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- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_log_best.json +1 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table.csv +4 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv +4 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table_last.csv +4 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/log.txt +886 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/train_log.json +0 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_log.json +1 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json +1 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table.csv +5 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table_best.csv +5 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table_last.csv +5 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/log.txt +962 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/train_log.json +0 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/log.txt +247 -0
- input_space_v3/flat_lr1e-3_7/pretrain/config.yaml +102 -0
- input_space_v3/flat_lr1e-3_7/pretrain/log.json +100 -0
- input_space_v3/flat_lr1e-3_7/pretrain/log.txt +0 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic/log.txt +245 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/log.txt +245 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/log.txt +252 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/log.txt +241 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml +30 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv +203 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt +240 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/config.yaml +96 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log_best.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log_last.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table.csv +4 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv +4 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table_last.csv +4 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/log.txt +886 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/train_log.json +0 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/config.yaml +96 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json +1 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table.csv +5 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table_best.csv +5 -0
- input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table_last.csv +5 -0
input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_log_best.json
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{"eval/best/epoch": 12, "eval/best/id_best": 33, "eval/best/lr_best": 0.00129, "eval/best/wd_best": 0.05, "eval/best/train/loss": 0.00013906847743783146, "eval/best/train/acc": 1.0, "eval/best/train/acc_std": 0.0, "eval/best/train/f1": 1.0, "eval/best/train/f1_std": 0.0, "eval/best/validation/loss": 0.03187108412384987, "eval/best/validation/acc": 0.9937996031746031, "eval/best/validation/acc_std": 0.0012420946917515053, "eval/best/validation/f1": 0.993376921264988, "eval/best/validation/f1_std": 0.0014715304210984514, "eval/best/test/loss": 0.050566673278808594, "eval/best/test/acc": 0.9886904761904762, "eval/best/test/acc_std": 0.0014923629279915548, "eval/best/test/f1": 0.9864823498181688, "eval/best/test/f1_std": 0.001909035653281809}
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input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table.csv
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model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
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| 2 |
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flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",train,0.00013906847743783146,1.0,0.0,1.0,0.0
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| 3 |
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flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",validation,0.03187108412384987,0.9937996031746031,0.0012420946917515053,0.993376921264988,0.0014715304210984514
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| 4 |
+
flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",test,0.050566673278808594,0.9886904761904762,0.0014923629279915548,0.9864823498181688,0.001909035653281809
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input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv
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model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
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| 2 |
+
flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",train,0.00013906847743783146,1.0,0.0,1.0,0.0
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| 3 |
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flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",validation,0.03187108412384987,0.9937996031746031,0.0012420946917515053,0.993376921264988,0.0014715304210984514
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| 4 |
+
flat_mae,patch,attn,hcpya_task21,best,12,0.00129,0.05,33,"[4.3, 1.0]",test,0.050566673278808594,0.9886904761904762,0.0014923629279915548,0.9864823498181688,0.001909035653281809
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input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/eval_table_last.csv
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model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
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flat_mae,patch,attn,hcpya_task21,last,19,0.00129,0.05,33,"[4.3, 1.0]",train,0.000115940798423253,1.0,0.0,1.0,0.0
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| 3 |
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flat_mae,patch,attn,hcpya_task21,last,19,0.00129,0.05,33,"[4.3, 1.0]",validation,0.03186384588479996,0.9935515873015873,0.0012589120564729084,0.9928358487355211,0.0015613984788329439
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| 4 |
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flat_mae,patch,attn,hcpya_task21,last,19,0.00129,0.05,33,"[4.3, 1.0]",test,0.050439536571502686,0.9884920634920635,0.00152285332929164,0.9863384372404883,0.0019296356161460687
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input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/log.txt
ADDED
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|
| 1 |
+
fMRI foundation model probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 21:11:36
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_probe
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_7; eval v2 (hcpya_task21 patch attn)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
classifier_kwargs:
|
| 15 |
+
embed_dim: null
|
| 16 |
+
dropout: 0.0
|
| 17 |
+
xavier_init: true
|
| 18 |
+
norm: true
|
| 19 |
+
lr_scale_grid:
|
| 20 |
+
- 0.02
|
| 21 |
+
- 0.023
|
| 22 |
+
- 0.028
|
| 23 |
+
- 0.033
|
| 24 |
+
- 0.038
|
| 25 |
+
- 0.045
|
| 26 |
+
- 0.053
|
| 27 |
+
- 0.062
|
| 28 |
+
- 0.074
|
| 29 |
+
- 0.087
|
| 30 |
+
- 0.1
|
| 31 |
+
- 0.12
|
| 32 |
+
- 0.14
|
| 33 |
+
- 0.17
|
| 34 |
+
- 0.2
|
| 35 |
+
- 0.23
|
| 36 |
+
- 0.27
|
| 37 |
+
- 0.32
|
| 38 |
+
- 0.38
|
| 39 |
+
- 0.44
|
| 40 |
+
- 0.52
|
| 41 |
+
- 0.61
|
| 42 |
+
- 0.72
|
| 43 |
+
- 0.85
|
| 44 |
+
- 1
|
| 45 |
+
- 1.2
|
| 46 |
+
- 1.4
|
| 47 |
+
- 1.6
|
| 48 |
+
- 1.9
|
| 49 |
+
- 2.3
|
| 50 |
+
- 2.7
|
| 51 |
+
- 3.1
|
| 52 |
+
- 3.7
|
| 53 |
+
- 4.3
|
| 54 |
+
- 5.1
|
| 55 |
+
- 6
|
| 56 |
+
- 7.1
|
| 57 |
+
- 8.3
|
| 58 |
+
- 9.8
|
| 59 |
+
- 12
|
| 60 |
+
- 14
|
| 61 |
+
- 16
|
| 62 |
+
- 19
|
| 63 |
+
- 22
|
| 64 |
+
- 26
|
| 65 |
+
- 31
|
| 66 |
+
- 36
|
| 67 |
+
- 43
|
| 68 |
+
- 50
|
| 69 |
+
wd_scale_grid:
|
| 70 |
+
- 1.0
|
| 71 |
+
num_workers: 8
|
| 72 |
+
prefetch_factor: null
|
| 73 |
+
balanced_sampling: false
|
| 74 |
+
epochs: 20
|
| 75 |
+
steps_per_epoch: 200
|
| 76 |
+
batch_size: 64
|
| 77 |
+
accum_iter: 2
|
| 78 |
+
lr: 0.0003
|
| 79 |
+
warmup_epochs: 5
|
| 80 |
+
no_decay: false
|
| 81 |
+
weight_decay: 0.05
|
| 82 |
+
clip_grad: 1.0
|
| 83 |
+
metrics:
|
| 84 |
+
- acc
|
| 85 |
+
- f1
|
| 86 |
+
cv_metric: acc
|
| 87 |
+
early_stopping: true
|
| 88 |
+
amp: true
|
| 89 |
+
device: cuda
|
| 90 |
+
seed: 4466
|
| 91 |
+
debug: false
|
| 92 |
+
wandb: false
|
| 93 |
+
wandb_entity: null
|
| 94 |
+
wandb_project: fMRI-fm-eval
|
| 95 |
+
name: input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn
|
| 96 |
+
model: flat_mae
|
| 97 |
+
representation: patch
|
| 98 |
+
classifier: attn
|
| 99 |
+
dataset: hcpya_task21
|
| 100 |
+
distributed: false
|
| 101 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn
|
| 102 |
+
remote_dir: null
|
| 103 |
+
|
| 104 |
+
creating frozen backbone model: flat_mae
|
| 105 |
+
backbone:
|
| 106 |
+
MaskedEncoderWrapper(
|
| 107 |
+
(model): MaskedEncoder(
|
| 108 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 109 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 110 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 111 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 112 |
+
(blocks): ModuleList(
|
| 113 |
+
(0-11): 12 x Block(
|
| 114 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 115 |
+
(attn): Attention(
|
| 116 |
+
num_heads=12
|
| 117 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 118 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 119 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 120 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 121 |
+
)
|
| 122 |
+
(drop_path1): Identity()
|
| 123 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 124 |
+
(mlp): Mlp(
|
| 125 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 126 |
+
(act): GELU(approximate='none')
|
| 127 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 128 |
+
)
|
| 129 |
+
(drop_path2): Identity()
|
| 130 |
+
)
|
| 131 |
+
)
|
| 132 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 133 |
+
)
|
| 134 |
+
)
|
| 135 |
+
creating dataset: hcpya_task21 (flat)
|
| 136 |
+
train (n=18999):
|
| 137 |
+
HFDataset(
|
| 138 |
+
dataset=Dataset({
|
| 139 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 140 |
+
num_rows: 18999
|
| 141 |
+
}),
|
| 142 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 143 |
+
counts=[ 832 1248 3201 1660 832 832 832 832 832 1248 1247 1243 832 416
|
| 144 |
+
416 416 416 416 416 416 416]
|
| 145 |
+
)
|
| 146 |
+
|
| 147 |
+
validation (n=4032):
|
| 148 |
+
HFDataset(
|
| 149 |
+
dataset=Dataset({
|
| 150 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 151 |
+
num_rows: 4032
|
| 152 |
+
}),
|
| 153 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 154 |
+
counts=[176 264 688 352 176 176 176 176 176 264 264 264 176 88 88 88 88 88
|
| 155 |
+
88 88 88]
|
| 156 |
+
)
|
| 157 |
+
|
| 158 |
+
test (n=5040):
|
| 159 |
+
HFDataset(
|
| 160 |
+
dataset=Dataset({
|
| 161 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 162 |
+
num_rows: 5040
|
| 163 |
+
}),
|
| 164 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 165 |
+
counts=[220 330 860 440 220 220 220 220 220 330 330 330 220 110 110 110 110 110
|
| 166 |
+
110 110 110]
|
| 167 |
+
)
|
| 168 |
+
|
| 169 |
+
running backbone on example batch to get embedding dim
|
| 170 |
+
embedding feature dim (patch): 768
|
| 171 |
+
initializing sweep of classifier heads
|
| 172 |
+
classifiers:
|
| 173 |
+
ModuleList(
|
| 174 |
+
(0-48): 49 x AttnPoolClassifier(
|
| 175 |
+
(kv): Linear(in_features=768, out_features=1536, bias=True)
|
| 176 |
+
(linear): Linear(in_features=768, out_features=21, bias=True)
|
| 177 |
+
)
|
| 178 |
+
)
|
| 179 |
+
classifier params (train): 58.7M (58.7M)
|
| 180 |
+
setting up optimizer
|
| 181 |
+
total batch size: 128 = 64 bs per gpu x 2 accum
|
| 182 |
+
lr: 3.00e-04
|
| 183 |
+
full schedule: epochs = 20 (steps = 4000) (decay = True)
|
| 184 |
+
warmup: epochs = 5 (steps = 1000)
|
| 185 |
+
start training for 20 epochs
|
| 186 |
+
train: [0] [ 0/400] eta: 0:26:19 lr: nan time: 3.9478 data: 3.4575 max mem: 21740
|
| 187 |
+
train: [0] [ 20/400] eta: 0:04:04 lr: 0.000003 loss: 3.0858 (3.0879) grad: 0.2615 (0.2719) time: 0.4774 data: 0.0021 max mem: 22446
|
| 188 |
+
train: [0] [ 40/400] eta: 0:03:25 lr: 0.000006 loss: 3.0634 (3.0545) grad: 0.2624 (0.2715) time: 0.4940 data: 0.0037 max mem: 22446
|
| 189 |
+
train: [0] [ 60/400] eta: 0:03:03 lr: 0.000009 loss: 2.9647 (2.9986) grad: 0.2624 (0.2673) time: 0.4771 data: 0.0038 max mem: 22446
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train: [0] [ 80/400] eta: 0:02:47 lr: 0.000012 loss: 2.8043 (2.9393) grad: 0.2502 (0.2603) time: 0.4765 data: 0.0034 max mem: 22446
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train: [0] [100/400] eta: 0:02:34 lr: 0.000015 loss: 2.6582 (2.8721) grad: 0.2363 (0.2566) time: 0.4741 data: 0.0034 max mem: 22446
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train: [0] [120/400] eta: 0:02:21 lr: 0.000018 loss: 2.5365 (2.8029) grad: 0.2366 (0.2519) time: 0.4686 data: 0.0035 max mem: 22446
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train: [0] [140/400] eta: 0:02:10 lr: 0.000021 loss: 2.3781 (2.7362) grad: 0.2328 (0.2501) time: 0.4763 data: 0.0031 max mem: 22446
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train: [0] [160/400] eta: 0:01:59 lr: 0.000024 loss: 2.2724 (2.6755) grad: 0.2246 (0.2450) time: 0.4765 data: 0.0034 max mem: 22446
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train: [0] [180/400] eta: 0:01:48 lr: 0.000027 loss: 2.1792 (2.6151) grad: 0.2028 (0.2408) time: 0.4637 data: 0.0033 max mem: 22446
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train: [0] [200/400] eta: 0:01:38 lr: 0.000030 loss: 2.1222 (2.5597) grad: 0.2103 (0.2379) time: 0.4625 data: 0.0033 max mem: 22446
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train: [0] [220/400] eta: 0:01:28 lr: 0.000033 loss: 1.9984 (2.5066) grad: 0.1950 (0.2342) time: 0.4759 data: 0.0033 max mem: 22446
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train: [0] [240/400] eta: 0:01:18 lr: 0.000036 loss: 1.9040 (2.4527) grad: 0.2006 (0.2319) time: 0.4723 data: 0.0033 max mem: 22446
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train: [0] [260/400] eta: 0:01:08 lr: 0.000039 loss: 1.8319 (2.4035) grad: 0.2030 (0.2295) time: 0.4747 data: 0.0033 max mem: 22446
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train: [0] [280/400] eta: 0:00:58 lr: 0.000042 loss: 1.7976 (2.3600) grad: 0.1880 (0.2262) time: 0.4772 data: 0.0034 max mem: 22446
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train: [0] [300/400] eta: 0:00:49 lr: 0.000045 loss: 1.7696 (2.3178) grad: 0.1787 (0.2229) time: 0.6400 data: 0.1810 max mem: 22446
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train: [0] [320/400] eta: 0:00:39 lr: 0.000048 loss: 1.6917 (2.2766) grad: 0.1741 (0.2201) time: 0.4720 data: 0.0031 max mem: 22446
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train: [0] [340/400] eta: 0:00:29 lr: 0.000051 loss: 1.6155 (2.2371) grad: 0.1829 (0.2182) time: 0.4713 data: 0.0035 max mem: 22446
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train: [0] [360/400] eta: 0:00:19 lr: 0.000054 loss: 1.5903 (2.2013) grad: 0.1777 (0.2159) time: 0.4674 data: 0.0034 max mem: 22446
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train: [0] [380/400] eta: 0:00:09 lr: 0.000057 loss: 1.5549 (2.1663) grad: 0.1711 (0.2134) time: 0.4815 data: 0.0036 max mem: 22446
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train: [0] [399/400] eta: 0:00:00 lr: 0.000060 loss: 1.5015 (2.1317) grad: 0.1694 (0.2116) time: 0.4877 data: 0.0036 max mem: 22446
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train: [0] Total time: 0:03:16 (0.4922 s / it)
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train: [0] Summary: lr: 0.000060 loss: 1.5015 (2.1317) grad: 0.1694 (0.2116)
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eval (validation): [0] [ 0/63] eta: 0:03:38 time: 3.4711 data: 3.1576 max mem: 22446
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eval (validation): [0] [20/63] eta: 0:00:25 time: 0.4579 data: 0.0045 max mem: 22446
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eval (validation): [0] [40/63] eta: 0:00:11 time: 0.3946 data: 0.0038 max mem: 22446
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eval (validation): [0] [60/63] eta: 0:00:01 time: 0.3576 data: 0.0033 max mem: 22446
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eval (validation): [0] [62/63] eta: 0:00:00 time: 0.3584 data: 0.0033 max mem: 22446
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eval (validation): [0] Total time: 0:00:28 (0.4560 s / it)
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cv: [0] best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00') loss: 0.052 acc: 0.986 f1: 0.985
|
| 216 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
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saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
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train: [1] [ 0/400] eta: 0:23:45 lr: nan time: 3.5640 data: 3.1389 max mem: 22446
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train: [1] [ 20/400] eta: 0:04:05 lr: 0.000063 loss: 1.4498 (1.4576) grad: 0.1665 (0.1706) time: 0.5000 data: 0.0037 max mem: 22446
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train: [1] [ 40/400] eta: 0:03:25 lr: 0.000066 loss: 1.4231 (1.4366) grad: 0.1691 (0.1705) time: 0.4894 data: 0.0035 max mem: 22446
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train: [1] [ 60/400] eta: 0:03:02 lr: 0.000069 loss: 1.3838 (1.4132) grad: 0.1643 (0.1672) time: 0.4657 data: 0.0033 max mem: 22446
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train: [1] [ 80/400] eta: 0:02:48 lr: 0.000072 loss: 1.3536 (1.3963) grad: 0.1552 (0.1651) time: 0.4967 data: 0.0035 max mem: 22446
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train: [1] [100/400] eta: 0:02:35 lr: 0.000075 loss: 1.3365 (1.3838) grad: 0.1567 (0.1642) time: 0.4804 data: 0.0034 max mem: 22446
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train: [1] [120/400] eta: 0:02:22 lr: 0.000078 loss: 1.3070 (1.3657) grad: 0.1552 (0.1631) time: 0.4606 data: 0.0032 max mem: 22446
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train: [1] [140/400] eta: 0:02:11 lr: 0.000081 loss: 1.2559 (1.3505) grad: 0.1495 (0.1612) time: 0.5013 data: 0.0035 max mem: 22446
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train: [1] [160/400] eta: 0:02:00 lr: 0.000084 loss: 1.2435 (1.3350) grad: 0.1491 (0.1601) time: 0.4855 data: 0.0034 max mem: 22446
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train: [1] [180/400] eta: 0:01:49 lr: 0.000087 loss: 1.2221 (1.3206) grad: 0.1493 (0.1592) time: 0.4652 data: 0.0031 max mem: 22446
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train: [1] [200/400] eta: 0:01:39 lr: 0.000090 loss: 1.1746 (1.3055) grad: 0.1478 (0.1582) time: 0.4740 data: 0.0032 max mem: 22446
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train: [1] [220/400] eta: 0:01:29 lr: 0.000093 loss: 1.1426 (1.2898) grad: 0.1505 (0.1582) time: 0.4800 data: 0.0034 max mem: 22446
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train: [1] [240/400] eta: 0:01:18 lr: 0.000096 loss: 1.1269 (1.2759) grad: 0.1514 (0.1573) time: 0.4675 data: 0.0033 max mem: 22446
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train: [1] [260/400] eta: 0:01:08 lr: 0.000099 loss: 1.1181 (1.2635) grad: 0.1455 (0.1563) time: 0.4743 data: 0.0033 max mem: 22446
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train: [1] [280/400] eta: 0:00:58 lr: 0.000102 loss: 1.0883 (1.2499) grad: 0.1388 (0.1557) time: 0.4687 data: 0.0034 max mem: 22446
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train: [1] [300/400] eta: 0:00:50 lr: 0.000105 loss: 1.0655 (1.2376) grad: 0.1371 (0.1542) time: 0.6394 data: 0.1828 max mem: 22446
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train: [1] [320/400] eta: 0:00:39 lr: 0.000108 loss: 1.0601 (1.2259) grad: 0.1371 (0.1534) time: 0.4770 data: 0.0037 max mem: 22446
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train: [1] [340/400] eta: 0:00:29 lr: 0.000111 loss: 1.0260 (1.2134) grad: 0.1311 (0.1520) time: 0.4680 data: 0.0029 max mem: 22446
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train: [1] [360/400] eta: 0:00:19 lr: 0.000114 loss: 1.0135 (1.2026) grad: 0.1279 (0.1507) time: 0.4720 data: 0.0034 max mem: 22446
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train: [1] [380/400] eta: 0:00:09 lr: 0.000117 loss: 0.9860 (1.1917) grad: 0.1308 (0.1499) time: 0.4873 data: 0.0035 max mem: 22446
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train: [1] [399/400] eta: 0:00:00 lr: 0.000120 loss: 0.9787 (1.1812) grad: 0.1307 (0.1487) time: 0.4749 data: 0.0033 max mem: 22446
|
| 239 |
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train: [1] Total time: 0:03:17 (0.4944 s / it)
|
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train: [1] Summary: lr: 0.000120 loss: 0.9787 (1.1812) grad: 0.1307 (0.1487)
|
| 241 |
+
eval (validation): [1] [ 0/63] eta: 0:03:23 time: 3.2227 data: 2.9826 max mem: 22446
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eval (validation): [1] [20/63] eta: 0:00:21 time: 0.3739 data: 0.0038 max mem: 22446
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eval (validation): [1] [40/63] eta: 0:00:09 time: 0.3560 data: 0.0029 max mem: 22446
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eval (validation): [1] [60/63] eta: 0:00:01 time: 0.3460 data: 0.0032 max mem: 22446
|
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eval (validation): [1] [62/63] eta: 0:00:00 time: 0.3446 data: 0.0032 max mem: 22446
|
| 246 |
+
eval (validation): [1] Total time: 0:00:25 (0.4093 s / it)
|
| 247 |
+
cv: [1] best hparam: (8.3, 1.0) (037) ('037_lr8.3e+00_wd1.0e+00') loss: 0.032 acc: 0.990 f1: 0.988
|
| 248 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 249 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 250 |
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train: [2] [ 0/400] eta: 0:22:59 lr: nan time: 3.4478 data: 3.0317 max mem: 22446
|
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train: [2] [ 20/400] eta: 0:04:16 lr: 0.000123 loss: 0.9126 (0.9262) grad: 0.1389 (0.1347) time: 0.5367 data: 0.0043 max mem: 22446
|
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train: [2] [ 40/400] eta: 0:03:26 lr: 0.000126 loss: 0.9323 (0.9357) grad: 0.1389 (0.1385) time: 0.4672 data: 0.0032 max mem: 22446
|
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train: [2] [ 60/400] eta: 0:03:04 lr: 0.000129 loss: 0.9287 (0.9276) grad: 0.1395 (0.1397) time: 0.4811 data: 0.0034 max mem: 22446
|
| 254 |
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train: [2] [ 80/400] eta: 0:02:47 lr: 0.000132 loss: 0.9116 (0.9245) grad: 0.1435 (0.1430) time: 0.4628 data: 0.0033 max mem: 22446
|
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train: [2] [100/400] eta: 0:02:33 lr: 0.000135 loss: 0.9116 (0.9197) grad: 0.1521 (0.1446) time: 0.4709 data: 0.0032 max mem: 22446
|
| 256 |
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train: [2] [120/400] eta: 0:02:21 lr: 0.000138 loss: 0.9013 (0.9196) grad: 0.1537 (0.1492) time: 0.4733 data: 0.0033 max mem: 22446
|
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train: [2] [140/400] eta: 0:02:10 lr: 0.000141 loss: 0.8726 (0.9126) grad: 0.1723 (0.1532) time: 0.4639 data: 0.0034 max mem: 22446
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train: [2] [160/400] eta: 0:01:58 lr: 0.000144 loss: 0.8812 (0.9121) grad: 0.1756 (0.1590) time: 0.4620 data: 0.0034 max mem: 22446
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train: [2] [180/400] eta: 0:01:48 lr: 0.000147 loss: 0.8710 (0.9078) grad: 0.1711 (0.1611) time: 0.4717 data: 0.0033 max mem: 22446
|
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train: [2] [200/400] eta: 0:01:37 lr: 0.000150 loss: 0.8261 (0.8988) grad: 0.1656 (0.1608) time: 0.4611 data: 0.0034 max mem: 22446
|
| 261 |
+
train: [2] [220/400] eta: 0:01:27 lr: 0.000153 loss: 0.8227 (0.8987) grad: 0.1682 (0.1623) time: 0.4598 data: 0.0034 max mem: 22446
|
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train: [2] [240/400] eta: 0:01:17 lr: 0.000156 loss: 0.9058 (0.8947) grad: 0.1682 (0.1621) time: 0.4556 data: 0.0033 max mem: 22446
|
| 263 |
+
train: [2] [260/400] eta: 0:01:07 lr: 0.000159 loss: 0.8006 (0.8917) grad: 0.1698 (0.1646) time: 0.4708 data: 0.0032 max mem: 22446
|
| 264 |
+
train: [2] [280/400] eta: 0:00:57 lr: 0.000162 loss: 0.8525 (0.8893) grad: 0.2002 (0.1681) time: 0.4685 data: 0.0034 max mem: 22446
|
| 265 |
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train: [2] [300/400] eta: 0:00:49 lr: 0.000165 loss: 0.8050 (0.8839) grad: 0.1962 (0.1690) time: 0.6502 data: 0.1854 max mem: 22446
|
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+
train: [2] [320/400] eta: 0:00:39 lr: 0.000168 loss: 0.7773 (0.8828) grad: 0.1836 (0.1719) time: 0.4755 data: 0.0035 max mem: 22446
|
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train: [2] [340/400] eta: 0:00:29 lr: 0.000171 loss: 0.8330 (0.8807) grad: 0.2215 (0.1799) time: 0.4795 data: 0.0033 max mem: 22446
|
| 268 |
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train: [2] [360/400] eta: 0:00:19 lr: 0.000174 loss: 0.8330 (0.8790) grad: 0.2221 (0.1813) time: 0.4936 data: 0.0034 max mem: 22446
|
| 269 |
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train: [2] [380/400] eta: 0:00:09 lr: 0.000177 loss: 0.8161 (0.8735) grad: 0.2054 (0.1831) time: 0.4747 data: 0.0033 max mem: 22446
|
| 270 |
+
train: [2] [399/400] eta: 0:00:00 lr: 0.000180 loss: 0.7310 (0.8659) grad: 0.2159 (0.1852) time: 0.4629 data: 0.0032 max mem: 22446
|
| 271 |
+
train: [2] Total time: 0:03:15 (0.4898 s / it)
|
| 272 |
+
train: [2] Summary: lr: 0.000180 loss: 0.7310 (0.8659) grad: 0.2159 (0.1852)
|
| 273 |
+
eval (validation): [2] [ 0/63] eta: 0:03:27 time: 3.2995 data: 3.0334 max mem: 22446
|
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+
eval (validation): [2] [20/63] eta: 0:00:22 time: 0.3883 data: 0.0036 max mem: 22446
|
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+
eval (validation): [2] [40/63] eta: 0:00:10 time: 0.3471 data: 0.0029 max mem: 22446
|
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+
eval (validation): [2] [60/63] eta: 0:00:01 time: 0.3354 data: 0.0030 max mem: 22446
|
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eval (validation): [2] [62/63] eta: 0:00:00 time: 0.3357 data: 0.0030 max mem: 22446
|
| 278 |
+
eval (validation): [2] Total time: 0:00:25 (0.4081 s / it)
|
| 279 |
+
cv: [2] best hparam: (12, 1.0) (039) ('039_lr1.2e+01_wd1.0e+00') loss: 0.040 acc: 0.990 f1: 0.987
|
| 280 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 281 |
+
train: [3] [ 0/400] eta: 0:22:44 lr: nan time: 3.4119 data: 3.0526 max mem: 22446
|
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+
train: [3] [ 20/400] eta: 0:03:56 lr: 0.000183 loss: 0.6710 (0.7036) grad: 0.2185 (0.2116) time: 0.4820 data: 0.0030 max mem: 22446
|
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train: [3] [ 40/400] eta: 0:03:15 lr: 0.000186 loss: 0.7159 (0.7132) grad: 0.2050 (0.2203) time: 0.4603 data: 0.0031 max mem: 22446
|
| 284 |
+
train: [3] [ 60/400] eta: 0:02:57 lr: 0.000189 loss: 0.7242 (0.7306) grad: 0.2050 (0.2245) time: 0.4784 data: 0.0033 max mem: 22446
|
| 285 |
+
train: [3] [ 80/400] eta: 0:02:42 lr: 0.000192 loss: 0.7487 (0.7482) grad: 0.2132 (0.2305) time: 0.4628 data: 0.0032 max mem: 22446
|
| 286 |
+
train: [3] [100/400] eta: 0:02:29 lr: 0.000195 loss: 0.6828 (0.7413) grad: 0.2066 (0.2235) time: 0.4666 data: 0.0034 max mem: 22446
|
| 287 |
+
train: [3] [120/400] eta: 0:02:18 lr: 0.000198 loss: 0.6926 (0.7423) grad: 0.1987 (0.2220) time: 0.4664 data: 0.0034 max mem: 22446
|
| 288 |
+
train: [3] [140/400] eta: 0:02:07 lr: 0.000201 loss: 0.7114 (0.7433) grad: 0.2097 (0.2216) time: 0.4666 data: 0.0034 max mem: 22446
|
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+
train: [3] [160/400] eta: 0:01:56 lr: 0.000204 loss: 0.6841 (0.7409) grad: 0.2353 (0.2243) time: 0.4644 data: 0.0035 max mem: 22446
|
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+
train: [3] [180/400] eta: 0:01:46 lr: 0.000207 loss: 0.6753 (0.7407) grad: 0.2520 (0.2301) time: 0.4747 data: 0.0034 max mem: 22446
|
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+
train: [3] [200/400] eta: 0:01:36 lr: 0.000210 loss: 0.7287 (0.7488) grad: 0.2621 (0.2322) time: 0.4703 data: 0.0034 max mem: 22446
|
| 292 |
+
train: [3] [220/400] eta: 0:01:26 lr: 0.000213 loss: 0.7263 (0.7444) grad: 0.2418 (0.2353) time: 0.4652 data: 0.0034 max mem: 22446
|
| 293 |
+
train: [3] [240/400] eta: 0:01:16 lr: 0.000216 loss: 0.7137 (0.7600) grad: 0.2800 (0.2399) time: 0.4616 data: 0.0034 max mem: 22446
|
| 294 |
+
train: [3] [260/400] eta: 0:01:07 lr: 0.000219 loss: 0.7175 (0.7586) grad: 0.2985 (0.2440) time: 0.4572 data: 0.0033 max mem: 22446
|
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+
train: [3] [280/400] eta: 0:00:57 lr: 0.000222 loss: 0.6758 (0.7557) grad: 0.2925 (0.2457) time: 0.4617 data: 0.0034 max mem: 22446
|
| 296 |
+
train: [3] [300/400] eta: 0:00:48 lr: 0.000225 loss: 0.7427 (0.7598) grad: 0.2891 (0.2498) time: 0.6550 data: 0.1806 max mem: 22446
|
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+
train: [3] [320/400] eta: 0:00:39 lr: 0.000228 loss: 0.7070 (0.7571) grad: 0.2891 (0.2519) time: 0.4662 data: 0.0030 max mem: 22446
|
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+
train: [3] [340/400] eta: 0:00:29 lr: 0.000231 loss: 0.6362 (0.7470) grad: 0.2755 (0.2541) time: 0.4701 data: 0.0033 max mem: 22446
|
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train: [3] [360/400] eta: 0:00:19 lr: 0.000234 loss: 0.5745 (0.7366) grad: 0.2514 (0.2547) time: 0.4758 data: 0.0031 max mem: 22446
|
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+
train: [3] [380/400] eta: 0:00:09 lr: 0.000237 loss: 0.5829 (0.7373) grad: 0.2539 (0.2558) time: 0.4742 data: 0.0033 max mem: 22446
|
| 301 |
+
train: [3] [399/400] eta: 0:00:00 lr: 0.000240 loss: 0.6936 (0.7353) grad: 0.2740 (0.2581) time: 0.4732 data: 0.0033 max mem: 22446
|
| 302 |
+
train: [3] Total time: 0:03:14 (0.4852 s / it)
|
| 303 |
+
train: [3] Summary: lr: 0.000240 loss: 0.6936 (0.7353) grad: 0.2740 (0.2581)
|
| 304 |
+
eval (validation): [3] [ 0/63] eta: 0:03:23 time: 3.2325 data: 2.9448 max mem: 22446
|
| 305 |
+
eval (validation): [3] [20/63] eta: 0:00:21 time: 0.3685 data: 0.0032 max mem: 22446
|
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+
eval (validation): [3] [40/63] eta: 0:00:09 time: 0.3507 data: 0.0029 max mem: 22446
|
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+
eval (validation): [3] [60/63] eta: 0:00:01 time: 0.3417 data: 0.0031 max mem: 22446
|
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eval (validation): [3] [62/63] eta: 0:00:00 time: 0.3431 data: 0.0031 max mem: 22446
|
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eval (validation): [3] Total time: 0:00:25 (0.4045 s / it)
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cv: [3] best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00') loss: 0.039 acc: 0.988 f1: 0.985
|
| 311 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
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train: [4] [ 0/400] eta: 0:22:47 lr: nan time: 3.4178 data: 3.0102 max mem: 22446
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train: [4] [ 20/400] eta: 0:03:58 lr: 0.000243 loss: 0.6781 (0.7050) grad: 0.2983 (0.3243) time: 0.4874 data: 0.0032 max mem: 22446
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train: [4] [ 40/400] eta: 0:03:17 lr: 0.000246 loss: 0.7028 (0.7483) grad: 0.2787 (0.3120) time: 0.4668 data: 0.0030 max mem: 22446
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train: [4] [ 60/400] eta: 0:02:58 lr: 0.000249 loss: 0.7010 (0.7187) grad: 0.2852 (0.3148) time: 0.4745 data: 0.0036 max mem: 22446
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train: [4] [ 80/400] eta: 0:02:43 lr: 0.000252 loss: 0.6218 (0.6933) grad: 0.2863 (0.3041) time: 0.4660 data: 0.0034 max mem: 22446
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train: [4] [100/400] eta: 0:02:31 lr: 0.000255 loss: 0.6001 (0.6986) grad: 0.3127 (0.3156) time: 0.4848 data: 0.0033 max mem: 22446
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train: [4] [120/400] eta: 0:02:20 lr: 0.000258 loss: 0.6446 (0.7082) grad: 0.3216 (0.3195) time: 0.4787 data: 0.0034 max mem: 22446
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train: [4] [140/400] eta: 0:02:09 lr: 0.000261 loss: 0.6819 (0.7076) grad: 0.3216 (0.3212) time: 0.4715 data: 0.0036 max mem: 22446
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train: [4] [160/400] eta: 0:01:58 lr: 0.000264 loss: 0.7317 (0.7257) grad: 0.3384 (0.3387) time: 0.4793 data: 0.0035 max mem: 22446
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train: [4] [180/400] eta: 0:01:48 lr: 0.000267 loss: 0.6946 (0.7228) grad: 0.3548 (0.3407) time: 0.4823 data: 0.0034 max mem: 22446
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train: [4] [200/400] eta: 0:01:38 lr: 0.000270 loss: 0.6411 (0.7189) grad: 0.3349 (0.3439) time: 0.4684 data: 0.0033 max mem: 22446
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train: [4] [220/400] eta: 0:01:28 lr: 0.000273 loss: 0.7232 (0.7345) grad: 0.3623 (0.3536) time: 0.4775 data: 0.0034 max mem: 22446
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train: [4] [240/400] eta: 0:01:18 lr: 0.000276 loss: 0.6323 (0.7283) grad: 0.3584 (0.3550) time: 0.4806 data: 0.0035 max mem: 22446
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train: [4] [260/400] eta: 0:01:08 lr: 0.000279 loss: 0.6481 (0.7553) grad: 0.3556 (0.3653) time: 0.4856 data: 0.0034 max mem: 22446
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+
train: [4] [280/400] eta: 0:00:58 lr: 0.000282 loss: 0.9674 (0.7681) grad: 0.4683 (0.3749) time: 0.4538 data: 0.0032 max mem: 22446
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train: [4] [300/400] eta: 0:00:49 lr: 0.000285 loss: 0.8666 (0.7799) grad: 0.5317 (0.3878) time: 0.6591 data: 0.1867 max mem: 22446
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train: [4] [320/400] eta: 0:00:39 lr: 0.000288 loss: 0.6333 (0.7720) grad: 0.4478 (0.3909) time: 0.4693 data: 0.0031 max mem: 22446
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train: [4] [340/400] eta: 0:00:29 lr: 0.000291 loss: 0.5998 (0.7670) grad: 0.3970 (0.3925) time: 0.4657 data: 0.0035 max mem: 22446
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train: [4] [360/400] eta: 0:00:19 lr: 0.000294 loss: 0.6294 (0.7713) grad: 0.4435 (0.3977) time: 0.4598 data: 0.0033 max mem: 22446
|
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train: [4] [380/400] eta: 0:00:09 lr: 0.000297 loss: 0.7919 (0.7891) grad: 0.4853 (0.4062) time: 0.4613 data: 0.0033 max mem: 22446
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train: [4] [399/400] eta: 0:00:00 lr: 0.000300 loss: 0.8858 (0.7941) grad: 0.4799 (0.4121) time: 0.4601 data: 0.0033 max mem: 22446
|
| 333 |
+
train: [4] Total time: 0:03:15 (0.4893 s / it)
|
| 334 |
+
train: [4] Summary: lr: 0.000300 loss: 0.8858 (0.7941) grad: 0.4799 (0.4121)
|
| 335 |
+
eval (validation): [4] [ 0/63] eta: 0:03:26 time: 3.2776 data: 2.9810 max mem: 22446
|
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eval (validation): [4] [20/63] eta: 0:00:22 time: 0.3812 data: 0.0032 max mem: 22446
|
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eval (validation): [4] [40/63] eta: 0:00:10 time: 0.3530 data: 0.0032 max mem: 22446
|
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eval (validation): [4] [60/63] eta: 0:00:01 time: 0.3426 data: 0.0031 max mem: 22446
|
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eval (validation): [4] [62/63] eta: 0:00:00 time: 0.3420 data: 0.0031 max mem: 22446
|
| 340 |
+
eval (validation): [4] Total time: 0:00:25 (0.4099 s / it)
|
| 341 |
+
cv: [4] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.044 acc: 0.989 f1: 0.986
|
| 342 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 343 |
+
train: [5] [ 0/400] eta: 0:22:44 lr: nan time: 3.4115 data: 3.0088 max mem: 22446
|
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train: [5] [ 20/400] eta: 0:03:53 lr: 0.000300 loss: 0.6598 (0.7559) grad: 0.4980 (0.6317) time: 0.4744 data: 0.0033 max mem: 22446
|
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+
train: [5] [ 40/400] eta: 0:03:15 lr: 0.000300 loss: 0.7016 (0.8417) grad: 0.5073 (0.8359) time: 0.4700 data: 0.0032 max mem: 22446
|
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+
train: [5] [ 60/400] eta: 0:02:56 lr: 0.000300 loss: 0.8762 (0.8804) grad: 0.4946 (0.7221) time: 0.4661 data: 0.0034 max mem: 22446
|
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+
train: [5] [ 80/400] eta: 0:02:41 lr: 0.000300 loss: 0.8053 (0.8933) grad: 0.4581 (0.6673) time: 0.4684 data: 0.0034 max mem: 22446
|
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+
train: [5] [100/400] eta: 0:02:29 lr: 0.000300 loss: 0.8433 (0.9545) grad: 0.5179 (0.6429) time: 0.4750 data: 0.0034 max mem: 22446
|
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train: [5] [120/400] eta: 0:02:18 lr: 0.000300 loss: 1.3487 (1.0490) grad: 0.5405 (0.6326) time: 0.4723 data: 0.0033 max mem: 22446
|
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+
train: [5] [140/400] eta: 0:02:07 lr: 0.000300 loss: 1.3023 (1.0583) grad: 0.5918 (0.6426) time: 0.4707 data: 0.0033 max mem: 22446
|
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train: [5] [160/400] eta: 0:01:57 lr: 0.000299 loss: 1.1195 (1.0717) grad: 0.5783 (0.6336) time: 0.4693 data: 0.0033 max mem: 22446
|
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+
train: [5] [180/400] eta: 0:01:47 lr: 0.000299 loss: 1.0569 (1.0565) grad: 0.5468 (0.6259) time: 0.4659 data: 0.0034 max mem: 22446
|
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+
train: [5] [200/400] eta: 0:01:36 lr: 0.000299 loss: 0.8650 (1.0690) grad: 0.6224 (0.6346) time: 0.4614 data: 0.0034 max mem: 22446
|
| 354 |
+
train: [5] [220/400] eta: 0:01:26 lr: 0.000299 loss: 0.9228 (1.0627) grad: 0.5557 (0.6238) time: 0.4736 data: 0.0034 max mem: 22446
|
| 355 |
+
train: [5] [240/400] eta: 0:01:17 lr: 0.000299 loss: 0.9228 (1.0528) grad: 0.5422 (0.6232) time: 0.4652 data: 0.0034 max mem: 22446
|
| 356 |
+
train: [5] [260/400] eta: 0:01:07 lr: 0.000299 loss: 1.0953 (1.0787) grad: 0.5725 (0.6210) time: 0.4623 data: 0.0035 max mem: 22446
|
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+
train: [5] [280/400] eta: 0:00:57 lr: 0.000298 loss: 1.3262 (1.0994) grad: 0.5892 (0.6483) time: 0.4709 data: 0.0035 max mem: 22446
|
| 358 |
+
train: [5] [300/400] eta: 0:00:49 lr: 0.000298 loss: 1.0024 (1.0958) grad: 0.5892 (0.6438) time: 0.6650 data: 0.1826 max mem: 22446
|
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+
train: [5] [320/400] eta: 0:00:39 lr: 0.000298 loss: 0.7118 (1.0772) grad: 0.4486 (0.6331) time: 0.4670 data: 0.0031 max mem: 22446
|
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+
train: [5] [340/400] eta: 0:00:29 lr: 0.000298 loss: 0.8032 (1.0716) grad: 0.5296 (0.6291) time: 0.4681 data: 0.0035 max mem: 22446
|
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+
train: [5] [360/400] eta: 0:00:19 lr: 0.000297 loss: 0.8032 (1.0571) grad: 0.5666 (0.6229) time: 0.4680 data: 0.0034 max mem: 22446
|
| 362 |
+
train: [5] [380/400] eta: 0:00:09 lr: 0.000297 loss: 0.8637 (1.0593) grad: 0.5229 (0.6198) time: 0.4741 data: 0.0034 max mem: 22446
|
| 363 |
+
train: [5] [399/400] eta: 0:00:00 lr: 0.000297 loss: 0.8637 (1.0408) grad: 0.4537 (0.6080) time: 0.4820 data: 0.0034 max mem: 22446
|
| 364 |
+
train: [5] Total time: 0:03:14 (0.4871 s / it)
|
| 365 |
+
train: [5] Summary: lr: 0.000297 loss: 0.8637 (1.0408) grad: 0.4537 (0.6080)
|
| 366 |
+
eval (validation): [5] [ 0/63] eta: 0:03:20 time: 3.1824 data: 2.9371 max mem: 22446
|
| 367 |
+
eval (validation): [5] [20/63] eta: 0:00:21 time: 0.3542 data: 0.0059 max mem: 22446
|
| 368 |
+
eval (validation): [5] [40/63] eta: 0:00:09 time: 0.3539 data: 0.0033 max mem: 22446
|
| 369 |
+
eval (validation): [5] [60/63] eta: 0:00:01 time: 0.3542 data: 0.0022 max mem: 22446
|
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+
eval (validation): [5] [62/63] eta: 0:00:00 time: 0.3585 data: 0.0024 max mem: 22446
|
| 371 |
+
eval (validation): [5] Total time: 0:00:25 (0.4049 s / it)
|
| 372 |
+
cv: [5] best hparam: (3.1, 1.0) (031) ('031_lr3.1e+00_wd1.0e+00') loss: 0.027 acc: 0.992 f1: 0.992
|
| 373 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 374 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 375 |
+
train: [6] [ 0/400] eta: 0:22:51 lr: nan time: 3.4283 data: 3.0613 max mem: 22446
|
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+
train: [6] [ 20/400] eta: 0:03:53 lr: 0.000296 loss: 0.5686 (0.5966) grad: 0.3633 (0.4655) time: 0.4738 data: 0.0035 max mem: 22446
|
| 377 |
+
train: [6] [ 40/400] eta: 0:03:17 lr: 0.000296 loss: 0.5116 (0.5964) grad: 0.3802 (0.4470) time: 0.4807 data: 0.0034 max mem: 22446
|
| 378 |
+
train: [6] [ 60/400] eta: 0:02:59 lr: 0.000296 loss: 0.5116 (0.6700) grad: 0.4426 (0.4601) time: 0.4822 data: 0.0034 max mem: 22446
|
| 379 |
+
train: [6] [ 80/400] eta: 0:02:44 lr: 0.000295 loss: 0.8365 (0.7462) grad: 0.4594 (0.4627) time: 0.4720 data: 0.0034 max mem: 22446
|
| 380 |
+
train: [6] [100/400] eta: 0:02:31 lr: 0.000295 loss: 0.7529 (0.7381) grad: 0.4452 (0.4594) time: 0.4683 data: 0.0033 max mem: 22446
|
| 381 |
+
train: [6] [120/400] eta: 0:02:20 lr: 0.000295 loss: 0.6186 (0.7053) grad: 0.3694 (0.4514) time: 0.4921 data: 0.0035 max mem: 22446
|
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+
train: [6] [140/400] eta: 0:02:09 lr: 0.000294 loss: 0.6186 (0.7078) grad: 0.3694 (0.4443) time: 0.4758 data: 0.0033 max mem: 22446
|
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+
train: [6] [160/400] eta: 0:01:58 lr: 0.000294 loss: 0.5136 (0.6846) grad: 0.3857 (0.4390) time: 0.4734 data: 0.0033 max mem: 22446
|
| 384 |
+
train: [6] [180/400] eta: 0:01:48 lr: 0.000293 loss: 0.5964 (0.6902) grad: 0.4121 (0.4410) time: 0.4734 data: 0.0032 max mem: 22446
|
| 385 |
+
train: [6] [200/400] eta: 0:01:38 lr: 0.000293 loss: 0.6218 (0.7279) grad: 0.4121 (0.4487) time: 0.4726 data: 0.0033 max mem: 22446
|
| 386 |
+
train: [6] [220/400] eta: 0:01:28 lr: 0.000292 loss: 0.5059 (0.7196) grad: 0.4343 (0.4486) time: 0.4740 data: 0.0032 max mem: 22446
|
| 387 |
+
train: [6] [240/400] eta: 0:01:18 lr: 0.000292 loss: 0.5952 (0.7134) grad: 0.4393 (0.4489) time: 0.4753 data: 0.0032 max mem: 22446
|
| 388 |
+
train: [6] [260/400] eta: 0:01:08 lr: 0.000291 loss: 0.5952 (0.7055) grad: 0.4183 (0.4508) time: 0.4710 data: 0.0033 max mem: 22446
|
| 389 |
+
train: [6] [280/400] eta: 0:00:58 lr: 0.000291 loss: 0.6836 (0.7197) grad: 0.3794 (0.4505) time: 0.4758 data: 0.0034 max mem: 22446
|
| 390 |
+
train: [6] [300/400] eta: 0:00:49 lr: 0.000290 loss: 0.5370 (0.7036) grad: 0.3324 (0.4452) time: 0.6282 data: 0.1798 max mem: 22446
|
| 391 |
+
train: [6] [320/400] eta: 0:00:39 lr: 0.000290 loss: 0.3915 (0.6924) grad: 0.3173 (0.4386) time: 0.4592 data: 0.0030 max mem: 22446
|
| 392 |
+
train: [6] [340/400] eta: 0:00:29 lr: 0.000289 loss: 0.4334 (0.6812) grad: 0.3173 (0.4337) time: 0.4613 data: 0.0033 max mem: 22446
|
| 393 |
+
train: [6] [360/400] eta: 0:00:19 lr: 0.000288 loss: 0.4334 (0.6713) grad: 0.3503 (0.4294) time: 0.4672 data: 0.0033 max mem: 22446
|
| 394 |
+
train: [6] [380/400] eta: 0:00:09 lr: 0.000288 loss: 0.5117 (0.6633) grad: 0.3503 (0.4229) time: 0.4697 data: 0.0033 max mem: 22446
|
| 395 |
+
train: [6] [399/400] eta: 0:00:00 lr: 0.000287 loss: 0.3595 (0.6480) grad: 0.3223 (0.4162) time: 0.4696 data: 0.0033 max mem: 22446
|
| 396 |
+
train: [6] Total time: 0:03:15 (0.4884 s / it)
|
| 397 |
+
train: [6] Summary: lr: 0.000287 loss: 0.3595 (0.6480) grad: 0.3223 (0.4162)
|
| 398 |
+
eval (validation): [6] [ 0/63] eta: 0:03:25 time: 3.2655 data: 2.9690 max mem: 22446
|
| 399 |
+
eval (validation): [6] [20/63] eta: 0:00:21 time: 0.3716 data: 0.0038 max mem: 22446
|
| 400 |
+
eval (validation): [6] [40/63] eta: 0:00:09 time: 0.3525 data: 0.0029 max mem: 22446
|
| 401 |
+
eval (validation): [6] [60/63] eta: 0:00:01 time: 0.3493 data: 0.0033 max mem: 22446
|
| 402 |
+
eval (validation): [6] [62/63] eta: 0:00:00 time: 0.3527 data: 0.0033 max mem: 22446
|
| 403 |
+
eval (validation): [6] Total time: 0:00:25 (0.4091 s / it)
|
| 404 |
+
cv: [6] best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00') loss: 0.030 acc: 0.991 f1: 0.989
|
| 405 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 406 |
+
train: [7] [ 0/400] eta: 0:22:27 lr: nan time: 3.3682 data: 2.9728 max mem: 22446
|
| 407 |
+
train: [7] [ 20/400] eta: 0:03:52 lr: 0.000286 loss: 0.3120 (0.4559) grad: 0.2504 (0.2978) time: 0.4748 data: 0.0032 max mem: 22446
|
| 408 |
+
train: [7] [ 40/400] eta: 0:03:15 lr: 0.000286 loss: 0.2964 (0.4116) grad: 0.2504 (0.2816) time: 0.4679 data: 0.0032 max mem: 22446
|
| 409 |
+
train: [7] [ 60/400] eta: 0:02:55 lr: 0.000285 loss: 0.3509 (0.4257) grad: 0.2597 (0.2835) time: 0.4671 data: 0.0033 max mem: 22446
|
| 410 |
+
train: [7] [ 80/400] eta: 0:02:42 lr: 0.000284 loss: 0.3509 (0.4039) grad: 0.2712 (0.2821) time: 0.4817 data: 0.0036 max mem: 22446
|
| 411 |
+
train: [7] [100/400] eta: 0:02:30 lr: 0.000284 loss: 0.3054 (0.4059) grad: 0.2964 (0.2884) time: 0.4692 data: 0.0034 max mem: 22446
|
| 412 |
+
train: [7] [120/400] eta: 0:02:18 lr: 0.000283 loss: 0.3566 (0.4034) grad: 0.3145 (0.2904) time: 0.4577 data: 0.0032 max mem: 22446
|
| 413 |
+
train: [7] [140/400] eta: 0:02:07 lr: 0.000282 loss: 0.3566 (0.4076) grad: 0.2848 (0.2918) time: 0.4599 data: 0.0035 max mem: 22446
|
| 414 |
+
train: [7] [160/400] eta: 0:01:56 lr: 0.000282 loss: 0.3720 (0.4079) grad: 0.2856 (0.2952) time: 0.4659 data: 0.0033 max mem: 22446
|
| 415 |
+
train: [7] [180/400] eta: 0:01:46 lr: 0.000281 loss: 0.3720 (0.4116) grad: 0.2962 (0.2952) time: 0.4644 data: 0.0032 max mem: 22446
|
| 416 |
+
train: [7] [200/400] eta: 0:01:36 lr: 0.000280 loss: 0.3442 (0.4085) grad: 0.2872 (0.2966) time: 0.4707 data: 0.0034 max mem: 22446
|
| 417 |
+
train: [7] [220/400] eta: 0:01:26 lr: 0.000279 loss: 0.2549 (0.4049) grad: 0.2690 (0.2944) time: 0.4775 data: 0.0034 max mem: 22446
|
| 418 |
+
train: [7] [240/400] eta: 0:01:16 lr: 0.000278 loss: 0.2462 (0.4039) grad: 0.2906 (0.2980) time: 0.4727 data: 0.0033 max mem: 22446
|
| 419 |
+
train: [7] [260/400] eta: 0:01:07 lr: 0.000278 loss: 0.2991 (0.4006) grad: 0.2943 (0.2974) time: 0.4707 data: 0.0033 max mem: 22446
|
| 420 |
+
train: [7] [280/400] eta: 0:00:57 lr: 0.000277 loss: 0.2991 (0.4031) grad: 0.2790 (0.2950) time: 0.4914 data: 0.0033 max mem: 22446
|
| 421 |
+
train: [7] [300/400] eta: 0:00:49 lr: 0.000276 loss: 0.3804 (0.4053) grad: 0.2899 (0.2968) time: 0.6441 data: 0.1686 max mem: 22446
|
| 422 |
+
train: [7] [320/400] eta: 0:00:39 lr: 0.000275 loss: 0.3877 (0.4047) grad: 0.3047 (0.2959) time: 0.4720 data: 0.0026 max mem: 22446
|
| 423 |
+
train: [7] [340/400] eta: 0:00:29 lr: 0.000274 loss: 0.2750 (0.3975) grad: 0.2447 (0.2926) time: 0.4805 data: 0.0037 max mem: 22446
|
| 424 |
+
train: [7] [360/400] eta: 0:00:19 lr: 0.000273 loss: 0.2684 (0.3907) grad: 0.2243 (0.2894) time: 0.4585 data: 0.0034 max mem: 22446
|
| 425 |
+
train: [7] [380/400] eta: 0:00:09 lr: 0.000272 loss: 0.2621 (0.3854) grad: 0.2152 (0.2865) time: 0.4642 data: 0.0035 max mem: 22446
|
| 426 |
+
train: [7] [399/400] eta: 0:00:00 lr: 0.000271 loss: 0.2579 (0.3786) grad: 0.2308 (0.2844) time: 0.4822 data: 0.0034 max mem: 22446
|
| 427 |
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train: [7] Total time: 0:03:14 (0.4871 s / it)
|
| 428 |
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train: [7] Summary: lr: 0.000271 loss: 0.2579 (0.3786) grad: 0.2308 (0.2844)
|
| 429 |
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eval (validation): [7] [ 0/63] eta: 0:03:23 time: 3.2233 data: 2.9241 max mem: 22446
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eval (validation): [7] [20/63] eta: 0:00:21 time: 0.3558 data: 0.0038 max mem: 22446
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eval (validation): [7] [40/63] eta: 0:00:09 time: 0.3503 data: 0.0029 max mem: 22446
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eval (validation): [7] [60/63] eta: 0:00:01 time: 0.3462 data: 0.0032 max mem: 22446
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eval (validation): [7] [62/63] eta: 0:00:00 time: 0.3461 data: 0.0032 max mem: 22446
|
| 434 |
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eval (validation): [7] Total time: 0:00:25 (0.4016 s / it)
|
| 435 |
+
cv: [7] best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00') loss: 0.034 acc: 0.993 f1: 0.992
|
| 436 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 437 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 438 |
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train: [8] [ 0/400] eta: 0:22:15 lr: nan time: 3.3392 data: 2.9798 max mem: 22446
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train: [8] [ 20/400] eta: 0:03:51 lr: 0.000270 loss: 0.1675 (0.2049) grad: 0.1768 (0.1977) time: 0.4731 data: 0.0032 max mem: 22446
|
| 440 |
+
train: [8] [ 40/400] eta: 0:03:13 lr: 0.000270 loss: 0.2227 (0.2662) grad: 0.2178 (0.2044) time: 0.4643 data: 0.0034 max mem: 22446
|
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train: [8] [ 60/400] eta: 0:02:55 lr: 0.000269 loss: 0.2584 (0.2651) grad: 0.1878 (0.2017) time: 0.4680 data: 0.0034 max mem: 22446
|
| 442 |
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train: [8] [ 80/400] eta: 0:02:41 lr: 0.000268 loss: 0.2240 (0.2691) grad: 0.1878 (0.2063) time: 0.4693 data: 0.0033 max mem: 22446
|
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train: [8] [100/400] eta: 0:02:28 lr: 0.000267 loss: 0.1962 (0.2649) grad: 0.2102 (0.2059) time: 0.4629 data: 0.0032 max mem: 22446
|
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train: [8] [120/400] eta: 0:02:17 lr: 0.000266 loss: 0.2070 (0.2708) grad: 0.2195 (0.2075) time: 0.4612 data: 0.0033 max mem: 22446
|
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train: [8] [140/400] eta: 0:02:06 lr: 0.000265 loss: 0.2070 (0.2684) grad: 0.2195 (0.2097) time: 0.4712 data: 0.0034 max mem: 22446
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train: [8] [160/400] eta: 0:01:56 lr: 0.000264 loss: 0.2005 (0.2666) grad: 0.2133 (0.2107) time: 0.4730 data: 0.0033 max mem: 22446
|
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train: [8] [180/400] eta: 0:01:46 lr: 0.000263 loss: 0.1970 (0.2643) grad: 0.2100 (0.2072) time: 0.4757 data: 0.0034 max mem: 22446
|
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train: [8] [200/400] eta: 0:01:36 lr: 0.000262 loss: 0.1696 (0.2583) grad: 0.1690 (0.2050) time: 0.4659 data: 0.0033 max mem: 22446
|
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train: [8] [220/400] eta: 0:01:26 lr: 0.000260 loss: 0.1939 (0.2551) grad: 0.1629 (0.2030) time: 0.4689 data: 0.0035 max mem: 22446
|
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train: [8] [240/400] eta: 0:01:16 lr: 0.000259 loss: 0.2478 (0.2559) grad: 0.1942 (0.2047) time: 0.4738 data: 0.0033 max mem: 22446
|
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train: [8] [260/400] eta: 0:01:07 lr: 0.000258 loss: 0.2127 (0.2556) grad: 0.1866 (0.2038) time: 0.4694 data: 0.0032 max mem: 22446
|
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train: [8] [280/400] eta: 0:00:57 lr: 0.000257 loss: 0.1914 (0.2571) grad: 0.1883 (0.2046) time: 0.4689 data: 0.0033 max mem: 22446
|
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train: [8] [300/400] eta: 0:00:48 lr: 0.000256 loss: 0.2046 (0.2599) grad: 0.2115 (0.2063) time: 0.6256 data: 0.1772 max mem: 22446
|
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train: [8] [320/400] eta: 0:00:38 lr: 0.000255 loss: 0.2147 (0.2569) grad: 0.2095 (0.2050) time: 0.4584 data: 0.0033 max mem: 22446
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train: [8] [340/400] eta: 0:00:29 lr: 0.000254 loss: 0.2019 (0.2547) grad: 0.1973 (0.2057) time: 0.4746 data: 0.0033 max mem: 22446
|
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train: [8] [360/400] eta: 0:00:19 lr: 0.000253 loss: 0.1691 (0.2502) grad: 0.1875 (0.2019) time: 0.4727 data: 0.0033 max mem: 22446
|
| 457 |
+
train: [8] [380/400] eta: 0:00:09 lr: 0.000252 loss: 0.1615 (0.2480) grad: 0.1577 (0.2014) time: 0.4631 data: 0.0031 max mem: 22446
|
| 458 |
+
train: [8] [399/400] eta: 0:00:00 lr: 0.000250 loss: 0.1822 (0.2463) grad: 0.1809 (0.2005) time: 0.4693 data: 0.0033 max mem: 22446
|
| 459 |
+
train: [8] Total time: 0:03:13 (0.4840 s / it)
|
| 460 |
+
train: [8] Summary: lr: 0.000250 loss: 0.1822 (0.2463) grad: 0.1809 (0.2005)
|
| 461 |
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eval (validation): [8] [ 0/63] eta: 0:03:21 time: 3.1983 data: 2.9179 max mem: 22446
|
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eval (validation): [8] [20/63] eta: 0:00:22 time: 0.3799 data: 0.0043 max mem: 22446
|
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eval (validation): [8] [40/63] eta: 0:00:10 time: 0.3580 data: 0.0031 max mem: 22446
|
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eval (validation): [8] [60/63] eta: 0:00:01 time: 0.3402 data: 0.0033 max mem: 22446
|
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eval (validation): [8] [62/63] eta: 0:00:00 time: 0.3415 data: 0.0033 max mem: 22446
|
| 466 |
+
eval (validation): [8] Total time: 0:00:25 (0.4098 s / it)
|
| 467 |
+
cv: [8] best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00') loss: 0.034 acc: 0.992 f1: 0.991
|
| 468 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 469 |
+
train: [9] [ 0/400] eta: 0:21:51 lr: nan time: 3.2786 data: 2.9143 max mem: 22446
|
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+
train: [9] [ 20/400] eta: 0:04:00 lr: 0.000249 loss: 0.1818 (0.2327) grad: 0.1365 (0.1688) time: 0.5002 data: 0.0026 max mem: 22446
|
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+
train: [9] [ 40/400] eta: 0:03:22 lr: 0.000248 loss: 0.1818 (0.2101) grad: 0.1585 (0.1708) time: 0.4877 data: 0.0034 max mem: 22446
|
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+
train: [9] [ 60/400] eta: 0:03:02 lr: 0.000247 loss: 0.1610 (0.2008) grad: 0.1585 (0.1675) time: 0.4844 data: 0.0033 max mem: 22446
