]  eta: 0:00:09    time: 0.3052  data: 0.0038  max mem: 3953
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3011  data: 0.0041  max mem: 3953
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.2907  data: 0.0039  max mem: 3953
+eval (test): [20] Total time: 0:00:29 (0.3522 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:44    time: 3.4753  data: 3.1963  max mem: 3953
+eval (testid): [20]  [20/82]  eta: 0:00:32    time: 0.3804  data: 0.0056  max mem: 3953
+eval (testid): [20]  [40/82]  eta: 0:00:18    time: 0.3252  data: 0.0037  max mem: 3953
+eval (testid): [20]  [60/82]  eta: 0:00:08    time: 0.3068  data: 0.0041  max mem: 3953
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.3177  data: 0.0039  max mem: 3953
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.3079  data: 0.0034  max mem: 3953
+eval (testid): [20] Total time: 0:00:30 (0.3722 s / it)
+evaluating best checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__patch__linear/checkpoint-best.pth
+eval model info:
+{"score": 0.11517165005537099, "hparam": [36, 1.0], "hparam_id": 46, "epoch": 12, "is_best": true, "best_score": 0.11517165005537099}
+eval (train): [20]  [  0/509]  eta: 0:28:12    time: 3.3252  data: 3.0939  max mem: 3953
+eval (train): [20]  [ 20/509]  eta: 0:04:05    time: 0.3607  data: 0.0394  max mem: 3953
+eval (train): [20]  [ 40/509]  eta: 0:03:15    time: 0.3268  data: 0.0036  max mem: 3953
+eval (train): [20]  [ 60/509]  eta: 0:02:58    time: 0.3568  data: 0.0048  max mem: 3953
+eval (train): [20]  [ 80/509]  eta: 0:02:42    time: 0.3223  data: 0.0039  max mem: 3953
+eval (train): [20]  [100/509]  eta: 0:02:32    time: 0.3559  data: 0.0048  max mem: 3953
+eval (train): [20]  [120/509]  eta: 0:02:24    time: 0.3534  data: 0.0044  max mem: 3953
+eval (train): [20]  [140/509]  eta: 0:02:15    time: 0.3381  data: 0.0045  max mem: 3953
+eval (train): [20]  [160/509]  eta: 0:02:07    time: 0.3602  data: 0.0043  max mem: 3953
+eval (train): [20]  [180/509]  eta: 0:01:58    time: 0.3329  data: 0.0044  max mem: 3953
+eval (train): [20]  [200/509]  eta: 0:01:50    time: 0.3232  data: 0.0045  max mem: 3953
+eval (train): [20]  [220/509]  eta: 0:01:42    time: 0.3338  data: 0.0039  max mem: 3953
+eval (train): [20]  [240/509]  eta: 0:01:35    time: 0.3494  data: 0.0044  max mem: 3953
+eval (train): [20]  [260/509]  eta: 0:01:27    time: 0.3323  data: 0.0041  max mem: 3953
+eval (train): [20]  [280/509]  eta: 0:01:20    time: 0.3272  data: 0.0048  max mem: 3953
+eval (train): [20]  [300/509]  eta: 0:01:13    time: 0.3300  data: 0.0041  max mem: 3953
+eval (train): [20]  [320/509]  eta: 0:01:06    time: 0.3626  data: 0.0044  max mem: 3953
+eval (train): [20]  [340/509]  eta: 0:00:59    time: 0.3349  data: 0.0045  max mem: 3953
+eval (train): [20]  [360/509]  eta: 0:00:52    time: 0.3376  data: 0.0044  max mem: 3953
+eval (train): [20]  [380/509]  eta: 0:00:45    time: 0.3503  data: 0.0048  max mem: 3953
+eval (train): [20]  [400/509]  eta: 0:00:38    time: 0.3407  data: 0.0041  max mem: 3953
+eval (train): [20]  [420/509]  eta: 0:00:31    time: 0.3561  data: 0.0043  max mem: 3953
+eval (train): [20]  [440/509]  eta: 0:00:24    time: 0.3438  data: 0.0041  max mem: 3953
+eval (train): [20]  [460/509]  eta: 0:00:17    time: 0.3564  data: 0.0043  max mem: 3953
+eval (train): [20]  [480/509]  eta: 0:00:10    time: 0.3442  data: 0.0042  max mem: 3953
+eval (train): [20]  [500/509]  eta: 0:00:03    time: 0.3049  data: 0.0044  max mem: 3953
+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.2832  data: 0.0040  max mem: 3953
+eval (train): [20] Total time: 0:02:56 (0.3476 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:50    time: 3.4141  data: 3.1282  max mem: 3953
+eval (validation): [20]  [20/85]  eta: 0:00:31    time: 0.3418  data: 0.0052  max mem: 3953
+eval (validation): [20]  [40/85]  eta: 0:00:18    time: 0.3380  data: 0.0038  max mem: 3953
+eval (validation): [20]  [60/85]  eta: 0:00:09    time: 0.3221  data: 0.0035  max mem: 3953
+eval (validation): [20]  [80/85]  eta: 0:00:01    time: 0.2953  data: 0.0038  max mem: 3953
+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.2927  data: 0.0038  max mem: 3953
+eval (validation): [20] Total time: 0:00:30 (0.3624 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:45    time: 3.3590  data: 3.1290  max mem: 3953
+eval (test): [20]  [20/85]  eta: 0:00:34    time: 0.3877  data: 0.0183  max mem: 3953
+eval (test): [20]  [40/85]  eta: 0:00:19    time: 0.3478  data: 0.0036  max mem: 3953
+eval (test): [20]  [60/85]  eta: 0:00:10    time: 0.3390  data: 0.0046  max mem: 3953
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3190  data: 0.0040  max mem: 3953
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.2962  data: 0.0037  max mem: 3953
+eval (test): [20] Total time: 0:00:32 (0.3832 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:37    time: 3.3865  data: 3.1487  max mem: 3953
+eval (testid): [20]  [20/82]  eta: 0:00:34    time: 0.4225  data: 0.0191  max mem: 3953
+eval (testid): [20]  [40/82]  eta: 0:00:19    time: 0.3686  data: 0.0044  max mem: 3953
+eval (testid): [20]  [60/82]  eta: 0:00:09    time: 0.3402  data: 0.0042  max mem: 3953
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.2933  data: 0.0038  max mem: 3953
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.2825  data: 0.0037  max mem: 3953
+eval (testid): [20] Total time: 0:00:32 (0.3933 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  | linear | nsd_cococlip | best   |      12 | 0.0108 | 0.05 |          46 | [36, 1.0] | train      | 2.9562 | 0.13442 | 0.00164   | 0.073603 | 0.0011895 |
+| flat_mae | patch  | linear | nsd_cococlip | best   |      12 | 0.0108 | 0.05 |          46 | [36, 1.0] | validation | 3.05   | 0.11517 | 0.0037074 | 0.056893 | 0.0025099 |
+| flat_mae | patch  | linear | nsd_cococlip | best   |      12 | 0.0108 | 0.05 |          46 | [36, 1.0] | test       | 3.0346 | 0.11707 | 0.003631  | 0.050479 | 0.0020236 |
+| flat_mae | patch  | linear | nsd_cococlip | best   |      12 | 0.0108 | 0.05 |          46 | [36, 1.0] | testid     | 3.0452 | 0.10777 | 0.0037217 | 0.055349 | 0.0025538 |
+
+
+done! total time: 1:11:03

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/config.yaml

@@ -0,0 +1,96 @@
+output_root: experiments/decoders/output
+name_prefix: eval_probe
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (nsd_cococlip reg linear)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: false
+  norm: false
+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: decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear
+model: flat_mae
+representation: reg
+classifier: linear
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear
+remote_dir: null

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/eval_log.json

@@ -0,0 +1 @@
+{"eval/epoch": 18, "eval/id_best": 43, "eval/lr_best": 0.006599999999999999, "eval/wd_best": 0.05, "eval/train/loss": 2.879481077194214, "eval/train/acc": 0.16165217124066505, "eval/train/acc_std": 0.0019354588929069469, "eval/train/f1": 0.12374415963161867, "eval/train/f1_std": 0.001887014383860702, "eval/validation/loss": 3.0988214015960693, "eval/validation/acc": 0.09985234403839055, "eval/validation/acc_std": 0.003640842031181821, "eval/validation/f1": 0.06452619678823363, "eval/validation/f1_std": 0.0030336272268828616, "eval/test/loss": 3.1099181175231934, "eval/test/acc": 0.09554730983302412, "eval/test/acc_std": 0.003614483775401817, "eval/test/f1": 0.05449483415174141, "eval/test/f1_std": 0.002546630842390943, "eval/testid/loss": 3.0890469551086426, "eval/testid/acc": 0.08906882591093117, "eval/testid/acc_std": 0.0037257488471356074, "eval/testid/f1": 0.05759498017735291, "eval/testid/f1_std": 0.002917014074849025}

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/eval_log_best.json

@@ -0,0 +1 @@
+{"eval/best/epoch": 18, "eval/best/id_best": 43, "eval/best/lr_best": 0.006599999999999999, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.879481077194214, "eval/best/train/acc": 0.16165217124066505, "eval/best/train/acc_std": 0.0019354588929069469, "eval/best/train/f1": 0.12374415963161867, "eval/best/train/f1_std": 0.001887014383860702, "eval/best/validation/loss": 3.0988214015960693, "eval/best/validation/acc": 0.09985234403839055, "eval/best/validation/acc_std": 0.003640842031181821, "eval/best/validation/f1": 0.06452619678823363, "eval/best/validation/f1_std": 0.0030336272268828616, "eval/best/test/loss": 3.1099181175231934, "eval/best/test/acc": 0.09554730983302412, "eval/best/test/acc_std": 0.003614483775401817, "eval/best/test/f1": 0.05449483415174141, "eval/best/test/f1_std": 0.002546630842390943, "eval/best/testid/loss": 3.0890469551086426, "eval/best/testid/acc": 0.08906882591093117, "eval/best/testid/acc_std": 0.0037257488471356074, "eval/best/testid/f1": 0.05759498017735291, "eval/best/testid/f1_std": 0.002917014074849025}

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/eval_log_last.json

@@ -0,0 +1 @@
+{"eval/last/epoch": 19, "eval/last/id_best": 42, "eval/last/lr_best": 0.005699999999999999, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.8835718631744385, "eval/last/train/acc": 0.16057653892252374, "eval/last/train/acc_std": 0.0018725516137403234, "eval/last/train/f1": 0.12181827700813404, "eval/last/train/f1_std": 0.0018328703108259601, "eval/last/validation/loss": 3.0897200107574463, "eval/last/validation/acc": 0.0991140642303433, "eval/last/validation/acc_std": 0.0036937271359197563, "eval/last/validation/f1": 0.06544945249719547, "eval/last/validation/f1_std": 0.0031338644378837398, "eval/last/test/loss": 3.1047823429107666, "eval/last/test/acc": 0.09591836734693877, "eval/last/test/acc_std": 0.0036089969227164906, "eval/last/test/f1": 0.054489990563806094, "eval/last/test/f1_std": 0.0024231825441281235, "eval/last/testid/loss": 3.0852785110473633, "eval/last/testid/acc": 0.09196067090803933, "eval/last/testid/acc_std": 0.003719449500282173, "eval/last/testid/f1": 0.05927300427590613, "eval/last/testid/f1_std": 0.0028982974633330084}

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/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,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",train,2.879481077194214,0.16165217124066505,0.0019354588929069469,0.12374415963161867,0.001887014383860702
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",validation,3.0988214015960693,0.09985234403839055,0.003640842031181821,0.06452619678823363,0.0030336272268828616
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",test,3.1099181175231934,0.09554730983302412,0.003614483775401817,0.05449483415174141,0.002546630842390943
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",testid,3.0890469551086426,0.08906882591093117,0.0037257488471356074,0.05759498017735291,0.002917014074849025

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/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,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",train,2.879481077194214,0.16165217124066505,0.0019354588929069469,0.12374415963161867,0.001887014383860702
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",validation,3.0988214015960693,0.09985234403839055,0.003640842031181821,0.06452619678823363,0.0030336272268828616
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",test,3.1099181175231934,0.09554730983302412,0.003614483775401817,0.05449483415174141,0.002546630842390943
+flat_mae,reg,linear,nsd_cococlip,best,18,0.006599999999999999,0.05,43,"[22, 1.0]",testid,3.0890469551086426,0.08906882591093117,0.0037257488471356074,0.05759498017735291,0.002917014074849025

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/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,reg,linear,nsd_cococlip,last,19,0.005699999999999999,0.05,42,"[19, 1.0]",train,2.8835718631744385,0.16057653892252374,0.0018725516137403234,0.12181827700813404,0.0018328703108259601
+flat_mae,reg,linear,nsd_cococlip,last,19,0.005699999999999999,0.05,42,"[19, 1.0]",validation,3.0897200107574463,0.0991140642303433,0.0036937271359197563,0.06544945249719547,0.0031338644378837398
+flat_mae,reg,linear,nsd_cococlip,last,19,0.005699999999999999,0.05,42,"[19, 1.0]",test,3.1047823429107666,0.09591836734693877,0.0036089969227164906,0.054489990563806094,0.0024231825441281235
+flat_mae,reg,linear,nsd_cococlip,last,19,0.005699999999999999,0.05,42,"[19, 1.0]",testid,3.0852785110473633,0.09196067090803933,0.003719449500282173,0.05927300427590613,0.0028982974633330084

decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/log.txt

@@ -0,0 +1,957 @@
+fMRI foundation model probe eval
+version: 0.1.dev65+g4003a1397
+sha: 6c01b606db98add5848cecd23e5d599250c0bf86, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-02-24 21:52:40
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_probe
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (nsd_cococlip reg linear)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+classifier_kwargs:
+  embed_dim: null
+  dropout: 0.0
+  xavier_init: false
+  norm: false
+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: decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear
+model: flat_mae
+representation: reg
+classifier: linear
+dataset: nsd_cococlip
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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 (reg): 768
+initializing sweep of classifier heads
+classifiers:
+ModuleList(
+  (0-48): 49 x LinearClassifier(
+    (linear): Linear(in_features=768, out_features=24, bias=True)
+  )
+)
+classifier params (train): 0.9M (0.9M)
+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: 0:22:15  lr: nan  time: 3.3394  data: 2.9969  max mem: 3910
+train: [0]  [ 20/400]  eta: 0:03:02  lr: 0.000003  loss: 3.1594 (3.1900)  grad: 0.3217 (0.3215)  time: 0.3382  data: 0.0060  max mem: 3953
+train: [0]  [ 40/400]  eta: 0:02:29  lr: 0.000006  loss: 3.2019 (3.1962)  grad: 0.3170 (0.3093)  time: 0.3436  data: 0.0038  max mem: 3953
+train: [0]  [ 60/400]  eta: 0:02:11  lr: 0.000009  loss: 3.2098 (3.2012)  grad: 0.2866 (0.3014)  time: 0.3342  data: 0.0042  max mem: 3953
+train: [0]  [ 80/400]  eta: 0:01:59  lr: 0.000012  loss: 3.1924 (3.1951)  grad: 0.2756 (0.2941)  time: 0.3338  data: 0.0042  max mem: 3953
+train: [0]  [100/400]  eta: 0:01:49  lr: 0.000015  loss: 3.1825 (3.1939)  grad: 0.2668 (0.2898)  time: 0.3261  data: 0.0037  max mem: 3953
+train: [0]  [120/400]  eta: 0:01:40  lr: 0.000018  loss: 3.1948 (3.1957)  grad: 0.2703 (0.2891)  time: 0.3344  data: 0.0044  max mem: 3953
+train: [0]  [140/400]  eta: 0:01:32  lr: 0.000021  loss: 3.1834 (3.1921)  grad: 0.2876 (0.2899)  time: 0.3344  data: 0.0039  max mem: 3953
+train: [0]  [160/400]  eta: 0:01:25  lr: 0.000024  loss: 3.1831 (3.1929)  grad: 0.2945 (0.2904)  time: 0.3421  data: 0.0037  max mem: 3953
+train: [0]  [180/400]  eta: 0:01:17  lr: 0.000027  loss: 3.1941 (3.1911)  grad: 0.2914 (0.2897)  time: 0.3340  data: 0.0036  max mem: 3953
+train: [0]  [200/400]  eta: 0:01:10  lr: 0.000030  loss: 3.1953 (3.1909)  grad: 0.2808 (0.2882)  time: 0.3368  data: 0.0036  max mem: 3953
+train: [0]  [220/400]  eta: 0:01:02  lr: 0.000033  loss: 3.1734 (3.1884)  grad: 0.2685 (0.2871)  time: 0.3348  data: 0.0036  max mem: 3953
+train: [0]  [240/400]  eta: 0:00:55  lr: 0.000036  loss: 3.1576 (3.1882)  grad: 0.2737 (0.2871)  time: 0.3389  data: 0.0036  max mem: 3953
+train: [0]  [260/400]  eta: 0:00:48  lr: 0.000039  loss: 3.1835 (3.1871)  grad: 0.2730 (0.2860)  time: 0.3482  data: 0.0037  max mem: 3953
+train: [0]  [280/400]  eta: 0:00:41  lr: 0.000042  loss: 3.1835 (3.1875)  grad: 0.2706 (0.2859)  time: 0.3427  data: 0.0042  max mem: 3953
+train: [0]  [300/400]  eta: 0:00:34  lr: 0.000045  loss: 3.1766 (3.1867)  grad: 0.2952 (0.2869)  time: 0.3446  data: 0.0041  max mem: 3953
+train: [0]  [320/400]  eta: 0:00:27  lr: 0.000048  loss: 3.1709 (3.1848)  grad: 0.2953 (0.2866)  time: 0.3550  data: 0.0040  max mem: 3953
+train: [0]  [340/400]  eta: 0:00:20  lr: 0.000051  loss: 3.1798 (3.1857)  grad: 0.2920 (0.2863)  time: 0.3364  data: 0.0044  max mem: 3953
+train: [0]  [360/400]  eta: 0:00:13  lr: 0.000054  loss: 3.1896 (3.1856)  grad: 0.2799 (0.2865)  time: 0.3393  data: 0.0036  max mem: 3953
+train: [0]  [380/400]  eta: 0:00:06  lr: 0.000057  loss: 3.1707 (3.1843)  grad: 0.2799 (0.2863)  time: 0.3447  data: 0.0040  max mem: 3953
+train: [0]  [399/400]  eta: 0:00:00  lr: 0.000060  loss: 3.1637 (3.1835)  grad: 0.2815 (0.2871)  time: 0.3393  data: 0.0039  max mem: 3953
+train: [0] Total time: 0:02:18 (0.3468 s / it)
+train: [0] Summary: lr: 0.000060  loss: 3.1637 (3.1835)  grad: 0.2815 (0.2871)
+eval (validation): [0]  [ 0/85]  eta: 0:04:43    time: 3.3296  data: 3.0751  max mem: 3953
+eval (validation): [0]  [20/85]  eta: 0:00:32    time: 0.3514  data: 0.0041  max mem: 3953
+eval (validation): [0]  [40/85]  eta: 0:00:18    time: 0.3205  data: 0.0042  max mem: 3953
+eval (validation): [0]  [60/85]  eta: 0:00:09    time: 0.3127  data: 0.0046  max mem: 3953
+eval (validation): [0]  [80/85]  eta: 0:00:01    time: 0.3104  data: 0.0039  max mem: 3953
+eval (validation): [0]  [84/85]  eta: 0:00:00    time: 0.3009  data: 0.0039  max mem: 3953
+eval (validation): [0] Total time: 0:00:30 (0.3609 s / it)
+cv: [0]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 3.157  acc: 0.065  f1: 0.017
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
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+train: [1]  [ 20/400]  eta: 0:03:21  lr: 0.000063  loss: 3.1458 (3.1548)  grad: 0.2733 (0.2762)  time: 0.3885  data: 0.0049  max mem: 3953
+train: [1]  [ 40/400]  eta: 0:02:38  lr: 0.000066  loss: 3.1604 (3.1589)  grad: 0.2722 (0.2742)  time: 0.3452  data: 0.0038  max mem: 3953
+train: [1]  [ 60/400]  eta: 0:02:17  lr: 0.000069  loss: 3.1632 (3.1634)  grad: 0.2755 (0.2785)  time: 0.3354  data: 0.0043  max mem: 3953
+train: [1]  [ 80/400]  eta: 0:02:04  lr: 0.000072  loss: 3.1569 (3.1595)  grad: 0.2834 (0.2817)  time: 0.3324  data: 0.0042  max mem: 3953
+train: [1]  [100/400]  eta: 0:01:53  lr: 0.000075  loss: 3.1801 (3.1651)  grad: 0.2834 (0.2822)  time: 0.3358  data: 0.0048  max mem: 3953
+train: [1]  [120/400]  eta: 0:01:43  lr: 0.000078  loss: 3.1720 (3.1635)  grad: 0.2717 (0.2792)  time: 0.3251  data: 0.0041  max mem: 3953
+train: [1]  [140/400]  eta: 0:01:34  lr: 0.000081  loss: 3.1577 (3.1622)  grad: 0.2769 (0.2795)  time: 0.3407  data: 0.0043  max mem: 3953
+train: [1]  [160/400]  eta: 0:01:26  lr: 0.000084  loss: 3.1831 (3.1666)  grad: 0.2789 (0.2786)  time: 0.3444  data: 0.0045  max mem: 3953
+train: [1]  [180/400]  eta: 0:01:19  lr: 0.000087  loss: 3.1852 (3.1668)  grad: 0.2695 (0.2780)  time: 0.3440  data: 0.0040  max mem: 3953
+train: [1]  [200/400]  eta: 0:01:11  lr: 0.000090  loss: 3.1669 (3.1660)  grad: 0.2678 (0.2767)  time: 0.3451  data: 0.0044  max mem: 3953
+train: [1]  [220/400]  eta: 0:01:04  lr: 0.000093  loss: 3.1741 (3.1662)  grad: 0.2874 (0.2789)  time: 0.3363  data: 0.0039  max mem: 3953
+train: [1]  [240/400]  eta: 0:00:56  lr: 0.000096  loss: 3.1636 (3.1656)  grad: 0.2948 (0.2795)  time: 0.3357  data: 0.0038  max mem: 3953
+train: [1]  [260/400]  eta: 0:00:49  lr: 0.000099  loss: 3.1571 (3.1682)  grad: 0.2905 (0.2798)  time: 0.3324  data: 0.0041  max mem: 3953
+train: [1]  [280/400]  eta: 0:00:42  lr: 0.000102  loss: 3.1571 (3.1658)  grad: 0.2905 (0.2808)  time: 0.3323  data: 0.0042  max mem: 3953
+train: [1]  [300/400]  eta: 0:00:35  lr: 0.000105  loss: 3.1436 (3.1654)  grad: 0.2842 (0.2803)  time: 0.3339  data: 0.0036  max mem: 3953
+train: [1]  [320/400]  eta: 0:00:27  lr: 0.000108  loss: 3.1724 (3.1663)  grad: 0.2787 (0.2797)  time: 0.3281  data: 0.0043  max mem: 3953
+train: [1]  [340/400]  eta: 0:00:20  lr: 0.000111  loss: 3.1760 (3.1663)  grad: 0.2762 (0.2798)  time: 0.3300  data: 0.0040  max mem: 3953
+train: [1]  [360/400]  eta: 0:00:13  lr: 0.000114  loss: 3.1685 (3.1669)  grad: 0.2691 (0.2792)  time: 0.3216  data: 0.0041  max mem: 3953
+train: [1]  [380/400]  eta: 0:00:06  lr: 0.000117  loss: 3.1634 (3.1663)  grad: 0.2729 (0.2803)  time: 0.3375  data: 0.0042  max mem: 3953
+train: [1]  [399/400]  eta: 0:00:00  lr: 0.000120  loss: 3.1528 (3.1661)  grad: 0.2906 (0.2805)  time: 0.3549  data: 0.0045  max mem: 3953
+train: [1] Total time: 0:02:18 (0.3467 s / it)
+train: [1] Summary: lr: 0.000120  loss: 3.1528 (3.1661)  grad: 0.2906 (0.2805)
+eval (validation): [1]  [ 0/85]  eta: 0:04:55    time: 3.4819  data: 3.2091  max mem: 3953
+eval (validation): [1]  [20/85]  eta: 0:00:34    time: 0.3765  data: 0.0052  max mem: 3953
+eval (validation): [1]  [40/85]  eta: 0:00:19    time: 0.3205  data: 0.0039  max mem: 3953
+eval (validation): [1]  [60/85]  eta: 0:00:09    time: 0.3323  data: 0.0048  max mem: 3953
+eval (validation): [1]  [80/85]  eta: 0:00:01    time: 0.2969  data: 0.0042  max mem: 3953
+eval (validation): [1]  [84/85]  eta: 0:00:00    time: 0.2919  data: 0.0039  max mem: 3953
+eval (validation): [1] Total time: 0:00:31 (0.3707 s / it)
+cv: [1]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 3.260  acc: 0.068  f1: 0.020
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+train: [2]  [  0/400]  eta: 0:21:39  lr: nan  time: 3.2499  data: 3.0354  max mem: 3953
+train: [2]  [ 20/400]  eta: 0:03:08  lr: 0.000123  loss: 3.1352 (3.1518)  grad: 0.2633 (0.2728)  time: 0.3597  data: 0.0050  max mem: 3953
+train: [2]  [ 40/400]  eta: 0:02:33  lr: 0.000126  loss: 3.1534 (3.1601)  grad: 0.2672 (0.2785)  time: 0.3511  data: 0.0035  max mem: 3953
+train: [2]  [ 60/400]  eta: 0:02:16  lr: 0.000129  loss: 3.1504 (3.1569)  grad: 0.2687 (0.2747)  time: 0.3482  data: 0.0043  max mem: 3953
+train: [2]  [ 80/400]  eta: 0:02:04  lr: 0.000132  loss: 3.1499 (3.1516)  grad: 0.2629 (0.2758)  time: 0.3501  data: 0.0041  max mem: 3953
+train: [2]  [100/400]  eta: 0:01:52  lr: 0.000135  loss: 3.1499 (3.1517)  grad: 0.2788 (0.2794)  time: 0.3301  data: 0.0042  max mem: 3953
+train: [2]  [120/400]  eta: 0:01:44  lr: 0.000138  loss: 3.1670 (3.1575)  grad: 0.2860 (0.2809)  time: 0.3567  data: 0.0046  max mem: 3953
+train: [2]  [140/400]  eta: 0:01:35  lr: 0.000141  loss: 3.1777 (3.1594)  grad: 0.2829 (0.2811)  time: 0.3400  data: 0.0040  max mem: 3953
+train: [2]  [160/400]  eta: 0:01:27  lr: 0.000144  loss: 3.1692 (3.1623)  grad: 0.2810 (0.2819)  time: 0.3416  data: 0.0039  max mem: 3953
+train: [2]  [180/400]  eta: 0:01:19  lr: 0.000147  loss: 3.1759 (3.1632)  grad: 0.2850 (0.2827)  time: 0.3336  data: 0.0042  max mem: 3953
+train: [2]  [200/400]  eta: 0:01:11  lr: 0.000150  loss: 3.1526 (3.1623)  grad: 0.2843 (0.2837)  time: 0.3354  data: 0.0043  max mem: 3953
+train: [2]  [220/400]  eta: 0:01:04  lr: 0.000153  loss: 3.1496 (3.1603)  grad: 0.2843 (0.2850)  time: 0.3330  data: 0.0041  max mem: 3953
+train: [2]  [240/400]  eta: 0:00:56  lr: 0.000156  loss: 3.1594 (3.1621)  grad: 0.2778 (0.2861)  time: 0.3323  data: 0.0042  max mem: 3953
+train: [2]  [260/400]  eta: 0:00:49  lr: 0.000159  loss: 3.1743 (3.1632)  grad: 0.2778 (0.2856)  time: 0.3486  data: 0.0041  max mem: 3953
+train: [2]  [280/400]  eta: 0:00:42  lr: 0.000162  loss: 3.1647 (3.1622)  grad: 0.2867 (0.2862)  time: 0.3409  data: 0.0038  max mem: 3953
+train: [2]  [300/400]  eta: 0:00:35  lr: 0.000165  loss: 3.1511 (3.1625)  grad: 0.2867 (0.2856)  time: 0.3528  data: 0.0038  max mem: 3953
+train: [2]  [320/400]  eta: 0:00:28  lr: 0.000168  loss: 3.1589 (3.1627)  grad: 0.2845 (0.2852)  time: 0.3536  data: 0.0044  max mem: 3953
+train: [2]  [340/400]  eta: 0:00:21  lr: 0.000171  loss: 3.1664 (3.1636)  grad: 0.2867 (0.2856)  time: 0.3346  data: 0.0043  max mem: 3953
+train: [2]  [360/400]  eta: 0:00:14  lr: 0.000174  loss: 3.1557 (3.1635)  grad: 0.2722 (0.2844)  time: 0.3432  data: 0.0037  max mem: 3953
+train: [2]  [380/400]  eta: 0:00:07  lr: 0.000177  loss: 3.1642 (3.1655)  grad: 0.2639 (0.2841)  time: 0.3407  data: 0.0042  max mem: 3953
+train: [2]  [399/400]  eta: 0:00:00  lr: 0.000180  loss: 3.1897 (3.1670)  grad: 0.2880 (0.2849)  time: 0.3450  data: 0.0044  max mem: 3953
+train: [2] Total time: 0:02:20 (0.3511 s / it)
+train: [2] Summary: lr: 0.000180  loss: 3.1897 (3.1670)  grad: 0.2880 (0.2849)
+eval (validation): [2]  [ 0/85]  eta: 0:04:52    time: 3.4458  data: 3.1876  max mem: 3953
+eval (validation): [2]  [20/85]  eta: 0:00:33    time: 0.3732  data: 0.0062  max mem: 3953
+eval (validation): [2]  [40/85]  eta: 0:00:19    time: 0.3218  data: 0.0040  max mem: 3953
+eval (validation): [2]  [60/85]  eta: 0:00:09    time: 0.3412  data: 0.0042  max mem: 3953
+eval (validation): [2]  [80/85]  eta: 0:00:01    time: 0.3393  data: 0.0042  max mem: 3953
+eval (validation): [2]  [84/85]  eta: 0:00:00    time: 0.3146  data: 0.0041  max mem: 3953
+eval (validation): [2] Total time: 0:00:32 (0.3822 s / it)
+cv: [2]  best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00')  loss: 3.139  acc: 0.068  f1: 0.026
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [3]  [  0/400]  eta: 0:22:48  lr: nan  time: 3.4211  data: 3.2090  max mem: 3953
+train: [3]  [ 20/400]  eta: 0:03:07  lr: 0.000183  loss: 3.1593 (3.1664)  grad: 0.2881 (0.2911)  time: 0.3479  data: 0.0041  max mem: 3953
+train: [3]  [ 40/400]  eta: 0:02:29  lr: 0.000186  loss: 3.1504 (3.1520)  grad: 0.2770 (0.2858)  time: 0.3352  data: 0.0040  max mem: 3953
+train: [3]  [ 60/400]  eta: 0:02:12  lr: 0.000189  loss: 3.1566 (3.1651)  grad: 0.2755 (0.2850)  time: 0.3364  data: 0.0040  max mem: 3953
+train: [3]  [ 80/400]  eta: 0:02:02  lr: 0.000192  loss: 3.1770 (3.1655)  grad: 0.2891 (0.2919)  time: 0.3583  data: 0.0044  max mem: 3953
+train: [3]  [100/400]  eta: 0:01:52  lr: 0.000195  loss: 3.1662 (3.1669)  grad: 0.2960 (0.2896)  time: 0.3478  data: 0.0043  max mem: 3953
+train: [3]  [120/400]  eta: 0:01:43  lr: 0.000198  loss: 3.1579 (3.1647)  grad: 0.2791 (0.2872)  time: 0.3436  data: 0.0042  max mem: 3953
+train: [3]  [140/400]  eta: 0:01:35  lr: 0.000201  loss: 3.1493 (3.1665)  grad: 0.2818 (0.2881)  time: 0.3426  data: 0.0042  max mem: 3953
+train: [3]  [160/400]  eta: 0:01:27  lr: 0.000204  loss: 3.1411 (3.1638)  grad: 0.2922 (0.2905)  time: 0.3390  data: 0.0041  max mem: 3953
+train: [3]  [180/400]  eta: 0:01:19  lr: 0.000207  loss: 3.1539 (3.1652)  grad: 0.3073 (0.2930)  time: 0.3383  data: 0.0039  max mem: 3953
+train: [3]  [200/400]  eta: 0:01:11  lr: 0.000210  loss: 3.1773 (3.1672)  grad: 0.3060 (0.2935)  time: 0.3428  data: 0.0042  max mem: 3953
+train: [3]  [220/400]  eta: 0:01:04  lr: 0.000213  loss: 3.1780 (3.1687)  grad: 0.2905 (0.2936)  time: 0.3428  data: 0.0041  max mem: 3953
+train: [3]  [240/400]  eta: 0:00:56  lr: 0.000216  loss: 3.1780 (3.1707)  grad: 0.2879 (0.2941)  time: 0.3420  data: 0.0040  max mem: 3953
+train: [3]  [260/400]  eta: 0:00:49  lr: 0.000219  loss: 3.1779 (3.1712)  grad: 0.2992 (0.2948)  time: 0.3380  data: 0.0040  max mem: 3953
+train: [3]  [280/400]  eta: 0:00:42  lr: 0.000222  loss: 3.1779 (3.1712)  grad: 0.3010 (0.2955)  time: 0.3246  data: 0.0041  max mem: 3953
+train: [3]  [300/400]  eta: 0:00:35  lr: 0.000225  loss: 3.1592 (3.1704)  grad: 0.2972 (0.2950)  time: 0.3495  data: 0.0042  max mem: 3953
+train: [3]  [320/400]  eta: 0:00:28  lr: 0.000228  loss: 3.1757 (3.1714)  grad: 0.2877 (0.2950)  time: 0.3440  data: 0.0041  max mem: 3953
+train: [3]  [340/400]  eta: 0:00:21  lr: 0.000231  loss: 3.1782 (3.1713)  grad: 0.2917 (0.2954)  time: 0.3425  data: 0.0041  max mem: 3953
+train: [3]  [360/400]  eta: 0:00:14  lr: 0.000234  loss: 3.1741 (3.1715)  grad: 0.2852 (0.2949)  time: 0.3404  data: 0.0040  max mem: 3953
+train: [3]  [380/400]  eta: 0:00:06  lr: 0.000237  loss: 3.1713 (3.1710)  grad: 0.2839 (0.2952)  time: 0.3181  data: 0.0042  max mem: 3953
+train: [3]  [399/400]  eta: 0:00:00  lr: 0.000240  loss: 3.1719 (3.1718)  grad: 0.3025 (0.2957)  time: 0.3536  data: 0.0041  max mem: 3953
+train: [3] Total time: 0:02:19 (0.3493 s / it)
+train: [3] Summary: lr: 0.000240  loss: 3.1719 (3.1718)  grad: 0.3025 (0.2957)
+eval (validation): [3]  [ 0/85]  eta: 0:04:59    time: 3.5284  data: 3.2492  max mem: 3953
+eval (validation): [3]  [20/85]  eta: 0:00:32    time: 0.3500  data: 0.0053  max mem: 3953
+eval (validation): [3]  [40/85]  eta: 0:00:18    time: 0.3134  data: 0.0040  max mem: 3953
+eval (validation): [3]  [60/85]  eta: 0:00:09    time: 0.3659  data: 0.0041  max mem: 3953
+eval (validation): [3]  [80/85]  eta: 0:00:01    time: 0.3150  data: 0.0039  max mem: 3953
+eval (validation): [3]  [84/85]  eta: 0:00:00    time: 0.3079  data: 0.0042  max mem: 3953
+eval (validation): [3] Total time: 0:00:31 (0.3757 s / it)
+cv: [3]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 3.179  acc: 0.082  f1: 0.037
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
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+train: [4]  [ 60/400]  eta: 0:02:16  lr: 0.000249  loss: 3.1369 (3.1488)  grad: 0.2765 (0.2805)  time: 0.3473  data: 0.0039  max mem: 3953
+train: [4]  [ 80/400]  eta: 0:02:03  lr: 0.000252  loss: 3.1451 (3.1472)  grad: 0.2900 (0.2841)  time: 0.3374  data: 0.0044  max mem: 3953
+train: [4]  [100/400]  eta: 0:01:53  lr: 0.000255  loss: 3.1570 (3.1510)  grad: 0.2974 (0.2898)  time: 0.3388  data: 0.0041  max mem: 3953
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+train: [4]  [399/400]  eta: 0:00:00  lr: 0.000300  loss: 3.1775 (3.1695)  grad: 0.2975 (0.3005)  time: 0.3477  data: 0.0042  max mem: 3953
+train: [4] Total time: 0:02:21 (0.3540 s / it)
+train: [4] Summary: lr: 0.000300  loss: 3.1775 (3.1695)  grad: 0.2975 (0.3005)
+eval (validation): [4]  [ 0/85]  eta: 0:04:47    time: 3.3814  data: 3.1803  max mem: 3953
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+eval (validation): [4]  [84/85]  eta: 0:00:00    time: 0.3182  data: 0.0041  max mem: 3953
+eval (validation): [4] Total time: 0:00:32 (0.3797 s / it)
+cv: [4]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 3.130  acc: 0.079  f1: 0.030
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [5]  [  0/400]  eta: 0:22:57  lr: nan  time: 3.4437  data: 3.1768  max mem: 3953
+train: [5]  [ 20/400]  eta: 0:03:09  lr: 0.000300  loss: 3.1416 (3.1367)  grad: 0.3007 (0.3011)  time: 0.3518  data: 0.0151  max mem: 3953
+train: [5]  [ 40/400]  eta: 0:02:30  lr: 0.000300  loss: 3.1554 (3.1484)  grad: 0.3007 (0.2969)  time: 0.3349  data: 0.0107  max mem: 3953
+train: [5]  [ 60/400]  eta: 0:02:17  lr: 0.000300  loss: 3.1721 (3.1559)  grad: 0.2972 (0.2989)  time: 0.3707  data: 0.0034  max mem: 3953
+train: [5]  [ 80/400]  eta: 0:02:04  lr: 0.000300  loss: 3.1690 (3.1574)  grad: 0.2949 (0.2976)  time: 0.3481  data: 0.0044  max mem: 3953
+train: [5]  [100/400]  eta: 0:01:54  lr: 0.000300  loss: 3.1427 (3.1555)  grad: 0.3020 (0.2986)  time: 0.3488  data: 0.0040  max mem: 3953
+train: [5]  [120/400]  eta: 0:01:44  lr: 0.000300  loss: 3.1528 (3.1602)  grad: 0.2993 (0.2980)  time: 0.3394  data: 0.0040  max mem: 3953
+train: [5]  [140/400]  eta: 0:01:35  lr: 0.000300  loss: 3.1652 (3.1608)  grad: 0.2967 (0.2992)  time: 0.3333  data: 0.0035  max mem: 3953
+train: [5]  [160/400]  eta: 0:01:27  lr: 0.000299  loss: 3.1536 (3.1598)  grad: 0.3106 (0.3021)  time: 0.3366  data: 0.0040  max mem: 3953
+train: [5]  [180/400]  eta: 0:01:20  lr: 0.000299  loss: 3.1506 (3.1603)  grad: 0.3133 (0.3044)  time: 0.3558  data: 0.0040  max mem: 3953
+train: [5]  [200/400]  eta: 0:01:12  lr: 0.000299  loss: 3.1354 (3.1574)  grad: 0.3041 (0.3046)  time: 0.3452  data: 0.0040  max mem: 3953
+train: [5]  [220/400]  eta: 0:01:04  lr: 0.000299  loss: 3.1331 (3.1567)  grad: 0.2971 (0.3037)  time: 0.3465  data: 0.0037  max mem: 3953
+train: [5]  [240/400]  eta: 0:00:57  lr: 0.000299  loss: 3.1727 (3.1589)  grad: 0.3063 (0.3044)  time: 0.3556  data: 0.0039  max mem: 3953
+train: [5]  [260/400]  eta: 0:00:50  lr: 0.000299  loss: 3.1788 (3.1604)  grad: 0.3063 (0.3047)  time: 0.3302  data: 0.0039  max mem: 3953
+train: [5]  [280/400]  eta: 0:00:42  lr: 0.000298  loss: 3.1678 (3.1607)  grad: 0.3031 (0.3044)  time: 0.3307  data: 0.0041  max mem: 3953
+train: [5]  [300/400]  eta: 0:00:35  lr: 0.000298  loss: 3.1653 (3.1622)  grad: 0.2954 (0.3036)  time: 0.3367  data: 0.0042  max mem: 3953
+train: [5]  [320/400]  eta: 0:00:28  lr: 0.000298  loss: 3.1809 (3.1626)  grad: 0.2882 (0.3028)  time: 0.3338  data: 0.0042  max mem: 3953
+train: [5]  [340/400]  eta: 0:00:21  lr: 0.000298  loss: 3.1536 (3.1611)  grad: 0.2895 (0.3027)  time: 0.3335  data: 0.0041  max mem: 3953
+train: [5]  [360/400]  eta: 0:00:14  lr: 0.000297  loss: 3.1398 (3.1612)  grad: 0.3048 (0.3033)  time: 0.3303  data: 0.0039  max mem: 3953
+train: [5]  [380/400]  eta: 0:00:07  lr: 0.000297  loss: 3.1767 (3.1624)  grad: 0.2956 (0.3026)  time: 0.3348  data: 0.0040  max mem: 3953
+train: [5]  [399/400]  eta: 0:00:00  lr: 0.000297  loss: 3.1818 (3.1625)  grad: 0.2952 (0.3026)  time: 0.3351  data: 0.0041  max mem: 3953
+train: [5] Total time: 0:02:19 (0.3496 s / it)
+train: [5] Summary: lr: 0.000297  loss: 3.1818 (3.1625)  grad: 0.2952 (0.3026)
+eval (validation): [5]  [ 0/85]  eta: 0:04:43    time: 3.3316  data: 3.0732  max mem: 3953
+eval (validation): [5]  [20/85]  eta: 0:00:32    time: 0.3652  data: 0.0125  max mem: 3953
+eval (validation): [5]  [40/85]  eta: 0:00:19    time: 0.3459  data: 0.0035  max mem: 3953
+eval (validation): [5]  [60/85]  eta: 0:00:10    time: 0.3569  data: 0.0045  max mem: 3953
+eval (validation): [5]  [80/85]  eta: 0:00:01    time: 0.3400  data: 0.0046  max mem: 3953
+eval (validation): [5]  [84/85]  eta: 0:00:00    time: 0.3259  data: 0.0043  max mem: 3953
+eval (validation): [5] Total time: 0:00:33 (0.3889 s / it)
+cv: [5]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 3.169  acc: 0.080  f1: 0.036
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [6]  [  0/400]  eta: 0:28:33  lr: nan  time: 4.2842  data: 4.0140  max mem: 3953
+train: [6]  [ 20/400]  eta: 0:03:23  lr: 0.000296  loss: 3.1160 (3.1546)  grad: 0.3017 (0.3075)  time: 0.3470  data: 0.0030  max mem: 3953
+train: [6]  [ 40/400]  eta: 0:02:38  lr: 0.000296  loss: 3.1275 (3.1439)  grad: 0.2976 (0.3014)  time: 0.3401  data: 0.0045  max mem: 3953
+train: [6]  [ 60/400]  eta: 0:02:17  lr: 0.000296  loss: 3.1275 (3.1439)  grad: 0.2882 (0.3006)  time: 0.3318  data: 0.0029  max mem: 3953
+train: [6]  [ 80/400]  eta: 0:02:03  lr: 0.000295  loss: 3.1439 (3.1431)  grad: 0.2929 (0.3010)  time: 0.3285  data: 0.0039  max mem: 3953
+train: [6]  [100/400]  eta: 0:01:51  lr: 0.000295  loss: 3.1506 (3.1450)  grad: 0.2957 (0.2998)  time: 0.3235  data: 0.0040  max mem: 3953
+train: [6]  [120/400]  eta: 0:01:42  lr: 0.000295  loss: 3.1564 (3.1455)  grad: 0.2956 (0.2976)  time: 0.3222  data: 0.0041  max mem: 3953
+train: [6]  [140/400]  eta: 0:01:33  lr: 0.000294  loss: 3.1534 (3.1478)  grad: 0.2972 (0.2986)  time: 0.3320  data: 0.0036  max mem: 3953
+train: [6]  [160/400]  eta: 0:01:25  lr: 0.000294  loss: 3.1523 (3.1466)  grad: 0.3024 (0.2995)  time: 0.3392  data: 0.0042  max mem: 3953
+train: [6]  [180/400]  eta: 0:01:18  lr: 0.000293  loss: 3.1411 (3.1497)  grad: 0.3024 (0.3008)  time: 0.3335  data: 0.0041  max mem: 3953
+train: [6]  [200/400]  eta: 0:01:10  lr: 0.000293  loss: 3.1411 (3.1487)  grad: 0.2990 (0.3006)  time: 0.3301  data: 0.0041  max mem: 3953
+train: [6]  [220/400]  eta: 0:01:03  lr: 0.000292  loss: 3.1444 (3.1482)  grad: 0.3039 (0.3011)  time: 0.3348  data: 0.0040  max mem: 3953
+train: [6]  [240/400]  eta: 0:00:55  lr: 0.000292  loss: 3.1462 (3.1495)  grad: 0.3073 (0.3013)  time: 0.3254  data: 0.0040  max mem: 3953
+train: [6]  [260/400]  eta: 0:00:48  lr: 0.000291  loss: 3.1432 (3.1494)  grad: 0.3035 (0.3018)  time: 0.3428  data: 0.0042  max mem: 3953
+train: [6]  [280/400]  eta: 0:00:41  lr: 0.000291  loss: 3.1378 (3.1478)  grad: 0.3006 (0.3014)  time: 0.3314  data: 0.0044  max mem: 3953
+train: [6]  [300/400]  eta: 0:00:34  lr: 0.000290  loss: 3.1543 (3.1497)  grad: 0.3016 (0.3017)  time: 0.3550  data: 0.0040  max mem: 3953
+train: [6]  [320/400]  eta: 0:00:27  lr: 0.000290  loss: 3.1629 (3.1501)  grad: 0.3029 (0.3015)  time: 0.3392  data: 0.0040  max mem: 3953
+train: [6]  [340/400]  eta: 0:00:20  lr: 0.000289  loss: 3.1576 (3.1503)  grad: 0.2876 (0.3002)  time: 0.3365  data: 0.0043  max mem: 3953
+train: [6]  [360/400]  eta: 0:00:13  lr: 0.000288  loss: 3.1719 (3.1509)  grad: 0.2858 (0.3001)  time: 0.3265  data: 0.0042  max mem: 3953
+train: [6]  [380/400]  eta: 0:00:06  lr: 0.000288  loss: 3.1549 (3.1501)  grad: 0.3014 (0.3004)  time: 0.3674  data: 0.0040  max mem: 3953
+train: [6]  [399/400]  eta: 0:00:00  lr: 0.000287  loss: 3.1514 (3.1506)  grad: 0.2885 (0.2995)  time: 0.3479  data: 0.0044  max mem: 3953
+train: [6] Total time: 0:02:18 (0.3468 s / it)
+train: [6] Summary: lr: 0.000287  loss: 3.1514 (3.1506)  grad: 0.2885 (0.2995)
+eval (validation): [6]  [ 0/85]  eta: 0:04:55    time: 3.4774  data: 3.2374  max mem: 3953
+eval (validation): [6]  [20/85]  eta: 0:00:30    time: 0.3205  data: 0.0034  max mem: 3953
+eval (validation): [6]  [40/85]  eta: 0:00:18    time: 0.3562  data: 0.0048  max mem: 3953
+eval (validation): [6]  [60/85]  eta: 0:00:09    time: 0.3203  data: 0.0042  max mem: 3953
+eval (validation): [6]  [80/85]  eta: 0:00:01    time: 0.3101  data: 0.0038  max mem: 3953
+eval (validation): [6]  [84/85]  eta: 0:00:00    time: 0.3011  data: 0.0041  max mem: 3953
+eval (validation): [6] Total time: 0:00:31 (0.3664 s / it)
+cv: [6]  best hparam: (19, 1.0) (042) ('042_lr1.9e+01_wd1.0e+00')  loss: 3.291  acc: 0.087  f1: 0.043
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+train: [7]  [  0/400]  eta: 0:21:58  lr: nan  time: 3.2951  data: 3.0396  max mem: 3953
+train: [7]  [ 20/400]  eta: 0:03:31  lr: 0.000286  loss: 3.1220 (3.1083)  grad: 0.2864 (0.2973)  time: 0.4199  data: 0.0281  max mem: 3953
+train: [7]  [ 40/400]  eta: 0:02:42  lr: 0.000286  loss: 3.1098 (3.0976)  grad: 0.2876 (0.3014)  time: 0.3418  data: 0.0036  max mem: 3953
+train: [7]  [ 60/400]  eta: 0:02:25  lr: 0.000285  loss: 3.1038 (3.1031)  grad: 0.2971 (0.3013)  time: 0.3752  data: 0.0041  max mem: 3953
+train: [7]  [ 80/400]  eta: 0:02:10  lr: 0.000284  loss: 3.1275 (3.1147)  grad: 0.2971 (0.3015)  time: 0.3541  data: 0.0047  max mem: 3953
+train: [7]  [100/400]  eta: 0:01:58  lr: 0.000284  loss: 3.1496 (3.1220)  grad: 0.2992 (0.3007)  time: 0.3317  data: 0.0039  max mem: 3953
+train: [7]  [120/400]  eta: 0:01:47  lr: 0.000283  loss: 3.1409 (3.1214)  grad: 0.3060 (0.3033)  time: 0.3359  data: 0.0041  max mem: 3953
+train: [7]  [140/400]  eta: 0:01:37  lr: 0.000282  loss: 3.1480 (3.1275)  grad: 0.3055 (0.3036)  time: 0.3327  data: 0.0040  max mem: 3953
+train: [7]  [160/400]  eta: 0:01:29  lr: 0.000282  loss: 3.1816 (3.1353)  grad: 0.3022 (0.3044)  time: 0.3392  data: 0.0046  max mem: 3953
+train: [7]  [180/400]  eta: 0:01:22  lr: 0.000281  loss: 3.1346 (3.1332)  grad: 0.3000 (0.3042)  time: 0.3819  data: 0.0043  max mem: 3953
+train: [7]  [200/400]  eta: 0:01:13  lr: 0.000280  loss: 3.1492 (3.1371)  grad: 0.3019 (0.3046)  time: 0.3289  data: 0.0047  max mem: 3953
+train: [7]  [220/400]  eta: 0:01:05  lr: 0.000279  loss: 3.1609 (3.1386)  grad: 0.3019 (0.3038)  time: 0.3229  data: 0.0039  max mem: 3953
+train: [7]  [240/400]  eta: 0:00:57  lr: 0.000278  loss: 3.1510 (3.1393)  grad: 0.3068 (0.3052)  time: 0.3186  data: 0.0038  max mem: 3953
+train: [7]  [260/400]  eta: 0:00:50  lr: 0.000278  loss: 3.1649 (3.1430)  grad: 0.3093 (0.3047)  time: 0.3510  data: 0.0041  max mem: 3953
+train: [7]  [280/400]  eta: 0:00:43  lr: 0.000277  loss: 3.1649 (3.1447)  grad: 0.3068 (0.3059)  time: 0.3465  data: 0.0043  max mem: 3953
+train: [7]  [300/400]  eta: 0:00:35  lr: 0.000276  loss: 3.1417 (3.1429)  grad: 0.3081 (0.3061)  time: 0.3313  data: 0.0042  max mem: 3953
+train: [7]  [320/400]  eta: 0:00:28  lr: 0.000275  loss: 3.1485 (3.1444)  grad: 0.3075 (0.3063)  time: 0.3327  data: 0.0041  max mem: 3953
+train: [7]  [340/400]  eta: 0:00:21  lr: 0.000274  loss: 3.1503 (3.1427)  grad: 0.3108 (0.3065)  time: 0.3126  data: 0.0040  max mem: 3953
+train: [7]  [360/400]  eta: 0:00:14  lr: 0.000273  loss: 3.1110 (3.1432)  grad: 0.3108 (0.3064)  time: 0.3363  data: 0.0042  max mem: 3953
+train: [7]  [380/400]  eta: 0:00:07  lr: 0.000272  loss: 3.1501 (3.1436)  grad: 0.3007 (0.3066)  time: 0.3340  data: 0.0044  max mem: 3953
+train: [7]  [399/400]  eta: 0:00:00  lr: 0.000271  loss: 3.1249 (3.1415)  grad: 0.2986 (0.3069)  time: 0.3313  data: 0.0041  max mem: 3953
+train: [7] Total time: 0:02:20 (0.3507 s / it)
+train: [7] Summary: lr: 0.000271  loss: 3.1249 (3.1415)  grad: 0.2986 (0.3069)
+eval (validation): [7]  [ 0/85]  eta: 0:04:43    time: 3.3352  data: 3.1243  max mem: 3953
+eval (validation): [7]  [20/85]  eta: 0:00:30    time: 0.3277  data: 0.0036  max mem: 3953
+eval (validation): [7]  [40/85]  eta: 0:00:17    time: 0.3249  data: 0.0042  max mem: 3953
+eval (validation): [7]  [60/85]  eta: 0:00:09    time: 0.3570  data: 0.0049  max mem: 3953
+eval (validation): [7]  [80/85]  eta: 0:00:01    time: 0.3325  data: 0.0039  max mem: 3953
+eval (validation): [7]  [84/85]  eta: 0:00:00    time: 0.3190  data: 0.0038  max mem: 3953
+eval (validation): [7] Total time: 0:00:31 (0.3726 s / it)
+cv: [7]  best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00')  loss: 3.229  acc: 0.085  f1: 0.039
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [8]  [  0/400]  eta: 0:22:35  lr: nan  time: 3.3881  data: 3.1342  max mem: 3953
+train: [8]  [ 20/400]  eta: 0:02:59  lr: 0.000270  loss: 3.1209 (3.1364)  grad: 0.2948 (0.2966)  time: 0.3266  data: 0.0040  max mem: 3953
+train: [8]  [ 40/400]  eta: 0:02:26  lr: 0.000270  loss: 3.1272 (3.1351)  grad: 0.2948 (0.2960)  time: 0.3374  data: 0.0043  max mem: 3953
+train: [8]  [ 60/400]  eta: 0:02:10  lr: 0.000269  loss: 3.1272 (3.1368)  grad: 0.2869 (0.2971)  time: 0.3401  data: 0.0037  max mem: 3953
+train: [8]  [ 80/400]  eta: 0:01:59  lr: 0.000268  loss: 3.1196 (3.1303)  grad: 0.2849 (0.2945)  time: 0.3402  data: 0.0041  max mem: 3953
+train: [8]  [100/400]  eta: 0:01:49  lr: 0.000267  loss: 3.1309 (3.1341)  grad: 0.2971 (0.2976)  time: 0.3361  data: 0.0042  max mem: 3953
+train: [8]  [120/400]  eta: 0:01:41  lr: 0.000266  loss: 3.1232 (3.1290)  grad: 0.2897 (0.2951)  time: 0.3349  data: 0.0045  max mem: 3953
+train: [8]  [140/400]  eta: 0:01:32  lr: 0.000265  loss: 3.1033 (3.1250)  grad: 0.2823 (0.2950)  time: 0.3332  data: 0.0044  max mem: 3953
+train: [8]  [160/400]  eta: 0:01:25  lr: 0.000264  loss: 3.1118 (3.1253)  grad: 0.2805 (0.2939)  time: 0.3534  data: 0.0047  max mem: 3953
+train: [8]  [180/400]  eta: 0:01:18  lr: 0.000263  loss: 3.1173 (3.1249)  grad: 0.2866 (0.2932)  time: 0.3396  data: 0.0042  max mem: 3953
+train: [8]  [200/400]  eta: 0:01:11  lr: 0.000262  loss: 3.1167 (3.1250)  grad: 0.2872 (0.2928)  time: 0.3592  data: 0.0043  max mem: 3953
+train: [8]  [220/400]  eta: 0:01:03  lr: 0.000260  loss: 3.1141 (3.1249)  grad: 0.3064 (0.2939)  time: 0.3395  data: 0.0043  max mem: 3953
+train: [8]  [240/400]  eta: 0:00:56  lr: 0.000259  loss: 3.1251 (3.1261)  grad: 0.3059 (0.2943)  time: 0.3342  data: 0.0044  max mem: 3953
+train: [8]  [260/400]  eta: 0:00:49  lr: 0.000258  loss: 3.1169 (3.1238)  grad: 0.3002 (0.2952)  time: 0.3307  data: 0.0042  max mem: 3953
+train: [8]  [280/400]  eta: 0:00:41  lr: 0.000257  loss: 3.1247 (3.1263)  grad: 0.2917 (0.2954)  time: 0.3289  data: 0.0043  max mem: 3953
+train: [8]  [300/400]  eta: 0:00:34  lr: 0.000256  loss: 3.1541 (3.1273)  grad: 0.2815 (0.2943)  time: 0.3434  data: 0.0037  max mem: 3953
+train: [8]  [320/400]  eta: 0:00:27  lr: 0.000255  loss: 3.1541 (3.1293)  grad: 0.2865 (0.2943)  time: 0.3442  data: 0.0040  max mem: 3953
+train: [8]  [340/400]  eta: 0:00:20  lr: 0.000254  loss: 3.1712 (3.1293)  grad: 0.2975 (0.2944)  time: 0.3494  data: 0.0045  max mem: 3953
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+train: [8]  [380/400]  eta: 0:00:06  lr: 0.000252  loss: 3.0900 (3.1269)  grad: 0.2937 (0.2941)  time: 0.3286  data: 0.0039  max mem: 3953
+train: [8]  [399/400]  eta: 0:00:00  lr: 0.000250  loss: 3.0928 (3.1260)  grad: 0.2842 (0.2934)  time: 0.3448  data: 0.0042  max mem: 3953
+train: [8] Total time: 0:02:19 (0.3487 s / it)
+train: [8] Summary: lr: 0.000250  loss: 3.0928 (3.1260)  grad: 0.2842 (0.2934)
+eval (validation): [8]  [ 0/85]  eta: 0:04:44    time: 3.3517  data: 3.1432  max mem: 3953
+eval (validation): [8]  [20/85]  eta: 0:00:29    time: 0.3107  data: 0.0073  max mem: 3953
+eval (validation): [8]  [40/85]  eta: 0:00:17    time: 0.3259  data: 0.0041  max mem: 3953
+eval (validation): [8]  [60/85]  eta: 0:00:09    time: 0.3172  data: 0.0041  max mem: 3953
+eval (validation): [8]  [80/85]  eta: 0:00:01    time: 0.3226  data: 0.0042  max mem: 3953
+eval (validation): [8]  [84/85]  eta: 0:00:00    time: 0.3177  data: 0.0040  max mem: 3953
+eval (validation): [8] Total time: 0:00:30 (0.3574 s / it)
+cv: [8]  best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00')  loss: 3.126  acc: 0.084  f1: 0.040
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [9]  [  0/400]  eta: 0:22:20  lr: nan  time: 3.3503  data: 3.0972  max mem: 3953
+train: [9]  [ 20/400]  eta: 0:03:03  lr: 0.000249  loss: 3.0943 (3.1054)  grad: 0.2752 (0.2822)  time: 0.3388  data: 0.0054  max mem: 3953
+train: [9]  [ 40/400]  eta: 0:02:26  lr: 0.000248  loss: 3.0943 (3.1048)  grad: 0.2759 (0.2836)  time: 0.3263  data: 0.0044  max mem: 3953
+train: [9]  [ 60/400]  eta: 0:02:09  lr: 0.000247  loss: 3.0857 (3.1084)  grad: 0.2894 (0.2849)  time: 0.3322  data: 0.0042  max mem: 3953
+train: [9]  [ 80/400]  eta: 0:01:58  lr: 0.000246  loss: 3.0982 (3.1101)  grad: 0.2915 (0.2894)  time: 0.3343  data: 0.0039  max mem: 3953
+train: [9]  [100/400]  eta: 0:01:49  lr: 0.000244  loss: 3.1007 (3.1097)  grad: 0.2915 (0.2894)  time: 0.3470  data: 0.0041  max mem: 3953
+train: [9]  [120/400]  eta: 0:01:40  lr: 0.000243  loss: 3.0927 (3.1065)  grad: 0.2786 (0.2888)  time: 0.3354  data: 0.0044  max mem: 3953
+train: [9]  [140/400]  eta: 0:01:32  lr: 0.000242  loss: 3.1071 (3.1061)  grad: 0.2892 (0.2895)  time: 0.3215  data: 0.0034  max mem: 3953
+train: [9]  [160/400]  eta: 0:01:24  lr: 0.000241  loss: 3.1223 (3.1102)  grad: 0.2866 (0.2894)  time: 0.3465  data: 0.0041  max mem: 3953
+train: [9]  [180/400]  eta: 0:01:17  lr: 0.000240  loss: 3.1321 (3.1124)  grad: 0.2752 (0.2875)  time: 0.3458  data: 0.0042  max mem: 3953
+train: [9]  [200/400]  eta: 0:01:10  lr: 0.000238  loss: 3.1309 (3.1122)  grad: 0.2865 (0.2882)  time: 0.3399  data: 0.0040  max mem: 3953
+train: [9]  [220/400]  eta: 0:01:02  lr: 0.000237  loss: 3.1267 (3.1122)  grad: 0.2992 (0.2897)  time: 0.3253  data: 0.0040  max mem: 3953
+train: [9]  [240/400]  eta: 0:00:55  lr: 0.000236  loss: 3.1154 (3.1125)  grad: 0.3114 (0.2922)  time: 0.3200  data: 0.0041  max mem: 3953
+train: [9]  [260/400]  eta: 0:00:48  lr: 0.000234  loss: 3.1042 (3.1143)  grad: 0.3077 (0.2923)  time: 0.3258  data: 0.0042  max mem: 3953
+train: [9]  [280/400]  eta: 0:00:41  lr: 0.000233  loss: 3.0894 (3.1119)  grad: 0.2870 (0.2918)  time: 0.3220  data: 0.0038  max mem: 3953
+train: [9]  [300/400]  eta: 0:00:34  lr: 0.000232  loss: 3.0894 (3.1126)  grad: 0.2870 (0.2915)  time: 0.3192  data: 0.0040  max mem: 3953
+train: [9]  [320/400]  eta: 0:00:27  lr: 0.000230  loss: 3.1261 (3.1137)  grad: 0.2944 (0.2919)  time: 0.3301  data: 0.0039  max mem: 3953
+train: [9]  [340/400]  eta: 0:00:20  lr: 0.000229  loss: 3.1169 (3.1133)  grad: 0.2827 (0.2912)  time: 0.3326  data: 0.0040  max mem: 3953
+train: [9]  [360/400]  eta: 0:00:13  lr: 0.000228  loss: 3.1249 (3.1153)  grad: 0.2840 (0.2917)  time: 0.3282  data: 0.0039  max mem: 3953
+train: [9]  [380/400]  eta: 0:00:06  lr: 0.000226  loss: 3.1438 (3.1164)  grad: 0.2943 (0.2914)  time: 0.3382  data: 0.0041  max mem: 3953
+train: [9]  [399/400]  eta: 0:00:00  lr: 0.000225  loss: 3.1014 (3.1147)  grad: 0.2810 (0.2906)  time: 0.3341  data: 0.0038  max mem: 3953
+train: [9] Total time: 0:02:15 (0.3400 s / it)
+train: [9] Summary: lr: 0.000225  loss: 3.1014 (3.1147)  grad: 0.2810 (0.2906)
+eval (validation): [9]  [ 0/85]  eta: 0:04:48    time: 3.3945  data: 3.1787  max mem: 3953
+eval (validation): [9]  [20/85]  eta: 0:00:32    time: 0.3574  data: 0.0213  max mem: 3953
+eval (validation): [9]  [40/85]  eta: 0:00:19    time: 0.3430  data: 0.0044  max mem: 3953
+eval (validation): [9]  [60/85]  eta: 0:00:09    time: 0.3132  data: 0.0039  max mem: 3953
+eval (validation): [9]  [80/85]  eta: 0:00:01    time: 0.3137  data: 0.0043  max mem: 3953
+eval (validation): [9]  [84/85]  eta: 0:00:00    time: 0.3082  data: 0.0042  max mem: 3953
+eval (validation): [9] Total time: 0:00:31 (0.3705 s / it)
+cv: [9]  best hparam: (7.1, 1.0) (036) ('036_lr7.1e+00_wd1.0e+00')  loss: 3.144  acc: 0.086  f1: 0.046
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [10]  [  0/400]  eta: 0:22:31  lr: nan  time: 3.3778  data: 3.1293  max mem: 3953
+train: [10]  [ 20/400]  eta: 0:03:00  lr: 0.000224  loss: 3.1093 (3.1172)  grad: 0.2873 (0.2831)  time: 0.3300  data: 0.0037  max mem: 3953
+train: [10]  [ 40/400]  eta: 0:02:23  lr: 0.000222  loss: 3.1039 (3.1043)  grad: 0.2873 (0.2882)  time: 0.3195  data: 0.0039  max mem: 3953
+train: [10]  [ 60/400]  eta: 0:02:07  lr: 0.000221  loss: 3.0839 (3.0975)  grad: 0.2936 (0.2897)  time: 0.3248  data: 0.0038  max mem: 3953
+train: [10]  [ 80/400]  eta: 0:01:55  lr: 0.000220  loss: 3.0922 (3.1020)  grad: 0.2870 (0.2878)  time: 0.3232  data: 0.0040  max mem: 3953
+train: [10]  [100/400]  eta: 0:01:46  lr: 0.000218  loss: 3.1008 (3.0983)  grad: 0.2773 (0.2904)  time: 0.3213  data: 0.0041  max mem: 3953
+train: [10]  [120/400]  eta: 0:01:37  lr: 0.000217  loss: 3.1146 (3.1027)  grad: 0.2885 (0.2905)  time: 0.3261  data: 0.0040  max mem: 3953
+train: [10]  [140/400]  eta: 0:01:30  lr: 0.000215  loss: 3.1146 (3.1040)  grad: 0.2919 (0.2917)  time: 0.3287  data: 0.0039  max mem: 3953
+train: [10]  [160/400]  eta: 0:01:22  lr: 0.000214  loss: 3.0993 (3.1048)  grad: 0.2959 (0.2924)  time: 0.3247  data: 0.0038  max mem: 3953
+train: [10]  [180/400]  eta: 0:01:15  lr: 0.000213  loss: 3.1257 (3.1083)  grad: 0.2920 (0.2928)  time: 0.3215  data: 0.0040  max mem: 3953
+train: [10]  [200/400]  eta: 0:01:08  lr: 0.000211  loss: 3.1257 (3.1075)  grad: 0.2826 (0.2922)  time: 0.3295  data: 0.0039  max mem: 3953
+train: [10]  [220/400]  eta: 0:01:01  lr: 0.000210  loss: 3.0955 (3.1055)  grad: 0.2795 (0.2908)  time: 0.3377  data: 0.0039  max mem: 3953
+train: [10]  [240/400]  eta: 0:00:54  lr: 0.000208  loss: 3.0617 (3.1017)  grad: 0.2758 (0.2900)  time: 0.3224  data: 0.0042  max mem: 3953
+train: [10]  [260/400]  eta: 0:00:47  lr: 0.000207  loss: 3.0686 (3.1016)  grad: 0.2758 (0.2893)  time: 0.3270  data: 0.0042  max mem: 3953
+train: [10]  [280/400]  eta: 0:00:40  lr: 0.000205  loss: 3.1101 (3.1027)  grad: 0.2736 (0.2883)  time: 0.3160  data: 0.0040  max mem: 3953
+train: [10]  [300/400]  eta: 0:00:33  lr: 0.000204  loss: 3.1185 (3.1031)  grad: 0.2686 (0.2880)  time: 0.3281  data: 0.0039  max mem: 3953
+train: [10]  [320/400]  eta: 0:00:26  lr: 0.000202  loss: 3.0966 (3.1030)  grad: 0.2828 (0.2884)  time: 0.3294  data: 0.0041  max mem: 3953
+train: [10]  [340/400]  eta: 0:00:20  lr: 0.000201  loss: 3.0922 (3.1026)  grad: 0.2841 (0.2881)  time: 0.3466  data: 0.0041  max mem: 3953
+train: [10]  [360/400]  eta: 0:00:13  lr: 0.000199  loss: 3.0800 (3.1021)  grad: 0.2822 (0.2875)  time: 0.3314  data: 0.0040  max mem: 3953
+train: [10]  [380/400]  eta: 0:00:06  lr: 0.000198  loss: 3.0808 (3.1022)  grad: 0.2775 (0.2874)  time: 0.3098  data: 0.0039  max mem: 3953
+train: [10]  [399/400]  eta: 0:00:00  lr: 0.000196  loss: 3.1049 (3.1028)  grad: 0.2839 (0.2882)  time: 0.3253  data: 0.0040  max mem: 3953
+train: [10] Total time: 0:02:13 (0.3341 s / it)
+train: [10] Summary: lr: 0.000196  loss: 3.1049 (3.1028)  grad: 0.2839 (0.2882)
+eval (validation): [10]  [ 0/85]  eta: 0:04:46    time: 3.3719  data: 3.1368  max mem: 3953
+eval (validation): [10]  [20/85]  eta: 0:00:33    time: 0.3674  data: 0.0156  max mem: 3953
+eval (validation): [10]  [40/85]  eta: 0:00:18    time: 0.3240  data: 0.0048  max mem: 3953
+eval (validation): [10]  [60/85]  eta: 0:00:09    time: 0.3043  data: 0.0037  max mem: 3953
+eval (validation): [10]  [80/85]  eta: 0:00:01    time: 0.2975  data: 0.0041  max mem: 3953
+eval (validation): [10]  [84/85]  eta: 0:00:00    time: 0.2920  data: 0.0039  max mem: 3953
+eval (validation): [10] Total time: 0:00:30 (0.3624 s / it)