|
| 473 |
+
train: [9] [ 80/400] eta: 0:02:48 lr: 0.000246 loss: 0.1560 (0.2012) grad: 0.1563 (0.1660) time: 0.4920 data: 0.0034 max mem: 22446
|
| 474 |
+
train: [9] [100/400] eta: 0:02:35 lr: 0.000244 loss: 0.1541 (0.1950) grad: 0.1563 (0.1685) time: 0.4849 data: 0.0033 max mem: 22446
|
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+
train: [9] [120/400] eta: 0:02:22 lr: 0.000243 loss: 0.1839 (0.2015) grad: 0.1680 (0.1677) time: 0.4639 data: 0.0032 max mem: 22446
|
| 476 |
+
train: [9] [140/400] eta: 0:02:11 lr: 0.000242 loss: 0.1798 (0.1968) grad: 0.1567 (0.1658) time: 0.4882 data: 0.0035 max mem: 22446
|
| 477 |
+
train: [9] [160/400] eta: 0:02:01 lr: 0.000241 loss: 0.1439 (0.1938) grad: 0.1379 (0.1642) time: 0.5012 data: 0.0034 max mem: 22446
|
| 478 |
+
train: [9] [180/400] eta: 0:01:50 lr: 0.000240 loss: 0.1394 (0.1887) grad: 0.1436 (0.1618) time: 0.4633 data: 0.0032 max mem: 22446
|
| 479 |
+
train: [9] [200/400] eta: 0:01:39 lr: 0.000238 loss: 0.1413 (0.1885) grad: 0.1451 (0.1597) time: 0.4754 data: 0.0035 max mem: 22446
|
| 480 |
+
train: [9] [220/400] eta: 0:01:29 lr: 0.000237 loss: 0.1730 (0.1874) grad: 0.1339 (0.1577) time: 0.4712 data: 0.0034 max mem: 22446
|
| 481 |
+
train: [9] [240/400] eta: 0:01:18 lr: 0.000236 loss: 0.1740 (0.1860) grad: 0.1277 (0.1558) time: 0.4700 data: 0.0033 max mem: 22446
|
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+
train: [9] [260/400] eta: 0:01:08 lr: 0.000234 loss: 0.1560 (0.1850) grad: 0.1520 (0.1556) time: 0.4644 data: 0.0032 max mem: 22446
|
| 483 |
+
train: [9] [280/400] eta: 0:00:58 lr: 0.000233 loss: 0.1493 (0.1846) grad: 0.1612 (0.1554) time: 0.4674 data: 0.0033 max mem: 22446
|
| 484 |
+
train: [9] [300/400] eta: 0:00:49 lr: 0.000232 loss: 0.1438 (0.1825) grad: 0.1425 (0.1552) time: 0.6363 data: 0.1750 max mem: 22446
|
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+
train: [9] [320/400] eta: 0:00:39 lr: 0.000230 loss: 0.1392 (0.1800) grad: 0.1391 (0.1544) time: 0.4670 data: 0.0030 max mem: 22446
|
| 486 |
+
train: [9] [340/400] eta: 0:00:29 lr: 0.000229 loss: 0.1416 (0.1797) grad: 0.1373 (0.1545) time: 0.4673 data: 0.0034 max mem: 22446
|
| 487 |
+
train: [9] [360/400] eta: 0:00:19 lr: 0.000228 loss: 0.1466 (0.1783) grad: 0.1198 (0.1525) time: 0.4675 data: 0.0034 max mem: 22446
|
| 488 |
+
train: [9] [380/400] eta: 0:00:09 lr: 0.000226 loss: 0.1281 (0.1757) grad: 0.1067 (0.1499) time: 0.4663 data: 0.0035 max mem: 22446
|
| 489 |
+
train: [9] [399/400] eta: 0:00:00 lr: 0.000225 loss: 0.1302 (0.1745) grad: 0.1080 (0.1485) time: 0.4673 data: 0.0034 max mem: 22446
|
| 490 |
+
train: [9] Total time: 0:03:16 (0.4916 s / it)
|
| 491 |
+
train: [9] Summary: lr: 0.000225 loss: 0.1302 (0.1745) grad: 0.1080 (0.1485)
|
| 492 |
+
eval (validation): [9] [ 0/63] eta: 0:03:17 time: 3.1420 data: 2.8886 max mem: 22446
|
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+
eval (validation): [9] [20/63] eta: 0:00:21 time: 0.3655 data: 0.0033 max mem: 22446
|
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+
eval (validation): [9] [40/63] eta: 0:00:09 time: 0.3547 data: 0.0027 max mem: 22446
|
| 495 |
+
eval (validation): [9] [60/63] eta: 0:00:01 time: 0.3464 data: 0.0032 max mem: 22446
|
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+
eval (validation): [9] [62/63] eta: 0:00:00 time: 0.3452 data: 0.0032 max mem: 22446
|
| 497 |
+
eval (validation): [9] Total time: 0:00:25 (0.4054 s / it)
|
| 498 |
+
cv: [9] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.993 f1: 0.993
|
| 499 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 500 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 501 |
+
train: [10] [ 0/400] eta: 0:22:52 lr: nan time: 3.4321 data: 3.0350 max mem: 22446
|
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+
train: [10] [ 20/400] eta: 0:03:51 lr: 0.000224 loss: 0.1530 (0.1686) grad: 0.1116 (0.1160) time: 0.4680 data: 0.0030 max mem: 22446
|
| 503 |
+
train: [10] [ 40/400] eta: 0:03:13 lr: 0.000222 loss: 0.1470 (0.1505) grad: 0.1077 (0.1107) time: 0.4632 data: 0.0033 max mem: 22446
|
| 504 |
+
train: [10] [ 60/400] eta: 0:02:57 lr: 0.000221 loss: 0.1426 (0.1483) grad: 0.1077 (0.1168) time: 0.4858 data: 0.0035 max mem: 22446
|
| 505 |
+
train: [10] [ 80/400] eta: 0:02:42 lr: 0.000220 loss: 0.1422 (0.1429) grad: 0.1256 (0.1180) time: 0.4665 data: 0.0034 max mem: 22446
|
| 506 |
+
train: [10] [100/400] eta: 0:02:30 lr: 0.000218 loss: 0.1204 (0.1412) grad: 0.1105 (0.1134) time: 0.4732 data: 0.0033 max mem: 22446
|
| 507 |
+
train: [10] [120/400] eta: 0:02:18 lr: 0.000217 loss: 0.1199 (0.1385) grad: 0.1115 (0.1144) time: 0.4645 data: 0.0034 max mem: 22446
|
| 508 |
+
train: [10] [140/400] eta: 0:02:07 lr: 0.000215 loss: 0.1209 (0.1389) grad: 0.0965 (0.1115) time: 0.4687 data: 0.0032 max mem: 22446
|
| 509 |
+
train: [10] [160/400] eta: 0:01:57 lr: 0.000214 loss: 0.1133 (0.1377) grad: 0.0896 (0.1112) time: 0.4676 data: 0.0032 max mem: 22446
|
| 510 |
+
train: [10] [180/400] eta: 0:01:47 lr: 0.000213 loss: 0.1133 (0.1383) grad: 0.1111 (0.1110) time: 0.4736 data: 0.0033 max mem: 22446
|
| 511 |
+
train: [10] [200/400] eta: 0:01:37 lr: 0.000211 loss: 0.1364 (0.1379) grad: 0.1096 (0.1117) time: 0.4745 data: 0.0034 max mem: 22446
|
| 512 |
+
train: [10] [220/400] eta: 0:01:27 lr: 0.000210 loss: 0.1211 (0.1370) grad: 0.1039 (0.1110) time: 0.4722 data: 0.0033 max mem: 22446
|
| 513 |
+
train: [10] [240/400] eta: 0:01:17 lr: 0.000208 loss: 0.1254 (0.1369) grad: 0.1202 (0.1119) time: 0.4744 data: 0.0032 max mem: 22446
|
| 514 |
+
train: [10] [260/400] eta: 0:01:07 lr: 0.000207 loss: 0.1307 (0.1371) grad: 0.1262 (0.1123) time: 0.4744 data: 0.0033 max mem: 22446
|
| 515 |
+
train: [10] [280/400] eta: 0:00:57 lr: 0.000205 loss: 0.1192 (0.1358) grad: 0.1129 (0.1110) time: 0.4704 data: 0.0034 max mem: 22446
|
| 516 |
+
train: [10] [300/400] eta: 0:00:49 lr: 0.000204 loss: 0.1069 (0.1349) grad: 0.1129 (0.1115) time: 0.6820 data: 0.1913 max mem: 22446
|
| 517 |
+
train: [10] [320/400] eta: 0:00:39 lr: 0.000202 loss: 0.1038 (0.1338) grad: 0.0959 (0.1096) time: 0.5067 data: 0.0032 max mem: 22446
|
| 518 |
+
train: [10] [340/400] eta: 0:00:29 lr: 0.000201 loss: 0.1024 (0.1321) grad: 0.0852 (0.1088) time: 0.4907 data: 0.0034 max mem: 22446
|
| 519 |
+
train: [10] [360/400] eta: 0:00:19 lr: 0.000199 loss: 0.1024 (0.1314) grad: 0.0798 (0.1075) time: 0.5094 data: 0.0037 max mem: 22446
|
| 520 |
+
train: [10] [380/400] eta: 0:00:09 lr: 0.000198 loss: 0.1080 (0.1308) grad: 0.0970 (0.1075) time: 0.5094 data: 0.0034 max mem: 22446
|
| 521 |
+
train: [10] [399/400] eta: 0:00:00 lr: 0.000196 loss: 0.1125 (0.1299) grad: 0.0863 (0.1059) time: 0.4796 data: 0.0033 max mem: 22446
|
| 522 |
+
train: [10] Total time: 0:03:18 (0.4965 s / it)
|
| 523 |
+
train: [10] Summary: lr: 0.000196 loss: 0.1125 (0.1299) grad: 0.0863 (0.1059)
|
| 524 |
+
eval (validation): [10] [ 0/63] eta: 0:03:42 time: 3.5289 data: 3.2647 max mem: 22446
|
| 525 |
+
eval (validation): [10] [20/63] eta: 0:00:23 time: 0.4022 data: 0.0029 max mem: 22446
|
| 526 |
+
eval (validation): [10] [40/63] eta: 0:00:10 time: 0.3876 data: 0.0031 max mem: 22446
|
| 527 |
+
eval (validation): [10] [60/63] eta: 0:00:01 time: 0.3650 data: 0.0031 max mem: 22446
|
| 528 |
+
eval (validation): [10] [62/63] eta: 0:00:00 time: 0.3663 data: 0.0031 max mem: 22446
|
| 529 |
+
eval (validation): [10] Total time: 0:00:27 (0.4399 s / it)
|
| 530 |
+
cv: [10] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.993 f1: 0.993
|
| 531 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 532 |
+
train: [11] [ 0/400] eta: 0:24:58 lr: nan time: 3.7471 data: 3.3004 max mem: 22446
|
| 533 |
+
train: [11] [ 20/400] eta: 0:04:14 lr: 0.000195 loss: 0.1123 (0.1187) grad: 0.0692 (0.0787) time: 0.5171 data: 0.0025 max mem: 22446
|
| 534 |
+
train: [11] [ 40/400] eta: 0:03:28 lr: 0.000193 loss: 0.1123 (0.1214) grad: 0.0735 (0.0772) time: 0.4844 data: 0.0035 max mem: 22446
|
| 535 |
+
train: [11] [ 60/400] eta: 0:03:08 lr: 0.000192 loss: 0.1114 (0.1206) grad: 0.0775 (0.0764) time: 0.5023 data: 0.0036 max mem: 22446
|
| 536 |
+
train: [11] [ 80/400] eta: 0:02:52 lr: 0.000190 loss: 0.1057 (0.1166) grad: 0.0777 (0.0766) time: 0.4963 data: 0.0035 max mem: 22446
|
| 537 |
+
train: [11] [100/400] eta: 0:02:39 lr: 0.000189 loss: 0.0986 (0.1130) grad: 0.0624 (0.0752) time: 0.4940 data: 0.0034 max mem: 22446
|
| 538 |
+
train: [11] [120/400] eta: 0:02:26 lr: 0.000187 loss: 0.0987 (0.1121) grad: 0.0729 (0.0778) time: 0.4891 data: 0.0034 max mem: 22446
|
| 539 |
+
train: [11] [140/400] eta: 0:02:15 lr: 0.000186 loss: 0.1064 (0.1128) grad: 0.0783 (0.0786) time: 0.5016 data: 0.0036 max mem: 22446
|
| 540 |
+
train: [11] [160/400] eta: 0:02:03 lr: 0.000184 loss: 0.1069 (0.1129) grad: 0.0783 (0.0795) time: 0.4808 data: 0.0034 max mem: 22446
|
| 541 |
+
train: [11] [180/400] eta: 0:01:52 lr: 0.000183 loss: 0.1052 (0.1126) grad: 0.0851 (0.0815) time: 0.4800 data: 0.0030 max mem: 22446
|
| 542 |
+
train: [11] [200/400] eta: 0:01:41 lr: 0.000181 loss: 0.0977 (0.1117) grad: 0.0845 (0.0818) time: 0.4923 data: 0.0033 max mem: 22446
|
| 543 |
+
train: [11] [220/400] eta: 0:01:31 lr: 0.000180 loss: 0.0943 (0.1104) grad: 0.0723 (0.0812) time: 0.4836 data: 0.0033 max mem: 22446
|
| 544 |
+
train: [11] [240/400] eta: 0:01:20 lr: 0.000178 loss: 0.0969 (0.1098) grad: 0.0663 (0.0799) time: 0.4803 data: 0.0033 max mem: 22446
|
| 545 |
+
train: [11] [260/400] eta: 0:01:10 lr: 0.000177 loss: 0.1021 (0.1108) grad: 0.0801 (0.0809) time: 0.4885 data: 0.0034 max mem: 22446
|
| 546 |
+
train: [11] [280/400] eta: 0:01:00 lr: 0.000175 loss: 0.1085 (0.1116) grad: 0.0868 (0.0827) time: 0.4757 data: 0.0033 max mem: 22446
|
| 547 |
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train: [11] [300/400] eta: 0:00:51 lr: 0.000174 loss: 0.1161 (0.1128) grad: 0.1039 (0.0843) time: 0.6399 data: 0.1765 max mem: 22446
|
| 548 |
+
train: [11] [320/400] eta: 0:00:40 lr: 0.000172 loss: 0.1021 (0.1124) grad: 0.1028 (0.0843) time: 0.4890 data: 0.0030 max mem: 22446
|
| 549 |
+
train: [11] [340/400] eta: 0:00:30 lr: 0.000170 loss: 0.1016 (0.1117) grad: 0.0798 (0.0838) time: 0.4866 data: 0.0034 max mem: 22446
|
| 550 |
+
train: [11] [360/400] eta: 0:00:20 lr: 0.000169 loss: 0.0977 (0.1111) grad: 0.0674 (0.0833) time: 0.4805 data: 0.0034 max mem: 22446
|
| 551 |
+
train: [11] [380/400] eta: 0:00:10 lr: 0.000167 loss: 0.0977 (0.1106) grad: 0.0736 (0.0834) time: 0.5024 data: 0.0034 max mem: 22446
|
| 552 |
+
train: [11] [399/400] eta: 0:00:00 lr: 0.000166 loss: 0.0980 (0.1102) grad: 0.0779 (0.0830) time: 0.4950 data: 0.0034 max mem: 22446
|
| 553 |
+
train: [11] Total time: 0:03:22 (0.5062 s / it)
|
| 554 |
+
train: [11] Summary: lr: 0.000166 loss: 0.0980 (0.1102) grad: 0.0779 (0.0830)
|
| 555 |
+
eval (validation): [11] [ 0/63] eta: 0:03:28 time: 3.3098 data: 3.0356 max mem: 22446
|
| 556 |
+
eval (validation): [11] [20/63] eta: 0:00:21 time: 0.3712 data: 0.0042 max mem: 22446
|
| 557 |
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eval (validation): [11] [40/63] eta: 0:00:10 time: 0.3812 data: 0.0029 max mem: 22446
|
| 558 |
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eval (validation): [11] [60/63] eta: 0:00:01 time: 0.3580 data: 0.0034 max mem: 22446
|
| 559 |
+
eval (validation): [11] [62/63] eta: 0:00:00 time: 0.3614 data: 0.0034 max mem: 22446
|
| 560 |
+
eval (validation): [11] Total time: 0:00:26 (0.4245 s / it)
|
| 561 |
+
cv: [11] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 562 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 563 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 564 |
+
train: [12] [ 0/400] eta: 0:23:02 lr: nan time: 3.4550 data: 3.0980 max mem: 22446
|
| 565 |
+
train: [12] [ 20/400] eta: 0:03:54 lr: 0.000164 loss: 0.0992 (0.1116) grad: 0.0823 (0.0824) time: 0.4746 data: 0.0036 max mem: 22446
|
| 566 |
+
train: [12] [ 40/400] eta: 0:03:20 lr: 0.000163 loss: 0.0992 (0.1071) grad: 0.0910 (0.0915) time: 0.4916 data: 0.0030 max mem: 22446
|
| 567 |
+
train: [12] [ 60/400] eta: 0:03:01 lr: 0.000161 loss: 0.0927 (0.1017) grad: 0.0716 (0.0814) time: 0.4913 data: 0.0035 max mem: 22446
|
| 568 |
+
train: [12] [ 80/400] eta: 0:02:46 lr: 0.000160 loss: 0.0911 (0.1002) grad: 0.0660 (0.0808) time: 0.4770 data: 0.0034 max mem: 22446
|
| 569 |
+
train: [12] [100/400] eta: 0:02:33 lr: 0.000158 loss: 0.0986 (0.1032) grad: 0.0767 (0.0822) time: 0.4758 data: 0.0034 max mem: 22446
|
| 570 |
+
train: [12] [120/400] eta: 0:02:22 lr: 0.000156 loss: 0.1047 (0.1060) grad: 0.0812 (0.0825) time: 0.4936 data: 0.0035 max mem: 22446
|
| 571 |
+
train: [12] [140/400] eta: 0:02:11 lr: 0.000155 loss: 0.0959 (0.1048) grad: 0.0767 (0.0819) time: 0.4886 data: 0.0034 max mem: 22446
|
| 572 |
+
train: [12] [160/400] eta: 0:02:00 lr: 0.000153 loss: 0.0904 (0.1033) grad: 0.0687 (0.0797) time: 0.4893 data: 0.0033 max mem: 22446
|
| 573 |
+
train: [12] [180/400] eta: 0:01:50 lr: 0.000152 loss: 0.0913 (0.1033) grad: 0.0670 (0.0790) time: 0.4993 data: 0.0034 max mem: 22446
|
| 574 |
+
train: [12] [200/400] eta: 0:01:40 lr: 0.000150 loss: 0.0850 (0.1019) grad: 0.0605 (0.0765) time: 0.5053 data: 0.0034 max mem: 22446
|
| 575 |
+
train: [12] [220/400] eta: 0:01:30 lr: 0.000149 loss: 0.0862 (0.1027) grad: 0.0636 (0.0766) time: 0.4785 data: 0.0034 max mem: 22446
|
| 576 |
+
train: [12] [240/400] eta: 0:01:20 lr: 0.000147 loss: 0.0980 (0.1035) grad: 0.0676 (0.0770) time: 0.5115 data: 0.0036 max mem: 22446
|
| 577 |
+
train: [12] [260/400] eta: 0:01:10 lr: 0.000145 loss: 0.0968 (0.1028) grad: 0.0717 (0.0766) time: 0.4934 data: 0.0033 max mem: 22446
|
| 578 |
+
train: [12] [280/400] eta: 0:00:59 lr: 0.000144 loss: 0.0937 (0.1022) grad: 0.0757 (0.0768) time: 0.4754 data: 0.0033 max mem: 22446
|
| 579 |
+
train: [12] [300/400] eta: 0:00:51 lr: 0.000142 loss: 0.0948 (0.1024) grad: 0.0722 (0.0767) time: 0.6625 data: 0.1939 max mem: 22446
|
| 580 |
+
train: [12] [320/400] eta: 0:00:40 lr: 0.000141 loss: 0.0924 (0.1017) grad: 0.0648 (0.0749) time: 0.4854 data: 0.0066 max mem: 22446
|
| 581 |
+
train: [12] [340/400] eta: 0:00:30 lr: 0.000139 loss: 0.0921 (0.1015) grad: 0.0455 (0.0743) time: 0.4811 data: 0.0034 max mem: 22446
|
| 582 |
+
train: [12] [360/400] eta: 0:00:20 lr: 0.000138 loss: 0.0921 (0.1009) grad: 0.0610 (0.0735) time: 0.4775 data: 0.0031 max mem: 22446
|
| 583 |
+
train: [12] [380/400] eta: 0:00:10 lr: 0.000136 loss: 0.0879 (0.1009) grad: 0.0529 (0.0726) time: 0.4923 data: 0.0036 max mem: 22446
|
| 584 |
+
train: [12] [399/400] eta: 0:00:00 lr: 0.000134 loss: 0.0913 (0.1004) grad: 0.0469 (0.0718) time: 0.4855 data: 0.0035 max mem: 22446
|
| 585 |
+
train: [12] Total time: 0:03:21 (0.5042 s / it)
|
| 586 |
+
train: [12] Summary: lr: 0.000134 loss: 0.0913 (0.1004) grad: 0.0469 (0.0718)
|
| 587 |
+
eval (validation): [12] [ 0/63] eta: 0:03:28 time: 3.3077 data: 3.0087 max mem: 22446
|
| 588 |
+
eval (validation): [12] [20/63] eta: 0:00:23 time: 0.4008 data: 0.0033 max mem: 22446
|
| 589 |
+
eval (validation): [12] [40/63] eta: 0:00:10 time: 0.3940 data: 0.0036 max mem: 22446
|
| 590 |
+
eval (validation): [12] [60/63] eta: 0:00:01 time: 0.3631 data: 0.0033 max mem: 22446
|
| 591 |
+
eval (validation): [12] [62/63] eta: 0:00:00 time: 0.3618 data: 0.0033 max mem: 22446
|
| 592 |
+
eval (validation): [12] Total time: 0:00:27 (0.4375 s / it)
|
| 593 |
+
cv: [12] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 594 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 595 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 596 |
+
train: [13] [ 0/400] eta: 0:23:32 lr: nan time: 3.5318 data: 3.1579 max mem: 22446
|
| 597 |
+
train: [13] [ 20/400] eta: 0:03:56 lr: 0.000133 loss: 0.0854 (0.0895) grad: 0.0376 (0.0415) time: 0.4782 data: 0.0034 max mem: 22446
|
| 598 |
+
train: [13] [ 40/400] eta: 0:03:22 lr: 0.000131 loss: 0.0808 (0.0851) grad: 0.0398 (0.0444) time: 0.4986 data: 0.0035 max mem: 22446
|
| 599 |
+
train: [13] [ 60/400] eta: 0:03:02 lr: 0.000130 loss: 0.0798 (0.0854) grad: 0.0427 (0.0467) time: 0.4796 data: 0.0035 max mem: 22446
|
| 600 |
+
train: [13] [ 80/400] eta: 0:02:46 lr: 0.000128 loss: 0.0805 (0.0859) grad: 0.0437 (0.0475) time: 0.4756 data: 0.0034 max mem: 22446
|
| 601 |
+
train: [13] [100/400] eta: 0:02:33 lr: 0.000127 loss: 0.0817 (0.0859) grad: 0.0466 (0.0493) time: 0.4805 data: 0.0034 max mem: 22446
|
| 602 |
+
train: [13] [120/400] eta: 0:02:22 lr: 0.000125 loss: 0.0816 (0.0862) grad: 0.0532 (0.0511) time: 0.4952 data: 0.0037 max mem: 22446
|
| 603 |
+
train: [13] [140/400] eta: 0:02:11 lr: 0.000124 loss: 0.0806 (0.0865) grad: 0.0562 (0.0516) time: 0.4887 data: 0.0034 max mem: 22446
|
| 604 |
+
train: [13] [160/400] eta: 0:02:00 lr: 0.000122 loss: 0.0799 (0.0863) grad: 0.0562 (0.0529) time: 0.4825 data: 0.0034 max mem: 22446
|
| 605 |
+
train: [13] [180/400] eta: 0:01:50 lr: 0.000120 loss: 0.0933 (0.0871) grad: 0.0623 (0.0532) time: 0.4816 data: 0.0034 max mem: 22446
|
| 606 |
+
train: [13] [200/400] eta: 0:01:40 lr: 0.000119 loss: 0.0933 (0.0877) grad: 0.0582 (0.0536) time: 0.4901 data: 0.0035 max mem: 22446
|
| 607 |
+
train: [13] [220/400] eta: 0:01:29 lr: 0.000117 loss: 0.0884 (0.0878) grad: 0.0523 (0.0534) time: 0.4885 data: 0.0035 max mem: 22446
|
| 608 |
+
train: [13] [240/400] eta: 0:01:19 lr: 0.000116 loss: 0.0887 (0.0875) grad: 0.0480 (0.0532) time: 0.4849 data: 0.0035 max mem: 22446
|
| 609 |
+
train: [13] [260/400] eta: 0:01:09 lr: 0.000114 loss: 0.0816 (0.0874) grad: 0.0480 (0.0531) time: 0.4743 data: 0.0034 max mem: 22446
|
| 610 |
+
train: [13] [280/400] eta: 0:00:59 lr: 0.000113 loss: 0.0796 (0.0868) grad: 0.0461 (0.0527) time: 0.4834 data: 0.0036 max mem: 22446
|
| 611 |
+
train: [13] [300/400] eta: 0:00:50 lr: 0.000111 loss: 0.0796 (0.0869) grad: 0.0499 (0.0531) time: 0.6614 data: 0.1852 max mem: 22446
|
| 612 |
+
train: [13] [320/400] eta: 0:00:40 lr: 0.000110 loss: 0.0874 (0.0869) grad: 0.0441 (0.0523) time: 0.4685 data: 0.0031 max mem: 22446
|
| 613 |
+
train: [13] [340/400] eta: 0:00:30 lr: 0.000108 loss: 0.0808 (0.0866) grad: 0.0400 (0.0522) time: 0.4717 data: 0.0034 max mem: 22446
|
| 614 |
+
train: [13] [360/400] eta: 0:00:20 lr: 0.000107 loss: 0.0696 (0.0858) grad: 0.0417 (0.0517) time: 0.4800 data: 0.0034 max mem: 22446
|
| 615 |
+
train: [13] [380/400] eta: 0:00:09 lr: 0.000105 loss: 0.0749 (0.0859) grad: 0.0420 (0.0513) time: 0.4786 data: 0.0032 max mem: 22446
|
| 616 |
+
train: [13] [399/400] eta: 0:00:00 lr: 0.000104 loss: 0.0845 (0.0860) grad: 0.0420 (0.0512) time: 0.4836 data: 0.0034 max mem: 22446
|
| 617 |
+
train: [13] Total time: 0:03:19 (0.4992 s / it)
|
| 618 |
+
train: [13] Summary: lr: 0.000104 loss: 0.0845 (0.0860) grad: 0.0420 (0.0512)
|
| 619 |
+
eval (validation): [13] [ 0/63] eta: 0:03:34 time: 3.4122 data: 3.1268 max mem: 22446
|
| 620 |
+
eval (validation): [13] [20/63] eta: 0:00:22 time: 0.3805 data: 0.0024 max mem: 22446
|
| 621 |
+
eval (validation): [13] [40/63] eta: 0:00:10 time: 0.3820 data: 0.0033 max mem: 22446
|
| 622 |
+
eval (validation): [13] [60/63] eta: 0:00:01 time: 0.3739 data: 0.0035 max mem: 22446
|
| 623 |
+
eval (validation): [13] [62/63] eta: 0:00:00 time: 0.3723 data: 0.0034 max mem: 22446
|
| 624 |
+
eval (validation): [13] Total time: 0:00:27 (0.4329 s / it)
|
| 625 |
+
cv: [13] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 626 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 627 |
+
train: [14] [ 0/400] eta: 0:24:27 lr: nan time: 3.6684 data: 3.2395 max mem: 22446
|
| 628 |
+
train: [14] [ 20/400] eta: 0:03:54 lr: 0.000102 loss: 0.0770 (0.0838) grad: 0.0404 (0.0429) time: 0.4633 data: 0.0025 max mem: 22446
|
| 629 |
+
train: [14] [ 40/400] eta: 0:03:22 lr: 0.000101 loss: 0.0820 (0.0834) grad: 0.0415 (0.0454) time: 0.5048 data: 0.0034 max mem: 22446
|
| 630 |
+
train: [14] [ 60/400] eta: 0:03:04 lr: 0.000099 loss: 0.0780 (0.0815) grad: 0.0424 (0.0451) time: 0.5062 data: 0.0035 max mem: 22446
|
| 631 |
+
train: [14] [ 80/400] eta: 0:02:48 lr: 0.000098 loss: 0.0710 (0.0811) grad: 0.0444 (0.0458) time: 0.4780 data: 0.0034 max mem: 22446
|
| 632 |
+
train: [14] [100/400] eta: 0:02:35 lr: 0.000096 loss: 0.0710 (0.0807) grad: 0.0446 (0.0455) time: 0.4855 data: 0.0033 max mem: 22446
|
| 633 |
+
train: [14] [120/400] eta: 0:02:24 lr: 0.000095 loss: 0.0795 (0.0828) grad: 0.0420 (0.0447) time: 0.5050 data: 0.0035 max mem: 22446
|
| 634 |
+
train: [14] [140/400] eta: 0:02:13 lr: 0.000093 loss: 0.0855 (0.0838) grad: 0.0423 (0.0453) time: 0.4950 data: 0.0035 max mem: 22446
|
| 635 |
+
train: [14] [160/400] eta: 0:02:02 lr: 0.000092 loss: 0.0722 (0.0827) grad: 0.0403 (0.0445) time: 0.4755 data: 0.0034 max mem: 22446
|
| 636 |
+
train: [14] [180/400] eta: 0:01:51 lr: 0.000090 loss: 0.0694 (0.0817) grad: 0.0369 (0.0444) time: 0.4719 data: 0.0034 max mem: 22446
|
| 637 |
+
train: [14] [200/400] eta: 0:01:40 lr: 0.000089 loss: 0.0758 (0.0823) grad: 0.0404 (0.0442) time: 0.4865 data: 0.0034 max mem: 22446
|
| 638 |
+
train: [14] [220/400] eta: 0:01:30 lr: 0.000088 loss: 0.0770 (0.0819) grad: 0.0395 (0.0439) time: 0.4879 data: 0.0034 max mem: 22446
|
| 639 |
+
train: [14] [240/400] eta: 0:01:19 lr: 0.000086 loss: 0.0792 (0.0817) grad: 0.0399 (0.0440) time: 0.4763 data: 0.0033 max mem: 22446
|
| 640 |
+
train: [14] [260/400] eta: 0:01:09 lr: 0.000085 loss: 0.0729 (0.0811) grad: 0.0412 (0.0439) time: 0.4819 data: 0.0034 max mem: 22446
|
| 641 |
+
train: [14] [280/400] eta: 0:00:59 lr: 0.000083 loss: 0.0785 (0.0819) grad: 0.0427 (0.0438) time: 0.4872 data: 0.0034 max mem: 22446
|
| 642 |
+
train: [14] [300/400] eta: 0:00:50 lr: 0.000082 loss: 0.0799 (0.0818) grad: 0.0427 (0.0438) time: 0.6610 data: 0.1900 max mem: 22446
|
| 643 |
+
train: [14] [320/400] eta: 0:00:40 lr: 0.000081 loss: 0.0789 (0.0816) grad: 0.0411 (0.0436) time: 0.4711 data: 0.0048 max mem: 22446
|
| 644 |
+
train: [14] [340/400] eta: 0:00:30 lr: 0.000079 loss: 0.0830 (0.0822) grad: 0.0399 (0.0434) time: 0.4800 data: 0.0031 max mem: 22446
|
| 645 |
+
train: [14] [360/400] eta: 0:00:20 lr: 0.000078 loss: 0.0856 (0.0823) grad: 0.0422 (0.0434) time: 0.4777 data: 0.0033 max mem: 22446
|
| 646 |
+
train: [14] [380/400] eta: 0:00:10 lr: 0.000076 loss: 0.0856 (0.0825) grad: 0.0423 (0.0433) time: 0.4770 data: 0.0033 max mem: 22446
|
| 647 |
+
train: [14] [399/400] eta: 0:00:00 lr: 0.000075 loss: 0.0784 (0.0824) grad: 0.0399 (0.0433) time: 0.4788 data: 0.0035 max mem: 22446
|
| 648 |
+
train: [14] Total time: 0:03:20 (0.5009 s / it)
|
| 649 |
+
train: [14] Summary: lr: 0.000075 loss: 0.0784 (0.0824) grad: 0.0399 (0.0433)
|
| 650 |
+
eval (validation): [14] [ 0/63] eta: 0:03:23 time: 3.2318 data: 2.9453 max mem: 22446
|
| 651 |
+
eval (validation): [14] [20/63] eta: 0:00:22 time: 0.3791 data: 0.0035 max mem: 22446
|
| 652 |
+
eval (validation): [14] [40/63] eta: 0:00:10 time: 0.3560 data: 0.0027 max mem: 22446
|
| 653 |
+
eval (validation): [14] [60/63] eta: 0:00:01 time: 0.3570 data: 0.0032 max mem: 22446
|
| 654 |
+
eval (validation): [14] [62/63] eta: 0:00:00 time: 0.3585 data: 0.0032 max mem: 22446
|
| 655 |
+
eval (validation): [14] Total time: 0:00:26 (0.4145 s / it)
|
| 656 |
+
cv: [14] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 657 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 658 |
+
train: [15] [ 0/400] eta: 0:22:45 lr: nan time: 3.4139 data: 3.0626 max mem: 22446
|
| 659 |
+
train: [15] [ 20/400] eta: 0:03:53 lr: 0.000074 loss: 0.0786 (0.0824) grad: 0.0372 (0.0402) time: 0.4732 data: 0.0027 max mem: 22446
|
| 660 |
+
train: [15] [ 40/400] eta: 0:03:17 lr: 0.000072 loss: 0.0829 (0.0822) grad: 0.0423 (0.0441) time: 0.4830 data: 0.0036 max mem: 22446
|
| 661 |
+
train: [15] [ 60/400] eta: 0:02:58 lr: 0.000071 loss: 0.0765 (0.0784) grad: 0.0412 (0.0424) time: 0.4743 data: 0.0035 max mem: 22446
|
| 662 |
+
train: [15] [ 80/400] eta: 0:02:44 lr: 0.000070 loss: 0.0680 (0.0773) grad: 0.0364 (0.0414) time: 0.4822 data: 0.0034 max mem: 22446
|
| 663 |
+
train: [15] [100/400] eta: 0:02:32 lr: 0.000068 loss: 0.0680 (0.0762) grad: 0.0364 (0.0416) time: 0.4814 data: 0.0032 max mem: 22446
|
| 664 |
+
train: [15] [120/400] eta: 0:02:20 lr: 0.000067 loss: 0.0694 (0.0759) grad: 0.0393 (0.0414) time: 0.4764 data: 0.0032 max mem: 22446
|
| 665 |
+
train: [15] [140/400] eta: 0:02:10 lr: 0.000066 loss: 0.0750 (0.0763) grad: 0.0393 (0.0411) time: 0.4878 data: 0.0034 max mem: 22446
|
| 666 |
+
train: [15] [160/400] eta: 0:01:59 lr: 0.000064 loss: 0.0750 (0.0759) grad: 0.0396 (0.0417) time: 0.4882 data: 0.0034 max mem: 22446
|
| 667 |
+
train: [15] [180/400] eta: 0:01:49 lr: 0.000063 loss: 0.0708 (0.0765) grad: 0.0397 (0.0415) time: 0.4696 data: 0.0034 max mem: 22446
|
| 668 |
+
train: [15] [200/400] eta: 0:01:38 lr: 0.000062 loss: 0.0756 (0.0770) grad: 0.0381 (0.0412) time: 0.4716 data: 0.0032 max mem: 22446
|
| 669 |
+
train: [15] [220/400] eta: 0:01:28 lr: 0.000061 loss: 0.0733 (0.0764) grad: 0.0391 (0.0413) time: 0.4915 data: 0.0033 max mem: 22446
|
| 670 |
+
train: [15] [240/400] eta: 0:01:18 lr: 0.000059 loss: 0.0733 (0.0763) grad: 0.0413 (0.0416) time: 0.4910 data: 0.0033 max mem: 22446
|
| 671 |
+
train: [15] [260/400] eta: 0:01:08 lr: 0.000058 loss: 0.0730 (0.0765) grad: 0.0377 (0.0412) time: 0.4842 data: 0.0033 max mem: 22446
|
| 672 |
+
train: [15] [280/400] eta: 0:00:59 lr: 0.000057 loss: 0.0721 (0.0766) grad: 0.0371 (0.0414) time: 0.5004 data: 0.0035 max mem: 22446
|
| 673 |
+
train: [15] [300/400] eta: 0:00:50 lr: 0.000056 loss: 0.0726 (0.0768) grad: 0.0387 (0.0414) time: 0.6442 data: 0.1751 max mem: 22446
|
| 674 |
+
train: [15] [320/400] eta: 0:00:40 lr: 0.000054 loss: 0.0692 (0.0766) grad: 0.0389 (0.0416) time: 0.4975 data: 0.0034 max mem: 22446
|
| 675 |
+
train: [15] [340/400] eta: 0:00:30 lr: 0.000053 loss: 0.0690 (0.0769) grad: 0.0433 (0.0417) time: 0.4941 data: 0.0034 max mem: 22446
|
| 676 |
+
train: [15] [360/400] eta: 0:00:20 lr: 0.000052 loss: 0.0677 (0.0771) grad: 0.0415 (0.0418) time: 0.4787 data: 0.0031 max mem: 22446
|
| 677 |
+
train: [15] [380/400] eta: 0:00:09 lr: 0.000051 loss: 0.0671 (0.0765) grad: 0.0389 (0.0415) time: 0.4785 data: 0.0032 max mem: 22446
|
| 678 |
+
train: [15] [399/400] eta: 0:00:00 lr: 0.000050 loss: 0.0752 (0.0769) grad: 0.0376 (0.0414) time: 0.4818 data: 0.0033 max mem: 22446
|
| 679 |
+
train: [15] Total time: 0:03:19 (0.4991 s / it)
|
| 680 |
+
train: [15] Summary: lr: 0.000050 loss: 0.0752 (0.0769) grad: 0.0376 (0.0414)
|
| 681 |
+
eval (validation): [15] [ 0/63] eta: 0:03:34 time: 3.4083 data: 3.0990 max mem: 22446
|
| 682 |
+
eval (validation): [15] [20/63] eta: 0:00:23 time: 0.4126 data: 0.0031 max mem: 22446
|
| 683 |
+
eval (validation): [15] [40/63] eta: 0:00:10 time: 0.3656 data: 0.0033 max mem: 22446
|
| 684 |
+
eval (validation): [15] [60/63] eta: 0:00:01 time: 0.3532 data: 0.0032 max mem: 22446
|
| 685 |
+
eval (validation): [15] [62/63] eta: 0:00:00 time: 0.3491 data: 0.0032 max mem: 22446
|
| 686 |
+
eval (validation): [15] Total time: 0:00:27 (0.4299 s / it)
|
| 687 |
+
cv: [15] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 688 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 689 |
+
train: [16] [ 0/400] eta: 0:23:36 lr: nan time: 3.5419 data: 3.1336 max mem: 22446
|
| 690 |
+
train: [16] [ 20/400] eta: 0:03:58 lr: 0.000048 loss: 0.0819 (0.0802) grad: 0.0382 (0.0406) time: 0.4811 data: 0.0019 max mem: 22446
|
| 691 |
+
train: [16] [ 40/400] eta: 0:03:22 lr: 0.000047 loss: 0.0801 (0.0777) grad: 0.0386 (0.0405) time: 0.4944 data: 0.0036 max mem: 22446
|
| 692 |
+
train: [16] [ 60/400] eta: 0:03:01 lr: 0.000046 loss: 0.0738 (0.0766) grad: 0.0354 (0.0389) time: 0.4769 data: 0.0035 max mem: 22446
|
| 693 |
+
train: [16] [ 80/400] eta: 0:02:46 lr: 0.000045 loss: 0.0738 (0.0768) grad: 0.0349 (0.0390) time: 0.4742 data: 0.0034 max mem: 22446
|
| 694 |
+
train: [16] [100/400] eta: 0:02:33 lr: 0.000044 loss: 0.0712 (0.0769) grad: 0.0381 (0.0392) time: 0.4810 data: 0.0033 max mem: 22446
|
| 695 |
+
train: [16] [120/400] eta: 0:02:21 lr: 0.000043 loss: 0.0682 (0.0763) grad: 0.0376 (0.0393) time: 0.4804 data: 0.0033 max mem: 22446
|
| 696 |
+
train: [16] [140/400] eta: 0:02:10 lr: 0.000042 loss: 0.0716 (0.0760) grad: 0.0376 (0.0395) time: 0.4793 data: 0.0033 max mem: 22446
|
| 697 |
+
train: [16] [160/400] eta: 0:01:59 lr: 0.000041 loss: 0.0732 (0.0761) grad: 0.0380 (0.0400) time: 0.4761 data: 0.0035 max mem: 22446
|
| 698 |
+
train: [16] [180/400] eta: 0:01:49 lr: 0.000040 loss: 0.0768 (0.0763) grad: 0.0380 (0.0397) time: 0.4761 data: 0.0033 max mem: 22446
|
| 699 |
+
train: [16] [200/400] eta: 0:01:38 lr: 0.000039 loss: 0.0771 (0.0770) grad: 0.0394 (0.0401) time: 0.4632 data: 0.0034 max mem: 22446
|
| 700 |
+
train: [16] [220/400] eta: 0:01:28 lr: 0.000038 loss: 0.0716 (0.0766) grad: 0.0398 (0.0398) time: 0.4718 data: 0.0034 max mem: 22446
|
| 701 |
+
train: [16] [240/400] eta: 0:01:18 lr: 0.000036 loss: 0.0679 (0.0763) grad: 0.0352 (0.0395) time: 0.4741 data: 0.0032 max mem: 22446
|
| 702 |
+
train: [16] [260/400] eta: 0:01:08 lr: 0.000035 loss: 0.0757 (0.0768) grad: 0.0373 (0.0395) time: 0.4704 data: 0.0032 max mem: 22446
|
| 703 |
+
train: [16] [280/400] eta: 0:00:58 lr: 0.000034 loss: 0.0715 (0.0764) grad: 0.0389 (0.0398) time: 0.4809 data: 0.0033 max mem: 22446
|
| 704 |
+
train: [16] [300/400] eta: 0:00:49 lr: 0.000033 loss: 0.0710 (0.0765) grad: 0.0414 (0.0401) time: 0.6522 data: 0.1802 max mem: 22446
|
| 705 |
+
train: [16] [320/400] eta: 0:00:39 lr: 0.000032 loss: 0.0754 (0.0764) grad: 0.0395 (0.0401) time: 0.4836 data: 0.0034 max mem: 22446
|
| 706 |
+
train: [16] [340/400] eta: 0:00:29 lr: 0.000031 loss: 0.0813 (0.0770) grad: 0.0386 (0.0403) time: 0.4708 data: 0.0034 max mem: 22446
|
| 707 |
+
train: [16] [360/400] eta: 0:00:19 lr: 0.000031 loss: 0.0804 (0.0770) grad: 0.0376 (0.0403) time: 0.4713 data: 0.0034 max mem: 22446
|
| 708 |
+
train: [16] [380/400] eta: 0:00:09 lr: 0.000030 loss: 0.0696 (0.0765) grad: 0.0365 (0.0401) time: 0.4822 data: 0.0035 max mem: 22446
|
| 709 |
+
train: [16] [399/400] eta: 0:00:00 lr: 0.000029 loss: 0.0663 (0.0763) grad: 0.0406 (0.0403) time: 0.4808 data: 0.0034 max mem: 22446
|
| 710 |
+
train: [16] Total time: 0:03:17 (0.4940 s / it)
|
| 711 |
+
train: [16] Summary: lr: 0.000029 loss: 0.0663 (0.0763) grad: 0.0406 (0.0403)
|
| 712 |
+
eval (validation): [16] [ 0/63] eta: 0:03:21 time: 3.1927 data: 2.9541 max mem: 22446
|
| 713 |
+
eval (validation): [16] [20/63] eta: 0:00:22 time: 0.3874 data: 0.0032 max mem: 22446
|
| 714 |
+
eval (validation): [16] [40/63] eta: 0:00:10 time: 0.3706 data: 0.0029 max mem: 22446
|
| 715 |
+
eval (validation): [16] [60/63] eta: 0:00:01 time: 0.3704 data: 0.0033 max mem: 22446
|
| 716 |
+
eval (validation): [16] [62/63] eta: 0:00:00 time: 0.3705 data: 0.0032 max mem: 22446
|
| 717 |
+
eval (validation): [16] Total time: 0:00:26 (0.4257 s / it)
|
| 718 |
+
cv: [16] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 719 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 720 |
+
train: [17] [ 0/400] eta: 0:23:14 lr: nan time: 3.4871 data: 3.0858 max mem: 22446
|
| 721 |
+
train: [17] [ 20/400] eta: 0:03:58 lr: 0.000028 loss: 0.0626 (0.0705) grad: 0.0378 (0.0388) time: 0.4842 data: 0.0028 max mem: 22446
|
| 722 |
+
train: [17] [ 40/400] eta: 0:03:24 lr: 0.000027 loss: 0.0714 (0.0757) grad: 0.0378 (0.0402) time: 0.5055 data: 0.0031 max mem: 22446
|
| 723 |
+
train: [17] [ 60/400] eta: 0:03:03 lr: 0.000026 loss: 0.0761 (0.0778) grad: 0.0382 (0.0394) time: 0.4782 data: 0.0037 max mem: 22446
|
| 724 |
+
train: [17] [ 80/400] eta: 0:02:47 lr: 0.000025 loss: 0.0833 (0.0794) grad: 0.0382 (0.0395) time: 0.4799 data: 0.0035 max mem: 22446
|
| 725 |
+
train: [17] [100/400] eta: 0:02:36 lr: 0.000024 loss: 0.0754 (0.0783) grad: 0.0377 (0.0393) time: 0.5081 data: 0.0036 max mem: 22446
|
| 726 |
+
train: [17] [120/400] eta: 0:02:23 lr: 0.000023 loss: 0.0747 (0.0780) grad: 0.0374 (0.0388) time: 0.4798 data: 0.0033 max mem: 22446
|
| 727 |
+
train: [17] [140/400] eta: 0:02:12 lr: 0.000023 loss: 0.0733 (0.0779) grad: 0.0373 (0.0389) time: 0.4937 data: 0.0035 max mem: 22446
|
| 728 |
+
train: [17] [160/400] eta: 0:02:02 lr: 0.000022 loss: 0.0758 (0.0779) grad: 0.0376 (0.0391) time: 0.4957 data: 0.0035 max mem: 22446
|
| 729 |
+
train: [17] [180/400] eta: 0:01:51 lr: 0.000021 loss: 0.0730 (0.0771) grad: 0.0396 (0.0389) time: 0.4693 data: 0.0034 max mem: 22446
|
| 730 |
+
train: [17] [200/400] eta: 0:01:40 lr: 0.000020 loss: 0.0661 (0.0765) grad: 0.0390 (0.0390) time: 0.4678 data: 0.0034 max mem: 22446
|
| 731 |
+
train: [17] [220/400] eta: 0:01:30 lr: 0.000019 loss: 0.0686 (0.0761) grad: 0.0369 (0.0389) time: 0.4896 data: 0.0035 max mem: 22446
|
| 732 |
+
train: [17] [240/400] eta: 0:01:19 lr: 0.000019 loss: 0.0701 (0.0760) grad: 0.0341 (0.0389) time: 0.4798 data: 0.0036 max mem: 22446
|
| 733 |
+
train: [17] [260/400] eta: 0:01:09 lr: 0.000018 loss: 0.0701 (0.0762) grad: 0.0400 (0.0390) time: 0.4707 data: 0.0034 max mem: 22446
|
| 734 |
+
train: [17] [280/400] eta: 0:00:59 lr: 0.000017 loss: 0.0717 (0.0760) grad: 0.0387 (0.0387) time: 0.4805 data: 0.0035 max mem: 22446
|
| 735 |
+
train: [17] [300/400] eta: 0:00:50 lr: 0.000016 loss: 0.0704 (0.0759) grad: 0.0387 (0.0389) time: 0.6362 data: 0.1853 max mem: 22446
|
| 736 |
+
train: [17] [320/400] eta: 0:00:40 lr: 0.000016 loss: 0.0699 (0.0759) grad: 0.0405 (0.0391) time: 0.4766 data: 0.0035 max mem: 22446
|
| 737 |
+
train: [17] [340/400] eta: 0:00:30 lr: 0.000015 loss: 0.0699 (0.0759) grad: 0.0375 (0.0389) time: 0.4646 data: 0.0027 max mem: 22446
|
| 738 |
+
train: [17] [360/400] eta: 0:00:19 lr: 0.000014 loss: 0.0674 (0.0756) grad: 0.0371 (0.0388) time: 0.4745 data: 0.0034 max mem: 22446
|
| 739 |
+
train: [17] [380/400] eta: 0:00:09 lr: 0.000014 loss: 0.0676 (0.0756) grad: 0.0368 (0.0387) time: 0.4848 data: 0.0036 max mem: 22446
|
| 740 |
+
train: [17] [399/400] eta: 0:00:00 lr: 0.000013 loss: 0.0678 (0.0754) grad: 0.0374 (0.0387) time: 0.4804 data: 0.0036 max mem: 22446
|
| 741 |
+
train: [17] Total time: 0:03:19 (0.4979 s / it)
|
| 742 |
+
train: [17] Summary: lr: 0.000013 loss: 0.0678 (0.0754) grad: 0.0374 (0.0387)
|
| 743 |
+
eval (validation): [17] [ 0/63] eta: 0:03:19 time: 3.1733 data: 2.9372 max mem: 22446
|
| 744 |
+
eval (validation): [17] [20/63] eta: 0:00:21 time: 0.3735 data: 0.0037 max mem: 22446
|
| 745 |
+
eval (validation): [17] [40/63] eta: 0:00:10 time: 0.3699 data: 0.0031 max mem: 22446
|
| 746 |
+
eval (validation): [17] [60/63] eta: 0:00:01 time: 0.3480 data: 0.0029 max mem: 22446
|
| 747 |
+
eval (validation): [17] [62/63] eta: 0:00:00 time: 0.3451 data: 0.0032 max mem: 22446
|
| 748 |
+
eval (validation): [17] Total time: 0:00:25 (0.4127 s / it)
|
| 749 |
+
cv: [17] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 750 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 751 |
+
train: [18] [ 0/400] eta: 0:22:49 lr: nan time: 3.4244 data: 3.0650 max mem: 22446
|
| 752 |
+
train: [18] [ 20/400] eta: 0:03:50 lr: 0.000012 loss: 0.0673 (0.0713) grad: 0.0343 (0.0363) time: 0.4653 data: 0.0026 max mem: 22446
|
| 753 |
+
train: [18] [ 40/400] eta: 0:03:17 lr: 0.000012 loss: 0.0690 (0.0751) grad: 0.0366 (0.0380) time: 0.4891 data: 0.0030 max mem: 22446
|
| 754 |
+
train: [18] [ 60/400] eta: 0:02:58 lr: 0.000011 loss: 0.0653 (0.0744) grad: 0.0375 (0.0382) time: 0.4724 data: 0.0033 max mem: 22446
|
| 755 |
+
train: [18] [ 80/400] eta: 0:02:45 lr: 0.000011 loss: 0.0724 (0.0757) grad: 0.0390 (0.0386) time: 0.4909 data: 0.0033 max mem: 22446
|
| 756 |
+
train: [18] [100/400] eta: 0:02:32 lr: 0.000010 loss: 0.0724 (0.0759) grad: 0.0393 (0.0387) time: 0.4817 data: 0.0035 max mem: 22446
|
| 757 |
+
train: [18] [120/400] eta: 0:02:21 lr: 0.000009 loss: 0.0653 (0.0739) grad: 0.0368 (0.0383) time: 0.4792 data: 0.0033 max mem: 22446
|
| 758 |
+
train: [18] [140/400] eta: 0:02:10 lr: 0.000009 loss: 0.0696 (0.0744) grad: 0.0373 (0.0386) time: 0.4768 data: 0.0033 max mem: 22446
|
| 759 |
+
train: [18] [160/400] eta: 0:01:59 lr: 0.000008 loss: 0.0725 (0.0739) grad: 0.0363 (0.0386) time: 0.4750 data: 0.0033 max mem: 22446
|
| 760 |
+
train: [18] [180/400] eta: 0:01:49 lr: 0.000008 loss: 0.0666 (0.0733) grad: 0.0393 (0.0390) time: 0.4831 data: 0.0034 max mem: 22446
|
| 761 |
+
train: [18] [200/400] eta: 0:01:38 lr: 0.000007 loss: 0.0682 (0.0732) grad: 0.0394 (0.0389) time: 0.4719 data: 0.0035 max mem: 22446
|
| 762 |
+
train: [18] [220/400] eta: 0:01:28 lr: 0.000007 loss: 0.0682 (0.0731) grad: 0.0377 (0.0388) time: 0.4748 data: 0.0036 max mem: 22446
|
| 763 |
+
train: [18] [240/400] eta: 0:01:18 lr: 0.000006 loss: 0.0690 (0.0729) grad: 0.0381 (0.0389) time: 0.4838 data: 0.0034 max mem: 22446
|
| 764 |
+
train: [18] [260/400] eta: 0:01:08 lr: 0.000006 loss: 0.0690 (0.0734) grad: 0.0356 (0.0387) time: 0.4837 data: 0.0036 max mem: 22446
|
| 765 |
+
train: [18] [280/400] eta: 0:00:58 lr: 0.000006 loss: 0.0747 (0.0737) grad: 0.0404 (0.0390) time: 0.4731 data: 0.0036 max mem: 22446
|
| 766 |
+
train: [18] [300/400] eta: 0:00:49 lr: 0.000005 loss: 0.0732 (0.0734) grad: 0.0412 (0.0391) time: 0.6406 data: 0.1799 max mem: 22446
|
| 767 |
+
train: [18] [320/400] eta: 0:00:39 lr: 0.000005 loss: 0.0671 (0.0732) grad: 0.0363 (0.0390) time: 0.4922 data: 0.0035 max mem: 22446
|
| 768 |
+
train: [18] [340/400] eta: 0:00:29 lr: 0.000004 loss: 0.0735 (0.0735) grad: 0.0365 (0.0390) time: 0.4738 data: 0.0035 max mem: 22446
|
| 769 |
+
train: [18] [360/400] eta: 0:00:19 lr: 0.000004 loss: 0.0742 (0.0734) grad: 0.0365 (0.0391) time: 0.4693 data: 0.0032 max mem: 22446
|
| 770 |
+
train: [18] [380/400] eta: 0:00:09 lr: 0.000004 loss: 0.0671 (0.0730) grad: 0.0349 (0.0390) time: 0.5071 data: 0.0036 max mem: 22446
|
| 771 |
+
train: [18] [399/400] eta: 0:00:00 lr: 0.000003 loss: 0.0655 (0.0726) grad: 0.0366 (0.0389) time: 0.4889 data: 0.0036 max mem: 22446
|
| 772 |
+
train: [18] Total time: 0:03:18 (0.4963 s / it)
|
| 773 |
+
train: [18] Summary: lr: 0.000003 loss: 0.0655 (0.0726) grad: 0.0366 (0.0389)
|
| 774 |
+
eval (validation): [18] [ 0/63] eta: 0:03:26 time: 3.2805 data: 2.9950 max mem: 22446
|
| 775 |
+
eval (validation): [18] [20/63] eta: 0:00:25 time: 0.4604 data: 0.0032 max mem: 22446
|
| 776 |
+
eval (validation): [18] [40/63] eta: 0:00:11 time: 0.3982 data: 0.0036 max mem: 22446
|
| 777 |
+
eval (validation): [18] [60/63] eta: 0:00:01 time: 0.3499 data: 0.0032 max mem: 22446
|
| 778 |
+
eval (validation): [18] [62/63] eta: 0:00:00 time: 0.3431 data: 0.0031 max mem: 22446
|
| 779 |
+
eval (validation): [18] Total time: 0:00:28 (0.4513 s / it)
|
| 780 |
+
cv: [18] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 781 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 782 |
+
train: [19] [ 0/400] eta: 0:22:21 lr: nan time: 3.3525 data: 3.0102 max mem: 22446
|
| 783 |
+
train: [19] [ 20/400] eta: 0:03:58 lr: 0.000003 loss: 0.0712 (0.0710) grad: 0.0360 (0.0355) time: 0.4907 data: 0.0030 max mem: 22446
|
| 784 |
+
train: [19] [ 40/400] eta: 0:03:20 lr: 0.000003 loss: 0.0712 (0.0710) grad: 0.0376 (0.0376) time: 0.4821 data: 0.0035 max mem: 22446
|
| 785 |
+
train: [19] [ 60/400] eta: 0:03:00 lr: 0.000002 loss: 0.0695 (0.0717) grad: 0.0392 (0.0374) time: 0.4765 data: 0.0036 max mem: 22446
|
| 786 |
+
train: [19] [ 80/400] eta: 0:02:45 lr: 0.000002 loss: 0.0699 (0.0729) grad: 0.0393 (0.0379) time: 0.4832 data: 0.0036 max mem: 22446
|
| 787 |
+
train: [19] [100/400] eta: 0:02:32 lr: 0.000002 loss: 0.0746 (0.0751) grad: 0.0388 (0.0382) time: 0.4703 data: 0.0034 max mem: 22446
|
| 788 |
+
train: [19] [120/400] eta: 0:02:20 lr: 0.000002 loss: 0.0708 (0.0740) grad: 0.0362 (0.0377) time: 0.4747 data: 0.0034 max mem: 22446
|
| 789 |
+
train: [19] [140/400] eta: 0:02:09 lr: 0.000001 loss: 0.0687 (0.0732) grad: 0.0361 (0.0378) time: 0.4689 data: 0.0034 max mem: 22446
|
| 790 |
+
train: [19] [160/400] eta: 0:01:58 lr: 0.000001 loss: 0.0701 (0.0737) grad: 0.0364 (0.0378) time: 0.4680 data: 0.0034 max mem: 22446
|
| 791 |
+
train: [19] [180/400] eta: 0:01:48 lr: 0.000001 loss: 0.0737 (0.0738) grad: 0.0365 (0.0379) time: 0.4744 data: 0.0034 max mem: 22446
|
| 792 |
+
train: [19] [200/400] eta: 0:01:38 lr: 0.000001 loss: 0.0692 (0.0738) grad: 0.0365 (0.0379) time: 0.4690 data: 0.0034 max mem: 22446
|
| 793 |
+
train: [19] [220/400] eta: 0:01:27 lr: 0.000001 loss: 0.0731 (0.0744) grad: 0.0372 (0.0380) time: 0.4754 data: 0.0034 max mem: 22446
|
| 794 |
+
train: [19] [240/400] eta: 0:01:18 lr: 0.000001 loss: 0.0746 (0.0740) grad: 0.0375 (0.0380) time: 0.4781 data: 0.0035 max mem: 22446
|
| 795 |
+
train: [19] [260/400] eta: 0:01:08 lr: 0.000000 loss: 0.0661 (0.0739) grad: 0.0373 (0.0379) time: 0.4748 data: 0.0033 max mem: 22446
|
| 796 |
+
train: [19] [280/400] eta: 0:00:58 lr: 0.000000 loss: 0.0710 (0.0742) grad: 0.0378 (0.0381) time: 0.4739 data: 0.0034 max mem: 22446
|
| 797 |
+
train: [19] [300/400] eta: 0:00:49 lr: 0.000000 loss: 0.0713 (0.0740) grad: 0.0388 (0.0381) time: 0.6250 data: 0.1799 max mem: 22446
|
| 798 |
+
train: [19] [320/400] eta: 0:00:39 lr: 0.000000 loss: 0.0709 (0.0738) grad: 0.0406 (0.0383) time: 0.4632 data: 0.0028 max mem: 22446
|
| 799 |
+
train: [19] [340/400] eta: 0:00:29 lr: 0.000000 loss: 0.0661 (0.0735) grad: 0.0393 (0.0383) time: 0.4675 data: 0.0034 max mem: 22446
|
| 800 |
+
train: [19] [360/400] eta: 0:00:19 lr: 0.000000 loss: 0.0661 (0.0736) grad: 0.0378 (0.0384) time: 0.4692 data: 0.0034 max mem: 22446
|
| 801 |
+
train: [19] [380/400] eta: 0:00:09 lr: 0.000000 loss: 0.0660 (0.0735) grad: 0.0373 (0.0384) time: 0.4845 data: 0.0034 max mem: 22446
|
| 802 |
+
train: [19] [399/400] eta: 0:00:00 lr: 0.000000 loss: 0.0706 (0.0734) grad: 0.0359 (0.0384) time: 0.4827 data: 0.0034 max mem: 22446
|
| 803 |
+
train: [19] Total time: 0:03:16 (0.4901 s / it)
|
| 804 |
+
train: [19] Summary: lr: 0.000000 loss: 0.0706 (0.0734) grad: 0.0359 (0.0384)
|
| 805 |
+
eval (validation): [19] [ 0/63] eta: 0:03:27 time: 3.2956 data: 3.0053 max mem: 22446
|
| 806 |
+
eval (validation): [19] [20/63] eta: 0:00:21 time: 0.3645 data: 0.0032 max mem: 22446
|
| 807 |
+
eval (validation): [19] [40/63] eta: 0:00:10 time: 0.3743 data: 0.0029 max mem: 22446
|
| 808 |
+
eval (validation): [19] [60/63] eta: 0:00:01 time: 0.3581 data: 0.0033 max mem: 22446
|
| 809 |
+
eval (validation): [19] [62/63] eta: 0:00:00 time: 0.3535 data: 0.0033 max mem: 22446
|
| 810 |
+
eval (validation): [19] Total time: 0:00:26 (0.4171 s / it)
|
| 811 |
+
cv: [19] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.032 acc: 0.994 f1: 0.993
|
| 812 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 813 |
+
evaluating last checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 814 |
+
eval model info:
|
| 815 |
+
{"score": 0.9935515873015873, "hparam": [4.3, 1.0], "hparam_id": 33, "epoch": 19, "is_best": false, "best_score": 0.9937996031746031}
|
| 816 |
+
eval (train): [20] [ 0/297] eta: 0:15:13 time: 3.0752 data: 2.8208 max mem: 22446
|
| 817 |
+
eval (train): [20] [ 20/297] eta: 0:02:12 time: 0.3469 data: 0.0035 max mem: 22446
|
| 818 |
+
eval (train): [20] [ 40/297] eta: 0:01:49 time: 0.3764 data: 0.0027 max mem: 22446
|
| 819 |
+
eval (train): [20] [ 60/297] eta: 0:01:37 time: 0.3787 data: 0.0034 max mem: 22446
|
| 820 |
+
eval (train): [20] [ 80/297] eta: 0:01:26 time: 0.3652 data: 0.0033 max mem: 22446
|
| 821 |
+
eval (train): [20] [100/297] eta: 0:01:17 time: 0.3728 data: 0.0036 max mem: 22446
|
| 822 |
+
eval (train): [20] [120/297] eta: 0:01:09 time: 0.3674 data: 0.0034 max mem: 22446
|
| 823 |
+
eval (train): [20] [140/297] eta: 0:01:00 time: 0.3625 data: 0.0036 max mem: 22446
|
| 824 |
+
eval (train): [20] [160/297] eta: 0:00:52 time: 0.3659 data: 0.0034 max mem: 22446
|
| 825 |
+
eval (train): [20] [180/297] eta: 0:00:44 time: 0.3679 data: 0.0033 max mem: 22446
|
| 826 |
+
eval (train): [20] [200/297] eta: 0:00:37 time: 0.3777 data: 0.0035 max mem: 22446
|
| 827 |
+
eval (train): [20] [220/297] eta: 0:00:29 time: 0.4107 data: 0.0037 max mem: 22446
|
| 828 |
+
eval (train): [20] [240/297] eta: 0:00:21 time: 0.3805 data: 0.0037 max mem: 22446
|
| 829 |
+
eval (train): [20] [260/297] eta: 0:00:14 time: 0.3963 data: 0.0034 max mem: 22446
|
| 830 |
+
eval (train): [20] [280/297] eta: 0:00:06 time: 0.4209 data: 0.0040 max mem: 22446
|
| 831 |
+
eval (train): [20] [296/297] eta: 0:00:00 time: 0.3585 data: 0.0032 max mem: 22446
|
| 832 |
+
eval (train): [20] Total time: 0:01:55 (0.3877 s / it)
|
| 833 |
+
eval (validation): [20] [ 0/63] eta: 0:03:19 time: 3.1640 data: 2.8645 max mem: 22446
|
| 834 |
+
eval (validation): [20] [20/63] eta: 0:00:23 time: 0.4199 data: 0.0034 max mem: 22446
|
| 835 |
+
eval (validation): [20] [40/63] eta: 0:00:10 time: 0.3733 data: 0.0032 max mem: 22446
|
| 836 |
+
eval (validation): [20] [60/63] eta: 0:00:01 time: 0.3700 data: 0.0033 max mem: 22446
|
| 837 |
+
eval (validation): [20] [62/63] eta: 0:00:00 time: 0.3703 data: 0.0033 max mem: 22446
|
| 838 |
+
eval (validation): [20] Total time: 0:00:27 (0.4360 s / it)
|
| 839 |
+
eval (test): [20] [ 0/79] eta: 0:04:18 time: 3.2689 data: 3.0144 max mem: 22446
|
| 840 |
+
eval (test): [20] [20/79] eta: 0:00:32 time: 0.4131 data: 0.0033 max mem: 22446
|
| 841 |
+
eval (test): [20] [40/79] eta: 0:00:18 time: 0.4176 data: 0.0035 max mem: 22446
|
| 842 |
+
eval (test): [20] [60/79] eta: 0:00:08 time: 0.4110 data: 0.0036 max mem: 22446
|
| 843 |
+
eval (test): [20] [78/79] eta: 0:00:00 time: 0.3607 data: 0.0034 max mem: 22446
|
| 844 |
+
eval (test): [20] Total time: 0:00:34 (0.4393 s / it)
|
| 845 |
+
evaluating best checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 846 |
+
eval model info:
|
| 847 |
+
{"score": 0.9937996031746031, "hparam": [4.3, 1.0], "hparam_id": 33, "epoch": 12, "is_best": true, "best_score": 0.9937996031746031}
|
| 848 |
+
eval (train): [20] [ 0/297] eta: 0:16:02 time: 3.2408 data: 2.9251 max mem: 22446
|
| 849 |
+
eval (train): [20] [ 20/297] eta: 0:02:40 time: 0.4478 data: 0.0030 max mem: 22446
|
| 850 |
+
eval (train): [20] [ 40/297] eta: 0:02:06 time: 0.3986 data: 0.0032 max mem: 22446
|
| 851 |
+
eval (train): [20] [ 60/297] eta: 0:01:50 time: 0.4148 data: 0.0038 max mem: 22446
|
| 852 |
+
eval (train): [20] [ 80/297] eta: 0:01:37 time: 0.3939 data: 0.0033 max mem: 22446
|
| 853 |
+
eval (train): [20] [100/297] eta: 0:01:27 time: 0.4219 data: 0.0037 max mem: 22446
|
| 854 |
+
eval (train): [20] [120/297] eta: 0:01:17 time: 0.4167 data: 0.0037 max mem: 22446
|
| 855 |
+
eval (train): [20] [140/297] eta: 0:01:07 time: 0.3851 data: 0.0035 max mem: 22446
|
| 856 |
+
eval (train): [20] [160/297] eta: 0:00:59 time: 0.4260 data: 0.0039 max mem: 22446
|
| 857 |
+
eval (train): [20] [180/297] eta: 0:00:49 time: 0.3769 data: 0.0035 max mem: 22446
|
| 858 |
+
eval (train): [20] [200/297] eta: 0:00:42 time: 0.5127 data: 0.0038 max mem: 22446
|
| 859 |
+
eval (train): [20] [220/297] eta: 0:00:33 time: 0.4674 data: 0.0037 max mem: 22446
|
| 860 |
+
eval (train): [20] [240/297] eta: 0:00:24 time: 0.4232 data: 0.0036 max mem: 22446
|
| 861 |
+
eval (train): [20] [260/297] eta: 0:00:16 time: 0.3969 data: 0.0035 max mem: 22446
|
| 862 |
+
eval (train): [20] [280/297] eta: 0:00:07 time: 0.3518 data: 0.0031 max mem: 22446
|
| 863 |
+
eval (train): [20] [296/297] eta: 0:00:00 time: 0.3650 data: 0.0026 max mem: 22446
|
| 864 |
+
eval (train): [20] Total time: 0:02:06 (0.4262 s / it)
|
| 865 |
+
eval (validation): [20] [ 0/63] eta: 0:04:07 time: 3.9327 data: 3.6339 max mem: 22446
|
| 866 |
+
eval (validation): [20] [20/63] eta: 0:00:23 time: 0.3863 data: 0.0046 max mem: 22446
|
| 867 |
+
eval (validation): [20] [40/63] eta: 0:00:10 time: 0.3905 data: 0.0031 max mem: 22446
|
| 868 |
+
eval (validation): [20] [60/63] eta: 0:00:01 time: 0.3641 data: 0.0027 max mem: 22446
|
| 869 |
+
eval (validation): [20] [62/63] eta: 0:00:00 time: 0.3620 data: 0.0020 max mem: 22446
|
| 870 |
+
eval (validation): [20] Total time: 0:00:27 (0.4412 s / it)
|
| 871 |
+
eval (test): [20] [ 0/79] eta: 0:04:23 time: 3.3298 data: 3.0659 max mem: 22446
|
| 872 |
+
eval (test): [20] [20/79] eta: 0:00:32 time: 0.4134 data: 0.0124 max mem: 22446
|
| 873 |
+
eval (test): [20] [40/79] eta: 0:00:18 time: 0.4164 data: 0.0041 max mem: 22446
|
| 874 |
+
eval (test): [20] [60/79] eta: 0:00:08 time: 0.4187 data: 0.0024 max mem: 22446
|
| 875 |
+
eval (test): [20] [78/79] eta: 0:00:00 time: 0.3785 data: 0.0033 max mem: 22446
|
| 876 |
+
eval (test): [20] Total time: 0:00:35 (0.4480 s / it)
|
| 877 |
+
eval results:
|
| 878 |
+
|
| 879 |
+
| model | repr | clf | dataset | ckpt | epoch | lr | wd | hparam_id | hparam | split | loss | acc | acc_std | f1 | f1_std |
|
| 880 |
+
|:---------|:-------|:------|:-------------|:-------|--------:|--------:|-----:|------------:|:-----------|:-----------|-----------:|--------:|----------:|--------:|----------:|
|
| 881 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 12 | 0.00129 | 0.05 | 33 | [4.3, 1.0] | train | 0.00013907 | 1 | 0 | 1 | 0 |
|
| 882 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 12 | 0.00129 | 0.05 | 33 | [4.3, 1.0] | validation | 0.031871 | 0.9938 | 0.0012421 | 0.99338 | 0.0014715 |
|
| 883 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 12 | 0.00129 | 0.05 | 33 | [4.3, 1.0] | test | 0.050567 | 0.98869 | 0.0014924 | 0.98648 | 0.001909 |
|
| 884 |
+
|
| 885 |
+
|
| 886 |
+
done! total time: 1:23:07
|
input_space_v3/flat_lr1e-3_7/eval_v2/hcpya_task21__patch__attn/train_log.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_log.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/epoch": 6, "eval/id_best": 23, "eval/lr_best": 0.00025499999999999996, "eval/wd_best": 0.05, "eval/train/loss": 2.0630671977996826, "eval/train/acc": 0.3784381818740588, "eval/train/acc_std": 0.00244757592770233, "eval/train/f1": 0.3234273297144906, "eval/train/f1_std": 0.0025095023375174873, "eval/validation/loss": 2.377534866333008, "eval/validation/acc": 0.2847914359542267, "eval/validation/acc_std": 0.005602043771634644, "eval/validation/f1": 0.22734592839636894, "eval/validation/f1_std": 0.004996049254373245, "eval/test/loss": 2.261892080307007, "eval/test/acc": 0.3122448979591837, "eval/test/acc_std": 0.005657944543525103, "eval/test/f1": 0.24509500704012688, "eval/test/f1_std": 0.005593589131455349, "eval/testid/loss": 2.263169527053833, "eval/testid/acc": 0.30113745903219585, "eval/testid/acc_std": 0.005672820666769621, "eval/testid/f1": 0.24914930634884794, "eval/testid/f1_std": 0.005151254460367082}
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/last/epoch": 19, "eval/last/id_best": 18, "eval/last/lr_best": 0.00011399999999999999, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.0003859996795654, "eval/last/train/acc": 0.39779956360060237, "eval/last/train/acc_std": 0.0025100284137563288, "eval/last/train/f1": 0.34137344560063854, "eval/last/train/f1_std": 0.0027090557875320315, "eval/last/validation/loss": 2.3957407474517822, "eval/last/validation/acc": 0.27759320782576596, "eval/last/validation/acc_std": 0.005505266876296761, "eval/last/validation/f1": 0.21630703904731372, "eval/last/validation/f1_std": 0.005199600185665768, "eval/last/test/loss": 2.2761619091033936, "eval/last/test/acc": 0.311873840445269, "eval/last/test/acc_std": 0.005269614588879479, "eval/last/test/f1": 0.24215795398776396, "eval/last/test/f1_std": 0.005286074856183143, "eval/last/testid/loss": 2.2344677448272705, "eval/last/testid/acc": 0.3117408906882591, "eval/last/testid/acc_std": 0.005577460641942456, "eval/last/testid/f1": 0.25699569135237676, "eval/last/testid/f1_std": 0.005401577920094937}
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",train,2.0630671977996826,0.3784381818740588,0.00244757592770233,0.3234273297144906,0.0025095023375174873
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",validation,2.377534866333008,0.2847914359542267,0.005602043771634644,0.22734592839636894,0.004996049254373245
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",test,2.261892080307007,0.3122448979591837,0.005657944543525103,0.24509500704012688,0.005593589131455349
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",testid,2.263169527053833,0.30113745903219585,0.005672820666769621,0.24914930634884794,0.005151254460367082
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table_best.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",train,2.0630671977996826,0.3784381818740588,0.00244757592770233,0.3234273297144906,0.0025095023375174873
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",validation,2.377534866333008,0.2847914359542267,0.005602043771634644,0.22734592839636894,0.004996049254373245
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",test,2.261892080307007,0.3122448979591837,0.005657944543525103,0.24509500704012688,0.005593589131455349
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00025499999999999996,0.05,23,"[0.85, 1.0]",testid,2.263169527053833,0.30113745903219585,0.005672820666769621,0.24914930634884794,0.005151254460367082
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/eval_table_last.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,0.00011399999999999999,0.05,18,"[0.38, 1.0]",train,2.0003859996795654,0.39779956360060237,0.0025100284137563288,0.34137344560063854,0.0027090557875320315
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,0.00011399999999999999,0.05,18,"[0.38, 1.0]",validation,2.3957407474517822,0.27759320782576596,0.005505266876296761,0.21630703904731372,0.005199600185665768
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,0.00011399999999999999,0.05,18,"[0.38, 1.0]",test,2.2761619091033936,0.311873840445269,0.005269614588879479,0.24215795398776396,0.005286074856183143
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,0.00011399999999999999,0.05,18,"[0.38, 1.0]",testid,2.2344677448272705,0.3117408906882591,0.005577460641942456,0.25699569135237676,0.005401577920094937
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/log.txt
ADDED
|
@@ -0,0 +1,962 @@
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|
| 1 |
+
fMRI foundation model probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 21:11:42
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_probe
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_7; eval v2 (nsd_cococlip patch attn)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
classifier_kwargs:
|
| 15 |
+
embed_dim: null
|
| 16 |
+
dropout: 0.0
|
| 17 |
+
xavier_init: true
|
| 18 |
+
norm: true
|
| 19 |
+
lr_scale_grid:
|
| 20 |
+
- 0.02
|
| 21 |
+
- 0.023
|
| 22 |
+
- 0.028
|
| 23 |
+
- 0.033
|
| 24 |
+
- 0.038
|
| 25 |
+
- 0.045
|
| 26 |
+
- 0.053
|
| 27 |
+
- 0.062
|
| 28 |
+
- 0.074
|
| 29 |
+
- 0.087
|
| 30 |
+
- 0.1
|
| 31 |
+
- 0.12
|
| 32 |
+
- 0.14
|
| 33 |
+
- 0.17
|
| 34 |
+
- 0.2
|
| 35 |
+
- 0.23
|
| 36 |
+
- 0.27
|
| 37 |
+
- 0.32
|
| 38 |
+
- 0.38
|
| 39 |
+
- 0.44
|
| 40 |
+
- 0.52
|
| 41 |
+
- 0.61
|
| 42 |
+
- 0.72
|
| 43 |
+
- 0.85
|
| 44 |
+
- 1
|
| 45 |
+
- 1.2
|
| 46 |
+
- 1.4
|
| 47 |
+
- 1.6
|
| 48 |
+
- 1.9
|
| 49 |
+
- 2.3
|
| 50 |
+
- 2.7
|
| 51 |
+
- 3.1
|
| 52 |
+
- 3.7
|
| 53 |
+
- 4.3
|
| 54 |
+
- 5.1
|
| 55 |
+
- 6
|
| 56 |
+
- 7.1
|
| 57 |
+
- 8.3
|
| 58 |
+
- 9.8
|
| 59 |
+
- 12
|
| 60 |
+
- 14
|
| 61 |
+
- 16
|
| 62 |
+
- 19
|
| 63 |
+
- 22
|
| 64 |
+
- 26
|
| 65 |
+
- 31
|
| 66 |
+
- 36
|
| 67 |
+
- 43
|
| 68 |
+
- 50
|
| 69 |
+
wd_scale_grid:
|
| 70 |
+
- 1.0
|
| 71 |
+
num_workers: 8
|
| 72 |
+
prefetch_factor: null
|
| 73 |
+
balanced_sampling: false
|
| 74 |
+
epochs: 20
|
| 75 |
+
steps_per_epoch: 200
|
| 76 |
+
batch_size: 64
|
| 77 |
+
accum_iter: 2
|
| 78 |
+
lr: 0.0003
|
| 79 |
+
warmup_epochs: 5
|
| 80 |
+
no_decay: false
|
| 81 |
+
weight_decay: 0.05
|
| 82 |
+
clip_grad: 1.0
|
| 83 |
+
metrics:
|
| 84 |
+
- acc
|
| 85 |
+
- f1
|
| 86 |
+
cv_metric: acc
|
| 87 |
+
early_stopping: true
|
| 88 |
+
amp: true
|
| 89 |
+
device: cuda
|
| 90 |
+
seed: 4466
|
| 91 |
+
debug: false
|
| 92 |
+
wandb: false
|
| 93 |
+
wandb_entity: null
|
| 94 |
+
wandb_project: fMRI-fm-eval
|
| 95 |
+
name: input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn
|
| 96 |
+
model: flat_mae
|
| 97 |
+
representation: patch
|
| 98 |
+
classifier: attn
|
| 99 |
+
dataset: nsd_cococlip
|
| 100 |
+
distributed: false
|
| 101 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn
|
| 102 |
+
remote_dir: null
|
| 103 |
+
|
| 104 |
+
creating frozen backbone model: flat_mae
|
| 105 |
+
backbone:
|
| 106 |
+
MaskedEncoderWrapper(
|
| 107 |
+
(model): MaskedEncoder(
|
| 108 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 109 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 110 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 111 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 112 |
+
(blocks): ModuleList(
|
| 113 |
+
(0-11): 12 x Block(
|
| 114 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 115 |
+
(attn): Attention(
|
| 116 |
+
num_heads=12
|
| 117 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 118 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 119 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 120 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 121 |
+
)
|
| 122 |
+
(drop_path1): Identity()
|
| 123 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 124 |
+
(mlp): Mlp(
|
| 125 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 126 |
+
(act): GELU(approximate='none')
|
| 127 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 128 |
+
)
|
| 129 |
+
(drop_path2): Identity()
|
| 130 |
+
)
|
| 131 |
+
)
|
| 132 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 133 |
+
)
|
| 134 |
+
)
|
| 135 |
+
creating dataset: nsd_cococlip (flat)
|
| 136 |
+
train (n=32539):
|
| 137 |
+
HFDataset(
|
| 138 |
+
dataset=Dataset({
|
| 139 |
+
features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 140 |
+
num_rows: 32539
|
| 141 |
+
}),
|
| 142 |
+
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],
|
| 143 |
+
counts=[1286 1180 1639 1868 834 824 1026 1042 913 1853 1503 2092 1001 1410
|
| 144 |
+
794 1241 1904 1872 2267 1428 889 904 1447 1322]
|
| 145 |
+
)
|
| 146 |
+
|
| 147 |
+
validation (n=5418):
|
| 148 |
+
HFDataset(
|
| 149 |
+
dataset=Dataset({
|
| 150 |
+
features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 151 |
+
num_rows: 5418
|
| 152 |
+
}),
|
| 153 |
+
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],
|
| 154 |
+
counts=[197 161 276 345 126 142 143 185 112 295 285 387 169 250 159 193 316 334
|
| 155 |
+
343 215 172 141 226 246]
|
| 156 |
+
)
|
| 157 |
+
|
| 158 |
+
test (n=5390):
|
| 159 |
+
HFDataset(
|
| 160 |
+
dataset=Dataset({
|
| 161 |
+
features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 162 |
+
num_rows: 5390
|
| 163 |
+
}),
|
| 164 |
+
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],
|
| 165 |
+
counts=[202 172 274 298 144 180 134 182 186 293 218 343 165 185 140 177 346 333
|
| 166 |
+
345 271 165 140 251 246]
|
| 167 |
+
)
|
| 168 |
+
|
| 169 |
+
testid (n=5187):
|
| 170 |
+
HFDataset(
|
| 171 |
+
dataset=Dataset({
|
| 172 |
+
features: ['sub', 'ses', 'run', 'trial_id', 'nsd_id', 'category_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 173 |
+
num_rows: 5187
|
| 174 |
+
}),
|
| 175 |
+
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],
|
| 176 |
+
counts=[197 159 267 273 123 153 175 184 139 310 215 386 153 230 118 192 330 306
|
| 177 |
+
349 223 143 127 249 186]
|
| 178 |
+
)
|
| 179 |
+
|
| 180 |
+
running backbone on example batch to get embedding dim
|
| 181 |
+
embedding feature dim (patch): 768
|
| 182 |
+
initializing sweep of classifier heads
|
| 183 |
+
classifiers:
|
| 184 |
+
ModuleList(
|
| 185 |
+
(0-48): 49 x AttnPoolClassifier(
|
| 186 |
+
(kv): Linear(in_features=768, out_features=1536, bias=True)
|
| 187 |
+
(linear): Linear(in_features=768, out_features=24, bias=True)
|
| 188 |
+
)
|
| 189 |
+
)
|
| 190 |
+
classifier params (train): 58.8M (58.8M)
|
| 191 |
+
setting up optimizer
|
| 192 |
+
total batch size: 128 = 64 bs per gpu x 2 accum
|
| 193 |
+
lr: 3.00e-04
|
| 194 |
+
full schedule: epochs = 20 (steps = 4000) (decay = True)
|
| 195 |
+
warmup: epochs = 5 (steps = 1000)
|
| 196 |
+
start training for 20 epochs
|
| 197 |
+
train: [0] [ 0/400] eta: 0:25:16 lr: nan time: 3.7907 data: 3.2787 max mem: 21740
|
| 198 |
+
train: [0] [ 20/400] eta: 0:04:03 lr: 0.000003 loss: 3.1779 (3.1835) grad: 0.1537 (0.1565) time: 0.4843 data: 0.0034 max mem: 22448
|
| 199 |
+
train: [0] [ 40/400] eta: 0:03:23 lr: 0.000006 loss: 3.1726 (3.1764) grad: 0.1510 (0.1562) time: 0.4838 data: 0.0043 max mem: 22448
|
| 200 |
+
train: [0] [ 60/400] eta: 0:03:01 lr: 0.000009 loss: 3.1626 (3.1753) grad: 0.1510 (0.1558) time: 0.4703 data: 0.0042 max mem: 22448
|
| 201 |
+
train: [0] [ 80/400] eta: 0:02:47 lr: 0.000012 loss: 3.1624 (3.1730) grad: 0.1511 (0.1547) time: 0.4975 data: 0.0047 max mem: 22448
|
| 202 |
+
train: [0] [100/400] eta: 0:02:34 lr: 0.000015 loss: 3.1647 (3.1718) grad: 0.1460 (0.1531) time: 0.4808 data: 0.0043 max mem: 22448
|
| 203 |
+
train: [0] [120/400] eta: 0:02:22 lr: 0.000018 loss: 3.1629 (3.1688) grad: 0.1383 (0.1507) time: 0.4760 data: 0.0042 max mem: 22448
|
| 204 |
+
train: [0] [140/400] eta: 0:02:11 lr: 0.000021 loss: 3.1535 (3.1669) grad: 0.1403 (0.1506) time: 0.4861 data: 0.0042 max mem: 22448
|
| 205 |
+
train: [0] [160/400] eta: 0:02:00 lr: 0.000024 loss: 3.1456 (3.1633) grad: 0.1570 (0.1518) time: 0.4677 data: 0.0041 max mem: 22448
|
| 206 |
+
train: [0] [180/400] eta: 0:01:49 lr: 0.000027 loss: 3.1387 (3.1606) grad: 0.1551 (0.1516) time: 0.4664 data: 0.0043 max mem: 22448
|
| 207 |
+
train: [0] [200/400] eta: 0:01:39 lr: 0.000030 loss: 3.1430 (3.1595) grad: 0.1409 (0.1504) time: 0.4763 data: 0.0042 max mem: 22448
|
| 208 |
+
train: [0] [220/400] eta: 0:01:28 lr: 0.000033 loss: 3.1439 (3.1580) grad: 0.1414 (0.1498) time: 0.4695 data: 0.0041 max mem: 22448
|
| 209 |
+
train: [0] [240/400] eta: 0:01:18 lr: 0.000036 loss: 3.1390 (3.1557) grad: 0.1454 (0.1494) time: 0.4706 data: 0.0041 max mem: 22448
|
| 210 |
+
train: [0] [260/400] eta: 0:01:08 lr: 0.000039 loss: 3.1260 (3.1530) grad: 0.1450 (0.1488) time: 0.4700 data: 0.0042 max mem: 22448
|
| 211 |
+
train: [0] [280/400] eta: 0:00:58 lr: 0.000042 loss: 3.1091 (3.1492) grad: 0.1450 (0.1486) time: 0.4550 data: 0.0040 max mem: 22448
|
| 212 |
+
train: [0] [300/400] eta: 0:00:48 lr: 0.000045 loss: 3.0842 (3.1441) grad: 0.1459 (0.1488) time: 0.4779 data: 0.0042 max mem: 22448
|
| 213 |
+
train: [0] [320/400] eta: 0:00:38 lr: 0.000048 loss: 3.0702 (3.1401) grad: 0.1525 (0.1494) time: 0.4749 data: 0.0040 max mem: 22448
|
| 214 |
+
train: [0] [340/400] eta: 0:00:29 lr: 0.000051 loss: 3.0772 (3.1367) grad: 0.1530 (0.1497) time: 0.4683 data: 0.0041 max mem: 22448
|
| 215 |
+
train: [0] [360/400] eta: 0:00:19 lr: 0.000054 loss: 3.0541 (3.1317) grad: 0.1559 (0.1504) time: 0.4681 data: 0.0042 max mem: 22448
|
| 216 |
+
train: [0] [380/400] eta: 0:00:09 lr: 0.000057 loss: 3.0506 (3.1277) grad: 0.1672 (0.1513) time: 0.4804 data: 0.0043 max mem: 22448
|
| 217 |
+
train: [0] [399/400] eta: 0:00:00 lr: 0.000060 loss: 3.0559 (3.1249) grad: 0.1731 (0.1522) time: 0.4836 data: 0.0043 max mem: 22448
|
| 218 |
+
train: [0] Total time: 0:03:13 (0.4840 s / it)
|
| 219 |
+
train: [0] Summary: lr: 0.000060 loss: 3.0559 (3.1249) grad: 0.1731 (0.1522)
|
| 220 |
+
eval (validation): [0] [ 0/85] eta: 0:05:03 time: 3.5739 data: 3.2826 max mem: 22448
|
| 221 |
+
eval (validation): [0] [20/85] eta: 0:00:38 time: 0.4474 data: 0.0035 max mem: 22448
|
| 222 |
+
eval (validation): [0] [40/85] eta: 0:00:21 time: 0.3612 data: 0.0042 max mem: 22448
|
| 223 |
+
eval (validation): [0] [60/85] eta: 0:00:10 time: 0.3533 data: 0.0040 max mem: 22448
|
| 224 |
+
eval (validation): [0] [80/85] eta: 0:00:02 time: 0.3973 data: 0.0041 max mem: 22448
|
| 225 |
+
eval (validation): [0] [84/85] eta: 0:00:00 time: 0.3991 data: 0.0041 max mem: 22448
|
| 226 |
+
eval (validation): [0] Total time: 0:00:36 (0.4292 s / it)
|
| 227 |
+
cv: [0] best hparam: (31, 1.0) (045) ('045_lr3.1e+01_wd1.0e+00') loss: 2.551 acc: 0.241 f1: 0.173
|
| 228 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 229 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 230 |
+
train: [1] [ 0/400] eta: 0:24:06 lr: nan time: 3.6151 data: 3.2510 max mem: 22448
|
| 231 |
+
train: [1] [ 20/400] eta: 0:04:02 lr: 0.000063 loss: 2.9989 (3.0062) grad: 0.1638 (0.1634) time: 0.4891 data: 0.0028 max mem: 22448
|
| 232 |
+
train: [1] [ 40/400] eta: 0:03:22 lr: 0.000066 loss: 3.0070 (3.0066) grad: 0.1586 (0.1593) time: 0.4826 data: 0.0041 max mem: 22448
|
| 233 |
+
train: [1] [ 60/400] eta: 0:03:02 lr: 0.000069 loss: 2.9893 (2.9960) grad: 0.1572 (0.1610) time: 0.4872 data: 0.0043 max mem: 22448
|
| 234 |
+
train: [1] [ 80/400] eta: 0:02:46 lr: 0.000072 loss: 2.9891 (2.9973) grad: 0.1618 (0.1637) time: 0.4706 data: 0.0040 max mem: 22448
|
| 235 |
+
train: [1] [100/400] eta: 0:02:34 lr: 0.000075 loss: 2.9891 (2.9889) grad: 0.1618 (0.1649) time: 0.4849 data: 0.0040 max mem: 22448
|
| 236 |
+
train: [1] [120/400] eta: 0:02:23 lr: 0.000078 loss: 2.9699 (2.9867) grad: 0.1668 (0.1657) time: 0.4958 data: 0.0043 max mem: 22448
|
| 237 |
+
train: [1] [140/400] eta: 0:02:11 lr: 0.000081 loss: 2.9807 (2.9854) grad: 0.1721 (0.1671) time: 0.4654 data: 0.0041 max mem: 22448
|
| 238 |
+
train: [1] [160/400] eta: 0:01:59 lr: 0.000084 loss: 2.9838 (2.9858) grad: 0.1721 (0.1675) time: 0.4650 data: 0.0040 max mem: 22448
|
| 239 |
+
train: [1] [180/400] eta: 0:01:49 lr: 0.000087 loss: 2.9847 (2.9859) grad: 0.1663 (0.1681) time: 0.4890 data: 0.0041 max mem: 22448
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train: [1] [200/400] eta: 0:01:39 lr: 0.000090 loss: 2.9561 (2.9827) grad: 0.1678 (0.1689) time: 0.4684 data: 0.0041 max mem: 22448
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train: [1] [220/400] eta: 0:01:28 lr: 0.000093 loss: 2.9127 (2.9744) grad: 0.1796 (0.1706) time: 0.4677 data: 0.0039 max mem: 22448
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train: [1] [240/400] eta: 0:01:18 lr: 0.000096 loss: 2.8986 (2.9706) grad: 0.1801 (0.1710) time: 0.4704 data: 0.0040 max mem: 22448
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train: [1] [260/400] eta: 0:01:08 lr: 0.000099 loss: 2.9310 (2.9686) grad: 0.1741 (0.1716) time: 0.4634 data: 0.0039 max mem: 22448
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train: [1] [280/400] eta: 0:00:58 lr: 0.000102 loss: 2.9322 (2.9647) grad: 0.1729 (0.1718) time: 0.4789 data: 0.0040 max mem: 22448
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+
train: [1] [300/400] eta: 0:00:48 lr: 0.000105 loss: 2.9246 (2.9628) grad: 0.1769 (0.1725) time: 0.4832 data: 0.0040 max mem: 22448
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+
train: [1] [320/400] eta: 0:00:38 lr: 0.000108 loss: 2.8939 (2.9585) grad: 0.1817 (0.1732) time: 0.4725 data: 0.0041 max mem: 22448
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train: [1] [340/400] eta: 0:00:29 lr: 0.000111 loss: 2.8683 (2.9540) grad: 0.1824 (0.1739) time: 0.4817 data: 0.0040 max mem: 22448
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+
train: [1] [360/400] eta: 0:00:19 lr: 0.000114 loss: 2.8787 (2.9513) grad: 0.1806 (0.1741) time: 0.4893 data: 0.0042 max mem: 22448
|
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+
train: [1] [380/400] eta: 0:00:09 lr: 0.000117 loss: 2.8762 (2.9479) grad: 0.1851 (0.1751) time: 0.4732 data: 0.0040 max mem: 22448
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+
train: [1] [399/400] eta: 0:00:00 lr: 0.000120 loss: 2.8762 (2.9461) grad: 0.1929 (0.1764) time: 0.4774 data: 0.0041 max mem: 22448
|
| 251 |
+
train: [1] Total time: 0:03:14 (0.4860 s / it)
|
| 252 |
+
train: [1] Summary: lr: 0.000120 loss: 2.8762 (2.9461) grad: 0.1929 (0.1764)
|
| 253 |
+
eval (validation): [1] [ 0/85] eta: 0:04:43 time: 3.3327 data: 3.0178 max mem: 22448
|
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eval (validation): [1] [20/85] eta: 0:00:33 time: 0.3780 data: 0.0054 max mem: 22448
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eval (validation): [1] [40/85] eta: 0:00:19 time: 0.3504 data: 0.0035 max mem: 22448
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eval (validation): [1] [60/85] eta: 0:00:10 time: 0.3645 data: 0.0041 max mem: 22448
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eval (validation): [1] [80/85] eta: 0:00:01 time: 0.3335 data: 0.0037 max mem: 22448
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eval (validation): [1] [84/85] eta: 0:00:00 time: 0.3278 data: 0.0036 max mem: 22448
|
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+
eval (validation): [1] Total time: 0:00:33 (0.3943 s / it)
|
| 260 |
+
cv: [1] best hparam: (16, 1.0) (041) ('041_lr1.6e+01_wd1.0e+00') loss: 2.478 acc: 0.248 f1: 0.179
|
| 261 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 262 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 263 |
+
train: [2] [ 0/400] eta: 0:22:22 lr: nan time: 3.3554 data: 3.0130 max mem: 22448
|
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train: [2] [ 20/400] eta: 0:03:48 lr: 0.000123 loss: 2.9036 (2.8856) grad: 0.2222 (0.2183) time: 0.4625 data: 0.0034 max mem: 22448
|
| 265 |
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train: [2] [ 40/400] eta: 0:03:12 lr: 0.000126 loss: 2.8996 (2.8883) grad: 0.2156 (0.2156) time: 0.4650 data: 0.0039 max mem: 22448
|
| 266 |
+
train: [2] [ 60/400] eta: 0:02:54 lr: 0.000129 loss: 2.8996 (2.9054) grad: 0.2246 (0.2658) time: 0.4724 data: 0.0040 max mem: 22448
|
| 267 |
+
train: [2] [ 80/400] eta: 0:02:40 lr: 0.000132 loss: 3.0051 (3.0269) grad: 0.6552 (0.4567) time: 0.4610 data: 0.0041 max mem: 22448
|
| 268 |
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train: [2] [100/400] eta: 0:02:28 lr: 0.000135 loss: 3.5409 (3.1404) grad: 1.1532 (0.6162) time: 0.4668 data: 0.0040 max mem: 22448
|
| 269 |
+
WARNING: classifier 48 (50, 1.0) diverged (loss=69.89 > 63.56) at step 452. Freezing.
|
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+
train: [2] [120/400] eta: 0:02:16 lr: 0.000138 loss: 3.4178 (3.1224) grad: 1.0588 (0.5873) time: 0.4565 data: 0.0040 max mem: 22448
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train: [2] [140/400] eta: 0:02:05 lr: 0.000141 loss: 2.8771 (3.0884) grad: 0.2034 (0.5336) time: 0.4581 data: 0.0040 max mem: 22448
|
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+
train: [2] [160/400] eta: 0:01:55 lr: 0.000144 loss: 2.8246 (3.0542) grad: 0.2059 (0.4928) time: 0.4603 data: 0.0040 max mem: 22448
|
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+
train: [2] [180/400] eta: 0:01:45 lr: 0.000147 loss: 2.7960 (3.0266) grad: 0.2059 (0.4612) time: 0.4535 data: 0.0039 max mem: 22448
|
| 274 |
+
train: [2] [200/400] eta: 0:01:35 lr: 0.000150 loss: 2.8184 (3.0069) grad: 0.2110 (0.4367) time: 0.4544 data: 0.0040 max mem: 22448
|
| 275 |
+
train: [2] [220/400] eta: 0:01:25 lr: 0.000153 loss: 2.8513 (2.9962) grad: 0.2327 (0.4248) time: 0.4592 data: 0.0040 max mem: 22448
|
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+
train: [2] [240/400] eta: 0:01:15 lr: 0.000156 loss: 2.9386 (3.0007) grad: 0.4040 (0.4399) time: 0.4573 data: 0.0041 max mem: 22448
|
| 277 |
+
WARNING: classifier 47 (43, 1.0) diverged (loss=82.90 > 63.56) at step 529. Freezing.
|
| 278 |
+
train: [2] [260/400] eta: 0:01:06 lr: 0.000159 loss: 3.2089 (3.0478) grad: 0.7532 (0.5163) time: 0.4660 data: 0.0043 max mem: 22448
|
| 279 |
+
train: [2] [280/400] eta: 0:00:56 lr: 0.000162 loss: 2.8683 (3.0323) grad: 0.2493 (0.4957) time: 0.4854 data: 0.0045 max mem: 22448
|
| 280 |
+
train: [2] [300/400] eta: 0:00:47 lr: 0.000165 loss: 2.8071 (3.0171) grad: 0.2241 (0.4771) time: 0.4664 data: 0.0042 max mem: 22448
|
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+
train: [2] [320/400] eta: 0:00:37 lr: 0.000168 loss: 2.7914 (3.0038) grad: 0.2120 (0.4604) time: 0.4698 data: 0.0041 max mem: 22448
|
| 282 |
+
train: [2] [340/400] eta: 0:00:28 lr: 0.000171 loss: 2.8053 (2.9938) grad: 0.2082 (0.4453) time: 0.4868 data: 0.0042 max mem: 22448
|
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+
train: [2] [360/400] eta: 0:00:18 lr: 0.000174 loss: 2.8100 (2.9843) grad: 0.2066 (0.4323) time: 0.4599 data: 0.0039 max mem: 22448
|
| 284 |
+
train: [2] [380/400] eta: 0:00:09 lr: 0.000177 loss: 2.8290 (2.9767) grad: 0.2157 (0.4216) time: 0.4637 data: 0.0043 max mem: 22448
|
| 285 |
+
train: [2] [399/400] eta: 0:00:00 lr: 0.000180 loss: 2.8290 (2.9676) grad: 0.2257 (0.4118) time: 0.5310 data: 0.0047 max mem: 22448
|
| 286 |
+
train: [2] Total time: 0:03:10 (0.4753 s / it)
|
| 287 |
+
train: [2] Summary: lr: 0.000180 loss: 2.8290 (2.9676) grad: 0.2257 (0.4118)
|
| 288 |
+
eval (validation): [2] [ 0/85] eta: 0:04:29 time: 3.1657 data: 2.9228 max mem: 22448
|
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+
eval (validation): [2] [20/85] eta: 0:00:30 time: 0.3388 data: 0.0040 max mem: 22448
|
| 290 |
+
eval (validation): [2] [40/85] eta: 0:00:18 time: 0.3371 data: 0.0032 max mem: 22448
|
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+
eval (validation): [2] [60/85] eta: 0:00:09 time: 0.3855 data: 0.0042 max mem: 22448
|
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+
eval (validation): [2] [80/85] eta: 0:00:01 time: 0.3495 data: 0.0039 max mem: 22448
|
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+
eval (validation): [2] [84/85] eta: 0:00:00 time: 0.3375 data: 0.0038 max mem: 22448
|
| 294 |
+
eval (validation): [2] Total time: 0:00:32 (0.3878 s / it)
|
| 295 |
+
cv: [2] best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00') loss: 2.388 acc: 0.275 f1: 0.207
|
| 296 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 297 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 298 |
+
train: [3] [ 0/400] eta: 0:22:23 lr: nan time: 3.3595 data: 2.9779 max mem: 22448
|
| 299 |
+
train: [3] [ 20/400] eta: 0:03:55 lr: 0.000183 loss: 2.7554 (2.7832) grad: 0.2374 (0.2343) time: 0.4817 data: 0.0033 max mem: 22448
|
| 300 |
+
train: [3] [ 40/400] eta: 0:03:15 lr: 0.000186 loss: 2.7889 (2.8029) grad: 0.2366 (0.2361) time: 0.4660 data: 0.0041 max mem: 22448
|
| 301 |
+
train: [3] [ 60/400] eta: 0:02:56 lr: 0.000189 loss: 2.7889 (2.7982) grad: 0.2370 (0.2425) time: 0.4650 data: 0.0042 max mem: 22448
|
| 302 |
+
train: [3] [ 80/400] eta: 0:02:41 lr: 0.000192 loss: 2.8027 (2.8048) grad: 0.2638 (0.2630) time: 0.4693 data: 0.0044 max mem: 22448
|
| 303 |
+
train: [3] [100/400] eta: 0:02:29 lr: 0.000195 loss: 2.8654 (2.8697) grad: 0.4728 (0.3969) time: 0.4652 data: 0.0042 max mem: 22448
|
| 304 |
+
WARNING: classifier 46 (36, 1.0) diverged (loss=68.57 > 63.56) at step 655. Freezing.
|
| 305 |
+
train: [3] [120/400] eta: 0:02:17 lr: 0.000198 loss: 2.9892 (2.9303) grad: 0.8184 (0.4728) time: 0.4630 data: 0.0041 max mem: 22448
|
| 306 |
+
train: [3] [140/400] eta: 0:02:06 lr: 0.000201 loss: 2.7992 (2.9108) grad: 0.2296 (0.4377) time: 0.4642 data: 0.0039 max mem: 22448
|
| 307 |
+
train: [3] [160/400] eta: 0:01:56 lr: 0.000204 loss: 2.8009 (2.8988) grad: 0.2250 (0.4110) time: 0.4625 data: 0.0041 max mem: 22448
|
| 308 |
+
train: [3] [180/400] eta: 0:01:46 lr: 0.000207 loss: 2.8090 (2.8859) grad: 0.2347 (0.3931) time: 0.4652 data: 0.0040 max mem: 22448
|
| 309 |
+
train: [3] [200/400] eta: 0:01:36 lr: 0.000210 loss: 2.7978 (2.8818) grad: 0.2736 (0.3860) time: 0.4540 data: 0.0041 max mem: 22448
|
| 310 |
+
train: [3] [220/400] eta: 0:01:26 lr: 0.000213 loss: 2.9080 (2.9054) grad: 0.3846 (0.4389) time: 0.4567 data: 0.0040 max mem: 22448
|
| 311 |
+
WARNING: classifier 45 (31, 1.0) diverged (loss=87.77 > 63.56) at step 715. Freezing.
|
| 312 |
+
train: [3] [240/400] eta: 0:01:16 lr: 0.000216 loss: 2.9812 (2.9306) grad: 0.8410 (0.4658) time: 0.4572 data: 0.0040 max mem: 22448
|
| 313 |
+
train: [3] [260/400] eta: 0:01:06 lr: 0.000219 loss: 2.8167 (2.9173) grad: 0.2196 (0.4465) time: 0.4641 data: 0.0041 max mem: 22448
|
| 314 |
+
train: [3] [280/400] eta: 0:00:56 lr: 0.000222 loss: 2.7517 (2.9058) grad: 0.2160 (0.4302) time: 0.4664 data: 0.0041 max mem: 22448
|
| 315 |
+
train: [3] [300/400] eta: 0:00:47 lr: 0.000225 loss: 2.7559 (2.8968) grad: 0.2092 (0.4155) time: 0.4576 data: 0.0041 max mem: 22448
|
| 316 |
+
train: [3] [320/400] eta: 0:00:37 lr: 0.000228 loss: 2.7559 (2.8871) grad: 0.2153 (0.4047) time: 0.4642 data: 0.0041 max mem: 22448
|
| 317 |
+
train: [3] [340/400] eta: 0:00:28 lr: 0.000231 loss: 2.7755 (2.8827) grad: 0.2525 (0.3987) time: 0.4576 data: 0.0042 max mem: 22448
|
| 318 |
+
train: [3] [360/400] eta: 0:00:18 lr: 0.000234 loss: 2.8860 (2.8944) grad: 0.3809 (0.4185) time: 0.4665 data: 0.0040 max mem: 22448
|
| 319 |
+
WARNING: classifier 44 (26, 1.0) diverged (loss=69.60 > 63.56) at step 784. Freezing.
|
| 320 |
+
train: [3] [380/400] eta: 0:00:09 lr: 0.000237 loss: 3.0173 (2.9061) grad: 0.6230 (0.4372) time: 0.4680 data: 0.0041 max mem: 22448
|
| 321 |
+
train: [3] [399/400] eta: 0:00:00 lr: 0.000240 loss: 2.7597 (2.8981) grad: 0.2181 (0.4259) time: 0.4715 data: 0.0042 max mem: 22448
|
| 322 |
+
train: [3] Total time: 0:03:08 (0.4718 s / it)
|
| 323 |
+
train: [3] Summary: lr: 0.000240 loss: 2.7597 (2.8981) grad: 0.2181 (0.4259)
|
| 324 |
+
eval (validation): [3] [ 0/85] eta: 0:04:21 time: 3.0709 data: 2.8384 max mem: 22448
|
| 325 |
+
eval (validation): [3] [20/85] eta: 0:00:30 time: 0.3393 data: 0.0043 max mem: 22448
|
| 326 |
+
eval (validation): [3] [40/85] eta: 0:00:19 time: 0.3900 data: 0.0034 max mem: 22448
|
| 327 |
+
eval (validation): [3] [60/85] eta: 0:00:10 time: 0.3507 data: 0.0040 max mem: 22448
|
| 328 |
+
eval (validation): [3] [80/85] eta: 0:00:01 time: 0.3476 data: 0.0042 max mem: 22448
|
| 329 |
+
eval (validation): [3] [84/85] eta: 0:00:00 time: 0.3359 data: 0.0040 max mem: 22448
|
| 330 |
+
eval (validation): [3] Total time: 0:00:33 (0.3914 s / it)
|
| 331 |
+
cv: [3] best hparam: (3.7, 1.0) (032) ('032_lr3.7e+00_wd1.0e+00') loss: 2.442 acc: 0.263 f1: 0.195
|
| 332 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 333 |
+
train: [4] [ 0/400] eta: 0:21:52 lr: nan time: 3.2816 data: 2.9436 max mem: 22448
|
| 334 |
+
train: [4] [ 20/400] eta: 0:03:44 lr: 0.000243 loss: 2.6782 (2.7062) grad: 0.2028 (0.2122) time: 0.4550 data: 0.0043 max mem: 22448
|
| 335 |
+
train: [4] [ 40/400] eta: 0:03:09 lr: 0.000246 loss: 2.7017 (2.7194) grad: 0.2059 (0.2108) time: 0.4600 data: 0.0039 max mem: 22448
|
| 336 |
+
train: [4] [ 60/400] eta: 0:02:52 lr: 0.000249 loss: 2.7184 (2.7229) grad: 0.2066 (0.2107) time: 0.4671 data: 0.0043 max mem: 22448
|
| 337 |
+
train: [4] [ 80/400] eta: 0:02:39 lr: 0.000252 loss: 2.6991 (2.7209) grad: 0.2072 (0.2090) time: 0.4696 data: 0.0045 max mem: 22448
|
| 338 |
+
train: [4] [100/400] eta: 0:02:27 lr: 0.000255 loss: 2.7402 (2.7305) grad: 0.2085 (0.2092) time: 0.4657 data: 0.0042 max mem: 22448
|
| 339 |
+
train: [4] [120/400] eta: 0:02:16 lr: 0.000258 loss: 2.7313 (2.7254) grad: 0.2114 (0.2108) time: 0.4615 data: 0.0042 max mem: 22448
|
| 340 |
+
train: [4] [140/400] eta: 0:02:06 lr: 0.000261 loss: 2.6897 (2.7232) grad: 0.2245 (0.2137) time: 0.4768 data: 0.0045 max mem: 22448
|
| 341 |
+
train: [4] [160/400] eta: 0:01:55 lr: 0.000264 loss: 2.7096 (2.7234) grad: 0.2324 (0.2163) time: 0.4556 data: 0.0039 max mem: 22448
|
| 342 |
+
train: [4] [180/400] eta: 0:01:45 lr: 0.000267 loss: 2.7104 (2.7256) grad: 0.2368 (0.2181) time: 0.4717 data: 0.0041 max mem: 22448
|
| 343 |
+
train: [4] [200/400] eta: 0:01:36 lr: 0.000270 loss: 2.7059 (2.7219) grad: 0.2267 (0.2188) time: 0.4814 data: 0.0041 max mem: 22448
|
| 344 |
+
train: [4] [220/400] eta: 0:01:26 lr: 0.000273 loss: 2.7060 (2.7244) grad: 0.2259 (0.2201) time: 0.4543 data: 0.0039 max mem: 22448
|
| 345 |
+
train: [4] [240/400] eta: 0:01:16 lr: 0.000276 loss: 2.7275 (2.7250) grad: 0.2309 (0.2216) time: 0.4506 data: 0.0038 max mem: 22448
|
| 346 |
+
train: [4] [260/400] eta: 0:01:06 lr: 0.000279 loss: 2.7587 (2.7277) grad: 0.2374 (0.2232) time: 0.4865 data: 0.0043 max mem: 22448
|
| 347 |
+
train: [4] [280/400] eta: 0:00:57 lr: 0.000282 loss: 2.7691 (2.7303) grad: 0.2386 (0.2262) time: 0.4645 data: 0.0041 max mem: 22448
|
| 348 |
+
train: [4] [300/400] eta: 0:00:47 lr: 0.000285 loss: 2.7945 (2.7399) grad: 0.3095 (0.2441) time: 0.4591 data: 0.0039 max mem: 22448
|
| 349 |
+
train: [4] [320/400] eta: 0:00:37 lr: 0.000288 loss: 3.0426 (2.7823) grad: 0.7753 (0.3114) time: 0.4551 data: 0.0040 max mem: 22448
|
| 350 |
+
WARNING: classifier 43 (22, 1.0) diverged (loss=65.91 > 63.56) at step 963. Freezing.
|
| 351 |
+
train: [4] [340/400] eta: 0:00:28 lr: 0.000291 loss: 3.2773 (2.7994) grad: 1.1283 (0.3323) time: 0.4601 data: 0.0041 max mem: 22448
|
| 352 |
+
train: [4] [360/400] eta: 0:00:18 lr: 0.000294 loss: 2.7534 (2.7968) grad: 0.2200 (0.3258) time: 0.4483 data: 0.0040 max mem: 22448
|
| 353 |
+
train: [4] [380/400] eta: 0:00:09 lr: 0.000297 loss: 2.7204 (2.7923) grad: 0.2152 (0.3199) time: 0.4662 data: 0.0042 max mem: 22448
|
| 354 |
+
train: [4] [399/400] eta: 0:00:00 lr: 0.000300 loss: 2.7097 (2.7876) grad: 0.2095 (0.3143) time: 0.4647 data: 0.0043 max mem: 22448
|
| 355 |
+
train: [4] Total time: 0:03:08 (0.4710 s / it)
|
| 356 |
+
train: [4] Summary: lr: 0.000300 loss: 2.7097 (2.7876) grad: 0.2095 (0.3143)
|
| 357 |
+
eval (validation): [4] [ 0/85] eta: 0:04:34 time: 3.2276 data: 2.9453 max mem: 22448
|
| 358 |
+
eval (validation): [4] [20/85] eta: 0:00:31 time: 0.3465 data: 0.0033 max mem: 22448
|
| 359 |
+
eval (validation): [4] [40/85] eta: 0:00:19 time: 0.3672 data: 0.0038 max mem: 22448
|
| 360 |
+
eval (validation): [4] [60/85] eta: 0:00:10 time: 0.3544 data: 0.0043 max mem: 22448
|
| 361 |
+
eval (validation): [4] [80/85] eta: 0:00:01 time: 0.3485 data: 0.0042 max mem: 22448
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eval (validation): [4] [84/85] eta: 0:00:00 time: 0.3409 data: 0.0041 max mem: 22448
|
| 363 |
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eval (validation): [4] Total time: 0:00:33 (0.3901 s / it)
|
| 364 |
+
cv: [4] best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00') loss: 2.414 acc: 0.272 f1: 0.216
|
| 365 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 366 |
+
train: [5] [ 0/400] eta: 0:22:14 lr: nan time: 3.3356 data: 3.0003 max mem: 22448
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train: [5] [ 20/400] eta: 0:03:48 lr: 0.000300 loss: 2.6256 (2.6063) grad: 0.2127 (0.2149) time: 0.4649 data: 0.0038 max mem: 22448
|
| 368 |
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train: [5] [ 40/400] eta: 0:03:13 lr: 0.000300 loss: 2.6303 (2.6486) grad: 0.2166 (0.2199) time: 0.4681 data: 0.0039 max mem: 22448
|
| 369 |
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train: [5] [ 60/400] eta: 0:02:53 lr: 0.000300 loss: 2.6839 (2.6603) grad: 0.2260 (0.2235) time: 0.4525 data: 0.0042 max mem: 22448
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+
train: [5] [ 80/400] eta: 0:02:38 lr: 0.000300 loss: 2.6858 (2.6579) grad: 0.2266 (0.2233) time: 0.4553 data: 0.0041 max mem: 22448
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train: [5] [100/400] eta: 0:02:26 lr: 0.000300 loss: 2.6822 (2.6647) grad: 0.2281 (0.2270) time: 0.4587 data: 0.0041 max mem: 22448
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train: [5] [120/400] eta: 0:02:15 lr: 0.000300 loss: 2.6306 (2.6576) grad: 0.2305 (0.2269) time: 0.4619 data: 0.0042 max mem: 22448
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train: [5] [140/400] eta: 0:02:04 lr: 0.000300 loss: 2.6238 (2.6514) grad: 0.2195 (0.2249) time: 0.4496 data: 0.0041 max mem: 22448
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train: [5] [160/400] eta: 0:01:54 lr: 0.000299 loss: 2.6211 (2.6499) grad: 0.2157 (0.2242) time: 0.4655 data: 0.0042 max mem: 22448
|
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+
train: [5] [180/400] eta: 0:01:44 lr: 0.000299 loss: 2.6459 (2.6532) grad: 0.2164 (0.2236) time: 0.4541 data: 0.0043 max mem: 22448
|
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train: [5] [200/400] eta: 0:01:34 lr: 0.000299 loss: 2.6684 (2.6529) grad: 0.2223 (0.2239) time: 0.4496 data: 0.0043 max mem: 22448
|
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train: [5] [220/400] eta: 0:01:24 lr: 0.000299 loss: 2.6469 (2.6529) grad: 0.2225 (0.2232) time: 0.4572 data: 0.0041 max mem: 22448
|
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+
train: [5] [240/400] eta: 0:01:15 lr: 0.000299 loss: 2.6469 (2.6523) grad: 0.2215 (0.2234) time: 0.4504 data: 0.0040 max mem: 22448
|
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+
train: [5] [260/400] eta: 0:01:05 lr: 0.000299 loss: 2.6192 (2.6486) grad: 0.2193 (0.2231) time: 0.4685 data: 0.0041 max mem: 22448
|
| 380 |
+
train: [5] [280/400] eta: 0:00:56 lr: 0.000298 loss: 2.6192 (2.6505) grad: 0.2216 (0.2237) time: 0.4541 data: 0.0041 max mem: 22448
|
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+
train: [5] [300/400] eta: 0:00:46 lr: 0.000298 loss: 2.6463 (2.6471) grad: 0.2274 (0.2237) time: 0.4529 data: 0.0041 max mem: 22448
|
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+
train: [5] [320/400] eta: 0:00:37 lr: 0.000298 loss: 2.6234 (2.6484) grad: 0.2251 (0.2240) time: 0.4586 data: 0.0042 max mem: 22448
|
| 383 |
+
train: [5] [340/400] eta: 0:00:27 lr: 0.000298 loss: 2.6349 (2.6478) grad: 0.2271 (0.2241) time: 0.4591 data: 0.0042 max mem: 22448
|
| 384 |
+
train: [5] [360/400] eta: 0:00:18 lr: 0.000297 loss: 2.6349 (2.6474) grad: 0.2248 (0.2242) time: 0.4662 data: 0.0042 max mem: 22448
|
| 385 |
+
train: [5] [380/400] eta: 0:00:09 lr: 0.000297 loss: 2.6131 (2.6471) grad: 0.2248 (0.2244) time: 0.4756 data: 0.0042 max mem: 22448
|
| 386 |
+
train: [5] [399/400] eta: 0:00:00 lr: 0.000297 loss: 2.5823 (2.6434) grad: 0.2223 (0.2239) time: 0.4697 data: 0.0040 max mem: 22448
|
| 387 |
+
train: [5] Total time: 0:03:06 (0.4671 s / it)
|
| 388 |
+
train: [5] Summary: lr: 0.000297 loss: 2.5823 (2.6434) grad: 0.2223 (0.2239)
|
| 389 |
+
eval (validation): [5] [ 0/85] eta: 0:04:32 time: 3.2060 data: 2.9609 max mem: 22448
|
| 390 |
+
eval (validation): [5] [20/85] eta: 0:00:32 time: 0.3687 data: 0.0054 max mem: 22448
|
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+
eval (validation): [5] [40/85] eta: 0:00:19 time: 0.3776 data: 0.0041 max mem: 22448
|
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eval (validation): [5] [60/85] eta: 0:00:10 time: 0.3561 data: 0.0038 max mem: 22448
|
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+
eval (validation): [5] [80/85] eta: 0:00:01 time: 0.3503 data: 0.0043 max mem: 22448
|
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+
eval (validation): [5] [84/85] eta: 0:00:00 time: 0.3524 data: 0.0042 max mem: 22448
|
| 395 |
+
eval (validation): [5] Total time: 0:00:34 (0.4016 s / it)
|
| 396 |
+
cv: [5] best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00') loss: 2.378 acc: 0.280 f1: 0.218
|
| 397 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 398 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 399 |
+
train: [6] [ 0/400] eta: 0:22:51 lr: nan time: 3.4299 data: 3.0412 max mem: 22448
|
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+
train: [6] [ 20/400] eta: 0:03:49 lr: 0.000296 loss: 2.5615 (2.5615) grad: 0.2204 (0.2173) time: 0.4634 data: 0.0035 max mem: 22448
|
| 401 |
+
train: [6] [ 40/400] eta: 0:03:10 lr: 0.000296 loss: 2.5847 (2.5834) grad: 0.2233 (0.2216) time: 0.4504 data: 0.0039 max mem: 22448
|
| 402 |
+
train: [6] [ 60/400] eta: 0:02:52 lr: 0.000296 loss: 2.5770 (2.5802) grad: 0.2236 (0.2234) time: 0.4623 data: 0.0040 max mem: 22448
|
| 403 |
+
train: [6] [ 80/400] eta: 0:02:38 lr: 0.000295 loss: 2.5644 (2.5718) grad: 0.2246 (0.2241) time: 0.4635 data: 0.0042 max mem: 22448
|
| 404 |
+
train: [6] [100/400] eta: 0:02:26 lr: 0.000295 loss: 2.5598 (2.5666) grad: 0.2266 (0.2245) time: 0.4562 data: 0.0040 max mem: 22448
|
| 405 |
+
train: [6] [120/400] eta: 0:02:15 lr: 0.000295 loss: 2.5736 (2.5683) grad: 0.2287 (0.2254) time: 0.4550 data: 0.0039 max mem: 22448
|
| 406 |
+
train: [6] [140/400] eta: 0:02:04 lr: 0.000294 loss: 2.5955 (2.5758) grad: 0.2219 (0.2258) time: 0.4556 data: 0.0041 max mem: 22448
|
| 407 |
+
train: [6] [160/400] eta: 0:01:54 lr: 0.000294 loss: 2.6370 (2.5806) grad: 0.2281 (0.2255) time: 0.4564 data: 0.0041 max mem: 22448
|
| 408 |
+
train: [6] [180/400] eta: 0:01:44 lr: 0.000293 loss: 2.5854 (2.5794) grad: 0.2293 (0.2264) time: 0.4560 data: 0.0041 max mem: 22448
|
| 409 |
+
train: [6] [200/400] eta: 0:01:34 lr: 0.000293 loss: 2.5854 (2.5814) grad: 0.2317 (0.2269) time: 0.4525 data: 0.0040 max mem: 22448
|
| 410 |
+
train: [6] [220/400] eta: 0:01:24 lr: 0.000292 loss: 2.5974 (2.5791) grad: 0.2326 (0.2279) time: 0.4544 data: 0.0042 max mem: 22448
|
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+
train: [6] [240/400] eta: 0:01:14 lr: 0.000292 loss: 2.5974 (2.5813) grad: 0.2367 (0.2285) time: 0.4464 data: 0.0041 max mem: 22448
|
| 412 |
+
train: [6] [260/400] eta: 0:01:05 lr: 0.000291 loss: 2.5781 (2.5790) grad: 0.2308 (0.2287) time: 0.4618 data: 0.0042 max mem: 22448
|
| 413 |
+
train: [6] [280/400] eta: 0:00:55 lr: 0.000291 loss: 2.5794 (2.5803) grad: 0.2292 (0.2290) time: 0.4449 data: 0.0039 max mem: 22448
|
| 414 |
+
train: [6] [300/400] eta: 0:00:46 lr: 0.000290 loss: 2.6094 (2.5819) grad: 0.2369 (0.2294) time: 0.4522 data: 0.0041 max mem: 22448
|
| 415 |
+
train: [6] [320/400] eta: 0:00:37 lr: 0.000290 loss: 2.6139 (2.5828) grad: 0.2369 (0.2298) time: 0.4638 data: 0.0041 max mem: 22448
|
| 416 |
+
train: [6] [340/400] eta: 0:00:27 lr: 0.000289 loss: 2.6139 (2.5846) grad: 0.2317 (0.2300) time: 0.4554 data: 0.0040 max mem: 22448
|
| 417 |
+
train: [6] [360/400] eta: 0:00:18 lr: 0.000288 loss: 2.5639 (2.5826) grad: 0.2342 (0.2304) time: 0.4637 data: 0.0042 max mem: 22448
|
| 418 |
+
train: [6] [380/400] eta: 0:00:09 lr: 0.000288 loss: 2.5464 (2.5848) grad: 0.2355 (0.2306) time: 0.4674 data: 0.0042 max mem: 22448
|
| 419 |
+
train: [6] [399/400] eta: 0:00:00 lr: 0.000287 loss: 2.5902 (2.5849) grad: 0.2200 (0.2300) time: 0.4618 data: 0.0043 max mem: 22448
|
| 420 |
+
train: [6] Total time: 0:03:05 (0.4649 s / it)
|
| 421 |
+
train: [6] Summary: lr: 0.000287 loss: 2.5902 (2.5849) grad: 0.2200 (0.2300)
|
| 422 |
+
eval (validation): [6] [ 0/85] eta: 0:04:34 time: 3.2283 data: 2.9897 max mem: 22448
|
| 423 |
+
eval (validation): [6] [20/85] eta: 0:00:31 time: 0.3521 data: 0.0045 max mem: 22448
|
| 424 |
+
eval (validation): [6] [40/85] eta: 0:00:19 time: 0.3604 data: 0.0035 max mem: 22448
|
| 425 |
+
eval (validation): [6] [60/85] eta: 0:00:10 time: 0.3554 data: 0.0044 max mem: 22448
|
| 426 |
+
eval (validation): [6] [80/85] eta: 0:00:01 time: 0.3475 data: 0.0044 max mem: 22448
|
| 427 |
+
eval (validation): [6] [84/85] eta: 0:00:00 time: 0.3439 data: 0.0044 max mem: 22448
|
| 428 |
+
eval (validation): [6] Total time: 0:00:33 (0.3902 s / it)
|
| 429 |
+
cv: [6] best hparam: (0.85, 1.0) (023) ('023_lr8.5e-01_wd1.0e+00') loss: 2.378 acc: 0.285 f1: 0.227
|
| 430 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 431 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 432 |
+
train: [7] [ 0/400] eta: 0:22:13 lr: nan time: 3.3345 data: 3.0055 max mem: 22448
|
| 433 |
+
train: [7] [ 20/400] eta: 0:03:42 lr: 0.000286 loss: 2.4870 (2.4976) grad: 0.2188 (0.2267) time: 0.4472 data: 0.0033 max mem: 22448
|
| 434 |
+
train: [7] [ 40/400] eta: 0:03:06 lr: 0.000286 loss: 2.4847 (2.4947) grad: 0.2242 (0.2300) time: 0.4490 data: 0.0038 max mem: 22448
|
| 435 |
+
train: [7] [ 60/400] eta: 0:02:49 lr: 0.000285 loss: 2.4771 (2.4849) grad: 0.2330 (0.2333) time: 0.4557 data: 0.0042 max mem: 22448
|
| 436 |
+
train: [7] [ 80/400] eta: 0:02:36 lr: 0.000284 loss: 2.5003 (2.5017) grad: 0.2370 (0.2317) time: 0.4564 data: 0.0042 max mem: 22448
|
| 437 |
+
train: [7] [100/400] eta: 0:02:24 lr: 0.000284 loss: 2.5026 (2.4963) grad: 0.2316 (0.2320) time: 0.4574 data: 0.0040 max mem: 22448
|
| 438 |
+
train: [7] [120/400] eta: 0:02:13 lr: 0.000283 loss: 2.4907 (2.4964) grad: 0.2351 (0.2337) time: 0.4555 data: 0.0041 max mem: 22448
|
| 439 |
+
train: [7] [140/400] eta: 0:02:03 lr: 0.000282 loss: 2.5087 (2.5026) grad: 0.2346 (0.2341) time: 0.4601 data: 0.0040 max mem: 22448
|
| 440 |
+
train: [7] [160/400] eta: 0:01:53 lr: 0.000282 loss: 2.5141 (2.5047) grad: 0.2327 (0.2339) time: 0.4691 data: 0.0041 max mem: 22448
|
| 441 |
+
train: [7] [180/400] eta: 0:01:43 lr: 0.000281 loss: 2.5498 (2.5119) grad: 0.2398 (0.2353) time: 0.4598 data: 0.0041 max mem: 22448
|
| 442 |
+
train: [7] [200/400] eta: 0:01:34 lr: 0.000280 loss: 2.5610 (2.5128) grad: 0.2398 (0.2354) time: 0.4587 data: 0.0041 max mem: 22448
|
| 443 |
+
train: [7] [220/400] eta: 0:01:24 lr: 0.000279 loss: 2.5210 (2.5114) grad: 0.2355 (0.2358) time: 0.4755 data: 0.0043 max mem: 22448
|
| 444 |
+
train: [7] [240/400] eta: 0:01:15 lr: 0.000278 loss: 2.5265 (2.5143) grad: 0.2336 (0.2359) time: 0.4541 data: 0.0041 max mem: 22448
|
| 445 |
+
train: [7] [260/400] eta: 0:01:05 lr: 0.000278 loss: 2.5265 (2.5129) grad: 0.2277 (0.2349) time: 0.4603 data: 0.0039 max mem: 22448
|
| 446 |
+
train: [7] [280/400] eta: 0:00:56 lr: 0.000277 loss: 2.4810 (2.5100) grad: 0.2238 (0.2345) time: 0.4635 data: 0.0042 max mem: 22448
|
| 447 |
+
train: [7] [300/400] eta: 0:00:46 lr: 0.000276 loss: 2.4398 (2.5074) grad: 0.2279 (0.2348) time: 0.4632 data: 0.0041 max mem: 22448
|
| 448 |
+
train: [7] [320/400] eta: 0:00:37 lr: 0.000275 loss: 2.4664 (2.5082) grad: 0.2348 (0.2348) time: 0.4466 data: 0.0040 max mem: 22448
|
| 449 |
+
train: [7] [340/400] eta: 0:00:28 lr: 0.000274 loss: 2.4794 (2.5069) grad: 0.2304 (0.2346) time: 0.4603 data: 0.0041 max mem: 22448
|
| 450 |
+
train: [7] [360/400] eta: 0:00:18 lr: 0.000273 loss: 2.4808 (2.5080) grad: 0.2332 (0.2346) time: 0.4580 data: 0.0042 max mem: 22448
|
| 451 |
+
train: [7] [380/400] eta: 0:00:09 lr: 0.000272 loss: 2.5016 (2.5075) grad: 0.2337 (0.2351) time: 0.4661 data: 0.0043 max mem: 22448
|
| 452 |
+
train: [7] [399/400] eta: 0:00:00 lr: 0.000271 loss: 2.5394 (2.5089) grad: 0.2366 (0.2355) time: 0.4599 data: 0.0041 max mem: 22448
|
| 453 |
+
train: [7] Total time: 0:03:06 (0.4663 s / it)
|
| 454 |
+
train: [7] Summary: lr: 0.000271 loss: 2.5394 (2.5089) grad: 0.2366 (0.2355)
|
| 455 |
+
eval (validation): [7] [ 0/85] eta: 0:04:27 time: 3.1474 data: 2.8486 max mem: 22448
|
| 456 |
+
eval (validation): [7] [20/85] eta: 0:00:32 time: 0.3699 data: 0.0038 max mem: 22448
|
| 457 |
+
eval (validation): [7] [40/85] eta: 0:00:19 time: 0.3725 data: 0.0044 max mem: 22448
|
| 458 |
+
eval (validation): [7] [60/85] eta: 0:00:10 time: 0.3516 data: 0.0039 max mem: 22448
|
| 459 |
+
eval (validation): [7] [80/85] eta: 0:00:01 time: 0.3436 data: 0.0039 max mem: 22448
|
| 460 |
+
eval (validation): [7] [84/85] eta: 0:00:00 time: 0.3380 data: 0.0039 max mem: 22448
|
| 461 |
+
eval (validation): [7] Total time: 0:00:33 (0.3951 s / it)
|
| 462 |
+
cv: [7] best hparam: (0.61, 1.0) (021) ('021_lr6.1e-01_wd1.0e+00') loss: 2.374 acc: 0.277 f1: 0.218
|
| 463 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 464 |
+
train: [8] [ 0/400] eta: 0:22:40 lr: nan time: 3.4014 data: 3.0234 max mem: 22448
|
| 465 |
+
train: [8] [ 20/400] eta: 0:03:51 lr: 0.000270 loss: 2.3456 (2.3751) grad: 0.2148 (0.2207) time: 0.4703 data: 0.0043 max mem: 22448
|
| 466 |
+
train: [8] [ 40/400] eta: 0:03:11 lr: 0.000270 loss: 2.3831 (2.4048) grad: 0.2263 (0.2275) time: 0.4487 data: 0.0041 max mem: 22448
|
| 467 |
+
train: [8] [ 60/400] eta: 0:02:51 lr: 0.000269 loss: 2.4557 (2.4199) grad: 0.2325 (0.2291) time: 0.4479 data: 0.0041 max mem: 22448
|
| 468 |
+
train: [8] [ 80/400] eta: 0:02:37 lr: 0.000268 loss: 2.4870 (2.4355) grad: 0.2332 (0.2327) time: 0.4539 data: 0.0039 max mem: 22448
|
| 469 |
+
train: [8] [100/400] eta: 0:02:25 lr: 0.000267 loss: 2.4676 (2.4348) grad: 0.2467 (0.2369) time: 0.4558 data: 0.0040 max mem: 22448
|
| 470 |
+
train: [8] [120/400] eta: 0:02:14 lr: 0.000266 loss: 2.4494 (2.4352) grad: 0.2568 (0.2403) time: 0.4572 data: 0.0041 max mem: 22448
|
| 471 |
+
train: [8] [140/400] eta: 0:02:04 lr: 0.000265 loss: 2.4416 (2.4402) grad: 0.2514 (0.2415) time: 0.4590 data: 0.0039 max mem: 22448
|
| 472 |
+
train: [8] [160/400] eta: 0:01:53 lr: 0.000264 loss: 2.4647 (2.4390) grad: 0.2543 (0.2445) time: 0.4589 data: 0.0041 max mem: 22448
|
| 473 |
+
train: [8] [180/400] eta: 0:01:44 lr: 0.000263 loss: 2.4519 (2.4393) grad: 0.2523 (0.2447) time: 0.4600 data: 0.0041 max mem: 22448
|
| 474 |
+
train: [8] [200/400] eta: 0:01:34 lr: 0.000262 loss: 2.4460 (2.4416) grad: 0.2485 (0.2454) time: 0.4527 data: 0.0041 max mem: 22448
|
| 475 |
+
train: [8] [220/400] eta: 0:01:24 lr: 0.000260 loss: 2.4741 (2.4442) grad: 0.2478 (0.2456) time: 0.4530 data: 0.0041 max mem: 22448
|
| 476 |
+
train: [8] [240/400] eta: 0:01:14 lr: 0.000259 loss: 2.4695 (2.4441) grad: 0.2454 (0.2454) time: 0.4493 data: 0.0039 max mem: 22448
|
| 477 |
+
train: [8] [260/400] eta: 0:01:05 lr: 0.000258 loss: 2.4607 (2.4466) grad: 0.2421 (0.2456) time: 0.4581 data: 0.0040 max mem: 22448
|
| 478 |
+
train: [8] [280/400] eta: 0:00:55 lr: 0.000257 loss: 2.4435 (2.4470) grad: 0.2426 (0.2459) time: 0.4535 data: 0.0040 max mem: 22448
|
| 479 |
+
train: [8] [300/400] eta: 0:00:46 lr: 0.000256 loss: 2.4279 (2.4472) grad: 0.2426 (0.2463) time: 0.4512 data: 0.0040 max mem: 22448
|
| 480 |
+
train: [8] [320/400] eta: 0:00:37 lr: 0.000255 loss: 2.4345 (2.4466) grad: 0.2399 (0.2457) time: 0.4627 data: 0.0040 max mem: 22448
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train: [8] [340/400] eta: 0:00:27 lr: 0.000254 loss: 2.4399 (2.4458) grad: 0.2391 (0.2461) time: 0.4461 data: 0.0041 max mem: 22448
|
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train: [8] [360/400] eta: 0:00:18 lr: 0.000253 loss: 2.4399 (2.4447) grad: 0.2475 (0.2460) time: 0.4523 data: 0.0041 max mem: 22448
|
| 483 |
+
train: [8] [380/400] eta: 0:00:09 lr: 0.000252 loss: 2.4639 (2.4456) grad: 0.2400 (0.2456) time: 0.4701 data: 0.0041 max mem: 22448
|
| 484 |
+
train: [8] [399/400] eta: 0:00:00 lr: 0.000250 loss: 2.4791 (2.4466) grad: 0.2427 (0.2461) time: 0.4703 data: 0.0042 max mem: 22448
|
| 485 |
+
train: [8] Total time: 0:03:05 (0.4640 s / it)
|
| 486 |
+
train: [8] Summary: lr: 0.000250 loss: 2.4791 (2.4466) grad: 0.2427 (0.2461)
|
| 487 |
+
eval (validation): [8] [ 0/85] eta: 0:04:27 time: 3.1431 data: 2.8528 max mem: 22448
|
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eval (validation): [8] [20/85] eta: 0:00:32 time: 0.3613 data: 0.0050 max mem: 22448
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eval (validation): [8] [40/85] eta: 0:00:19 time: 0.3797 data: 0.0039 max mem: 22448
|
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eval (validation): [8] [60/85] eta: 0:00:10 time: 0.3613 data: 0.0043 max mem: 22448
|
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eval (validation): [8] [80/85] eta: 0:00:01 time: 0.3493 data: 0.0038 max mem: 22448
|
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eval (validation): [8] [84/85] eta: 0:00:00 time: 0.3402 data: 0.0037 max mem: 22448
|
| 493 |
+
eval (validation): [8] Total time: 0:00:33 (0.3983 s / it)
|
| 494 |
+
cv: [8] best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00') loss: 2.415 acc: 0.274 f1: 0.220
|
| 495 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 496 |
+
train: [9] [ 0/400] eta: 0:21:41 lr: nan time: 3.2533 data: 2.9237 max mem: 22448
|
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train: [9] [ 20/400] eta: 0:03:57 lr: 0.000249 loss: 2.3850 (2.3892) grad: 0.2460 (0.2557) time: 0.4941 data: 0.0047 max mem: 22448
|
| 498 |
+
train: [9] [ 40/400] eta: 0:03:16 lr: 0.000248 loss: 2.4452 (2.4121) grad: 0.2441 (0.2479) time: 0.4619 data: 0.0037 max mem: 22448
|
| 499 |
+
train: [9] [ 60/400] eta: 0:02:54 lr: 0.000247 loss: 2.4269 (2.4026) grad: 0.2353 (0.2438) time: 0.4490 data: 0.0041 max mem: 22448
|
| 500 |
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train: [9] [ 80/400] eta: 0:02:40 lr: 0.000246 loss: 2.3984 (2.4127) grad: 0.2373 (0.2442) time: 0.4609 data: 0.0042 max mem: 22448
|
| 501 |
+
train: [9] [100/400] eta: 0:02:28 lr: 0.000244 loss: 2.4030 (2.4079) grad: 0.2459 (0.2447) time: 0.4650 data: 0.0043 max mem: 22448
|
| 502 |
+
train: [9] [120/400] eta: 0:02:15 lr: 0.000243 loss: 2.3927 (2.4048) grad: 0.2463 (0.2451) time: 0.4449 data: 0.0038 max mem: 22448
|
| 503 |
+
train: [9] [140/400] eta: 0:02:05 lr: 0.000242 loss: 2.3928 (2.4061) grad: 0.2472 (0.2460) time: 0.4580 data: 0.0040 max mem: 22448
|
| 504 |
+
train: [9] [160/400] eta: 0:01:55 lr: 0.000241 loss: 2.4116 (2.4056) grad: 0.2503 (0.2467) time: 0.4675 data: 0.0041 max mem: 22448
|
| 505 |
+
train: [9] [180/400] eta: 0:01:44 lr: 0.000240 loss: 2.4027 (2.4081) grad: 0.2465 (0.2472) time: 0.4470 data: 0.0041 max mem: 22448
|
| 506 |
+
train: [9] [200/400] eta: 0:01:34 lr: 0.000238 loss: 2.3688 (2.4035) grad: 0.2482 (0.2476) time: 0.4566 data: 0.0040 max mem: 22448
|
| 507 |
+
train: [9] [220/400] eta: 0:01:24 lr: 0.000237 loss: 2.3541 (2.4008) grad: 0.2525 (0.2483) time: 0.4493 data: 0.0044 max mem: 22448
|
| 508 |
+
train: [9] [240/400] eta: 0:01:15 lr: 0.000236 loss: 2.4116 (2.4058) grad: 0.2546 (0.2481) time: 0.4458 data: 0.0033 max mem: 22448
|
| 509 |
+
train: [9] [260/400] eta: 0:01:05 lr: 0.000234 loss: 2.4377 (2.4050) grad: 0.2393 (0.2474) time: 0.4499 data: 0.0039 max mem: 22448
|
| 510 |
+
train: [9] [280/400] eta: 0:00:56 lr: 0.000233 loss: 2.4109 (2.4046) grad: 0.2400 (0.2474) time: 0.4584 data: 0.0039 max mem: 22448
|
| 511 |
+
train: [9] [300/400] eta: 0:00:46 lr: 0.000232 loss: 2.3832 (2.4039) grad: 0.2471 (0.2476) time: 0.4514 data: 0.0040 max mem: 22448
|
| 512 |
+
train: [9] [320/400] eta: 0:00:37 lr: 0.000230 loss: 2.3748 (2.4031) grad: 0.2477 (0.2473) time: 0.4545 data: 0.0040 max mem: 22448
|
| 513 |
+
train: [9] [340/400] eta: 0:00:27 lr: 0.000229 loss: 2.3512 (2.4012) grad: 0.2430 (0.2472) time: 0.4509 data: 0.0041 max mem: 22448
|
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+
train: [9] [360/400] eta: 0:00:18 lr: 0.000228 loss: 2.3929 (2.4014) grad: 0.2529 (0.2479) time: 0.4502 data: 0.0040 max mem: 22448
|
| 515 |
+
train: [9] [380/400] eta: 0:00:09 lr: 0.000226 loss: 2.3660 (2.4002) grad: 0.2518 (0.2481) time: 0.4590 data: 0.0040 max mem: 22448
|
| 516 |
+
train: [9] [399/400] eta: 0:00:00 lr: 0.000225 loss: 2.3947 (2.4009) grad: 0.2518 (0.2486) time: 0.4649 data: 0.0041 max mem: 22448
|
| 517 |
+
train: [9] Total time: 0:03:05 (0.4642 s / it)
|
| 518 |
+
train: [9] Summary: lr: 0.000225 loss: 2.3947 (2.4009) grad: 0.2518 (0.2486)
|
| 519 |
+
eval (validation): [9] [ 0/85] eta: 0:04:36 time: 3.2557 data: 3.0042 max mem: 22448
|
| 520 |
+
eval (validation): [9] [20/85] eta: 0:00:31 time: 0.3453 data: 0.0140 max mem: 22448
|
| 521 |
+
eval (validation): [9] [40/85] eta: 0:00:18 time: 0.3485 data: 0.0035 max mem: 22448
|
| 522 |
+
eval (validation): [9] [60/85] eta: 0:00:09 time: 0.3624 data: 0.0042 max mem: 22448
|
| 523 |
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eval (validation): [9] [80/85] eta: 0:00:01 time: 0.3464 data: 0.0040 max mem: 22448
|
| 524 |
+
eval (validation): [9] [84/85] eta: 0:00:00 time: 0.3309 data: 0.0039 max mem: 22448
|
| 525 |
+
eval (validation): [9] Total time: 0:00:32 (0.3870 s / it)
|
| 526 |
+
cv: [9] best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00') loss: 2.400 acc: 0.274 f1: 0.209
|
| 527 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 528 |
+
train: [10] [ 0/400] eta: 0:22:31 lr: nan time: 3.3786 data: 2.9902 max mem: 22448
|
| 529 |
+
train: [10] [ 20/400] eta: 0:03:48 lr: 0.000224 loss: 2.3264 (2.3404) grad: 0.2527 (0.2571) time: 0.4618 data: 0.0038 max mem: 22448
|
| 530 |
+
train: [10] [ 40/400] eta: 0:03:09 lr: 0.000222 loss: 2.3263 (2.3285) grad: 0.2461 (0.2498) time: 0.4499 data: 0.0042 max mem: 22448
|
| 531 |
+
train: [10] [ 60/400] eta: 0:02:50 lr: 0.000221 loss: 2.3202 (2.3439) grad: 0.2424 (0.2458) time: 0.4517 data: 0.0042 max mem: 22448
|
| 532 |
+
train: [10] [ 80/400] eta: 0:02:36 lr: 0.000220 loss: 2.3521 (2.3356) grad: 0.2350 (0.2438) time: 0.4545 data: 0.0041 max mem: 22448
|
| 533 |
+
train: [10] [100/400] eta: 0:02:25 lr: 0.000218 loss: 2.3135 (2.3303) grad: 0.2402 (0.2438) time: 0.4546 data: 0.0039 max mem: 22448
|
| 534 |
+
train: [10] [120/400] eta: 0:02:14 lr: 0.000217 loss: 2.3202 (2.3312) grad: 0.2442 (0.2450) time: 0.4549 data: 0.0040 max mem: 22448
|
| 535 |
+
train: [10] [140/400] eta: 0:02:03 lr: 0.000215 loss: 2.3222 (2.3354) grad: 0.2496 (0.2453) time: 0.4570 data: 0.0040 max mem: 22448
|
| 536 |
+
train: [10] [160/400] eta: 0:01:53 lr: 0.000214 loss: 2.3378 (2.3361) grad: 0.2471 (0.2454) time: 0.4600 data: 0.0040 max mem: 22448
|
| 537 |
+
train: [10] [180/400] eta: 0:01:43 lr: 0.000213 loss: 2.3423 (2.3379) grad: 0.2469 (0.2460) time: 0.4574 data: 0.0041 max mem: 22448
|
| 538 |
+
train: [10] [200/400] eta: 0:01:34 lr: 0.000211 loss: 2.3534 (2.3394) grad: 0.2498 (0.2468) time: 0.4605 data: 0.0041 max mem: 22448
|
| 539 |
+
train: [10] [220/400] eta: 0:01:24 lr: 0.000210 loss: 2.3767 (2.3407) grad: 0.2512 (0.2473) time: 0.4595 data: 0.0041 max mem: 22448
|
| 540 |
+
train: [10] [240/400] eta: 0:01:14 lr: 0.000208 loss: 2.3468 (2.3410) grad: 0.2485 (0.2471) time: 0.4515 data: 0.0040 max mem: 22448
|
| 541 |
+
train: [10] [260/400] eta: 0:01:05 lr: 0.000207 loss: 2.3362 (2.3405) grad: 0.2459 (0.2470) time: 0.4527 data: 0.0039 max mem: 22448
|
| 542 |
+
train: [10] [280/400] eta: 0:00:56 lr: 0.000205 loss: 2.3362 (2.3399) grad: 0.2465 (0.2470) time: 0.5042 data: 0.0043 max mem: 22448
|
| 543 |
+
train: [10] [300/400] eta: 0:00:47 lr: 0.000204 loss: 2.2569 (2.3353) grad: 0.2437 (0.2466) time: 0.4791 data: 0.0043 max mem: 22448
|
| 544 |
+
train: [10] [320/400] eta: 0:00:37 lr: 0.000202 loss: 2.2591 (2.3332) grad: 0.2457 (0.2469) time: 0.4776 data: 0.0041 max mem: 22448
|
| 545 |
+
train: [10] [340/400] eta: 0:00:28 lr: 0.000201 loss: 2.3355 (2.3341) grad: 0.2485 (0.2472) time: 0.4784 data: 0.0041 max mem: 22448
|
| 546 |
+
train: [10] [360/400] eta: 0:00:18 lr: 0.000199 loss: 2.3355 (2.3325) grad: 0.2445 (0.2470) time: 0.4721 data: 0.0041 max mem: 22448
|
| 547 |
+
train: [10] [380/400] eta: 0:00:09 lr: 0.000198 loss: 2.2846 (2.3319) grad: 0.2445 (0.2470) time: 0.4704 data: 0.0038 max mem: 22448
|
| 548 |
+
train: [10] [399/400] eta: 0:00:00 lr: 0.000196 loss: 2.3051 (2.3332) grad: 0.2469 (0.2472) time: 0.5048 data: 0.0046 max mem: 22448
|
| 549 |
+
train: [10] Total time: 0:03:09 (0.4732 s / it)
|
| 550 |
+
train: [10] Summary: lr: 0.000196 loss: 2.3051 (2.3332) grad: 0.2469 (0.2472)
|
| 551 |
+
eval (validation): [10] [ 0/85] eta: 0:04:56 time: 3.4907 data: 3.2171 max mem: 22448
|
| 552 |
+
eval (validation): [10] [20/85] eta: 0:00:35 time: 0.3921 data: 0.0050 max mem: 22448
|
| 553 |
+
eval (validation): [10] [40/85] eta: 0:00:20 time: 0.3624 data: 0.0038 max mem: 22448
|
| 554 |
+
eval (validation): [10] [60/85] eta: 0:00:10 time: 0.3990 data: 0.0045 max mem: 22448
|
| 555 |
+
eval (validation): [10] [80/85] eta: 0:00:02 time: 0.3670 data: 0.0041 max mem: 22448
|
| 556 |
+
eval (validation): [10] [84/85] eta: 0:00:00 time: 0.3535 data: 0.0040 max mem: 22448
|
| 557 |
+
eval (validation): [10] Total time: 0:00:35 (0.4191 s / it)
|
| 558 |
+
cv: [10] best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00') loss: 2.383 acc: 0.280 f1: 0.220
|
| 559 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 560 |
+
train: [11] [ 0/400] eta: 0:23:34 lr: nan time: 3.5374 data: 3.1905 max mem: 22448
|
| 561 |
+
train: [11] [ 20/400] eta: 0:03:54 lr: 0.000195 loss: 2.2469 (2.2738) grad: 0.2372 (0.2412) time: 0.4725 data: 0.0032 max mem: 22448
|
| 562 |
+
train: [11] [ 40/400] eta: 0:03:18 lr: 0.000193 loss: 2.2740 (2.2825) grad: 0.2418 (0.2432) time: 0.4799 data: 0.0036 max mem: 22448
|
| 563 |
+
train: [11] [ 60/400] eta: 0:02:59 lr: 0.000192 loss: 2.2571 (2.2722) grad: 0.2448 (0.2452) time: 0.4812 data: 0.0041 max mem: 22448
|
| 564 |
+
train: [11] [ 80/400] eta: 0:02:44 lr: 0.000190 loss: 2.2615 (2.2849) grad: 0.2464 (0.2479) time: 0.4757 data: 0.0041 max mem: 22448
|
| 565 |
+
train: [11] [100/400] eta: 0:02:31 lr: 0.000189 loss: 2.2708 (2.2800) grad: 0.2481 (0.2479) time: 0.4656 data: 0.0040 max mem: 22448
|
| 566 |
+
train: [11] [120/400] eta: 0:02:19 lr: 0.000187 loss: 2.2240 (2.2668) grad: 0.2481 (0.2488) time: 0.4681 data: 0.0041 max mem: 22448
|
| 567 |
+
train: [11] [140/400] eta: 0:02:08 lr: 0.000186 loss: 2.2480 (2.2705) grad: 0.2506 (0.2497) time: 0.4774 data: 0.0040 max mem: 22448
|
| 568 |
+
train: [11] [160/400] eta: 0:01:58 lr: 0.000184 loss: 2.2923 (2.2738) grad: 0.2540 (0.2502) time: 0.4770 data: 0.0042 max mem: 22448
|
| 569 |
+
train: [11] [180/400] eta: 0:01:48 lr: 0.000183 loss: 2.2974 (2.2771) grad: 0.2613 (0.2523) time: 0.4793 data: 0.0040 max mem: 22448
|
| 570 |
+
train: [11] [200/400] eta: 0:01:38 lr: 0.000181 loss: 2.3117 (2.2801) grad: 0.2617 (0.2524) time: 0.4819 data: 0.0041 max mem: 22448
|
| 571 |
+
train: [11] [220/400] eta: 0:01:28 lr: 0.000180 loss: 2.3364 (2.2871) grad: 0.2529 (0.2523) time: 0.4784 data: 0.0043 max mem: 22448
|
| 572 |
+
train: [11] [240/400] eta: 0:01:18 lr: 0.000178 loss: 2.3359 (2.2883) grad: 0.2533 (0.2529) time: 0.4741 data: 0.0042 max mem: 22448
|
| 573 |
+