+cv: [10]  best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00')  loss: 3.113  acc: 0.084  f1: 0.037
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [11]  [  0/400]  eta: 0:22:00  lr: nan  time: 3.3024  data: 3.0392  max mem: 3953
+train: [11]  [ 20/400]  eta: 0:03:01  lr: 0.000195  loss: 3.0948 (3.1010)  grad: 0.2951 (0.2908)  time: 0.3376  data: 0.0050  max mem: 3953
+train: [11]  [ 40/400]  eta: 0:02:25  lr: 0.000193  loss: 3.0948 (3.0943)  grad: 0.2898 (0.2905)  time: 0.3254  data: 0.0034  max mem: 3953
+train: [11]  [ 60/400]  eta: 0:02:10  lr: 0.000192  loss: 3.0885 (3.0895)  grad: 0.2890 (0.2959)  time: 0.3418  data: 0.0042  max mem: 3953
+train: [11]  [ 80/400]  eta: 0:01:59  lr: 0.000190  loss: 3.0755 (3.0893)  grad: 0.2944 (0.2940)  time: 0.3461  data: 0.0042  max mem: 3953
+train: [11]  [100/400]  eta: 0:01:51  lr: 0.000189  loss: 3.0755 (3.0933)  grad: 0.2863 (0.2928)  time: 0.3612  data: 0.0044  max mem: 3953
+train: [11]  [120/400]  eta: 0:01:41  lr: 0.000187  loss: 3.0678 (3.0856)  grad: 0.2791 (0.2903)  time: 0.3178  data: 0.0038  max mem: 3953
+train: [11]  [140/400]  eta: 0:01:33  lr: 0.000186  loss: 3.0907 (3.0877)  grad: 0.2753 (0.2883)  time: 0.3386  data: 0.0040  max mem: 3953
+train: [11]  [160/400]  eta: 0:01:25  lr: 0.000184  loss: 3.0865 (3.0857)  grad: 0.2809 (0.2884)  time: 0.3315  data: 0.0039  max mem: 3953
+train: [11]  [180/400]  eta: 0:01:17  lr: 0.000183  loss: 3.0798 (3.0865)  grad: 0.2851 (0.2876)  time: 0.3242  data: 0.0039  max mem: 3953
+train: [11]  [200/400]  eta: 0:01:10  lr: 0.000181  loss: 3.0842 (3.0847)  grad: 0.2810 (0.2870)  time: 0.3362  data: 0.0041  max mem: 3953
+train: [11]  [220/400]  eta: 0:01:02  lr: 0.000180  loss: 3.0987 (3.0860)  grad: 0.2716 (0.2864)  time: 0.3117  data: 0.0042  max mem: 3953
+train: [11]  [240/400]  eta: 0:00:55  lr: 0.000178  loss: 3.1091 (3.0873)  grad: 0.2723 (0.2851)  time: 0.3299  data: 0.0044  max mem: 3953
+train: [11]  [260/400]  eta: 0:00:48  lr: 0.000177  loss: 3.1018 (3.0875)  grad: 0.2667 (0.2837)  time: 0.3198  data: 0.0042  max mem: 3953
+train: [11]  [280/400]  eta: 0:00:41  lr: 0.000175  loss: 3.0679 (3.0863)  grad: 0.2667 (0.2825)  time: 0.3191  data: 0.0042  max mem: 3953
+train: [11]  [300/400]  eta: 0:00:34  lr: 0.000174  loss: 3.0793 (3.0863)  grad: 0.2617 (0.2824)  time: 0.3202  data: 0.0040  max mem: 3953
+train: [11]  [320/400]  eta: 0:00:27  lr: 0.000172  loss: 3.0906 (3.0872)  grad: 0.2844 (0.2826)  time: 0.3183  data: 0.0041  max mem: 3953
+train: [11]  [340/400]  eta: 0:00:20  lr: 0.000170  loss: 3.1041 (3.0890)  grad: 0.2763 (0.2820)  time: 0.3234  data: 0.0042  max mem: 3953
+train: [11]  [360/400]  eta: 0:00:13  lr: 0.000169  loss: 3.0916 (3.0879)  grad: 0.2579 (0.2815)  time: 0.3094  data: 0.0043  max mem: 3953
+train: [11]  [380/400]  eta: 0:00:06  lr: 0.000167  loss: 3.0745 (3.0881)  grad: 0.2893 (0.2822)  time: 0.3197  data: 0.0041  max mem: 3953
+train: [11]  [399/400]  eta: 0:00:00  lr: 0.000166  loss: 3.0813 (3.0882)  grad: 0.2923 (0.2833)  time: 0.3138  data: 0.0040  max mem: 3953
+train: [11] Total time: 0:02:14 (0.3350 s / it)
+train: [11] Summary: lr: 0.000166  loss: 3.0813 (3.0882)  grad: 0.2923 (0.2833)
+eval (validation): [11]  [ 0/85]  eta: 0:04:32    time: 3.2070  data: 3.0089  max mem: 3953
+eval (validation): [11]  [20/85]  eta: 0:00:30    time: 0.3371  data: 0.0052  max mem: 3953
+eval (validation): [11]  [40/85]  eta: 0:00:17    time: 0.3190  data: 0.0047  max mem: 3953
+eval (validation): [11]  [60/85]  eta: 0:00:09    time: 0.3130  data: 0.0045  max mem: 3953
+eval (validation): [11]  [80/85]  eta: 0:00:01    time: 0.2996  data: 0.0041  max mem: 3953
+eval (validation): [11]  [84/85]  eta: 0:00:00    time: 0.2966  data: 0.0040  max mem: 3953
+eval (validation): [11] Total time: 0:00:30 (0.3540 s / it)
+cv: [11]  best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00')  loss: 3.111  acc: 0.083  f1: 0.039
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [12]  [  0/400]  eta: 0:26:51  lr: nan  time: 4.0291  data: 3.7590  max mem: 3953
+train: [12]  [ 20/400]  eta: 0:03:12  lr: 0.000164  loss: 3.0624 (3.0651)  grad: 0.2908 (0.2868)  time: 0.3292  data: 0.0031  max mem: 3953
+train: [12]  [ 40/400]  eta: 0:02:31  lr: 0.000163  loss: 3.0624 (3.0598)  grad: 0.2739 (0.2777)  time: 0.3294  data: 0.0041  max mem: 3953
+train: [12]  [ 60/400]  eta: 0:02:11  lr: 0.000161  loss: 3.0836 (3.0713)  grad: 0.2739 (0.2779)  time: 0.3159  data: 0.0041  max mem: 3953
+train: [12]  [ 80/400]  eta: 0:01:57  lr: 0.000160  loss: 3.0534 (3.0595)  grad: 0.2805 (0.2786)  time: 0.3150  data: 0.0042  max mem: 3953
+train: [12]  [100/400]  eta: 0:01:47  lr: 0.000158  loss: 3.0484 (3.0628)  grad: 0.2861 (0.2814)  time: 0.3197  data: 0.0043  max mem: 3953
+train: [12]  [120/400]  eta: 0:01:38  lr: 0.000156  loss: 3.0800 (3.0660)  grad: 0.2878 (0.2824)  time: 0.3215  data: 0.0042  max mem: 3953
+train: [12]  [140/400]  eta: 0:01:30  lr: 0.000155  loss: 3.0807 (3.0658)  grad: 0.2647 (0.2805)  time: 0.3211  data: 0.0041  max mem: 3953
+train: [12]  [160/400]  eta: 0:01:22  lr: 0.000153  loss: 3.0807 (3.0685)  grad: 0.2820 (0.2824)  time: 0.3282  data: 0.0042  max mem: 3953
+train: [12]  [180/400]  eta: 0:01:15  lr: 0.000152  loss: 3.0818 (3.0702)  grad: 0.2834 (0.2810)  time: 0.3328  data: 0.0043  max mem: 3953
+train: [12]  [200/400]  eta: 0:01:08  lr: 0.000150  loss: 3.0636 (3.0687)  grad: 0.2801 (0.2820)  time: 0.3437  data: 0.0043  max mem: 3953
+train: [12]  [220/400]  eta: 0:01:01  lr: 0.000149  loss: 3.0636 (3.0675)  grad: 0.2673 (0.2800)  time: 0.3264  data: 0.0041  max mem: 3953
+train: [12]  [240/400]  eta: 0:00:54  lr: 0.000147  loss: 3.0816 (3.0695)  grad: 0.2640 (0.2797)  time: 0.3346  data: 0.0041  max mem: 3953
+train: [12]  [260/400]  eta: 0:00:48  lr: 0.000145  loss: 3.0901 (3.0712)  grad: 0.2722 (0.2798)  time: 0.3565  data: 0.0042  max mem: 3953
+train: [12]  [280/400]  eta: 0:00:41  lr: 0.000144  loss: 3.0895 (3.0727)  grad: 0.2769 (0.2791)  time: 0.3332  data: 0.0040  max mem: 3953
+train: [12]  [300/400]  eta: 0:00:34  lr: 0.000142  loss: 3.0612 (3.0707)  grad: 0.2678 (0.2781)  time: 0.3323  data: 0.0040  max mem: 3953
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+train: [12]  [340/400]  eta: 0:00:20  lr: 0.000139  loss: 3.0661 (3.0707)  grad: 0.2679 (0.2773)  time: 0.3170  data: 0.0042  max mem: 3953
+train: [12]  [360/400]  eta: 0:00:13  lr: 0.000138  loss: 3.1058 (3.0736)  grad: 0.2665 (0.2763)  time: 0.3282  data: 0.0041  max mem: 3953
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+train: [12] Total time: 0:02:15 (0.3383 s / it)
+train: [12] Summary: lr: 0.000134  loss: 3.0952 (3.0755)  grad: 0.2846 (0.2776)
+eval (validation): [12]  [ 0/85]  eta: 0:04:33    time: 3.2137  data: 3.0253  max mem: 3953
+eval (validation): [12]  [20/85]  eta: 0:00:28    time: 0.2952  data: 0.0048  max mem: 3953
+eval (validation): [12]  [40/85]  eta: 0:00:16    time: 0.3042  data: 0.0044  max mem: 3953
+eval (validation): [12]  [60/85]  eta: 0:00:08    time: 0.3007  data: 0.0036  max mem: 3953
+eval (validation): [12]  [80/85]  eta: 0:00:01    time: 0.2929  data: 0.0039  max mem: 3953
+eval (validation): [12]  [84/85]  eta: 0:00:00    time: 0.2902  data: 0.0034  max mem: 3953
+eval (validation): [12] Total time: 0:00:28 (0.3359 s / it)
+cv: [12]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 3.129  acc: 0.093  f1: 0.049
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+train: [13]  [  0/400]  eta: 0:21:32  lr: nan  time: 3.2306  data: 2.9874  max mem: 3953
+train: [13]  [ 20/400]  eta: 0:03:07  lr: 0.000133  loss: 3.0463 (3.0522)  grad: 0.2674 (0.2684)  time: 0.3573  data: 0.0264  max mem: 3953
+train: [13]  [ 40/400]  eta: 0:02:27  lr: 0.000131  loss: 3.0454 (3.0425)  grad: 0.2620 (0.2675)  time: 0.3184  data: 0.0035  max mem: 3953
+train: [13]  [ 60/400]  eta: 0:02:08  lr: 0.000130  loss: 3.0385 (3.0460)  grad: 0.2632 (0.2709)  time: 0.3136  data: 0.0042  max mem: 3953
+train: [13]  [ 80/400]  eta: 0:01:56  lr: 0.000128  loss: 3.0634 (3.0495)  grad: 0.2809 (0.2731)  time: 0.3255  data: 0.0043  max mem: 3953
+train: [13]  [100/400]  eta: 0:01:46  lr: 0.000127  loss: 3.0637 (3.0542)  grad: 0.2806 (0.2739)  time: 0.3200  data: 0.0042  max mem: 3953
+train: [13]  [120/400]  eta: 0:01:38  lr: 0.000125  loss: 3.0654 (3.0543)  grad: 0.2735 (0.2745)  time: 0.3250  data: 0.0043  max mem: 3953
+train: [13]  [140/400]  eta: 0:01:30  lr: 0.000124  loss: 3.0701 (3.0575)  grad: 0.2742 (0.2747)  time: 0.3212  data: 0.0043  max mem: 3953
+train: [13]  [160/400]  eta: 0:01:22  lr: 0.000122  loss: 3.0508 (3.0565)  grad: 0.2681 (0.2730)  time: 0.3119  data: 0.0042  max mem: 3953
+train: [13]  [180/400]  eta: 0:01:15  lr: 0.000120  loss: 3.0470 (3.0581)  grad: 0.2815 (0.2736)  time: 0.3340  data: 0.0042  max mem: 3953
+train: [13]  [200/400]  eta: 0:01:07  lr: 0.000119  loss: 3.0687 (3.0598)  grad: 0.2875 (0.2746)  time: 0.3194  data: 0.0042  max mem: 3953
+train: [13]  [220/400]  eta: 0:01:00  lr: 0.000117  loss: 3.0490 (3.0579)  grad: 0.2884 (0.2747)  time: 0.3183  data: 0.0044  max mem: 3953
+train: [13]  [240/400]  eta: 0:00:53  lr: 0.000116  loss: 3.0391 (3.0579)  grad: 0.2846 (0.2750)  time: 0.3360  data: 0.0042  max mem: 3953
+train: [13]  [260/400]  eta: 0:00:46  lr: 0.000114  loss: 3.0780 (3.0598)  grad: 0.2548 (0.2733)  time: 0.3183  data: 0.0041  max mem: 3953
+train: [13]  [280/400]  eta: 0:00:40  lr: 0.000113  loss: 3.0780 (3.0606)  grad: 0.2548 (0.2740)  time: 0.3170  data: 0.0044  max mem: 3953
+train: [13]  [300/400]  eta: 0:00:33  lr: 0.000111  loss: 3.0622 (3.0601)  grad: 0.2702 (0.2737)  time: 0.3208  data: 0.0041  max mem: 3953
+train: [13]  [320/400]  eta: 0:00:26  lr: 0.000110  loss: 3.0575 (3.0611)  grad: 0.2551 (0.2728)  time: 0.3202  data: 0.0041  max mem: 3953
+train: [13]  [340/400]  eta: 0:00:19  lr: 0.000108  loss: 3.0478 (3.0605)  grad: 0.2537 (0.2719)  time: 0.3362  data: 0.0042  max mem: 3953
+train: [13]  [360/400]  eta: 0:00:13  lr: 0.000107  loss: 3.0412 (3.0606)  grad: 0.2551 (0.2714)  time: 0.3325  data: 0.0042  max mem: 3953
+train: [13]  [380/400]  eta: 0:00:06  lr: 0.000105  loss: 3.0719 (3.0618)  grad: 0.2717 (0.2714)  time: 0.3406  data: 0.0041  max mem: 3953
+train: [13]  [399/400]  eta: 0:00:00  lr: 0.000104  loss: 3.0705 (3.0618)  grad: 0.2717 (0.2719)  time: 0.3202  data: 0.0042  max mem: 3953
+train: [13] Total time: 0:02:13 (0.3328 s / it)
+train: [13] Summary: lr: 0.000104  loss: 3.0705 (3.0618)  grad: 0.2717 (0.2719)
+eval (validation): [13]  [ 0/85]  eta: 0:04:40    time: 3.3014  data: 3.0421  max mem: 3953
+eval (validation): [13]  [20/85]  eta: 0:00:29    time: 0.3163  data: 0.0063  max mem: 3953
+eval (validation): [13]  [40/85]  eta: 0:00:17    time: 0.3355  data: 0.0041  max mem: 3953
+eval (validation): [13]  [60/85]  eta: 0:00:09    time: 0.3485  data: 0.0044  max mem: 3953
+eval (validation): [13]  [80/85]  eta: 0:00:01    time: 0.3090  data: 0.0040  max mem: 3953
+eval (validation): [13]  [84/85]  eta: 0:00:00    time: 0.3002  data: 0.0039  max mem: 3953
+eval (validation): [13] Total time: 0:00:30 (0.3643 s / it)
+cv: [13]  best hparam: (50, 1.0) (048) ('048_lr5.0e+01_wd1.0e+00')  loss: 3.243  acc: 0.094  f1: 0.046
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+train: [14]  [  0/400]  eta: 0:22:03  lr: nan  time: 3.3093  data: 3.0559  max mem: 3953
+train: [14]  [ 20/400]  eta: 0:03:09  lr: 0.000102  loss: 3.0772 (3.0721)  grad: 0.2628 (0.2807)  time: 0.3592  data: 0.0048  max mem: 3953
+train: [14]  [ 40/400]  eta: 0:02:29  lr: 0.000101  loss: 3.0555 (3.0623)  grad: 0.2695 (0.2762)  time: 0.3248  data: 0.0034  max mem: 3953
+train: [14]  [ 60/400]  eta: 0:02:11  lr: 0.000099  loss: 3.0561 (3.0654)  grad: 0.2714 (0.2743)  time: 0.3343  data: 0.0042  max mem: 3953
+train: [14]  [ 80/400]  eta: 0:01:59  lr: 0.000098  loss: 3.0643 (3.0655)  grad: 0.2594 (0.2683)  time: 0.3295  data: 0.0043  max mem: 3953
+train: [14]  [100/400]  eta: 0:01:48  lr: 0.000096  loss: 3.0567 (3.0609)  grad: 0.2523 (0.2688)  time: 0.3211  data: 0.0043  max mem: 3953
+train: [14]  [120/400]  eta: 0:01:39  lr: 0.000095  loss: 3.0530 (3.0590)  grad: 0.2705 (0.2678)  time: 0.3186  data: 0.0043  max mem: 3953
+train: [14]  [140/400]  eta: 0:01:31  lr: 0.000093  loss: 3.0589 (3.0597)  grad: 0.2705 (0.2697)  time: 0.3163  data: 0.0044  max mem: 3953
+train: [14]  [160/400]  eta: 0:01:23  lr: 0.000092  loss: 3.0637 (3.0616)  grad: 0.2721 (0.2701)  time: 0.3179  data: 0.0042  max mem: 3953
+train: [14]  [180/400]  eta: 0:01:15  lr: 0.000090  loss: 3.0557 (3.0607)  grad: 0.2731 (0.2702)  time: 0.3215  data: 0.0042  max mem: 3953
+train: [14]  [200/400]  eta: 0:01:08  lr: 0.000089  loss: 3.0507 (3.0598)  grad: 0.2565 (0.2683)  time: 0.3169  data: 0.0041  max mem: 3953
+train: [14]  [220/400]  eta: 0:01:00  lr: 0.000088  loss: 3.0385 (3.0582)  grad: 0.2626 (0.2689)  time: 0.3186  data: 0.0043  max mem: 3953
+train: [14]  [240/400]  eta: 0:00:54  lr: 0.000086  loss: 3.0442 (3.0585)  grad: 0.2724 (0.2696)  time: 0.3279  data: 0.0042  max mem: 3953
+train: [14]  [260/400]  eta: 0:00:47  lr: 0.000085  loss: 3.0623 (3.0588)  grad: 0.2717 (0.2695)  time: 0.3220  data: 0.0043  max mem: 3953
+train: [14]  [280/400]  eta: 0:00:40  lr: 0.000083  loss: 3.0354 (3.0584)  grad: 0.2609 (0.2687)  time: 0.3178  data: 0.0043  max mem: 3953
+train: [14]  [300/400]  eta: 0:00:33  lr: 0.000082  loss: 3.0407 (3.0580)  grad: 0.2582 (0.2682)  time: 0.3240  data: 0.0042  max mem: 3953
+train: [14]  [320/400]  eta: 0:00:26  lr: 0.000081  loss: 3.0401 (3.0564)  grad: 0.2658 (0.2679)  time: 0.3125  data: 0.0041  max mem: 3953
+train: [14]  [340/400]  eta: 0:00:19  lr: 0.000079  loss: 3.0361 (3.0575)  grad: 0.2641 (0.2680)  time: 0.3221  data: 0.0041  max mem: 3953
+train: [14]  [360/400]  eta: 0:00:13  lr: 0.000078  loss: 3.0367 (3.0573)  grad: 0.2612 (0.2680)  time: 0.3311  data: 0.0041  max mem: 3953
+train: [14]  [380/400]  eta: 0:00:06  lr: 0.000076  loss: 3.0361 (3.0562)  grad: 0.2653 (0.2679)  time: 0.3209  data: 0.0042  max mem: 3953
+train: [14]  [399/400]  eta: 0:00:00  lr: 0.000075  loss: 3.0375 (3.0557)  grad: 0.2607 (0.2675)  time: 0.3197  data: 0.0042  max mem: 3953
+train: [14] Total time: 0:02:12 (0.3316 s / it)
+train: [14] Summary: lr: 0.000075  loss: 3.0375 (3.0557)  grad: 0.2607 (0.2675)
+eval (validation): [14]  [ 0/85]  eta: 0:04:39    time: 3.2901  data: 3.0453  max mem: 3953
+eval (validation): [14]  [20/85]  eta: 0:00:31    time: 0.3509  data: 0.0039  max mem: 3953
+eval (validation): [14]  [40/85]  eta: 0:00:18    time: 0.3090  data: 0.0040  max mem: 3953
+eval (validation): [14]  [60/85]  eta: 0:00:09    time: 0.3025  data: 0.0042  max mem: 3953
+eval (validation): [14]  [80/85]  eta: 0:00:01    time: 0.3010  data: 0.0042  max mem: 3953
+eval (validation): [14]  [84/85]  eta: 0:00:00    time: 0.2882  data: 0.0041  max mem: 3953
+eval (validation): [14] Total time: 0:00:29 (0.3522 s / it)
+cv: [14]  best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00')  loss: 3.128  acc: 0.093  f1: 0.053
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [15]  [  0/400]  eta: 0:22:49  lr: nan  time: 3.4246  data: 3.2149  max mem: 3953
+train: [15]  [ 20/400]  eta: 0:03:03  lr: 0.000074  loss: 3.0550 (3.0518)  grad: 0.2753 (0.2796)  time: 0.3356  data: 0.0033  max mem: 3953
+train: [15]  [ 40/400]  eta: 0:02:29  lr: 0.000072  loss: 3.0550 (3.0453)  grad: 0.2683 (0.2654)  time: 0.3437  data: 0.0036  max mem: 3953
+train: [15]  [ 60/400]  eta: 0:02:11  lr: 0.000071  loss: 3.0181 (3.0363)  grad: 0.2529 (0.2631)  time: 0.3281  data: 0.0039  max mem: 3953
+train: [15]  [ 80/400]  eta: 0:01:58  lr: 0.000070  loss: 3.0040 (3.0320)  grad: 0.2514 (0.2593)  time: 0.3243  data: 0.0043  max mem: 3953
+train: [15]  [100/400]  eta: 0:01:49  lr: 0.000068  loss: 3.0344 (3.0348)  grad: 0.2462 (0.2572)  time: 0.3341  data: 0.0042  max mem: 3953
+train: [15]  [120/400]  eta: 0:01:39  lr: 0.000067  loss: 3.0270 (3.0314)  grad: 0.2687 (0.2608)  time: 0.3199  data: 0.0040  max mem: 3953
+train: [15]  [140/400]  eta: 0:01:31  lr: 0.000066  loss: 3.0069 (3.0304)  grad: 0.2691 (0.2619)  time: 0.3353  data: 0.0043  max mem: 3953
+train: [15]  [160/400]  eta: 0:01:24  lr: 0.000064  loss: 3.0275 (3.0320)  grad: 0.2726 (0.2635)  time: 0.3487  data: 0.0043  max mem: 3953
+train: [15]  [180/400]  eta: 0:01:17  lr: 0.000063  loss: 3.0525 (3.0364)  grad: 0.2636 (0.2642)  time: 0.3312  data: 0.0043  max mem: 3953
+train: [15]  [200/400]  eta: 0:01:09  lr: 0.000062  loss: 3.0550 (3.0378)  grad: 0.2615 (0.2638)  time: 0.3235  data: 0.0043  max mem: 3953
+train: [15]  [220/400]  eta: 0:01:02  lr: 0.000061  loss: 3.0319 (3.0378)  grad: 0.2622 (0.2637)  time: 0.3231  data: 0.0039  max mem: 3953
+train: [15]  [240/400]  eta: 0:00:55  lr: 0.000059  loss: 3.0282 (3.0369)  grad: 0.2639 (0.2638)  time: 0.3373  data: 0.0043  max mem: 3953
+train: [15]  [260/400]  eta: 0:00:48  lr: 0.000058  loss: 3.0406 (3.0387)  grad: 0.2627 (0.2627)  time: 0.3330  data: 0.0041  max mem: 3953
+train: [15]  [280/400]  eta: 0:00:41  lr: 0.000057  loss: 3.0546 (3.0396)  grad: 0.2671 (0.2633)  time: 0.3204  data: 0.0043  max mem: 3953
+train: [15]  [300/400]  eta: 0:00:34  lr: 0.000056  loss: 3.0514 (3.0401)  grad: 0.2698 (0.2635)  time: 0.3168  data: 0.0041  max mem: 3953
+train: [15]  [320/400]  eta: 0:00:27  lr: 0.000054  loss: 3.0461 (3.0401)  grad: 0.2698 (0.2639)  time: 0.3187  data: 0.0043  max mem: 3953
+train: [15]  [340/400]  eta: 0:00:20  lr: 0.000053  loss: 3.0283 (3.0384)  grad: 0.2574 (0.2637)  time: 0.3187  data: 0.0041  max mem: 3953
+train: [15]  [360/400]  eta: 0:00:13  lr: 0.000052  loss: 3.0477 (3.0411)  grad: 0.2541 (0.2631)  time: 0.3194  data: 0.0043  max mem: 3953
+train: [15]  [380/400]  eta: 0:00:06  lr: 0.000051  loss: 3.0452 (3.0406)  grad: 0.2541 (0.2630)  time: 0.3245  data: 0.0041  max mem: 3953
+train: [15]  [399/400]  eta: 0:00:00  lr: 0.000050  loss: 3.0377 (3.0415)  grad: 0.2612 (0.2632)  time: 0.3249  data: 0.0042  max mem: 3953
+train: [15] Total time: 0:02:14 (0.3361 s / it)
+train: [15] Summary: lr: 0.000050  loss: 3.0377 (3.0415)  grad: 0.2612 (0.2632)
+eval (validation): [15]  [ 0/85]  eta: 0:04:37    time: 3.2627  data: 3.0212  max mem: 3953
+eval (validation): [15]  [20/85]  eta: 0:00:30    time: 0.3304  data: 0.0046  max mem: 3953
+eval (validation): [15]  [40/85]  eta: 0:00:17    time: 0.3049  data: 0.0041  max mem: 3953
+eval (validation): [15]  [60/85]  eta: 0:00:09    time: 0.3184  data: 0.0042  max mem: 3953
+eval (validation): [15]  [80/85]  eta: 0:00:01    time: 0.3060  data: 0.0042  max mem: 3953
+eval (validation): [15]  [84/85]  eta: 0:00:00    time: 0.2945  data: 0.0040  max mem: 3953
+eval (validation): [15] Total time: 0:00:29 (0.3513 s / it)
+cv: [15]  best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00')  loss: 3.097  acc: 0.093  f1: 0.052
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [16]  [  0/400]  eta: 0:21:44  lr: nan  time: 3.2614  data: 3.0381  max mem: 3953
+train: [16]  [ 20/400]  eta: 0:03:03  lr: 0.000048  loss: 3.0262 (3.0306)  grad: 0.2378 (0.2489)  time: 0.3451  data: 0.0049  max mem: 3953
+train: [16]  [ 40/400]  eta: 0:02:26  lr: 0.000047  loss: 3.0262 (3.0320)  grad: 0.2503 (0.2575)  time: 0.3238  data: 0.0035  max mem: 3953
+train: [16]  [ 60/400]  eta: 0:02:08  lr: 0.000046  loss: 3.0514 (3.0446)  grad: 0.2693 (0.2614)  time: 0.3205  data: 0.0044  max mem: 3953
+train: [16]  [ 80/400]  eta: 0:01:56  lr: 0.000045  loss: 3.0569 (3.0402)  grad: 0.2553 (0.2602)  time: 0.3199  data: 0.0042  max mem: 3953
+train: [16]  [100/400]  eta: 0:01:46  lr: 0.000044  loss: 3.0476 (3.0437)  grad: 0.2615 (0.2635)  time: 0.3195  data: 0.0042  max mem: 3953
+train: [16]  [120/400]  eta: 0:01:37  lr: 0.000043  loss: 3.0490 (3.0428)  grad: 0.2630 (0.2638)  time: 0.3239  data: 0.0041  max mem: 3953
+train: [16]  [140/400]  eta: 0:01:29  lr: 0.000042  loss: 3.0313 (3.0427)  grad: 0.2615 (0.2636)  time: 0.3154  data: 0.0042  max mem: 3953
+train: [16]  [160/400]  eta: 0:01:22  lr: 0.000041  loss: 3.0313 (3.0440)  grad: 0.2515 (0.2632)  time: 0.3264  data: 0.0041  max mem: 3953
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+train: [16]  [200/400]  eta: 0:01:08  lr: 0.000039  loss: 3.0644 (3.0474)  grad: 0.2524 (0.2623)  time: 0.3392  data: 0.0039  max mem: 3953
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+train: [16]  [280/400]  eta: 0:00:40  lr: 0.000034  loss: 3.0056 (3.0438)  grad: 0.2547 (0.2617)  time: 0.3251  data: 0.0039  max mem: 3953
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+train: [16] Total time: 0:02:14 (0.3365 s / it)
+train: [16] Summary: lr: 0.000029  loss: 3.0172 (3.0399)  grad: 0.2499 (0.2597)
+eval (validation): [16]  [ 0/85]  eta: 0:04:39    time: 3.2860  data: 3.0405  max mem: 3953
+eval (validation): [16]  [20/85]  eta: 0:00:31    time: 0.3411  data: 0.0035  max mem: 3953
+eval (validation): [16]  [40/85]  eta: 0:00:17    time: 0.3060  data: 0.0041  max mem: 3953
+eval (validation): [16]  [60/85]  eta: 0:00:09    time: 0.3075  data: 0.0041  max mem: 3953
+eval (validation): [16]  [80/85]  eta: 0:00:01    time: 0.3052  data: 0.0039  max mem: 3953
+eval (validation): [16]  [84/85]  eta: 0:00:00    time: 0.2980  data: 0.0038  max mem: 3953
+eval (validation): [16] Total time: 0:00:29 (0.3518 s / it)
+cv: [16]  best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00')  loss: 3.100  acc: 0.093  f1: 0.056
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [17]  [  0/400]  eta: 0:22:08  lr: nan  time: 3.3217  data: 3.0692  max mem: 3953
+train: [17]  [ 20/400]  eta: 0:03:13  lr: 0.000028  loss: 3.0487 (3.0370)  grad: 0.2555 (0.2538)  time: 0.3682  data: 0.0040  max mem: 3953
+train: [17]  [ 40/400]  eta: 0:02:29  lr: 0.000027  loss: 3.0487 (3.0420)  grad: 0.2588 (0.2610)  time: 0.3148  data: 0.0038  max mem: 3953
+train: [17]  [ 60/400]  eta: 0:02:10  lr: 0.000026  loss: 3.0582 (3.0491)  grad: 0.2644 (0.2608)  time: 0.3219  data: 0.0043  max mem: 3953
+train: [17]  [ 80/400]  eta: 0:01:57  lr: 0.000025  loss: 3.0262 (3.0413)  grad: 0.2516 (0.2596)  time: 0.3220  data: 0.0044  max mem: 3953
+train: [17]  [100/400]  eta: 0:01:47  lr: 0.000024  loss: 3.0110 (3.0367)  grad: 0.2516 (0.2589)  time: 0.3186  data: 0.0043  max mem: 3953
+train: [17]  [120/400]  eta: 0:01:38  lr: 0.000023  loss: 3.0115 (3.0348)  grad: 0.2576 (0.2584)  time: 0.3132  data: 0.0041  max mem: 3953
+train: [17]  [140/400]  eta: 0:01:30  lr: 0.000023  loss: 3.0275 (3.0345)  grad: 0.2616 (0.2605)  time: 0.3189  data: 0.0041  max mem: 3953
+train: [17]  [160/400]  eta: 0:01:22  lr: 0.000022  loss: 3.0275 (3.0331)  grad: 0.2770 (0.2620)  time: 0.3132  data: 0.0040  max mem: 3953
+train: [17]  [180/400]  eta: 0:01:15  lr: 0.000021  loss: 3.0342 (3.0343)  grad: 0.2742 (0.2629)  time: 0.3413  data: 0.0044  max mem: 3953
+train: [17]  [200/400]  eta: 0:01:08  lr: 0.000020  loss: 3.0315 (3.0338)  grad: 0.2661 (0.2632)  time: 0.3281  data: 0.0042  max mem: 3953
+train: [17]  [220/400]  eta: 0:01:01  lr: 0.000019  loss: 3.0170 (3.0324)  grad: 0.2617 (0.2640)  time: 0.3254  data: 0.0041  max mem: 3953
+train: [17]  [240/400]  eta: 0:00:54  lr: 0.000019  loss: 3.0377 (3.0333)  grad: 0.2480 (0.2633)  time: 0.3196  data: 0.0041  max mem: 3953
+train: [17]  [260/400]  eta: 0:00:47  lr: 0.000018  loss: 3.0272 (3.0339)  grad: 0.2463 (0.2619)  time: 0.3219  data: 0.0038  max mem: 3953
+train: [17]  [280/400]  eta: 0:00:40  lr: 0.000017  loss: 3.0253 (3.0324)  grad: 0.2413 (0.2605)  time: 0.3233  data: 0.0039  max mem: 3953
+train: [17]  [300/400]  eta: 0:00:33  lr: 0.000016  loss: 3.0300 (3.0326)  grad: 0.2485 (0.2606)  time: 0.3259  data: 0.0041  max mem: 3953
+train: [17]  [320/400]  eta: 0:00:26  lr: 0.000016  loss: 3.0335 (3.0312)  grad: 0.2620 (0.2608)  time: 0.3179  data: 0.0042  max mem: 3953
+train: [17]  [340/400]  eta: 0:00:19  lr: 0.000015  loss: 3.0150 (3.0299)  grad: 0.2533 (0.2603)  time: 0.3210  data: 0.0043  max mem: 3953
+train: [17]  [360/400]  eta: 0:00:13  lr: 0.000014  loss: 3.0325 (3.0304)  grad: 0.2556 (0.2604)  time: 0.3362  data: 0.0043  max mem: 3953
+train: [17]  [380/400]  eta: 0:00:06  lr: 0.000014  loss: 3.0386 (3.0309)  grad: 0.2611 (0.2601)  time: 0.3346  data: 0.0043  max mem: 3953
+train: [17]  [399/400]  eta: 0:00:00  lr: 0.000013  loss: 3.0076 (3.0296)  grad: 0.2550 (0.2594)  time: 0.3256  data: 0.0041  max mem: 3953
+train: [17] Total time: 0:02:13 (0.3333 s / it)
+train: [17] Summary: lr: 0.000013  loss: 3.0076 (3.0296)  grad: 0.2550 (0.2594)
+eval (validation): [17]  [ 0/85]  eta: 0:05:35    time: 3.9504  data: 3.7366  max mem: 3953
+eval (validation): [17]  [20/85]  eta: 0:00:33    time: 0.3407  data: 0.0039  max mem: 3953
+eval (validation): [17]  [40/85]  eta: 0:00:18    time: 0.2998  data: 0.0039  max mem: 3953
+eval (validation): [17]  [60/85]  eta: 0:00:09    time: 0.3066  data: 0.0043  max mem: 3953
+eval (validation): [17]  [80/85]  eta: 0:00:01    time: 0.3178  data: 0.0042  max mem: 3953
+eval (validation): [17]  [84/85]  eta: 0:00:00    time: 0.3056  data: 0.0039  max mem: 3953
+eval (validation): [17] Total time: 0:00:30 (0.3621 s / it)
+cv: [17]  best hparam: (19, 1.0) (042) ('042_lr1.9e+01_wd1.0e+00')  loss: 3.096  acc: 0.094  f1: 0.059
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+train: [18]  [  0/400]  eta: 0:21:43  lr: nan  time: 3.2600  data: 3.0175  max mem: 3953
+train: [18]  [ 20/400]  eta: 0:03:14  lr: 0.000012  loss: 2.9799 (3.0068)  grad: 0.2502 (0.2554)  time: 0.3734  data: 0.0060  max mem: 3953
+train: [18]  [ 40/400]  eta: 0:02:31  lr: 0.000012  loss: 3.0316 (3.0317)  grad: 0.2504 (0.2567)  time: 0.3270  data: 0.0035  max mem: 3953
+train: [18]  [ 60/400]  eta: 0:02:11  lr: 0.000011  loss: 3.0320 (3.0332)  grad: 0.2504 (0.2527)  time: 0.3141  data: 0.0042  max mem: 3953
+train: [18]  [ 80/400]  eta: 0:01:59  lr: 0.000011  loss: 3.0248 (3.0298)  grad: 0.2569 (0.2538)  time: 0.3336  data: 0.0039  max mem: 3953
+train: [18]  [100/400]  eta: 0:01:49  lr: 0.000010  loss: 3.0336 (3.0335)  grad: 0.2608 (0.2563)  time: 0.3323  data: 0.0043  max mem: 3953
+train: [18]  [120/400]  eta: 0:01:40  lr: 0.000009  loss: 3.0498 (3.0340)  grad: 0.2590 (0.2539)  time: 0.3236  data: 0.0041  max mem: 3953
+train: [18]  [140/400]  eta: 0:01:31  lr: 0.000009  loss: 3.0120 (3.0298)  grad: 0.2510 (0.2534)  time: 0.3187  data: 0.0041  max mem: 3953
+train: [18]  [160/400]  eta: 0:01:23  lr: 0.000008  loss: 3.0097 (3.0295)  grad: 0.2459 (0.2524)  time: 0.3219  data: 0.0041  max mem: 3953
+train: [18]  [180/400]  eta: 0:01:16  lr: 0.000008  loss: 3.0213 (3.0290)  grad: 0.2422 (0.2524)  time: 0.3224  data: 0.0042  max mem: 3953
+train: [18]  [200/400]  eta: 0:01:08  lr: 0.000007  loss: 3.0489 (3.0314)  grad: 0.2634 (0.2548)  time: 0.3257  data: 0.0043  max mem: 3953
+train: [18]  [220/400]  eta: 0:01:01  lr: 0.000007  loss: 3.0450 (3.0315)  grad: 0.2582 (0.2541)  time: 0.3186  data: 0.0041  max mem: 3953
+train: [18]  [240/400]  eta: 0:00:54  lr: 0.000006  loss: 3.0142 (3.0294)  grad: 0.2404 (0.2531)  time: 0.3232  data: 0.0040  max mem: 3953
+train: [18]  [260/400]  eta: 0:00:47  lr: 0.000006  loss: 3.0010 (3.0272)  grad: 0.2424 (0.2528)  time: 0.3232  data: 0.0043  max mem: 3953
+train: [18]  [280/400]  eta: 0:00:40  lr: 0.000006  loss: 3.0010 (3.0263)  grad: 0.2424 (0.2524)  time: 0.3209  data: 0.0041  max mem: 3953
+train: [18]  [300/400]  eta: 0:00:33  lr: 0.000005  loss: 3.0201 (3.0266)  grad: 0.2373 (0.2520)  time: 0.3160  data: 0.0039  max mem: 3953
+train: [18]  [320/400]  eta: 0:00:26  lr: 0.000005  loss: 3.0335 (3.0275)  grad: 0.2499 (0.2524)  time: 0.3176  data: 0.0042  max mem: 3953
+train: [18]  [340/400]  eta: 0:00:20  lr: 0.000004  loss: 3.0368 (3.0282)  grad: 0.2527 (0.2522)  time: 0.3195  data: 0.0041  max mem: 3953
+train: [18]  [360/400]  eta: 0:00:13  lr: 0.000004  loss: 3.0179 (3.0273)  grad: 0.2538 (0.2530)  time: 0.3404  data: 0.0042  max mem: 3953
+train: [18]  [380/400]  eta: 0:00:06  lr: 0.000004  loss: 3.0170 (3.0277)  grad: 0.2600 (0.2526)  time: 0.3243  data: 0.0041  max mem: 3953
+train: [18]  [399/400]  eta: 0:00:00  lr: 0.000003  loss: 3.0235 (3.0283)  grad: 0.2405 (0.2519)  time: 0.3250  data: 0.0040  max mem: 3953
+train: [18] Total time: 0:02:13 (0.3338 s / it)
+train: [18] Summary: lr: 0.000003  loss: 3.0235 (3.0283)  grad: 0.2405 (0.2519)
+eval (validation): [18]  [ 0/85]  eta: 0:04:37    time: 3.2637  data: 3.0159  max mem: 3953
+eval (validation): [18]  [20/85]  eta: 0:00:31    time: 0.3493  data: 0.0051  max mem: 3953
+eval (validation): [18]  [40/85]  eta: 0:00:18    time: 0.3316  data: 0.0042  max mem: 3953
+eval (validation): [18]  [60/85]  eta: 0:00:09    time: 0.3099  data: 0.0042  max mem: 3953
+eval (validation): [18]  [80/85]  eta: 0:00:01    time: 0.3221  data: 0.0045  max mem: 3953
+eval (validation): [18]  [84/85]  eta: 0:00:00    time: 0.3106  data: 0.0042  max mem: 3953
+eval (validation): [18] Total time: 0:00:30 (0.3638 s / it)
+cv: [18]  best hparam: (22, 1.0) (043) ('043_lr2.2e+01_wd1.0e+00')  loss: 3.099  acc: 0.100  f1: 0.065
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+saving best checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+train: [19]  [  0/400]  eta: 0:23:59  lr: nan  time: 3.5980  data: 3.3451  max mem: 3953
+train: [19]  [ 20/400]  eta: 0:03:31  lr: 0.000003  loss: 3.0311 (3.0427)  grad: 0.2611 (0.2596)  time: 0.4053  data: 0.0045  max mem: 3953
+train: [19]  [ 40/400]  eta: 0:02:40  lr: 0.000003  loss: 3.0311 (3.0341)  grad: 0.2505 (0.2528)  time: 0.3288  data: 0.0040  max mem: 3953
+train: [19]  [ 60/400]  eta: 0:02:16  lr: 0.000002  loss: 3.0366 (3.0365)  grad: 0.2435 (0.2488)  time: 0.3147  data: 0.0036  max mem: 3953
+train: [19]  [ 80/400]  eta: 0:02:03  lr: 0.000002  loss: 3.0209 (3.0268)  grad: 0.2416 (0.2480)  time: 0.3338  data: 0.0036  max mem: 3953
+train: [19]  [100/400]  eta: 0:01:52  lr: 0.000002  loss: 3.0040 (3.0273)  grad: 0.2454 (0.2490)  time: 0.3286  data: 0.0040  max mem: 3953
+train: [19]  [120/400]  eta: 0:01:42  lr: 0.000002  loss: 3.0328 (3.0303)  grad: 0.2576 (0.2483)  time: 0.3240  data: 0.0040  max mem: 3953
+train: [19]  [140/400]  eta: 0:01:34  lr: 0.000001  loss: 3.0328 (3.0307)  grad: 0.2475 (0.2499)  time: 0.3411  data: 0.0041  max mem: 3953
+train: [19]  [160/400]  eta: 0:01:25  lr: 0.000001  loss: 3.0331 (3.0337)  grad: 0.2581 (0.2508)  time: 0.3211  data: 0.0043  max mem: 3953
+train: [19]  [180/400]  eta: 0:01:17  lr: 0.000001  loss: 3.0588 (3.0381)  grad: 0.2581 (0.2506)  time: 0.3219  data: 0.0042  max mem: 3953
+train: [19]  [200/400]  eta: 0:01:10  lr: 0.000001  loss: 3.0361 (3.0374)  grad: 0.2468 (0.2505)  time: 0.3246  data: 0.0042  max mem: 3953
+train: [19]  [220/400]  eta: 0:01:02  lr: 0.000001  loss: 3.0242 (3.0351)  grad: 0.2471 (0.2515)  time: 0.3140  data: 0.0041  max mem: 3953
+train: [19]  [240/400]  eta: 0:00:55  lr: 0.000001  loss: 3.0303 (3.0356)  grad: 0.2556 (0.2518)  time: 0.3185  data: 0.0040  max mem: 3953
+train: [19]  [260/400]  eta: 0:00:48  lr: 0.000000  loss: 3.0231 (3.0324)  grad: 0.2563 (0.2532)  time: 0.3293  data: 0.0040  max mem: 3953
+train: [19]  [280/400]  eta: 0:00:41  lr: 0.000000  loss: 3.0031 (3.0318)  grad: 0.2498 (0.2519)  time: 0.3192  data: 0.0040  max mem: 3953
+train: [19]  [300/400]  eta: 0:00:34  lr: 0.000000  loss: 3.0240 (3.0321)  grad: 0.2500 (0.2525)  time: 0.3150  data: 0.0041  max mem: 3953
+train: [19]  [320/400]  eta: 0:00:27  lr: 0.000000  loss: 3.0182 (3.0316)  grad: 0.2618 (0.2533)  time: 0.3161  data: 0.0041  max mem: 3953
+train: [19]  [340/400]  eta: 0:00:20  lr: 0.000000  loss: 3.0142 (3.0309)  grad: 0.2618 (0.2538)  time: 0.3136  data: 0.0041  max mem: 3953
+train: [19]  [360/400]  eta: 0:00:13  lr: 0.000000  loss: 3.0197 (3.0299)  grad: 0.2602 (0.2541)  time: 0.3204  data: 0.0043  max mem: 3953
+train: [19]  [380/400]  eta: 0:00:06  lr: 0.000000  loss: 3.0197 (3.0300)  grad: 0.2602 (0.2543)  time: 0.3164  data: 0.0041  max mem: 3953
+train: [19]  [399/400]  eta: 0:00:00  lr: 0.000000  loss: 3.0014 (3.0293)  grad: 0.2493 (0.2538)  time: 0.3175  data: 0.0040  max mem: 3953
+train: [19] Total time: 0:02:13 (0.3347 s / it)
+train: [19] Summary: lr: 0.000000  loss: 3.0014 (3.0293)  grad: 0.2493 (0.2538)
+eval (validation): [19]  [ 0/85]  eta: 0:04:37    time: 3.2688  data: 3.0340  max mem: 3953
+eval (validation): [19]  [20/85]  eta: 0:00:31    time: 0.3408  data: 0.0054  max mem: 3953
+eval (validation): [19]  [40/85]  eta: 0:00:17    time: 0.3020  data: 0.0040  max mem: 3953
+eval (validation): [19]  [60/85]  eta: 0:00:09    time: 0.3015  data: 0.0043  max mem: 3953
+eval (validation): [19]  [80/85]  eta: 0:00:01    time: 0.2877  data: 0.0044  max mem: 3953
+eval (validation): [19]  [84/85]  eta: 0:00:00    time: 0.2793  data: 0.0040  max mem: 3953
+eval (validation): [19] Total time: 0:00:29 (0.3437 s / it)
+cv: [19]  best hparam: (19, 1.0) (042) ('042_lr1.9e+01_wd1.0e+00')  loss: 3.090  acc: 0.099  f1: 0.065
+saving checkpoint experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+evaluating last checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-last.pth
+eval model info:
+{"score": 0.0991140642303433, "hparam": [19, 1.0], "hparam_id": 42, "epoch": 19, "is_best": false, "best_score": 0.09985234403839055}
+eval (train): [20]  [  0/509]  eta: 0:26:58    time: 3.1790  data: 2.9339  max mem: 3953
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+eval (train): [20]  [280/509]  eta: 0:01:15    time: 0.3192  data: 0.0036  max mem: 3953
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+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.2874  data: 0.0040  max mem: 3953
+eval (train): [20] Total time: 0:02:46 (0.3265 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:39    time: 3.2912  data: 3.0350  max mem: 3953
+eval (validation): [20]  [20/85]  eta: 0:00:31    time: 0.3445  data: 0.0037  max mem: 3953
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+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.2899  data: 0.0041  max mem: 3953
+eval (validation): [20] Total time: 0:00:30 (0.3538 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:32    time: 3.2030  data: 3.0036  max mem: 3953
+eval (test): [20]  [20/85]  eta: 0:00:31    time: 0.3539  data: 0.0051  max mem: 3953
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+eval (test): [20]  [60/85]  eta: 0:00:09    time: 0.3095  data: 0.0044  max mem: 3953
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.3073  data: 0.0043  max mem: 3953
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.2955  data: 0.0042  max mem: 3953
+eval (test): [20] Total time: 0:00:30 (0.3578 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:18    time: 3.1466  data: 2.9589  max mem: 3953
+eval (testid): [20]  [20/82]  eta: 0:00:27    time: 0.3146  data: 0.0125  max mem: 3953
+eval (testid): [20]  [40/82]  eta: 0:00:15    time: 0.3043  data: 0.0037  max mem: 3953
+eval (testid): [20]  [60/82]  eta: 0:00:07    time: 0.3179  data: 0.0038  max mem: 3953
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+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.2781  data: 0.0040  max mem: 3953
+eval (testid): [20] Total time: 0:00:28 (0.3428 s / it)
+evaluating best checkpoint: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/nsd_cococlip__reg__linear/checkpoint-best.pth
+eval model info:
+{"score": 0.09985234403839055, "hparam": [22, 1.0], "hparam_id": 43, "epoch": 18, "is_best": true, "best_score": 0.09985234403839055}
+eval (train): [20]  [  0/509]  eta: 0:27:25    time: 3.2329  data: 3.0360  max mem: 3953
+eval (train): [20]  [ 20/509]  eta: 0:03:32    time: 0.2938  data: 0.0041  max mem: 3953
+eval (train): [20]  [ 40/509]  eta: 0:03:02    time: 0.3402  data: 0.0048  max mem: 3953
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+eval (train): [20]  [ 80/509]  eta: 0:02:34    time: 0.3105  data: 0.0040  max mem: 3953
+eval (train): [20]  [100/509]  eta: 0:02:25    time: 0.3377  data: 0.0046  max mem: 3953
+eval (train): [20]  [120/509]  eta: 0:02:15    time: 0.3192  data: 0.0040  max mem: 3953
+eval (train): [20]  [140/509]  eta: 0:02:07    time: 0.3307  data: 0.0042  max mem: 3953
+eval (train): [20]  [160/509]  eta: 0:02:00    time: 0.3325  data: 0.0046  max mem: 3953
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+eval (train): [20]  [200/509]  eta: 0:01:44    time: 0.3197  data: 0.0045  max mem: 3953
+eval (train): [20]  [220/509]  eta: 0:01:36    time: 0.3086  data: 0.0044  max mem: 3953
+eval (train): [20]  [240/509]  eta: 0:01:29    time: 0.3188  data: 0.0044  max mem: 3953
+eval (train): [20]  [260/509]  eta: 0:01:22    time: 0.3071  data: 0.0037  max mem: 3953
+eval (train): [20]  [280/509]  eta: 0:01:16    time: 0.3351  data: 0.0042  max mem: 3953
+eval (train): [20]  [300/509]  eta: 0:01:09    time: 0.3198  data: 0.0046  max mem: 3953
+eval (train): [20]  [320/509]  eta: 0:01:02    time: 0.3253  data: 0.0042  max mem: 3953
+eval (train): [20]  [340/509]  eta: 0:00:55    time: 0.3130  data: 0.0045  max mem: 3953
+eval (train): [20]  [360/509]  eta: 0:00:49    time: 0.3144  data: 0.0042  max mem: 3953
+eval (train): [20]  [380/509]  eta: 0:00:42    time: 0.3059  data: 0.0044  max mem: 3953
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+eval (train): [20]  [508/509]  eta: 0:00:00    time: 0.2849  data: 0.0040  max mem: 3953
+eval (train): [20] Total time: 0:02:44 (0.3234 s / it)
+eval (validation): [20]  [ 0/85]  eta: 0:04:31    time: 3.1915  data: 2.9585  max mem: 3953
+eval (validation): [20]  [20/85]  eta: 0:00:28    time: 0.2973  data: 0.0137  max mem: 3953
+eval (validation): [20]  [40/85]  eta: 0:00:16    time: 0.2973  data: 0.0037  max mem: 3953
+eval (validation): [20]  [60/85]  eta: 0:00:08    time: 0.3434  data: 0.0046  max mem: 3953
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+eval (validation): [20]  [84/85]  eta: 0:00:00    time: 0.2892  data: 0.0036  max mem: 3953
+eval (validation): [20] Total time: 0:00:29 (0.3454 s / it)
+eval (test): [20]  [ 0/85]  eta: 0:04:31    time: 3.1987  data: 2.9628  max mem: 3953
+eval (test): [20]  [20/85]  eta: 0:00:30    time: 0.3252  data: 0.0236  max mem: 3953
+eval (test): [20]  [40/85]  eta: 0:00:17    time: 0.3286  data: 0.0040  max mem: 3953
+eval (test): [20]  [60/85]  eta: 0:00:09    time: 0.3236  data: 0.0045  max mem: 3953
+eval (test): [20]  [80/85]  eta: 0:00:01    time: 0.2945  data: 0.0044  max mem: 3953
+eval (test): [20]  [84/85]  eta: 0:00:00    time: 0.2895  data: 0.0040  max mem: 3953
+eval (test): [20] Total time: 0:00:30 (0.3540 s / it)
+eval (testid): [20]  [ 0/82]  eta: 0:04:19    time: 3.1658  data: 2.9621  max mem: 3953
+eval (testid): [20]  [20/82]  eta: 0:00:30    time: 0.3561  data: 0.0055  max mem: 3953
+eval (testid): [20]  [40/82]  eta: 0:00:17    time: 0.3239  data: 0.0044  max mem: 3953
+eval (testid): [20]  [60/82]  eta: 0:00:08    time: 0.3198  data: 0.0040  max mem: 3953
+eval (testid): [20]  [80/82]  eta: 0:00:00    time: 0.2931  data: 0.0039  max mem: 3953
+eval (testid): [20]  [81/82]  eta: 0:00:00    time: 0.2836  data: 0.0037  max mem: 3953
+eval (testid): [20] Total time: 0:00:29 (0.3593 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 | reg    | linear | nsd_cococlip | best   |      18 | 0.0066 | 0.05 |          43 | [22, 1.0] | train      | 2.8795 | 0.16165  | 0.0019355 | 0.12374  | 0.001887  |
+| flat_mae | reg    | linear | nsd_cococlip | best   |      18 | 0.0066 | 0.05 |          43 | [22, 1.0] | validation | 3.0988 | 0.099852 | 0.0036408 | 0.064526 | 0.0030336 |
+| flat_mae | reg    | linear | nsd_cococlip | best   |      18 | 0.0066 | 0.05 |          43 | [22, 1.0] | test       | 3.1099 | 0.095547 | 0.0036145 | 0.054495 | 0.0025466 |
+| flat_mae | reg    | linear | nsd_cococlip | best   |      18 | 0.0066 | 0.05 |          43 | [22, 1.0] | testid     | 3.089  | 0.089069 | 0.0037257 | 0.057595 | 0.002917  |
+
+
+done! total time: 1:05:51

decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null

decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
+model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
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+flat_mae,patch,logistic,ppmi_dx,,0.046415888336127774,test,0.62,0.03990414013608112,0.5386109762020399,0.049260935441646704,0.5478335478335479,0.04253909601080385
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+flat_mae,patch,logistic,ppmi_dx,2,0.005994842503189409,train,0.7313167259786477,0.01566997951500847,0.6862482669377946,0.02004857663549945,0.680033718689788,0.018046528651283807
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+flat_mae,patch,logistic,ppmi_dx,8,0.005994842503189409,test,0.67,0.04009337102315044,0.6239316239316239,0.04716111682626796,0.6218166383701189,0.04337524365732208
+flat_mae,patch,logistic,ppmi_dx,9,0.005994842503189409,train,0.7544483985765125,0.015589029206071355,0.7137098560354375,0.0200892892467773,0.7049079426247057,0.018360676444630974
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+flat_mae,patch,logistic,ppmi_dx,12,0.046415888336127774,train,0.8149466192170819,0.015428380212166654,0.7953272075302555,0.01781647300243942,0.7853511025476343,0.017829764838571526
+flat_mae,patch,logistic,ppmi_dx,12,0.046415888336127774,test,0.64,0.039113394125286555,0.5714285714285714,0.04863305855020712,0.5772495755517827,0.04235885246199127
+flat_mae,patch,logistic,ppmi_dx,13,0.005994842503189409,train,0.7366548042704626,0.016990790428494277,0.6929641934293097,0.021308626702586396,0.68610843502462,0.0193079219330032
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decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic/log.txt