train: [11] [260/400] eta: 0:01:08 lr: 0.000177 loss: 2.2840 (2.2868) grad: 0.2559 (0.2535) time: 0.4714 data: 0.0039 max mem: 22448
|
| 574 |
+
train: [11] [280/400] eta: 0:00:58 lr: 0.000175 loss: 2.2693 (2.2875) grad: 0.2592 (0.2539) time: 0.4839 data: 0.0041 max mem: 22448
|
| 575 |
+
train: [11] [300/400] eta: 0:00:48 lr: 0.000174 loss: 2.3333 (2.2919) grad: 0.2566 (0.2541) time: 0.4678 data: 0.0042 max mem: 22448
|
| 576 |
+
train: [11] [320/400] eta: 0:00:38 lr: 0.000172 loss: 2.3432 (2.2935) grad: 0.2568 (0.2548) time: 0.4645 data: 0.0042 max mem: 22448
|
| 577 |
+
train: [11] [340/400] eta: 0:00:29 lr: 0.000170 loss: 2.3235 (2.2939) grad: 0.2625 (0.2552) time: 0.4752 data: 0.0042 max mem: 22448
|
| 578 |
+
train: [11] [360/400] eta: 0:00:19 lr: 0.000169 loss: 2.2819 (2.2934) grad: 0.2510 (0.2553) time: 0.4792 data: 0.0044 max mem: 22448
|
| 579 |
+
train: [11] [380/400] eta: 0:00:09 lr: 0.000167 loss: 2.2535 (2.2903) grad: 0.2501 (0.2551) time: 0.4809 data: 0.0042 max mem: 22448
|
| 580 |
+
train: [11] [399/400] eta: 0:00:00 lr: 0.000166 loss: 2.2807 (2.2931) grad: 0.2474 (0.2547) time: 0.4864 data: 0.0041 max mem: 22448
|
| 581 |
+
train: [11] Total time: 0:03:13 (0.4840 s / it)
|
| 582 |
+
train: [11] Summary: lr: 0.000166 loss: 2.2807 (2.2931) grad: 0.2474 (0.2547)
|
| 583 |
+
eval (validation): [11] [ 0/85] eta: 0:04:50 time: 3.4217 data: 3.1241 max mem: 22448
|
| 584 |
+
eval (validation): [11] [20/85] eta: 0:00:33 time: 0.3639 data: 0.0036 max mem: 22448
|
| 585 |
+
eval (validation): [11] [40/85] eta: 0:00:19 time: 0.3635 data: 0.0036 max mem: 22448
|
| 586 |
+
eval (validation): [11] [60/85] eta: 0:00:10 time: 0.3690 data: 0.0044 max mem: 22448
|
| 587 |
+
eval (validation): [11] [80/85] eta: 0:00:02 time: 0.3560 data: 0.0041 max mem: 22448
|
| 588 |
+
eval (validation): [11] [84/85] eta: 0:00:00 time: 0.3504 data: 0.0042 max mem: 22448
|
| 589 |
+
eval (validation): [11] Total time: 0:00:34 (0.4021 s / it)
|
| 590 |
+
cv: [11] best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00') loss: 2.396 acc: 0.279 f1: 0.216
|
| 591 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 592 |
+
train: [12] [ 0/400] eta: 0:22:56 lr: nan time: 3.4416 data: 3.0921 max mem: 22448
|
| 593 |
+
train: [12] [ 20/400] eta: 0:03:50 lr: 0.000164 loss: 2.1775 (2.1885) grad: 0.2399 (0.2384) time: 0.4645 data: 0.0041 max mem: 22448
|
| 594 |
+
train: [12] [ 40/400] eta: 0:03:14 lr: 0.000163 loss: 2.1801 (2.2047) grad: 0.2403 (0.2434) time: 0.4684 data: 0.0038 max mem: 22448
|
| 595 |
+
train: [12] [ 60/400] eta: 0:02:53 lr: 0.000161 loss: 2.2211 (2.2094) grad: 0.2441 (0.2432) time: 0.4516 data: 0.0041 max mem: 22448
|
| 596 |
+
train: [12] [ 80/400] eta: 0:02:41 lr: 0.000160 loss: 2.2250 (2.2131) grad: 0.2441 (0.2439) time: 0.4823 data: 0.0043 max mem: 22448
|
| 597 |
+
train: [12] [100/400] eta: 0:02:30 lr: 0.000158 loss: 2.2250 (2.2171) grad: 0.2487 (0.2451) time: 0.4863 data: 0.0043 max mem: 22448
|
| 598 |
+
train: [12] [120/400] eta: 0:02:19 lr: 0.000156 loss: 2.2197 (2.2204) grad: 0.2477 (0.2445) time: 0.4816 data: 0.0043 max mem: 22448
|
| 599 |
+
train: [12] [140/400] eta: 0:02:08 lr: 0.000155 loss: 2.2177 (2.2189) grad: 0.2506 (0.2470) time: 0.4801 data: 0.0042 max mem: 22448
|
| 600 |
+
train: [12] [160/400] eta: 0:01:58 lr: 0.000153 loss: 2.2236 (2.2218) grad: 0.2623 (0.2493) time: 0.4914 data: 0.0041 max mem: 22448
|
| 601 |
+
train: [12] [180/400] eta: 0:01:48 lr: 0.000152 loss: 2.2434 (2.2248) grad: 0.2617 (0.2500) time: 0.4653 data: 0.0040 max mem: 22448
|
| 602 |
+
train: [12] [200/400] eta: 0:01:37 lr: 0.000150 loss: 2.2552 (2.2322) grad: 0.2536 (0.2504) time: 0.4749 data: 0.0041 max mem: 22448
|
| 603 |
+
train: [12] [220/400] eta: 0:01:28 lr: 0.000149 loss: 2.2950 (2.2363) grad: 0.2486 (0.2504) time: 0.4957 data: 0.0043 max mem: 22448
|
| 604 |
+
train: [12] [240/400] eta: 0:01:18 lr: 0.000147 loss: 2.2240 (2.2335) grad: 0.2486 (0.2515) time: 0.4668 data: 0.0041 max mem: 22448
|
| 605 |
+
train: [12] [260/400] eta: 0:01:08 lr: 0.000145 loss: 2.1958 (2.2331) grad: 0.2522 (0.2512) time: 0.4608 data: 0.0039 max mem: 22448
|
| 606 |
+
train: [12] [280/400] eta: 0:00:58 lr: 0.000144 loss: 2.1958 (2.2299) grad: 0.2499 (0.2512) time: 0.4764 data: 0.0041 max mem: 22448
|
| 607 |
+
train: [12] [300/400] eta: 0:00:48 lr: 0.000142 loss: 2.2018 (2.2313) grad: 0.2525 (0.2518) time: 0.4794 data: 0.0042 max mem: 22448
|
| 608 |
+
train: [12] [320/400] eta: 0:00:38 lr: 0.000141 loss: 2.2064 (2.2320) grad: 0.2542 (0.2518) time: 0.4647 data: 0.0041 max mem: 22448
|
| 609 |
+
train: [12] [340/400] eta: 0:00:28 lr: 0.000139 loss: 2.1977 (2.2324) grad: 0.2584 (0.2519) time: 0.4624 data: 0.0042 max mem: 22448
|
| 610 |
+
train: [12] [360/400] eta: 0:00:19 lr: 0.000138 loss: 2.1977 (2.2320) grad: 0.2522 (0.2518) time: 0.4849 data: 0.0044 max mem: 22448
|
| 611 |
+
train: [12] [380/400] eta: 0:00:09 lr: 0.000136 loss: 2.2307 (2.2330) grad: 0.2522 (0.2520) time: 0.4664 data: 0.0042 max mem: 22448
|
| 612 |
+
train: [12] [399/400] eta: 0:00:00 lr: 0.000134 loss: 2.2298 (2.2326) grad: 0.2549 (0.2524) time: 0.4844 data: 0.0041 max mem: 22448
|
| 613 |
+
train: [12] Total time: 0:03:12 (0.4822 s / it)
|
| 614 |
+
train: [12] Summary: lr: 0.000134 loss: 2.2298 (2.2326) grad: 0.2549 (0.2524)
|
| 615 |
+
eval (validation): [12] [ 0/85] eta: 0:04:43 time: 3.3349 data: 3.0805 max mem: 22448
|
| 616 |
+
eval (validation): [12] [20/85] eta: 0:00:33 time: 0.3706 data: 0.0042 max mem: 22448
|
| 617 |
+
eval (validation): [12] [40/85] eta: 0:00:19 time: 0.3717 data: 0.0034 max mem: 22448
|
| 618 |
+
eval (validation): [12] [60/85] eta: 0:00:10 time: 0.3822 data: 0.0043 max mem: 22448
|
| 619 |
+
eval (validation): [12] [80/85] eta: 0:00:02 time: 0.3674 data: 0.0041 max mem: 22448
|
| 620 |
+
eval (validation): [12] [84/85] eta: 0:00:00 time: 0.3558 data: 0.0039 max mem: 22448
|
| 621 |
+
eval (validation): [12] Total time: 0:00:34 (0.4105 s / it)
|
| 622 |
+
cv: [12] best hparam: (0.52, 1.0) (020) ('020_lr5.2e-01_wd1.0e+00') loss: 2.442 acc: 0.271 f1: 0.214
|
| 623 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 624 |
+
train: [13] [ 0/400] eta: 0:22:58 lr: nan time: 3.4461 data: 3.0803 max mem: 22448
|
| 625 |
+
train: [13] [ 20/400] eta: 0:03:58 lr: 0.000133 loss: 2.1642 (2.1849) grad: 0.2456 (0.2508) time: 0.4872 data: 0.0041 max mem: 22448
|
| 626 |
+
train: [13] [ 40/400] eta: 0:03:17 lr: 0.000131 loss: 2.1783 (2.1826) grad: 0.2482 (0.2506) time: 0.4631 data: 0.0041 max mem: 22448
|
| 627 |
+
train: [13] [ 60/400] eta: 0:02:56 lr: 0.000130 loss: 2.1931 (2.1866) grad: 0.2493 (0.2501) time: 0.4599 data: 0.0042 max mem: 22448
|
| 628 |
+
train: [13] [ 80/400] eta: 0:02:41 lr: 0.000128 loss: 2.1672 (2.1856) grad: 0.2527 (0.2512) time: 0.4626 data: 0.0041 max mem: 22448
|
| 629 |
+
train: [13] [100/400] eta: 0:02:29 lr: 0.000127 loss: 2.1635 (2.1815) grad: 0.2487 (0.2499) time: 0.4712 data: 0.0041 max mem: 22448
|
| 630 |
+
train: [13] [120/400] eta: 0:02:18 lr: 0.000125 loss: 2.1534 (2.1734) grad: 0.2484 (0.2505) time: 0.4676 data: 0.0042 max mem: 22448
|
| 631 |
+
train: [13] [140/400] eta: 0:02:07 lr: 0.000124 loss: 2.1845 (2.1825) grad: 0.2631 (0.2518) time: 0.4685 data: 0.0041 max mem: 22448
|
| 632 |
+
train: [13] [160/400] eta: 0:01:56 lr: 0.000122 loss: 2.1938 (2.1829) grad: 0.2583 (0.2523) time: 0.4642 data: 0.0040 max mem: 22448
|
| 633 |
+
train: [13] [180/400] eta: 0:01:46 lr: 0.000120 loss: 2.1960 (2.1877) grad: 0.2529 (0.2524) time: 0.4809 data: 0.0041 max mem: 22448
|
| 634 |
+
train: [13] [200/400] eta: 0:01:36 lr: 0.000119 loss: 2.1472 (2.1810) grad: 0.2470 (0.2523) time: 0.4702 data: 0.0040 max mem: 22448
|
| 635 |
+
train: [13] [220/400] eta: 0:01:26 lr: 0.000117 loss: 2.1393 (2.1808) grad: 0.2451 (0.2524) time: 0.4720 data: 0.0041 max mem: 22448
|
| 636 |
+
train: [13] [240/400] eta: 0:01:17 lr: 0.000116 loss: 2.1888 (2.1815) grad: 0.2507 (0.2525) time: 0.4649 data: 0.0042 max mem: 22448
|
| 637 |
+
train: [13] [260/400] eta: 0:01:07 lr: 0.000114 loss: 2.1878 (2.1837) grad: 0.2507 (0.2520) time: 0.4711 data: 0.0041 max mem: 22448
|
| 638 |
+
train: [13] [280/400] eta: 0:00:57 lr: 0.000113 loss: 2.1214 (2.1806) grad: 0.2406 (0.2516) time: 0.4580 data: 0.0039 max mem: 22448
|
| 639 |
+
train: [13] [300/400] eta: 0:00:47 lr: 0.000111 loss: 2.1176 (2.1785) grad: 0.2407 (0.2507) time: 0.4728 data: 0.0042 max mem: 22448
|
| 640 |
+
train: [13] [320/400] eta: 0:00:38 lr: 0.000110 loss: 2.1843 (2.1809) grad: 0.2518 (0.2514) time: 0.4654 data: 0.0041 max mem: 22448
|
| 641 |
+
train: [13] [340/400] eta: 0:00:28 lr: 0.000108 loss: 2.1983 (2.1816) grad: 0.2518 (0.2510) time: 0.4522 data: 0.0042 max mem: 22448
|
| 642 |
+
train: [13] [360/400] eta: 0:00:19 lr: 0.000107 loss: 2.1791 (2.1823) grad: 0.2508 (0.2514) time: 0.4620 data: 0.0041 max mem: 22448
|
| 643 |
+
train: [13] [380/400] eta: 0:00:09 lr: 0.000105 loss: 2.1841 (2.1825) grad: 0.2600 (0.2518) time: 0.4713 data: 0.0041 max mem: 22448
|
| 644 |
+
train: [13] [399/400] eta: 0:00:00 lr: 0.000104 loss: 2.1841 (2.1837) grad: 0.2520 (0.2518) time: 0.4700 data: 0.0042 max mem: 22448
|
| 645 |
+
train: [13] Total time: 0:03:10 (0.4755 s / it)
|
| 646 |
+
train: [13] Summary: lr: 0.000104 loss: 2.1841 (2.1837) grad: 0.2520 (0.2518)
|
| 647 |
+
eval (validation): [13] [ 0/85] eta: 0:05:09 time: 3.6378 data: 3.3369 max mem: 22448
|
| 648 |
+
eval (validation): [13] [20/85] eta: 0:00:36 time: 0.4068 data: 0.0042 max mem: 22448
|
| 649 |
+
eval (validation): [13] [40/85] eta: 0:00:21 time: 0.3752 data: 0.0037 max mem: 22448
|
| 650 |
+
eval (validation): [13] [60/85] eta: 0:00:10 time: 0.3687 data: 0.0042 max mem: 22448
|
| 651 |
+
eval (validation): [13] [80/85] eta: 0:00:02 time: 0.3696 data: 0.0040 max mem: 22448
|
| 652 |
+
eval (validation): [13] [84/85] eta: 0:00:00 time: 0.3586 data: 0.0039 max mem: 22448
|
| 653 |
+
eval (validation): [13] Total time: 0:00:35 (0.4205 s / it)
|
| 654 |
+
cv: [13] best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00') loss: 2.403 acc: 0.276 f1: 0.215
|
| 655 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 656 |
+
train: [14] [ 0/400] eta: 0:22:49 lr: nan time: 3.4228 data: 3.0314 max mem: 22448
|
| 657 |
+
train: [14] [ 20/400] eta: 0:04:04 lr: 0.000102 loss: 2.0943 (2.0925) grad: 0.2355 (0.2389) time: 0.5046 data: 0.0041 max mem: 22448
|
| 658 |
+
train: [14] [ 40/400] eta: 0:03:23 lr: 0.000101 loss: 2.0973 (2.1162) grad: 0.2347 (0.2401) time: 0.4842 data: 0.0046 max mem: 22448
|
| 659 |
+
train: [14] [ 60/400] eta: 0:03:00 lr: 0.000099 loss: 2.1056 (2.1169) grad: 0.2399 (0.2425) time: 0.4616 data: 0.0040 max mem: 22448
|
| 660 |
+
train: [14] [ 80/400] eta: 0:02:45 lr: 0.000098 loss: 2.1056 (2.1230) grad: 0.2384 (0.2419) time: 0.4730 data: 0.0041 max mem: 22448
|
| 661 |
+
train: [14] [100/400] eta: 0:02:33 lr: 0.000096 loss: 2.1332 (2.1298) grad: 0.2431 (0.2436) time: 0.4820 data: 0.0041 max mem: 22448
|
| 662 |
+
train: [14] [120/400] eta: 0:02:20 lr: 0.000095 loss: 2.1298 (2.1270) grad: 0.2505 (0.2452) time: 0.4698 data: 0.0043 max mem: 22448
|
| 663 |
+
train: [14] [140/400] eta: 0:02:09 lr: 0.000093 loss: 2.0816 (2.1258) grad: 0.2534 (0.2460) time: 0.4682 data: 0.0044 max mem: 22448
|
| 664 |
+
train: [14] [160/400] eta: 0:01:58 lr: 0.000092 loss: 2.0719 (2.1238) grad: 0.2525 (0.2458) time: 0.4581 data: 0.0042 max mem: 22448
|
| 665 |
+
train: [14] [180/400] eta: 0:01:48 lr: 0.000090 loss: 2.1001 (2.1237) grad: 0.2435 (0.2460) time: 0.4730 data: 0.0041 max mem: 22448
|
| 666 |
+
train: [14] [200/400] eta: 0:01:37 lr: 0.000089 loss: 2.0983 (2.1236) grad: 0.2456 (0.2466) time: 0.4638 data: 0.0039 max mem: 22448
|
| 667 |
+
train: [14] [220/400] eta: 0:01:27 lr: 0.000088 loss: 2.1073 (2.1225) grad: 0.2514 (0.2474) time: 0.4595 data: 0.0042 max mem: 22448
|
| 668 |
+
train: [14] [240/400] eta: 0:01:17 lr: 0.000086 loss: 2.1297 (2.1272) grad: 0.2514 (0.2475) time: 0.4662 data: 0.0041 max mem: 22448
|
| 669 |
+
train: [14] [260/400] eta: 0:01:07 lr: 0.000085 loss: 2.1297 (2.1283) grad: 0.2499 (0.2476) time: 0.4716 data: 0.0042 max mem: 22448
|
| 670 |
+
train: [14] [280/400] eta: 0:00:57 lr: 0.000083 loss: 2.1105 (2.1273) grad: 0.2411 (0.2470) time: 0.4519 data: 0.0040 max mem: 22448
|
| 671 |
+
train: [14] [300/400] eta: 0:00:48 lr: 0.000082 loss: 2.1342 (2.1322) grad: 0.2465 (0.2472) time: 0.4827 data: 0.0041 max mem: 22448
|
| 672 |
+
train: [14] [320/400] eta: 0:00:38 lr: 0.000081 loss: 2.1653 (2.1326) grad: 0.2545 (0.2477) time: 0.4655 data: 0.0042 max mem: 22448
|
| 673 |
+
train: [14] [340/400] eta: 0:00:28 lr: 0.000079 loss: 2.1550 (2.1330) grad: 0.2561 (0.2483) time: 0.4610 data: 0.0041 max mem: 22448
|
| 674 |
+
train: [14] [360/400] eta: 0:00:19 lr: 0.000078 loss: 2.1400 (2.1334) grad: 0.2495 (0.2480) time: 0.4746 data: 0.0041 max mem: 22448
|
| 675 |
+
train: [14] [380/400] eta: 0:00:09 lr: 0.000076 loss: 2.1017 (2.1327) grad: 0.2468 (0.2481) time: 0.4605 data: 0.0040 max mem: 22448
|
| 676 |
+
train: [14] [399/400] eta: 0:00:00 lr: 0.000075 loss: 2.0953 (2.1328) grad: 0.2470 (0.2485) time: 0.4692 data: 0.0042 max mem: 22448
|
| 677 |
+
train: [14] Total time: 0:03:11 (0.4778 s / it)
|
| 678 |
+
train: [14] Summary: lr: 0.000075 loss: 2.0953 (2.1328) grad: 0.2470 (0.2485)
|
| 679 |
+
eval (validation): [14] [ 0/85] eta: 0:04:57 time: 3.4952 data: 3.1773 max mem: 22448
|
| 680 |
+
eval (validation): [14] [20/85] eta: 0:00:34 time: 0.3881 data: 0.0046 max mem: 22448
|
| 681 |
+
eval (validation): [14] [40/85] eta: 0:00:21 time: 0.4063 data: 0.0035 max mem: 22448
|
| 682 |
+
eval (validation): [14] [60/85] eta: 0:00:10 time: 0.3583 data: 0.0039 max mem: 22448
|
| 683 |
+
eval (validation): [14] [80/85] eta: 0:00:02 time: 0.3716 data: 0.0041 max mem: 22448
|
| 684 |
+
eval (validation): [14] [84/85] eta: 0:00:00 time: 0.3603 data: 0.0040 max mem: 22448
|
| 685 |
+
eval (validation): [14] Total time: 0:00:35 (0.4194 s / it)
|
| 686 |
+
cv: [14] best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00') loss: 2.395 acc: 0.278 f1: 0.217
|
| 687 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 688 |
+
train: [15] [ 0/400] eta: 0:22:42 lr: nan time: 3.4057 data: 3.0672 max mem: 22448
|
| 689 |
+
train: [15] [ 20/400] eta: 0:03:44 lr: 0.000074 loss: 2.0814 (2.0800) grad: 0.2337 (0.2390) time: 0.4511 data: 0.0037 max mem: 22448
|
| 690 |
+
train: [15] [ 40/400] eta: 0:03:08 lr: 0.000072 loss: 2.0814 (2.0750) grad: 0.2337 (0.2384) time: 0.4511 data: 0.0042 max mem: 22448
|
| 691 |
+
train: [15] [ 60/400] eta: 0:02:51 lr: 0.000071 loss: 2.0579 (2.0764) grad: 0.2421 (0.2399) time: 0.4665 data: 0.0042 max mem: 22448
|
| 692 |
+
train: [15] [ 80/400] eta: 0:02:38 lr: 0.000070 loss: 2.0263 (2.0684) grad: 0.2334 (0.2391) time: 0.4661 data: 0.0041 max mem: 22448
|
| 693 |
+
train: [15] [100/400] eta: 0:02:26 lr: 0.000068 loss: 2.0137 (2.0610) grad: 0.2335 (0.2399) time: 0.4618 data: 0.0040 max mem: 22448
|
| 694 |
+
train: [15] [120/400] eta: 0:02:15 lr: 0.000067 loss: 2.0797 (2.0709) grad: 0.2468 (0.2421) time: 0.4706 data: 0.0043 max mem: 22448
|
| 695 |
+
train: [15] [140/400] eta: 0:02:05 lr: 0.000066 loss: 2.1216 (2.0776) grad: 0.2571 (0.2444) time: 0.4660 data: 0.0041 max mem: 22448
|
| 696 |
+
train: [15] [160/400] eta: 0:01:55 lr: 0.000064 loss: 2.0818 (2.0790) grad: 0.2475 (0.2449) time: 0.4649 data: 0.0040 max mem: 22448
|
| 697 |
+
train: [15] [180/400] eta: 0:01:45 lr: 0.000063 loss: 2.1041 (2.0886) grad: 0.2475 (0.2462) time: 0.4600 data: 0.0040 max mem: 22448
|
| 698 |
+
train: [15] [200/400] eta: 0:01:35 lr: 0.000062 loss: 2.1199 (2.0857) grad: 0.2437 (0.2456) time: 0.4799 data: 0.0042 max mem: 22448
|
| 699 |
+
train: [15] [220/400] eta: 0:01:26 lr: 0.000061 loss: 2.0673 (2.0848) grad: 0.2425 (0.2457) time: 0.4829 data: 0.0040 max mem: 22448
|
| 700 |
+
train: [15] [240/400] eta: 0:01:16 lr: 0.000059 loss: 2.0943 (2.0861) grad: 0.2476 (0.2460) time: 0.4620 data: 0.0040 max mem: 22448
|
| 701 |
+
train: [15] [260/400] eta: 0:01:06 lr: 0.000058 loss: 2.1191 (2.0894) grad: 0.2460 (0.2458) time: 0.4846 data: 0.0042 max mem: 22448
|
| 702 |
+
train: [15] [280/400] eta: 0:00:57 lr: 0.000057 loss: 2.0981 (2.0869) grad: 0.2343 (0.2451) time: 0.4643 data: 0.0040 max mem: 22448
|
| 703 |
+
train: [15] [300/400] eta: 0:00:47 lr: 0.000056 loss: 2.0573 (2.0869) grad: 0.2341 (0.2451) time: 0.4857 data: 0.0042 max mem: 22448
|
| 704 |
+
train: [15] [320/400] eta: 0:00:38 lr: 0.000054 loss: 2.1130 (2.0889) grad: 0.2445 (0.2452) time: 0.4862 data: 0.0042 max mem: 22448
|
| 705 |
+
train: [15] [340/400] eta: 0:00:28 lr: 0.000053 loss: 2.1237 (2.0911) grad: 0.2460 (0.2453) time: 0.4758 data: 0.0040 max mem: 22448
|
| 706 |
+
train: [15] [360/400] eta: 0:00:19 lr: 0.000052 loss: 2.1217 (2.0920) grad: 0.2472 (0.2455) time: 0.4508 data: 0.0041 max mem: 22448
|
| 707 |
+
train: [15] [380/400] eta: 0:00:09 lr: 0.000051 loss: 2.0729 (2.0897) grad: 0.2404 (0.2452) time: 0.4747 data: 0.0042 max mem: 22448
|
| 708 |
+
train: [15] [399/400] eta: 0:00:00 lr: 0.000050 loss: 2.0552 (2.0898) grad: 0.2362 (0.2449) time: 0.4824 data: 0.0041 max mem: 22448
|
| 709 |
+
train: [15] Total time: 0:03:10 (0.4769 s / it)
|
| 710 |
+
train: [15] Summary: lr: 0.000050 loss: 2.0552 (2.0898) grad: 0.2362 (0.2449)
|
| 711 |
+
eval (validation): [15] [ 0/85] eta: 0:04:47 time: 3.3868 data: 3.1371 max mem: 22448
|
| 712 |
+
eval (validation): [15] [20/85] eta: 0:00:34 time: 0.3896 data: 0.0043 max mem: 22448
|
| 713 |
+
eval (validation): [15] [40/85] eta: 0:00:20 time: 0.3717 data: 0.0038 max mem: 22448
|
| 714 |
+
eval (validation): [15] [60/85] eta: 0:00:10 time: 0.3536 data: 0.0042 max mem: 22448
|
| 715 |
+
eval (validation): [15] [80/85] eta: 0:00:02 time: 0.3623 data: 0.0041 max mem: 22448
|
| 716 |
+
eval (validation): [15] [84/85] eta: 0:00:00 time: 0.3552 data: 0.0040 max mem: 22448
|
| 717 |
+
eval (validation): [15] Total time: 0:00:34 (0.4074 s / it)
|
| 718 |
+
cv: [15] best hparam: (0.44, 1.0) (019) ('019_lr4.4e-01_wd1.0e+00') loss: 2.413 acc: 0.278 f1: 0.220
|
| 719 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 720 |
+
train: [16] [ 0/400] eta: 0:23:21 lr: nan time: 3.5045 data: 3.1081 max mem: 22448
|
| 721 |
+
train: [16] [ 20/400] eta: 0:03:53 lr: 0.000048 loss: 2.0094 (2.0318) grad: 0.2280 (0.2280) time: 0.4702 data: 0.0044 max mem: 22448
|
| 722 |
+
train: [16] [ 40/400] eta: 0:03:13 lr: 0.000047 loss: 2.0094 (2.0289) grad: 0.2280 (0.2289) time: 0.4552 data: 0.0041 max mem: 22448
|
| 723 |
+
train: [16] [ 60/400] eta: 0:02:54 lr: 0.000046 loss: 2.0059 (2.0299) grad: 0.2318 (0.2312) time: 0.4692 data: 0.0042 max mem: 22448
|
| 724 |
+
train: [16] [ 80/400] eta: 0:02:40 lr: 0.000045 loss: 2.0796 (2.0433) grad: 0.2383 (0.2353) time: 0.4647 data: 0.0042 max mem: 22448
|
| 725 |
+
train: [16] [100/400] eta: 0:02:29 lr: 0.000044 loss: 2.0709 (2.0396) grad: 0.2410 (0.2368) time: 0.4742 data: 0.0042 max mem: 22448
|
| 726 |
+
train: [16] [120/400] eta: 0:02:17 lr: 0.000043 loss: 2.0400 (2.0415) grad: 0.2396 (0.2377) time: 0.4715 data: 0.0042 max mem: 22448
|
| 727 |
+
train: [16] [140/400] eta: 0:02:07 lr: 0.000042 loss: 2.0484 (2.0444) grad: 0.2383 (0.2383) time: 0.4679 data: 0.0042 max mem: 22448
|
| 728 |
+
train: [16] [160/400] eta: 0:01:56 lr: 0.000041 loss: 2.0608 (2.0513) grad: 0.2424 (0.2395) time: 0.4652 data: 0.0044 max mem: 22448
|
| 729 |
+
train: [16] [180/400] eta: 0:01:46 lr: 0.000040 loss: 2.0714 (2.0508) grad: 0.2424 (0.2395) time: 0.4563 data: 0.0040 max mem: 22448
|
| 730 |
+
train: [16] [200/400] eta: 0:01:36 lr: 0.000039 loss: 2.0255 (2.0485) grad: 0.2373 (0.2389) time: 0.4627 data: 0.0042 max mem: 22448
|
| 731 |
+
train: [16] [220/400] eta: 0:01:26 lr: 0.000038 loss: 2.0255 (2.0502) grad: 0.2329 (0.2387) time: 0.4640 data: 0.0041 max mem: 22448
|
| 732 |
+
train: [16] [240/400] eta: 0:01:16 lr: 0.000036 loss: 2.0544 (2.0504) grad: 0.2330 (0.2390) time: 0.4578 data: 0.0040 max mem: 22448
|
| 733 |
+
train: [16] [260/400] eta: 0:01:06 lr: 0.000035 loss: 2.0666 (2.0539) grad: 0.2476 (0.2398) time: 0.4692 data: 0.0042 max mem: 22448
|
| 734 |
+
train: [16] [280/400] eta: 0:00:56 lr: 0.000034 loss: 2.0475 (2.0531) grad: 0.2408 (0.2398) time: 0.4486 data: 0.0042 max mem: 22448
|
| 735 |
+
train: [16] [300/400] eta: 0:00:47 lr: 0.000033 loss: 2.0485 (2.0551) grad: 0.2394 (0.2401) time: 0.4742 data: 0.0042 max mem: 22448
|
| 736 |
+
train: [16] [320/400] eta: 0:00:37 lr: 0.000032 loss: 2.1138 (2.0575) grad: 0.2405 (0.2403) time: 0.4558 data: 0.0042 max mem: 22448
|
| 737 |
+
train: [16] [340/400] eta: 0:00:28 lr: 0.000031 loss: 2.0476 (2.0573) grad: 0.2408 (0.2403) time: 0.4573 data: 0.0042 max mem: 22448
|
| 738 |
+
train: [16] [360/400] eta: 0:00:18 lr: 0.000031 loss: 2.0428 (2.0580) grad: 0.2396 (0.2406) time: 0.4573 data: 0.0042 max mem: 22448
|
| 739 |
+
train: [16] [380/400] eta: 0:00:09 lr: 0.000030 loss: 2.0590 (2.0574) grad: 0.2437 (0.2412) time: 0.4589 data: 0.0040 max mem: 22448
|
| 740 |
+
train: [16] [399/400] eta: 0:00:00 lr: 0.000029 loss: 2.0257 (2.0578) grad: 0.2490 (0.2418) time: 0.4607 data: 0.0040 max mem: 22448
|
| 741 |
+
train: [16] Total time: 0:03:08 (0.4709 s / it)
|
| 742 |
+
train: [16] Summary: lr: 0.000029 loss: 2.0257 (2.0578) grad: 0.2490 (0.2418)
|
| 743 |
+
eval (validation): [16] [ 0/85] eta: 0:04:59 time: 3.5255 data: 3.2029 max mem: 22448
|
| 744 |
+
eval (validation): [16] [20/85] eta: 0:00:34 time: 0.3788 data: 0.0033 max mem: 22448
|
| 745 |
+
eval (validation): [16] [40/85] eta: 0:00:20 time: 0.3941 data: 0.0043 max mem: 22448
|
| 746 |
+
eval (validation): [16] [60/85] eta: 0:00:10 time: 0.3548 data: 0.0043 max mem: 22448
|
| 747 |
+
eval (validation): [16] [80/85] eta: 0:00:02 time: 0.3612 data: 0.0041 max mem: 22448
|
| 748 |
+
eval (validation): [16] [84/85] eta: 0:00:00 time: 0.3538 data: 0.0040 max mem: 22448
|
| 749 |
+
eval (validation): [16] Total time: 0:00:34 (0.4113 s / it)
|
| 750 |
+
cv: [16] best hparam: (0.32, 1.0) (017) ('017_lr3.2e-01_wd1.0e+00') loss: 2.395 acc: 0.276 f1: 0.214
|
| 751 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 752 |
+
train: [17] [ 0/400] eta: 0:23:53 lr: nan time: 3.5847 data: 3.1487 max mem: 22448
|
| 753 |
+
train: [17] [ 20/400] eta: 0:04:10 lr: 0.000028 loss: 2.0041 (2.0036) grad: 0.2187 (0.2293) time: 0.5140 data: 0.0143 max mem: 22448
|
| 754 |
+
train: [17] [ 40/400] eta: 0:03:22 lr: 0.000027 loss: 2.0076 (2.0207) grad: 0.2347 (0.2326) time: 0.4606 data: 0.0041 max mem: 22448
|
| 755 |
+
train: [17] [ 60/400] eta: 0:03:01 lr: 0.000026 loss: 2.0276 (2.0366) grad: 0.2333 (0.2319) time: 0.4737 data: 0.0042 max mem: 22448
|
| 756 |
+
train: [17] [ 80/400] eta: 0:02:47 lr: 0.000025 loss: 2.0082 (2.0227) grad: 0.2289 (0.2320) time: 0.4868 data: 0.0043 max mem: 22448
|
| 757 |
+
train: [17] [100/400] eta: 0:02:32 lr: 0.000024 loss: 2.0131 (2.0281) grad: 0.2267 (0.2313) time: 0.4563 data: 0.0042 max mem: 22448
|
| 758 |
+
train: [17] [120/400] eta: 0:02:20 lr: 0.000023 loss: 2.0310 (2.0265) grad: 0.2268 (0.2321) time: 0.4759 data: 0.0046 max mem: 22448
|
| 759 |
+
train: [17] [140/400] eta: 0:02:10 lr: 0.000023 loss: 2.0310 (2.0253) grad: 0.2298 (0.2325) time: 0.4786 data: 0.0036 max mem: 22448
|
| 760 |
+
train: [17] [160/400] eta: 0:01:58 lr: 0.000022 loss: 2.0156 (2.0239) grad: 0.2345 (0.2332) time: 0.4546 data: 0.0041 max mem: 22448
|
| 761 |
+
train: [17] [180/400] eta: 0:01:47 lr: 0.000021 loss: 2.0171 (2.0235) grad: 0.2335 (0.2329) time: 0.4474 data: 0.0040 max mem: 22448
|
| 762 |
+
train: [17] [200/400] eta: 0:01:37 lr: 0.000020 loss: 2.0292 (2.0243) grad: 0.2305 (0.2325) time: 0.4812 data: 0.0044 max mem: 22448
|
| 763 |
+
train: [17] [220/400] eta: 0:01:27 lr: 0.000019 loss: 2.0393 (2.0258) grad: 0.2349 (0.2332) time: 0.4750 data: 0.0044 max mem: 22448
|
| 764 |
+
train: [17] [240/400] eta: 0:01:17 lr: 0.000019 loss: 2.0507 (2.0245) grad: 0.2345 (0.2338) time: 0.4610 data: 0.0041 max mem: 22448
|
| 765 |
+
train: [17] [260/400] eta: 0:01:07 lr: 0.000018 loss: 1.9885 (2.0242) grad: 0.2345 (0.2342) time: 0.4613 data: 0.0041 max mem: 22448
|
| 766 |
+
train: [17] [280/400] eta: 0:00:57 lr: 0.000017 loss: 1.9903 (2.0254) grad: 0.2359 (0.2343) time: 0.4499 data: 0.0041 max mem: 22448
|
| 767 |
+
train: [17] [300/400] eta: 0:00:48 lr: 0.000016 loss: 2.0555 (2.0273) grad: 0.2352 (0.2342) time: 0.4858 data: 0.0042 max mem: 22448
|
| 768 |
+
train: [17] [320/400] eta: 0:00:38 lr: 0.000016 loss: 2.0534 (2.0273) grad: 0.2265 (0.2335) time: 0.4610 data: 0.0041 max mem: 22448
|
| 769 |
+
train: [17] [340/400] eta: 0:00:28 lr: 0.000015 loss: 2.0251 (2.0282) grad: 0.2294 (0.2340) time: 0.4530 data: 0.0040 max mem: 22448
|
| 770 |
+
train: [17] [360/400] eta: 0:00:19 lr: 0.000014 loss: 2.0245 (2.0290) grad: 0.2371 (0.2344) time: 0.4577 data: 0.0043 max mem: 22448
|
| 771 |
+
train: [17] [380/400] eta: 0:00:09 lr: 0.000014 loss: 2.0153 (2.0277) grad: 0.2404 (0.2350) time: 0.4758 data: 0.0044 max mem: 22448
|
| 772 |
+
train: [17] [399/400] eta: 0:00:00 lr: 0.000013 loss: 1.9957 (2.0263) grad: 0.2362 (0.2346) time: 0.4610 data: 0.0043 max mem: 22448
|
| 773 |
+
train: [17] Total time: 0:03:10 (0.4766 s / it)
|
| 774 |
+
train: [17] Summary: lr: 0.000013 loss: 1.9957 (2.0263) grad: 0.2362 (0.2346)
|
| 775 |
+
eval (validation): [17] [ 0/85] eta: 0:04:48 time: 3.3964 data: 3.1426 max mem: 22448
|
| 776 |
+
eval (validation): [17] [20/85] eta: 0:00:35 time: 0.3995 data: 0.0047 max mem: 22448
|
| 777 |
+
eval (validation): [17] [40/85] eta: 0:00:20 time: 0.3516 data: 0.0037 max mem: 22448
|
| 778 |
+
eval (validation): [17] [60/85] eta: 0:00:10 time: 0.3486 data: 0.0040 max mem: 22448
|
| 779 |
+
eval (validation): [17] [80/85] eta: 0:00:02 time: 0.3620 data: 0.0040 max mem: 22448
|
| 780 |
+
eval (validation): [17] [84/85] eta: 0:00:00 time: 0.3521 data: 0.0039 max mem: 22448
|
| 781 |
+
eval (validation): [17] Total time: 0:00:34 (0.4039 s / it)
|
| 782 |
+
cv: [17] best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00') loss: 2.392 acc: 0.279 f1: 0.219
|
| 783 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 784 |
+
train: [18] [ 0/400] eta: 0:23:04 lr: nan time: 3.4615 data: 3.1302 max mem: 22448
|
| 785 |
+
train: [18] [ 20/400] eta: 0:03:44 lr: 0.000012 loss: 2.0161 (2.0380) grad: 0.2305 (0.2299) time: 0.4485 data: 0.0037 max mem: 22448
|
| 786 |
+
train: [18] [ 40/400] eta: 0:03:09 lr: 0.000012 loss: 2.0106 (2.0052) grad: 0.2283 (0.2286) time: 0.4573 data: 0.0036 max mem: 22448
|
| 787 |
+
train: [18] [ 60/400] eta: 0:02:53 lr: 0.000011 loss: 1.9464 (1.9942) grad: 0.2369 (0.2319) time: 0.4731 data: 0.0041 max mem: 22448
|
| 788 |
+
train: [18] [ 80/400] eta: 0:02:38 lr: 0.000011 loss: 1.9821 (1.9946) grad: 0.2349 (0.2298) time: 0.4595 data: 0.0042 max mem: 22448
|
| 789 |
+
train: [18] [100/400] eta: 0:02:27 lr: 0.000010 loss: 2.0078 (1.9996) grad: 0.2247 (0.2301) time: 0.4641 data: 0.0041 max mem: 22448
|
| 790 |
+
train: [18] [120/400] eta: 0:02:15 lr: 0.000009 loss: 1.9846 (1.9964) grad: 0.2275 (0.2299) time: 0.4591 data: 0.0042 max mem: 22448
|
| 791 |
+
train: [18] [140/400] eta: 0:02:05 lr: 0.000009 loss: 1.9896 (2.0009) grad: 0.2293 (0.2301) time: 0.4703 data: 0.0043 max mem: 22448
|
| 792 |
+
train: [18] [160/400] eta: 0:01:55 lr: 0.000008 loss: 2.0045 (2.0024) grad: 0.2293 (0.2295) time: 0.4654 data: 0.0042 max mem: 22448
|
| 793 |
+
train: [18] [180/400] eta: 0:01:45 lr: 0.000008 loss: 1.9785 (2.0008) grad: 0.2291 (0.2302) time: 0.4606 data: 0.0041 max mem: 22448
|
| 794 |
+
train: [18] [200/400] eta: 0:01:35 lr: 0.000007 loss: 2.0074 (2.0029) grad: 0.2307 (0.2303) time: 0.4617 data: 0.0042 max mem: 22448
|
| 795 |
+
train: [18] [220/400] eta: 0:01:25 lr: 0.000007 loss: 2.0154 (2.0049) grad: 0.2307 (0.2302) time: 0.4650 data: 0.0042 max mem: 22448
|
| 796 |
+
train: [18] [240/400] eta: 0:01:15 lr: 0.000006 loss: 2.0366 (2.0098) grad: 0.2290 (0.2303) time: 0.4639 data: 0.0043 max mem: 22448
|
| 797 |
+
train: [18] [260/400] eta: 0:01:06 lr: 0.000006 loss: 2.0368 (2.0102) grad: 0.2262 (0.2301) time: 0.4673 data: 0.0043 max mem: 22448
|
| 798 |
+
train: [18] [280/400] eta: 0:00:56 lr: 0.000006 loss: 2.0187 (2.0102) grad: 0.2280 (0.2303) time: 0.4475 data: 0.0040 max mem: 22448
|
| 799 |
+
train: [18] [300/400] eta: 0:00:47 lr: 0.000005 loss: 2.0173 (2.0088) grad: 0.2280 (0.2301) time: 0.4797 data: 0.0042 max mem: 22448
|
| 800 |
+
train: [18] [320/400] eta: 0:00:37 lr: 0.000005 loss: 2.0267 (2.0114) grad: 0.2244 (0.2301) time: 0.4692 data: 0.0041 max mem: 22448
|
| 801 |
+
train: [18] [340/400] eta: 0:00:28 lr: 0.000004 loss: 2.0075 (2.0088) grad: 0.2326 (0.2299) time: 0.4589 data: 0.0042 max mem: 22448
|
| 802 |
+
train: [18] [360/400] eta: 0:00:18 lr: 0.000004 loss: 1.9931 (2.0090) grad: 0.2326 (0.2298) time: 0.4841 data: 0.0044 max mem: 22448
|
| 803 |
+
train: [18] [380/400] eta: 0:00:09 lr: 0.000004 loss: 1.9988 (2.0093) grad: 0.2323 (0.2299) time: 0.4747 data: 0.0043 max mem: 22448
|
| 804 |
+
train: [18] [399/400] eta: 0:00:00 lr: 0.000003 loss: 1.9950 (2.0079) grad: 0.2300 (0.2303) time: 0.4628 data: 0.0040 max mem: 22448
|
| 805 |
+
train: [18] Total time: 0:03:08 (0.4724 s / it)
|
| 806 |
+
train: [18] Summary: lr: 0.000003 loss: 1.9950 (2.0079) grad: 0.2300 (0.2303)
|
| 807 |
+
eval (validation): [18] [ 0/85] eta: 0:04:42 time: 3.3205 data: 3.0396 max mem: 22448
|
| 808 |
+
eval (validation): [18] [20/85] eta: 0:00:34 time: 0.3970 data: 0.0053 max mem: 22448
|
| 809 |
+
eval (validation): [18] [40/85] eta: 0:00:20 time: 0.3896 data: 0.0037 max mem: 22448
|
| 810 |
+
eval (validation): [18] [60/85] eta: 0:00:10 time: 0.3409 data: 0.0041 max mem: 22448
|
| 811 |
+
eval (validation): [18] [80/85] eta: 0:00:02 time: 0.3574 data: 0.0040 max mem: 22448
|
| 812 |
+
eval (validation): [18] [84/85] eta: 0:00:00 time: 0.3625 data: 0.0041 max mem: 22448
|
| 813 |
+
eval (validation): [18] Total time: 0:00:34 (0.4103 s / it)
|
| 814 |
+
cv: [18] best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00') loss: 2.397 acc: 0.277 f1: 0.217
|
| 815 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 816 |
+
train: [19] [ 0/400] eta: 0:24:01 lr: nan time: 3.6050 data: 3.1957 max mem: 22448
|
| 817 |
+
train: [19] [ 20/400] eta: 0:04:08 lr: 0.000003 loss: 2.0160 (2.0099) grad: 0.2228 (0.2255) time: 0.5059 data: 0.0037 max mem: 22448
|
| 818 |
+
train: [19] [ 40/400] eta: 0:03:21 lr: 0.000003 loss: 1.9750 (1.9819) grad: 0.2228 (0.2276) time: 0.4625 data: 0.0041 max mem: 22448
|
| 819 |
+
train: [19] [ 60/400] eta: 0:03:00 lr: 0.000002 loss: 2.0146 (2.0051) grad: 0.2264 (0.2280) time: 0.4706 data: 0.0041 max mem: 22448
|
| 820 |
+
train: [19] [ 80/400] eta: 0:02:45 lr: 0.000002 loss: 2.0240 (2.0081) grad: 0.2252 (0.2269) time: 0.4704 data: 0.0043 max mem: 22448
|
| 821 |
+
train: [19] [100/400] eta: 0:02:31 lr: 0.000002 loss: 2.0195 (2.0097) grad: 0.2283 (0.2277) time: 0.4610 data: 0.0044 max mem: 22448
|
| 822 |
+
train: [19] [120/400] eta: 0:02:19 lr: 0.000002 loss: 1.9984 (2.0108) grad: 0.2289 (0.2283) time: 0.4619 data: 0.0041 max mem: 22448
|
| 823 |
+
train: [19] [140/400] eta: 0:02:07 lr: 0.000001 loss: 1.9959 (2.0106) grad: 0.2246 (0.2278) time: 0.4551 data: 0.0044 max mem: 22448
|
| 824 |
+
train: [19] [160/400] eta: 0:01:57 lr: 0.000001 loss: 1.9890 (2.0055) grad: 0.2205 (0.2269) time: 0.4626 data: 0.0042 max mem: 22448
|
| 825 |
+
train: [19] [180/400] eta: 0:01:46 lr: 0.000001 loss: 1.9593 (2.0029) grad: 0.2286 (0.2279) time: 0.4654 data: 0.0042 max mem: 22448
|
| 826 |
+
train: [19] [200/400] eta: 0:01:36 lr: 0.000001 loss: 1.9968 (2.0031) grad: 0.2345 (0.2278) time: 0.4618 data: 0.0044 max mem: 22448
|
| 827 |
+
train: [19] [220/400] eta: 0:01:26 lr: 0.000001 loss: 1.9965 (2.0010) grad: 0.2255 (0.2275) time: 0.4630 data: 0.0042 max mem: 22448
|
| 828 |
+
train: [19] [240/400] eta: 0:01:16 lr: 0.000001 loss: 1.9654 (1.9988) grad: 0.2255 (0.2275) time: 0.4690 data: 0.0041 max mem: 22448
|
| 829 |
+
train: [19] [260/400] eta: 0:01:07 lr: 0.000000 loss: 1.9591 (1.9977) grad: 0.2277 (0.2275) time: 0.4669 data: 0.0043 max mem: 22448
|
| 830 |
+
train: [19] [280/400] eta: 0:00:57 lr: 0.000000 loss: 2.0095 (1.9997) grad: 0.2264 (0.2274) time: 0.4465 data: 0.0040 max mem: 22448
|
| 831 |
+
train: [19] [300/400] eta: 0:00:47 lr: 0.000000 loss: 2.0132 (1.9999) grad: 0.2237 (0.2271) time: 0.4677 data: 0.0042 max mem: 22448
|
| 832 |
+
train: [19] [320/400] eta: 0:00:37 lr: 0.000000 loss: 1.9927 (2.0019) grad: 0.2230 (0.2273) time: 0.4521 data: 0.0042 max mem: 22448
|
| 833 |
+
train: [19] [340/400] eta: 0:00:28 lr: 0.000000 loss: 2.0114 (2.0040) grad: 0.2230 (0.2271) time: 0.4585 data: 0.0042 max mem: 22448
|
| 834 |
+
train: [19] [360/400] eta: 0:00:18 lr: 0.000000 loss: 2.0324 (2.0040) grad: 0.2224 (0.2268) time: 0.4663 data: 0.0042 max mem: 22448
|
| 835 |
+
train: [19] [380/400] eta: 0:00:09 lr: 0.000000 loss: 1.9952 (2.0039) grad: 0.2214 (0.2267) time: 0.4588 data: 0.0045 max mem: 22448
|
| 836 |
+
train: [19] [399/400] eta: 0:00:00 lr: 0.000000 loss: 2.0028 (2.0062) grad: 0.2214 (0.2268) time: 0.4619 data: 0.0043 max mem: 22448
|
| 837 |
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train: [19] Total time: 0:03:09 (0.4725 s / it)
|
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train: [19] Summary: lr: 0.000000 loss: 2.0028 (2.0062) grad: 0.2214 (0.2268)
|
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eval (validation): [19] Total time: 0:00:34 (0.4057 s / it)
|
| 846 |
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cv: [19] best hparam: (0.38, 1.0) (018) ('018_lr3.8e-01_wd1.0e+00') loss: 2.396 acc: 0.278 f1: 0.216
|
| 847 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 848 |
+
evaluating last checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-last.pth
|
| 849 |
+
eval model info:
|
| 850 |
+
{"score": 0.27759320782576596, "hparam": [0.38, 1.0], "hparam_id": 18, "epoch": 19, "is_best": false, "best_score": 0.2847914359542267}
|
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eval (test): [20] Total time: 0:00:40 (0.4707 s / it)
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eval (testid): [20] Total time: 0:00:36 (0.4438 s / it)
|
| 900 |
+
evaluating best checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/checkpoint-best.pth
|
| 901 |
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eval model info:
|
| 902 |
+
{"score": 0.2847914359542267, "hparam": [0.85, 1.0], "hparam_id": 23, "epoch": 6, "is_best": true, "best_score": 0.2847914359542267}
|
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eval (train): [20] Total time: 0:03:46 (0.4445 s / it)
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eval (validation): [20] Total time: 0:00:36 (0.4284 s / it)
|
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eval (test): [20] [84/85] eta: 0:00:00 time: 0.3293 data: 0.0039 max mem: 22448
|
| 944 |
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eval (test): [20] Total time: 0:00:33 (0.3938 s / it)
|
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eval (testid): [20] [ 0/82] eta: 0:03:54 time: 2.8553 data: 2.6264 max mem: 22448
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|
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|
| 951 |
+
eval (testid): [20] Total time: 0:00:31 (0.3859 s / it)
|
| 952 |
+
eval results:
|
| 953 |
+
|
| 954 |
+
| model | repr | clf | dataset | ckpt | epoch | lr | wd | hparam_id | hparam | split | loss | acc | acc_std | f1 | f1_std |
|
| 955 |
+
|:---------|:-------|:------|:-------------|:-------|--------:|---------:|-----:|------------:|:------------|:-----------|-------:|--------:|----------:|--------:|----------:|
|
| 956 |
+
| flat_mae | patch | attn | nsd_cococlip | best | 6 | 0.000255 | 0.05 | 23 | [0.85, 1.0] | train | 2.0631 | 0.37844 | 0.0024476 | 0.32343 | 0.0025095 |
|
| 957 |
+
| flat_mae | patch | attn | nsd_cococlip | best | 6 | 0.000255 | 0.05 | 23 | [0.85, 1.0] | validation | 2.3775 | 0.28479 | 0.005602 | 0.22735 | 0.004996 |
|
| 958 |
+
| flat_mae | patch | attn | nsd_cococlip | best | 6 | 0.000255 | 0.05 | 23 | [0.85, 1.0] | test | 2.2619 | 0.31224 | 0.0056579 | 0.2451 | 0.0055936 |
|
| 959 |
+
| flat_mae | patch | attn | nsd_cococlip | best | 6 | 0.000255 | 0.05 | 23 | [0.85, 1.0] | testid | 2.2632 | 0.30114 | 0.0056728 | 0.24915 | 0.0051513 |
|
| 960 |
+
|
| 961 |
+
|
| 962 |
+
done! total time: 1:28:00
|
input_space_v3/flat_lr1e-3_7/eval_v2/nsd_cococlip__patch__attn/train_log.json
ADDED
|
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|
|
|
input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
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|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_7; eval v2 (ppmi_dx patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: ppmi_dx
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/eval_table.csv
ADDED
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| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,ppmi_dx,,0.005994842503189409,train,0.7295373665480427,0.017170429807707523,0.6875585205992509,0.021285746755557954,0.6813998530688573,0.019431547022636712
|
| 3 |
+
flat_mae,patch,logistic,ppmi_dx,,0.005994842503189409,test,0.64,0.03536233023996015,0.54337899543379,0.04795637134012401,0.5581295581295581,0.038660270484329684
|
| 4 |
+
flat_mae,patch,logistic,ppmi_dx,1,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 5 |
+
flat_mae,patch,logistic,ppmi_dx,1,2.782559402207126,test,0.57,0.04896529383144759,0.5413333333333333,0.051135105670696596,0.5411714770797962,0.05077313475860722
|
| 6 |
+
flat_mae,patch,logistic,ppmi_dx,2,0.3593813663804626,train,0.9181494661921709,0.011986328821665596,0.9132157052314964,0.012782067455238307,0.9117828088203811,0.013277616307539262
|
| 7 |
+
flat_mae,patch,logistic,ppmi_dx,2,0.3593813663804626,test,0.66,0.043871535190827315,0.6353496353496353,0.047654742468156604,0.634125636672326,0.04709153071914889
|
| 8 |
+
flat_mae,patch,logistic,ppmi_dx,3,0.005994842503189409,train,0.7241992882562278,0.016005509287558114,0.6809099067748494,0.019993461289709063,0.6751230999785913,0.018195049832687743
|
| 9 |
+
flat_mae,patch,logistic,ppmi_dx,3,0.005994842503189409,test,0.61,0.04215639453273964,0.5555555555555556,0.04936738445852324,0.5581494057724957,0.04521066572088864
|
| 10 |
+
flat_mae,patch,logistic,ppmi_dx,4,0.005994842503189409,train,0.7153024911032029,0.01685961605171297,0.6701151955389244,0.02105355686973722,0.6652884821237423,0.019069639180664302
|
| 11 |
+
flat_mae,patch,logistic,ppmi_dx,4,0.005994842503189409,test,0.67,0.04177123890908671,0.6349153667441089,0.047371959853541264,0.6320033955857385,0.045331932002187034
|
| 12 |
+
flat_mae,patch,logistic,ppmi_dx,5,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 13 |
+
flat_mae,patch,logistic,ppmi_dx,5,21.54434690031882,test,0.56,0.04954866698509658,0.548440065681445,0.04986935831775812,0.5534804753820034,0.0513903497224727
|
| 14 |
+
flat_mae,patch,logistic,ppmi_dx,6,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 15 |
+
flat_mae,patch,logistic,ppmi_dx,6,10000.0,test,0.62,0.04851168519027143,0.6041666666666667,0.05010538137282557,0.6069609507640068,0.05110303156572456
|
| 16 |
+
flat_mae,patch,logistic,ppmi_dx,7,0.046415888336127774,train,0.8274021352313167,0.015498525437144056,0.8097926399933009,0.01762309505706666,0.799815350032113,0.01766848460479383
|
| 17 |
+
flat_mae,patch,logistic,ppmi_dx,7,0.046415888336127774,test,0.61,0.04623454985181537,0.5741893219783819,0.05046296474471986,0.5734295415959253,0.04868840334647908
|
| 18 |
+
flat_mae,patch,logistic,ppmi_dx,8,2.782559402207126,train,0.99644128113879,0.002539985571426718,0.9962334964144495,0.0026957204874845757,0.9953703703703703,0.003304333081346787
|
| 19 |
+
flat_mae,patch,logistic,ppmi_dx,8,2.782559402207126,test,0.62,0.0472263443429618,0.6006725514922235,0.0493162600328295,0.6018675721561969,0.049608277124440416
|
| 20 |
+
flat_mae,patch,logistic,ppmi_dx,9,0.046415888336127774,train,0.7935943060498221,0.016267208711217652,0.7738650238650239,0.018211882097841785,0.7662706058659816,0.01802459289717544
|
| 21 |
+
flat_mae,patch,logistic,ppmi_dx,9,0.046415888336127774,test,0.7,0.03886230049804051,0.6553308823529411,0.04759301676666227,0.6511035653650254,0.04359982082973969
|
| 22 |
+
flat_mae,patch,logistic,ppmi_dx,10,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 23 |
+
flat_mae,patch,logistic,ppmi_dx,10,21.54434690031882,test,0.56,0.04836370126448139,0.5535714285714286,0.0481081395717187,0.5636672325976231,0.049531908678583605
|
| 24 |
+
flat_mae,patch,logistic,ppmi_dx,11,0.046415888336127774,train,0.8042704626334519,0.015858323282909568,0.7855616605616605,0.018024558196916465,0.7775503104260331,0.018058001248010443
|
| 25 |
+
flat_mae,patch,logistic,ppmi_dx,11,0.046415888336127774,test,0.65,0.04582722335031875,0.6224786970121885,0.04987210772586323,0.6209677419354839,0.048902426769228936
|
| 26 |
+
flat_mae,patch,logistic,ppmi_dx,12,0.005994842503189409,train,0.7348754448398577,0.01654176592396451,0.6932617813513068,0.020656982308896392,0.6864028045386428,0.0188682897409243
|
| 27 |
+
flat_mae,patch,logistic,ppmi_dx,12,0.005994842503189409,test,0.6,0.03672043572726229,0.503968253968254,0.04631793269111327,0.5246179966044142,0.0383095280123279
|
| 28 |
+
flat_mae,patch,logistic,ppmi_dx,13,0.3593813663804626,train,0.9110320284697508,0.011336187634595311,0.9053234501347709,0.01213907229385398,0.9025235495611218,0.01264175553638083
|
| 29 |
+
flat_mae,patch,logistic,ppmi_dx,13,0.3593813663804626,test,0.66,0.043700704799808435,0.6212121212121212,0.05050234890501691,0.6188455008488964,0.04784937070794841
|
| 30 |
+
flat_mae,patch,logistic,ppmi_dx,14,0.3593813663804626,train,0.9199288256227758,0.011793927192977295,0.9147115063586972,0.01268675373447741,0.9114884393063584,0.013303644584404174
|
| 31 |
+
flat_mae,patch,logistic,ppmi_dx,14,0.3593813663804626,test,0.61,0.0503988730032726,0.584,0.05370627211851491,0.583616298811545,0.05333175281380221
|
| 32 |
+
flat_mae,patch,logistic,ppmi_dx,15,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 33 |
+
flat_mae,patch,logistic,ppmi_dx,15,2.782559402207126,test,0.54,0.04976293801615817,0.5118845500848896,0.05067506290926889,0.5118845500848896,0.050620063900067215
|
| 34 |
+
flat_mae,patch,logistic,ppmi_dx,16,0.005994842503189409,train,0.7170818505338078,0.01658094880042457,0.6716746949438388,0.02064171478475658,0.6667335688289445,0.018742058250425464
|
| 35 |
+
flat_mae,patch,logistic,ppmi_dx,16,0.005994842503189409,test,0.65,0.04280166351907365,0.5944849959448499,0.05171758129373175,0.5955008488964346,0.04676611496179813
|
| 36 |
+
flat_mae,patch,logistic,ppmi_dx,17,0.005994842503189409,train,0.7241992882562278,0.016945754924071174,0.6809099067748494,0.02129802005454315,0.6751230999785913,0.019410480009525305
|
| 37 |
+
flat_mae,patch,logistic,ppmi_dx,17,0.005994842503189409,test,0.64,0.04262549002650879,0.592944369063772,0.04946524321936867,0.5925297113752122,0.04597365387991084
|
| 38 |
+
flat_mae,patch,logistic,ppmi_dx,18,0.005994842503189409,train,0.7384341637010676,0.016734835241295657,0.7017921923222697,0.020180848230949705,0.6945113466067223,0.018813328525305373
|
| 39 |
+
flat_mae,patch,logistic,ppmi_dx,18,0.005994842503189409,test,0.59,0.04400172269354917,0.5327635327635327,0.049803000728750976,0.5369269949066213,0.045837658370374366
|
| 40 |
+
flat_mae,patch,logistic,ppmi_dx,19,2.782559402207126,train,0.998220640569395,0.001779825561720624,0.9981184064710746,0.0018851367523917892,0.9976851851851851,0.002315421216868036
|
| 41 |
+
flat_mae,patch,logistic,ppmi_dx,19,2.782559402207126,test,0.54,0.04912033794672019,0.5208333333333334,0.05036000566465248,0.5220713073005093,0.0511931054193485
|
| 42 |
+
flat_mae,patch,logistic,ppmi_dx,20,0.005994842503189409,train,0.7259786476868327,0.017103531614346707,0.6824858757062147,0.021371071649117265,0.6765681866837936,0.019369867742989714
|
| 43 |
+
flat_mae,patch,logistic,ppmi_dx,20,0.005994842503189409,test,0.66,0.0389209455178057,0.5952380952380952,0.050308687107881986,0.5984719864176571,0.04337869615479128
|
| 44 |
+
flat_mae,patch,logistic,ppmi_dx,21,0.046415888336127774,train,0.7864768683274022,0.015484212733004635,0.7644099769440369,0.017722697821452763,0.7561416184971098,0.0175177218443615
|
| 45 |
+
flat_mae,patch,logistic,ppmi_dx,21,0.046415888336127774,test,0.72,0.042332380041759994,0.6834011759384894,0.05077122953789308,0.6774193548387097,0.047824365313582126
|
| 46 |
+
flat_mae,patch,logistic,ppmi_dx,22,0.046415888336127774,train,0.802491103202847,0.016600774666636117,0.7833619836432776,0.01879160227394038,0.7752354956112182,0.018687924024145208
|
| 47 |
+
flat_mae,patch,logistic,ppmi_dx,22,0.046415888336127774,test,0.63,0.04207294142319979,0.5847828526540231,0.04801789515738247,0.5844651952461799,0.04486319655419706
|
| 48 |
+
flat_mae,patch,logistic,ppmi_dx,23,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 49 |
+
flat_mae,patch,logistic,ppmi_dx,23,21.54434690031882,test,0.64,0.04517281926114419,0.6043956043956044,0.05061132834809695,0.6027164685908319,0.04867377231075253
|
| 50 |
+
flat_mae,patch,logistic,ppmi_dx,24,0.005994842503189409,train,0.7259786476868327,0.015700029890564154,0.6824858757062147,0.019806646550183767,0.6765681866837936,0.018060143513008647
|
| 51 |
+
flat_mae,patch,logistic,ppmi_dx,24,0.005994842503189409,test,0.65,0.04633540331107521,0.5944849959448499,0.05567927987631211,0.5955008488964346,0.050094199129048486
|
| 52 |
+
flat_mae,patch,logistic,ppmi_dx,25,0.005994842503189409,train,0.7330960854092526,0.016083620345837393,0.6878332740846071,0.02039970017010762,0.6814788053949904,0.01837266392020624
|
| 53 |
+
flat_mae,patch,logistic,ppmi_dx,25,0.005994842503189409,test,0.68,0.03840818142010891,0.6114618746964546,0.05221490365162682,0.6146010186757216,0.04383233661429553
|
| 54 |
+
flat_mae,patch,logistic,ppmi_dx,26,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 55 |
+
flat_mae,patch,logistic,ppmi_dx,26,2.782559402207126,test,0.6,0.04980327700061513,0.5796553173602353,0.05132262421770619,0.5806451612903225,0.051507933338997565
|
| 56 |
+
flat_mae,patch,logistic,ppmi_dx,27,0.3593813663804626,train,0.9217081850533808,0.010816217097394535,0.9166846361185984,0.011590548856495271,0.9138032541211731,0.012177996495352247
|
| 57 |
+
flat_mae,patch,logistic,ppmi_dx,27,0.3593813663804626,test,0.57,0.04739881433116233,0.5305164319248826,0.051298262929650514,0.5309847198641766,0.04973345566980027
|
| 58 |
+
flat_mae,patch,logistic,ppmi_dx,28,0.3593813663804626,train,0.9163701067615658,0.011066424968276825,0.9104060457433205,0.012014715806308907,0.9051193534575037,0.012679165449254662
|
| 59 |
+
flat_mae,patch,logistic,ppmi_dx,28,0.3593813663804626,test,0.67,0.04254211560324662,0.6396986570586308,0.047780643061751886,0.6370967741935484,0.04649056838478631
|
| 60 |
+
flat_mae,patch,logistic,ppmi_dx,29,0.005994842503189409,train,0.7348754448398577,0.016943587458477332,0.6923240851989433,0.021350286037596256,0.6855330764290302,0.0194032927832332
|
| 61 |
+
flat_mae,patch,logistic,ppmi_dx,29,0.005994842503189409,test,0.67,0.04084203227068897,0.6033177064551027,0.05233199062513069,0.6065365025466893,0.044878486112100005
|
| 62 |
+
flat_mae,patch,logistic,ppmi_dx,30,2.782559402207126,train,0.998220640569395,0.001642336223700383,0.9981184064710746,0.0017392343628209858,0.9976851851851851,0.0021365577724991093
|
| 63 |
+
flat_mae,patch,logistic,ppmi_dx,30,2.782559402207126,test,0.64,0.046048995645942156,0.5989304812834224,0.0517154377851105,0.597623089983022,0.049017326405453344
|
| 64 |
+
flat_mae,patch,logistic,ppmi_dx,31,0.005994842503189409,train,0.7241992882562278,0.017021475078480782,0.6846616927849756,0.020874180817441006,0.6786020124170413,0.019331347772171072
|
| 65 |
+
flat_mae,patch,logistic,ppmi_dx,31,0.005994842503189409,test,0.61,0.04600140867408302,0.5555555555555556,0.052303075466308344,0.5581494057724957,0.04824641404903407
|
| 66 |
+
flat_mae,patch,logistic,ppmi_dx,32,0.046415888336127774,train,0.806049822064057,0.016816991722589297,0.7867677516595135,0.019219520243950173,0.7781256690216227,0.01915336001870753
|
| 67 |
+
flat_mae,patch,logistic,ppmi_dx,32,0.046415888336127774,test,0.69,0.04505319078600316,0.6570417081535569,0.05122446383387725,0.6532258064516129,0.0489966132903305
|
| 68 |
+
flat_mae,patch,logistic,ppmi_dx,33,0.046415888336127774,train,0.8149466192170819,0.016183908963380515,0.7967874278561992,0.018498874415902877,0.7879602868764719,0.018592090550493465
|
| 69 |
+
flat_mae,patch,logistic,ppmi_dx,33,0.046415888336127774,test,0.6,0.04803113573506252,0.570999570999571,0.05109407202009144,0.5704584040747029,0.050179394157369994
|
| 70 |
+
flat_mae,patch,logistic,ppmi_dx,34,0.046415888336127774,train,0.806049822064057,0.016772532698354225,0.7857475123725584,0.019168673622259137,0.7763862128023977,0.01890494982424526
|
| 71 |
+
flat_mae,patch,logistic,ppmi_dx,34,0.046415888336127774,test,0.68,0.04571214280691728,0.6567996567996568,0.0502533820162311,0.6553480475382003,0.05005658248723505
|
| 72 |
+
flat_mae,patch,logistic,ppmi_dx,35,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 73 |
+
flat_mae,patch,logistic,ppmi_dx,35,166.81005372000556,test,0.6,0.046245622495540054,0.570999570999571,0.049915815416379836,0.5704584040747029,0.04921077525839578
|
| 74 |
+
flat_mae,patch,logistic,ppmi_dx,36,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 75 |
+
flat_mae,patch,logistic,ppmi_dx,36,21.54434690031882,test,0.59,0.04726612317506059,0.5523528769516323,0.051748394040090503,0.5522071307300509,0.050057518232247
|
| 76 |
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|
| 162 |
+
flat_mae,patch,logistic,ppmi_dx,80,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 163 |
+
flat_mae,patch,logistic,ppmi_dx,80,2.782559402207126,test,0.61,0.04847875823492182,0.5953937130407718,0.048997780603058885,0.5988964346349746,0.04959503724184109
|
| 164 |
+
flat_mae,patch,logistic,ppmi_dx,81,0.005994842503189409,train,0.7188612099644128,0.016643200553653748,0.6771524141942991,0.02078393782247932,0.6716575679725969,0.01919113104102182
|
| 165 |
+
flat_mae,patch,logistic,ppmi_dx,81,0.005994842503189409,test,0.65,0.04260405614492592,0.612789025334661,0.04635968548851016,0.6107809847198642,0.04439241793580153
|
| 166 |
+
flat_mae,patch,logistic,ppmi_dx,82,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 167 |
+
flat_mae,patch,logistic,ppmi_dx,82,21.54434690031882,test,0.64,0.044939710724480636,0.6138996138996139,0.04865221837002007,0.6129032258064516,0.04831991953120557
|
| 168 |
+
flat_mae,patch,logistic,ppmi_dx,83,0.3593813663804626,train,0.9288256227758007,0.010628729770353508,0.9245353958534751,0.011285557205013992,0.9230625133804324,0.011574978479835664
|
| 169 |
+
flat_mae,patch,logistic,ppmi_dx,83,0.3593813663804626,test,0.58,0.05007410109028418,0.5689655172413793,0.05064717434544389,0.5747028862478778,0.05215937658180453
|
| 170 |
+
flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,train,0.7366548042704626,0.016504084269001063,0.6984672496048492,0.0203106019204889,0.691326803682295,0.01886203427270938
|
| 171 |
+
flat_mae,patch,logistic,ppmi_dx,84,0.005994842503189409,test,0.62,0.040939228131463355,0.5476190476190476,0.05154964794431277,0.5560271646859083,0.0445260282510156
|
| 172 |
+
flat_mae,patch,logistic,ppmi_dx,85,0.046415888336127774,train,0.802491103202847,0.015993954734913475,0.7818162740674676,0.018284225415216144,0.7726263112823806,0.018013461124279123
|
| 173 |
+
flat_mae,patch,logistic,ppmi_dx,85,0.046415888336127774,test,0.6,0.03998111554221568,0.5324918186068257,0.04851434958599507,0.5398981324278438,0.042961499604386166
|
| 174 |
+
flat_mae,patch,logistic,ppmi_dx,86,0.000774263682681127,train,0.6850533807829181,0.015101461959565174,0.605003275954494,0.021544024633235712,0.6137604367373153,0.017188733238759957
|
| 175 |
+
flat_mae,patch,logistic,ppmi_dx,86,0.000774263682681127,test,0.61,0.031432537282249436,0.4729017434788485,0.04651184789027144,0.517402376910017,0.03355436315412795
|
| 176 |
+
flat_mae,patch,logistic,ppmi_dx,87,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 177 |
+
flat_mae,patch,logistic,ppmi_dx,87,2.782559402207126,test,0.61,0.04920739375337816,0.5741893219783819,0.053970194728414723,0.5734295415959253,0.05204391544278411
|
| 178 |
+
flat_mae,patch,logistic,ppmi_dx,88,0.005994842503189409,train,0.7170818505338078,0.017542478572774727,0.6736603376683137,0.021655328846982024,0.6684730250481695,0.01977089597930567
|
| 179 |
+
flat_mae,patch,logistic,ppmi_dx,88,0.005994842503189409,test,0.67,0.038712499273490464,0.6033177064551027,0.051480611018538985,0.6065365025466893,0.04364382123717895
|
| 180 |
+
flat_mae,patch,logistic,ppmi_dx,89,0.005994842503189409,train,0.7241992882562278,0.016821209551228105,0.6799344510458807,0.020924040763372093,0.6742533718689788,0.018967714978297547
|
| 181 |
+
flat_mae,patch,logistic,ppmi_dx,89,0.005994842503189409,test,0.71,0.036012103520899744,0.6363636363636364,0.053412330091927,0.6387945670628183,0.04311481638033361
|
| 182 |
+
flat_mae,patch,logistic,ppmi_dx,90,0.046415888336127774,train,0.8042704626334519,0.014955927875758524,0.782987208110423,0.01731262643342801,0.7732016698779705,0.017130564803957568
|
| 183 |
+
flat_mae,patch,logistic,ppmi_dx,90,0.046415888336127774,test,0.67,0.04346589927747957,0.6349153667441089,0.04838621624108758,0.6320033955857385,0.0463905183559024
|
| 184 |
+
flat_mae,patch,logistic,ppmi_dx,91,0.005994842503189409,train,0.7348754448398577,0.01652659651887109,0.6884225409759819,0.0212500565303433,0.6820541639905802,0.01899827513805073
|
| 185 |
+
flat_mae,patch,logistic,ppmi_dx,91,0.005994842503189409,test,0.66,0.042940498366926295,0.6155585707824514,0.05000576551588744,0.6137521222410866,0.04655447452806663
|
| 186 |
+
flat_mae,patch,logistic,ppmi_dx,92,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 187 |
+
flat_mae,patch,logistic,ppmi_dx,92,21.54434690031882,test,0.58,0.04816752017698233,0.5543293718166383,0.05073530177432512,0.5543293718166383,0.05077112687914479
|
| 188 |
+
flat_mae,patch,logistic,ppmi_dx,93,0.046415888336127774,train,0.8078291814946619,0.016265303930575736,0.7899414427509448,0.018159390717372756,0.7821799400556626,0.018086534082174976
|
| 189 |
+
flat_mae,patch,logistic,ppmi_dx,93,0.046415888336127774,test,0.6,0.04855460843215605,0.5796553173602353,0.05022600280723422,0.5806451612903225,0.0507304501734926
|
| 190 |
+
flat_mae,patch,logistic,ppmi_dx,94,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 191 |
+
flat_mae,patch,logistic,ppmi_dx,94,166.81005372000556,test,0.65,0.048027925210235765,0.6266666666666667,0.0510089807462999,0.6260611205432938,0.05081857552784964
|
| 192 |
+
flat_mae,patch,logistic,ppmi_dx,95,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 193 |
+
flat_mae,patch,logistic,ppmi_dx,95,10000.0,test,0.62,0.04599937390878272,0.5824175824175825,0.05068613736305663,0.5814940577249575,0.04847032118429192
|
| 194 |
+
flat_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,train,0.797153024911032,0.016342515429538663,0.7787707182320442,0.018517639124732185,0.7717699636052238,0.018650380818869005
|
| 195 |
+
flat_mae,patch,logistic,ppmi_dx,96,0.046415888336127774,test,0.57,0.04245894016576485,0.5242836596968692,0.04676714061429211,0.5258913412563667,0.044389162794048226
|
| 196 |
+
flat_mae,patch,logistic,ppmi_dx,97,2.782559402207126,train,0.99644128113879,0.002463690041371193,0.9962334964144495,0.0026143544463221218,0.9953703703703703,0.0032050782482653003
|
| 197 |
+
flat_mae,patch,logistic,ppmi_dx,97,2.782559402207126,test,0.68,0.04567791151092614,0.6604414261460102,0.04834565871696938,0.6604414261460102,0.04842415412211496
|
| 198 |
+
flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,train,0.9270462633451957,0.011101021776837094,0.9225788676126188,0.011867101410358794,0.9207476985656176,0.012413233978358992
|
| 199 |
+
flat_mae,patch,logistic,ppmi_dx,98,0.3593813663804626,test,0.62,0.046371866470954134,0.5766488413547237,0.05284264686470824,0.5764006791171477,0.04990701067156035
|
| 200 |
+
flat_mae,patch,logistic,ppmi_dx,99,0.046415888336127774,train,0.8096085409252669,0.015826609855365024,0.7886438324868636,0.01843720105696226,0.7784066581031899,0.018270276751975283
|
| 201 |
+
flat_mae,patch,logistic,ppmi_dx,99,0.046415888336127774,test,0.67,0.04544332294188002,0.6396986570586308,0.05072757019414787,0.6370967741935484,0.04926250333524091
|
| 202 |
+
flat_mae,patch,logistic,ppmi_dx,100,0.005994842503189409,train,0.7277580071174378,0.01618032142396934,0.6830862108999237,0.020442512734302535,0.6771435452793835,0.01852887619255486
|
| 203 |
+
flat_mae,patch,logistic,ppmi_dx,100,0.005994842503189409,test,0.64,0.04432911458624005,0.5989304812834224,0.05088777275289724,0.597623089983022,0.04790808684956218
|
input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic/log.txt
ADDED
|
@@ -0,0 +1,247 @@
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:55:21
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_7; eval v2 (ppmi_dx patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: ppmi_dx
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/eval_v2/ppmi_dx__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: ppmi_dx (flat)
|
| 70 |
+
train (n=463):
|
| 71 |
+
HFDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 463
|
| 75 |
+
}),
|
| 76 |
+
labels=['PD' 'Prodromal'],
|
| 77 |
+
counts=[178 285]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=99):
|
| 81 |
+
HFDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 99
|
| 85 |
+
}),
|
| 86 |
+
labels=['PD' 'Prodromal'],
|
| 87 |
+
counts=[39 60]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=100):
|
| 91 |
+
HFDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'ses', 'dir', 'sex', 'age', 'age_bin', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 100
|
| 95 |
+
}),
|
| 96 |
+
labels=['PD' 'Prodromal'],
|
| 97 |
+
counts=[37 63]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/232] eta: 0:17:01 time: 4.4025 data: 3.5704 max mem: 2698
|
| 102 |
+
extract (train) [ 20/232] eta: 0:01:23 time: 0.1918 data: 0.0646 max mem: 2851
|
| 103 |
+
extract (train) [ 40/232] eta: 0:00:54 time: 0.1714 data: 0.0533 max mem: 2851
|
| 104 |
+
extract (train) [ 60/232] eta: 0:00:44 time: 0.2108 data: 0.0722 max mem: 2851
|
| 105 |
+
extract (train) [ 80/232] eta: 0:00:37 time: 0.2161 data: 0.0715 max mem: 2851
|
| 106 |
+
extract (train) [100/232] eta: 0:00:31 time: 0.2039 data: 0.0696 max mem: 2851
|
| 107 |
+
extract (train) [120/232] eta: 0:00:26 time: 0.2088 data: 0.0703 max mem: 2851
|
| 108 |
+
extract (train) [140/232] eta: 0:00:21 time: 0.2151 data: 0.0738 max mem: 2851
|
| 109 |
+
extract (train) [160/232] eta: 0:00:16 time: 0.2275 data: 0.0754 max mem: 2851
|
| 110 |
+
extract (train) [180/232] eta: 0:00:11 time: 0.2215 data: 0.0783 max mem: 2851
|
| 111 |
+
extract (train) [200/232] eta: 0:00:07 time: 0.1723 data: 0.0517 max mem: 2851
|
| 112 |
+
extract (train) [220/232] eta: 0:00:02 time: 0.1646 data: 0.0503 max mem: 2851
|
| 113 |
+
extract (train) [231/232] eta: 0:00:00 time: 0.1590 data: 0.0484 max mem: 2851
|
| 114 |
+
extract (train) Total time: 0:00:50 (0.2181 s / it)
|
| 115 |
+
extract (validation) [ 0/50] eta: 0:03:40 time: 4.4191 data: 4.2298 max mem: 2851
|
| 116 |
+
extract (validation) [20/50] eta: 0:00:13 time: 0.2404 data: 0.0773 max mem: 2851
|
| 117 |
+
extract (validation) [40/50] eta: 0:00:03 time: 0.1586 data: 0.0463 max mem: 2851
|
| 118 |
+
extract (validation) [49/50] eta: 0:00:00 time: 0.1604 data: 0.0506 max mem: 2851
|
| 119 |
+
extract (validation) Total time: 0:00:14 (0.2828 s / it)
|
| 120 |
+
extract (test) [ 0/50] eta: 0:03:29 time: 4.1991 data: 4.0240 max mem: 2851
|
| 121 |
+
extract (test) [20/50] eta: 0:00:12 time: 0.2396 data: 0.0888 max mem: 2851
|
| 122 |
+
extract (test) [40/50] eta: 0:00:02 time: 0.1654 data: 0.0496 max mem: 2851
|
| 123 |
+
extract (test) [49/50] eta: 0:00:00 time: 0.1580 data: 0.0472 max mem: 2851
|
| 124 |
+
extract (test) Total time: 0:00:13 (0.2787 s / it)
|
| 125 |
+
feature extraction time: 0:01:18
|
| 126 |
+
train features: (463, 768)
|
| 127 |
+
validation features: (99, 768)
|
| 128 |
+
test features: (100, 768)
|
| 129 |
+
evaluating fixed splits
|
| 130 |
+
eval results (fixed splits):
|
| 131 |
+
|
| 132 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 133 |
+
|:---------|:-------|:---------|:----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 134 |
+
| flat_mae | patch | logistic | ppmi_dx | | 0.0059948 | train | 0.72954 | 0.01717 | 0.68756 | 0.021286 | 0.6814 | 0.019432 |
|
| 135 |
+
| flat_mae | patch | logistic | ppmi_dx | | 0.0059948 | test | 0.64 | 0.035362 | 0.54338 | 0.047956 | 0.55813 | 0.03866 |
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
evaluating random splits (n=100)
|
| 139 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 1, "C": 2.782559402207126, "split": "test", "acc": 0.57, "acc_std": 0.04896529383144759, "f1": 0.5413333333333333, "f1_std": 0.051135105670696596, "bacc": 0.5411714770797962, "bacc_std": 0.05077313475860722}
|
| 140 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.66, "acc_std": 0.043871535190827315, "f1": 0.6353496353496353, "f1_std": 0.047654742468156604, "bacc": 0.634125636672326, "bacc_std": 0.04709153071914889}
|
| 141 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04215639453273964, "f1": 0.5555555555555556, "f1_std": 0.04936738445852324, "bacc": 0.5581494057724957, "bacc_std": 0.04521066572088864}
|
| 142 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04177123890908671, "f1": 0.6349153667441089, "f1_std": 0.047371959853541264, "bacc": 0.6320033955857385, "bacc_std": 0.045331932002187034}
|
| 143 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 5, "C": 21.54434690031882, "split": "test", "acc": 0.56, "acc_std": 0.04954866698509658, "f1": 0.548440065681445, "f1_std": 0.04986935831775812, "bacc": 0.5534804753820034, "bacc_std": 0.0513903497224727}
|
| 144 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 6, "C": 10000.0, "split": "test", "acc": 0.62, "acc_std": 0.04851168519027143, "f1": 0.6041666666666667, "f1_std": 0.05010538137282557, "bacc": 0.6069609507640068, "bacc_std": 0.05110303156572456}
|
| 145 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.61, "acc_std": 0.04623454985181537, "f1": 0.5741893219783819, "f1_std": 0.05046296474471986, "bacc": 0.5734295415959253, "bacc_std": 0.04868840334647908}
|
| 146 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 8, "C": 2.782559402207126, "split": "test", "acc": 0.62, "acc_std": 0.0472263443429618, "f1": 0.6006725514922235, "f1_std": 0.0493162600328295, "bacc": 0.6018675721561969, "bacc_std": 0.049608277124440416}
|
| 147 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.7, "acc_std": 0.03886230049804051, "f1": 0.6553308823529411, "f1_std": 0.04759301676666227, "bacc": 0.6511035653650254, "bacc_std": 0.04359982082973969}
|
| 148 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 10, "C": 21.54434690031882, "split": "test", "acc": 0.56, "acc_std": 0.04836370126448139, "f1": 0.5535714285714286, "f1_std": 0.0481081395717187, "bacc": 0.5636672325976231, "bacc_std": 0.049531908678583605}
|
| 149 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.65, "acc_std": 0.04582722335031875, "f1": 0.6224786970121885, "f1_std": 0.04987210772586323, "bacc": 0.6209677419354839, "bacc_std": 0.048902426769228936}
|
| 150 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.03672043572726229, "f1": 0.503968253968254, "f1_std": 0.04631793269111327, "bacc": 0.5246179966044142, "bacc_std": 0.0383095280123279}
|
| 151 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 13, "C": 0.3593813663804626, "split": "test", "acc": 0.66, "acc_std": 0.043700704799808435, "f1": 0.6212121212121212, "f1_std": 0.05050234890501691, "bacc": 0.6188455008488964, "bacc_std": 0.04784937070794841}
|
| 152 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.61, "acc_std": 0.0503988730032726, "f1": 0.584, "f1_std": 0.05370627211851491, "bacc": 0.583616298811545, "bacc_std": 0.05333175281380221}
|
| 153 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 15, "C": 2.782559402207126, "split": "test", "acc": 0.54, "acc_std": 0.04976293801615817, "f1": 0.5118845500848896, "f1_std": 0.05067506290926889, "bacc": 0.5118845500848896, "bacc_std": 0.050620063900067215}
|
| 154 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 16, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.04280166351907365, "f1": 0.5944849959448499, "f1_std": 0.05171758129373175, "bacc": 0.5955008488964346, "bacc_std": 0.04676611496179813}
|
| 155 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 17, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04262549002650879, "f1": 0.592944369063772, "f1_std": 0.04946524321936867, "bacc": 0.5925297113752122, "bacc_std": 0.04597365387991084}
|
| 156 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.59, "acc_std": 0.04400172269354917, "f1": 0.5327635327635327, "f1_std": 0.049803000728750976, "bacc": 0.5369269949066213, "bacc_std": 0.045837658370374366}
|
| 157 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 19, "C": 2.782559402207126, "split": "test", "acc": 0.54, "acc_std": 0.04912033794672019, "f1": 0.5208333333333334, "f1_std": 0.05036000566465248, "bacc": 0.5220713073005093, "bacc_std": 0.0511931054193485}
|
| 158 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.66, "acc_std": 0.0389209455178057, "f1": 0.5952380952380952, "f1_std": 0.050308687107881986, "bacc": 0.5984719864176571, "bacc_std": 0.04337869615479128}
|
| 159 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.72, "acc_std": 0.042332380041759994, "f1": 0.6834011759384894, "f1_std": 0.05077122953789308, "bacc": 0.6774193548387097, "bacc_std": 0.047824365313582126}
|
| 160 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 22, "C": 0.046415888336127774, "split": "test", "acc": 0.63, "acc_std": 0.04207294142319979, "f1": 0.5847828526540231, "f1_std": 0.04801789515738247, "bacc": 0.5844651952461799, "bacc_std": 0.04486319655419706}
|
| 161 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 23, "C": 21.54434690031882, "split": "test", "acc": 0.64, "acc_std": 0.04517281926114419, "f1": 0.6043956043956044, "f1_std": 0.05061132834809695, "bacc": 0.6027164685908319, "bacc_std": 0.04867377231075253}
|
| 162 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 24, "C": 0.005994842503189409, "split": "test", "acc": 0.65, "acc_std": 0.04633540331107521, "f1": 0.5944849959448499, "f1_std": 0.05567927987631211, "bacc": 0.5955008488964346, "bacc_std": 0.050094199129048486}
|
| 163 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 25, "C": 0.005994842503189409, "split": "test", "acc": 0.68, "acc_std": 0.03840818142010891, "f1": 0.6114618746964546, "f1_std": 0.05221490365162682, "bacc": 0.6146010186757216, "bacc_std": 0.04383233661429553}
|
| 164 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 26, "C": 2.782559402207126, "split": "test", "acc": 0.6, "acc_std": 0.04980327700061513, "f1": 0.5796553173602353, "f1_std": 0.05132262421770619, "bacc": 0.5806451612903225, "bacc_std": 0.051507933338997565}
|
| 165 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 27, "C": 0.3593813663804626, "split": "test", "acc": 0.57, "acc_std": 0.04739881433116233, "f1": 0.5305164319248826, "f1_std": 0.051298262929650514, "bacc": 0.5309847198641766, "bacc_std": 0.04973345566980027}
|
| 166 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.67, "acc_std": 0.04254211560324662, "f1": 0.6396986570586308, "f1_std": 0.047780643061751886, "bacc": 0.6370967741935484, "bacc_std": 0.04649056838478631}
|
| 167 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.67, "acc_std": 0.04084203227068897, "f1": 0.6033177064551027, "f1_std": 0.05233199062513069, "bacc": 0.6065365025466893, "bacc_std": 0.044878486112100005}
|
| 168 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 30, "C": 2.782559402207126, "split": "test", "acc": 0.64, "acc_std": 0.046048995645942156, "f1": 0.5989304812834224, "f1_std": 0.0517154377851105, "bacc": 0.597623089983022, "bacc_std": 0.049017326405453344}
|
| 169 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 31, "C": 0.005994842503189409, "split": "test", "acc": 0.61, "acc_std": 0.04600140867408302, "f1": 0.5555555555555556, "f1_std": 0.052303075466308344, "bacc": 0.5581494057724957, "bacc_std": 0.04824641404903407}
|
| 170 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 32, "C": 0.046415888336127774, "split": "test", "acc": 0.69, "acc_std": 0.04505319078600316, "f1": 0.6570417081535569, "f1_std": 0.05122446383387725, "bacc": 0.6532258064516129, "bacc_std": 0.0489966132903305}
|
| 171 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 33, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.04803113573506252, "f1": 0.570999570999571, "f1_std": 0.05109407202009144, "bacc": 0.5704584040747029, "bacc_std": 0.050179394157369994}
|
| 172 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.68, "acc_std": 0.04571214280691728, "f1": 0.6567996567996568, "f1_std": 0.0502533820162311, "bacc": 0.6553480475382003, "bacc_std": 0.05005658248723505}
|
| 173 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 35, "C": 166.81005372000556, "split": "test", "acc": 0.6, "acc_std": 0.046245622495540054, "f1": 0.570999570999571, "f1_std": 0.049915815416379836, "bacc": 0.5704584040747029, "bacc_std": 0.04921077525839578}
|
| 174 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 36, "C": 21.54434690031882, "split": "test", "acc": 0.59, "acc_std": 0.04726612317506059, "f1": 0.5523528769516323, "f1_std": 0.051748394040090503, "bacc": 0.5522071307300509, "bacc_std": 0.050057518232247}
|
| 175 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 37, "C": 0.3593813663804626, "split": "test", "acc": 0.65, "acc_std": 0.04687600665585753, "f1": 0.6338529134846741, "f1_std": 0.04819263477803123, "bacc": 0.6362478777589134, "bacc_std": 0.04887240277903581}
|
| 176 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 38, "C": 166.81005372000556, "split": "test", "acc": 0.51, "acc_std": 0.048509376413225516, "f1": 0.4916485112563544, "f1_std": 0.04825228174616227, "bacc": 0.49278438030560273, "bacc_std": 0.04896050711367196}
|
| 177 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 39, "C": 0.005994842503189409, "split": "test", "acc": 0.69, "acc_std": 0.039750597479786376, "f1": 0.6343908479773559, "f1_std": 0.05107657213125451, "bacc": 0.6328522920203735, "bacc_std": 0.0447603484199541}
|
| 178 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 40, "C": 0.046415888336127774, "split": "test", "acc": 0.66, "acc_std": 0.046923785013572805, "f1": 0.6353496353496353, "f1_std": 0.05042223162030356, "bacc": 0.634125636672326, "bacc_std": 0.05013842124979934}
|
| 179 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 41, "C": 0.005994842503189409, "split": "test", "acc": 0.71, "acc_std": 0.04292458037069203, "f1": 0.6791680495630048, "f1_std": 0.04879999057660205, "bacc": 0.6744482173174873, "bacc_std": 0.046794572876227826}
|
| 180 |
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{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 95, "C": 10000.0, "split": "test", "acc": 0.62, "acc_std": 0.04599937390878272, "f1": 0.5824175824175825, "f1_std": 0.05068613736305663, "bacc": 0.5814940577249575, "bacc_std": 0.04847032118429192}
|
| 234 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.57, "acc_std": 0.04245894016576485, "f1": 0.5242836596968692, "f1_std": 0.04676714061429211, "bacc": 0.5258913412563667, "bacc_std": 0.044389162794048226}
|
| 235 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 97, "C": 2.782559402207126, "split": "test", "acc": 0.68, "acc_std": 0.04567791151092614, "f1": 0.6604414261460102, "f1_std": 0.04834565871696938, "bacc": 0.6604414261460102, "bacc_std": 0.04842415412211496}
|
| 236 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.62, "acc_std": 0.046371866470954134, "f1": 0.5766488413547237, "f1_std": 0.05284264686470824, "bacc": 0.5764006791171477, "bacc_std": 0.04990701067156035}
|
| 237 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.67, "acc_std": 0.04544332294188002, "f1": 0.6396986570586308, "f1_std": 0.05072757019414787, "bacc": 0.6370967741935484, "bacc_std": 0.04926250333524091}
|
| 238 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "ppmi_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.64, "acc_std": 0.04432911458624005, "f1": 0.5989304812834224, "f1_std": 0.05088777275289724, "bacc": 0.597623089983022, "bacc_std": 0.04790808684956218}
|
| 239 |
+
eval results (random splits):
|
| 240 |
+
|
| 241 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 242 |
+
|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 243 |
+
| flat_mae | patch | logistic | ppmi_dx | train | 100 | 210.73 | 1406 | 0.85431 | 0.11334 | 0.83482 | 0.1312 | 0.83035 | 0.13367 |
|
| 244 |
+
| flat_mae | patch | logistic | ppmi_dx | test | 100 | 210.73 | 1406 | 0.6359 | 0.047399 | 0.59725 | 0.050435 | 0.59814 | 0.047521 |
|
| 245 |
+
|
| 246 |
+
|
| 247 |
+
done! total time: 0:05:31
|
input_space_v3/flat_lr1e-3_7/pretrain/config.yaml
ADDED
|
@@ -0,0 +1,102 @@
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|
|
| 1 |
+
name: input_space_v3/flat_lr1e-3_7/pretrain
|
| 2 |
+
notes: input_space ablation v3 flat_lr1e-3_7; use tube masking and on-disk data (input_space=flat
|
| 3 |
+
base_lr=1e-3 seed=5407)
|
| 4 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_7/pretrain
|
| 5 |
+
input_space: flat
|
| 6 |
+
patch_size: 16
|
| 7 |
+
num_frames: 16
|
| 8 |
+
t_patch_size: 4
|
| 9 |
+
mask_ratio: 0.9
|
| 10 |
+
pred_mask_ratio: null
|
| 11 |
+
masking: tube
|
| 12 |
+
masking_kwargs: {}
|
| 13 |
+
mask_patch_size: null
|
| 14 |
+
model: mae_vit_base
|
| 15 |
+
model_kwargs:
|
| 16 |
+
decoding: attn
|
| 17 |
+
pos_embed: sep
|
| 18 |
+
target_norm: null
|
| 19 |
+
pca_norm_nc: 2
|
| 20 |
+
t_pred_stride: 2
|
| 21 |
+
no_decode_pos: true
|
| 22 |
+
mask_drop_scale: false
|
| 23 |
+
pred_edge_pad: 0
|
| 24 |
+
gauss_sigma: null
|
| 25 |
+
class_token: true
|
| 26 |
+
reg_tokens: 0
|
| 27 |
+
no_embed_class: true
|
| 28 |
+
head_init_scale: 0.0
|
| 29 |
+
decoder_depth: 4
|
| 30 |
+
drop_path_rate: 0.0
|
| 31 |
+
datasets:
|
| 32 |
+
hcp-train:
|
| 33 |
+
type: wds
|
| 34 |
+
url: /data/fmri-datasets/pretrain/hcpya-all.${input_space}.wds/hcpya-all-${input_space}-{00000..01799}.tar
|
| 35 |
+
clipping: random
|
| 36 |
+
clipping_kwargs:
|
| 37 |
+
oversample: 4.0
|
| 38 |
+
shuffle: true
|
| 39 |
+
buffer_size: 2000
|
| 40 |
+
samples_per_epoch: 200000
|
| 41 |
+
hcp-train-subset:
|
| 42 |
+
type: arrow
|
| 43 |
+
root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/validation
|
| 44 |
+
split_range:
|
| 45 |
+
- 0
|
| 46 |
+
- 2000
|
| 47 |
+
shuffle: false
|
| 48 |
+
hcp-val:
|
| 49 |
+
type: arrow
|
| 50 |
+
root: s3://medarc/fmri-datasets/eval/hcpya-clips.${input_space}.arrow/test
|
| 51 |
+
split_range:
|
| 52 |
+
- 0
|
| 53 |
+
- 2000
|
| 54 |
+
shuffle: false
|
| 55 |
+
train_dataset: hcp-train
|
| 56 |
+
eval_datasets:
|
| 57 |
+
- hcp-train-subset
|
| 58 |
+
- hcp-val
|
| 59 |
+
val_dataset: null
|
| 60 |
+
clip_vmax: 3.0
|
| 61 |
+
normalize: frame
|
| 62 |
+
tr_scale: null
|
| 63 |
+
crop_scale: null
|
| 64 |
+
crop_aspect: null
|
| 65 |
+
gray_jitter: null
|
| 66 |
+
num_workers: 16
|
| 67 |
+
epochs: 100
|
| 68 |
+
batch_size: 32
|
| 69 |
+
accum_iter: 1
|
| 70 |
+
base_lr: 0.001
|
| 71 |
+
min_lr: 0.0
|
| 72 |
+
warmup_epochs: 5
|
| 73 |
+
weight_decay: 0.05
|
| 74 |
+
betas:
|
| 75 |
+
- 0.9
|
| 76 |
+
- 0.95
|
| 77 |
+
clip_grad: 1.0
|
| 78 |
+
amp: true
|
| 79 |
+
amp_dtype: float16
|
| 80 |
+
ckpt: null
|
| 81 |
+
resume: true
|
| 82 |
+
auto_resume: true
|
| 83 |
+
start_epoch: 0
|
| 84 |
+
max_checkpoints: 0
|
| 85 |
+
checkpoint_period: null
|
| 86 |
+
plot_period: 5
|
| 87 |
+
device: cuda
|
| 88 |
+
presend_cuda: false
|
| 89 |
+
seed: 5407
|
| 90 |
+
debug: false
|
| 91 |
+
wandb: true
|
| 92 |
+
wandb_entity: null
|
| 93 |
+
wandb_project: fMRI-foundation-model
|
| 94 |
+
rank: 0
|
| 95 |
+
world_size: 1
|
| 96 |
+
gpu: 0
|
| 97 |
+
distributed: true
|
| 98 |
+
dist_backend: nccl
|
| 99 |
+
in_chans: 1
|
| 100 |
+
img_size:
|
| 101 |
+
- 224
|
| 102 |
+
- 560
|
input_space_v3/flat_lr1e-3_7/pretrain/log.json
ADDED
|
@@ -0,0 +1,100 @@
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
|
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|
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|
|
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|
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|
|
| 1 |
+
{"epoch": 0, "train/lr": 1.2502400076802458e-05, "train/grad": 0.051318237952739, "train/loss": 0.9932481551551818, "eval/hcp-train-subset/loss": 0.9914839652276808, "eval/hcp-val/loss": 0.9902881326213959}
|
| 2 |
+
{"epoch": 1, "train/lr": 3.750320010240327e-05, "train/grad": 0.07872986304968596, "train/loss": 0.9886723026275634, "eval/hcp-train-subset/loss": 0.9890674179600131, "eval/hcp-val/loss": 0.9874959838005805}
|
| 3 |
+
{"epoch": 2, "train/lr": 6.250400012800409e-05, "train/grad": 0.09565139282256288, "train/loss": 0.9856002246379852, "eval/hcp-train-subset/loss": 0.9844473129318606, "eval/hcp-val/loss": 0.9813570062960347}
|
| 4 |
+
{"epoch": 3, "train/lr": 8.75048001536049e-05, "train/grad": 0.19644920042120922, "train/loss": 0.977650799741745, "eval/hcp-train-subset/loss": 0.9746499782608401, "eval/hcp-val/loss": 0.9712076783180237}
|
| 5 |
+
{"epoch": 4, "train/lr": 0.00011250559953918529, "train/grad": 0.220742986431518, "train/loss": 0.9484211838340759, "eval/hcp-train-subset/loss": 0.9266697581737272, "eval/hcp-val/loss": 0.9211686330456887}
|
| 6 |
+
{"epoch": 5, "train/lr": 0.00012498860637884563, "train/grad": 0.1659507494009602, "train/loss": 0.9057202558517456, "eval/hcp-train-subset/loss": 0.8871332493520552, "eval/hcp-val/loss": 0.8809960215322433}
|
| 7 |
+
{"epoch": 6, "train/lr": 0.0001249202705377922, "train/grad": 0.11131781063527918, "train/loss": 0.8760839134597779, "eval/hcp-train-subset/loss": 0.8716520494030368, "eval/hcp-val/loss": 0.8655036101418156}
|
| 8 |
+
{"epoch": 7, "train/lr": 0.0001247836790473516, "train/grad": 0.08860026354032179, "train/loss": 0.8643902563667297, "eval/hcp-train-subset/loss": 0.8638759841842036, "eval/hcp-val/loss": 0.8578131583429152}
|
| 9 |
+
{"epoch": 8, "train/lr": 0.000124578981268311, "train/grad": 0.07822065106169744, "train/loss": 0.8565729024600983, "eval/hcp-train-subset/loss": 0.8592404069439057, "eval/hcp-val/loss": 0.8533052359857867}
|
| 10 |
+
{"epoch": 9, "train/lr": 0.00012430640103468907, "train/grad": 0.07164085922982166, "train/loss": 0.8528463499069214, "eval/hcp-train-subset/loss": 0.8571089775331558, "eval/hcp-val/loss": 0.851263552904129}
|
| 11 |
+
{"epoch": 10, "train/lr": 0.00012396623640896796, "train/grad": 0.06758198433002414, "train/loss": 0.8518233715248108, "eval/hcp-train-subset/loss": 0.854705652882976, "eval/hcp-val/loss": 0.8491854821482012}
|
| 12 |
+
{"epoch": 11, "train/lr": 0.0001235588593561712, "train/grad": 0.0661553424492786, "train/loss": 0.8486806791877747, "eval/hcp-train-subset/loss": 0.8524173421244468, "eval/hcp-val/loss": 0.8475650414343803}
|
| 13 |
+
{"epoch": 12, "train/lr": 0.00012308471533712604, "train/grad": 0.06474030662233769, "train/loss": 0.8459527962875366, "eval/hcp-train-subset/loss": 0.8516986793087374, "eval/hcp-val/loss": 0.8472732718913786}
|
| 14 |
+
{"epoch": 13, "train/lr": 0.00012254432282135565, "train/grad": 0.06406116059286074, "train/loss": 0.8456400387763977, "eval/hcp-train-subset/loss": 0.8507142115023828, "eval/hcp-val/loss": 0.8450746411277402}
|
| 15 |
+
{"epoch": 14, "train/lr": 0.00012193827272014171, "train/grad": 0.06473639132927585, "train/loss": 0.842682253074646, "eval/hcp-train-subset/loss": 0.8501002144428992, "eval/hcp-val/loss": 0.8449677949951541}
|
| 16 |
+
{"epoch": 15, "train/lr": 0.00012126722774037197, "train/grad": 0.06478186274929958, "train/loss": 0.8420601556015015, "eval/hcp-train-subset/loss": 0.8488184242479263, "eval/hcp-val/loss": 0.843672776414502}
|
| 17 |
+
{"epoch": 16, "train/lr": 0.00012053192165988122, "train/grad": 0.06351993417172637, "train/loss": 0.8421802336788178, "eval/hcp-train-subset/loss": 0.8481352781095812, "eval/hcp-val/loss": 0.8428266067658702}
|
| 18 |
+
{"epoch": 17, "train/lr": 0.00011973315852507104, "train/grad": 0.06423509396343145, "train/loss": 0.8403414786815643, "eval/hcp-train-subset/loss": 0.847106070287766, "eval/hcp-val/loss": 0.8424779282462213}
|
| 19 |
+
{"epoch": 18, "train/lr": 0.00011887181177170142, "train/grad": 0.06450779241999842, "train/loss": 0.8401277018547058, "eval/hcp-train-subset/loss": 0.8463582434961873, "eval/hcp-val/loss": 0.8418605125719502}
|
| 20 |
+
{"epoch": 19, "train/lr": 0.00011794882326980209, "train/grad": 0.06530534589702804, "train/loss": 0.8364746505832672, "eval/hcp-train-subset/loss": 0.8462067092618635, "eval/hcp-val/loss": 0.8413706479534027}
|
| 21 |
+
{"epoch": 20, "train/lr": 0.00011696520229374954, "train/grad": 0.06409047377029381, "train/loss": 0.8392694521331787, "eval/hcp-train-subset/loss": 0.8460315802404957, "eval/hcp-val/loss": 0.8417887370432576}
|
| 22 |
+
{"epoch": 21, "train/lr": 0.00011592202441863837, "train/grad": 0.0657469117305833, "train/loss": 0.8364691058635711, "eval/hcp-train-subset/loss": 0.8454175254990978, "eval/hcp-val/loss": 0.8411511942263572}
|
| 23 |
+
{"epoch": 22, "train/lr": 0.00011482043034415979, "train/grad": 0.0666262399888712, "train/loss": 0.8351339110565186, "eval/hcp-train-subset/loss": 0.8448935006895373, "eval/hcp-val/loss": 0.8409005634246334}
|
| 24 |
+
{"epoch": 23, "train/lr": 0.00011366162464726024, "train/grad": 0.06970518596657604, "train/loss": 0.8336493396949768, "eval/hcp-train-subset/loss": 0.844740294641064, "eval/hcp-val/loss": 0.8406562795562129}
|
| 25 |
+
{"epoch": 24, "train/lr": 0.0001124468744649569, "train/grad": 0.06732999360207921, "train/loss": 0.8336801349353791, "eval/hcp-train-subset/loss": 0.8445381147246207, "eval/hcp-val/loss": 0.8399153236419924}
|
| 26 |
+
{"epoch": 25, "train/lr": 0.0001111775081087387, "train/grad": 0.06880465712131176, "train/loss": 0.8338369365596772, "eval/hcp-train-subset/loss": 0.8434914648532867, "eval/hcp-val/loss": 0.8394699548521349}
|
| 27 |
+
{"epoch": 26, "train/lr": 0.0001098549136120796, "train/grad": 0.07022664252425911, "train/loss": 0.8317651797962189, "eval/hcp-train-subset/loss": 0.8436133496222957, "eval/hcp-val/loss": 0.8394377885326263}
|
| 28 |
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|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (aabc_age patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: aabc_age
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic/eval_table.csv
ADDED
|
@@ -0,0 +1,203 @@
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|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,aabc_age,,0.005994842503189409,train,0.6889763779527559,0.02065305661633393,0.687379909938261,0.02080508549044507,0.6895820735066854,0.02059430079305293
|
| 3 |
+
flat_mae,patch,logistic,aabc_age,,0.005994842503189409,test,0.4807692307692308,0.0606534925115294,0.4527777777777777,0.06542637057474919,0.47046703296703296,0.060883559676262335
|
| 4 |
+
flat_mae,patch,logistic,aabc_age,1,0.005994842503189409,train,0.687007874015748,0.020434539036659743,0.6867954788427307,0.020609345313258583,0.6882496801812084,0.020457883724401698
|
| 5 |
+
flat_mae,patch,logistic,aabc_age,1,0.005994842503189409,test,0.5192307692307693,0.061332636562374414,0.4953655690951517,0.06162404814840793,0.5141941391941393,0.0607938293016322
|
| 6 |
+
flat_mae,patch,logistic,aabc_age,2,9.999999999999999e-05,train,0.5,0.020213236050910386,0.4821009041685569,0.02099379834975323,0.49866941956964955,0.020118342828020594
|
| 7 |
+
flat_mae,patch,logistic,aabc_age,2,9.999999999999999e-05,test,0.5,0.06426788425602295,0.482051282051282,0.06744818570988333,0.4965659340659341,0.06378453286452215
|
| 8 |
+
flat_mae,patch,logistic,aabc_age,3,0.005994842503189409,train,0.6830708661417323,0.02047007254622562,0.6825830423428505,0.02062381105446182,0.6835323530032279,0.020568158450846974
|
| 9 |
+
flat_mae,patch,logistic,aabc_age,3,0.005994842503189409,test,0.5769230769230769,0.06662684214845063,0.5734901169683778,0.06784693000164928,0.5753205128205129,0.06692645344384526
|
| 10 |
+
flat_mae,patch,logistic,aabc_age,4,0.005994842503189409,train,0.6850393700787402,0.020287206748133536,0.6837622678636504,0.020630218757926196,0.6856984420461498,0.0202824378134047
|
| 11 |
+
flat_mae,patch,logistic,aabc_age,4,0.005994842503189409,test,0.6153846153846154,0.06316423366273974,0.6108333333333333,0.06446921505143058,0.6167582417582418,0.063421628864998
|
| 12 |
+
flat_mae,patch,logistic,aabc_age,5,0.3593813663804626,train,0.9665354330708661,0.007999786871734799,0.9668496645510188,0.007919788100958003,0.9670488349389803,0.007849471503216836
|
| 13 |
+
flat_mae,patch,logistic,aabc_age,5,0.3593813663804626,test,0.4807692307692308,0.06385934757096527,0.479790008841733,0.06261315097768128,0.48466117216117216,0.06456400994206035
|
| 14 |
+
flat_mae,patch,logistic,aabc_age,6,0.005994842503189409,train,0.6830708661417323,0.020727408358280446,0.6820594412626817,0.021051424884050842,0.6840674621060283,0.020704247821594843
|
| 15 |
+
flat_mae,patch,logistic,aabc_age,6,0.005994842503189409,test,0.5769230769230769,0.06527251702891455,0.5764568764568765,0.0663760384022554,0.5753205128205128,0.06536058233548393
|
| 16 |
+
flat_mae,patch,logistic,aabc_age,7,0.046415888336127774,train,0.8405511811023622,0.015467018344118993,0.8405288306702872,0.015559334184914451,0.840900699861332,0.015450215166912107
|
| 17 |
+
flat_mae,patch,logistic,aabc_age,7,0.046415888336127774,test,0.5576923076923077,0.06264056382797326,0.545530303030303,0.06368234893628982,0.5487637362637363,0.06284930828256875
|
| 18 |
+
flat_mae,patch,logistic,aabc_age,8,0.046415888336127774,train,0.8366141732283464,0.016782449576901105,0.836312020043367,0.01697999949588474,0.8374035508996162,0.016760373879935064
|
| 19 |
+
flat_mae,patch,logistic,aabc_age,8,0.046415888336127774,test,0.5576923076923077,0.06039931227321155,0.5561904761904761,0.060722807756423074,0.565018315018315,0.06064624446892985
|
| 20 |
+
flat_mae,patch,logistic,aabc_age,9,0.005994842503189409,train,0.6791338582677166,0.01996289687231046,0.6778784244996917,0.02040625328920363,0.6811230434474068,0.019926473361866186
|
| 21 |
+
flat_mae,patch,logistic,aabc_age,9,0.005994842503189409,test,0.5,0.06345583890489716,0.4846253343916277,0.06896119249050346,0.4951923076923077,0.06336380441252033
|
| 22 |
+
flat_mae,patch,logistic,aabc_age,10,0.005994842503189409,train,0.6811023622047244,0.018608123508954917,0.6788581416173026,0.019061022179647103,0.6817337918521488,0.01863092706599777
|
| 23 |
+
flat_mae,patch,logistic,aabc_age,10,0.005994842503189409,test,0.46153846153846156,0.06505023093255471,0.46374458874458874,0.06508099399828349,0.4610805860805861,0.06504589386975812
|
| 24 |
+
flat_mae,patch,logistic,aabc_age,11,0.046415888336127774,train,0.8326771653543307,0.01629787120274337,0.8312389833723853,0.01672935736112472,0.832753831592151,0.01639205505337341
|
| 25 |
+
flat_mae,patch,logistic,aabc_age,11,0.046415888336127774,test,0.6346153846153846,0.06846776116922933,0.6394594988344988,0.06875650362612189,0.6334706959706959,0.06874947059909439
|
| 26 |
+
flat_mae,patch,logistic,aabc_age,12,0.005994842503189409,train,0.687007874015748,0.020856817111286174,0.6852547029619724,0.021107216783045214,0.687782178948923,0.020813835724029672
|
| 27 |
+
flat_mae,patch,logistic,aabc_age,12,0.005994842503189409,test,0.4423076923076923,0.06486530194525965,0.44277809218386466,0.06618035774577584,0.4420787545787546,0.06501361411029183
|
| 28 |
+
flat_mae,patch,logistic,aabc_age,13,0.005994842503189409,train,0.6830708661417323,0.020515324558901895,0.6809859527565068,0.02084628704043359,0.6834823663330066,0.02061285994192004
|
| 29 |
+
flat_mae,patch,logistic,aabc_age,13,0.005994842503189409,test,0.4807692307692308,0.07029787635567908,0.485,0.06985421927982287,0.48054029304029305,0.07052476709338637
|
| 30 |
+
flat_mae,patch,logistic,aabc_age,14,0.005994842503189409,train,0.6850393700787402,0.01994315189378586,0.6830282774493899,0.02023502759257582,0.6857660499166649,0.01996095751191026
|
| 31 |
+
flat_mae,patch,logistic,aabc_age,14,0.005994842503189409,test,0.6730769230769231,0.06212474328164787,0.6791213768115942,0.06246465388819099,0.674908424908425,0.062104241066679014
|
| 32 |
+
flat_mae,patch,logistic,aabc_age,15,0.005994842503189409,train,0.6850393700787402,0.02013335901443895,0.68303854581099,0.020483457179449084,0.685548482035486,0.020175041038575776
|
| 33 |
+
flat_mae,patch,logistic,aabc_age,15,0.005994842503189409,test,0.5192307692307693,0.06735602660138607,0.5231318681318682,0.06638132167128738,0.5203754578754579,0.067456051283033
|
| 34 |
+
flat_mae,patch,logistic,aabc_age,16,0.000774263682681127,train,0.594488188976378,0.0205466835969315,0.587982440945846,0.020978911833052016,0.593329788315442,0.0204781041233661
|
| 35 |
+
flat_mae,patch,logistic,aabc_age,16,0.000774263682681127,test,0.38461538461538464,0.06562733230569509,0.39421121085880817,0.06649951395623602,0.38255494505494503,0.06551601577068833
|
| 36 |
+
flat_mae,patch,logistic,aabc_age,17,0.000774263682681127,train,0.5885826771653543,0.021004849842534798,0.5844359649514289,0.021582415007604123,0.5890043230678055,0.020962036950635986
|
| 37 |
+
flat_mae,patch,logistic,aabc_age,17,0.000774263682681127,test,0.4423076923076923,0.06051230986312136,0.41347035890379547,0.06417034414980266,0.440018315018315,0.060186586561582114
|
| 38 |
+
flat_mae,patch,logistic,aabc_age,18,0.046415888336127774,train,0.84251968503937,0.014679692826716896,0.8415588899165541,0.014816983994483527,0.8428668422233687,0.014664823496953673
|
| 39 |
+
flat_mae,patch,logistic,aabc_age,18,0.046415888336127774,test,0.38461538461538464,0.07016776177775239,0.3899777034559643,0.06982302459039914,0.3855311355311355,0.07032144728063312
|
| 40 |
+
flat_mae,patch,logistic,aabc_age,19,0.046415888336127774,train,0.8405511811023622,0.01593504070754277,0.840393606492023,0.016041325818463473,0.8412682277531747,0.015877190273356226
|
| 41 |
+
flat_mae,patch,logistic,aabc_age,19,0.046415888336127774,test,0.4807692307692308,0.06942889385389305,0.48621794871794877,0.06991777253725229,0.4821428571428571,0.06941035015728743
|
| 42 |
+
flat_mae,patch,logistic,aabc_age,20,0.005994842503189409,train,0.6948818897637795,0.02058900191398309,0.6939328224012324,0.02093058989114876,0.6956115059964825,0.020502611350692843
|
| 43 |
+
flat_mae,patch,logistic,aabc_age,20,0.005994842503189409,test,0.5384615384615384,0.05847039406612991,0.5092982456140351,0.06166627656830343,0.532051282051282,0.05813997893472555
|
| 44 |
+
flat_mae,patch,logistic,aabc_age,21,0.046415888336127774,train,0.8248031496062992,0.016435239661599255,0.8240734369855364,0.016616342410140178,0.8250068566847402,0.01640886494498677
|
| 45 |
+
flat_mae,patch,logistic,aabc_age,21,0.046415888336127774,test,0.4807692307692308,0.05839585125492206,0.45812007067266536,0.05732381041700727,0.48443223443223443,0.05905999042986378
|
| 46 |
+
flat_mae,patch,logistic,aabc_age,22,0.046415888336127774,train,0.8346456692913385,0.015785476643501967,0.8343960965840966,0.01593222458020457,0.8337144866884418,0.015882792509937054
|
| 47 |
+
flat_mae,patch,logistic,aabc_age,22,0.046415888336127774,test,0.4230769230769231,0.0682310185582124,0.4292807417807418,0.06853322937847145,0.42147435897435903,0.06838049696130587
|
| 48 |
+
flat_mae,patch,logistic,aabc_age,23,0.3593813663804626,train,0.9783464566929134,0.006214808544733343,0.9782320076081572,0.006242628880154386,0.9782253509375917,0.0062416914789318895
|
| 49 |
+
flat_mae,patch,logistic,aabc_age,23,0.3593813663804626,test,0.5192307692307693,0.0667424064337513,0.5195156695156695,0.06598791753476108,0.5203754578754579,0.06726883148429068
|
| 50 |
+
flat_mae,patch,logistic,aabc_age,24,0.046415888336127774,train,0.8346456692913385,0.01658946692696381,0.8346910180911075,0.016687121226220174,0.834267216991536,0.01662025677385754
|
| 51 |
+
flat_mae,patch,logistic,aabc_age,24,0.046415888336127774,test,0.4807692307692308,0.06528231816570822,0.4767032967032967,0.06575228804629657,0.4832875457875458,0.06561587549473874
|
| 52 |
+
flat_mae,patch,logistic,aabc_age,25,0.005994842503189409,train,0.7047244094488189,0.01909709536547647,0.7036964687369998,0.019151754924884357,0.7052070287251937,0.019012529558686934
|
| 53 |
+
flat_mae,patch,logistic,aabc_age,25,0.005994842503189409,test,0.40384615384615385,0.06476698104991097,0.4009316770186335,0.06514863824273062,0.40041208791208793,0.06462133425653956
|
| 54 |
+
flat_mae,patch,logistic,aabc_age,26,0.046415888336127774,train,0.8267716535433071,0.015821513601441083,0.8265170704181626,0.01596893839926745,0.826687823295083,0.01591901563499165
|
| 55 |
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| 81 |
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| 192 |
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| 193 |
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| 194 |
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| 196 |
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| 197 |
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|
| 200 |
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flat_mae,patch,logistic,aabc_age,99,9.999999999999999e-05,test,0.46153846153846156,0.0673283045361754,0.442821223316913,0.06736273303175964,0.4578754578754579,0.06668862364462898
|
| 202 |
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flat_mae,patch,logistic,aabc_age,100,0.005994842503189409,train,0.6948818897637795,0.02063010571234484,0.6938816823247941,0.020917645745517546,0.6971844678349562,0.02056839263261899
|
| 203 |
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flat_mae,patch,logistic,aabc_age,100,0.005994842503189409,test,0.5384615384615384,0.06770160556773896,0.5349777034559643,0.06872894092352676,0.5368589743589743,0.06772043634222613
|
input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic/log.txt
ADDED
|
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:56:57
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (aabc_age patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: aabc_age
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/aabc_age__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: aabc_age (flat)
|
| 70 |
+
train (n=455):
|
| 71 |
+
HFDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 471
|
| 75 |
+
}),
|
| 76 |
+
labels=[0 1 2 3],
|
| 77 |
+
counts=[110 127 109 109]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=53):
|
| 81 |
+
HFDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 58
|
| 85 |
+
}),
|
| 86 |
+
labels=[0 1 2 3],
|
| 87 |
+
counts=[14 13 12 14]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=52):
|
| 91 |
+
HFDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 55
|
| 95 |
+
}),
|
| 96 |
+
labels=[0 1 2 3],
|
| 97 |
+
counts=[13 13 12 14]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/228] eta: 0:18:29 time: 4.8683 data: 4.1498 max mem: 3205
|
| 102 |
+
extract (train) [ 20/228] eta: 0:01:38 time: 0.2516 data: 0.0977 max mem: 3393
|
| 103 |
+
extract (train) [ 40/228] eta: 0:01:04 time: 0.2049 data: 0.0650 max mem: 3393
|
| 104 |
+
extract (train) [ 60/228] eta: 0:00:49 time: 0.2051 data: 0.0699 max mem: 3393
|
| 105 |
+
extract (train) [ 80/228] eta: 0:00:40 time: 0.2096 data: 0.0693 max mem: 3393
|
| 106 |
+
extract (train) [100/228] eta: 0:00:33 time: 0.2099 data: 0.0698 max mem: 3393
|
| 107 |
+
extract (train) [120/228] eta: 0:00:27 time: 0.2057 data: 0.0682 max mem: 3393
|
| 108 |
+
extract (train) [140/228] eta: 0:00:21 time: 0.2156 data: 0.0744 max mem: 3393
|
| 109 |
+
extract (train) [160/228] eta: 0:00:16 time: 0.2170 data: 0.0733 max mem: 3393
|
| 110 |
+
extract (train) [180/228] eta: 0:00:11 time: 0.2125 data: 0.0722 max mem: 3393
|
| 111 |
+
extract (train) [200/228] eta: 0:00:06 time: 0.1982 data: 0.0629 max mem: 3393
|
| 112 |
+
extract (train) [220/228] eta: 0:00:01 time: 0.1879 data: 0.0577 max mem: 3393
|
| 113 |
+
extract (train) [227/228] eta: 0:00:00 time: 0.1792 data: 0.0551 max mem: 3393
|
| 114 |
+
extract (train) Total time: 0:00:52 (0.2318 s / it)
|
| 115 |
+
extract (validation) [ 0/27] eta: 0:01:54 time: 4.2281 data: 4.0924 max mem: 3393
|
| 116 |
+
extract (validation) [20/27] eta: 0:00:02 time: 0.1919 data: 0.0605 max mem: 3393
|
| 117 |
+
extract (validation) [26/27] eta: 0:00:00 time: 0.1691 data: 0.0510 max mem: 3393
|
| 118 |
+
extract (validation) Total time: 0:00:09 (0.3475 s / it)
|
| 119 |
+
extract (test) [ 0/26] eta: 0:01:51 time: 4.2921 data: 4.1664 max mem: 3393
|
| 120 |
+
extract (test) [20/26] eta: 0:00:02 time: 0.1728 data: 0.0504 max mem: 3393
|
| 121 |
+
extract (test) [25/26] eta: 0:00:00 time: 0.1703 data: 0.0498 max mem: 3393
|
| 122 |
+
extract (test) Total time: 0:00:08 (0.3444 s / it)
|
| 123 |
+
feature extraction time: 0:01:11
|
| 124 |
+
train features: (455, 768)
|
| 125 |
+
validation features: (53, 768)
|
| 126 |
+
test features: (52, 768)
|
| 127 |
+
evaluating fixed splits
|
| 128 |
+
eval results (fixed splits):
|
| 129 |
+
|
| 130 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 131 |
+
|:---------|:-------|:---------|:----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 132 |
+
| flat_mae | patch | logistic | aabc_age | | 0.0059948 | train | 0.68898 | 0.020653 | 0.68738 | 0.020805 | 0.68958 | 0.020594 |
|
| 133 |
+
| flat_mae | patch | logistic | aabc_age | | 0.0059948 | test | 0.48077 | 0.060653 | 0.45278 | 0.065426 | 0.47047 | 0.060884 |
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
evaluating random splits (n=100)
|
| 137 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.061332636562374414, "f1": 0.4953655690951517, "f1_std": 0.06162404814840793, "bacc": 0.5141941391941393, "bacc_std": 0.0607938293016322}
|
| 138 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 2, "C": 9.999999999999999e-05, "split": "test", "acc": 0.5, "acc_std": 0.06426788425602295, "f1": 0.482051282051282, "f1_std": 0.06744818570988333, "bacc": 0.4965659340659341, "bacc_std": 0.06378453286452215}
|
| 139 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06662684214845063, "f1": 0.5734901169683778, "f1_std": 0.06784693000164928, "bacc": 0.5753205128205129, "bacc_std": 0.06692645344384526}
|
| 140 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.06316423366273974, "f1": 0.6108333333333333, "f1_std": 0.06446921505143058, "bacc": 0.6167582417582418, "bacc_std": 0.063421628864998}
|
| 141 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06385934757096527, "f1": 0.479790008841733, "f1_std": 0.06261315097768128, "bacc": 0.48466117216117216, "bacc_std": 0.06456400994206035}
|
| 142 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06527251702891455, "f1": 0.5764568764568765, "f1_std": 0.0663760384022554, "bacc": 0.5753205128205128, "bacc_std": 0.06536058233548393}
|
| 143 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 7, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06264056382797326, "f1": 0.545530303030303, "f1_std": 0.06368234893628982, "bacc": 0.5487637362637363, "bacc_std": 0.06284930828256875}
|
| 144 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06039931227321155, "f1": 0.5561904761904761, "f1_std": 0.060722807756423074, "bacc": 0.565018315018315, "bacc_std": 0.06064624446892985}
|
| 145 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.5, "acc_std": 0.06345583890489716, "f1": 0.4846253343916277, "f1_std": 0.06896119249050346, "bacc": 0.4951923076923077, "bacc_std": 0.06336380441252033}
|
| 146 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 10, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06505023093255471, "f1": 0.46374458874458874, "f1_std": 0.06508099399828349, "bacc": 0.4610805860805861, "bacc_std": 0.06504589386975812}
|
| 147 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.6346153846153846, "acc_std": 0.06846776116922933, "f1": 0.6394594988344988, "f1_std": 0.06875650362612189, "bacc": 0.6334706959706959, "bacc_std": 0.06874947059909439}
|
| 148 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 12, "C": 0.005994842503189409, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06486530194525965, "f1": 0.44277809218386466, "f1_std": 0.06618035774577584, "bacc": 0.4420787545787546, "bacc_std": 0.06501361411029183}
|
| 149 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.07029787635567908, "f1": 0.485, "f1_std": 0.06985421927982287, "bacc": 0.48054029304029305, "bacc_std": 0.07052476709338637}
|
| 150 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 14, "C": 0.005994842503189409, "split": "test", "acc": 0.6730769230769231, "acc_std": 0.06212474328164787, "f1": 0.6791213768115942, "f1_std": 0.06246465388819099, "bacc": 0.674908424908425, "bacc_std": 0.062104241066679014}
|
| 151 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 15, "C": 0.005994842503189409, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06735602660138607, "f1": 0.5231318681318682, "f1_std": 0.06638132167128738, "bacc": 0.5203754578754579, "bacc_std": 0.067456051283033}
|
| 152 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 16, "C": 0.000774263682681127, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.06562733230569509, "f1": 0.39421121085880817, "f1_std": 0.06649951395623602, "bacc": 0.38255494505494503, "bacc_std": 0.06551601577068833}
|
| 153 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 17, "C": 0.000774263682681127, "split": "test", "acc": 0.4423076923076923, "acc_std": 0.06051230986312136, "f1": 0.41347035890379547, "f1_std": 0.06417034414980266, "bacc": 0.440018315018315, "bacc_std": 0.060186586561582114}
|
| 154 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 18, "C": 0.046415888336127774, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.07016776177775239, "f1": 0.3899777034559643, "f1_std": 0.06982302459039914, "bacc": 0.3855311355311355, "bacc_std": 0.07032144728063312}
|
| 155 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06942889385389305, "f1": 0.48621794871794877, "f1_std": 0.06991777253725229, "bacc": 0.4821428571428571, "bacc_std": 0.06941035015728743}
|
| 156 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05847039406612991, "f1": 0.5092982456140351, "f1_std": 0.06166627656830343, "bacc": 0.532051282051282, "bacc_std": 0.05813997893472555}
|
| 157 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.05839585125492206, "f1": 0.45812007067266536, "f1_std": 0.05732381041700727, "bacc": 0.48443223443223443, "bacc_std": 0.05905999042986378}
|
| 158 |
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| 209 |
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{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 73, "C": 0.005994842503189409, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06548041624213001, "f1": 0.559886570756136, "f1_std": 0.06509113018413572, "bacc": 0.5590659340659341, "bacc_std": 0.06563889772967434}
|
| 210 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 74, "C": 0.005994842503189409, "split": "test", "acc": 0.5961538461538461, "acc_std": 0.06708727645926531, "f1": 0.5956102863997601, "f1_std": 0.06936260899154212, "bacc": 0.5931776556776557, "bacc_std": 0.06719706571812696}
|
| 211 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 75, "C": 0.000774263682681127, "split": "test", "acc": 0.5, "acc_std": 0.06125863059590221, "f1": 0.462134109916368, "f1_std": 0.05911597729655413, "bacc": 0.49061355311355315, "bacc_std": 0.060178679062129654}
|
| 212 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 76, "C": 0.046415888336127774, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06043328513876042, "f1": 0.49837662337662336, "f1_std": 0.06480426052276629, "bacc": 0.5201465201465201, "bacc_std": 0.060601744274187234}
|
| 213 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 77, "C": 0.005994842503189409, "split": "test", "acc": 0.5, "acc_std": 0.06791783889551173, "f1": 0.49898711358606407, "f1_std": 0.06884766133802536, "bacc": 0.5011446886446886, "bacc_std": 0.06807104882460052}
|
| 214 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 78, "C": 0.046415888336127774, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06432419506691021, "f1": 0.540383771850107, "f1_std": 0.06725953279848086, "bacc": 0.551510989010989, "bacc_std": 0.06420363989672136}
|
| 215 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 79, "C": 0.046415888336127774, "split": "test", "acc": 0.40384615384615385, "acc_std": 0.06325185639392814, "f1": 0.38942259075740726, "f1_std": 0.06261514330336873, "bacc": 0.4001831501831502, "bacc_std": 0.06268412360577143}
|
| 216 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 80, "C": 0.046415888336127774, "split": "test", "acc": 0.4230769230769231, "acc_std": 0.061664426367963085, "f1": 0.414686887325568, "f1_std": 0.06039674941143165, "bacc": 0.4237637362637363, "bacc_std": 0.061912799026014965}
|
| 217 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 81, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.06777272321394735, "f1": 0.49879629629629635, "f1_std": 0.06863163377111266, "bacc": 0.4983974358974359, "bacc_std": 0.06776373050146516}
|
| 218 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 82, "C": 0.005994842503189409, "split": "test", "acc": 0.5576923076923077, "acc_std": 0.06758622588215799, "f1": 0.5541229603729604, "f1_std": 0.0692384158062655, "bacc": 0.5560897435897436, "bacc_std": 0.0678054839808067}
|
| 219 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 83, "C": 0.005994842503189409, "split": "test", "acc": 0.5, "acc_std": 0.06257520918649595, "f1": 0.4833333333333333, "f1_std": 0.060553574106180966, "bacc": 0.49496336996337, "bacc_std": 0.06185649387824937}
|
| 220 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 84, "C": 0.046415888336127774, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06464610075785907, "f1": 0.5693035426731079, "f1_std": 0.06637580347948843, "bacc": 0.5753205128205128, "bacc_std": 0.06486475749886551}
|
| 221 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 85, "C": 0.000774263682681127, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.055163702048254874, "f1": 0.44474467418546365, "f1_std": 0.0590062101230067, "bacc": 0.47435897435897434, "bacc_std": 0.05457489956723591}
|
| 222 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 86, "C": 0.046415888336127774, "split": "test", "acc": 0.6346153846153846, "acc_std": 0.051460489238470346, "f1": 0.5747983870967742, "f1_std": 0.05206738801517167, "bacc": 0.6197344322344321, "bacc_std": 0.051228278475952085}
|
| 223 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 87, "C": 0.005994842503189409, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06455005855281308, "f1": 0.57158267020336, "f1_std": 0.06498803568042019, "bacc": 0.5842490842490842, "bacc_std": 0.06459063692499249}
|
| 224 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 88, "C": 0.005994842503189409, "split": "test", "acc": 0.5192307692307693, "acc_std": 0.06482607591350255, "f1": 0.50679579041648, "f1_std": 0.06734518188386604, "bacc": 0.5217490842490842, "bacc_std": 0.06505947044440571}
|
| 225 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 89, "C": 9.999999999999999e-05, "split": "test", "acc": 0.38461538461538464, "acc_std": 0.062084822202604355, "f1": 0.3565785543608124, "f1_std": 0.059805049714273965, "bacc": 0.3807234432234432, "bacc_std": 0.06120552662864557}
|
| 226 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 90, "C": 0.005994842503189409, "split": "test", "acc": 0.5769230769230769, "acc_std": 0.06040979386876165, "f1": 0.5642578668894458, "f1_std": 0.06307373551994835, "bacc": 0.575091575091575, "bacc_std": 0.060365873405650285}
|
| 227 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 91, "C": 9.999999999999999e-05, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06024274122101753, "f1": 0.42892695539754366, "f1_std": 0.06299844125799782, "bacc": 0.475503663003663, "bacc_std": 0.059500644018211785}
|
| 228 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 92, "C": 0.3593813663804626, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.06523625702605286, "f1": 0.4812250712250712, "f1_std": 0.0649251158600009, "bacc": 0.4832875457875458, "bacc_std": 0.06569814051208371}
|
| 229 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 93, "C": 0.046415888336127774, "split": "test", "acc": 0.4807692307692308, "acc_std": 0.0620206469725008, "f1": 0.47959549071618035, "f1_std": 0.059365989164830775, "bacc": 0.48305860805860806, "bacc_std": 0.06250748147871822}
|
| 230 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 94, "C": 0.000774263682681127, "split": "test", "acc": 0.34615384615384615, "acc_std": 0.060059376438861334, "f1": 0.3364514712340799, "f1_std": 0.060762272836028246, "bacc": 0.34386446886446886, "bacc_std": 0.05956647214424661}
|
| 231 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 95, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.06206772403483073, "f1": 0.4887820512820512, "f1_std": 0.05524209646362442, "bacc": 0.49061355311355315, "bacc_std": 0.06115825598413073}