@@ -0,0 +1,247 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:57:17
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (ppmi_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/ppmi_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:19:55    time: 5.1511  data: 4.0234  max mem: 2698
+extract (train)  [ 20/232]  eta: 0:01:39    time: 0.2362  data: 0.0884  max mem: 3005
+extract (train)  [ 40/232]  eta: 0:01:03    time: 0.1834  data: 0.0601  max mem: 3005
+extract (train)  [ 60/232]  eta: 0:00:49    time: 0.1950  data: 0.0687  max mem: 3005
+extract (train)  [ 80/232]  eta: 0:00:39    time: 0.1766  data: 0.0582  max mem: 3005
+extract (train)  [100/232]  eta: 0:00:31    time: 0.1710  data: 0.0571  max mem: 3005
+extract (train)  [120/232]  eta: 0:00:25    time: 0.1669  data: 0.0536  max mem: 3005
+extract (train)  [140/232]  eta: 0:00:20    time: 0.1829  data: 0.0622  max mem: 3005
+extract (train)  [160/232]  eta: 0:00:15    time: 0.1624  data: 0.0513  max mem: 3005
+extract (train)  [180/232]  eta: 0:00:10    time: 0.1627  data: 0.0517  max mem: 3005
+extract (train)  [200/232]  eta: 0:00:06    time: 0.1845  data: 0.0617  max mem: 3005
+extract (train)  [220/232]  eta: 0:00:02    time: 0.1596  data: 0.0502  max mem: 3005
+extract (train)  [231/232]  eta: 0:00:00    time: 0.1556  data: 0.0487  max mem: 3005
+extract (train) Total time: 0:00:46 (0.2020 s / it)
+extract (validation)  [ 0/50]  eta: 0:02:50    time: 3.4117  data: 3.2507  max mem: 3005
+extract (validation)  [20/50]  eta: 0:00:10    time: 0.2098  data: 0.0768  max mem: 3005
+extract (validation)  [40/50]  eta: 0:00:02    time: 0.1451  data: 0.0409  max mem: 3005
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.1478  data: 0.0446  max mem: 3005
+extract (validation) Total time: 0:00:12 (0.2434 s / it)
+extract (test)  [ 0/50]  eta: 0:03:06    time: 3.7350  data: 3.5107  max mem: 3005
+extract (test)  [20/50]  eta: 0:00:11    time: 0.2063  data: 0.0744  max mem: 3005
+extract (test)  [40/50]  eta: 0:00:02    time: 0.1493  data: 0.0430  max mem: 3005
+extract (test)  [49/50]  eta: 0:00:00    time: 0.1511  data: 0.0442  max mem: 3005
+extract (test) Total time: 0:00:12 (0.2516 s / it)
+feature extraction time: 0:01:11
+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   |         | 0.046416 | train   | 0.81673 |  0.015011 | 0.79705 | 0.017475 | 0.78661 |   0.017423 |
+| flat_mae | patch  | logistic | ppmi_dx   |         | 0.046416 | test    | 0.62    |  0.039904 | 0.53861 | 0.049261 | 0.54783 |   0.042539 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.04351608438267395, "f1": 0.6026180458158018, "f1_std": 0.05348800420035311, "bacc": 0.6035653650254669, "bacc_std": 0.0477754169644722}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.005994842503189409, "split": "test", "acc": 0.68, "acc_std": 0.04127054155205622, "f1": 0.6323529411764706, "f1_std": 0.05020928766836231, "bacc": 0.6298811544991512, "bacc_std": 0.046045348457448676}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.041220863649370575, "f1": 0.5636277862955537, "f1_std": 0.051867540337308476, "bacc": 0.5691850594227504, "bacc_std": 0.04517756018602569}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.04456191647584291, "f1": 0.6212121212121212, "f1_std": 0.04968895579944259, "bacc": 0.6188455008488964, "bacc_std": 0.047120091797266166}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.044350760985579496, "f1": 0.6178622120318812, "f1_std": 0.04913234805992817, "bacc": 0.615874363327674, "bacc_std": 0.047545420042052325}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04379440603547444, "f1": 0.5792426367461431, "f1_std": 0.05450481665304654, "bacc": 0.5823429541595926, "bacc_std": 0.04798877377186772}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.0467744545665687, "f1": 0.5755517826825127, "f1_std": 0.04992052189576302, "bacc": 0.5755517826825127, "bacc_std": 0.04998428196306503}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04009337102315044, "f1": 0.6239316239316239, "f1_std": 0.04716111682626796, "bacc": 0.6218166383701189, "bacc_std": 0.04337524365732208}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.72, "acc_std": 0.03803793369782329, "f1": 0.6666666666666667, "f1_std": 0.0502818283119577, "bacc": 0.6621392190152802, "bacc_std": 0.044171427933047375}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.71, "acc_std": 0.03967457120121149, "f1": 0.6640018537828757, "f1_std": 0.048583211883612316, "bacc": 0.6591680814940577, "bacc_std": 0.04415224280363166}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04498655354658766, "f1": 0.6296711929076422, "f1_std": 0.05201982146977735, "bacc": 0.6269100169779287, "bacc_std": 0.04879030120868502}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.039113394125286555, "f1": 0.5714285714285714, "f1_std": 0.04863305855020712, "bacc": 0.5772495755517827, "bacc_std": 0.04235885246199127}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.040240651088172014, "f1": 0.6176572818908586, "f1_std": 0.049134900855981246, "bacc": 0.616723259762309, "bacc_std": 0.04400799319339476}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.03803397954461246, "f1": 0.5783730158730158, "f1_std": 0.053457628358837085, "bacc": 0.5882852292020373, "bacc_std": 0.04310708474937606}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.04060139899067518, "f1": 0.66078697421981, "f1_std": 0.048891224639434454, "bacc": 0.6561969439728353, "bacc_std": 0.04568817696058325}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 16, "C": 1291.5496650148827, "split": "test", "acc": 0.62, "acc_std": 0.046701481775207096, "f1": 0.6006725514922235, "f1_std": 0.0483212366169874, "bacc": 0.6018675721561969, "bacc_std": 0.048812763629875106}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.58, "acc_std": 0.04223967329419109, "f1": 0.5174632352941176, "f1_std": 0.047753999177591426, "bacc": 0.5237691001697793, "bacc_std": 0.043651854987671955}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.59, "acc_std": 0.044603546944161285, "f1": 0.5249681381068243, "f1_std": 0.051248332593987395, "bacc": 0.5318336162988115, "bacc_std": 0.0462666725317313}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 19, "C": 0.3593813663804626, "split": "test", "acc": 0.62, "acc_std": 0.04810260284017903, "f1": 0.6100164203612479, "f1_std": 0.04867103642129283, "bacc": 0.6171477079796265, "bacc_std": 0.04983891001570774}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.03843683129499621, "f1": 0.5952380952380952, "f1_std": 0.04999959043331171, "bacc": 0.5984719864176571, "bacc_std": 0.042835900702690444}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.03962815160968273, "f1": 0.6493688639551192, "f1_std": 0.050336163564792896, "bacc": 0.6460101867572157, "bacc_std": 0.04506881317652539}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.045663372630588735, "f1": 0.5906626839252129, "f1_std": 0.050152792386993626, "bacc": 0.5895585738539898, "bacc_std": 0.04808722834542306}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.045875483648676656, "f1": 0.6296711929076422, "f1_std": 0.05375044665802366, "bacc": 0.6269100169779287, "bacc_std": 0.050451958505389326}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04265056154378275, "f1": 0.5714285714285714, "f1_std": 0.0533558552268493, "bacc": 0.5772495755517827, "bacc_std": 0.04625832482992758}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 25, "C": 0.005994842503189409, "split": "test", "acc": 0.69, "acc_std": 0.03846423793603611, "f1": 0.627359057579036, "f1_std": 0.05157446144813842, "bacc": 0.6277589134125636, "bacc_std": 0.04400182805702498}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 26, "C": 0.3593813663804626, "split": "test", "acc": 0.57, "acc_std": 0.050336225523970296, "f1": 0.5501621508525996, "f1_std": 0.05113078301735352, "bacc": 0.551358234295416, "bacc_std": 0.051528495584043585}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.03938821651204837, "f1": 0.5706048337627285, "f1_std": 0.05343300219223018, "bacc": 0.580220713073005, "bacc_std": 0.04411414299087545}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 28, "C": 0.046415888336127774, "split": "test", "acc": 0.69, "acc_std": 0.040464675953231104, "f1": 0.6467236467236468, "f1_std": 0.0483475483466815, "bacc": 0.6430390492359932, "bacc_std": 0.04461013925169826}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 29, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.044397459386771226, "f1": 0.5989304812834224, "f1_std": 0.05075168320996248, "bacc": 0.597623089983022, "bacc_std": 0.04785729767237496}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 30, "C": 0.005994842503189409, "split": "test", "acc": 0.71, "acc_std": 0.03419999415204628, "f1": 0.6277756385573097, "f1_std": 0.05399901708505434, "bacc": 0.6337011884550086, "bacc_std": 0.04143297574966433}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.04836881640065219, "f1": 0.609375, "f1_std": 0.05143805076340605, "bacc": 0.6078098471986417, "bacc_std": 0.050193191013174515}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 32, "C": 1291.5496650148827, "split": "test", "acc": 0.59, "acc_std": 0.04722323157091221, "f1": 0.5710848415106182, "f1_std": 0.04918003504938041, "bacc": 0.5725806451612903, "bacc_std": 0.04990330326497817}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 33, "C": 0.3593813663804626, "split": "test", "acc": 0.64, "acc_std": 0.04563325103474439, "f1": 0.6216897856242118, "f1_std": 0.047684013984501006, "bacc": 0.6230899830220713, "bacc_std": 0.048240541885276336}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.64, "acc_std": 0.04700503802785399, "f1": 0.6043956043956044, "f1_std": 0.052401693075633096, "bacc": 0.6027164685908319, "bacc_std": 0.050360989978081014}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.046106186135918895, "f1": 0.6178622120318812, "f1_std": 0.05058459272144312, "bacc": 0.615874363327674, "bacc_std": 0.04919154286318017}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 36, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.038, "f1": 0.5706048337627285, "f1_std": 0.05111362213711494, "bacc": 0.580220713073005, "bacc_std": 0.04216550963915673}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 37, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.04354190625133448, "f1": 0.5960257670051315, "f1_std": 0.04884268996584415, "bacc": 0.5946519524617997, "bacc_std": 0.047436959986519756}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 38, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04456586586166592, "f1": 0.5989304812834224, "f1_std": 0.05130434850559296, "bacc": 0.597623089983022, "bacc_std": 0.04865022888800847}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.75, "acc_std": 0.03834790215904908, "f1": 0.7103464256748928, "f1_std": 0.04819102622466781, "bacc": 0.7016129032258065, "bacc_std": 0.04412215109458873}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 40, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.041854887408760276, "f1": 0.6493688639551192, "f1_std": 0.05228315287751872, "bacc": 0.6460101867572157, "bacc_std": 0.04691954014230938}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 41, "C": 0.005994842503189409, "split": "test", "acc": 0.72, "acc_std": 0.04161120522167076, "f1": 0.6923076923076923, "f1_std": 0.0470971605290822, "bacc": 0.6876061120543294, "bacc_std": 0.04578174321899416}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 42, "C": 0.005994842503189409, "split": "test", "acc": 0.68, "acc_std": 0.037831119465329056, "f1": 0.6114618746964546, "f1_std": 0.050545403309114945, "bacc": 0.6146010186757216, "bacc_std": 0.042817880543508574}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 43, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04024005964210291, "f1": 0.6108031607500884, "f1_std": 0.05030351873782526, "bacc": 0.6116298811544991, "bacc_std": 0.0441540627377756}
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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 | ppmi_dx   | train   |        100 | 38.773 |  221.43 | 0.76746 |  0.058837 | 0.7299  | 0.072044 | 0.72276 |   0.072351 |
+| flat_mae | patch  | logistic | ppmi_dx   | test    |        100 | 38.773 |  221.43 | 0.6548  |  0.046089 | 0.60301 | 0.047876 | 0.60406 |   0.044318 |
+
+
+done! total time: 0:05:37

decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (ppmi_dx reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic
+model: flat_mae
+representation: reg
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic
+remote_dir: null

decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,reg,logistic,ppmi_dx,89,0.005994842503189409,train,0.7526690391459074,0.015704479162701002,0.7155570120341543,0.019353638996313403,0.7069417683579533,0.01793740883038409
+flat_mae,reg,logistic,ppmi_dx,89,0.005994842503189409,test,0.72,0.037846315540617695,0.6600291403593979,0.05263354070721799,0.6570458404074703,0.04456218211612602
+flat_mae,reg,logistic,ppmi_dx,90,0.005994842503189409,train,0.7562277580071174,0.015765323777891787,0.7196497168969723,0.019182133163729114,0.7107016698779705,0.01780830083075481
+flat_mae,reg,logistic,ppmi_dx,90,0.005994842503189409,test,0.69,0.040342905200295126,0.6467236467236468,0.04820229273364632,0.6430390492359932,0.044520192177676075
+flat_mae,reg,logistic,ppmi_dx,91,0.005994842503189409,train,0.7544483985765125,0.015792654481217718,0.7128066121578386,0.020205111058870864,0.7040382145150931,0.018330859573538468
+flat_mae,reg,logistic,ppmi_dx,91,0.005994842503189409,test,0.68,0.040294595171064804,0.6190476190476191,0.05265926153525735,0.6196943972835314,0.04544159027943502
+flat_mae,reg,logistic,ppmi_dx,92,0.005994842503189409,train,0.7615658362989324,0.01589253041980216,0.7228715261422516,0.0201534704668844,0.7132974737743524,0.018575618577706603
+flat_mae,reg,logistic,ppmi_dx,92,0.005994842503189409,test,0.72,0.040977340079609854,0.6783088235294117,0.0501138311373395,0.6723259762308998,0.04645936042282219
+flat_mae,reg,logistic,ppmi_dx,93,0.005994842503189409,train,0.7793594306049823,0.01544446430164782,0.7473644523716303,0.018927708541041283,0.7364456219225005,0.017877364549571288
+flat_mae,reg,logistic,ppmi_dx,93,0.005994842503189409,test,0.58,0.04424811408410532,0.5442708333333334,0.04816688835173655,0.5441426146010186,0.04683554669445004
+flat_mae,reg,logistic,ppmi_dx,94,0.046415888336127774,train,0.8665480427046264,0.013309144186620566,0.8549367951184426,0.014806720121217743,0.8463926354099764,0.015191836027644009
+flat_mae,reg,logistic,ppmi_dx,94,0.046415888336127774,test,0.7,0.04194489718666622,0.6657754010695187,0.048520056537520125,0.6612903225806452,0.04617753866710422
+flat_mae,reg,logistic,ppmi_dx,95,0.005994842503189409,train,0.7704626334519573,0.015639870591152686,0.7375571507694312,0.019266854920547857,0.727480732177264,0.01817907610654874
+flat_mae,reg,logistic,ppmi_dx,95,0.005994842503189409,test,0.63,0.039054321143760784,0.5552350042072365,0.04946181806089218,0.5640916808149405,0.04218734848041521
+flat_mae,reg,logistic,ppmi_dx,96,0.005994842503189409,train,0.7740213523131673,0.015914277207741634,0.7377527437601732,0.02047011012612059,0.7268919931492186,0.0189781108204649
+flat_mae,reg,logistic,ppmi_dx,96,0.005994842503189409,test,0.65,0.04260255391405544,0.6072270227808326,0.048595741777500666,0.6056876061120543,0.04584531272881027
+flat_mae,reg,logistic,ppmi_dx,97,0.005994842503189409,train,0.7669039145907474,0.016308457332783525,0.7342501849946758,0.019992435320534987,0.7245905587668593,0.018908327363086765
+flat_mae,reg,logistic,ppmi_dx,97,0.005994842503189409,test,0.67,0.037430527647897234,0.6033177064551027,0.04843023381731555,0.6065365025466893,0.04132224699183524
+flat_mae,reg,logistic,ppmi_dx,98,0.046415888336127774,train,0.8718861209964412,0.013235872020842667,0.8608910891089109,0.014835072678793047,0.8524673517448084,0.015455570416905788
+flat_mae,reg,logistic,ppmi_dx,98,0.046415888336127774,test,0.61,0.04952686139863902,0.568536342515765,0.05517021841674904,0.5683361629881154,0.05258804308028223
+flat_mae,reg,logistic,ppmi_dx,99,0.005994842503189409,train,0.7686832740213523,0.016113782726093425,0.732780313670412,0.02053714221332322,0.7225567330336116,0.01910311215544916
+flat_mae,reg,logistic,ppmi_dx,99,0.005994842503189409,test,0.66,0.03950139238052248,0.6026180458158018,0.04938583523477659,0.6035653650254669,0.0438495772030738
+flat_mae,reg,logistic,ppmi_dx,100,0.046415888336127774,train,0.8576512455516014,0.013602357828654286,0.8444010687044036,0.015349632562783,0.8348185613359024,0.015789739457557014
+flat_mae,reg,logistic,ppmi_dx,100,0.046415888336127774,test,0.61,0.04690949157686534,0.5920075321686369,0.04891873324298145,0.5938030560271647,0.04969080449024414

decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic/log.txt

@@ -0,0 +1,247 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:30:12
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations cross_reg1_pep4; eval v2 (ppmi_dx reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/cross_reg1_pep4/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic
+model: flat_mae
+representation: reg
+dataset: ppmi_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/eval_v2/ppmi_dx__reg__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:19:00    time: 4.9165  data: 4.1681  max mem: 2698
+extract (train)  [ 20/232]  eta: 0:01:35    time: 0.2285  data: 0.0827  max mem: 3005
+extract (train)  [ 40/232]  eta: 0:01:02    time: 0.1933  data: 0.0644  max mem: 3005
+extract (train)  [ 60/232]  eta: 0:00:49    time: 0.2054  data: 0.0729  max mem: 3005
+extract (train)  [ 80/232]  eta: 0:00:39    time: 0.1696  data: 0.0555  max mem: 3005
+extract (train)  [100/232]  eta: 0:00:31    time: 0.1784  data: 0.0595  max mem: 3005
+extract (train)  [120/232]  eta: 0:00:26    time: 0.2094  data: 0.0770  max mem: 3005
+extract (train)  [140/232]  eta: 0:00:21    time: 0.1840  data: 0.0607  max mem: 3005
+extract (train)  [160/232]  eta: 0:00:15    time: 0.1596  data: 0.0511  max mem: 3005
+extract (train)  [180/232]  eta: 0:00:11    time: 0.1822  data: 0.0625  max mem: 3005
+extract (train)  [200/232]  eta: 0:00:06    time: 0.1690  data: 0.0563  max mem: 3005
+extract (train)  [220/232]  eta: 0:00:02    time: 0.1524  data: 0.0457  max mem: 3005
+extract (train)  [231/232]  eta: 0:00:00    time: 0.1433  data: 0.0416  max mem: 3005
+extract (train) Total time: 0:00:47 (0.2043 s / it)
+extract (validation)  [ 0/50]  eta: 0:02:59    time: 3.5822  data: 3.3811  max mem: 3005
+extract (validation)  [20/50]  eta: 0:00:11    time: 0.2100  data: 0.0725  max mem: 3005
+extract (validation)  [40/50]  eta: 0:00:02    time: 0.1440  data: 0.0374  max mem: 3005
+extract (validation)  [49/50]  eta: 0:00:00    time: 0.1434  data: 0.0395  max mem: 3005
+extract (validation) Total time: 0:00:12 (0.2457 s / it)
+extract (test)  [ 0/50]  eta: 0:02:52    time: 3.4564  data: 3.3262  max mem: 3005
+extract (test)  [20/50]  eta: 0:00:10    time: 0.1892  data: 0.0602  max mem: 3005
+extract (test)  [40/50]  eta: 0:00:02    time: 0.1417  data: 0.0373  max mem: 3005
+extract (test)  [49/50]  eta: 0:00:00    time: 0.1416  data: 0.0379  max mem: 3005
+extract (test) Total time: 0:00:11 (0.2333 s / it)
+feature extraction time: 0:01:11
+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 | reg    | logistic | ppmi_dx   |         | 0.35938 | train   | 0.96619 | 0.0073417 | 0.96399 | 0.0079015 | 0.95964 |  0.0088613 |
+| flat_mae | reg    | logistic | ppmi_dx   |         | 0.35938 | test    | 0.66    | 0.044403  | 0.61556 | 0.049959  | 0.61304 |  0.047385  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.62, "acc_std": 0.04952145393665255, "f1": 0.5766488413547237, "f1_std": 0.05570788566604609, "bacc": 0.5764006791171477, "bacc_std": 0.05285197280380837}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.69, "acc_std": 0.042512943911237194, "f1": 0.6570417081535569, "f1_std": 0.04850288626645294, "bacc": 0.6532258064516129, "bacc_std": 0.04650848350253953}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.64, "acc_std": 0.04748983470175486, "f1": 0.625, "f1_std": 0.04898638042359684, "bacc": 0.6281833616298811, "bacc_std": 0.050018224884669185}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.043453883600893486, "f1": 0.6212121212121212, "f1_std": 0.04892343990985682, "bacc": 0.6188455008488964, "bacc_std": 0.04627842002989132}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.61, "acc_std": 0.04642335188243089, "f1": 0.584, "f1_std": 0.049116337642184524, "bacc": 0.583616298811545, "bacc_std": 0.04889873994606864}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 1291.5496650148827, "split": "test", "acc": 0.6, "acc_std": 0.045008172591208366, "f1": 0.5755517826825127, "f1_std": 0.04767560151533296, "bacc": 0.5755517826825127, "bacc_std": 0.04774967770780004}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.59, "acc_std": 0.05198761006239852, "f1": 0.5626666666666666, "f1_std": 0.053903271421685335, "bacc": 0.5623938879456706, "bacc_std": 0.05356077009907835}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.038251880999501185, "f1": 0.6033177064551027, "f1_std": 0.05010412317671512, "bacc": 0.6065365025466893, "bacc_std": 0.04270222918510206}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.041802100425696306, "f1": 0.66078697421981, "f1_std": 0.04937492808436804, "bacc": 0.6561969439728353, "bacc_std": 0.04633498010277153}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.68, "acc_std": 0.03980280894610328, "f1": 0.6323529411764706, "f1_std": 0.04734298374316082, "bacc": 0.6298811544991512, "bacc_std": 0.04347786473163675}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.7, "acc_std": 0.0386621209971724, "f1": 0.6428571428571428, "f1_std": 0.05098502038705891, "bacc": 0.6409168081494058, "bacc_std": 0.04405285057483844}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.63, "acc_std": 0.039616415789417395, "f1": 0.5552350042072365, "f1_std": 0.04976977811400029, "bacc": 0.5640916808149405, "bacc_std": 0.04279524994480864}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04043837781118327, "f1": 0.6176572818908586, "f1_std": 0.049505278046492046, "bacc": 0.616723259762309, "bacc_std": 0.044440226175004795}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.041426677395127884, "f1": 0.587178241864983, "f1_std": 0.053508759064354525, "bacc": 0.5933786078098472, "bacc_std": 0.04533967367777042}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 15, "C": 2.782559402207126, "split": "test", "acc": 0.64, "acc_std": 0.04920162598939186, "f1": 0.6138996138996139, "f1_std": 0.052867099398570455, "bacc": 0.6129032258064516, "bacc_std": 0.05222047395292094}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.59, "acc_std": 0.047230206436135756, "f1": 0.5670995670995671, "f1_std": 0.04971475761902281, "bacc": 0.5674872665534805, "bacc_std": 0.05015254481354647}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.041532753339984574, "f1": 0.5944849959448499, "f1_std": 0.050738435677190157, "bacc": 0.5955008488964346, "bacc_std": 0.04557578939969868}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04625753992594072, "f1": 0.5481404240528328, "f1_std": 0.0544008819393325, "bacc": 0.5530560271646858, "bacc_std": 0.049157151499731516}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.04713444175971536, "f1": 0.6338529134846741, "f1_std": 0.048887575334434705, "bacc": 0.6362478777589134, "bacc_std": 0.049438984069135515}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.69, "acc_std": 0.0388910015299169, "f1": 0.6408295678368672, "f1_std": 0.04784690609885569, "bacc": 0.6379456706281834, "bacc_std": 0.0431162133886556}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.04952686139863902, "f1": 0.568536342515765, "f1_std": 0.05517021841674904, "bacc": 0.5683361629881154, "bacc_std": 0.05258804308028223}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.04690949157686534, "f1": 0.5920075321686369, "f1_std": 0.04891873324298145, "bacc": 0.5938030560271647, "bacc_std": 0.04969080449024414}
+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 | reg    | logistic | ppmi_dx   | train   |        100 | 13.997 |  129.12 | 0.84418 |  0.088309 | 0.82322 | 0.10287  | 0.81555 |   0.10595  |
+| flat_mae | reg    | logistic | ppmi_dx   | test    |        100 | 13.997 |  129.12 | 0.6449  |  0.046308 | 0.60341 | 0.047062 | 0.60392 |   0.044913 |
+
+
+done! total time: 0:05:12

decoders/cross_reg1_pep4/pretrain/config.yaml

@@ -0,0 +1,100 @@
+name: decoders/cross_reg1_pep4/pretrain
+notes: decoder ablations cross_reg1_pep4 (model_kwargs.decoding=cross model_kwargs.reg_tokens=1
+  model_kwargs.pred_edge_pad=4)
+output_dir: experiments/decoders/output/decoders/cross_reg1_pep4/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: cross
+  pos_embed: sep
+  target_norm: null
+  t_pred_stride: 2
+  no_decode_pos: true
+  mask_drop_scale: false
+  pred_edge_pad: 4
+  class_token: false
+  reg_tokens: 1
+  no_embed_class: true
+  head_init_scale: 0.0
+  decoder_depth: 4
+  drop_path_rate: 0.0
+datasets:
+  hcp-train:
+    type: wds
+    url: /data/fmri-datasets/pretrain/hcpya-all.${input_space}.wds/hcpya-all-${input_space}-{00000..01799}.tar
+    clipping: random
+    clipping_kwargs:
+      oversample: 4.0
+    shuffle: true
+    buffer_size: 2000
+    samples_per_epoch: 200000
+  hcp-train-subset:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/validation
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+  hcp-val:
+    type: arrow
+    root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/test
+    split_range:
+    - 0
+    - 2000
+    shuffle: false
+train_dataset: hcp-train
+eval_datasets:
+- hcp-train-subset
+- hcp-val
+clip_vmax: 3.0
+normalize: frame
+tr_scale: null
+crop_scale: null
+crop_aspect: null
+gray_jitter: null
+gauss_sigma: 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: 5
+checkpoint_period: 20
+plot_period: 5
+device: cuda
+presend_cuda: false
+seed: 7338
+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