|
| 232 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 96, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.0705181163512456, "f1": 0.5423326673326674, "f1_std": 0.06983880488831903, "bacc": 0.5398351648351648, "bacc_std": 0.0706716181601235}
|
| 233 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.5, "acc_std": 0.0652915514166307, "f1": 0.4934116809116809, "f1_std": 0.0635612869705407, "bacc": 0.4965659340659341, "bacc_std": 0.06495351446312596}
|
| 234 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.06022417441054674, "f1": 0.4330026455026455, "f1_std": 0.06832216125619005, "bacc": 0.4608516483516484, "bacc_std": 0.06042759544820207}
|
| 235 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 99, "C": 9.999999999999999e-05, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.0673283045361754, "f1": 0.442821223316913, "f1_std": 0.06736273303175964, "bacc": 0.4578754578754579, "bacc_std": 0.06668862364462898}
|
| 236 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_age", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06770160556773896, "f1": 0.5349777034559643, "f1_std": 0.06872894092352676, "bacc": 0.5368589743589743, "bacc_std": 0.06772043634222613}
|
| 237 |
+
eval results (random splits):
|
| 238 |
+
|
| 239 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 240 |
+
|:---------|:-------|:---------|:----------|:--------|-----------:|--------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 241 |
+
| flat_mae | patch | logistic | aabc_age | train | 100 | 0.30478 | 2.1811 | 0.74069 | 0.11782 | 0.7384 | 0.12096 | 0.74105 | 0.11801 |
|
| 242 |
+
| flat_mae | patch | logistic | aabc_age | test | 100 | 0.30478 | 2.1811 | 0.50231 | 0.064467 | 0.49459 | 0.064819 | 0.50076 | 0.064435 |
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
done! total time: 0:05:49
|
input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
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|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (aabc_sex patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: aabc_sex
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/eval_table.csv
ADDED
|
@@ -0,0 +1,203 @@
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|
|
| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,aabc_sex,,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 3 |
+
flat_mae,patch,logistic,aabc_sex,,166.81005372000556,test,0.8909090909090909,0.04128027163726373,0.8863636363636364,0.04342816104978585,0.8863636363636364,0.04420472397460774
|
| 4 |
+
flat_mae,patch,logistic,aabc_sex,1,0.046415888336127774,train,0.943289224952741,0.010692186956412181,0.9415598762704375,0.01107721377283634,0.9394252469298632,0.01152513306070632
|
| 5 |
+
flat_mae,patch,logistic,aabc_sex,1,0.046415888336127774,test,0.8363636363636363,0.050454843569962826,0.8354935194416749,0.0503234195980057,0.8471467391304348,0.047972461283406154
|
| 6 |
+
flat_mae,patch,logistic,aabc_sex,2,0.3593813663804626,train,0.9886578449905482,0.004663966223064355,0.9883572497579012,0.004792900002579047,0.98776341627832,0.005034349323642883
|
| 7 |
+
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| 174 |
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flat_mae,patch,logistic,aabc_sex,86,0.3593813663804626,train,0.9848771266540642,0.005468858407105781,0.9844763330105349,0.0056216570301282414,0.9838872768838478,0.005893595391906561
|
| 175 |
+
flat_mae,patch,logistic,aabc_sex,86,0.3593813663804626,test,0.9090909090909091,0.03964155095330494,0.905982905982906,0.0414572076291978,0.9035326086956521,0.04245339620666587
|
| 176 |
+
flat_mae,patch,logistic,aabc_sex,87,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 177 |
+
flat_mae,patch,logistic,aabc_sex,87,166.81005372000556,test,0.8909090909090909,0.041706078990878394,0.8863636363636364,0.04425388863669605,0.8817934782608696,0.04510999038460782
|
| 178 |
+
flat_mae,patch,logistic,aabc_sex,88,0.3593813663804626,train,0.9886578449905482,0.0047214058045590285,0.9883715818165831,0.004841978409863608,0.9883715818165831,0.0049211909166970175
|
| 179 |
+
flat_mae,patch,logistic,aabc_sex,88,0.3593813663804626,test,0.8727272727272727,0.045419098906237056,0.8663658451926415,0.04881090662111341,0.8600543478260869,0.049637871696698074
|
| 180 |
+
flat_mae,patch,logistic,aabc_sex,89,0.3593813663804626,train,0.9886578449905482,0.00476829226736829,0.9883715818165831,0.004890310892423709,0.9883715818165831,0.004993957928843414
|
| 181 |
+
flat_mae,patch,logistic,aabc_sex,89,0.3593813663804626,test,0.8545454545454545,0.04504212903970848,0.8428571428571429,0.05208508067669945,0.8322010869565217,0.051709750486285004
|
| 182 |
+
flat_mae,patch,logistic,aabc_sex,90,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 183 |
+
flat_mae,patch,logistic,aabc_sex,90,166.81005372000556,test,0.8545454545454545,0.04739691380265731,0.8505434782608696,0.04900808806136071,0.8505434782608696,0.049338549629531304
|
| 184 |
+
flat_mae,patch,logistic,aabc_sex,91,0.3593813663804626,train,0.9867674858223062,0.004754651281602628,0.9864417081324122,0.0048709737818260455,0.9867375948884786,0.0048720288238841
|
| 185 |
+
flat_mae,patch,logistic,aabc_sex,91,0.3593813663804626,test,0.8909090909090909,0.041983294591937254,0.8879076086956521,0.043238161186696406,0.8879076086956521,0.04318645230496523
|
| 186 |
+
flat_mae,patch,logistic,aabc_sex,92,0.046415888336127774,train,0.9395085066162571,0.010915830853586755,0.9376638680217999,0.011313933407814137,0.9355491075353908,0.01173849692812982
|
| 187 |
+
flat_mae,patch,logistic,aabc_sex,92,0.046415888336127774,test,0.8909090909090909,0.0402388077966105,0.884453781512605,0.04456740794016554,0.8756793478260869,0.04575091730808843
|
| 188 |
+
flat_mae,patch,logistic,aabc_sex,93,0.046415888336127774,train,0.9489603024574669,0.009972273696996688,0.9475747398557507,0.010273590965403248,0.9467598698672295,0.01054434016332707
|
| 189 |
+
flat_mae,patch,logistic,aabc_sex,93,0.046415888336127774,test,0.8909090909090909,0.03942520903480432,0.8821428571428571,0.045983332111083505,0.8695652173913043,0.04713883688943995
|
| 190 |
+
flat_mae,patch,logistic,aabc_sex,94,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 191 |
+
flat_mae,patch,logistic,aabc_sex,94,166.81005372000556,test,0.9090909090909091,0.03664031108893885,0.905982905982906,0.03822593201944595,0.9035326086956521,0.03887950995074301
|
| 192 |
+
flat_mae,patch,logistic,aabc_sex,95,0.005994842503189409,train,0.9054820415879017,0.012627926939591905,0.9021935273932079,0.013189721641215527,0.8988393563703508,0.013585482886672573
|
| 193 |
+
flat_mae,patch,logistic,aabc_sex,95,0.005994842503189409,test,0.9090909090909091,0.038783126602827026,0.9071259709557582,0.039487743078217626,0.9096467391304348,0.038921795264619835
|
| 194 |
+
flat_mae,patch,logistic,aabc_sex,96,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 195 |
+
flat_mae,patch,logistic,aabc_sex,96,21.54434690031882,test,0.8727272727272727,0.0451596397001929,0.8683760683760684,0.046762322384120994,0.8661684782608696,0.0467904242323518
|
| 196 |
+
flat_mae,patch,logistic,aabc_sex,97,0.3593813663804626,train,0.9886578449905482,0.004626036035811461,0.9883715818165831,0.004741352563298169,0.9883715818165831,0.004758463947421513
|
| 197 |
+
flat_mae,patch,logistic,aabc_sex,97,0.3593813663804626,test,0.8545454545454545,0.04486329648945588,0.8505434782608696,0.046599284861006986,0.8505434782608696,0.047208813289693834
|
| 198 |
+
flat_mae,patch,logistic,aabc_sex,98,0.046415888336127774,train,0.9376181474480151,0.010087305112683779,0.935584214313389,0.010501392838807607,0.93269878953076,0.010955392926182911
|
| 199 |
+
flat_mae,patch,logistic,aabc_sex,98,0.046415888336127774,test,0.8727272727272727,0.04288005180704899,0.8663658451926415,0.04633913548983254,0.8600543478260869,0.047210798069724655
|
| 200 |
+
flat_mae,patch,logistic,aabc_sex,99,0.005994842503189409,train,0.9073724007561437,0.013271268661086747,0.904218013856813,0.01382116943138742,0.9010815088367186,0.014136483188109644
|
| 201 |
+
flat_mae,patch,logistic,aabc_sex,99,0.005994842503189409,test,0.8727272727272727,0.04426628663250854,0.8663658451926415,0.048245934766743444,0.8600543478260869,0.04913660330949244
|
| 202 |
+
flat_mae,patch,logistic,aabc_sex,100,0.046415888336127774,train,0.941398865784499,0.010657935518183493,0.9398080346491953,0.010979056278522454,0.9390075910782849,0.011266383859395861
|
| 203 |
+
flat_mae,patch,logistic,aabc_sex,100,0.046415888336127774,test,0.8727272727272727,0.04376156281676065,0.8711943793911007,0.043898138508973145,0.8783967391304348,0.04220448266227801
|
input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic/log.txt
ADDED
|
@@ -0,0 +1,245 @@
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:57:19
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (aabc_sex patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: aabc_sex
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/aabc_sex__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: aabc_sex (flat)
|
| 70 |
+
train (n=471):
|
| 71 |
+
HFDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 471
|
| 75 |
+
}),
|
| 76 |
+
labels=[0 1],
|
| 77 |
+
counts=[269 202]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=58):
|
| 81 |
+
HFDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 58
|
| 85 |
+
}),
|
| 86 |
+
labels=[0 1],
|
| 87 |
+
counts=[36 22]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=55):
|
| 91 |
+
HFDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'segment', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 55
|
| 95 |
+
}),
|
| 96 |
+
labels=[0 1],
|
| 97 |
+
counts=[33 22]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/236] eta: 0:21:34 time: 5.4861 data: 4.5806 max mem: 3205
|
| 102 |
+
extract (train) [ 20/236] eta: 0:01:51 time: 0.2677 data: 0.1025 max mem: 3393
|
| 103 |
+
extract (train) [ 40/236] eta: 0:01:12 time: 0.2195 data: 0.0764 max mem: 3393
|
| 104 |
+
extract (train) [ 60/236] eta: 0:00:56 time: 0.2224 data: 0.0781 max mem: 3393
|
| 105 |
+
extract (train) [ 80/236] eta: 0:00:46 time: 0.2233 data: 0.0795 max mem: 3393
|
| 106 |
+
extract (train) [100/236] eta: 0:00:38 time: 0.2122 data: 0.0723 max mem: 3393
|
| 107 |
+
extract (train) [120/236] eta: 0:00:31 time: 0.2251 data: 0.0794 max mem: 3393
|
| 108 |
+
extract (train) [140/236] eta: 0:00:25 time: 0.2199 data: 0.0782 max mem: 3393
|
| 109 |
+
extract (train) [160/236] eta: 0:00:19 time: 0.2399 data: 0.0842 max mem: 3393
|
| 110 |
+
extract (train) [180/236] eta: 0:00:14 time: 0.2118 data: 0.0698 max mem: 3393
|
| 111 |
+
extract (train) [200/236] eta: 0:00:09 time: 0.2256 data: 0.0777 max mem: 3393
|
| 112 |
+
extract (train) [220/236] eta: 0:00:03 time: 0.1927 data: 0.0595 max mem: 3393
|
| 113 |
+
extract (train) [235/236] eta: 0:00:00 time: 0.1866 data: 0.0577 max mem: 3393
|
| 114 |
+
extract (train) Total time: 0:00:58 (0.2460 s / it)
|
| 115 |
+
extract (validation) [ 0/29] eta: 0:02:15 time: 4.6875 data: 4.5393 max mem: 3393
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extract (validation) [20/29] eta: 0:00:03 time: 0.2087 data: 0.0660 max mem: 3393
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extract (validation) [28/29] eta: 0:00:00 time: 0.1895 data: 0.0591 max mem: 3393
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| 118 |
+
extract (validation) Total time: 0:00:10 (0.3773 s / it)
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+
extract (test) [ 0/28] eta: 0:02:12 time: 4.7454 data: 4.6016 max mem: 3393
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+
extract (test) [20/28] eta: 0:00:03 time: 0.1922 data: 0.0588 max mem: 3393
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+
extract (test) [27/28] eta: 0:00:00 time: 0.1826 data: 0.0568 max mem: 3393
|
| 122 |
+
extract (test) Total time: 0:00:10 (0.3679 s / it)
|
| 123 |
+
feature extraction time: 0:01:19
|
| 124 |
+
train features: (471, 768)
|
| 125 |
+
validation features: (58, 768)
|
| 126 |
+
test features: (55, 768)
|
| 127 |
+
evaluating fixed splits
|
| 128 |
+
eval results (fixed splits):
|
| 129 |
+
|
| 130 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 131 |
+
|:---------|:-------|:---------|:----------|:--------|-------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 132 |
+
| flat_mae | patch | logistic | aabc_sex | | 166.81 | train | 1 | 0 | 1 | 0 | 1 | 0 |
|
| 133 |
+
| flat_mae | patch | logistic | aabc_sex | | 166.81 | test | 0.89091 | 0.04128 | 0.88636 | 0.043428 | 0.88636 | 0.044205 |
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
evaluating random splits (n=100)
|
| 137 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 1, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.050454843569962826, "f1": 0.8354935194416749, "f1_std": 0.0503234195980057, "bacc": 0.8471467391304348, "bacc_std": 0.047972461283406154}
|
| 138 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 2, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03836146482436606, "f1": 0.9071259709557582, "f1_std": 0.039130250560899714, "bacc": 0.9096467391304348, "bacc_std": 0.03882479718222039}
|
| 139 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 3, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.057064950670629, "f1": 0.7518222839291913, "f1_std": 0.061340610667798494, "bacc": 0.7479619565217391, "bacc_std": 0.060368124231165166}
|
| 140 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 4, "C": 21.54434690031882, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04937827005595685, "f1": 0.8343927735028438, "f1_std": 0.049410569785512316, "bacc": 0.8410326086956521, "bacc_std": 0.047958528021319065}
|
| 141 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 5, "C": 0.3593813663804626, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.042709102519445756, "f1": 0.8639095086603039, "f1_std": 0.04811742785616033, "bacc": 0.8539402173913043, "bacc_std": 0.04860580687592688}
|
| 142 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.04177691244243811, "f1": 0.9071259709557582, "f1_std": 0.0424605866206815, "bacc": 0.9096467391304348, "bacc_std": 0.04175858362187671}
|
| 143 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04652391547090377, "f1": 0.8521505376344086, "f1_std": 0.04708195730415968, "bacc": 0.8566576086956521, "bacc_std": 0.04661730323044952}
|
| 144 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.05253974174669985, "f1": 0.795677136102668, "f1_std": 0.053761764767330654, "bacc": 0.7975543478260869, "bacc_std": 0.05411603893049472}
|
| 145 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 9, "C": 0.046415888336127774, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04114170633311166, "f1": 0.8863636363636364, "f1_std": 0.04365531978580378, "bacc": 0.8817934782608696, "bacc_std": 0.04477956636721617}
|
| 146 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 10, "C": 2.782559402207126, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.03545967328885119, "f1": 0.9252717391304348, "f1_std": 0.03641136688583493, "bacc": 0.9252717391304348, "bacc_std": 0.03633752968519464}
|
| 147 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.03636952679564093, "f1": 0.9252717391304348, "f1_std": 0.03746778687156803, "bacc": 0.9252717391304348, "bacc_std": 0.03765877233902594}
|
| 148 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 12, "C": 21.54434690031882, "split": "test", "acc": 0.7818181818181819, "acc_std": 0.05290377980215942, "f1": 0.76890756302521, "f1_std": 0.05806064813470633, "bacc": 0.7635869565217391, "bacc_std": 0.05684580612786276}
|
| 149 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 13, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.047606916644503366, "f1": 0.8505434782608696, "f1_std": 0.04877499267051389, "bacc": 0.8505434782608696, "bacc_std": 0.04852564878021723}
|
| 150 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 14, "C": 2.782559402207126, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.037414824480533, "f1": 0.9079959852793577, "f1_std": 0.03753805884669445, "bacc": 0.9157608695652174, "bacc_std": 0.03534543878392868}
|
| 151 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.05013832106951148, "f1": 0.8328267477203647, "f1_std": 0.05115135937123936, "bacc": 0.8349184782608696, "bacc_std": 0.050874498143016186}
|
| 152 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 16, "C": 0.3593813663804626, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04843291005978787, "f1": 0.84593837535014, "f1_std": 0.053512560645436566, "bacc": 0.8383152173913043, "bacc_std": 0.053733606877205124}
|
| 153 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 17, "C": 21.54434690031882, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05116169796676975, "f1": 0.8074229691876751, "f1_std": 0.055828741724009624, "bacc": 0.8009510869565217, "bacc_std": 0.05513477287129559}
|
| 154 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.045189613375473704, "f1": 0.8711943793911007, "f1_std": 0.04540466189175861, "bacc": 0.8783967391304348, "bacc_std": 0.04415182593824274}
|
| 155 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 19, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04518324855976184, "f1": 0.8521505376344086, "f1_std": 0.0456528182215267, "bacc": 0.8566576086956521, "bacc_std": 0.04494647565997492}
|
| 156 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 20, "C": 0.046415888336127774, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03650262528767708, "f1": 0.9045470322804582, "f1_std": 0.039787944491865505, "bacc": 0.8974184782608696, "bacc_std": 0.041681285104959824}
|
| 157 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 21, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.051062159494398615, "f1": 0.7931623931623932, "f1_std": 0.05342931783207414, "bacc": 0.7914402173913043, "bacc_std": 0.0534669734430349}
|
| 158 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 22, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.038778642839788345, "f1": 0.9086075108009306, "f1_std": 0.03860858508097085, "bacc": 0.921875, "bacc_std": 0.03332539619044311}
|
| 159 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 23, "C": 0.3593813663804626, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.04854544092611315, "f1": 0.8354935194416749, "f1_std": 0.04855653997703018, "bacc": 0.8471467391304348, "bacc_std": 0.046512353501378965}
|
| 160 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 24, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03790549361611048, "f1": 0.9045470322804582, "f1_std": 0.041088205234120455, "bacc": 0.8974184782608696, "bacc_std": 0.0426822426337656}
|
| 161 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 25, "C": 0.005994842503189409, "split": "test", "acc": 0.9454545454545454, "acc_std": 0.029647939735814914, "f1": 0.9447975911676145, "f1_std": 0.029659371393288052, "bacc": 0.953125, "bacc_std": 0.025478698210465937}
|
| 162 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 26, "C": 2.782559402207126, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04385838970034623, "f1": 0.84593837535014, "f1_std": 0.0483708857976522, "bacc": 0.8383152173913043, "bacc_std": 0.04862041274311972}
|
| 163 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 27, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.037295053180489465, "f1": 0.905982905982906, "f1_std": 0.038919945007058115, "bacc": 0.9035326086956521, "bacc_std": 0.03987595079321527}
|
| 164 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 28, "C": 0.3593813663804626, "split": "test", "acc": 0.8, "acc_std": 0.05403499325314815, "f1": 0.790003471017008, "f1_std": 0.05848119388534846, "bacc": 0.7853260869565217, "bacc_std": 0.05781804947053326}
|
| 165 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 29, "C": 2.782559402207126, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.041195362011271, "f1": 0.8863636363636364, "f1_std": 0.04361683956555933, "bacc": 0.8817934782608696, "bacc_std": 0.04444393632649216}
|
| 166 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 30, "C": 0.3593813663804626, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.043629635844995796, "f1": 0.8891129032258065, "f1_std": 0.044206407521975945, "bacc": 0.8940217391304348, "bacc_std": 0.043273523469567074}
|
| 167 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 31, "C": 0.005994842503189409, "split": "test", "acc": 0.8, "acc_std": 0.05061081451087844, "f1": 0.795677136102668, "f1_std": 0.0515024264673852, "bacc": 0.7975543478260869, "bacc_std": 0.05120847342094255}
|
| 168 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 32, "C": 166.81005372000556, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.038125256807155686, "f1": 0.9045470322804582, "f1_std": 0.041253414405784034, "bacc": 0.8974184782608696, "bacc_std": 0.04296966662108761}
|
| 169 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 33, "C": 0.046415888336127774, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.035227243401747335, "f1": 0.9252717391304348, "f1_std": 0.036226000698534655, "bacc": 0.9252717391304348, "bacc_std": 0.03630914016631629}
|
| 170 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.8545454545454545, "acc_std": 0.04868758220693468, "f1": 0.8505434782608696, "f1_std": 0.050172524345692686, "bacc": 0.8505434782608696, "bacc_std": 0.05039794169287616}
|
| 171 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.044819874576183194, "f1": 0.8683760683760684, "f1_std": 0.046700844580755504, "bacc": 0.8661684782608696, "bacc_std": 0.04703219891854326}
|
| 172 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 36, "C": 0.3593813663804626, "split": "test", "acc": 0.9454545454545454, "acc_std": 0.03007947599332572, "f1": 0.9435897435897436, "f1_std": 0.03151785539256354, "bacc": 0.9408967391304348, "bacc_std": 0.03307103946130984}
|
| 173 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 37, "C": 0.3593813663804626, "split": "test", "acc": 0.7636363636363637, "acc_std": 0.0570396411380456, "f1": 0.7555555555555555, "f1_std": 0.0597636002347408, "bacc": 0.7540760869565217, "bacc_std": 0.05955263044560587}
|
| 174 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 38, "C": 0.005994842503189409, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.05100991315096918, "f1": 0.8074229691876751, "f1_std": 0.05586428525852598, "bacc": 0.8009510869565217, "bacc_std": 0.055221591115610515}
|
| 175 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 39, "C": 21.54434690031882, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.043062898452618056, "f1": 0.8683760683760684, "f1_std": 0.04475052034623057, "bacc": 0.8661684782608696, "bacc_std": 0.04498637988653198}
|
| 176 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 40, "C": 0.3593813663804626, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.036980656100451155, "f1": 0.905982905982906, "f1_std": 0.0385802806083221, "bacc": 0.9035326086956521, "bacc_std": 0.03930188818967241}
|
| 177 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 41, "C": 0.005994842503189409, "split": "test", "acc": 0.8909090909090909, "acc_std": 0.04268359856873433, "f1": 0.8879076086956521, "f1_std": 0.04402556638984178, "bacc": 0.8879076086956521, "bacc_std": 0.04416115309771717}
|
| 178 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 42, "C": 2.782559402207126, "split": "test", "acc": 0.8363636363636363, "acc_std": 0.051477142484282326, "f1": 0.8354935194416749, "f1_std": 0.05146702567803053, "bacc": 0.8471467391304348, "bacc_std": 0.049199328334977796}
|
| 179 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 43, "C": 0.046415888336127774, "split": "test", "acc": 0.9090909090909091, "acc_std": 0.03758452292681609, "f1": 0.905982905982906, "f1_std": 0.03912807990939497, "bacc": 0.9035326086956521, "bacc_std": 0.03994921075819425}
|
| 180 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 44, "C": 0.005994842503189409, "split": "test", "acc": 0.8181818181818182, "acc_std": 0.04938958300179205, "f1": 0.8106060606060606, "f1_std": 0.05248764992148359, "bacc": 0.8070652173913043, "bacc_std": 0.05234408171113471}
|
| 181 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 45, "C": 0.3593813663804626, "split": "test", "acc": 0.9272727272727272, "acc_std": 0.034413143750751954, "f1": 0.9260752688172043, "f1_std": 0.03471431751135977, "bacc": 0.9313858695652174, "bacc_std": 0.0330286442262296}
|
| 182 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 46, "C": 2.782559402207126, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.047201120591824575, "f1": 0.8699763593380614, "f1_std": 0.048289141384129396, "bacc": 0.8722826086956521, "bacc_std": 0.04836046041262313}
|
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| 234 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04288005180704899, "f1": 0.8663658451926415, "f1_std": 0.04633913548983254, "bacc": 0.8600543478260869, "bacc_std": 0.047210798069724655}
|
| 235 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 99, "C": 0.005994842503189409, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04426628663250854, "f1": 0.8663658451926415, "f1_std": 0.048245934766743444, "bacc": 0.8600543478260869, "bacc_std": 0.04913660330949244}
|
| 236 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "aabc_sex", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.8727272727272727, "acc_std": 0.04376156281676065, "f1": 0.8711943793911007, "f1_std": 0.043898138508973145, "bacc": 0.8783967391304348, "bacc_std": 0.04220448266227801}
|
| 237 |
+
eval results (random splits):
|
| 238 |
+
|
| 239 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 240 |
+
|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 241 |
+
| flat_mae | patch | logistic | aabc_sex | train | 100 | 8.8955 | 32.908 | 0.96684 | 0.035268 | 0.9658 | 0.036491 | 0.96489 | 0.037762 |
|
| 242 |
+
| flat_mae | patch | logistic | aabc_sex | test | 100 | 8.8955 | 32.908 | 0.87364 | 0.043809 | 0.86919 | 0.045491 | 0.86811 | 0.046161 |
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
done! total time: 0:05:12
|
input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
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|
|
|
|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (abide_dx patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: abide_dx
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/eval_table.csv
ADDED
|
@@ -0,0 +1,203 @@
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|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
|
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|
|
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|
|
|
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|
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|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
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|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,abide_dx,,0.046415888336127774,train,0.7849002849002849,0.015118144695688708,0.7797841509904333,0.015579162502962006,0.7774722568783242,0.015445790188745555
|
| 3 |
+
flat_mae,patch,logistic,abide_dx,,0.046415888336127774,test,0.6532258064516129,0.0419016333601904,0.6480760345851759,0.04282866981832994,0.6476826394344068,0.04240158692300933
|
| 4 |
+
flat_mae,patch,logistic,abide_dx,1,0.005994842503189409,train,0.7150997150997151,0.016558819256654513,0.7039898125268813,0.017661072595878336,0.7026208933185678,0.01712152666224204
|
| 5 |
+
flat_mae,patch,logistic,abide_dx,1,0.005994842503189409,test,0.6612903225806451,0.03928344595259573,0.6522435897435898,0.041091280663619445,0.6517857142857143,0.04006559993742523
|
| 6 |
+
flat_mae,patch,logistic,abide_dx,2,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 7 |
+
flat_mae,patch,logistic,abide_dx,2,21.54434690031882,test,0.5887096774193549,0.04170376426912036,0.5826018084614877,0.04216207708144935,0.5824579831932774,0.041780475006468254
|
| 8 |
+
flat_mae,patch,logistic,abide_dx,3,2.782559402207126,train,0.9772079772079773,0.005811839421004346,0.9769790778513806,0.005868712952118336,0.9772609819121447,0.005849550123309814
|
| 9 |
+
flat_mae,patch,logistic,abide_dx,3,2.782559402207126,test,0.5806451612903226,0.04622951778091552,0.5766806722689075,0.0468248307949034,0.5766806722689075,0.0467510604624925
|
| 10 |
+
flat_mae,patch,logistic,abide_dx,4,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 11 |
+
flat_mae,patch,logistic,abide_dx,4,1291.5496650148827,test,0.6048387096774194,0.04570509003319836,0.6017043592264831,0.046442506174170874,0.601890756302521,0.04641468425862333
|
| 12 |
+
flat_mae,patch,logistic,abide_dx,5,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 13 |
+
flat_mae,patch,logistic,abide_dx,5,21.54434690031882,test,0.6532258064516129,0.03943284178760655,0.6408702094699266,0.04184913581894623,0.641281512605042,0.04041252237084422
|
| 14 |
+
flat_mae,patch,logistic,abide_dx,6,0.046415888336127774,train,0.7877492877492878,0.015174991366853123,0.78378970563005,0.015589610517456064,0.7820967146548542,0.015587412987847044
|
| 15 |
+
flat_mae,patch,logistic,abide_dx,6,0.046415888336127774,test,0.5564516129032258,0.04582433771653672,0.551522325244953,0.045954764314943934,0.5514705882352942,0.0458026710767851
|
| 16 |
+
flat_mae,patch,logistic,abide_dx,7,0.005994842503189409,train,0.7108262108262108,0.016394938591292284,0.7010415901819897,0.01731352398347828,0.6996308600959764,0.016902966150707972
|
| 17 |
+
flat_mae,patch,logistic,abide_dx,7,0.005994842503189409,test,0.6532258064516129,0.04007166771660508,0.63853298081486,0.04318718629010362,0.6397058823529411,0.041131645917398346
|
| 18 |
+
flat_mae,patch,logistic,abide_dx,8,0.005994842503189409,train,0.7207977207977208,0.01602342498131765,0.7126795856999666,0.016675384580591784,0.7110372831303065,0.016368060364794306
|
| 19 |
+
flat_mae,patch,logistic,abide_dx,8,0.005994842503189409,test,0.6129032258064516,0.04019091730113612,0.6025641025641025,0.04179707597672801,0.6029411764705883,0.04087570139434808
|
| 20 |
+
flat_mae,patch,logistic,abide_dx,9,0.005994842503189409,train,0.717948717948718,0.016372905816310238,0.711111111111111,0.0169186160324222,0.7096345514950166,0.01672216793451734
|
| 21 |
+
flat_mae,patch,logistic,abide_dx,9,0.005994842503189409,test,0.6612903225806451,0.03984034213711646,0.6371237458193979,0.044602159762021365,0.6423319327731093,0.04096820891227882
|
| 22 |
+
flat_mae,patch,logistic,abide_dx,10,0.3593813663804626,train,0.9002849002849003,0.011560388226747289,0.8989728702888957,0.011753199609195346,0.8980435585086748,0.01189828778401462
|
| 23 |
+
flat_mae,patch,logistic,abide_dx,10,0.3593813663804626,test,0.5725806451612904,0.04292564294288852,0.5643931861867832,0.043501130093190975,0.5646008403361344,0.0431174458566251
|
| 24 |
+
flat_mae,patch,logistic,abide_dx,11,0.046415888336127774,train,0.7877492877492878,0.01629518330429159,0.7836179521923066,0.016734788231907567,0.7818014027316353,0.016692776410707915
|
| 25 |
+
flat_mae,patch,logistic,abide_dx,11,0.046415888336127774,test,0.5887096774193549,0.04515846477335987,0.5826018084614877,0.04581411729099879,0.5824579831932774,0.04553780440426183
|
| 26 |
+
flat_mae,patch,logistic,abide_dx,12,0.046415888336127774,train,0.7891737891737892,0.014832570994125079,0.7851559592049431,0.015232877979516151,0.7833887043189369,0.015201459823677356
|
| 27 |
+
flat_mae,patch,logistic,abide_dx,12,0.046415888336127774,test,0.5725806451612904,0.04600258724570443,0.5691904293674206,0.04610894016959845,0.569327731092437,0.046038904299753755
|
| 28 |
+
flat_mae,patch,logistic,abide_dx,13,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 29 |
+
flat_mae,patch,logistic,abide_dx,13,166.81005372000556,test,0.6370967741935484,0.044038753508857886,0.6342182890855457,0.044530383973118266,0.634453781512605,0.044554833091261015
|
| 30 |
+
flat_mae,patch,logistic,abide_dx,14,0.3593813663804626,train,0.8888888888888888,0.011644603079121471,0.8874269126076266,0.011818079458878208,0.8865263935031378,0.011899348599747818
|
| 31 |
+
flat_mae,patch,logistic,abide_dx,14,0.3593813663804626,test,0.5725806451612904,0.04464815333531615,0.5678306043269548,0.045245385859806767,0.5677521008403361,0.04501376202873272
|
| 32 |
+
flat_mae,patch,logistic,abide_dx,15,0.046415888336127774,train,0.7877492877492878,0.015085168502456111,0.7834423388674605,0.015546161111898923,0.7815060908084164,0.01550972091830348
|
| 33 |
+
flat_mae,patch,logistic,abide_dx,15,0.046415888336127774,test,0.6370967741935484,0.04261514975397352,0.626380984265149,0.04468875559837201,0.6265756302521008,0.04343985236373257
|
| 34 |
+
flat_mae,patch,logistic,abide_dx,16,0.3593813663804626,train,0.8988603988603988,0.011661995662825025,0.8975631110462571,0.011847090087948426,0.8967515688445922,0.011942120703317126
|
| 35 |
+
flat_mae,patch,logistic,abide_dx,16,0.3593813663804626,test,0.6048387096774194,0.041311042558290156,0.602745995423341,0.04153606123868081,0.6034663865546219,0.04153539072677747
|
| 36 |
+
flat_mae,patch,logistic,abide_dx,17,0.3593813663804626,train,0.8846153846153846,0.012183129026833517,0.8829795334574977,0.012388423780518647,0.8817644887412329,0.012458165827888584
|
| 37 |
+
flat_mae,patch,logistic,abide_dx,17,0.3593813663804626,test,0.6129032258064516,0.04327864788973161,0.6063492063492064,0.04474002044553822,0.60609243697479,0.044235978212460135
|
| 38 |
+
flat_mae,patch,logistic,abide_dx,18,0.005994842503189409,train,0.7222222222222222,0.016414023981880042,0.7131265206557706,0.017179329216186714,0.7114433370247324,0.016800415508091884
|
| 39 |
+
flat_mae,patch,logistic,abide_dx,18,0.005994842503189409,test,0.6693548387096774,0.04110825218253145,0.6575739206573719,0.04359517738110354,0.657563025210084,0.04205109609247913
|
| 40 |
+
flat_mae,patch,logistic,abide_dx,19,0.3593813663804626,train,0.8803418803418803,0.012403334725614994,0.8784315342431864,0.012639381313024356,0.8767072720561093,0.012706198917748938
|
| 41 |
+
flat_mae,patch,logistic,abide_dx,19,0.3593813663804626,test,0.5645161290322581,0.04249464372197081,0.5374412821221332,0.04697664975602971,0.546218487394958,0.04339358690030381
|
| 42 |
+
flat_mae,patch,logistic,abide_dx,20,0.3593813663804626,train,0.8846153846153846,0.011316349416896389,0.8831353802077018,0.011514298748928007,0.8823551125876707,0.011678356174099874
|
| 43 |
+
flat_mae,patch,logistic,abide_dx,20,0.3593813663804626,test,0.5806451612903226,0.04353008874825322,0.5735449735449736,0.04455114598610447,0.5735294117647058,0.04400721252137609
|
| 44 |
+
flat_mae,patch,logistic,abide_dx,21,0.046415888336127774,train,0.7905982905982906,0.015091697866238915,0.7871775252999178,0.015478887067402792,0.7858619416758952,0.015551687569602138
|
| 45 |
+
flat_mae,patch,logistic,abide_dx,21,0.046415888336127774,test,0.6129032258064516,0.04465703506097381,0.6063492063492064,0.04559938269887082,0.60609243697479,0.045089608168265016
|
| 46 |
+
flat_mae,patch,logistic,abide_dx,22,2.782559402207126,train,0.9829059829059829,0.004882378833237064,0.9827242524916944,0.004934791548114525,0.9827242524916944,0.004964501333716309
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| 200 |
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|
input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic/log.txt
ADDED
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:55:54
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (abide_dx patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: abide_dx
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/abide_dx__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: abide_dx (flat)
|
| 70 |
+
train (n=578):
|
| 71 |
+
HFDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 578
|
| 75 |
+
}),
|
| 76 |
+
labels=['Autism' 'Control'],
|
| 77 |
+
counts=[260 318]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=124):
|
| 81 |
+
HFDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 124
|
| 85 |
+
}),
|
| 86 |
+
labels=['Autism' 'Control'],
|
| 87 |
+
counts=[54 70]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=124):
|
| 91 |
+
HFDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'site', 'dataset', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 124
|
| 95 |
+
}),
|
| 96 |
+
labels=['Autism' 'Control'],
|
| 97 |
+
counts=[57 67]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/289] eta: 0:22:21 time: 4.6424 data: 3.4264 max mem: 2698
|
| 102 |
+
extract (train) [ 20/289] eta: 0:01:43 time: 0.1731 data: 0.0589 max mem: 2851
|
| 103 |
+
extract (train) [ 40/289] eta: 0:01:08 time: 0.1582 data: 0.0498 max mem: 2851
|
| 104 |
+
extract (train) [ 60/289] eta: 0:00:55 time: 0.1808 data: 0.0630 max mem: 2851
|
| 105 |
+
extract (train) [ 80/289] eta: 0:00:46 time: 0.1623 data: 0.0520 max mem: 2851
|
| 106 |
+
extract (train) [100/289] eta: 0:00:39 time: 0.1575 data: 0.0513 max mem: 2851
|
| 107 |
+
extract (train) [120/289] eta: 0:00:33 time: 0.1515 data: 0.0467 max mem: 2851
|
| 108 |
+
extract (train) [140/289] eta: 0:00:29 time: 0.1674 data: 0.0542 max mem: 2851
|
| 109 |
+
extract (train) [160/289] eta: 0:00:24 time: 0.1619 data: 0.0514 max mem: 2851
|
| 110 |
+
extract (train) [180/289] eta: 0:00:20 time: 0.1649 data: 0.0546 max mem: 2851
|
| 111 |
+
extract (train) [200/289] eta: 0:00:16 time: 0.1553 data: 0.0495 max mem: 2851
|
| 112 |
+
extract (train) [220/289] eta: 0:00:12 time: 0.1521 data: 0.0473 max mem: 2851
|
| 113 |
+
extract (train) [240/289] eta: 0:00:08 time: 0.1515 data: 0.0471 max mem: 2851
|
| 114 |
+
extract (train) [260/289] eta: 0:00:05 time: 0.1532 data: 0.0481 max mem: 2851
|
| 115 |
+
extract (train) [280/289] eta: 0:00:01 time: 0.1464 data: 0.0429 max mem: 2851
|
| 116 |
+
extract (train) [288/289] eta: 0:00:00 time: 0.1467 data: 0.0432 max mem: 2851
|
| 117 |
+
extract (train) Total time: 0:00:50 (0.1764 s / it)
|
| 118 |
+
extract (validation) [ 0/62] eta: 0:03:32 time: 3.4281 data: 3.2271 max mem: 2851
|
| 119 |
+
extract (validation) [20/62] eta: 0:00:15 time: 0.2081 data: 0.0736 max mem: 2851
|
| 120 |
+
extract (validation) [40/62] eta: 0:00:05 time: 0.1470 data: 0.0438 max mem: 2851
|
| 121 |
+
extract (validation) [60/62] eta: 0:00:00 time: 0.1440 data: 0.0428 max mem: 2851
|
| 122 |
+
extract (validation) [61/62] eta: 0:00:00 time: 0.1440 data: 0.0429 max mem: 2851
|
| 123 |
+
extract (validation) Total time: 0:00:13 (0.2240 s / it)
|
| 124 |
+
extract (test) [ 0/62] eta: 0:03:47 time: 3.6745 data: 3.4548 max mem: 2851
|
| 125 |
+
extract (test) [20/62] eta: 0:00:15 time: 0.2067 data: 0.0718 max mem: 2851
|
| 126 |
+
extract (test) [40/62] eta: 0:00:05 time: 0.1473 data: 0.0431 max mem: 2851
|
| 127 |
+
extract (test) [60/62] eta: 0:00:00 time: 0.1478 data: 0.0458 max mem: 2851
|
| 128 |
+
extract (test) [61/62] eta: 0:00:00 time: 0.1478 data: 0.0459 max mem: 2851
|
| 129 |
+
extract (test) Total time: 0:00:14 (0.2288 s / it)
|
| 130 |
+
feature extraction time: 0:01:19
|
| 131 |
+
train features: (578, 768)
|
| 132 |
+
validation features: (124, 768)
|
| 133 |
+
test features: (124, 768)
|
| 134 |
+
evaluating fixed splits
|
| 135 |
+
eval results (fixed splits):
|
| 136 |
+
|
| 137 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 138 |
+
|:---------|:-------|:---------|:----------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 139 |
+
| flat_mae | patch | logistic | abide_dx | | 0.046416 | train | 0.7849 | 0.015118 | 0.77978 | 0.015579 | 0.77747 | 0.015446 |
|
| 140 |
+
| flat_mae | patch | logistic | abide_dx | | 0.046416 | test | 0.65323 | 0.041902 | 0.64808 | 0.042829 | 0.64768 | 0.042402 |
|
| 141 |
+
|
| 142 |
+
|
| 143 |
+
evaluating random splits (n=100)
|
| 144 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.03928344595259573, "f1": 0.6522435897435898, "f1_std": 0.041091280663619445, "bacc": 0.6517857142857143, "bacc_std": 0.04006559993742523}
|
| 145 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 2, "C": 21.54434690031882, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04170376426912036, "f1": 0.5826018084614877, "f1_std": 0.04216207708144935, "bacc": 0.5824579831932774, "bacc_std": 0.041780475006468254}
|
| 146 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 3, "C": 2.782559402207126, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04622951778091552, "f1": 0.5766806722689075, "f1_std": 0.0468248307949034, "bacc": 0.5766806722689075, "bacc_std": 0.0467510604624925}
|
| 147 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 4, "C": 1291.5496650148827, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04570509003319836, "f1": 0.6017043592264831, "f1_std": 0.046442506174170874, "bacc": 0.601890756302521, "bacc_std": 0.04641468425862333}
|
| 148 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 5, "C": 21.54434690031882, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.03943284178760655, "f1": 0.6408702094699266, "f1_std": 0.04184913581894623, "bacc": 0.641281512605042, "bacc_std": 0.04041252237084422}
|
| 149 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 6, "C": 0.046415888336127774, "split": "test", "acc": 0.5564516129032258, "acc_std": 0.04582433771653672, "f1": 0.551522325244953, "f1_std": 0.045954764314943934, "bacc": 0.5514705882352942, "bacc_std": 0.0458026710767851}
|
| 150 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04007166771660508, "f1": 0.63853298081486, "f1_std": 0.04318718629010362, "bacc": 0.6397058823529411, "bacc_std": 0.041131645917398346}
|
| 151 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 8, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04019091730113612, "f1": 0.6025641025641025, "f1_std": 0.04179707597672801, "bacc": 0.6029411764705883, "bacc_std": 0.04087570139434808}
|
| 152 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.6612903225806451, "acc_std": 0.03984034213711646, "f1": 0.6371237458193979, "f1_std": 0.044602159762021365, "bacc": 0.6423319327731093, "bacc_std": 0.04096820891227882}
|
| 153 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 10, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04292564294288852, "f1": 0.5643931861867832, "f1_std": 0.043501130093190975, "bacc": 0.5646008403361344, "bacc_std": 0.0431174458566251}
|
| 154 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04515846477335987, "f1": 0.5826018084614877, "f1_std": 0.04581411729099879, "bacc": 0.5824579831932774, "bacc_std": 0.04553780440426183}
|
| 155 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04600258724570443, "f1": 0.5691904293674206, "f1_std": 0.04610894016959845, "bacc": 0.569327731092437, "bacc_std": 0.046038904299753755}
|
| 156 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 13, "C": 166.81005372000556, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.044038753508857886, "f1": 0.6342182890855457, "f1_std": 0.044530383973118266, "bacc": 0.634453781512605, "bacc_std": 0.044554833091261015}
|
| 157 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 14, "C": 0.3593813663804626, "split": "test", "acc": 0.5725806451612904, "acc_std": 0.04464815333531615, "f1": 0.5678306043269548, "f1_std": 0.045245385859806767, "bacc": 0.5677521008403361, "bacc_std": 0.04501376202873272}
|
| 158 |
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{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 66, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04406509174044293, "f1": 0.6197559861681998, "f1_std": 0.044112577117600554, "bacc": 0.6213235294117647, "bacc_std": 0.044205540498311165}
|
| 210 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 67, "C": 1291.5496650148827, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04315442770554526, "f1": 0.6753076721654884, "f1_std": 0.04355924140351498, "bacc": 0.6759453781512605, "bacc_std": 0.04364951467279006}
|
| 211 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 68, "C": 0.3593813663804626, "split": "test", "acc": 0.6935483870967742, "acc_std": 0.03851761492784112, "f1": 0.6906512605042017, "f1_std": 0.038789081463712356, "bacc": 0.6906512605042017, "bacc_std": 0.0387079980017759}
|
| 212 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 69, "C": 0.005994842503189409, "split": "test", "acc": 0.7258064516129032, "acc_std": 0.03827274918090839, "f1": 0.7087593257806024, "f1_std": 0.0430141600084856, "bacc": 0.7090336134453781, "bacc_std": 0.03998447189726336}
|
| 213 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 70, "C": 0.005994842503189409, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04214803145665495, "f1": 0.5953379953379954, "f1_std": 0.04334982530904575, "bacc": 0.5955882352941176, "bacc_std": 0.042543526933981585}
|
| 214 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 71, "C": 0.046415888336127774, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.038275304824616004, "f1": 0.6190346145968457, "f1_std": 0.041471003166279746, "bacc": 0.6218487394957983, "bacc_std": 0.039266460070139626}
|
| 215 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 72, "C": 0.005994842503189409, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.042390018065975345, "f1": 0.5475675675675675, "f1_std": 0.04457506471606714, "bacc": 0.5509453781512605, "bacc_std": 0.0428883236382383}
|
| 216 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 73, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04016388124820451, "f1": 0.5921052631578947, "f1_std": 0.04398009569930661, "bacc": 0.5966386554621849, "bacc_std": 0.041175036954120045}
|
| 217 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 74, "C": 0.046415888336127774, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.041375828174527104, "f1": 0.6153389215233318, "f1_std": 0.04218028457125705, "bacc": 0.6150210084033614, "bacc_std": 0.041827702325897866}
|
| 218 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 75, "C": 1291.5496650148827, "split": "test", "acc": 0.5403225806451613, "acc_std": 0.04659516349564904, "f1": 0.537888198757764, "f1_std": 0.04699523484042215, "bacc": 0.5383403361344539, "bacc_std": 0.04710943757089698}
|
| 219 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 76, "C": 0.3593813663804626, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04080646436312437, "f1": 0.6688034188034189, "f1_std": 0.042743976319359146, "bacc": 0.6680672268907563, "bacc_std": 0.04169453957177175}
|
| 220 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 77, "C": 0.046415888336127774, "split": "test", "acc": 0.5080645161290323, "acc_std": 0.04383715490133812, "f1": 0.5054593004249754, "f1_std": 0.04402050127074825, "bacc": 0.5057773109243697, "bacc_std": 0.04393861427666274}
|
| 221 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 78, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.040134564886731255, "f1": 0.5972691721349506, "f1_std": 0.0411263019707682, "bacc": 0.5971638655462186, "bacc_std": 0.040719774250430246}
|
| 222 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 79, "C": 0.046415888336127774, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04328658161327534, "f1": 0.6063492063492064, "f1_std": 0.04451724571769944, "bacc": 0.60609243697479, "bacc_std": 0.04399251305047831}
|
| 223 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 80, "C": 0.005994842503189409, "split": "test", "acc": 0.6451612903225806, "acc_std": 0.045458180230228226, "f1": 0.6405797101449275, "f1_std": 0.045770885256993264, "bacc": 0.6402310924369747, "bacc_std": 0.04554298382456265}
|
| 224 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 81, "C": 0.005994842503189409, "split": "test", "acc": 0.6370967741935484, "acc_std": 0.041145850451354674, "f1": 0.6190346145968457, "f1_std": 0.04499820623350004, "bacc": 0.6218487394957983, "bacc_std": 0.042351017589933204}
|
| 225 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 82, "C": 0.046415888336127774, "split": "test", "acc": 0.5887096774193549, "acc_std": 0.04040681202292999, "f1": 0.5649122807017544, "f1_std": 0.04435583081547282, "bacc": 0.5714285714285714, "bacc_std": 0.04117154954462591}
|
| 226 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 83, "C": 2.782559402207126, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04638169861811178, "f1": 0.5989703649924097, "f1_std": 0.04698622701712921, "bacc": 0.5987394957983193, "bacc_std": 0.046659179976889224}
|
| 227 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 84, "C": 0.046415888336127774, "split": "test", "acc": 0.5967741935483871, "acc_std": 0.046203064887459545, "f1": 0.5915678524374176, "f1_std": 0.04665792265746383, "bacc": 0.5913865546218487, "bacc_std": 0.04629307264929039}
|
| 228 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 85, "C": 0.005994842503189409, "split": "test", "acc": 0.6774193548387096, "acc_std": 0.04157811436528522, "f1": 0.6648648648648648, "f1_std": 0.04379820004801466, "bacc": 0.6649159663865546, "bacc_std": 0.042364547497577}
|
| 229 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 86, "C": 2.782559402207126, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.0446319058962689, "f1": 0.6004471624909581, "f1_std": 0.045320529691054726, "bacc": 0.6003151260504203, "bacc_std": 0.0451775371155432}
|
| 230 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 87, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.0444465444931069, "f1": 0.602745995423341, "f1_std": 0.044483519200626286, "bacc": 0.6034663865546219, "bacc_std": 0.04439320933619249}
|
| 231 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 88, "C": 0.3593813663804626, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04455191116845277, "f1": 0.5752305665349143, "f1_std": 0.044838442393622696, "bacc": 0.5751050420168067, "bacc_std": 0.04465037746840965}
|
| 232 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 89, "C": 2.782559402207126, "split": "test", "acc": 0.5645161290322581, "acc_std": 0.0437908423898335, "f1": 0.5645161290322581, "f1_std": 0.04396545824884776, "bacc": 0.5698529411764706, "bacc_std": 0.04376482422590139}
|
| 233 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 90, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04529556938177449, "f1": 0.5880957223239103, "f1_std": 0.04789314425086675, "bacc": 0.5908613445378151, "bacc_std": 0.045911145944440485}
|
| 234 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 91, "C": 0.005994842503189409, "split": "test", "acc": 0.6129032258064516, "acc_std": 0.04227746415084535, "f1": 0.6025641025641025, "f1_std": 0.04359577347014083, "bacc": 0.6029411764705883, "bacc_std": 0.04268428947552194}
|
| 235 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 92, "C": 21.54434690031882, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.04594712610710925, "f1": 0.6118548118548119, "f1_std": 0.04696342508682928, "bacc": 0.6118697478991597, "bacc_std": 0.046141568251084016}
|
| 236 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 93, "C": 0.005994842503189409, "split": "test", "acc": 0.5806451612903226, "acc_std": 0.04294965956819604, "f1": 0.5643243243243243, "f1_std": 0.045074074794312614, "bacc": 0.5672268907563025, "bacc_std": 0.043438315622233964}
|
| 237 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 94, "C": 21.54434690031882, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.04433230806061501, "f1": 0.5454307410316837, "f1_std": 0.04470559761201011, "bacc": 0.5456932773109244, "bacc_std": 0.04465869974701527}
|
| 238 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 95, "C": 0.3593813663804626, "split": "test", "acc": 0.6048387096774194, "acc_std": 0.04289501194717132, "f1": 0.5989703649924097, "f1_std": 0.0439181127590175, "bacc": 0.5987394957983193, "bacc_std": 0.04364708021011115}
|
| 239 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 96, "C": 0.046415888336127774, "split": "test", "acc": 0.5483870967741935, "acc_std": 0.04724147842816823, "f1": 0.5407407407407407, "f1_std": 0.048178446105519385, "bacc": 0.5409663865546219, "bacc_std": 0.047563984903519145}
|
| 240 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.6854838709677419, "acc_std": 0.041221625690379474, "f1": 0.6829891838741396, "f1_std": 0.041714618252376906, "bacc": 0.6832983193277311, "bacc_std": 0.041872947776779613}
|
| 241 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 98, "C": 0.046415888336127774, "split": "test", "acc": 0.6290322580645161, "acc_std": 0.04435905845030183, "f1": 0.6191239316239316, "f1_std": 0.046176762425690486, "bacc": 0.6192226890756303, "bacc_std": 0.045111179242968084}
|
| 242 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.6209677419354839, "acc_std": 0.043523705670070675, "f1": 0.6137071651090342, "f1_std": 0.04460235748207617, "bacc": 0.6134453781512605, "bacc_std": 0.043959416064220234}
|
| 243 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "abide_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.6532258064516129, "acc_std": 0.04193244095659784, "f1": 0.6331613347093223, "f1_std": 0.045854848244350155, "bacc": 0.6365546218487395, "bacc_std": 0.043049585134371605}
|
| 244 |
+
eval results (random splits):
|
| 245 |
+
|
| 246 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 247 |
+
|:---------|:-------|:---------|:----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 248 |
+
| flat_mae | patch | logistic | abide_dx | train | 100 | 54.723 | 254.32 | 0.82313 | 0.10213 | 0.81865 | 0.10561 | 0.81747 | 0.10601 |
|
| 249 |
+
| flat_mae | patch | logistic | abide_dx | test | 100 | 54.723 | 254.32 | 0.60968 | 0.040493 | 0.6004 | 0.04047 | 0.60136 | 0.039779 |
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
done! total time: 0:05:48
|
input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/config.yaml
ADDED
|
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|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (adhd200_dx patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: adhd200_dx
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/eval_table.csv
ADDED
|
@@ -0,0 +1,203 @@
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| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,adhd200_dx,,0.005994842503189409,train,0.7506849315068493,0.021032728787301832,0.7417205153925708,0.022139390457965872,0.7389479147585027,0.021781925911702867
|
| 3 |
+
flat_mae,patch,logistic,adhd200_dx,,0.005994842503189409,test,0.6307692307692307,0.06011526600706839,0.6153846153846154,0.06399223555693029,0.6148648648648649,0.062105674853638014
|
| 4 |
+
flat_mae,patch,logistic,adhd200_dx,1,0.005994842503189409,train,0.7479452054794521,0.021825033787628203,0.736833855799373,0.023191371701551774,0.7336508518043597,0.022695035062631916