decoders/cross_reg1_pep4/pretrain/log.json

@@ -0,0 +1,100 @@
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decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,aabc_age,93,9.999999999999999e-05,train,0.5,0.019017133269836322,0.4765318086088405,0.02048457997251009,0.4992221498727438,0.01896619575843789
+flat_mae,patch,logistic,aabc_age,93,9.999999999999999e-05,test,0.46153846153846156,0.06431427091856816,0.4337973137973138,0.0676537617291445,0.45650183150183155,0.06380020582851925
+flat_mae,patch,logistic,aabc_age,94,9.999999999999999e-05,train,0.49015748031496065,0.02021644917906294,0.461382398795998,0.021101139468473702,0.48872399014938944,0.020046508406245563
+flat_mae,patch,logistic,aabc_age,94,9.999999999999999e-05,test,0.38461538461538464,0.0635177373540642,0.37503498199593277,0.06300010508328333,0.38095238095238093,0.06294466935086762
+flat_mae,patch,logistic,aabc_age,95,9.999999999999999e-05,train,0.5019685039370079,0.019783947970746243,0.47892506592931605,0.02074836496384494,0.5001504394991072,0.019697599851269388
+flat_mae,patch,logistic,aabc_age,95,9.999999999999999e-05,test,0.4230769230769231,0.05973580889136202,0.3759090909090909,0.05041904384039932,0.41483516483516486,0.058297762571809915
+flat_mae,patch,logistic,aabc_age,96,9.999999999999999e-05,train,0.4881889763779528,0.019857706895738143,0.46173097409534436,0.020599866937339086,0.48675784778735265,0.0196439682578789
+flat_mae,patch,logistic,aabc_age,96,9.999999999999999e-05,test,0.6153846153846154,0.05806260648807758,0.5907631421744325,0.06367602471177458,0.6078296703296704,0.05787854679329393
+flat_mae,patch,logistic,aabc_age,97,9.999999999999999e-05,train,0.5019685039370079,0.020715688083659336,0.47626607011487054,0.02196332392134213,0.5001004528288859,0.020581173856232423
+flat_mae,patch,logistic,aabc_age,97,9.999999999999999e-05,test,0.40384615384615385,0.062484211023417224,0.37820263975155277,0.06279048925561488,0.3985805860805861,0.0616452158902816
+flat_mae,patch,logistic,aabc_age,98,0.000774263682681127,train,0.59251968503937,0.020354141853248974,0.5901283766852268,0.020746763873422978,0.593136554472764,0.020317126260022418
+flat_mae,patch,logistic,aabc_age,98,0.000774263682681127,test,0.38461538461538464,0.06045286441987714,0.35666226498145004,0.05915458872101264,0.37934981684981683,0.05961772116045624
+flat_mae,patch,logistic,aabc_age,99,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_age,99,2.782559402207126,test,0.3269230769230769,0.06551763618208134,0.33327228327228325,0.06611447651334895,0.32623626373626374,0.06591228243857586
+flat_mae,patch,logistic,aabc_age,100,0.000774263682681127,train,0.6003937007874016,0.019601442968137147,0.594163505416654,0.020158759322085412,0.6017038142346693,0.019571337739303564
+flat_mae,patch,logistic,aabc_age,100,0.000774263682681127,test,0.36538461538461536,0.06169848185103013,0.35137788248732776,0.05689596287436201,0.3587454212454213,0.06058495752122709

decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:45:45
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_age patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_age
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_age__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:21:34    time: 5.6778  data: 4.5316  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:46    time: 0.2526  data: 0.0877  max mem: 3581
+extract (train)  [ 40/228]  eta: 0:01:10    time: 0.2335  data: 0.0822  max mem: 3581
+extract (train)  [ 60/228]  eta: 0:00:55    time: 0.2437  data: 0.0870  max mem: 3581
+extract (train)  [ 80/228]  eta: 0:00:45    time: 0.2180  data: 0.0731  max mem: 3581
+extract (train)  [100/228]  eta: 0:00:36    time: 0.2215  data: 0.0751  max mem: 3581
+extract (train)  [120/228]  eta: 0:00:29    time: 0.2254  data: 0.0774  max mem: 3581
+extract (train)  [140/228]  eta: 0:00:23    time: 0.2377  data: 0.0859  max mem: 3581
+extract (train)  [160/228]  eta: 0:00:18    time: 0.2275  data: 0.0786  max mem: 3581
+extract (train)  [180/228]  eta: 0:00:12    time: 0.1966  data: 0.0613  max mem: 3581
+extract (train)  [200/228]  eta: 0:00:07    time: 0.2271  data: 0.0776  max mem: 3581
+extract (train)  [220/228]  eta: 0:00:01    time: 0.1899  data: 0.0584  max mem: 3581
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1844  data: 0.0567  max mem: 3581
+extract (train) Total time: 0:00:56 (0.2497 s / it)
+extract (validation)  [ 0/27]  eta: 0:02:12    time: 4.8906  data: 4.6724  max mem: 3581
+extract (validation)  [20/27]  eta: 0:00:03    time: 0.2086  data: 0.0698  max mem: 3581
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1938  data: 0.0604  max mem: 3581
+extract (validation) Total time: 0:00:10 (0.3879 s / it)
+extract (test)  [ 0/26]  eta: 0:02:03    time: 4.7611  data: 4.6206  max mem: 3581
+extract (test)  [20/26]  eta: 0:00:02    time: 0.1971  data: 0.0636  max mem: 3581
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1957  data: 0.0629  max mem: 3581
+extract (test) Total time: 0:00:10 (0.3885 s / it)
+feature extraction time: 0:01:17
+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   | 0.50591 |  0.021344 | 0.48914 | 0.021988 | 0.50443 |   0.021175 |
+| flat_mae | patch  | logistic | aabc_age  |         | 0.0001 | test    | 0.36538 |  0.062272 | 0.33051 | 0.054681 | 0.3544  |   0.060802 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.06020974188411274, "f1": 0.36151368760064406, "f1_std": 0.05963039172932883, "bacc": 0.3660714285714286, "bacc_std": 0.06022285640664828}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06038147958540828, "f1": 0.4575892857142857, "f1_std": 0.061274292757061616, "bacc": 0.47435897435897434, "bacc_std": 0.05956821850009557}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 9.999999999999999e-05, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06409331215284396, "f1": 0.37353479853479854, "f1_std": 0.0673107152909018, "bacc": 0.37957875457875456, "bacc_std": 0.06377240653703946}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06242150099911286, "f1": 0.504235347985348, "f1_std": 0.06603969495289239, "bacc": 0.5455586080586081, "bacc_std": 0.061572755976112864}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.000774263682681127, "split": "test", "acc": 0.3076923076923077, "acc_std": 0.06264508603820197, "f1": 0.28638953638953635, "f1_std": 0.054221630014512946, "bacc": 0.30219780219780223, "bacc_std": 0.061249028626816075}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.000774263682681127, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06841937984563685, "f1": 0.5584160463192721, "f1_std": 0.0690016123457342, "bacc": 0.5560897435897436, "bacc_std": 0.06847088136652654}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.054170670463467026, "f1": 0.4250356125356125, "f1_std": 0.05784875003385051, "bacc": 0.47687728937728935, "bacc_std": 0.05355369820825996}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.05390484438824316, "f1": 0.34088669950738915, "f1_std": 0.04759603946916128, "bacc": 0.3983516483516484, "bacc_std": 0.05260278091581552}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.05965884272086494, "f1": 0.42133995037220845, "f1_std": 0.04847572869434771, "bacc": 0.47115384615384615, "bacc_std": 0.05795642551766949}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 9.999999999999999e-05, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.06131762056469557, "f1": 0.3408333333333333, "f1_std": 0.05880383646397843, "bacc": 0.36149267399267404, "bacc_std": 0.06036463418924889}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 166.81005372000556, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.0699162114764447, "f1": 0.3760143304970891, "f1_std": 0.07097004128295395, "bacc": 0.38278388278388276, "bacc_std": 0.06972501192447234}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06184456322103019, "f1": 0.39352779889864287, "f1_std": 0.06223769235434818, "bacc": 0.39880952380952384, "bacc_std": 0.06113074764501607}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.05722129522493295, "f1": 0.3981950844854071, "f1_std": 0.061121435954466063, "bacc": 0.42216117216117216, "bacc_std": 0.05728642623495482}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.061336821088981905, "f1": 0.4562745098039216, "f1_std": 0.062780746046052, "bacc": 0.47435897435897434, "bacc_std": 0.060551393184067076}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 0.000774263682681127, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.052893705512473195, "f1": 0.34926108374384235, "f1_std": 0.04317938774851884, "bacc": 0.3804945054945055, "bacc_std": 0.051827771347068445}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 9.999999999999999e-05, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.06338452203129795, "f1": 0.32134149375528687, "f1_std": 0.06428239021428947, "bacc": 0.3232600732600733, "bacc_std": 0.0628498815278943}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06004646869189614, "f1": 0.40844941348973607, "f1_std": 0.059440752775240704, "bacc": 0.4356684981684982, "bacc_std": 0.058944310174277204}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 18, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.058533091333578896, "f1": 0.4188834154351395, "f1_std": 0.0496187623151034, "bacc": 0.4697802197802198, "bacc_std": 0.056966769317070676}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06674568665007899, "f1": 0.40408026755852844, "f1_std": 0.0672549848198668, "bacc": 0.402014652014652, "bacc_std": 0.06667006335563559}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.0602174928878988, "f1": 0.455106100795756, "f1_std": 0.06060392674268266, "bacc": 0.46543040293040294, "bacc_std": 0.060674741015453296}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 21, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.05878256997226626, "f1": 0.39615199615199614, "f1_std": 0.061542344293316426, "bacc": 0.4384157509157509, "bacc_std": 0.058295852552716354}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 22, "C": 0.000774263682681127, "split": "test", "acc": 0.28846153846153844, "acc_std": 0.05814356848537092, "f1": 0.275093808630394, "f1_std": 0.053500790776044686, "bacc": 0.2802197802197802, "bacc_std": 0.05676531130509463}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.058923227555541545, "f1": 0.4767914653784219, "f1_std": 0.05659616541262062, "bacc": 0.4800824175824176, "bacc_std": 0.05884347283200148}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 24, "C": 0.000774263682681127, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06608532900772433, "f1": 0.4182027649769585, "f1_std": 0.06519898500470542, "bacc": 0.4196428571428571, "bacc_std": 0.06576807025158533}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 25, "C": 0.000774263682681127, "split": "test", "acc": 0.3076923076923077, "acc_std": 0.054440062584471594, "f1": 0.2731667664926842, "f1_std": 0.0464700383412188, "bacc": 0.30494505494505497, "bacc_std": 0.05363858850257645}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 81, "C": 0.000774263682681127, "split": "test", "acc": 0.28846153846153844, "acc_std": 0.05684884713095195, "f1": 0.2859694989106754, "f1_std": 0.05507328104685769, "bacc": 0.2859432234432234, "bacc_std": 0.0566416506303228}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 82, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06299331936025969, "f1": 0.46875, "f1_std": 0.06531918619849714, "bacc": 0.4773351648351648, "bacc_std": 0.06257073335785185}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.062484211023417224, "f1": 0.37820263975155277, "f1_std": 0.06279048925561488, "bacc": 0.3985805860805861, "bacc_std": 0.0616452158902816}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 0.000774263682681127, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06045286441987714, "f1": 0.35666226498145004, "f1_std": 0.05915458872101264, "bacc": 0.37934981684981683, "bacc_std": 0.05961772116045624}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 2.782559402207126, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.06551763618208134, "f1": 0.33327228327228325, "f1_std": 0.06611447651334895, "bacc": 0.32623626373626374, "bacc_std": 0.06591228243857586}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.000774263682681127, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.06169848185103013, "f1": 0.35137788248732776, "f1_std": 0.05689596287436201, "bacc": 0.3587454212454213, "bacc_std": 0.06058495752122709}
+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 | 1.7008 |   16.68 | 0.56567 |  0.11358  | 0.54941 | 0.12252  | 0.56494 |   0.11422  |
+| flat_mae | patch  | logistic | aabc_age  | test    |        100 | 1.7008 |   16.68 | 0.42154 |  0.066206 | 0.39173 | 0.060595 | 0.41686 |   0.065259 |
+
+
+done! total time: 0:05:37

decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_age reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic
+model: flat_mae
+representation: reg
+dataset: aabc_age
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:18:44
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_age reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic
+model: flat_mae
+representation: reg
+dataset: aabc_age
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_age__reg__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:19:24    time: 5.1085  data: 4.1793  max mem: 3205
+extract (train)  [ 20/228]  eta: 0:01:37    time: 0.2384  data: 0.0839  max mem: 3581
+extract (train)  [ 40/228]  eta: 0:01:05    time: 0.2208  data: 0.0729  max mem: 3581
+extract (train)  [ 60/228]  eta: 0:00:51    time: 0.2175  data: 0.0730  max mem: 3581
+extract (train)  [ 80/228]  eta: 0:00:41    time: 0.2134  data: 0.0711  max mem: 3581
+extract (train)  [100/228]  eta: 0:00:34    time: 0.2353  data: 0.0840  max mem: 3581
+extract (train)  [120/228]  eta: 0:00:28    time: 0.1880  data: 0.0554  max mem: 3581
+extract (train)  [140/228]  eta: 0:00:22    time: 0.2039  data: 0.0640  max mem: 3581
+extract (train)  [160/228]  eta: 0:00:16    time: 0.2151  data: 0.0717  max mem: 3581
+extract (train)  [180/228]  eta: 0:00:11    time: 0.2110  data: 0.0689  max mem: 3581
+extract (train)  [200/228]  eta: 0:00:06    time: 0.2057  data: 0.0668  max mem: 3581
+extract (train)  [220/228]  eta: 0:00:01    time: 0.1830  data: 0.0553  max mem: 3581
+extract (train)  [227/228]  eta: 0:00:00    time: 0.1808  data: 0.0547  max mem: 3581
+extract (train) Total time: 0:00:53 (0.2346 s / it)
+extract (validation)  [ 0/27]  eta: 0:01:54    time: 4.2408  data: 4.0964  max mem: 3581
+extract (validation)  [20/27]  eta: 0:00:02    time: 0.1763  data: 0.0460  max mem: 3581
+extract (validation)  [26/27]  eta: 0:00:00    time: 0.1568  data: 0.0395  max mem: 3581
+extract (validation) Total time: 0:00:09 (0.3341 s / it)
+extract (test)  [ 0/26]  eta: 0:01:45    time: 4.0684  data: 3.9339  max mem: 3581
+extract (test)  [20/26]  eta: 0:00:02    time: 0.1688  data: 0.0442  max mem: 3581
+extract (test)  [25/26]  eta: 0:00:00    time: 0.1661  data: 0.0432  max mem: 3581
+extract (test) Total time: 0:00:08 (0.3319 s / it)
+feature extraction time: 0:01:11
+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 | reg    | logistic | aabc_age  |         | 0.0001 | train   | 0.51575 |  0.020912 | 0.48957 | 0.021631 | 0.51192 |   0.020721 |
+| flat_mae | reg    | logistic | aabc_age  |         | 0.0001 | test    | 0.44231 |  0.063802 | 0.41697 | 0.062184 | 0.43155 |   0.06282  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.05873029066350786, "f1": 0.43643999678482437, "f1_std": 0.05828819657079274, "bacc": 0.44024725274725274, "bacc_std": 0.0584780500906276}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 9.999999999999999e-05, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.05824067591778916, "f1": 0.41863799283154124, "f1_std": 0.057254474265564265, "bacc": 0.4535256410256411, "bacc_std": 0.0572159119192848}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06545249869790165, "f1": 0.440625, "f1_std": 0.06632365428479227, "bacc": 0.4565018315018315, "bacc_std": 0.06503433857361934}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5961538461538461, "acc_std": 0.061863695847446144, "f1": 0.5552380952380952, "f1_std": 0.06898757825502741, "bacc": 0.5842490842490843, "bacc_std": 0.06166350954850679}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 9.999999999999999e-05, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.06028880198652495, "f1": 0.2944966910484152, "f1_std": 0.05135145789086585, "bacc": 0.3228021978021978, "bacc_std": 0.05902837260837487}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06923370720261737, "f1": 0.48001567398119127, "f1_std": 0.06889160966891524, "bacc": 0.4819139194139194, "bacc_std": 0.06943891662091849}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.05744834314562531, "f1": 0.45519303166752784, "f1_std": 0.06173118410257922, "bacc": 0.48443223443223443, "bacc_std": 0.05844143930161434}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.05725351078354427, "f1": 0.36, "f1_std": 0.05944952131467443, "bacc": 0.40132783882783885, "bacc_std": 0.05669684359754293}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06043054353318342, "f1": 0.42024886877828055, "f1_std": 0.04884771478425988, "bacc": 0.47115384615384615, "bacc_std": 0.058697604119079094}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 9.999999999999999e-05, "split": "test", "acc": 0.34615384615384615, "acc_std": 0.06163179220703295, "f1": 0.32975928641251223, "f1_std": 0.058605699560590356, "bacc": 0.34226190476190477, "bacc_std": 0.06070471438901633}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 166.81005372000556, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06940742418894667, "f1": 0.3839673913043478, "f1_std": 0.07042894282016683, "bacc": 0.39010989010989017, "bacc_std": 0.0697280947516965}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 0.000774263682681127, "split": "test", "acc": 0.34615384615384615, "acc_std": 0.061902899244795015, "f1": 0.3459404789583818, "f1_std": 0.06007309199734729, "bacc": 0.3424908424908425, "bacc_std": 0.06129882410799714}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 0.000774263682681127, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06860161771170357, "f1": 0.4407042510490786, "f1_std": 0.06923804588563694, "bacc": 0.44184981684981683, "bacc_std": 0.0687533422662764}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.000774263682681127, "split": "test", "acc": 0.5, "acc_std": 0.061560765188965014, "f1": 0.48825194413429707, "f1_std": 0.06416681918062424, "bacc": 0.4951923076923077, "bacc_std": 0.061137949062441806}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.05676042249781248, "f1": 0.4909366096866097, "f1_std": 0.05455272714285594, "bacc": 0.5112179487179487, "bacc_std": 0.0557181604747863}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 0.000774263682681127, "split": "test", "acc": 0.34615384615384615, "acc_std": 0.06504099753960378, "f1": 0.3514726507713885, "f1_std": 0.0657213112539017, "bacc": 0.34706959706959706, "bacc_std": 0.06536830865463213}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.0576868715387565, "f1": 0.40617668621700875, "f1_std": 0.05805627398985904, "bacc": 0.4342948717948718, "bacc_std": 0.05672475644934179}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.2692307692307692, "acc_std": 0.0625174413533758, "f1": 0.29247541407867494, "f1_std": 0.0603808311469473, "bacc": 0.2667124542124542, "bacc_std": 0.062075003621818614}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 19, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.0561100329603946, "f1": 0.33871090770404266, "f1_std": 0.05060397109457702, "bacc": 0.39148351648351654, "bacc_std": 0.05484926230675062}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 20, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06060224135132903, "f1": 0.45755469039673713, "f1_std": 0.06359656053297755, "bacc": 0.48008241758241754, "bacc_std": 0.060304122119134614}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 21, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.061931246474948555, "f1": 0.43495997536945813, "f1_std": 0.06409130122195261, "bacc": 0.4741300366300366, "bacc_std": 0.06111514141358766}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.2692307692307692, "acc_std": 0.05644911831534556, "f1": 0.26159951159951156, "f1_std": 0.05313445864676363, "bacc": 0.26694139194139194, "bacc_std": 0.05611395992712976}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 23, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.05639568576390454, "f1": 0.39083034647550774, "f1_std": 0.04825232054203755, "bacc": 0.43543956043956045, "bacc_std": 0.05494391283153634}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 24, "C": 0.000774263682681127, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.07176848874214477, "f1": 0.41646965937288516, "f1_std": 0.07172412031136939, "bacc": 0.41826923076923084, "bacc_std": 0.07156494237054259}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 25, "C": 0.000774263682681127, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.05773448458295243, "f1": 0.290927750410509, "f1_std": 0.049403509268661924, "bacc": 0.32280219780219777, "bacc_std": 0.05666235071534126}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 26, "C": 0.000774263682681127, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.0639227529381774, "f1": 0.424477969348659, "f1_std": 0.06507852501687421, "bacc": 0.43727106227106227, "bacc_std": 0.06370927738945657}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 27, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06206814111935233, "f1": 0.36948051948051946, "f1_std": 0.0631092830099514, "bacc": 0.394459706959707, "bacc_std": 0.06106723064500022}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 31, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.05706649033825917, "f1": 0.44523326572008115, "f1_std": 0.047486149632700536, "bacc": 0.5082417582417582, "bacc_std": 0.05548867448421321}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 32, "C": 0.000774263682681127, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.05671921655512547, "f1": 0.4266263997188009, "f1_std": 0.058568327838253255, "bacc": 0.46062271062271065, "bacc_std": 0.056534953296357965}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 36, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06220865823792789, "f1": 0.44078144078144077, "f1_std": 0.06330387055873683, "bacc": 0.4684065934065934, "bacc_std": 0.06289075659686603}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06749204423858385, "f1": 0.4148654244306418, "f1_std": 0.06626575676172156, "bacc": 0.40201465201465203, "bacc_std": 0.06745040248682672}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 40, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5, "acc_std": 0.05701660715198915, "f1": 0.43730842212815646, "f1_std": 0.048753660241390495, "bacc": 0.4903846153846154, "bacc_std": 0.05532419219473303}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 41, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.059388949622620084, "f1": 0.4573750749737592, "f1_std": 0.0639429277866896, "bacc": 0.48008241758241754, "bacc_std": 0.05938537399194924}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 42, "C": 9.999999999999999e-05, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.0561038238737293, "f1": 0.41767857142857145, "f1_std": 0.058514930425701224, "bacc": 0.45627289377289376, "bacc_std": 0.05530129927591583}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 43, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5, "acc_std": 0.06227220024278026, "f1": 0.4546703296703297, "f1_std": 0.0630136255476935, "bacc": 0.4892399267399268, "bacc_std": 0.0613985329199036}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 44, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.05969131640731229, "f1": 0.3873376623376623, "f1_std": 0.06032793468129017, "bacc": 0.41369047619047616, "bacc_std": 0.05867688301666563}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 45, "C": 0.000774263682681127, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06735689410470136, "f1": 0.42083850931677025, "f1_std": 0.06713024245708535, "bacc": 0.423992673992674, "bacc_std": 0.0675712312803574}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 46, "C": 0.005994842503189409, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.05972570437687286, "f1": 0.3847402597402597, "f1_std": 0.060033309196120564, "bacc": 0.3839285714285714, "bacc_std": 0.059746503854348064}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 47, "C": 9.999999999999999e-05, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06010119671149524, "f1": 0.33241221144446953, "f1_std": 0.05305388585555245, "bacc": 0.375, "bacc_std": 0.058577351294779374}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 48, "C": 0.005994842503189409, "split": "test", "acc": 0.34615384615384615, "acc_std": 0.06403441890681705, "f1": 0.34383713061132415, "f1_std": 0.06458136639580034, "bacc": 0.3427197802197802, "bacc_std": 0.06383822804359368}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 91, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.05836195972716532, "f1": 0.36210628452007765, "f1_std": 0.05889336248127792, "bacc": 0.3999542124542125, "bacc_std": 0.05747799508149825}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 92, "C": 0.046415888336127774, "split": "test", "acc": 0.36538461538461536, "acc_std": 0.06046266391592957, "f1": 0.3575135509168493, "f1_std": 0.06009931559223221, "bacc": 0.36904761904761907, "bacc_std": 0.0611849748234283}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 93, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06178406517913152, "f1": 0.36713381277275803, "f1_std": 0.062100202971111476, "bacc": 0.3985805860805861, "bacc_std": 0.06094421165326057}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 94, "C": 9.999999999999999e-05, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06356751058808945, "f1": 0.3873563218390805, "f1_std": 0.06616943554316208, "bacc": 0.39743589743589747, "bacc_std": 0.06303156060283724}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 95, "C": 0.000774263682681127, "split": "test", "acc": 0.3269230769230769, "acc_std": 0.058977718689138425, "f1": 0.3172172030867683, "f1_std": 0.05165772239655147, "bacc": 0.32005494505494503, "bacc_std": 0.057725544782255306}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 96, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.05676447499362409, "f1": 0.5428571428571428, "f1_std": 0.06191759307626926, "bacc": 0.5663919413919415, "bacc_std": 0.05645208212854524}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.06156730093465767, "f1": 0.3920284582423801, "f1_std": 0.06380491405204727, "bacc": 0.41506410256410253, "bacc_std": 0.06068667413816151}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 0.000774263682681127, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06297783023089726, "f1": 0.36989898989898995, "f1_std": 0.06296532246525437, "bacc": 0.3823260073260073, "bacc_std": 0.06262224305522504}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 0.000774263682681127, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06679176994618707, "f1": 0.44415007742681767, "f1_std": 0.06817257411784756, "bacc": 0.44184981684981683, "bacc_std": 0.06706898066505797}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 9.999999999999999e-05, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.061321383955171095, "f1": 0.4239772727272727, "f1_std": 0.06047895100557252, "bacc": 0.4535256410256411, "bacc_std": 0.06016935552031693}
+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 | reg    | logistic | aabc_age  | train   |        100 | 1.671 |  16.681 | 0.59012 |   0.12478 | 0.57215 | 0.13731  | 0.589   |   0.12628  |
+| flat_mae | reg    | logistic | aabc_age  | test    |        100 | 1.671 |  16.681 | 0.42731 |   0.06302 | 0.39957 | 0.059009 | 0.42256 |   0.061918 |
+
+
+done! total time: 0:05:26

decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic/eval_table.csv

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+flat_mae,patch,logistic,aabc_sex,89,0.046415888336127774,test,0.6909090909090909,0.06468296747206129,0.6803418803418804,0.06715178461016298,0.6793478260869565,0.06643907841114952
+flat_mae,patch,logistic,aabc_sex,90,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,aabc_sex,90,2.782559402207126,test,0.6909090909090909,0.06274651615234267,0.6803418803418804,0.06485063480057303,0.6793478260869565,0.06425217688444314
+flat_mae,patch,logistic,aabc_sex,91,0.046415888336127774,train,0.9262759924385633,0.01150848425981946,0.9243690085598548,0.011819972871137565,0.9241111990386588,0.011962696171546825
+flat_mae,patch,logistic,aabc_sex,91,0.046415888336127774,test,0.6727272727272727,0.06208813783247276,0.6637228260869565,0.06386039126513159,0.6637228260869565,0.06360053150513527
+flat_mae,patch,logistic,aabc_sex,92,0.046415888336127774,train,0.9357277882797732,0.010716636692881362,0.9340244152947736,0.011003991298375934,0.9334974647557079,0.011138633570765558
+flat_mae,patch,logistic,aabc_sex,92,0.046415888336127774,test,0.7272727272727273,0.05397856936241664,0.7020585048754062,0.06398106456460491,0.6983695652173914,0.05906705200200019
+flat_mae,patch,logistic,aabc_sex,93,0.005994842503189409,train,0.8544423440453687,0.014635567949351386,0.8496965000645744,0.015229379394125978,0.8474237228523697,0.01544513517980186
+flat_mae,patch,logistic,aabc_sex,93,0.005994842503189409,test,0.6909090909090909,0.057388903814167225,0.6623329721921272,0.06642024737230977,0.6610054347826086,0.06117769152747517
+flat_mae,patch,logistic,aabc_sex,94,0.046415888336127774,train,0.9206049149338374,0.011315994405039935,0.9186010727160819,0.01158247956671043,0.9186010727160819,0.011608841629864182
+flat_mae,patch,logistic,aabc_sex,94,0.046415888336127774,test,0.8,0.05625096068968418,0.7931623931623932,0.05861868899274305,0.7914402173913043,0.05856909002184618
+flat_mae,patch,logistic,aabc_sex,95,0.3593813663804626,train,0.9886578449905482,0.004757512402940818,0.9883572497579012,0.0048914202867483166,0.98776341627832,0.005165073030443774
+flat_mae,patch,logistic,aabc_sex,95,0.3593813663804626,test,0.8,0.05313834742389103,0.7931623931623932,0.0557110863702641,0.7914402173913043,0.05588930286503917
+flat_mae,patch,logistic,aabc_sex,96,0.046415888336127774,train,0.9206049149338374,0.012263202126993413,0.9186010727160819,0.01255908729018194,0.9186010727160819,0.012577801388422355
+flat_mae,patch,logistic,aabc_sex,96,0.046415888336127774,test,0.6909090909090909,0.06074816189444517,0.6892655367231638,0.06078603447551249,0.6976902173913043,0.06042438378239402
+flat_mae,patch,logistic,aabc_sex,97,0.3593813663804626,train,0.996219281663516,0.002747195964343182,0.9961190832526338,0.0028257174329615036,0.9955156950672646,0.0032584454375281274
+flat_mae,patch,logistic,aabc_sex,97,0.3593813663804626,test,0.7454545454545455,0.05744178224173967,0.7348484848484849,0.06070414212714564,0.7323369565217391,0.059760747902151884
+flat_mae,patch,logistic,aabc_sex,98,0.3593813663804626,train,0.9924385633270322,0.003884214782199223,0.9922381665052675,0.00399319946918552,0.9916395556727923,0.004319350814242418
+flat_mae,patch,logistic,aabc_sex,98,0.3593813663804626,test,0.6909090909090909,0.05964190661601446,0.6754599097535579,0.0635831110933265,0.6732336956521738,0.06212827256245735
+flat_mae,patch,logistic,aabc_sex,99,0.046415888336127774,train,0.9262759924385633,0.010979228832380138,0.9243690085598548,0.011247653646601864,0.9241111990386588,0.011262265841082607
+flat_mae,patch,logistic,aabc_sex,99,0.046415888336127774,test,0.7636363636363637,0.056320969187753984,0.7555555555555555,0.058771768862086474,0.7540760869565217,0.05804841316637023
+flat_mae,patch,logistic,aabc_sex,100,0.3593813663804626,train,0.9924385633270322,0.003643002177812924,0.9922477212110554,0.003735479983408941,0.9922477212110554,0.003803609326052018
+flat_mae,patch,logistic,aabc_sex,100,0.3593813663804626,test,0.7818181818181819,0.056135609122643766,0.78,0.05642636957451899,0.7880434782608696,0.056015518218168796

decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:45:49
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_sex patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic
+model: flat_mae
+representation: patch
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_sex__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:26:59    time: 6.8639  data: 5.5448  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:02:07    time: 0.2789  data: 0.1043  max mem: 3581
+extract (train)  [ 40/236]  eta: 0:01:22    time: 0.2367  data: 0.0830  max mem: 3581
+extract (train)  [ 60/236]  eta: 0:01:03    time: 0.2364  data: 0.0842  max mem: 3581
+extract (train)  [ 80/236]  eta: 0:00:51    time: 0.2338  data: 0.0813  max mem: 3581
+extract (train)  [100/236]  eta: 0:00:41    time: 0.2276  data: 0.0778  max mem: 3581
+extract (train)  [120/236]  eta: 0:00:34    time: 0.2214  data: 0.0754  max mem: 3581
+extract (train)  [140/236]  eta: 0:00:27    time: 0.2419  data: 0.0865  max mem: 3581
+extract (train)  [160/236]  eta: 0:00:21    time: 0.2102  data: 0.0690  max mem: 3581
+extract (train)  [180/236]  eta: 0:00:15    time: 0.2249  data: 0.0783  max mem: 3581
+extract (train)  [200/236]  eta: 0:00:09    time: 0.2713  data: 0.1055  max mem: 3581
+extract (train)  [220/236]  eta: 0:00:04    time: 0.2015  data: 0.0677  max mem: 3581
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1996  data: 0.0643  max mem: 3581
+extract (train) Total time: 0:01:01 (0.2627 s / it)
+extract (validation)  [ 0/29]  eta: 0:02:28    time: 5.1228  data: 4.9308  max mem: 3581
+extract (validation)  [20/29]  eta: 0:00:04    time: 0.2179  data: 0.0729  max mem: 3581
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1857  data: 0.0554  max mem: 3581
+extract (validation) Total time: 0:00:11 (0.3901 s / it)
+extract (test)  [ 0/28]  eta: 0:02:15    time: 4.8452  data: 4.6841  max mem: 3581
+extract (test)  [20/28]  eta: 0:00:03    time: 0.2177  data: 0.0745  max mem: 3581
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1962  data: 0.0647  max mem: 3581
+extract (test) Total time: 0:00:10 (0.3891 s / it)
+feature extraction time: 0:01:24
+train features: (471, 768)
+validation features: (58, 768)
+test features: (55, 768)
+evaluating fixed splits
+eval results (fixed splits):
+
+| model    | repr   | clf      | dataset   | trial   |         C | split   |     acc |   acc_std |      f1 |   f1_std |    bacc |   bacc_std |
+|:---------|:-------|:---------|:----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.0059948 | train   | 0.85066 |  0.015091 | 0.846   | 0.015608 | 0.84382 |   0.015683 |
+| flat_mae | patch  | logistic | aabc_sex  |         | 0.0059948 | test    | 0.76364 |  0.054249 | 0.76238 | 0.053998 | 0.7803  |   0.052207 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.059885632046592245, "f1": 0.6803418803418804, "f1_std": 0.062312056593004914, "bacc": 0.6793478260869565, "bacc_std": 0.06205869447657637}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 166.81005372000556, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.058703344407521625, "f1": 0.7348484848484849, "f1_std": 0.06160426238180184, "bacc": 0.7323369565217391, "bacc_std": 0.060888849817812334}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.056211395568296796, "f1": 0.78, "f1_std": 0.05648434217925837, "bacc": 0.7880434782608696, "bacc_std": 0.056373279418786004}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.055561970465269415, "f1": 0.7348484848484849, "f1_std": 0.058766793403595814, "bacc": 0.7323369565217391, "bacc_std": 0.05801141347371512}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06065091552251301, "f1": 0.7066666666666667, "f1_std": 0.0608828573328687, "bacc": 0.7133152173913043, "bacc_std": 0.060740790775012876}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05851043560237922, "f1": 0.7213779128672746, "f1_std": 0.06016850107603374, "bacc": 0.7228260869565217, "bacc_std": 0.06019587044988747}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 21.54434690031882, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.061867744837190174, "f1": 0.6673387096774194, "f1_std": 0.06328794528129167, "bacc": 0.6698369565217391, "bacc_std": 0.06379448973624834}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.0574680766574865, "f1": 0.7239879558380728, "f1_std": 0.05831388485667318, "bacc": 0.7289402173913043, "bacc_std": 0.058781968082110135}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05594957588152311, "f1": 0.7727272727272727, "f1_std": 0.058565752179610256, "bacc": 0.7697010869565217, "bacc_std": 0.0584091091425106}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05890572380949633, "f1": 0.7083775185577943, "f1_std": 0.0648516654700307, "bacc": 0.7044836956521738, "bacc_std": 0.06208469615050987}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05804277704367831, "f1": 0.7303921568627451, "f1_std": 0.06431344226440523, "bacc": 0.7262228260869565, "bacc_std": 0.06243357877713029}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05763813017080602, "f1": 0.7010869565217391, "f1_std": 0.0588704173205572, "bacc": 0.7010869565217391, "bacc_std": 0.058587691495223505}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.6181818181818182, "acc_std": 0.06547453230199733, "f1": 0.6176762661370407, "f1_std": 0.06568961285593536, "bacc": 0.6290760869565217, "bacc_std": 0.0657276018949385}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04300357855568581, "f1": 0.8699763593380614, "f1_std": 0.04394402425191906, "bacc": 0.8722826086956521, "bacc_std": 0.043879497619258655}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05944092143040743, "f1": 0.6694945210321668, "f1_std": 0.06605318664493222, "bacc": 0.6671195652173914, "bacc_std": 0.06266771711979759}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05885756444639876, "f1": 0.7585275244849713, "f1_std": 0.06011709326215933, "bacc": 0.7601902173913043, "bacc_std": 0.06015387942448184}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.058851543159303875, "f1": 0.6754599097535579, "f1_std": 0.06264610641071351, "bacc": 0.6732336956521738, "bacc_std": 0.060975447308642294}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 166.81005372000556, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.058498004486696994, "f1": 0.637278722665741, "f1_std": 0.06228674587335408, "bacc": 0.6358695652173914, "bacc_std": 0.06063274722182528}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 166.81005372000556, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06249015525770626, "f1": 0.7258225324027916, "f1_std": 0.06247189013782252, "bacc": 0.7350543478260869, "bacc_std": 0.06175023897009066}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.8, "acc_std": 0.0504825114959839, "f1": 0.7931623931623932, "f1_std": 0.05298607450028878, "bacc": 0.7914402173913043, "bacc_std": 0.05303487184055098}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.6545454545454545, "acc_std": 0.06316281990365709, "f1": 0.637278722665741, "f1_std": 0.06731942084251373, "bacc": 0.6358695652173914, "bacc_std": 0.06554065552676862}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 78, "C": 166.81005372000556, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.061275165853820625, "f1": 0.696969696969697, "f1_std": 0.06483605930009796, "bacc": 0.6949728260869565, "bacc_std": 0.06388359787065237}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 79, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05367148310950194, "f1": 0.76890756302521, "f1_std": 0.05930701113888251, "bacc": 0.7635869565217391, "bacc_std": 0.05788373580860351}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 80, "C": 2.782559402207126, "split": "test", "acc": 0.8, "acc_std": 0.05599641074684093, "f1": 0.7989365237620472, "f1_std": 0.05601892155356306, "bacc": 0.8097826086956521, "bacc_std": 0.054499141866108776}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 81, "C": 0.046415888336127774, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.05161428465226744, "f1": 0.6696696696696697, "f1_std": 0.06451894745597272, "bacc": 0.670516304347826, "bacc_std": 0.056425415022138777}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 82, "C": 166.81005372000556, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05423342264905843, "f1": 0.7811671087533156, "f1_std": 0.05422661118361694, "bacc": 0.7941576086956521, "bacc_std": 0.052722062121492455}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05964190661601446, "f1": 0.6754599097535579, "f1_std": 0.0635831110933265, "bacc": 0.6732336956521738, "bacc_std": 0.06212827256245735}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.056135609122643766, "f1": 0.78, "f1_std": 0.05642636957451899, "bacc": 0.7880434782608696, "bacc_std": 0.056015518218168796}
+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 | 114.86 |  999.53 | 0.94809 |  0.052856 | 0.94648 | 0.054975 | 0.946   |   0.055734 |
+| flat_mae | patch  | logistic | aabc_sex  | test    |        100 | 114.86 |  999.53 | 0.73782 |  0.051438 | 0.72809 | 0.054798 | 0.72853 |   0.055148 |
+
+
+done! total time: 0:05:25

decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_sex reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic
+model: flat_mae
+representation: reg
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic/log.txt

@@ -0,0 +1,245 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:19:08
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (aabc_sex reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic
+model: flat_mae
+representation: reg
+dataset: aabc_sex
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/aabc_sex__reg__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:20:30    time: 5.2121  data: 4.5042  max mem: 3205
+extract (train)  [ 20/236]  eta: 0:01:41    time: 0.2316  data: 0.0798  max mem: 3581
+extract (train)  [ 40/236]  eta: 0:01:05    time: 0.1882  data: 0.0575  max mem: 3581
+extract (train)  [ 60/236]  eta: 0:00:49    time: 0.1858  data: 0.0568  max mem: 3581
+extract (train)  [ 80/236]  eta: 0:00:41    time: 0.2036  data: 0.0666  max mem: 3581
+extract (train)  [100/236]  eta: 0:00:34    time: 0.2193  data: 0.0772  max mem: 3581
+extract (train)  [120/236]  eta: 0:00:28    time: 0.2082  data: 0.0689  max mem: 3581
+extract (train)  [140/236]  eta: 0:00:23    time: 0.2196  data: 0.0760  max mem: 3581
+extract (train)  [160/236]  eta: 0:00:17    time: 0.1886  data: 0.0599  max mem: 3581
+extract (train)  [180/236]  eta: 0:00:13    time: 0.2080  data: 0.0710  max mem: 3581
+extract (train)  [200/236]  eta: 0:00:08    time: 0.2064  data: 0.0705  max mem: 3581
+extract (train)  [220/236]  eta: 0:00:03    time: 0.1933  data: 0.0609  max mem: 3581
+extract (train)  [235/236]  eta: 0:00:00    time: 0.1774  data: 0.0563  max mem: 3581
+extract (train) Total time: 0:00:53 (0.2262 s / it)
+extract (validation)  [ 0/29]  eta: 0:02:07    time: 4.4089  data: 4.2545  max mem: 3581
+extract (validation)  [20/29]  eta: 0:00:03    time: 0.1932  data: 0.0603  max mem: 3581
+extract (validation)  [28/29]  eta: 0:00:00    time: 0.1709  data: 0.0499  max mem: 3581
+extract (validation) Total time: 0:00:10 (0.3452 s / it)
+extract (test)  [ 0/28]  eta: 0:02:01    time: 4.3402  data: 4.1975  max mem: 3581
+extract (test)  [20/28]  eta: 0:00:03    time: 0.2000  data: 0.0659  max mem: 3581
+extract (test)  [27/28]  eta: 0:00:00    time: 0.1719  data: 0.0529  max mem: 3581
+extract (test) Total time: 0:00:09 (0.3521 s / it)
+feature extraction time: 0:01:13
+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 | reg    | logistic | aabc_sex  |         | 0.046416 | train   | 0.9414  |  0.01016  | 0.94003 | 0.010387 | 0.94029 |   0.01041  |
+| flat_mae | reg    | logistic | aabc_sex  |         | 0.046416 | test    | 0.81818 |  0.050952 | 0.81667 | 0.050836 | 0.83333 |   0.048638 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05686435651022805, "f1": 0.741263440860215, "f1_std": 0.05780518248124952, "bacc": 0.7445652173913043, "bacc_std": 0.05813856314203359}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.045108075911046035, "f1": 0.8281846581048247, "f1_std": 0.04883491683309754, "bacc": 0.8226902173913043, "bacc_std": 0.0489720873066438}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.058983241771857174, "f1": 0.7066666666666667, "f1_std": 0.059443315963299936, "bacc": 0.7133152173913043, "bacc_std": 0.059719948927988896}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06090690905183037, "f1": 0.7043010752688172, "f1_std": 0.06186255079016478, "bacc": 0.7072010869565217, "bacc_std": 0.06226453783756901}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05826470232645219, "f1": 0.7239879558380728, "f1_std": 0.059322542319658146, "bacc": 0.7289402173913043, "bacc_std": 0.05936297651804229}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05657321738791099, "f1": 0.7555555555555555, "f1_std": 0.05926562723403219, "bacc": 0.7540760869565217, "bacc_std": 0.05939082964790558}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06000087051710099, "f1": 0.6754599097535579, "f1_std": 0.06498946610654509, "bacc": 0.6732336956521738, "bacc_std": 0.06325298261278592}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.054346047625949606, "f1": 0.7727272727272727, "f1_std": 0.057705932151778515, "bacc": 0.7697010869565217, "bacc_std": 0.057483461393271824}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.3593813663804626, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06060869206408441, "f1": 0.7384510869565217, "f1_std": 0.0621754429444348, "bacc": 0.7384510869565217, "bacc_std": 0.06176852943339552}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05772405144913582, "f1": 0.7585275244849713, "f1_std": 0.059019737975447274, "bacc": 0.7601902173913043, "bacc_std": 0.05881140262157492}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.005994842503189409, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.056419866379796124, "f1": 0.7555555555555555, "f1_std": 0.05910907874497819, "bacc": 0.7540760869565217, "bacc_std": 0.05868370354113446}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06030336531047498, "f1": 0.6803418803418804, "f1_std": 0.06313648953972331, "bacc": 0.6793478260869565, "bacc_std": 0.062444686579051153}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.000774263682681127, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06050344160691185, "f1": 0.7348484848484849, "f1_std": 0.06312614894348424, "bacc": 0.7323369565217391, "bacc_std": 0.062487001409989855}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.053471549552731736, "f1": 0.790003471017008, "f1_std": 0.05794684940331213, "bacc": 0.7853260869565217, "bacc_std": 0.05773269985192296}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05871405423992843, "f1": 0.7083775185577943, "f1_std": 0.06539705135685858, "bacc": 0.7044836956521738, "bacc_std": 0.06250435433496747}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.06017235849850601, "f1": 0.7384510869565217, "f1_std": 0.06231039893096374, "bacc": 0.7384510869565217, "bacc_std": 0.06230097706983681}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05639548660212899, "f1": 0.7136410968413746, "f1_std": 0.06060400635726474, "bacc": 0.7105978260869565, "bacc_std": 0.05935312120218117}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 2.782559402207126, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.0658721526585231, "f1": 0.6303763440860215, "f1_std": 0.06636224991091373, "bacc": 0.6324728260869565, "bacc_std": 0.06633442614493465}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.005994842503189409, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05933070226623783, "f1": 0.7384510869565217, "f1_std": 0.061112030859034695, "bacc": 0.7384510869565217, "bacc_std": 0.061178434136570106}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.054897421999113565, "f1": 0.795677136102668, "f1_std": 0.05620131585932542, "bacc": 0.7975543478260869, "bacc_std": 0.05603043979785281}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05712327976323744, "f1": 0.7348484848484849, "f1_std": 0.060834277722005885, "bacc": 0.7323369565217391, "bacc_std": 0.06014456225607166}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05516231795631913, "f1": 0.78, "f1_std": 0.055361044737783735, "bacc": 0.7880434782608696, "bacc_std": 0.054291640454489885}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.059257990359846024, "f1": 0.7213779128672746, "f1_std": 0.060892428929681816, "bacc": 0.7228260869565217, "bacc_std": 0.060999983052617814}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.056768970688729686, "f1": 0.7518222839291913, "f1_std": 0.061427975173266855, "bacc": 0.7479619565217391, "bacc_std": 0.06054477222475154}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.3593813663804626, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.060235076681364984, "f1": 0.7066666666666667, "f1_std": 0.06049694700768868, "bacc": 0.7133152173913043, "bacc_std": 0.06058201313226821}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 0.3593813663804626, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.059906472559694716, "f1": 0.717948717948718, "f1_std": 0.061872962103562704, "bacc": 0.7167119565217391, "bacc_std": 0.06145906093541241}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.05722544657350601, "f1": 0.7348484848484849, "f1_std": 0.06015377101312881, "bacc": 0.7323369565217391, "bacc_std": 0.059582090596493595}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.06052607934594589, "f1": 0.6803418803418804, "f1_std": 0.06333841138479059, "bacc": 0.6793478260869565, "bacc_std": 0.06297982430867961}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.6909090909090909, "acc_std": 0.05924733421488473, "f1": 0.6754599097535579, "f1_std": 0.06323499334919024, "bacc": 0.6732336956521738, "bacc_std": 0.0616323153013269}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.06211108150950999, "f1": 0.7258225324027916, "f1_std": 0.062184076098976825, "bacc": 0.7350543478260869, "bacc_std": 0.061294169170219494}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.7454545454545455, "acc_std": 0.0575121805280491, "f1": 0.741263440860215, "f1_std": 0.05845243566912999, "bacc": 0.7445652173913043, "bacc_std": 0.058563382568656314}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.057057036853883795, "f1": 0.7555555555555555, "f1_std": 0.05923354735340611, "bacc": 0.7540760869565217, "bacc_std": 0.058760358104306284}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 0.046415888336127774, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.05756624355205239, "f1": 0.7472605160834218, "f1_std": 0.06427914923876564, "bacc": 0.7418478260869565, "bacc_std": 0.06211539716274644}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 0.005994842503189409, "split": "test", "acc": 0.7272727272727273, "acc_std": 0.05755777837303722, "f1": 0.7083775185577943, "f1_std": 0.06306969324400177, "bacc": 0.7044836956521738, "bacc_std": 0.06076183425577814}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06063748397493022, "f1": 0.696969696969697, "f1_std": 0.06357647779132708, "bacc": 0.6949728260869565, "bacc_std": 0.06286764735774836}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 36, "C": 2.782559402207126, "split": "test", "acc": 0.7090909090909091, "acc_std": 0.06298454749492527, "f1": 0.7010869565217391, "f1_std": 0.06560497548760205, "bacc": 0.7010869565217391, "bacc_std": 0.06550089145475921}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 37, "C": 0.005994842503189409, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.051846882004164103, "f1": 0.8106060606060606, "f1_std": 0.05508712712865352, "bacc": 0.8070652173913043, "bacc_std": 0.05478176117235523}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 38, "C": 0.000774263682681127, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.055146458642658505, "f1": 0.7758152173913043, "f1_std": 0.056735038602484585, "bacc": 0.7758152173913043, "bacc_std": 0.05689894578381109}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.0581100807743657, "f1": 0.7555555555555555, "f1_std": 0.061003276269182706, "bacc": 0.7540760869565217, "bacc_std": 0.06054418132690288}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05596929500602158, "f1": 0.7758152173913043, "f1_std": 0.057900207829809036, "bacc": 0.7758152173913043, "bacc_std": 0.05815728167010988}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 41, "C": 0.005994842503189409, "split": "test", "acc": 0.6363636363636364, "acc_std": 0.06559720816474027, "f1": 0.6303763440860215, "f1_std": 0.066870267478454, "bacc": 0.6324728260869565, "bacc_std": 0.06724219039551839}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 42, "C": 21.54434690031882, "split": "test", "acc": 0.6727272727272727, "acc_std": 0.06633623344574666, "f1": 0.6673387096774194, "f1_std": 0.06714636079918689, "bacc": 0.6698369565217391, "bacc_std": 0.06692186277630337}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 43, "C": 0.005994842503189409, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05792601722088597, "f1": 0.7758152173913043, "f1_std": 0.05990458566968057, "bacc": 0.7758152173913043, "bacc_std": 0.05974629269984236}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 44, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.051833375762873134, "f1": 0.8343927735028438, "f1_std": 0.052015482655581695, "bacc": 0.8410326086956521, "bacc_std": 0.050723901818792216}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.054720627805590956, "f1": 0.7811671087533156, "f1_std": 0.05467831151228022, "bacc": 0.7941576086956521, "bacc_std": 0.052870420540671405}
+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 | reg    | logistic | aabc_sex  | train   |        100 | 0.60922 |  3.0549 | 0.92104 |  0.054794 | 0.91828 | 0.057358 | 0.91676 |    0.05869 |
+| flat_mae | reg    | logistic | aabc_sex  | test    |        100 | 0.60922 |  3.0549 | 0.75145 |  0.050578 | 0.74257 | 0.053585 | 0.743   |    0.05348 |
+
+
+done! total time: 0:04:58

decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,abide_dx,86,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,86,2.782559402207126,test,0.5403225806451613,0.042999963700587264,0.531517200238616,0.044615301423886644,0.5320378151260504,0.04372396976748105
+flat_mae,patch,logistic,abide_dx,87,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,87,166.81005372000556,test,0.6290322580645161,0.04447011120789863,0.628161668839635,0.044376468554319384,0.6302521008403361,0.0443212361699049
+flat_mae,patch,logistic,abide_dx,88,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,patch,logistic,abide_dx,88,166.81005372000556,test,0.5564516129032258,0.04248166648700364,0.5529334644378892,0.04288710029210345,0.553046218487395,0.04294487313266584
+flat_mae,patch,logistic,abide_dx,89,0.000774263682681127,train,0.6780626780626781,0.015043254113465156,0.6589947726803769,0.016571387904041685,0.6607604282022886,0.015516220077667323
+flat_mae,patch,logistic,abide_dx,89,0.000774263682681127,test,0.6290322580645161,0.042668930348270455,0.6169755573462261,0.044707924252205176,0.6176470588235294,0.04331782261636747
+flat_mae,patch,logistic,abide_dx,90,0.046415888336127774,train,0.8532763532763533,0.013506764949828188,0.8512965739352546,0.013718296247264021,0.8503875968992247,0.013771712090492574
+flat_mae,patch,logistic,abide_dx,90,0.046415888336127774,test,0.6209677419354839,0.04610787920143035,0.6167554415729598,0.04701270587431229,0.6165966386554622,0.046841702506412425
+flat_mae,patch,logistic,abide_dx,91,0.005994842503189409,train,0.7635327635327636,0.01588634338165091,0.7586349740267272,0.016451087485395074,0.7568844592100405,0.016396752121987667
+flat_mae,patch,logistic,abide_dx,91,0.005994842503189409,test,0.6290322580645161,0.04221412083555494,0.6210470369386127,0.04349770023752278,0.6207983193277311,0.04285306069906153
+flat_mae,patch,logistic,abide_dx,92,0.3593813663804626,train,0.9544159544159544,0.007395530144703387,0.9539037362925997,0.007481700236296725,0.9536360280546328,0.007534103321436029
+flat_mae,patch,logistic,abide_dx,92,0.3593813663804626,test,0.5887096774193549,0.04591990719950595,0.5841388834089565,0.04658710622977248,0.5840336134453781,0.046380208422524966
+flat_mae,patch,logistic,abide_dx,93,0.000774263682681127,train,0.6937321937321937,0.016387867314329004,0.6758230074810075,0.017919849164595613,0.6767441860465115,0.016904187088499383
+flat_mae,patch,logistic,abide_dx,93,0.000774263682681127,test,0.6048387096774194,0.039348307231670314,0.574718275355218,0.044538868681577026,0.5845588235294117,0.0403837887594712
+flat_mae,patch,logistic,abide_dx,94,0.046415888336127774,train,0.8433048433048433,0.014066377354936198,0.8408031996041727,0.014406793345262701,0.8392764857881136,0.014518507804889379
+flat_mae,patch,logistic,abide_dx,94,0.046415888336127774,test,0.5564516129032258,0.04176191125954305,0.5479551932126997,0.04236626419146795,0.5483193277310925,0.041884946072517575
+flat_mae,patch,logistic,abide_dx,95,0.000774263682681127,train,0.6809116809116809,0.01713789501532929,0.6647793164608773,0.01847791972763799,0.6654115909929863,0.017569493134448014
+flat_mae,patch,logistic,abide_dx,95,0.000774263682681127,test,0.5806451612903226,0.041704525291155946,0.5581140350877193,0.04567810868466739,0.5640756302521008,0.04262920222422063
+flat_mae,patch,logistic,abide_dx,96,0.000774263682681127,train,0.6780626780626781,0.016530040235181013,0.6585044556373498,0.018547787631551457,0.6604651162790698,0.01726003717170142
+flat_mae,patch,logistic,abide_dx,96,0.000774263682681127,test,0.5967741935483871,0.040313921650915706,0.575109649122807,0.04387450473932039,0.5803571428571428,0.04102089371018664
+flat_mae,patch,logistic,abide_dx,97,0.005994842503189409,train,0.7521367521367521,0.016654267674700865,0.7465746035170999,0.017220888377762675,0.7447766703580657,0.017106724746365192
+flat_mae,patch,logistic,abide_dx,97,0.005994842503189409,test,0.5967741935483871,0.040884761656294226,0.5810810810810811,0.04333494557102323,0.5835084033613446,0.04164021071216353
+flat_mae,patch,logistic,abide_dx,98,0.3593813663804626,train,0.9487179487179487,0.008404148427877857,0.948076923076923,0.008526310766311261,0.9472868217054264,0.008675337412900452
+flat_mae,patch,logistic,abide_dx,98,0.3593813663804626,test,0.6129032258064516,0.04350943299590779,0.610369206598586,0.04394723104831792,0.6108193277310925,0.04403938299881905
+flat_mae,patch,logistic,abide_dx,99,0.3593813663804626,train,0.9658119658119658,0.006632411935126407,0.9654485049833887,0.006701016958820985,0.9654485049833887,0.0067016513791176326
+flat_mae,patch,logistic,abide_dx,99,0.3593813663804626,test,0.6129032258064516,0.04388134068546445,0.6092436974789917,0.04426940309364152,0.6092436974789917,0.04412279845625132
+flat_mae,patch,logistic,abide_dx,100,0.046415888336127774,train,0.8547008547008547,0.0126855370265588,0.8526909150757077,0.012902651157123517,0.8516795865633076,0.012966407729831188
+flat_mae,patch,logistic,abide_dx,100,0.046415888336127774,test,0.6612903225806451,0.042874993742851225,0.6590730557737627,0.04307228407780876,0.6596638655462186,0.04303058077846806

decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:43:10
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (abide_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: abide_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/abide_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:23:46    time: 4.9345  data: 4.1383  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:59    time: 0.2197  data: 0.0841  max mem: 3005
+extract (train)  [ 40/289]  eta: 0:01:16    time: 0.1669  data: 0.0526  max mem: 3005
+extract (train)  [ 60/289]  eta: 0:01:01    time: 0.1807  data: 0.0629  max mem: 3005
+extract (train)  [ 80/289]  eta: 0:00:52    time: 0.2039  data: 0.0738  max mem: 3005
+extract (train)  [100/289]  eta: 0:00:45    time: 0.1904  data: 0.0670  max mem: 3005
+extract (train)  [120/289]  eta: 0:00:39    time: 0.1986  data: 0.0718  max mem: 3005
+extract (train)  [140/289]  eta: 0:00:33    time: 0.1996  data: 0.0720  max mem: 3005
+extract (train)  [160/289]  eta: 0:00:28    time: 0.1841  data: 0.0633  max mem: 3005
+extract (train)  [180/289]  eta: 0:00:23    time: 0.1832  data: 0.0620  max mem: 3005
+extract (train)  [200/289]  eta: 0:00:19    time: 0.1764  data: 0.0582  max mem: 3005
+extract (train)  [220/289]  eta: 0:00:14    time: 0.1748  data: 0.0571  max mem: 3005
+extract (train)  [240/289]  eta: 0:00:10    time: 0.1779  data: 0.0593  max mem: 3005
+extract (train)  [260/289]  eta: 0:00:06    time: 0.2020  data: 0.0743  max mem: 3005
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1582  data: 0.0497  max mem: 3005
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1586  data: 0.0497  max mem: 3005
+extract (train) Total time: 0:00:58 (0.2040 s / it)
+extract (validation)  [ 0/62]  eta: 0:04:22    time: 4.2399  data: 4.0744  max mem: 3005
+extract (validation)  [20/62]  eta: 0:00:17    time: 0.2251  data: 0.0854  max mem: 3005
+extract (validation)  [40/62]  eta: 0:00:06    time: 0.1796  data: 0.0625  max mem: 3005
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1688  data: 0.0555  max mem: 3005
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1691  data: 0.0557  max mem: 3005
+extract (validation) Total time: 0:00:16 (0.2630 s / it)
+extract (test)  [ 0/62]  eta: 0:04:11    time: 4.0521  data: 3.9180  max mem: 3005
+extract (test)  [20/62]  eta: 0:00:16    time: 0.2215  data: 0.0803  max mem: 3005
+extract (test)  [40/62]  eta: 0:00:06    time: 0.1690  data: 0.0525  max mem: 3005
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1736  data: 0.0562  max mem: 3005
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1747  data: 0.0568  max mem: 3005
+extract (test) Total time: 0:00:15 (0.2563 s / it)
+feature extraction time: 0:01:31
+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  |         | 0.35938 | train   | 0.96296 | 0.0071061 | 0.9625  | 0.0072079 | 0.96194 |  0.0073569 |
+| flat_mae | patch  | logistic | abide_dx  |         | 0.35938 | test    | 0.53226 | 0.044813  | 0.52784 | 0.044946  | 0.52789 |  0.044848  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04377692719731147, "f1": 0.6255252100840336, "f1_std": 0.044481887428298034, "bacc": 0.6255252100840336, "bacc_std": 0.04441878959292454}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.04328874510370435, "f1": 0.5473272490221643, "f1_std": 0.04324560861526853, "bacc": 0.5488445378151261, "bacc_std": 0.04314501015380844}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 1291.5496650148827, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.042551612169587756, "f1": 0.5953379953379954, "f1_std": 0.04388299792023957, "bacc": 0.5955882352941176, "bacc_std": 0.04304450952136265}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.3593813663804626, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04242290859638592, "f1": 0.6197559861681998, "f1_std": 0.042623309280797946, "bacc": 0.6213235294117647, "bacc_std": 0.042828472385558154}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.000774263682681127, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04248766730533587, "f1": 0.6145945945945945, "f1_std": 0.04526711285428143, "bacc": 0.6160714285714286, "bacc_std": 0.043450496095729145}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.000774263682681127, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.03855289486398319, "f1": 0.5077589317935763, "f1_std": 0.044444423355268346, "bacc": 0.5309873949579832, "bacc_std": 0.03910035705634258}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.000774263682681127, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.03839503848453504, "f1": 0.5881563140414104, "f1_std": 0.04459846788490249, "bacc": 0.5992647058823529, "bacc_std": 0.03959961296941128}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04439035528174495, "f1": 0.5941345902068604, "f1_std": 0.044767177849436283, "bacc": 0.5945378151260504, "bacc_std": 0.04486696554948261}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04166845504313284, "f1": 0.555142173797502, "f1_std": 0.04247270127119367, "bacc": 0.555672268907563, "bacc_std": 0.041874646639430614}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 2.782559402207126, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04363890234787019, "f1": 0.6428384393820372, "f1_std": 0.044146039347922966, "bacc": 0.6433823529411764, "bacc_std": 0.04431831886363558}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 21.54434690031882, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.044795230137595295, "f1": 0.551522325244953, "f1_std": 0.04514515495360564, "bacc": 0.5514705882352942, "bacc_std": 0.04513333160893818}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.0393280282817837, "f1": 0.6644445911160979, "f1_std": 0.040334940196631516, "bacc": 0.6638655462184874, "bacc_std": 0.039939439360959526}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 21.54434690031882, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.043663679522794485, "f1": 0.6450689565443664, "f1_std": 0.043757028242016265, "bacc": 0.6496848739495797, "bacc_std": 0.043327385976290454}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 21.54434690031882, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.04326564125029431, "f1": 0.5626959247648903, "f1_std": 0.04335304009637541, "bacc": 0.5635504201680672, "bacc_std": 0.0435570283829187}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04306199098553012, "f1": 0.6539994685091681, "f1_std": 0.04439593196954897, "bacc": 0.6533613445378151, "bacc_std": 0.043673558770929315}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.042874993742851225, "f1": 0.6590730557737627, "f1_std": 0.04307228407780876, "bacc": 0.6596638655462186, "bacc_std": 0.04303058077846806}
+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 | 140.94 |  1012.9 | 0.84734 |  0.12552  | 0.84128 | 0.13231  | 0.84109 |   0.13197  |
+| flat_mae | patch  | logistic | abide_dx  | test    |        100 | 140.94 |  1012.9 | 0.59258 |  0.034852 | 0.58099 | 0.038097 | 0.58359 |   0.036525 |
+
+
+done! total time: 0:05:55

decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (abide_dx reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic
+model: flat_mae
+representation: reg
+dataset: abide_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic/log.txt