|
| 5 |
+
flat_mae,patch,logistic,adhd200_dx,1,0.005994842503189409,test,0.6153846153846154,0.05657085205099131,0.6018132810585641,0.058867672202832906,0.6013513513513513,0.0577086690193027
|
| 6 |
+
flat_mae,patch,logistic,adhd200_dx,2,0.046415888336127774,train,0.8273972602739726,0.020183496004846868,0.8232363996955929,0.02077224878665521,0.8212584722476644,0.020799949078411636
|
| 7 |
+
flat_mae,patch,logistic,adhd200_dx,2,0.046415888336127774,test,0.7076923076923077,0.05586201088714073,0.6934723256391164,0.060083266628473006,0.6911196911196911,0.058027296614592064
|
| 8 |
+
flat_mae,patch,logistic,adhd200_dx,3,0.005994842503189409,train,0.7561643835616438,0.02220015062863192,0.7457122952038764,0.023719660973300468,0.7423673444464798,0.023307417806325753
|
| 9 |
+
flat_mae,patch,logistic,adhd200_dx,3,0.005994842503189409,test,0.5538461538461539,0.05657084368320474,0.5250692869740489,0.06082632171750404,0.5299227799227799,0.05750249631133549
|
| 10 |
+
flat_mae,patch,logistic,adhd200_dx,4,0.005994842503189409,train,0.7397260273972602,0.022551832418467956,0.7314700803072895,0.023611677191396674,0.7292391768944251,0.02339664820756321
|
| 11 |
+
flat_mae,patch,logistic,adhd200_dx,4,0.005994842503189409,test,0.6307692307692307,0.055447647892446564,0.6235521235521235,0.05708092120695752,0.6235521235521235,0.05713868765278625
|
| 12 |
+
flat_mae,patch,logistic,adhd200_dx,5,0.005994842503189409,train,0.7698630136986301,0.021768314837598956,0.7602739726027397,0.023438779786429408,0.7566556756426696,0.02310845944718046
|
| 13 |
+
flat_mae,patch,logistic,adhd200_dx,5,0.005994842503189409,test,0.5384615384615384,0.060269249914090516,0.5357142857142857,0.06087362111006021,0.5381274131274132,0.0618712679131066
|
| 14 |
+
flat_mae,patch,logistic,adhd200_dx,6,0.005994842503189409,train,0.7534246575342466,0.021209414522855807,0.7425548589341693,0.022619806937993894,0.7392226903584295,0.022112763167718773
|
| 15 |
+
flat_mae,patch,logistic,adhd200_dx,6,0.005994842503189409,test,0.6615384615384615,0.059627014250273905,0.6549227799227799,0.06133635184716082,0.6549227799227799,0.061049863037492456
|
| 16 |
+
flat_mae,patch,logistic,adhd200_dx,7,0.005994842503189409,train,0.736986301369863,0.023485765043793013,0.7294292068198666,0.024433949193718874,0.7275294620504366,0.02428236817184175
|
| 17 |
+
flat_mae,patch,logistic,adhd200_dx,7,0.005994842503189409,test,0.6153846153846154,0.05505655833217888,0.6018132810585641,0.057709651438696934,0.6013513513513513,0.056423667885912875
|
| 18 |
+
flat_mae,patch,logistic,adhd200_dx,8,0.046415888336127774,train,0.8356164383561644,0.018991324722408214,0.8305687937117039,0.01996611005977673,0.8271050864016609,0.020162403066628528
|
| 19 |
+
flat_mae,patch,logistic,adhd200_dx,8,0.046415888336127774,test,0.6,0.06367000724426854,0.5921814671814671,0.06533749192841934,0.5921814671814671,0.06472219754155917
|
| 20 |
+
flat_mae,patch,logistic,adhd200_dx,9,0.005994842503189409,train,0.7452054794520548,0.022637506511752108,0.738129218900675,0.02326380878446828,0.7362459546925566,0.023075266888108438
|
| 21 |
+
flat_mae,patch,logistic,adhd200_dx,9,0.005994842503189409,test,0.5846153846153846,0.06053548814905012,0.5578231292517006,0.06545658781457737,0.5612934362934363,0.06187049197900358
|
| 22 |
+
flat_mae,patch,logistic,adhd200_dx,10,0.046415888336127774,train,0.8465753424657534,0.01890973035358812,0.841864207464257,0.01974787057762469,0.8382487635098004,0.01983546628971271
|
| 23 |
+
flat_mae,patch,logistic,adhd200_dx,10,0.046415888336127774,test,0.6,0.061636621712214845,0.5921814671814671,0.06303245627588339,0.5921814671814671,0.06275480160168166
|
| 24 |
+
flat_mae,patch,logistic,adhd200_dx,11,0.046415888336127774,train,0.8465753424657534,0.01883937616274856,0.843010752688172,0.019477697846923894,0.841118641997924,0.019726892330627056
|
| 25 |
+
flat_mae,patch,logistic,adhd200_dx,11,0.046415888336127774,test,0.5384615384615384,0.05904114301101679,0.5248538011695907,0.061124803863014436,0.525096525096525,0.06010487491403585
|
| 26 |
+
flat_mae,patch,logistic,adhd200_dx,12,0.046415888336127774,train,0.8328767123287671,0.019426180746738513,0.8279113625648279,0.020249801334984895,0.8246779019356415,0.020296505906017795
|
| 27 |
+
flat_mae,patch,logistic,adhd200_dx,12,0.046415888336127774,test,0.5692307692307692,0.058723752036585995,0.5512820512820513,0.06287896035508381,0.5521235521235521,0.06070224342857615
|
| 28 |
+
flat_mae,patch,logistic,adhd200_dx,13,0.005994842503189409,train,0.7698630136986301,0.02257441147169292,0.7632505559673832,0.02335373547991562,0.7609604933748549,0.023219584085919145
|
| 29 |
+
flat_mae,patch,logistic,adhd200_dx,13,0.005994842503189409,test,0.5692307692307692,0.058928370921045185,0.5608108108108107,0.05983569873301991,0.5608108108108107,0.05963245183269374
|
| 30 |
+
flat_mae,patch,logistic,adhd200_dx,14,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 31 |
+
flat_mae,patch,logistic,adhd200_dx,14,1291.5496650148827,test,0.5692307692307692,0.05981323199672035,0.5565302144249512,0.06174517983844658,0.5564671814671815,0.06081732409808527
|
| 32 |
+
flat_mae,patch,logistic,adhd200_dx,15,0.046415888336127774,train,0.8301369863013699,0.019519401840903942,0.8249210868354273,0.02040913644741121,0.8215332478475912,0.020453213019935974
|
| 33 |
+
flat_mae,patch,logistic,adhd200_dx,15,0.046415888336127774,test,0.6,0.059008702597570475,0.599146110056926,0.05916422441349385,0.6052123552123552,0.05960291083022128
|
| 34 |
+
flat_mae,patch,logistic,adhd200_dx,16,0.046415888336127774,train,0.8328767123287671,0.018121915685443957,0.8282352941176471,0.01874013616495673,0.8253953715576724,0.018745191756459327
|
| 35 |
+
flat_mae,patch,logistic,adhd200_dx,16,0.046415888336127774,test,0.6461538461538462,0.061118567472911445,0.6375757575757576,0.06291265534749883,0.6370656370656371,0.06250825048776966
|
| 36 |
+
flat_mae,patch,logistic,adhd200_dx,17,0.3593813663804626,train,0.9424657534246575,0.012088004068990346,0.941078799898531,0.012461486579841268,0.9382670818831288,0.012911847149303518
|
| 37 |
+
flat_mae,patch,logistic,adhd200_dx,17,0.3593813663804626,test,0.5384615384615384,0.0585949813065891,0.5125,0.061814871274668344,0.5164092664092664,0.0590717913950893
|
| 38 |
+
flat_mae,patch,logistic,adhd200_dx,18,0.005994842503189409,train,0.7342465753424657,0.022296724634857743,0.7246910988250481,0.023410926610323114,0.7222323990962936,0.023031438677657043
|
| 39 |
+
flat_mae,patch,logistic,adhd200_dx,18,0.005994842503189409,test,0.6461538461538462,0.056830519344750696,0.6336682185738789,0.059552637021043145,0.6327220077220077,0.058212115227563706
|
| 40 |
+
flat_mae,patch,logistic,adhd200_dx,19,0.005994842503189409,train,0.7424657534246575,0.02352293156159042,0.7329212853406402,0.0250045039975823,0.7302314221163827,0.024685573715823098
|
| 41 |
+
flat_mae,patch,logistic,adhd200_dx,19,0.005994842503189409,test,0.6923076923076923,0.05602386642733127,0.675,0.06085961385867147,0.6732625482625483,0.05831985032217121
|
| 42 |
+
flat_mae,patch,logistic,adhd200_dx,20,0.005994842503189409,train,0.7178082191780822,0.023085406873359155,0.7104981480198058,0.0238451677182878,0.7091042315442388,0.02371159174713244
|
| 43 |
+
flat_mae,patch,logistic,adhd200_dx,20,0.005994842503189409,test,0.6153846153846154,0.05760032873015992,0.606060606060606,0.05934573660914931,0.6056949806949807,0.05883531048429123
|
| 44 |
+
flat_mae,patch,logistic,adhd200_dx,21,0.046415888336127774,train,0.8246575342465754,0.019970704759772227,0.8189147286821705,0.020938268572587133,0.8152439396714906,0.020938466951661593
|
| 45 |
+
flat_mae,patch,logistic,adhd200_dx,21,0.046415888336127774,test,0.676923076923077,0.057113685030397744,0.6741465743614228,0.05763711105489534,0.6771235521235521,0.05788486448308282
|
| 46 |
+
flat_mae,patch,logistic,adhd200_dx,22,0.005994842503189409,train,0.7506849315068493,0.02185562342908164,0.7422576414808837,0.022985485153403245,0.7396653843805336,0.022770114943486176
|
| 47 |
+
flat_mae,patch,logistic,adhd200_dx,22,0.005994842503189409,test,0.6,0.059927174738922485,0.588206627680312,0.062221000882740136,0.5878378378378378,0.061378710290656086
|
| 48 |
+
flat_mae,patch,logistic,adhd200_dx,23,0.005994842503189409,train,0.7506849315068493,0.02163368931590614,0.7422576414808837,0.02265790383975332,0.7396653843805336,0.02239726133949154
|
| 49 |
+
flat_mae,patch,logistic,adhd200_dx,23,0.005994842503189409,test,0.6615384615384615,0.05378760773228468,0.6366869918699187,0.059529724288847057,0.6375482625482626,0.05580578148811548
|
| 50 |
+
flat_mae,patch,logistic,adhd200_dx,24,0.046415888336127774,train,0.8191780821917808,0.019773978249212724,0.8139825796886583,0.020584542592404775,0.8111070403614825,0.02065020278575743
|
| 51 |
+
flat_mae,patch,logistic,adhd200_dx,24,0.046415888336127774,test,0.6,0.05913634031255342,0.5775,0.0631992315533304,0.5791505791505791,0.060230607542033444
|
| 52 |
+
flat_mae,patch,logistic,adhd200_dx,25,0.046415888336127774,train,0.8356164383561644,0.018574534269402337,0.8305687937117039,0.019370128230962794,0.8271050864016609,0.01945768332192771
|
| 53 |
+
flat_mae,patch,logistic,adhd200_dx,25,0.046415888336127774,test,0.5846153846153846,0.061619976780761115,0.578226387887527,0.06315977641360941,0.5786679536679536,0.06311785892153486
|
| 54 |
+
flat_mae,patch,logistic,adhd200_dx,26,0.005994842503189409,train,0.7424657534246575,0.021738841942818034,0.7334855828983347,0.022869334154562635,0.7309488917384136,0.02257580242698365
|
| 55 |
+
flat_mae,patch,logistic,adhd200_dx,26,0.005994842503189409,test,0.6615384615384615,0.058724098659014,0.6515594541910331,0.06074581919908525,0.6505791505791505,0.05993930209470447
|
| 56 |
+
flat_mae,patch,logistic,adhd200_dx,27,0.005994842503189409,train,0.7424657534246575,0.02263660990713542,0.7334855828983347,0.023978691089779656,0.7309488917384136,0.02368656922728229
|
| 57 |
+
flat_mae,patch,logistic,adhd200_dx,27,0.005994842503189409,test,0.6,0.056877520231566954,0.5833333333333333,0.05956843028997229,0.5834942084942085,0.057705504696220225
|
| 58 |
+
flat_mae,patch,logistic,adhd200_dx,28,0.046415888336127774,train,0.810958904109589,0.020320671134616376,0.8064017710951733,0.020839654197722143,0.8045429565854552,0.020776676745577972
|
| 59 |
+
flat_mae,patch,logistic,adhd200_dx,28,0.046415888336127774,test,0.6153846153846154,0.053930272755907956,0.5966741126830479,0.05742081052684551,0.597007722007722,0.055257243713488134
|
| 60 |
+
flat_mae,patch,logistic,adhd200_dx,29,0.005994842503189409,train,0.7561643835616438,0.022743290692710253,0.7484298647089345,0.023697866369226692,0.7459546925566343,0.023491036595379076
|
| 61 |
+
flat_mae,patch,logistic,adhd200_dx,29,0.005994842503189409,test,0.5692307692307692,0.06276478868143229,0.5565302144249512,0.06444298676237485,0.5564671814671815,0.0635548530250291
|
| 62 |
+
flat_mae,patch,logistic,adhd200_dx,30,0.046415888336127774,train,0.8356164383561644,0.01930405515217129,0.8298844146159583,0.020487109580543197,0.8256701471575991,0.020648897303180577
|
| 63 |
+
flat_mae,patch,logistic,adhd200_dx,30,0.046415888336127774,test,0.6307692307692307,0.05961564466444523,0.6285714285714286,0.059579812006900414,0.6322393822393823,0.059427272038066636
|
| 64 |
+
flat_mae,patch,logistic,adhd200_dx,31,0.046415888336127774,train,0.8328767123287671,0.019785981188822268,0.8282352941176471,0.0205238249906581,0.8253953715576724,0.020598521654381273
|
| 65 |
+
flat_mae,patch,logistic,adhd200_dx,31,0.046415888336127774,test,0.6461538461538462,0.05800530479719407,0.6336682185738789,0.06150288248052889,0.6327220077220077,0.06020596684783434
|
| 66 |
+
flat_mae,patch,logistic,adhd200_dx,32,0.005994842503189409,train,0.7452054794520548,0.022107585928848632,0.7371233417745046,0.02302185871275,0.7348110154484948,0.022764561343838356
|
| 67 |
+
flat_mae,patch,logistic,adhd200_dx,32,0.005994842503189409,test,0.6,0.0628561313989166,0.5921814671814671,0.06418376906639849,0.5921814671814671,0.06383197682459739
|
| 68 |
+
flat_mae,patch,logistic,adhd200_dx,33,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 69 |
+
flat_mae,patch,logistic,adhd200_dx,33,21.54434690031882,test,0.5538461538461539,0.0622304098116463,0.5500119360229172,0.0626387952436239,0.5516409266409266,0.06309120473251609
|
| 70 |
+
flat_mae,patch,logistic,adhd200_dx,34,0.046415888336127774,train,0.8465753424657534,0.018836076905953125,0.841864207464257,0.019732891649470532,0.8382487635098004,0.01994751070052175
|
| 71 |
+
flat_mae,patch,logistic,adhd200_dx,34,0.046415888336127774,test,0.5692307692307692,0.059307841963295166,0.5691287878787878,0.059511192364150374,0.5781853281853282,0.059506885850263154
|
| 72 |
+
flat_mae,patch,logistic,adhd200_dx,35,0.005994842503189409,train,0.7452054794520548,0.021911299718580773,0.7330129541218018,0.02351708576443995,0.7297887280942785,0.022860570074065095
|
| 73 |
+
flat_mae,patch,logistic,adhd200_dx,35,0.005994842503189409,test,0.5692307692307692,0.05900649648317493,0.545,0.06282426800961197,0.5477799227799228,0.06001120490252967
|
| 74 |
+
flat_mae,patch,logistic,adhd200_dx,36,0.005994842503189409,train,0.7315068493150685,0.02309707727007827,0.7221445438727319,0.024250074598592162,0.7198052146302741,0.023938386157765525
|
| 75 |
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flat_mae,patch,logistic,adhd200_dx,76,0.005994842503189409,test,0.5692307692307692,0.047984257773595294,0.49444444444444446,0.05945554593781028,0.5260617760617761,0.04889026439016208
|
| 156 |
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flat_mae,patch,logistic,adhd200_dx,77,0.005994842503189409,train,0.7287671232876712,0.021651613653575926,0.7184102863822326,0.022887097631540836,0.7159430909201929,0.022500325005264037
|
| 157 |
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flat_mae,patch,logistic,adhd200_dx,77,0.005994842503189409,test,0.7538461538461538,0.047778819798125774,0.7308488612836439,0.05699193322134115,0.7273166023166023,0.051978652905671045
|
| 158 |
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flat_mae,patch,logistic,adhd200_dx,78,0.046415888336127774,train,0.8465753424657534,0.018973123181625976,0.8435203331700147,0.01939751434460787,0.8425535812419858,0.019503414328163084
|
| 159 |
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flat_mae,patch,logistic,adhd200_dx,78,0.046415888336127774,test,0.5384615384615384,0.05826443696960106,0.5125,0.06199520002345972,0.5164092664092664,0.05920743896923701
|
| 160 |
+
flat_mae,patch,logistic,adhd200_dx,79,0.005994842503189409,train,0.7534246575342466,0.021891268178048415,0.7458531905675558,0.022825847524973318,0.7435275080906149,0.02263235333240217
|
| 161 |
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flat_mae,patch,logistic,adhd200_dx,79,0.005994842503189409,test,0.6307692307692307,0.06153556916279681,0.6198830409356726,0.06370970787584397,0.6192084942084942,0.06286786520344786
|
| 162 |
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flat_mae,patch,logistic,adhd200_dx,80,0.005994842503189409,train,0.7561643835616438,0.02175236945786224,0.7473969875817451,0.022953295386778223,0.7445197533125725,0.022658446821706976
|
| 163 |
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flat_mae,patch,logistic,adhd200_dx,80,0.005994842503189409,test,0.5692307692307692,0.06368278635633422,0.5512820512820513,0.06703395266268285,0.5521235521235521,0.06509156751962701
|
| 164 |
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flat_mae,patch,logistic,adhd200_dx,81,0.005994842503189409,train,0.7671232876712328,0.0216294899570954,0.7606557377049181,0.02239326150185837,0.7585333089088355,0.022310206337867743
|
| 165 |
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flat_mae,patch,logistic,adhd200_dx,81,0.005994842503189409,test,0.5692307692307692,0.05508103981176791,0.5376016260162602,0.06080192092237037,0.5434362934362934,0.05639591830290881
|
| 166 |
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flat_mae,patch,logistic,adhd200_dx,82,0.046415888336127774,train,0.8191780821917808,0.019835219098419468,0.8139825796886583,0.02063296181746342,0.8111070403614825,0.020646948326953726
|
| 167 |
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flat_mae,patch,logistic,adhd200_dx,82,0.046415888336127774,test,0.7538461538461538,0.05162019695553099,0.7533206831119544,0.05166426053161618,0.7620656370656371,0.0508424255968854
|
| 168 |
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flat_mae,patch,logistic,adhd200_dx,83,0.046415888336127774,train,0.8383561643835616,0.019095993396078505,0.8338669238187078,0.01976986693558508,0.8309672101117421,0.01977074055469175
|
| 169 |
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flat_mae,patch,logistic,adhd200_dx,83,0.046415888336127774,test,0.6307692307692307,0.05985116846698906,0.6285714285714286,0.059802071720001036,0.6322393822393823,0.059679002126566065
|
| 170 |
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flat_mae,patch,logistic,adhd200_dx,84,0.005994842503189409,train,0.7342465753424657,0.021879942633845018,0.7258168188400747,0.02272045341389497,0.7236673383403553,0.02246276912839147
|
| 171 |
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|
| 172 |
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flat_mae,patch,logistic,adhd200_dx,85,0.000774263682681127,train,0.6821917808219178,0.023663304100828494,0.664861955420466,0.025746745290707012,0.6639189106673994,0.024611211489327614
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|
| 174 |
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flat_mae,patch,logistic,adhd200_dx,86,0.005994842503189409,test,0.5846153846153846,0.059873436533799056,0.5578231292517006,0.06444256765520774,0.5612934362934363,0.06112126046316074
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flat_mae,patch,logistic,adhd200_dx,87,0.046415888336127774,train,0.8328767123287671,0.018794775348726103,0.8268675982301849,0.019745267605251925,0.8225254930695487,0.019694736563090107
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flat_mae,patch,logistic,adhd200_dx,87,0.046415888336127774,test,0.6,0.06034048558955614,0.588206627680312,0.06264732484240339,0.5878378378378378,0.061656597628047226
|
| 178 |
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flat_mae,patch,logistic,adhd200_dx,88,0.000774263682681127,train,0.6684931506849315,0.02398747343075741,0.6572876331778783,0.025033510093744,0.6560878060694877,0.02463193875182154
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| 180 |
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flat_mae,patch,logistic,adhd200_dx,89,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
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| 181 |
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flat_mae,patch,logistic,adhd200_dx,89,1291.5496650148827,test,0.5384615384615384,0.06321467135528122,0.5248538011695907,0.06458190248547759,0.525096525096525,0.06376958170742275
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| 182 |
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flat_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,train,0.7479452054794521,0.022257909252690257,0.7402054836912793,0.023218562078302467,0.7379556695365451,0.023029870083748735
|
| 183 |
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flat_mae,patch,logistic,adhd200_dx,90,0.005994842503189409,test,0.676923076923077,0.055375076101567916,0.656084656084656,0.06068773311275292,0.6554054054054055,0.05747458250179992
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| 184 |
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flat_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,train,0.7424657534246575,0.02191683285500999,0.7334855828983347,0.02299148988291436,0.7309488917384136,0.02273510110327788
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| 185 |
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flat_mae,patch,logistic,adhd200_dx,91,0.005994842503189409,test,0.6,0.052183417983359764,0.5626293995859213,0.05904808260148831,0.5704633204633205,0.053534701130698144
|
| 186 |
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flat_mae,patch,logistic,adhd200_dx,92,0.046415888336127774,train,0.8547945205479452,0.01756112323721347,0.8495734869868818,0.01855548151020401,0.8448128472858277,0.018740833476205424
|
| 187 |
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flat_mae,patch,logistic,adhd200_dx,92,0.046415888336127774,test,0.5692307692307692,0.06047489380780332,0.5512820512820513,0.06339115993779601,0.5521235521235521,0.06141448703895949
|
| 188 |
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flat_mae,patch,logistic,adhd200_dx,93,0.005994842503189409,train,0.7726027397260274,0.02112120878848576,0.763396099686819,0.022348323200471188,0.7598003297307199,0.022068402042590245
|
| 189 |
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flat_mae,patch,logistic,adhd200_dx,93,0.005994842503189409,test,0.6307692307692307,0.053560835289631896,0.5962732919254659,0.06107574232450808,0.6018339768339769,0.05557341325071298
|
| 190 |
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flat_mae,patch,logistic,adhd200_dx,94,21.54434690031882,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 191 |
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flat_mae,patch,logistic,adhd200_dx,94,21.54434690031882,test,0.46153846153846156,0.059742247151195735,0.42680776014109345,0.06237670618042945,0.4358108108108108,0.059775857995661465
|
| 192 |
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flat_mae,patch,logistic,adhd200_dx,95,0.3593813663804626,train,0.947945205479452,0.01092634066134226,0.9466903427653375,0.011291698740663674,0.9438389204371985,0.011899690614760962
|
| 193 |
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flat_mae,patch,logistic,adhd200_dx,95,0.3593813663804626,test,0.5230769230769231,0.05748071145479248,0.5157414083153088,0.05838678040157587,0.515926640926641,0.05851940936808584
|
| 194 |
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flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,train,0.7397260273972602,0.023218279208365885,0.7319931056337483,0.02415915329853506,0.729956646516456,0.023946321722341107
|
| 195 |
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flat_mae,patch,logistic,adhd200_dx,96,0.005994842503189409,test,0.6153846153846154,0.06276168883800703,0.61207925519217,0.06309640682047613,0.6143822393822393,0.0632309687726463
|
| 196 |
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flat_mae,patch,logistic,adhd200_dx,97,0.046415888336127774,train,0.8547945205479452,0.017658174212873266,0.8504803641956702,0.01849276676101158,0.8469652561519204,0.018751153941047056
|
| 197 |
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flat_mae,patch,logistic,adhd200_dx,97,0.046415888336127774,test,0.5538461538461539,0.05964162003363663,0.5250692869740489,0.06464514958753544,0.5299227799227799,0.060910273656580666
|
| 198 |
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flat_mae,patch,logistic,adhd200_dx,98,0.005994842503189409,train,0.7534246575342466,0.021601876443170763,0.7413059913059913,0.023202369280961723,0.7377877511143677,0.02259723061364177
|
| 199 |
+
flat_mae,patch,logistic,adhd200_dx,98,0.005994842503189409,test,0.7076923076923077,0.054406752104460464,0.6888384983623079,0.05994537932201663,0.6867760617760618,0.05665047589872905
|
| 200 |
+
flat_mae,patch,logistic,adhd200_dx,99,0.3593813663804626,train,0.9452054794520548,0.012091605067852135,0.9437353557775312,0.012564026925410515,0.9399767967271173,0.01324160548502163
|
| 201 |
+
flat_mae,patch,logistic,adhd200_dx,99,0.3593813663804626,test,0.5692307692307692,0.06084847024667873,0.564176245210728,0.061583299543997605,0.5651544401544402,0.06148896423730603
|
| 202 |
+
flat_mae,patch,logistic,adhd200_dx,100,0.005994842503189409,train,0.7424657534246575,0.022836880424542752,0.7334855828983347,0.02406249588896973,0.7309488917384136,0.023747700218743
|
| 203 |
+
flat_mae,patch,logistic,adhd200_dx,100,0.005994842503189409,test,0.6461538461538462,0.05363438797977001,0.6233308138070043,0.05929584067814305,0.6240347490347491,0.05585989378300959
|
input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic/log.txt
ADDED
|
@@ -0,0 +1,241 @@
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:56:19
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (adhd200_dx patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: adhd200_dx
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/adhd200_dx__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: adhd200_dx (flat)
|
| 70 |
+
train (n=301):
|
| 71 |
+
HFDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 301
|
| 75 |
+
}),
|
| 76 |
+
labels=['ADHD' 'Control'],
|
| 77 |
+
counts=[131 170]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=64):
|
| 81 |
+
HFDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 64
|
| 85 |
+
}),
|
| 86 |
+
labels=['ADHD' 'Control'],
|
| 87 |
+
counts=[28 36]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=65):
|
| 91 |
+
HFDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'site', 'gender', 'dx', 'path', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 65
|
| 95 |
+
}),
|
| 96 |
+
labels=['ADHD' 'Control'],
|
| 97 |
+
counts=[28 37]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/151] eta: 0:10:18 time: 4.0990 data: 3.3680 max mem: 2698
|
| 102 |
+
extract (train) [ 20/151] eta: 0:00:47 time: 0.1784 data: 0.0568 max mem: 2851
|
| 103 |
+
extract (train) [ 40/151] eta: 0:00:29 time: 0.1569 data: 0.0451 max mem: 2851
|
| 104 |
+
extract (train) [ 60/151] eta: 0:00:20 time: 0.1580 data: 0.0469 max mem: 2851
|
| 105 |
+
extract (train) [ 80/151] eta: 0:00:14 time: 0.1520 data: 0.0440 max mem: 2851
|
| 106 |
+
extract (train) [100/151] eta: 0:00:10 time: 0.1526 data: 0.0428 max mem: 2851
|
| 107 |
+
extract (train) [120/151] eta: 0:00:05 time: 0.1503 data: 0.0413 max mem: 2851
|
| 108 |
+
extract (train) [140/151] eta: 0:00:01 time: 0.1280 data: 0.0319 max mem: 2851
|
| 109 |
+
extract (train) [150/151] eta: 0:00:00 time: 0.1243 data: 0.0307 max mem: 2851
|
| 110 |
+
extract (train) Total time: 0:00:27 (0.1797 s / it)
|
| 111 |
+
extract (validation) [ 0/32] eta: 0:01:56 time: 3.6309 data: 3.5041 max mem: 2851
|
| 112 |
+
extract (validation) [20/32] eta: 0:00:03 time: 0.1555 data: 0.0436 max mem: 2851
|
| 113 |
+
extract (validation) [31/32] eta: 0:00:00 time: 0.1295 data: 0.0333 max mem: 2851
|
| 114 |
+
extract (validation) Total time: 0:00:08 (0.2623 s / it)
|
| 115 |
+
extract (test) [ 0/33] eta: 0:01:56 time: 3.5434 data: 3.3800 max mem: 2851
|
| 116 |
+
extract (test) [20/33] eta: 0:00:04 time: 0.1552 data: 0.0470 max mem: 2851
|
| 117 |
+
extract (test) [32/33] eta: 0:00:00 time: 0.1234 data: 0.0303 max mem: 2851
|
| 118 |
+
extract (test) Total time: 0:00:08 (0.2530 s / it)
|
| 119 |
+
feature extraction time: 0:00:43
|
| 120 |
+
train features: (301, 768)
|
| 121 |
+
validation features: (64, 768)
|
| 122 |
+
test features: (65, 768)
|
| 123 |
+
evaluating fixed splits
|
| 124 |
+
eval results (fixed splits):
|
| 125 |
+
|
| 126 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 127 |
+
|:---------|:-------|:---------|:-----------|:--------|----------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 128 |
+
| flat_mae | patch | logistic | adhd200_dx | | 0.0059948 | train | 0.75068 | 0.021033 | 0.74172 | 0.022139 | 0.73895 | 0.021782 |
|
| 129 |
+
| flat_mae | patch | logistic | adhd200_dx | | 0.0059948 | test | 0.63077 | 0.060115 | 0.61538 | 0.063992 | 0.61486 | 0.062106 |
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
evaluating random splits (n=100)
|
| 133 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 1, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05657085205099131, "f1": 0.6018132810585641, "f1_std": 0.058867672202832906, "bacc": 0.6013513513513513, "bacc_std": 0.0577086690193027}
|
| 134 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.05586201088714073, "f1": 0.6934723256391164, "f1_std": 0.060083266628473006, "bacc": 0.6911196911196911, "bacc_std": 0.058027296614592064}
|
| 135 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05657084368320474, "f1": 0.5250692869740489, "f1_std": 0.06082632171750404, "bacc": 0.5299227799227799, "bacc_std": 0.05750249631133549}
|
| 136 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 4, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.055447647892446564, "f1": 0.6235521235521235, "f1_std": 0.05708092120695752, "bacc": 0.6235521235521235, "bacc_std": 0.05713868765278625}
|
| 137 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 5, "C": 0.005994842503189409, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.060269249914090516, "f1": 0.5357142857142857, "f1_std": 0.06087362111006021, "bacc": 0.5381274131274132, "bacc_std": 0.0618712679131066}
|
| 138 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 6, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.059627014250273905, "f1": 0.6549227799227799, "f1_std": 0.06133635184716082, "bacc": 0.6549227799227799, "bacc_std": 0.061049863037492456}
|
| 139 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 7, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05505655833217888, "f1": 0.6018132810585641, "f1_std": 0.057709651438696934, "bacc": 0.6013513513513513, "bacc_std": 0.056423667885912875}
|
| 140 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 8, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.06367000724426854, "f1": 0.5921814671814671, "f1_std": 0.06533749192841934, "bacc": 0.5921814671814671, "bacc_std": 0.06472219754155917}
|
| 141 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 9, "C": 0.005994842503189409, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.06053548814905012, "f1": 0.5578231292517006, "f1_std": 0.06545658781457737, "bacc": 0.5612934362934363, "bacc_std": 0.06187049197900358}
|
| 142 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 10, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.061636621712214845, "f1": 0.5921814671814671, "f1_std": 0.06303245627588339, "bacc": 0.5921814671814671, "bacc_std": 0.06275480160168166}
|
| 143 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 11, "C": 0.046415888336127774, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.05904114301101679, "f1": 0.5248538011695907, "f1_std": 0.061124803863014436, "bacc": 0.525096525096525, "bacc_std": 0.06010487491403585}
|
| 144 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 12, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.058723752036585995, "f1": 0.5512820512820513, "f1_std": 0.06287896035508381, "bacc": 0.5521235521235521, "bacc_std": 0.06070224342857615}
|
| 145 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 13, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.058928370921045185, "f1": 0.5608108108108107, "f1_std": 0.05983569873301991, "bacc": 0.5608108108108107, "bacc_std": 0.05963245183269374}
|
| 146 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 14, "C": 1291.5496650148827, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05981323199672035, "f1": 0.5565302144249512, "f1_std": 0.06174517983844658, "bacc": 0.5564671814671815, "bacc_std": 0.06081732409808527}
|
| 147 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 15, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.059008702597570475, "f1": 0.599146110056926, "f1_std": 0.05916422441349385, "bacc": 0.6052123552123552, "bacc_std": 0.05960291083022128}
|
| 148 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 16, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.061118567472911445, "f1": 0.6375757575757576, "f1_std": 0.06291265534749883, "bacc": 0.6370656370656371, "bacc_std": 0.06250825048776966}
|
| 149 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 17, "C": 0.3593813663804626, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.0585949813065891, "f1": 0.5125, "f1_std": 0.061814871274668344, "bacc": 0.5164092664092664, "bacc_std": 0.0590717913950893}
|
| 150 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 18, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.056830519344750696, "f1": 0.6336682185738789, "f1_std": 0.059552637021043145, "bacc": 0.6327220077220077, "bacc_std": 0.058212115227563706}
|
| 151 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 19, "C": 0.005994842503189409, "split": "test", "acc": 0.6923076923076923, "acc_std": 0.05602386642733127, "f1": 0.675, "f1_std": 0.06085961385867147, "bacc": 0.6732625482625483, "bacc_std": 0.05831985032217121}
|
| 152 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 20, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.05760032873015992, "f1": 0.606060606060606, "f1_std": 0.05934573660914931, "bacc": 0.6056949806949807, "bacc_std": 0.05883531048429123}
|
| 153 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 21, "C": 0.046415888336127774, "split": "test", "acc": 0.676923076923077, "acc_std": 0.057113685030397744, "f1": 0.6741465743614228, "f1_std": 0.05763711105489534, "bacc": 0.6771235521235521, "bacc_std": 0.05788486448308282}
|
| 154 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 22, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.059927174738922485, "f1": 0.588206627680312, "f1_std": 0.062221000882740136, "bacc": 0.5878378378378378, "bacc_std": 0.061378710290656086}
|
| 155 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 23, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.05378760773228468, "f1": 0.6366869918699187, "f1_std": 0.059529724288847057, "bacc": 0.6375482625482626, "bacc_std": 0.05580578148811548}
|
| 156 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 24, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.05913634031255342, "f1": 0.5775, "f1_std": 0.0631992315533304, "bacc": 0.5791505791505791, "bacc_std": 0.060230607542033444}
|
| 157 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 25, "C": 0.046415888336127774, "split": "test", "acc": 0.5846153846153846, "acc_std": 0.061619976780761115, "f1": 0.578226387887527, "f1_std": 0.06315977641360941, "bacc": 0.5786679536679536, "bacc_std": 0.06311785892153486}
|
| 158 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 26, "C": 0.005994842503189409, "split": "test", "acc": 0.6615384615384615, "acc_std": 0.058724098659014, "f1": 0.6515594541910331, "f1_std": 0.06074581919908525, "bacc": 0.6505791505791505, "bacc_std": 0.05993930209470447}
|
| 159 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 27, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.056877520231566954, "f1": 0.5833333333333333, "f1_std": 0.05956843028997229, "bacc": 0.5834942084942085, "bacc_std": 0.057705504696220225}
|
| 160 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 28, "C": 0.046415888336127774, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.053930272755907956, "f1": 0.5966741126830479, "f1_std": 0.05742081052684551, "bacc": 0.597007722007722, "bacc_std": 0.055257243713488134}
|
| 161 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 29, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06276478868143229, "f1": 0.5565302144249512, "f1_std": 0.06444298676237485, "bacc": 0.5564671814671815, "bacc_std": 0.0635548530250291}
|
| 162 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 30, "C": 0.046415888336127774, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.05961564466444523, "f1": 0.6285714285714286, "f1_std": 0.059579812006900414, "bacc": 0.6322393822393823, "bacc_std": 0.059427272038066636}
|
| 163 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 31, "C": 0.046415888336127774, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05800530479719407, "f1": 0.6336682185738789, "f1_std": 0.06150288248052889, "bacc": 0.6327220077220077, "bacc_std": 0.06020596684783434}
|
| 164 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 32, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.0628561313989166, "f1": 0.5921814671814671, "f1_std": 0.06418376906639849, "bacc": 0.5921814671814671, "bacc_std": 0.06383197682459739}
|
| 165 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 33, "C": 21.54434690031882, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.0622304098116463, "f1": 0.5500119360229172, "f1_std": 0.0626387952436239, "bacc": 0.5516409266409266, "bacc_std": 0.06309120473251609}
|
| 166 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 34, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.059307841963295166, "f1": 0.5691287878787878, "f1_std": 0.059511192364150374, "bacc": 0.5781853281853282, "bacc_std": 0.059506885850263154}
|
| 167 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 35, "C": 0.005994842503189409, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.05900649648317493, "f1": 0.545, "f1_std": 0.06282426800961197, "bacc": 0.5477799227799228, "bacc_std": 0.06001120490252967}
|
| 168 |
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| 219 |
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{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 87, "C": 0.046415888336127774, "split": "test", "acc": 0.6, "acc_std": 0.06034048558955614, "f1": 0.588206627680312, "f1_std": 0.06264732484240339, "bacc": 0.5878378378378378, "bacc_std": 0.061656597628047226}
|
| 220 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 88, "C": 0.000774263682681127, "split": "test", "acc": 0.6, "acc_std": 0.057619679412691124, "f1": 0.588206627680312, "f1_std": 0.0598885616826529, "bacc": 0.5878378378378378, "bacc_std": 0.059032123575810815}
|
| 221 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 89, "C": 1291.5496650148827, "split": "test", "acc": 0.5384615384615384, "acc_std": 0.06321467135528122, "f1": 0.5248538011695907, "f1_std": 0.06458190248547759, "bacc": 0.525096525096525, "bacc_std": 0.06376958170742275}
|
| 222 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 90, "C": 0.005994842503189409, "split": "test", "acc": 0.676923076923077, "acc_std": 0.055375076101567916, "f1": 0.656084656084656, "f1_std": 0.06068773311275292, "bacc": 0.6554054054054055, "bacc_std": 0.05747458250179992}
|
| 223 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 91, "C": 0.005994842503189409, "split": "test", "acc": 0.6, "acc_std": 0.052183417983359764, "f1": 0.5626293995859213, "f1_std": 0.05904808260148831, "bacc": 0.5704633204633205, "bacc_std": 0.053534701130698144}
|
| 224 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 92, "C": 0.046415888336127774, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06047489380780332, "f1": 0.5512820512820513, "f1_std": 0.06339115993779601, "bacc": 0.5521235521235521, "bacc_std": 0.06141448703895949}
|
| 225 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 93, "C": 0.005994842503189409, "split": "test", "acc": 0.6307692307692307, "acc_std": 0.053560835289631896, "f1": 0.5962732919254659, "f1_std": 0.06107574232450808, "bacc": 0.6018339768339769, "bacc_std": 0.05557341325071298}
|
| 226 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 94, "C": 21.54434690031882, "split": "test", "acc": 0.46153846153846156, "acc_std": 0.059742247151195735, "f1": 0.42680776014109345, "f1_std": 0.06237670618042945, "bacc": 0.4358108108108108, "bacc_std": 0.059775857995661465}
|
| 227 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 95, "C": 0.3593813663804626, "split": "test", "acc": 0.5230769230769231, "acc_std": 0.05748071145479248, "f1": 0.5157414083153088, "f1_std": 0.05838678040157587, "bacc": 0.515926640926641, "bacc_std": 0.05851940936808584}
|
| 228 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 96, "C": 0.005994842503189409, "split": "test", "acc": 0.6153846153846154, "acc_std": 0.06276168883800703, "f1": 0.61207925519217, "f1_std": 0.06309640682047613, "bacc": 0.6143822393822393, "bacc_std": 0.0632309687726463}
|
| 229 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 97, "C": 0.046415888336127774, "split": "test", "acc": 0.5538461538461539, "acc_std": 0.05964162003363663, "f1": 0.5250692869740489, "f1_std": 0.06464514958753544, "bacc": 0.5299227799227799, "bacc_std": 0.060910273656580666}
|
| 230 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 98, "C": 0.005994842503189409, "split": "test", "acc": 0.7076923076923077, "acc_std": 0.054406752104460464, "f1": 0.6888384983623079, "f1_std": 0.05994537932201663, "bacc": 0.6867760617760618, "bacc_std": 0.05665047589872905}
|
| 231 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 99, "C": 0.3593813663804626, "split": "test", "acc": 0.5692307692307692, "acc_std": 0.06084847024667873, "f1": 0.564176245210728, "f1_std": 0.061583299543997605, "bacc": 0.5651544401544402, "bacc_std": 0.06148896423730603}
|
| 232 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adhd200_dx", "trial": 100, "C": 0.005994842503189409, "split": "test", "acc": 0.6461538461538462, "acc_std": 0.05363438797977001, "f1": 0.6233308138070043, "f1_std": 0.05929584067814305, "bacc": 0.6240347490347491, "bacc_std": 0.05585989378300959}
|
| 233 |
+
eval results (random splits):
|
| 234 |
+
|
| 235 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 236 |
+
|:---------|:-------|:---------|:-----------|:--------|-----------:|-------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 237 |
+
| flat_mae | patch | logistic | adhd200_dx | train | 100 | 126.32 | 1013.8 | 0.79353 | 0.079023 | 0.78631 | 0.082342 | 0.78377 | 0.082462 |
|
| 238 |
+
| flat_mae | patch | logistic | adhd200_dx | test | 100 | 126.32 | 1013.8 | 0.60523 | 0.05217 | 0.59077 | 0.054208 | 0.59243 | 0.052843 |
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
done! total time: 0:04:27
|
input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
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|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_logistic
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (adni_ad_vs_cn patch logistic)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
num_workers: 16
|
| 9 |
+
batch_size: 2
|
| 10 |
+
cv_folds: 5
|
| 11 |
+
max_iter: 1000
|
| 12 |
+
Cs: 10
|
| 13 |
+
balanced_sampling: false
|
| 14 |
+
metrics:
|
| 15 |
+
- acc
|
| 16 |
+
- f1
|
| 17 |
+
- bacc
|
| 18 |
+
cv_metric: bacc
|
| 19 |
+
n_trials: 100
|
| 20 |
+
amp: true
|
| 21 |
+
device: cuda
|
| 22 |
+
seed: 4466
|
| 23 |
+
debug: false
|
| 24 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic
|
| 25 |
+
model: flat_mae
|
| 26 |
+
representation: patch
|
| 27 |
+
dataset: adni_ad_vs_cn
|
| 28 |
+
distributed: false
|
| 29 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic
|
| 30 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/eval_table.csv
ADDED
|
@@ -0,0 +1,203 @@
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,trial,C,split,acc,acc_std,f1,f1_std,bacc,bacc_std
|
| 2 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,,0.046415888336127774,train,0.9105691056910569,0.013098978153955951,0.8652465003043214,0.021310051406490168,0.8381633651259477,0.02389231517124154
|
| 3 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,,0.046415888336127774,test,0.7560975609756098,0.06551595434431708,0.6440972222222222,0.08995999243480725,0.6440972222222222,0.09090872857134842
|
| 4 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,1,0.3593813663804626,train,0.967479674796748,0.008797534391208003,0.9529616724738676,0.01312430211515462,0.9383268962116854,0.017105453955539154
|
| 5 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,1,0.3593813663804626,test,0.8048780487804879,0.05772106716173145,0.7152777777777778,0.09188674737594844,0.7016129032258065,0.08885231441732565
|
| 6 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,2,0.046415888336127774,train,0.9024390243902439,0.013667614310351985,0.8493877551020408,0.022980665902568392,0.8190278576711316,0.02523840679092013
|
| 7 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,2,0.046415888336127774,test,0.7804878048780488,0.04948936154976352,0.6328358208955224,0.09509702852427378,0.6177419354838709,0.07814552574900863
|
| 8 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,3,0.005994842503189409,train,0.8346883468834688,0.013423608724966797,0.6985011452375531,0.030976551363869734,0.6655846823896787,0.02561035865841395
|
| 9 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,3,0.005994842503189409,test,0.7317073170731707,0.040315796831050556,0.4972129319955407,0.07530524418575595,0.5177419354838709,0.052955043367994135
|
| 10 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,4,0.046415888336127774,train,0.8943089430894309,0.014761842745193892,0.8360205558277596,0.025245095260099853,0.8056331662420906,0.027154302045573623
|
| 11 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,4,0.046415888336127774,test,0.9024390243902439,0.04333234184866118,0.8576388888888888,0.07048185550114676,0.8338709677419355,0.07640240011266705
|
| 12 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,5,1291.5496650148827,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 13 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,5,1291.5496650148827,test,0.6341463414634146,0.048474430112966975,0.3880597014925373,0.018404531053524797,0.41935483870967744,0.032055671526316866
|
| 14 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,6,0.3593813663804626,train,0.9701897018970189,0.008841761512589853,0.9570680628272251,0.013088803591268959,0.9441408497000575,0.01678417996589188
|
| 15 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,6,0.3593813663804626,test,0.7073170731707317,0.06160487013049667,0.5729166666666666,0.08542520268014693,0.5693548387096774,0.07954951801775696
|
| 16 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,7,0.3593813663804626,train,0.978319783197832,0.008034800301081658,0.969172932330827,0.011712220398906554,0.9615827101651737,0.015328055254749343
|
| 17 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,7,0.3593813663804626,test,0.7804878048780488,0.06336388213009106,0.7119437939110069,0.08181924682094377,0.7193548387096774,0.08586595414461688
|
| 18 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,8,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 19 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,8,2.782559402207126,test,0.7317073170731707,0.05510811395499199,0.5918552036199095,0.08479598631077041,0.5854838709677419,0.07617719710649008
|
| 20 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,9,166.81005372000556,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 21 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,9,166.81005372000556,test,0.6829268292682927,0.0743837502163761,0.6259649122807017,0.0833461020546785,0.6548387096774193,0.09394851269978446
|
| 22 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,10,0.3593813663804626,train,0.9701897018970189,0.008428382956729093,0.9570680628272251,0.012572412462751476,0.9441408497000575,0.016639284108281933
|
| 23 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,10,0.3593813663804626,test,0.7804878048780488,0.05448621086385996,0.6660633484162897,0.08981991787455793,0.6516129032258065,0.08230536647556087
|
| 24 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,11,0.3593813663804626,train,0.9701897018970189,0.008914582141185757,0.9570680628272251,0.01318756960408024,0.9441408497000575,0.01671302729239329
|
| 25 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,11,0.3593813663804626,test,0.8048780487804879,0.05031498641972994,0.6893939393939394,0.08941217908636467,0.667741935483871,0.07996798754663058
|
| 26 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,12,2.782559402207126,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 27 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,12,2.782559402207126,test,0.6829268292682927,0.07097470454539677,0.5839188134270101,0.08685483249181301,0.5870967741935484,0.08936845238090081
|
| 28 |
+
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flat_mae,patch,logistic,adni_ad_vs_cn,95,0.3593813663804626,train,0.975609756097561,0.007264755246840624,0.9645665510802881,0.010976574559082264,0.9476744186046512,0.015585434221419668
|
| 193 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,95,0.3593813663804626,test,0.7073170731707317,0.06187956293752953,0.5729166666666666,0.08759961991337345,0.5693548387096774,0.08124214035466365
|
| 194 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,96,10000.0,train,1.0,0.0,1.0,0.0,1.0,0.0
|
| 195 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,96,10000.0,test,0.6585365853658537,0.07088479648272528,0.5876436781609196,0.07819753363738673,0.6048387096774194,0.08711811751629948
|
| 196 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,train,0.9701897018970189,0.00808432290056499,0.9570680628272251,0.011963574042625578,0.9441408497000575,0.015378493343802844
|
| 197 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,97,0.3593813663804626,test,0.8048780487804879,0.05583173496665914,0.7152777777777778,0.08547602486554345,0.7016129032258065,0.0835419040952163
|
| 198 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,98,0.3593813663804626,train,0.975609756097561,0.007639152526111178,0.9648738695859115,0.011367492643705379,0.9517215876407263,0.01551739668335858
|
| 199 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,98,0.3593813663804626,test,0.6829268292682927,0.06347604906012318,0.5547201336675021,0.0858632849432197,0.5532258064516129,0.08246401390916165
|
| 200 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,99,0.046415888336127774,train,0.8997289972899729,0.01358691917855534,0.8444297580930026,0.023371130323093015,0.8132139041827595,0.025665609434646606
|
| 201 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,99,0.046415888336127774,test,0.8292682926829268,0.04503881596452131,0.7144278606965174,0.09544332344996444,0.6838709677419355,0.08096156068167465
|
| 202 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,100,0.046415888336127774,train,0.9105691056910569,0.013611464462223338,0.8626194478603744,0.022970821042755798,0.8324225491001725,0.025816371201896924
|
| 203 |
+
flat_mae,patch,logistic,adni_ad_vs_cn,100,0.046415888336127774,test,0.7317073170731707,0.050689851401421133,0.5512437810945273,0.08822454545201851,0.5516129032258065,0.07068005759125426
|
input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic/log.txt
ADDED
|
@@ -0,0 +1,240 @@
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|
| 1 |
+
fMRI foundation model logistic probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 22:56:22
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_logistic
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (adni_ad_vs_cn patch logistic)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
num_workers: 16
|
| 15 |
+
batch_size: 2
|
| 16 |
+
cv_folds: 5
|
| 17 |
+
max_iter: 1000
|
| 18 |
+
Cs: 10
|
| 19 |
+
balanced_sampling: false
|
| 20 |
+
metrics:
|
| 21 |
+
- acc
|
| 22 |
+
- f1
|
| 23 |
+
- bacc
|
| 24 |
+
cv_metric: bacc
|
| 25 |
+
n_trials: 100
|
| 26 |
+
amp: true
|
| 27 |
+
device: cuda
|
| 28 |
+
seed: 4466
|
| 29 |
+
debug: false
|
| 30 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic
|
| 31 |
+
model: flat_mae
|
| 32 |
+
representation: patch
|
| 33 |
+
dataset: adni_ad_vs_cn
|
| 34 |
+
distributed: false
|
| 35 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/adni_ad_vs_cn__patch__logistic
|
| 36 |
+
remote_dir: null
|
| 37 |
+
|
| 38 |
+
creating frozen backbone model: flat_mae
|
| 39 |
+
backbone:
|
| 40 |
+
MaskedEncoderWrapper(
|
| 41 |
+
(model): MaskedEncoder(
|
| 42 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 43 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 44 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 45 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 46 |
+
(blocks): ModuleList(
|
| 47 |
+
(0-11): 12 x Block(
|
| 48 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 49 |
+
(attn): Attention(
|
| 50 |
+
num_heads=12
|
| 51 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 52 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 53 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 54 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 55 |
+
)
|
| 56 |
+
(drop_path1): Identity()
|
| 57 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 58 |
+
(mlp): Mlp(
|
| 59 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 60 |
+
(act): GELU(approximate='none')
|
| 61 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 62 |
+
)
|
| 63 |
+
(drop_path2): Identity()
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 67 |
+
)
|
| 68 |
+
)
|
| 69 |
+
creating dataset: adni_ad_vs_cn (flat)
|
| 70 |
+
train (n=328):
|
| 71 |
+
ADNIDataset(
|
| 72 |
+
dataset=Dataset({
|
| 73 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
|
| 74 |
+
num_rows: 525
|
| 75 |
+
}),
|
| 76 |
+
labels=[0 1],
|
| 77 |
+
counts=[251 77]
|
| 78 |
+
)
|
| 79 |
+
|
| 80 |
+
validation (n=41):
|
| 81 |
+
ADNIDataset(
|
| 82 |
+
dataset=Dataset({
|
| 83 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
|
| 84 |
+
num_rows: 66
|
| 85 |
+
}),
|
| 86 |
+
labels=[0 1],
|
| 87 |
+
counts=[31 10]
|
| 88 |
+
)
|
| 89 |
+
|
| 90 |
+
test (n=41):
|
| 91 |
+
ADNIDataset(
|
| 92 |
+
dataset=Dataset({
|
| 93 |
+
features: ['sub', 'visit', 'mod', 'task', 'path', 'start', 'end', 'tr', 'bold', 'mean', 'std'],
|
| 94 |
+
num_rows: 66
|
| 95 |
+
}),
|
| 96 |
+
labels=[0 1],
|
| 97 |
+
counts=[32 9]
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
extracting features for all splits
|
| 101 |
+
extract (train) [ 0/164] eta: 0:12:08 time: 4.4426 data: 3.6036 max mem: 2698
|
| 102 |
+
extract (train) [ 20/164] eta: 0:00:55 time: 0.1855 data: 0.0658 max mem: 2851
|
| 103 |
+
extract (train) [ 40/164] eta: 0:00:36 time: 0.1898 data: 0.0643 max mem: 2851
|
| 104 |
+
extract (train) [ 60/164] eta: 0:00:26 time: 0.1717 data: 0.0551 max mem: 2851
|
| 105 |
+
extract (train) [ 80/164] eta: 0:00:19 time: 0.1747 data: 0.0562 max mem: 2851
|
| 106 |
+
extract (train) [100/164] eta: 0:00:14 time: 0.1780 data: 0.0566 max mem: 2851
|
| 107 |
+
extract (train) [120/164] eta: 0:00:09 time: 0.1780 data: 0.0591 max mem: 2851
|
| 108 |
+
extract (train) [140/164] eta: 0:00:04 time: 0.1685 data: 0.0531 max mem: 2851
|
| 109 |
+
extract (train) [160/164] eta: 0:00:00 time: 0.1508 data: 0.0460 max mem: 2851
|
| 110 |
+
extract (train) [163/164] eta: 0:00:00 time: 0.1513 data: 0.0458 max mem: 2851
|
| 111 |
+
extract (train) Total time: 0:00:33 (0.2030 s / it)
|
| 112 |
+
extract (validation) [ 0/21] eta: 0:01:21 time: 3.8688 data: 3.7415 max mem: 2851
|
| 113 |
+
extract (validation) [20/21] eta: 0:00:00 time: 0.1630 data: 0.0521 max mem: 2851
|