@@ -0,0 +1,252 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:18:00
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (abide_dx reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic
+model: flat_mae
+representation: reg
+dataset: abide_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/abide_dx__reg__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:19:02    time: 3.9534  data: 3.3025  max mem: 2698
+extract (train)  [ 20/289]  eta: 0:01:39    time: 0.1895  data: 0.0637  max mem: 3005
+extract (train)  [ 40/289]  eta: 0:01:05    time: 0.1525  data: 0.0466  max mem: 3005
+extract (train)  [ 60/289]  eta: 0:00:52    time: 0.1543  data: 0.0461  max mem: 3005
+extract (train)  [ 80/289]  eta: 0:00:44    time: 0.1644  data: 0.0532  max mem: 3005
+extract (train)  [100/289]  eta: 0:00:37    time: 0.1560  data: 0.0485  max mem: 3005
+extract (train)  [120/289]  eta: 0:00:32    time: 0.1551  data: 0.0476  max mem: 3005
+extract (train)  [140/289]  eta: 0:00:27    time: 0.1544  data: 0.0460  max mem: 3005
+extract (train)  [160/289]  eta: 0:00:23    time: 0.1642  data: 0.0536  max mem: 3005
+extract (train)  [180/289]  eta: 0:00:19    time: 0.1723  data: 0.0550  max mem: 3005
+extract (train)  [200/289]  eta: 0:00:16    time: 0.1668  data: 0.0534  max mem: 3005
+extract (train)  [220/289]  eta: 0:00:12    time: 0.1661  data: 0.0537  max mem: 3005
+extract (train)  [240/289]  eta: 0:00:08    time: 0.1657  data: 0.0553  max mem: 3005
+extract (train)  [260/289]  eta: 0:00:05    time: 0.1625  data: 0.0531  max mem: 3005
+extract (train)  [280/289]  eta: 0:00:01    time: 0.1548  data: 0.0476  max mem: 3005
+extract (train)  [288/289]  eta: 0:00:00    time: 0.1556  data: 0.0487  max mem: 3005
+extract (train) Total time: 0:00:51 (0.1771 s / it)
+extract (validation)  [ 0/62]  eta: 0:03:44    time: 3.6149  data: 3.4672  max mem: 3005
+extract (validation)  [20/62]  eta: 0:00:15    time: 0.2009  data: 0.0704  max mem: 3005
+extract (validation)  [40/62]  eta: 0:00:05    time: 0.1581  data: 0.0497  max mem: 3005
+extract (validation)  [60/62]  eta: 0:00:00    time: 0.1551  data: 0.0505  max mem: 3005
+extract (validation)  [61/62]  eta: 0:00:00    time: 0.1550  data: 0.0505  max mem: 3005
+extract (validation) Total time: 0:00:14 (0.2317 s / it)
+extract (test)  [ 0/62]  eta: 0:03:53    time: 3.7728  data: 3.5997  max mem: 3005
+extract (test)  [20/62]  eta: 0:00:15    time: 0.2034  data: 0.0694  max mem: 3005
+extract (test)  [40/62]  eta: 0:00:05    time: 0.1635  data: 0.0509  max mem: 3005
+extract (test)  [60/62]  eta: 0:00:00    time: 0.1545  data: 0.0476  max mem: 3005
+extract (test)  [61/62]  eta: 0:00:00    time: 0.1542  data: 0.0474  max mem: 3005
+extract (test) Total time: 0:00:14 (0.2370 s / it)
+feature extraction time: 0:01:20
+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 | reg    | logistic | abide_dx  |         | 0.0059948 | train   | 0.81624 |  0.013777 | 0.81266 | 0.014113 | 0.81068 |   0.014079 |
+| flat_mae | reg    | logistic | abide_dx  |         | 0.0059948 | test    | 0.58065 |  0.042322 | 0.56944 | 0.043825 | 0.57135 |   0.04273  |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04302262067445718, "f1": 0.6125, "f1_std": 0.04308666710504642, "bacc": 0.615546218487395, "bacc_std": 0.0430640372870933}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.042884740135909115, "f1": 0.5806451612903226, "f1_std": 0.04304070703248366, "bacc": 0.5861344537815126, "bacc_std": 0.042764587609295635}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.044117381974678495, "f1": 0.6045708211533352, "f1_std": 0.04549149727665772, "bacc": 0.6045168067226891, "bacc_std": 0.044784804126661554}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.042872666778633654, "f1": 0.6118548118548119, "f1_std": 0.04420678731524125, "bacc": 0.6118697478991597, "bacc_std": 0.043412533133996026}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.0418612867181886, "f1": 0.5929621848739496, "f1_std": 0.042517752034177646, "bacc": 0.5929621848739496, "bacc_std": 0.04245722637465348}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04127170384150949, "f1": 0.626380984265149, "f1_std": 0.042536629026512264, "bacc": 0.6265756302521008, "bacc_std": 0.04158219779200473}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.000774263682681127, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04028571217713727, "f1": 0.574718275355218, "f1_std": 0.045812694956875934, "bacc": 0.5845588235294117, "bacc_std": 0.04131633299312512}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.3593813663804626, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04206148564187509, "f1": 0.5941345902068604, "f1_std": 0.04231342099116469, "bacc": 0.5945378151260504, "bacc_std": 0.04223628625511693}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04160938657308653, "f1": 0.5765651155005022, "f1_std": 0.04268610893507543, "bacc": 0.5777310924369747, "bacc_std": 0.041833556588198685}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.043937861121797636, "f1": 0.6418067226890756, "f1_std": 0.04441502347280435, "bacc": 0.6418067226890756, "bacc_std": 0.04432888947668008}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 2.782559402207126, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04343932280628793, "f1": 0.5826018084614877, "f1_std": 0.04433749565180021, "bacc": 0.5824579831932774, "bacc_std": 0.04402226347314493}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.3593813663804626, "split": "test", "acc": 0.5161290322580645, "acc_std": 0.04390386278602168, "f1": 0.5129615082482325, "f1_std": 0.044174183552552586, "bacc": 0.5131302521008403, "bacc_std": 0.04423233565867662}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04438966081772879, "f1": 0.6041951664386684, "f1_std": 0.04437383028066331, "bacc": 0.6066176470588236, "bacc_std": 0.04439890154375602}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.043960590974109114, "f1": 0.6035753898349319, "f1_std": 0.04400106766929949, "bacc": 0.6050420168067226, "bacc_std": 0.04403133407234343}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.039730635699814096, "f1": 0.6526610644257702, "f1_std": 0.039766138423536226, "bacc": 0.6554621848739496, "bacc_std": 0.0398270830796502}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.03879320289755654, "f1": 0.5581140350877193, "f1_std": 0.04213117157077213, "bacc": 0.5640756302521008, "bacc_std": 0.03949008345253527}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 17, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.044949025933407306, "f1": 0.6119947848761408, "f1_std": 0.045094328383577416, "bacc": 0.6139705882352942, "bacc_std": 0.04525716203175383}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 18, "C": 166.81005372000556, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.042900121158586914, "f1": 0.5623043623043623, "f1_std": 0.044323968930829065, "bacc": 0.5630252100840336, "bacc_std": 0.04335847781129747}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 19, "C": 0.005994842503189409, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04328294249866049, "f1": 0.6153389215233318, "f1_std": 0.044105161532270364, "bacc": 0.6150210084033614, "bacc_std": 0.043838383793386705}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 20, "C": 2.782559402207126, "split": "test", "acc": 0.5241935483870968, "acc_std": 0.045225748930063286, "f1": 0.5150792072645324, "f1_std": 0.046107639264786775, "bacc": 0.5157563025210083, "bacc_std": 0.045554000234481945}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04505188071873942, "f1": 0.6125, "f1_std": 0.04521286897297581, "bacc": 0.615546218487395, "bacc_std": 0.04521116737625754}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04092897581658232, "f1": 0.5907590759075907, "f1_std": 0.0430340084514928, "bacc": 0.592436974789916, "bacc_std": 0.041520244590889585}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.0428686830382255, "f1": 0.5573516535327002, "f1_std": 0.04491170217156621, "bacc": 0.5598739495798319, "bacc_std": 0.04341343794202899}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.04087969965536183, "f1": 0.6391534391534391, "f1_std": 0.041795905441736135, "bacc": 0.6386554621848739, "bacc_std": 0.041498248203676535}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04263826441228081, "f1": 0.5529334644378892, "f1_std": 0.043010729355763534, "bacc": 0.553046218487395, "bacc_std": 0.04290706634341999}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 26, "C": 0.005994842503189409, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04368924650466842, "f1": 0.5694444444444444, "f1_std": 0.04555551950846896, "bacc": 0.5703781512605042, "bacc_std": 0.04446724336882759}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.04168404458975978, "f1": 0.626380984265149, "f1_std": 0.0433349745376252, "bacc": 0.6265756302521008, "bacc_std": 0.04227477092333316}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 28, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.04220056109721617, "f1": 0.6502820306204673, "f1_std": 0.04457058529396927, "bacc": 0.6502100840336134, "bacc_std": 0.04312623712672943}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.6693548387096774, "acc_std": 0.04198856442108495, "f1": 0.6595915634415801, "f1_std": 0.04404721572849947, "bacc": 0.6591386554621849, "bacc_std": 0.04294610920499959}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 30, "C": 0.000774263682681127, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04230599042952057, "f1": 0.5343756400628115, "f1_std": 0.04548069640892707, "bacc": 0.5404411764705883, "bacc_std": 0.04289293801952597}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 31, "C": 0.005994842503189409, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04296907389197765, "f1": 0.5931704050887178, "f1_std": 0.045058357262803786, "bacc": 0.5940126050420168, "bacc_std": 0.04379618098676921}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 32, "C": 2.782559402207126, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.04434968885059558, "f1": 0.5479166666666666, "f1_std": 0.04446141921597849, "bacc": 0.5504201680672269, "bacc_std": 0.04461821459023731}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 33, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.042092939511416384, "f1": 0.5860042735042735, "f1_std": 0.04365335817085198, "bacc": 0.5866596638655462, "bacc_std": 0.04271377486357096}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 34, "C": 0.000774263682681127, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.040166960993620854, "f1": 0.5639943741209563, "f1_std": 0.045382662437967, "bacc": 0.5756302521008403, "bacc_std": 0.040975089721394936}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 35, "C": 0.000774263682681127, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04239652274041667, "f1": 0.5826018084614877, "f1_std": 0.042925312185141125, "bacc": 0.5824579831932774, "bacc_std": 0.04273381108732241}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 36, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.0457373257379656, "f1": 0.5662332519305657, "f1_std": 0.046666879224178316, "bacc": 0.5661764705882353, "bacc_std": 0.04628617344892488}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 37, "C": 0.000774263682681127, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.0397776435465781, "f1": 0.6163150492264415, "f1_std": 0.04598212853974836, "bacc": 0.6244747899159664, "bacc_std": 0.041214430215073726}
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+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 93, "C": 0.005994842503189409, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.0430943445084854, "f1": 0.5716183895827798, "f1_std": 0.044477785561764835, "bacc": 0.571953781512605, "bacc_std": 0.04374033991024301}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 94, "C": 0.046415888336127774, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.0428446453087631, "f1": 0.5337093741606231, "f1_std": 0.04432221906985984, "bacc": 0.5362394957983193, "bacc_std": 0.04303151597686858}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 95, "C": 0.005994842503189409, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.041824406240090345, "f1": 0.5810810810810811, "f1_std": 0.044522771362656276, "bacc": 0.5835084033613446, "bacc_std": 0.04260002553785869}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 96, "C": 0.005994842503189409, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04558406660217663, "f1": 0.6118548118548119, "f1_std": 0.046964519351131155, "bacc": 0.6118697478991597, "bacc_std": 0.046028708670592555}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 97, "C": 0.000774263682681127, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.03747619431690826, "f1": 0.6129032258064516, "f1_std": 0.041952976161021976, "bacc": 0.6186974789915967, "bacc_std": 0.03851691529383629}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.044981355254143686, "f1": 0.6604954367666231, "f1_std": 0.04501709585655694, "bacc": 0.6628151260504203, "bacc_std": 0.045244331977391736}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.04427898499250746, "f1": 0.5941345902068604, "f1_std": 0.04458463843668474, "bacc": 0.5945378151260504, "bacc_std": 0.044557964897986795}
+{"model": "flat_mae", "repr": "reg", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04618960606713198, "f1": 0.610369206598586, "f1_std": 0.04647705815015329, "bacc": 0.6108193277310925, "bacc_std": 0.046406912166710036}
+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 | reg    | logistic | abide_dx  | train   |        100 | 18.255 |  131.76 | 0.83615 |  0.089283 | 0.83092 | 0.093953 | 0.82941 |   0.094028 |
+| flat_mae | reg    | logistic | abide_dx  | test    |        100 | 18.255 |  131.76 | 0.60073 |  0.035208 | 0.59052 | 0.036948 | 0.59248 |   0.036191 |
+
+
+done! total time: 0:05:16

decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic/eval_table.csv

@@ -0,0 +1,203 @@
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+flat_mae,patch,logistic,adhd200_dx,77,0.046415888336127774,train,0.8876712328767123,0.01668027557234742,0.8849633712304653,0.01721826742180085,0.8825486963424315,0.017477725372093748
+flat_mae,patch,logistic,adhd200_dx,77,0.046415888336127774,test,0.6461538461538462,0.05926044411482579,0.6431129147767964,0.05966966752445548,0.6457528957528957,0.059757786860315514
+flat_mae,patch,logistic,adhd200_dx,78,0.005994842503189409,train,0.821917808219178,0.01868338803732341,0.8151178647964153,0.019818499285175317,0.8106643463393783,0.019791207525405332
+flat_mae,patch,logistic,adhd200_dx,78,0.005994842503189409,test,0.47692307692307695,0.061051387802921575,0.4551282051282051,0.0619760071855894,0.45801158301158307,0.06088492985617606
+flat_mae,patch,logistic,adhd200_dx,79,0.000774263682681127,train,0.6821917808219178,0.021348588637598837,0.6569548872180451,0.024358349575495146,0.6581791536911522,0.022470223266245665
+flat_mae,patch,logistic,adhd200_dx,79,0.000774263682681127,test,0.5538461538461539,0.058463765139781465,0.5321419707123356,0.06114034570872993,0.5342664092664092,0.05916435797601838
+flat_mae,patch,logistic,adhd200_dx,80,0.005994842503189409,train,0.7780821917808219,0.020954355312627407,0.7705809775818855,0.02190977060536756,0.7675245771508823,0.021752855008472113
+flat_mae,patch,logistic,adhd200_dx,80,0.005994842503189409,test,0.5692307692307692,0.06127991070180587,0.5565302144249512,0.06280282923875505,0.5564671814671815,0.06194028999932819
+flat_mae,patch,logistic,adhd200_dx,81,0.000774263682681127,train,0.7013698630136986,0.022454264352566464,0.6801566054876236,0.025014157903792536,0.6794742626854735,0.023486754722150227
+flat_mae,patch,logistic,adhd200_dx,81,0.000774263682681127,test,0.5384615384615384,0.05218101402667517,0.4724025974025974,0.060158004402614845,0.49903474903474904,0.05260831262714374
+flat_mae,patch,logistic,adhd200_dx,82,0.000774263682681127,train,0.6794520547945205,0.022615850742845566,0.6474303405572754,0.02680962051651135,0.6514471514929474,0.024002136141771716
+flat_mae,patch,logistic,adhd200_dx,82,0.000774263682681127,test,0.676923076923077,0.0489377723453998,0.6351242983159583,0.0611871180939286,0.6423745173745173,0.05250820727485347
+flat_mae,patch,logistic,adhd200_dx,83,0.005994842503189409,train,0.8,0.018619639266382375,0.7923631404636664,0.0198091728692074,0.7883769921230994,0.019750696310077025
+flat_mae,patch,logistic,adhd200_dx,83,0.005994842503189409,test,0.5846153846153846,0.06346384890899066,0.578226387887527,0.0641602141892825,0.5786679536679536,0.06409260398977272
+flat_mae,patch,logistic,adhd200_dx,84,0.000774263682681127,train,0.6684931506849315,0.021823839659457354,0.6404116559871683,0.024686805654451388,0.6431733528729315,0.022701913880809006
+flat_mae,patch,logistic,adhd200_dx,84,0.000774263682681127,test,0.5538461538461539,0.052935410011976804,0.49612403100775193,0.06217646345795922,0.5168918918918919,0.05396233402707363
+flat_mae,patch,logistic,adhd200_dx,85,0.046415888336127774,train,0.9013698630136986,0.015445112595778175,0.8990783410138249,0.015928087777197772,0.8968370275386213,0.016206377956067688
+flat_mae,patch,logistic,adhd200_dx,85,0.046415888336127774,test,0.6,0.05892300461935712,0.5833333333333333,0.061891240280208905,0.5834942084942085,0.060313545219748965
+flat_mae,patch,logistic,adhd200_dx,86,0.005994842503189409,train,0.8027397260273973,0.019462396633436692,0.7945205479452055,0.02070420318749722,0.790086706967088,0.020490009404529497
+flat_mae,patch,logistic,adhd200_dx,86,0.005994842503189409,test,0.5230769230769231,0.05834566654631453,0.47320261437908495,0.06466354406444957,0.48986486486486486,0.05886899286792007
+flat_mae,patch,logistic,adhd200_dx,87,0.005994842503189409,train,0.8,0.019316273934987294,0.7919025937004553,0.02056401145267659,0.7876595225010685,0.020385316526981827
+flat_mae,patch,logistic,adhd200_dx,87,0.005994842503189409,test,0.5384615384615384,0.061933340764283425,0.5125,0.06547087754823969,0.5164092664092664,0.06289846557613754
+flat_mae,patch,logistic,adhd200_dx,88,0.000774263682681127,train,0.6904109589041096,0.02247195036483203,0.6674031723503939,0.0252909254782004,0.6676131159553031,0.023610412479549012
+flat_mae,patch,logistic,adhd200_dx,88,0.000774263682681127,test,0.5076923076923077,0.0609418463705626,0.48,0.06399638171450527,0.48503861003861004,0.061449262862256965
+flat_mae,patch,logistic,adhd200_dx,89,0.000774263682681127,train,0.6821917808219178,0.02345022141086151,0.6601271512972,0.02619178338395485,0.6603315625572449,0.024557706607275815
+flat_mae,patch,logistic,adhd200_dx,89,0.000774263682681127,test,0.6,0.051745723317553546,0.5533826638477801,0.06021039070041826,0.5661196911196912,0.05321109551061686
+flat_mae,patch,logistic,adhd200_dx,90,0.000774263682681127,train,0.6904109589041096,0.021704368965744278,0.6618957446982925,0.025189252462762372,0.6640257678451487,0.022863329392443628
+flat_mae,patch,logistic,adhd200_dx,90,0.000774263682681127,test,0.5846153846153846,0.04793758269165777,0.5054945054945055,0.06110656769397677,0.5395752895752896,0.04946804070786595
+flat_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,train,0.8082191780821918,0.019487183406861813,0.8002283105022832,0.020775010641736383,0.7956585455211578,0.020587692602755697
+flat_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,test,0.6153846153846154,0.05512331522737729,0.5905769715293525,0.06033127922087831,0.5926640926640927,0.05697228280879233
+flat_mae,patch,logistic,adhd200_dx,92,0.3593813663804626,train,0.9972602739726028,0.0028206992980341797,0.9972120591807273,0.002874655813627762,0.9968553459119497,0.00323759510623421
+flat_mae,patch,logistic,adhd200_dx,92,0.3593813663804626,test,0.5846153846153846,0.06314265992938989,0.578226387887527,0.06416906895999108,0.5786679536679536,0.06384694008231567
+flat_mae,patch,logistic,adhd200_dx,93,9.999999999999999e-05,train,0.6054794520547945,0.013286164015634023,0.487719298245614,0.022653624749318677,0.5529095682970019,0.014388927130394028
+flat_mae,patch,logistic,adhd200_dx,93,9.999999999999999e-05,test,0.6,0.019893206000301065,0.43666666666666665,0.04443838519122845,0.5357142857142857,0.023090328393206586
+flat_mae,patch,logistic,adhd200_dx,94,0.000774263682681127,train,0.6876712328767123,0.022005126750434395,0.6617733125243855,0.02488195864749761,0.663033522623191,0.022981090029836275
+flat_mae,patch,logistic,adhd200_dx,94,0.000774263682681127,test,0.6153846153846154,0.04246014490466847,0.5277535600116245,0.062355044822991194,0.5666023166023166,0.04565068633139476
+flat_mae,patch,logistic,adhd200_dx,95,0.3593813663804626,train,0.9972602739726028,0.0026918055699653486,0.9972120591807273,0.0027430305738328233,0.9968553459119497,0.003089651047287282
+flat_mae,patch,logistic,adhd200_dx,95,0.3593813663804626,test,0.5538461538461539,0.05437068475701538,0.5167905665214048,0.060329578583930184,0.5255791505791506,0.055351183024685446
+flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,train,0.8082191780821918,0.019327856642924943,0.8011115954664342,0.02038990628108996,0.7970934847652196,0.020296760531888804
+flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,test,0.5230769230769231,0.05978336433229209,0.5062484685126194,0.06187737438459926,0.5072393822393823,0.060451464920462834
+flat_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,train,0.7972602739726027,0.019678188566863423,0.7892872745428448,0.021015207506802178,0.7852323380350492,0.02089357743884958
+flat_mae,patch,logistic,adhd200_dx,97,0.005994842503189409,test,0.5384615384615384,0.05697231965701055,0.4846723044397463,0.06423068990692955,0.5033783783783784,0.057676133058271066
+flat_mae,patch,logistic,adhd200_dx,98,0.000774263682681127,train,0.6712328767123288,0.022186920489235235,0.6403232163973196,0.025881737951824836,0.6441655980948892,0.02339293470612363
+flat_mae,patch,logistic,adhd200_dx,98,0.000774263682681127,test,0.6,0.05307044776554559,0.5427489177489178,0.06350648693969092,0.5617760617760618,0.05449924135245666
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+flat_mae,patch,logistic,adhd200_dx,99,0.000774263682681127,test,0.5538461538461539,0.05515572360284874,0.5321419707123356,0.05807587600065702,0.5342664092664092,0.056046407809770674
+flat_mae,patch,logistic,adhd200_dx,100,0.000774263682681127,train,0.6904109589041096,0.021863153774399346,0.6652870764861026,0.025064338408878922,0.6661781767112414,0.023075001742908708
+flat_mae,patch,logistic,adhd200_dx,100,0.000774263682681127,test,0.5076923076923077,0.054612558973491945,0.4616977225672878,0.05981336694736562,0.47635135135135137,0.05515783886831236

decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic/log.txt

@@ -0,0 +1,241 @@
+fMRI foundation model logistic probe eval
+version: 0.1.dev66+g7ddd3aa04
+sha: aef99c83a386cf95c3d8ca503ecc968d8d5694af, status: has uncommitted changes, branch: dev/clane9
+cwd: /data/connor/fmri-fm
+start: 2026-03-07 21:43:27
+config:
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (adhd200_dx patch logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic
+model: flat_mae
+representation: patch
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/adhd200_dx__patch__logistic
+remote_dir: null
+
+creating frozen backbone model: flat_mae
+backbone:
+MaskedEncoderWrapper(
+  (model): MaskedEncoder(
+    class_token=False, reg_tokens=1, 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: 0:12:33    time: 4.9916  data: 4.0208  max mem: 2698
+extract (train)  [ 20/151]  eta: 0:00:57    time: 0.2081  data: 0.0750  max mem: 3005
+extract (train)  [ 40/151]  eta: 0:00:33    time: 0.1693  data: 0.0537  max mem: 3005
+extract (train)  [ 60/151]  eta: 0:00:23    time: 0.1689  data: 0.0550  max mem: 3005
+extract (train)  [ 80/151]  eta: 0:00:16    time: 0.1655  data: 0.0527  max mem: 3005
+extract (train)  [100/151]  eta: 0:00:11    time: 0.1649  data: 0.0523  max mem: 3005
+extract (train)  [120/151]  eta: 0:00:06    time: 0.1657  data: 0.0528  max mem: 3005
+extract (train)  [140/151]  eta: 0:00:02    time: 0.1668  data: 0.0538  max mem: 3005
+extract (train)  [150/151]  eta: 0:00:00    time: 0.1571  data: 0.0492  max mem: 3005
+extract (train) Total time: 0:00:31 (0.2054 s / it)
+extract (validation)  [ 0/32]  eta: 0:02:12    time: 4.1342  data: 3.9301  max mem: 3005
+extract (validation)  [20/32]  eta: 0:00:04    time: 0.1925  data: 0.0667  max mem: 3005
+extract (validation)  [31/32]  eta: 0:00:00    time: 0.1530  data: 0.0474  max mem: 3005
+extract (validation) Total time: 0:00:10 (0.3129 s / it)
+extract (test)  [ 0/33]  eta: 0:02:13    time: 4.0377  data: 3.8013  max mem: 3005
+extract (test)  [20/33]  eta: 0:00:04    time: 0.1988  data: 0.0701  max mem: 3005
+extract (test)  [32/33]  eta: 0:00:00    time: 0.1652  data: 0.0563  max mem: 3005
+extract (test) Total time: 0:00:10 (0.3149 s / it)
+feature extraction time: 0:00:51
+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 |         | 0.00077426 | train   | 0.64384 |  0.021598 | 0.61677 | 0.024422 | 0.61989 |   0.022521 |
+| flat_mae | patch  | logistic | adhd200_dx |         | 0.00077426 | test    | 0.63077 |  0.053016 | 0.58774 | 0.063718 | 0.59749 |   0.055648 |
+
+
+evaluating random splits (n=100)
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.000774263682681127, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05458277683140735, "f1": 0.6425000000000001, "f1_std": 0.05816779598910018, "bacc": 0.6418918918918919, "bacc_std": 0.05585433506353738}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.000774263682681127, "split": "test", "acc": 0.676923076923077, "acc_std": 0.05326767062419579, "f1": 0.6500897205844656, "f1_std": 0.060294453957874965, "bacc": 0.6510617760617761, "bacc_std": 0.05582676658916951}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05944413915999939, "f1": 0.5167905665214048, "f1_std": 0.06648543375191263, "bacc": 0.5255791505791506, "bacc_std": 0.06102948444079758}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 166.81005372000556, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.061334083693206067, "f1": 0.5230769230769231, "f1_std": 0.061730884070689676, "bacc": 0.5333011583011583, "bacc_std": 0.06199841832793676}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06372235634551544, "f1": 0.4532564287430906, "f1_std": 0.06419256675531515, "bacc": 0.4531853281853282, "bacc_std": 0.06439503835085006}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.05869956402366246, "f1": 0.5775, "f1_std": 0.06385545691331125, "bacc": 0.5791505791505791, "bacc_std": 0.060692420538455825}
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+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.005994842503189409, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.05978336433229209, "f1": 0.5062484685126194, "f1_std": 0.06187737438459926, "bacc": 0.5072393822393823, "bacc_std": 0.060451464920462834}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05697231965701055, "f1": 0.4846723044397463, "f1_std": 0.06423068990692955, "bacc": 0.5033783783783784, "bacc_std": 0.057676133058271066}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.000774263682681127, "split": "test", "acc": 0.6, "acc_std": 0.05307044776554559, "f1": 0.5427489177489178, "f1_std": 0.06350648693969092, "bacc": 0.5617760617760618, "bacc_std": 0.05449924135245666}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.000774263682681127, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05515572360284874, "f1": 0.5321419707123356, "f1_std": 0.05807587600065702, "bacc": 0.5342664092664092, "bacc_std": 0.056046407809770674}
+{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.000774263682681127, "split": "test", "acc": 0.5076923076923077, "acc_std": 0.054612558973491945, "f1": 0.4616977225672878, "f1_std": 0.05981336694736562, "bacc": 0.47635135135135137, "bacc_std": 0.05515783886831236}
+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 | 6.9757 |  32.863 | 0.78638 |  0.1135   | 0.77088 | 0.12643  | 0.77108 |   0.12352  |
+| flat_mae | patch  | logistic | adhd200_dx | test    |        100 | 6.9757 |  32.863 | 0.554   |  0.053674 | 0.52155 | 0.054878 | 0.53049 |   0.052023 |
+
+
+done! total time: 0:04:49

decoders/crossreg_reg1/eval_v2/adhd200_dx__reg__logistic/config.yaml

@@ -0,0 +1,30 @@
+output_root: experiments/decoders/output
+name_prefix: eval_logistic
+remote_root: null
+notes: decoder ablations crossreg_reg1; eval v2 (adhd200_dx reg logistic)
+model_kwargs:
+  ckpt_path: experiments/decoders/output/decoders/crossreg_reg1/pretrain/checkpoint-last.pth
+dataset_kwargs: {}
+num_workers: 16
+batch_size: 2
+cv_folds: 5
+max_iter: 1000
+Cs: 10
+balanced_sampling: false
+metrics:
+- acc
+- f1
+- bacc
+cv_metric: bacc
+n_trials: 100
+amp: true
+device: cuda
+seed: 4466
+debug: false
+name: decoders/crossreg_reg1/eval_v2/adhd200_dx__reg__logistic
+model: flat_mae
+representation: reg
+dataset: adhd200_dx
+distributed: false
+output_dir: experiments/decoders/output/decoders/crossreg_reg1/eval_v2/adhd200_dx__reg__logistic
+remote_dir: null

decoders/crossreg_reg1/eval_v2/adhd200_dx__reg__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,reg,logistic,adhd200_dx,,0.000774263682681127,test,0.6,0.05388626198549339,0.5533826638477801,0.06451576690751301,0.5661196911196912,0.05627885583026897
+flat_mae,reg,logistic,adhd200_dx,1,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
+flat_mae,reg,logistic,adhd200_dx,1,166.81005372000556,test,0.6153846153846154,0.05912719015296276,0.61207925519217,0.05997563328105542,0.6143822393822393,0.060568491092861575
+flat_mae,reg,logistic,adhd200_dx,2,0.000774263682681127,train,0.6931506849315069,0.02096566029524679,0.6666014223266131,0.02416896013899953,0.6678878915552299,0.02211522175586972
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+flat_mae,reg,logistic,adhd200_dx,3,0.005994842503189409,train,0.863013698630137,0.017557877056337545,0.8590777118853472,0.018278361019738818,0.8556817487940405,0.018505366246488623
+flat_mae,reg,logistic,adhd200_dx,3,0.005994842503189409,test,0.6153846153846154,0.056130498834486446,0.5905769715293525,0.06166988768994409,0.5926640926640927,0.057954397023842995
+flat_mae,reg,logistic,adhd200_dx,4,0.005994842503189409,train,0.8191780821917808,0.020793012380560326,0.8152834008097165,0.021335911527656876,0.8139769188496062,0.021476997930327823
+flat_mae,reg,logistic,adhd200_dx,4,0.005994842503189409,test,0.6461538461538462,0.057067583726893814,0.6233308138070043,0.06381744426563256,0.6240347490347491,0.05988501528961966
+flat_mae,reg,logistic,adhd200_dx,5,0.000774263682681127,train,0.6904109589041096,0.021810820385929994,0.6674031723503939,0.02483280578030318,0.6676131159553031,0.02302924998051709
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