| 114 |
+
extract (validation) Total time: 0:00:07 (0.3574 s / it)
|
| 115 |
+
extract (test) [ 0/21] eta: 0:01:23 time: 3.9748 data: 3.8661 max mem: 2851
|
| 116 |
+
extract (test) [20/21] eta: 0:00:00 time: 0.1448 data: 0.0402 max mem: 2851
|
| 117 |
+
extract (test) Total time: 0:00:07 (0.3410 s / it)
|
| 118 |
+
feature extraction time: 0:00:48
|
| 119 |
+
train features: (328, 768)
|
| 120 |
+
validation features: (41, 768)
|
| 121 |
+
test features: (41, 768)
|
| 122 |
+
evaluating fixed splits
|
| 123 |
+
eval results (fixed splits):
|
| 124 |
+
|
| 125 |
+
| model | repr | clf | dataset | trial | C | split | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 126 |
+
|:---------|:-------|:---------|:--------------|:--------|---------:|:--------|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 127 |
+
| flat_mae | patch | logistic | adni_ad_vs_cn | | 0.046416 | train | 0.91057 | 0.013099 | 0.86525 | 0.02131 | 0.83816 | 0.023892 |
|
| 128 |
+
| flat_mae | patch | logistic | adni_ad_vs_cn | | 0.046416 | test | 0.7561 | 0.065516 | 0.6441 | 0.08996 | 0.6441 | 0.090909 |
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
evaluating random splits (n=100)
|
| 132 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 1, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05772106716173145, "f1": 0.7152777777777778, "f1_std": 0.09188674737594844, "bacc": 0.7016129032258065, "bacc_std": 0.08885231441732565}
|
| 133 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 2, "C": 0.046415888336127774, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.04948936154976352, "f1": 0.6328358208955224, "f1_std": 0.09509702852427378, "bacc": 0.6177419354838709, "bacc_std": 0.07814552574900863}
|
| 134 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 3, "C": 0.005994842503189409, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.040315796831050556, "f1": 0.4972129319955407, "f1_std": 0.07530524418575595, "bacc": 0.5177419354838709, "bacc_std": 0.052955043367994135}
|
| 135 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 4, "C": 0.046415888336127774, "split": "test", "acc": 0.9024390243902439, "acc_std": 0.04333234184866118, "f1": 0.8576388888888888, "f1_std": 0.07048185550114676, "bacc": 0.8338709677419355, "bacc_std": 0.07640240011266705}
|
| 136 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 5, "C": 1291.5496650148827, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.048474430112966975, "f1": 0.3880597014925373, "f1_std": 0.018404531053524797, "bacc": 0.41935483870967744, "bacc_std": 0.032055671526316866}
|
| 137 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 6, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06160487013049667, "f1": 0.5729166666666666, "f1_std": 0.08542520268014693, "bacc": 0.5693548387096774, "bacc_std": 0.07954951801775696}
|
| 138 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 7, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.06336388213009106, "f1": 0.7119437939110069, "f1_std": 0.08181924682094377, "bacc": 0.7193548387096774, "bacc_std": 0.08586595414461688}
|
| 139 |
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{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 58, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.06056651055716179, "f1": 0.7354838709677419, "f1_std": 0.08229161822553517, "bacc": 0.7354838709677419, "bacc_std": 0.08504692338903062}
|
| 190 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 59, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.06539089436635748, "f1": 0.6893939393939394, "f1_std": 0.07873046781381904, "bacc": 0.7032258064516128, "bacc_std": 0.08486406857602193}
|
| 191 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 60, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.0727548997064177, "f1": 0.646551724137931, "f1_std": 0.08238667717675084, "bacc": 0.6709677419354838, "bacc_std": 0.08972453832902055}
|
| 192 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 61, "C": 0.046415888336127774, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06889177987916767, "f1": 0.5839188134270101, "f1_std": 0.08451462393625814, "bacc": 0.5870967741935484, "bacc_std": 0.0864585839750806}
|
| 193 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 62, "C": 166.81005372000556, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.0630877880936059, "f1": 0.6232247284878863, "f1_std": 0.08420461539163546, "bacc": 0.6193548387096774, "bacc_std": 0.08366190438650989}
|
| 194 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 63, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05094166217032762, "f1": 0.7152777777777778, "f1_std": 0.07911360883864425, "bacc": 0.7016129032258065, "bacc_std": 0.07798504852832124}
|
| 195 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 64, "C": 2.782559402207126, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.07447262931009828, "f1": 0.5651515151515152, "f1_std": 0.08483244617511154, "bacc": 0.5709677419354839, "bacc_std": 0.09090936447598608}
|
| 196 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 65, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.0625238752732031, "f1": 0.6917293233082706, "f1_std": 0.08760925198585878, "bacc": 0.685483870967742, "bacc_std": 0.0871346476197434}
|
| 197 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 66, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06861337595472779, "f1": 0.6272727272727273, "f1_std": 0.08447265824843692, "bacc": 0.6370967741935484, "bacc_std": 0.0889027971219591}
|
| 198 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 67, "C": 2.782559402207126, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.06133133311104423, "f1": 0.7354838709677419, "f1_std": 0.08441643699283095, "bacc": 0.7354838709677419, "bacc_std": 0.08648383751849287}
|
| 199 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 68, "C": 0.3593813663804626, "split": "test", "acc": 0.5853658536585366, "acc_std": 0.06926252122984561, "f1": 0.4558938329430133, "f1_std": 0.0760943926911703, "bacc": 0.45483870967741935, "bacc_std": 0.0792220496640024}
|
| 200 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 69, "C": 166.81005372000556, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.060298602598338163, "f1": 0.6440972222222222, "f1_std": 0.08912436484996128, "bacc": 0.635483870967742, "bacc_std": 0.08462197724148299}
|
| 201 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 70, "C": 0.3593813663804626, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.0536589578697431, "f1": 0.6117424242424243, "f1_std": 0.0903626150459482, "bacc": 0.6016129032258064, "bacc_std": 0.07768401027659094}
|
| 202 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 71, "C": 0.3593813663804626, "split": "test", "acc": 0.6341463414634146, "acc_std": 0.07031744118073288, "f1": 0.5199063231850116, "f1_std": 0.08065899176442085, "bacc": 0.5209677419354839, "bacc_std": 0.0822724042529503}
|
| 203 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 72, "C": 166.81005372000556, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.056422305005042944, "f1": 0.5918552036199095, "f1_std": 0.08697928486776278, "bacc": 0.5854838709677419, "bacc_std": 0.07775391421567457}
|
| 204 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 73, "C": 0.046415888336127774, "split": "test", "acc": 0.7560975609756098, "acc_std": 0.0648849253645235, "f1": 0.6693548387096775, "f1_std": 0.08680507375495873, "bacc": 0.6693548387096775, "bacc_std": 0.08864495815115005}
|
| 205 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 74, "C": 0.005994842503189409, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.02680992650920385, "f1": 0.4225352112676056, "f1_std": 0.009132940949010071, "bacc": 0.4838709677419355, "bacc_std": 0.01772914494963481}
|
| 206 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 75, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.05525208037246046, "f1": 0.5340909090909092, "f1_std": 0.08483795529801211, "bacc": 0.535483870967742, "bacc_std": 0.07183977170876543}
|
| 207 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 76, "C": 2.782559402207126, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06392075700960172, "f1": 0.5839188134270101, "f1_std": 0.08103905589431355, "bacc": 0.5870967741935484, "bacc_std": 0.08366696399226915}
|
| 208 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 77, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.048532148344808734, "f1": 0.6893939393939394, "f1_std": 0.08981962808715685, "bacc": 0.667741935483871, "bacc_std": 0.07950445140974573}
|
| 209 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 78, "C": 0.3593813663804626, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06281548098990296, "f1": 0.6232247284878863, "f1_std": 0.08690177929098686, "bacc": 0.6193548387096774, "bacc_std": 0.0845451427052248}
|
| 210 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 79, "C": 0.3593813663804626, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.05949011985615513, "f1": 0.7885040530582166, "f1_std": 0.07071713907221615, "bacc": 0.8193548387096774, "bacc_std": 0.07516119313298439}
|
| 211 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 80, "C": 21.54434690031882, "split": "test", "acc": 0.6097560975609756, "acc_std": 0.06528637322938653, "f1": 0.47096774193548385, "f1_std": 0.07364532451450354, "bacc": 0.47096774193548385, "bacc_std": 0.0739888370802447}
|
| 212 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 81, "C": 166.81005372000556, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.06849093176101467, "f1": 0.6479313036690086, "f1_std": 0.0850633369206886, "bacc": 0.6532258064516129, "bacc_std": 0.08720672056890416}
|
| 213 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 82, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.046644044260348315, "f1": 0.4831932773109243, "f1_std": 0.07115341714013741, "bacc": 0.5016129032258064, "bacc_std": 0.05423888992246073}
|
| 214 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 83, "C": 21.54434690031882, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.057979688334531626, "f1": 0.5918552036199095, "f1_std": 0.08869187215220145, "bacc": 0.5854838709677419, "bacc_std": 0.08053763153739031}
|
| 215 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 84, "C": 0.3593813663804626, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.05850608961730522, "f1": 0.6917293233082706, "f1_std": 0.08591493076995431, "bacc": 0.685483870967742, "bacc_std": 0.08731922345394154}
|
| 216 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 85, "C": 0.046415888336127774, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.045403367652302365, "f1": 0.7144278606965174, "f1_std": 0.09528981987165812, "bacc": 0.6838709677419355, "bacc_std": 0.08109725017182667}
|
| 217 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 86, "C": 21.54434690031882, "split": "test", "acc": 0.6097560975609756, "acc_std": 0.06788379334691869, "f1": 0.5287356321839081, "f1_std": 0.0733625447832434, "bacc": 0.5387096774193548, "bacc_std": 0.08223643611926733}
|
| 218 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 87, "C": 166.81005372000556, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.053301785235953206, "f1": 0.7402714932126697, "f1_std": 0.08971734001023186, "bacc": 0.717741935483871, "bacc_std": 0.08560935792547489}
|
| 219 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 88, "C": 0.046415888336127774, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06605049174567386, "f1": 0.5839188134270101, "f1_std": 0.08345530153222983, "bacc": 0.5870967741935484, "bacc_std": 0.087841389259355}
|
| 220 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 89, "C": 21.54434690031882, "split": "test", "acc": 0.7804878048780488, "acc_std": 0.061126759003484535, "f1": 0.6917293233082706, "f1_std": 0.08757832176105614, "bacc": 0.685483870967742, "bacc_std": 0.0885011316999665}
|
| 221 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 90, "C": 1291.5496650148827, "split": "test", "acc": 0.6097560975609756, "acc_std": 0.0729606740943843, "f1": 0.5494505494505495, "f1_std": 0.07573116610728349, "bacc": 0.5725806451612903, "bacc_std": 0.0863931118265328}
|
| 222 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 91, "C": 10000.0, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06748884059603502, "f1": 0.5839188134270101, "f1_std": 0.08557763571711303, "bacc": 0.5870967741935484, "bacc_std": 0.09062458726448049}
|
| 223 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 92, "C": 0.3593813663804626, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.07316936584091033, "f1": 0.5651515151515152, "f1_std": 0.08546870233525211, "bacc": 0.5709677419354839, "bacc_std": 0.09055927942806688}
|
| 224 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 93, "C": 0.3593813663804626, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.06900084059183291, "f1": 0.5651515151515152, "f1_std": 0.07943439972592196, "bacc": 0.5709677419354839, "bacc_std": 0.0841138029828473}
|
| 225 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 94, "C": 0.046415888336127774, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.054918142535377044, "f1": 0.4564393939393939, "f1_std": 0.06496705836046374, "bacc": 0.4693548387096774, "bacc_std": 0.05770650237393608}
|
| 226 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 95, "C": 0.3593813663804626, "split": "test", "acc": 0.7073170731707317, "acc_std": 0.06187956293752953, "f1": 0.5729166666666666, "f1_std": 0.08759961991337345, "bacc": 0.5693548387096774, "bacc_std": 0.08124214035466365}
|
| 227 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 96, "C": 10000.0, "split": "test", "acc": 0.6585365853658537, "acc_std": 0.07088479648272528, "f1": 0.5876436781609196, "f1_std": 0.07819753363738673, "bacc": 0.6048387096774194, "bacc_std": 0.08711811751629948}
|
| 228 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 97, "C": 0.3593813663804626, "split": "test", "acc": 0.8048780487804879, "acc_std": 0.05583173496665914, "f1": 0.7152777777777778, "f1_std": 0.08547602486554345, "bacc": 0.7016129032258065, "bacc_std": 0.0835419040952163}
|
| 229 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 98, "C": 0.3593813663804626, "split": "test", "acc": 0.6829268292682927, "acc_std": 0.06347604906012318, "f1": 0.5547201336675021, "f1_std": 0.0858632849432197, "bacc": 0.5532258064516129, "bacc_std": 0.08246401390916165}
|
| 230 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 99, "C": 0.046415888336127774, "split": "test", "acc": 0.8292682926829268, "acc_std": 0.04503881596452131, "f1": 0.7144278606965174, "f1_std": 0.09544332344996444, "bacc": 0.6838709677419355, "bacc_std": 0.08096156068167465}
|
| 231 |
+
{"model": "flat_mae", "repr": "patch", "clf": "logistic", "dataset": "adni_ad_vs_cn", "trial": 100, "C": 0.046415888336127774, "split": "test", "acc": 0.7317073170731707, "acc_std": 0.050689851401421133, "f1": 0.5512437810945273, "f1_std": 0.08822454545201851, "bacc": 0.5516129032258065, "bacc_std": 0.07068005759125426}
|
| 232 |
+
eval results (random splits):
|
| 233 |
+
|
| 234 |
+
| model | repr | clf | dataset | split | n_trials | C | C_std | acc | acc_std | f1 | f1_std | bacc | bacc_std |
|
| 235 |
+
|:---------|:-------|:---------|:--------------|:--------|-----------:|------:|--------:|--------:|----------:|--------:|---------:|--------:|-----------:|
|
| 236 |
+
| flat_mae | patch | logistic | adni_ad_vs_cn | train | 100 | 352.8 | 1719.6 | 0.96222 | 0.040459 | 0.94188 | 0.066378 | 0.9285 | 0.07747 |
|
| 237 |
+
| flat_mae | patch | logistic | adni_ad_vs_cn | test | 100 | 352.8 | 1719.6 | 0.73732 | 0.066489 | 0.62318 | 0.090434 | 0.62239 | 0.083655 |
|
| 238 |
+
|
| 239 |
+
|
| 240 |
+
done! total time: 0:04:47
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/config.yaml
ADDED
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_probe
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (hcpya_task21 patch attn)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
classifier_kwargs:
|
| 9 |
+
embed_dim: null
|
| 10 |
+
dropout: 0.0
|
| 11 |
+
xavier_init: true
|
| 12 |
+
norm: true
|
| 13 |
+
lr_scale_grid:
|
| 14 |
+
- 0.02
|
| 15 |
+
- 0.023
|
| 16 |
+
- 0.028
|
| 17 |
+
- 0.033
|
| 18 |
+
- 0.038
|
| 19 |
+
- 0.045
|
| 20 |
+
- 0.053
|
| 21 |
+
- 0.062
|
| 22 |
+
- 0.074
|
| 23 |
+
- 0.087
|
| 24 |
+
- 0.1
|
| 25 |
+
- 0.12
|
| 26 |
+
- 0.14
|
| 27 |
+
- 0.17
|
| 28 |
+
- 0.2
|
| 29 |
+
- 0.23
|
| 30 |
+
- 0.27
|
| 31 |
+
- 0.32
|
| 32 |
+
- 0.38
|
| 33 |
+
- 0.44
|
| 34 |
+
- 0.52
|
| 35 |
+
- 0.61
|
| 36 |
+
- 0.72
|
| 37 |
+
- 0.85
|
| 38 |
+
- 1
|
| 39 |
+
- 1.2
|
| 40 |
+
- 1.4
|
| 41 |
+
- 1.6
|
| 42 |
+
- 1.9
|
| 43 |
+
- 2.3
|
| 44 |
+
- 2.7
|
| 45 |
+
- 3.1
|
| 46 |
+
- 3.7
|
| 47 |
+
- 4.3
|
| 48 |
+
- 5.1
|
| 49 |
+
- 6
|
| 50 |
+
- 7.1
|
| 51 |
+
- 8.3
|
| 52 |
+
- 9.8
|
| 53 |
+
- 12
|
| 54 |
+
- 14
|
| 55 |
+
- 16
|
| 56 |
+
- 19
|
| 57 |
+
- 22
|
| 58 |
+
- 26
|
| 59 |
+
- 31
|
| 60 |
+
- 36
|
| 61 |
+
- 43
|
| 62 |
+
- 50
|
| 63 |
+
wd_scale_grid:
|
| 64 |
+
- 1.0
|
| 65 |
+
num_workers: 8
|
| 66 |
+
prefetch_factor: null
|
| 67 |
+
balanced_sampling: false
|
| 68 |
+
epochs: 20
|
| 69 |
+
steps_per_epoch: 200
|
| 70 |
+
batch_size: 64
|
| 71 |
+
accum_iter: 2
|
| 72 |
+
lr: 0.0003
|
| 73 |
+
warmup_epochs: 5
|
| 74 |
+
no_decay: false
|
| 75 |
+
weight_decay: 0.05
|
| 76 |
+
clip_grad: 1.0
|
| 77 |
+
metrics:
|
| 78 |
+
- acc
|
| 79 |
+
- f1
|
| 80 |
+
cv_metric: acc
|
| 81 |
+
early_stopping: true
|
| 82 |
+
amp: true
|
| 83 |
+
device: cuda
|
| 84 |
+
seed: 4466
|
| 85 |
+
debug: false
|
| 86 |
+
wandb: false
|
| 87 |
+
wandb_entity: null
|
| 88 |
+
wandb_project: fMRI-fm-eval
|
| 89 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn
|
| 90 |
+
model: flat_mae
|
| 91 |
+
representation: patch
|
| 92 |
+
classifier: attn
|
| 93 |
+
dataset: hcpya_task21
|
| 94 |
+
distributed: false
|
| 95 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn
|
| 96 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/epoch": 9, "eval/id_best": 34, "eval/lr_best": 0.0015299999999999997, "eval/wd_best": 0.05, "eval/train/loss": 0.000832904304843396, "eval/train/acc": 0.9998947313016474, "eval/train/acc_std": 7.67982592474577e-05, "eval/train/f1": 0.999895114061634, "eval/train/f1_std": 7.684286005177423e-05, "eval/validation/loss": 0.02902471460402012, "eval/validation/acc": 0.9933035714285714, "eval/validation/acc_std": 0.0013546527087215097, "eval/validation/f1": 0.9929024686603418, "eval/validation/f1_std": 0.0015440217723864998, "eval/test/loss": 0.05261553078889847, "eval/test/acc": 0.9876984126984127, "eval/test/acc_std": 0.0015318181988098604, "eval/test/f1": 0.98687795574217, "eval/test/f1_std": 0.0018108890242931395}
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log_best.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/best/epoch": 9, "eval/best/id_best": 34, "eval/best/lr_best": 0.0015299999999999997, "eval/best/wd_best": 0.05, "eval/best/train/loss": 0.000832904304843396, "eval/best/train/acc": 0.9998947313016474, "eval/best/train/acc_std": 7.67982592474577e-05, "eval/best/train/f1": 0.999895114061634, "eval/best/train/f1_std": 7.684286005177423e-05, "eval/best/validation/loss": 0.02902471460402012, "eval/best/validation/acc": 0.9933035714285714, "eval/best/validation/acc_std": 0.0013546527087215097, "eval/best/validation/f1": 0.9929024686603418, "eval/best/validation/f1_std": 0.0015440217723864998, "eval/best/test/loss": 0.05261553078889847, "eval/best/test/acc": 0.9876984126984127, "eval/best/test/acc_std": 0.0015318181988098604, "eval/best/test/f1": 0.98687795574217, "eval/best/test/f1_std": 0.0018108890242931395}
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_log_last.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/last/epoch": 19, "eval/last/id_best": 35, "eval/last/lr_best": 0.0018, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.8173144528409466e-05, "eval/last/train/acc": 1.0, "eval/last/train/acc_std": 0.0, "eval/last/train/f1": 1.0, "eval/last/train/f1_std": 0.0, "eval/last/validation/loss": 0.03954823687672615, "eval/last/validation/acc": 0.9930555555555556, "eval/last/validation/acc_std": 0.001342817579931478, "eval/last/validation/f1": 0.9915179400463655, "eval/last/validation/f1_std": 0.0018080190616117046, "eval/last/test/loss": 0.054730020463466644, "eval/last/test/acc": 0.989484126984127, "eval/last/test/acc_std": 0.0013613558366693656, "eval/last/test/f1": 0.9886990619628216, "eval/last/test/f1_std": 0.0016424276247519708}
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table.csv
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,0.000832904304843396,0.9998947313016474,7.67982592474577e-05,0.999895114061634,7.684286005177423e-05
|
| 3 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,0.02902471460402012,0.9933035714285714,0.0013546527087215097,0.9929024686603418,0.0015440217723864998
|
| 4 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,0.05261553078889847,0.9876984126984127,0.0015318181988098604,0.98687795574217,0.0018108890242931395
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table_best.csv
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",train,0.000832904304843396,0.9998947313016474,7.67982592474577e-05,0.999895114061634,7.684286005177423e-05
|
| 3 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",validation,0.02902471460402012,0.9933035714285714,0.0013546527087215097,0.9929024686603418,0.0015440217723864998
|
| 4 |
+
flat_mae,patch,attn,hcpya_task21,best,9,0.0015299999999999997,0.05,34,"[5.1, 1.0]",test,0.05261553078889847,0.9876984126984127,0.0015318181988098604,0.98687795574217,0.0018108890242931395
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/eval_table_last.csv
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,hcpya_task21,last,19,0.0018,0.05,35,"[6, 1.0]",train,2.8173144528409466e-05,1.0,0.0,1.0,0.0
|
| 3 |
+
flat_mae,patch,attn,hcpya_task21,last,19,0.0018,0.05,35,"[6, 1.0]",validation,0.03954823687672615,0.9930555555555556,0.001342817579931478,0.9915179400463655,0.0018080190616117046
|
| 4 |
+
flat_mae,patch,attn,hcpya_task21,last,19,0.0018,0.05,35,"[6, 1.0]",test,0.054730020463466644,0.989484126984127,0.0013613558366693656,0.9886990619628216,0.0016424276247519708
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/log.txt
ADDED
|
@@ -0,0 +1,886 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
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|
|
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|
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|
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|
| 1 |
+
fMRI foundation model probe eval
|
| 2 |
+
version: 0.1.dev86+gf97f52698
|
| 3 |
+
sha: bcce2b486277d3a5b520775efd9cdf1a2affce36, status: has uncommitted changes, branch: dev/clane9
|
| 4 |
+
cwd: /data/connor/fmri-fm
|
| 5 |
+
start: 2026-04-08 21:21:56
|
| 6 |
+
config:
|
| 7 |
+
output_root: experiments/input_space_v3/output
|
| 8 |
+
name_prefix: eval_probe
|
| 9 |
+
remote_root: null
|
| 10 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (hcpya_task21 patch attn)
|
| 11 |
+
model_kwargs:
|
| 12 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 13 |
+
dataset_kwargs: {}
|
| 14 |
+
classifier_kwargs:
|
| 15 |
+
embed_dim: null
|
| 16 |
+
dropout: 0.0
|
| 17 |
+
xavier_init: true
|
| 18 |
+
norm: true
|
| 19 |
+
lr_scale_grid:
|
| 20 |
+
- 0.02
|
| 21 |
+
- 0.023
|
| 22 |
+
- 0.028
|
| 23 |
+
- 0.033
|
| 24 |
+
- 0.038
|
| 25 |
+
- 0.045
|
| 26 |
+
- 0.053
|
| 27 |
+
- 0.062
|
| 28 |
+
- 0.074
|
| 29 |
+
- 0.087
|
| 30 |
+
- 0.1
|
| 31 |
+
- 0.12
|
| 32 |
+
- 0.14
|
| 33 |
+
- 0.17
|
| 34 |
+
- 0.2
|
| 35 |
+
- 0.23
|
| 36 |
+
- 0.27
|
| 37 |
+
- 0.32
|
| 38 |
+
- 0.38
|
| 39 |
+
- 0.44
|
| 40 |
+
- 0.52
|
| 41 |
+
- 0.61
|
| 42 |
+
- 0.72
|
| 43 |
+
- 0.85
|
| 44 |
+
- 1
|
| 45 |
+
- 1.2
|
| 46 |
+
- 1.4
|
| 47 |
+
- 1.6
|
| 48 |
+
- 1.9
|
| 49 |
+
- 2.3
|
| 50 |
+
- 2.7
|
| 51 |
+
- 3.1
|
| 52 |
+
- 3.7
|
| 53 |
+
- 4.3
|
| 54 |
+
- 5.1
|
| 55 |
+
- 6
|
| 56 |
+
- 7.1
|
| 57 |
+
- 8.3
|
| 58 |
+
- 9.8
|
| 59 |
+
- 12
|
| 60 |
+
- 14
|
| 61 |
+
- 16
|
| 62 |
+
- 19
|
| 63 |
+
- 22
|
| 64 |
+
- 26
|
| 65 |
+
- 31
|
| 66 |
+
- 36
|
| 67 |
+
- 43
|
| 68 |
+
- 50
|
| 69 |
+
wd_scale_grid:
|
| 70 |
+
- 1.0
|
| 71 |
+
num_workers: 8
|
| 72 |
+
prefetch_factor: null
|
| 73 |
+
balanced_sampling: false
|
| 74 |
+
epochs: 20
|
| 75 |
+
steps_per_epoch: 200
|
| 76 |
+
batch_size: 64
|
| 77 |
+
accum_iter: 2
|
| 78 |
+
lr: 0.0003
|
| 79 |
+
warmup_epochs: 5
|
| 80 |
+
no_decay: false
|
| 81 |
+
weight_decay: 0.05
|
| 82 |
+
clip_grad: 1.0
|
| 83 |
+
metrics:
|
| 84 |
+
- acc
|
| 85 |
+
- f1
|
| 86 |
+
cv_metric: acc
|
| 87 |
+
early_stopping: true
|
| 88 |
+
amp: true
|
| 89 |
+
device: cuda
|
| 90 |
+
seed: 4466
|
| 91 |
+
debug: false
|
| 92 |
+
wandb: false
|
| 93 |
+
wandb_entity: null
|
| 94 |
+
wandb_project: fMRI-fm-eval
|
| 95 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn
|
| 96 |
+
model: flat_mae
|
| 97 |
+
representation: patch
|
| 98 |
+
classifier: attn
|
| 99 |
+
dataset: hcpya_task21
|
| 100 |
+
distributed: false
|
| 101 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn
|
| 102 |
+
remote_dir: null
|
| 103 |
+
|
| 104 |
+
creating frozen backbone model: flat_mae
|
| 105 |
+
backbone:
|
| 106 |
+
MaskedEncoderWrapper(
|
| 107 |
+
(model): MaskedEncoder(
|
| 108 |
+
class_token=True, reg_tokens=0, no_embed_class=True, mask_drop_scale=False
|
| 109 |
+
(patchify): Patchify3D((16, 224, 560), (4, 16, 16), in_chans=1)
|
| 110 |
+
(patch_embed): Linear(in_features=1024, out_features=768, bias=True)
|
| 111 |
+
(pos_embed): SeparablePosEmbed(768, (4, 14, 35))
|
| 112 |
+
(blocks): ModuleList(
|
| 113 |
+
(0-11): 12 x Block(
|
| 114 |
+
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 115 |
+
(attn): Attention(
|
| 116 |
+
num_heads=12
|
| 117 |
+
(q): Linear(in_features=768, out_features=768, bias=True)
|
| 118 |
+
(k): Linear(in_features=768, out_features=768, bias=True)
|
| 119 |
+
(v): Linear(in_features=768, out_features=768, bias=True)
|
| 120 |
+
(proj): Linear(in_features=768, out_features=768, bias=True)
|
| 121 |
+
)
|
| 122 |
+
(drop_path1): Identity()
|
| 123 |
+
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 124 |
+
(mlp): Mlp(
|
| 125 |
+
(fc1): Linear(in_features=768, out_features=3072, bias=True)
|
| 126 |
+
(act): GELU(approximate='none')
|
| 127 |
+
(fc2): Linear(in_features=3072, out_features=768, bias=True)
|
| 128 |
+
)
|
| 129 |
+
(drop_path2): Identity()
|
| 130 |
+
)
|
| 131 |
+
)
|
| 132 |
+
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
|
| 133 |
+
)
|
| 134 |
+
)
|
| 135 |
+
creating dataset: hcpya_task21 (flat)
|
| 136 |
+
train (n=18999):
|
| 137 |
+
HFDataset(
|
| 138 |
+
dataset=Dataset({
|
| 139 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 140 |
+
num_rows: 18999
|
| 141 |
+
}),
|
| 142 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 143 |
+
counts=[ 832 1248 3201 1660 832 832 832 832 832 1248 1247 1243 832 416
|
| 144 |
+
416 416 416 416 416 416 416]
|
| 145 |
+
)
|
| 146 |
+
|
| 147 |
+
validation (n=4032):
|
| 148 |
+
HFDataset(
|
| 149 |
+
dataset=Dataset({
|
| 150 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 151 |
+
num_rows: 4032
|
| 152 |
+
}),
|
| 153 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 154 |
+
counts=[176 264 688 352 176 176 176 176 176 264 264 264 176 88 88 88 88 88
|
| 155 |
+
88 88 88]
|
| 156 |
+
)
|
| 157 |
+
|
| 158 |
+
test (n=5040):
|
| 159 |
+
HFDataset(
|
| 160 |
+
dataset=Dataset({
|
| 161 |
+
features: ['sub', 'task', 'cond', 'cond_id', 'path', 'start', 'end', 'n_frames', 'tr', 'bold', 'mean', 'std'],
|
| 162 |
+
num_rows: 5040
|
| 163 |
+
}),
|
| 164 |
+
labels=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20],
|
| 165 |
+
counts=[220 330 860 440 220 220 220 220 220 330 330 330 220 110 110 110 110 110
|
| 166 |
+
110 110 110]
|
| 167 |
+
)
|
| 168 |
+
|
| 169 |
+
running backbone on example batch to get embedding dim
|
| 170 |
+
embedding feature dim (patch): 768
|
| 171 |
+
initializing sweep of classifier heads
|
| 172 |
+
classifiers:
|
| 173 |
+
ModuleList(
|
| 174 |
+
(0-48): 49 x AttnPoolClassifier(
|
| 175 |
+
(kv): Linear(in_features=768, out_features=1536, bias=True)
|
| 176 |
+
(linear): Linear(in_features=768, out_features=21, bias=True)
|
| 177 |
+
)
|
| 178 |
+
)
|
| 179 |
+
classifier params (train): 58.7M (58.7M)
|
| 180 |
+
setting up optimizer
|
| 181 |
+
total batch size: 128 = 64 bs per gpu x 2 accum
|
| 182 |
+
lr: 3.00e-04
|
| 183 |
+
full schedule: epochs = 20 (steps = 4000) (decay = True)
|
| 184 |
+
warmup: epochs = 5 (steps = 1000)
|
| 185 |
+
start training for 20 epochs
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train: [0] [ 0/400] eta: 0:21:24 lr: nan time: 3.2105 data: 2.6988 max mem: 21740
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train: [0] [ 20/400] eta: 0:03:33 lr: 0.000003 loss: 3.0087 (3.0113) grad: 0.2415 (0.2503) time: 0.4286 data: 0.0024 max mem: 22446
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train: [0] [ 40/400] eta: 0:03:00 lr: 0.000006 loss: 2.9804 (2.9710) grad: 0.2416 (0.2492) time: 0.4363 data: 0.0033 max mem: 22446
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train: [0] [ 60/400] eta: 0:02:43 lr: 0.000009 loss: 2.8367 (2.9113) grad: 0.2447 (0.2458) time: 0.4416 data: 0.0034 max mem: 22446
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train: [0] [ 80/400] eta: 0:02:31 lr: 0.000012 loss: 2.7192 (2.8475) grad: 0.2315 (0.2404) time: 0.4498 data: 0.0032 max mem: 22446
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train: [0] [100/400] eta: 0:02:22 lr: 0.000015 loss: 2.5618 (2.7751) grad: 0.2176 (0.2369) time: 0.4846 data: 0.0034 max mem: 22446
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train: [0] [120/400] eta: 0:02:14 lr: 0.000018 loss: 2.4191 (2.7028) grad: 0.2186 (0.2328) time: 0.4987 data: 0.0035 max mem: 22446
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train: [0] [140/400] eta: 0:02:05 lr: 0.000021 loss: 2.2772 (2.6342) grad: 0.2181 (0.2314) time: 0.5021 data: 0.0033 max mem: 22446
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train: [0] [160/400] eta: 0:01:56 lr: 0.000024 loss: 2.1767 (2.5740) grad: 0.2049 (0.2272) time: 0.5153 data: 0.0035 max mem: 22446
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train: [0] [180/400] eta: 0:01:46 lr: 0.000027 loss: 2.0734 (2.5131) grad: 0.1900 (0.2234) time: 0.4630 data: 0.0033 max mem: 22446
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train: [0] [200/400] eta: 0:01:35 lr: 0.000030 loss: 2.0118 (2.4565) grad: 0.1931 (0.2206) time: 0.4314 data: 0.0033 max mem: 22446
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train: [0] [220/400] eta: 0:01:25 lr: 0.000033 loss: 1.8866 (2.4022) grad: 0.1881 (0.2173) time: 0.4381 data: 0.0034 max mem: 22446
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train: [0] [240/400] eta: 0:01:15 lr: 0.000036 loss: 1.8023 (2.3486) grad: 0.1882 (0.2155) time: 0.4442 data: 0.0033 max mem: 22446
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train: [0] [260/400] eta: 0:01:05 lr: 0.000039 loss: 1.7313 (2.2998) grad: 0.1895 (0.2134) time: 0.4291 data: 0.0033 max mem: 22446
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train: [0] [280/400] eta: 0:00:55 lr: 0.000042 loss: 1.7098 (2.2567) grad: 0.1765 (0.2106) time: 0.4311 data: 0.0034 max mem: 22446
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train: [0] [300/400] eta: 0:00:47 lr: 0.000045 loss: 1.6605 (2.2154) grad: 0.1676 (0.2077) time: 0.5959 data: 0.1700 max mem: 22446
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train: [0] [320/400] eta: 0:00:37 lr: 0.000048 loss: 1.6138 (2.1747) grad: 0.1656 (0.2052) time: 0.4576 data: 0.0031 max mem: 22446
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train: [0] [340/400] eta: 0:00:28 lr: 0.000051 loss: 1.5289 (2.1360) grad: 0.1697 (0.2035) time: 0.4565 data: 0.0033 max mem: 22446
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train: [0] [360/400] eta: 0:00:18 lr: 0.000054 loss: 1.5128 (2.1009) grad: 0.1697 (0.2015) time: 0.4559 data: 0.0032 max mem: 22446
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train: [0] [380/400] eta: 0:00:09 lr: 0.000057 loss: 1.4712 (2.0666) grad: 0.1631 (0.1993) time: 0.4473 data: 0.0032 max mem: 22446
|
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train: [0] [399/400] eta: 0:00:00 lr: 0.000060 loss: 1.4092 (2.0322) grad: 0.1599 (0.1977) time: 0.4559 data: 0.0032 max mem: 22446
|
| 207 |
+
train: [0] Total time: 0:03:08 (0.4703 s / it)
|
| 208 |
+
train: [0] Summary: lr: 0.000060 loss: 1.4092 (2.0322) grad: 0.1599 (0.1977)
|
| 209 |
+
eval (validation): [0] [ 0/63] eta: 0:03:15 time: 3.0987 data: 2.8628 max mem: 22446
|
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eval (validation): [0] [20/63] eta: 0:00:20 time: 0.3352 data: 0.0044 max mem: 22446
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eval (validation): [0] [40/63] eta: 0:00:09 time: 0.3257 data: 0.0028 max mem: 22446
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eval (validation): [0] [60/63] eta: 0:00:01 time: 0.3260 data: 0.0027 max mem: 22446
|
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eval (validation): [0] [62/63] eta: 0:00:00 time: 0.3215 data: 0.0031 max mem: 22446
|
| 214 |
+
eval (validation): [0] Total time: 0:00:23 (0.3775 s / it)
|
| 215 |
+
cv: [0] best hparam: (26, 1.0) (044) ('044_lr2.6e+01_wd1.0e+00') loss: 0.042 acc: 0.987 f1: 0.983
|
| 216 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 217 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 218 |
+
train: [1] [ 0/400] eta: 0:21:31 lr: nan time: 3.2289 data: 2.8913 max mem: 22446
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train: [1] [ 20/400] eta: 0:03:36 lr: 0.000063 loss: 1.3623 (1.3727) grad: 0.1563 (0.1616) time: 0.4366 data: 0.0031 max mem: 22446
|
| 220 |
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train: [1] [ 40/400] eta: 0:03:06 lr: 0.000066 loss: 1.3419 (1.3501) grad: 0.1586 (0.1609) time: 0.4651 data: 0.0031 max mem: 22446
|
| 221 |
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train: [1] [ 60/400] eta: 0:02:47 lr: 0.000069 loss: 1.2992 (1.3282) grad: 0.1551 (0.1578) time: 0.4436 data: 0.0034 max mem: 22446
|
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train: [1] [ 80/400] eta: 0:02:35 lr: 0.000072 loss: 1.2713 (1.3115) grad: 0.1492 (0.1562) time: 0.4557 data: 0.0033 max mem: 22446
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train: [1] [100/400] eta: 0:02:24 lr: 0.000075 loss: 1.2525 (1.2996) grad: 0.1505 (0.1558) time: 0.4619 data: 0.0032 max mem: 22446
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train: [1] [120/400] eta: 0:02:12 lr: 0.000078 loss: 1.2152 (1.2827) grad: 0.1472 (0.1550) time: 0.4448 data: 0.0033 max mem: 22446
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train: [1] [140/400] eta: 0:02:02 lr: 0.000081 loss: 1.1820 (1.2682) grad: 0.1462 (0.1536) time: 0.4410 data: 0.0033 max mem: 22446
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train: [1] [160/400] eta: 0:01:51 lr: 0.000084 loss: 1.1632 (1.2521) grad: 0.1399 (0.1525) time: 0.4425 data: 0.0030 max mem: 22446
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train: [1] [180/400] eta: 0:01:42 lr: 0.000087 loss: 1.1419 (1.2384) grad: 0.1440 (0.1517) time: 0.4459 data: 0.0032 max mem: 22446
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train: [1] [200/400] eta: 0:01:32 lr: 0.000090 loss: 1.1143 (1.2241) grad: 0.1402 (0.1507) time: 0.4457 data: 0.0032 max mem: 22446
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train: [1] [220/400] eta: 0:01:22 lr: 0.000093 loss: 1.0636 (1.2087) grad: 0.1432 (0.1506) time: 0.4484 data: 0.0033 max mem: 22446
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train: [1] [240/400] eta: 0:01:13 lr: 0.000096 loss: 1.0534 (1.1953) grad: 0.1445 (0.1499) time: 0.4528 data: 0.0031 max mem: 22446
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train: [1] [260/400] eta: 0:01:04 lr: 0.000099 loss: 1.0436 (1.1833) grad: 0.1389 (0.1491) time: 0.4466 data: 0.0031 max mem: 22446
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train: [1] [280/400] eta: 0:00:54 lr: 0.000102 loss: 1.0059 (1.1702) grad: 0.1360 (0.1488) time: 0.4451 data: 0.0033 max mem: 22446
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train: [1] [300/400] eta: 0:00:46 lr: 0.000105 loss: 0.9899 (1.1579) grad: 0.1314 (0.1476) time: 0.6225 data: 0.1740 max mem: 22446
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train: [1] [320/400] eta: 0:00:37 lr: 0.000108 loss: 0.9715 (1.1463) grad: 0.1314 (0.1466) time: 0.4387 data: 0.0030 max mem: 22446
|
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train: [1] [340/400] eta: 0:00:27 lr: 0.000111 loss: 0.9449 (1.1338) grad: 0.1303 (0.1455) time: 0.4443 data: 0.0033 max mem: 22446
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+
train: [1] [360/400] eta: 0:00:18 lr: 0.000114 loss: 0.9429 (1.1233) grad: 0.1277 (0.1445) time: 0.4473 data: 0.0032 max mem: 22446
|
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+
train: [1] [380/400] eta: 0:00:09 lr: 0.000117 loss: 0.9126 (1.1127) grad: 0.1349 (0.1442) time: 0.4453 data: 0.0032 max mem: 22446
|
| 238 |
+
train: [1] [399/400] eta: 0:00:00 lr: 0.000120 loss: 0.9126 (1.1030) grad: 0.1345 (0.1438) time: 0.4506 data: 0.0032 max mem: 22446
|
| 239 |
+
train: [1] Total time: 0:03:05 (0.4634 s / it)
|
| 240 |
+
train: [1] Summary: lr: 0.000120 loss: 0.9126 (1.1030) grad: 0.1345 (0.1438)
|
| 241 |
+
eval (validation): [1] [ 0/63] eta: 0:03:14 time: 3.0825 data: 2.8536 max mem: 22446
|
| 242 |
+
eval (validation): [1] [20/63] eta: 0:00:19 time: 0.3273 data: 0.0037 max mem: 22446
|
| 243 |
+
eval (validation): [1] [40/63] eta: 0:00:09 time: 0.3356 data: 0.0025 max mem: 22446
|
| 244 |
+
eval (validation): [1] [60/63] eta: 0:00:01 time: 0.3294 data: 0.0031 max mem: 22446
|
| 245 |
+
eval (validation): [1] [62/63] eta: 0:00:00 time: 0.3302 data: 0.0031 max mem: 22446
|
| 246 |
+
eval (validation): [1] Total time: 0:00:23 (0.3788 s / it)
|
| 247 |
+
cv: [1] best hparam: (14, 1.0) (040) ('040_lr1.4e+01_wd1.0e+00') loss: 0.033 acc: 0.990 f1: 0.988
|
| 248 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 249 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 250 |
+
train: [2] [ 0/400] eta: 0:21:36 lr: nan time: 3.2406 data: 2.8682 max mem: 22446
|
| 251 |
+
train: [2] [ 20/400] eta: 0:03:40 lr: 0.000123 loss: 0.8476 (0.8670) grad: 0.1388 (0.1379) time: 0.4482 data: 0.0033 max mem: 22446
|
| 252 |
+
train: [2] [ 40/400] eta: 0:03:05 lr: 0.000126 loss: 0.8704 (0.8736) grad: 0.1388 (0.1445) time: 0.4454 data: 0.0034 max mem: 22446
|
| 253 |
+
train: [2] [ 60/400] eta: 0:02:47 lr: 0.000129 loss: 0.8570 (0.8628) grad: 0.1363 (0.1505) time: 0.4459 data: 0.0036 max mem: 22446
|
| 254 |
+
train: [2] [ 80/400] eta: 0:02:33 lr: 0.000132 loss: 0.8371 (0.8592) grad: 0.1391 (0.1489) time: 0.4418 data: 0.0034 max mem: 22446
|
| 255 |
+
train: [2] [100/400] eta: 0:02:22 lr: 0.000135 loss: 0.8340 (0.8533) grad: 0.1371 (0.1470) time: 0.4499 data: 0.0031 max mem: 22446
|
| 256 |
+
train: [2] [120/400] eta: 0:02:11 lr: 0.000138 loss: 0.8116 (0.8495) grad: 0.1416 (0.1505) time: 0.4589 data: 0.0032 max mem: 22446
|
| 257 |
+
train: [2] [140/400] eta: 0:02:01 lr: 0.000141 loss: 0.7976 (0.8425) grad: 0.1495 (0.1505) time: 0.4502 data: 0.0033 max mem: 22446
|
| 258 |
+
train: [2] [160/400] eta: 0:01:51 lr: 0.000144 loss: 0.7820 (0.8388) grad: 0.1561 (0.1528) time: 0.4395 data: 0.0031 max mem: 22446
|
| 259 |
+
train: [2] [180/400] eta: 0:01:41 lr: 0.000147 loss: 0.7925 (0.8336) grad: 0.1596 (0.1550) time: 0.4512 data: 0.0033 max mem: 22446
|
| 260 |
+
train: [2] [200/400] eta: 0:01:32 lr: 0.000150 loss: 0.7681 (0.8257) grad: 0.1596 (0.1558) time: 0.4499 data: 0.0032 max mem: 22446
|
| 261 |
+
train: [2] [220/400] eta: 0:01:22 lr: 0.000153 loss: 0.7681 (0.8252) grad: 0.1513 (0.1566) time: 0.4503 data: 0.0032 max mem: 22446
|
| 262 |
+
train: [2] [240/400] eta: 0:01:13 lr: 0.000156 loss: 0.7742 (0.8208) grad: 0.1600 (0.1587) time: 0.4456 data: 0.0031 max mem: 22446
|
| 263 |
+
train: [2] [260/400] eta: 0:01:04 lr: 0.000159 loss: 0.7451 (0.8175) grad: 0.1637 (0.1600) time: 0.4468 data: 0.0032 max mem: 22446
|
| 264 |
+
train: [2] [280/400] eta: 0:00:54 lr: 0.000162 loss: 0.7451 (0.8148) grad: 0.1699 (0.1618) time: 0.4392 data: 0.0033 max mem: 22446
|
| 265 |
+
train: [2] [300/400] eta: 0:00:46 lr: 0.000165 loss: 0.7211 (0.8101) grad: 0.1888 (0.1641) time: 0.6064 data: 0.1726 max mem: 22446
|
| 266 |
+
train: [2] [320/400] eta: 0:00:37 lr: 0.000168 loss: 0.7097 (0.8060) grad: 0.1982 (0.1670) time: 0.4406 data: 0.0039 max mem: 22446
|
| 267 |
+
train: [2] [340/400] eta: 0:00:27 lr: 0.000171 loss: 0.7455 (0.8048) grad: 0.2014 (0.1703) time: 0.4394 data: 0.0029 max mem: 22446
|
| 268 |
+
train: [2] [360/400] eta: 0:00:18 lr: 0.000174 loss: 0.7496 (0.8034) grad: 0.2018 (0.1719) time: 0.4383 data: 0.0033 max mem: 22446
|
| 269 |
+
train: [2] [380/400] eta: 0:00:09 lr: 0.000177 loss: 0.6918 (0.7970) grad: 0.1821 (0.1724) time: 0.4379 data: 0.0033 max mem: 22446
|
| 270 |
+
train: [2] [399/400] eta: 0:00:00 lr: 0.000180 loss: 0.6472 (0.7894) grad: 0.1846 (0.1743) time: 0.4444 data: 0.0031 max mem: 22446
|
| 271 |
+
train: [2] Total time: 0:03:04 (0.4608 s / it)
|
| 272 |
+
train: [2] Summary: lr: 0.000180 loss: 0.6472 (0.7894) grad: 0.1846 (0.1743)
|
| 273 |
+
eval (validation): [2] [ 0/63] eta: 0:03:13 time: 3.0642 data: 2.8366 max mem: 22446
|
| 274 |
+
eval (validation): [2] [20/63] eta: 0:00:19 time: 0.3298 data: 0.0028 max mem: 22446
|
| 275 |
+
eval (validation): [2] [40/63] eta: 0:00:09 time: 0.3372 data: 0.0034 max mem: 22446
|
| 276 |
+
eval (validation): [2] [60/63] eta: 0:00:01 time: 0.3171 data: 0.0031 max mem: 22446
|
| 277 |
+
eval (validation): [2] [62/63] eta: 0:00:00 time: 0.3162 data: 0.0031 max mem: 22446
|
| 278 |
+
eval (validation): [2] Total time: 0:00:23 (0.3756 s / it)
|
| 279 |
+
cv: [2] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.031 acc: 0.988 f1: 0.987
|
| 280 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 281 |
+
train: [3] [ 0/400] eta: 0:21:11 lr: nan time: 3.1776 data: 2.8451 max mem: 22446
|
| 282 |
+
train: [3] [ 20/400] eta: 0:03:34 lr: 0.000183 loss: 0.6021 (0.6259) grad: 0.1826 (0.2027) time: 0.4329 data: 0.0028 max mem: 22446
|
| 283 |
+
train: [3] [ 40/400] eta: 0:03:01 lr: 0.000186 loss: 0.6503 (0.6737) grad: 0.1984 (0.2103) time: 0.4421 data: 0.0029 max mem: 22446
|
| 284 |
+
train: [3] [ 60/400] eta: 0:02:45 lr: 0.000189 loss: 0.7098 (0.7018) grad: 0.2131 (0.2276) time: 0.4479 data: 0.0032 max mem: 22446
|
| 285 |
+
train: [3] [ 80/400] eta: 0:02:33 lr: 0.000192 loss: 0.6842 (0.7124) grad: 0.2264 (0.2397) time: 0.4582 data: 0.0033 max mem: 22446
|
| 286 |
+
train: [3] [100/400] eta: 0:02:22 lr: 0.000195 loss: 0.6508 (0.7062) grad: 0.2185 (0.2377) time: 0.4564 data: 0.0033 max mem: 22446
|
| 287 |
+
train: [3] [120/400] eta: 0:02:11 lr: 0.000198 loss: 0.6585 (0.7062) grad: 0.2072 (0.2334) time: 0.4406 data: 0.0031 max mem: 22446
|
| 288 |
+
train: [3] [140/400] eta: 0:02:01 lr: 0.000201 loss: 0.6842 (0.7181) grad: 0.2356 (0.2455) time: 0.4622 data: 0.0031 max mem: 22446
|
| 289 |
+
train: [3] [160/400] eta: 0:01:51 lr: 0.000204 loss: 0.7223 (0.7162) grad: 0.2641 (0.2484) time: 0.4487 data: 0.0031 max mem: 22446
|
| 290 |
+
train: [3] [180/400] eta: 0:01:42 lr: 0.000207 loss: 0.7376 (0.7226) grad: 0.2560 (0.2532) time: 0.4524 data: 0.0031 max mem: 22446
|
| 291 |
+
train: [3] [200/400] eta: 0:01:32 lr: 0.000210 loss: 0.7334 (0.7249) grad: 0.2726 (0.2571) time: 0.4409 data: 0.0032 max mem: 22446
|
| 292 |
+
train: [3] [220/400] eta: 0:01:22 lr: 0.000213 loss: 0.7050 (0.7275) grad: 0.3152 (0.3011) time: 0.4416 data: 0.0032 max mem: 22446
|
| 293 |
+
train: [3] [240/400] eta: 0:01:13 lr: 0.000216 loss: 0.7582 (0.7486) grad: 0.3223 (0.3018) time: 0.4455 data: 0.0032 max mem: 22446
|
| 294 |
+
train: [3] [260/400] eta: 0:01:04 lr: 0.000219 loss: 0.7098 (0.7456) grad: 0.3223 (0.3044) time: 0.4465 data: 0.0031 max mem: 22446
|
| 295 |
+
train: [3] [280/400] eta: 0:00:54 lr: 0.000222 loss: 0.6091 (0.7396) grad: 0.3310 (0.3054) time: 0.4448 data: 0.0031 max mem: 22446
|
| 296 |
+
train: [3] [300/400] eta: 0:00:46 lr: 0.000225 loss: 0.6335 (0.7462) grad: 0.3597 (0.3107) time: 0.6142 data: 0.1665 max mem: 22446
|
| 297 |
+
train: [3] [320/400] eta: 0:00:37 lr: 0.000228 loss: 0.6919 (0.7454) grad: 0.3387 (0.3169) time: 0.4433 data: 0.0030 max mem: 22446
|
| 298 |
+
train: [3] [340/400] eta: 0:00:27 lr: 0.000231 loss: 0.6013 (0.7369) grad: 0.2822 (0.3158) time: 0.4504 data: 0.0032 max mem: 22446
|
| 299 |
+
train: [3] [360/400] eta: 0:00:18 lr: 0.000234 loss: 0.5964 (0.7297) grad: 0.3154 (0.3177) time: 0.4494 data: 0.0032 max mem: 22446
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train: [3] [380/400] eta: 0:00:09 lr: 0.000237 loss: 0.6082 (0.7296) grad: 0.3108 (0.3175) time: 0.4407 data: 0.0031 max mem: 22446
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train: [3] [399/400] eta: 0:00:00 lr: 0.000240 loss: 0.7205 (0.7357) grad: 0.3210 (0.3198) time: 0.4391 data: 0.0032 max mem: 22446
|
| 302 |
+
train: [3] Total time: 0:03:04 (0.4621 s / it)
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train: [3] Summary: lr: 0.000240 loss: 0.7205 (0.7357) grad: 0.3210 (0.3198)
|
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eval (validation): [3] [ 0/63] eta: 0:03:15 time: 3.1006 data: 2.8272 max mem: 22446
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eval (validation): [3] [20/63] eta: 0:00:20 time: 0.3409 data: 0.0039 max mem: 22446
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eval (validation): [3] [40/63] eta: 0:00:09 time: 0.3234 data: 0.0027 max mem: 22446
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eval (validation): [3] [60/63] eta: 0:00:01 time: 0.3116 data: 0.0029 max mem: 22446
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eval (validation): [3] [62/63] eta: 0:00:00 time: 0.3117 data: 0.0029 max mem: 22446
|
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eval (validation): [3] Total time: 0:00:23 (0.3733 s / it)
|
| 310 |
+
cv: [3] best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00') loss: 0.038 acc: 0.988 f1: 0.984
|
| 311 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
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train: [4] [ 0/400] eta: 0:21:39 lr: nan time: 3.2487 data: 2.9095 max mem: 22446
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train: [4] [ 20/400] eta: 0:03:38 lr: 0.000243 loss: 0.6176 (0.6521) grad: 0.3258 (0.3683) time: 0.4424 data: 0.0018 max mem: 22446
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train: [4] [ 40/400] eta: 0:03:04 lr: 0.000246 loss: 0.6949 (0.6905) grad: 0.3255 (0.3456) time: 0.4454 data: 0.0035 max mem: 22446
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train: [4] [ 60/400] eta: 0:02:46 lr: 0.000249 loss: 0.6382 (0.6442) grad: 0.3221 (0.4043) time: 0.4446 data: 0.0032 max mem: 22446
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train: [4] [ 80/400] eta: 0:02:34 lr: 0.000252 loss: 0.4994 (0.6340) grad: 0.2838 (0.3683) time: 0.4587 data: 0.0034 max mem: 22446
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train: [4] [100/400] eta: 0:02:23 lr: 0.000255 loss: 0.5978 (0.6355) grad: 0.2838 (0.3665) time: 0.4625 data: 0.0033 max mem: 22446
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train: [4] [120/400] eta: 0:02:13 lr: 0.000258 loss: 0.5783 (0.6404) grad: 0.3553 (0.3677) time: 0.4605 data: 0.0033 max mem: 22446
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train: [4] [140/400] eta: 0:02:03 lr: 0.000261 loss: 0.6462 (0.6516) grad: 0.3619 (0.3787) time: 0.4599 data: 0.0031 max mem: 22446
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train: [4] [160/400] eta: 0:01:53 lr: 0.000264 loss: 0.7752 (0.6845) grad: 0.4149 (0.3910) time: 0.4541 data: 0.0032 max mem: 22446
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train: [4] [180/400] eta: 0:01:43 lr: 0.000267 loss: 0.9525 (0.6970) grad: 0.4449 (0.4041) time: 0.4468 data: 0.0032 max mem: 22446
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train: [4] [200/400] eta: 0:01:33 lr: 0.000270 loss: 0.7213 (0.7051) grad: 0.4494 (0.4116) time: 0.4454 data: 0.0032 max mem: 22446
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train: [4] [220/400] eta: 0:01:23 lr: 0.000273 loss: 0.6848 (0.7174) grad: 0.4586 (0.4198) time: 0.4483 data: 0.0032 max mem: 22446
|
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train: [4] [240/400] eta: 0:01:14 lr: 0.000276 loss: 0.7474 (0.7197) grad: 0.4476 (0.4195) time: 0.4506 data: 0.0032 max mem: 22446
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train: [4] [260/400] eta: 0:01:04 lr: 0.000279 loss: 0.8331 (0.7473) grad: 0.4628 (0.4361) time: 0.4368 data: 0.0031 max mem: 22446
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train: [4] [280/400] eta: 0:00:55 lr: 0.000282 loss: 1.0497 (0.7693) grad: 0.5390 (0.4584) time: 0.4393 data: 0.0033 max mem: 22446
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train: [4] [300/400] eta: 0:00:46 lr: 0.000285 loss: 0.8867 (0.7903) grad: 0.5179 (0.4627) time: 0.5942 data: 0.1685 max mem: 22446
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train: [4] [320/400] eta: 0:00:37 lr: 0.000288 loss: 0.5922 (0.7734) grad: 0.4373 (0.4586) time: 0.4487 data: 0.0032 max mem: 22446
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train: [4] [340/400] eta: 0:00:27 lr: 0.000291 loss: 0.5672 (0.7630) grad: 0.3665 (0.4551) time: 0.4418 data: 0.0034 max mem: 22446
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train: [4] [360/400] eta: 0:00:18 lr: 0.000294 loss: 0.6040 (0.7614) grad: 0.3937 (0.4548) time: 0.4345 data: 0.0033 max mem: 22446
|
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train: [4] [380/400] eta: 0:00:09 lr: 0.000297 loss: 0.7116 (0.7731) grad: 0.4343 (0.4569) time: 0.4466 data: 0.0032 max mem: 22446
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train: [4] [399/400] eta: 0:00:00 lr: 0.000300 loss: 0.7116 (0.7748) grad: 0.4371 (0.4551) time: 0.4421 data: 0.0032 max mem: 22446
|
| 333 |
+
train: [4] Total time: 0:03:04 (0.4625 s / it)
|
| 334 |
+
train: [4] Summary: lr: 0.000300 loss: 0.7116 (0.7748) grad: 0.4371 (0.4551)
|
| 335 |
+
eval (validation): [4] [ 0/63] eta: 0:03:21 time: 3.1925 data: 2.9287 max mem: 22446
|
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eval (validation): [4] [20/63] eta: 0:00:20 time: 0.3531 data: 0.0031 max mem: 22446
|
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eval (validation): [4] [40/63] eta: 0:00:09 time: 0.3264 data: 0.0031 max mem: 22446
|
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eval (validation): [4] [60/63] eta: 0:00:01 time: 0.3192 data: 0.0030 max mem: 22446
|
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eval (validation): [4] [62/63] eta: 0:00:00 time: 0.3195 data: 0.0030 max mem: 22446
|
| 340 |
+
eval (validation): [4] Total time: 0:00:24 (0.3816 s / it)
|
| 341 |
+
cv: [4] best hparam: (1.4, 1.0) (026) ('026_lr1.4e+00_wd1.0e+00') loss: 0.032 acc: 0.990 f1: 0.988
|
| 342 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 343 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 344 |
+
train: [5] [ 0/400] eta: 0:21:04 lr: nan time: 3.1612 data: 2.7834 max mem: 22446
|
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+
train: [5] [ 20/400] eta: 0:03:38 lr: 0.000300 loss: 0.6242 (0.6103) grad: 0.4786 (0.6939) time: 0.4458 data: 0.0025 max mem: 22446
|
| 346 |
+
train: [5] [ 40/400] eta: 0:03:03 lr: 0.000300 loss: 0.6819 (0.7432) grad: 0.4825 (0.6094) time: 0.4439 data: 0.0033 max mem: 22446
|
| 347 |
+
train: [5] [ 60/400] eta: 0:02:46 lr: 0.000300 loss: 0.8347 (0.8107) grad: 0.5110 (0.5896) time: 0.4500 data: 0.0032 max mem: 22446
|
| 348 |
+
train: [5] [ 80/400] eta: 0:02:34 lr: 0.000300 loss: 0.6986 (0.7726) grad: 0.5007 (0.5557) time: 0.4619 data: 0.0034 max mem: 22446
|
| 349 |
+
train: [5] [100/400] eta: 0:02:22 lr: 0.000300 loss: 0.7077 (0.8071) grad: 0.4655 (0.5435) time: 0.4456 data: 0.0032 max mem: 22446
|
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+
train: [5] [120/400] eta: 0:02:12 lr: 0.000300 loss: 0.9279 (0.8407) grad: 0.4668 (0.5349) time: 0.4520 data: 0.0031 max mem: 22446
|
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+
train: [5] [140/400] eta: 0:02:02 lr: 0.000300 loss: 0.9319 (0.8424) grad: 0.4429 (0.5235) time: 0.4590 data: 0.0032 max mem: 22446
|
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+
train: [5] [160/400] eta: 0:01:52 lr: 0.000299 loss: 0.6634 (0.8474) grad: 0.4678 (0.5251) time: 0.4519 data: 0.0033 max mem: 22446
|
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+
train: [5] [180/400] eta: 0:01:42 lr: 0.000299 loss: 0.6176 (0.8334) grad: 0.5248 (0.5274) time: 0.4497 data: 0.0032 max mem: 22446
|
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+
train: [5] [200/400] eta: 0:01:32 lr: 0.000299 loss: 0.6635 (0.8452) grad: 0.5195 (0.5261) time: 0.4459 data: 0.0031 max mem: 22446
|
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+
train: [5] [220/400] eta: 0:01:23 lr: 0.000299 loss: 0.9057 (0.8510) grad: 0.4312 (0.5276) time: 0.4429 data: 0.0032 max mem: 22446
|
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+
train: [5] [240/400] eta: 0:01:13 lr: 0.000299 loss: 0.7567 (0.8372) grad: 0.4402 (0.5227) time: 0.4560 data: 0.0032 max mem: 22446
|
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+
train: [5] [260/400] eta: 0:01:04 lr: 0.000299 loss: 0.7567 (0.8648) grad: 0.4773 (0.5255) time: 0.4463 data: 0.0031 max mem: 22446
|
| 358 |
+
train: [5] [280/400] eta: 0:00:55 lr: 0.000298 loss: 0.9534 (0.8774) grad: 0.5171 (0.5257) time: 0.4574 data: 0.0034 max mem: 22446
|
| 359 |
+
train: [5] [300/400] eta: 0:00:47 lr: 0.000298 loss: 0.8499 (0.8781) grad: 0.5333 (0.5272) time: 0.6089 data: 0.1760 max mem: 22446
|
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+
train: [5] [320/400] eta: 0:00:37 lr: 0.000298 loss: 0.6136 (0.8599) grad: 0.4701 (0.5231) time: 0.4580 data: 0.0035 max mem: 22446
|
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+
train: [5] [340/400] eta: 0:00:28 lr: 0.000298 loss: 0.5828 (0.8540) grad: 0.4214 (0.5180) time: 0.4531 data: 0.0035 max mem: 22446
|
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+
train: [5] [360/400] eta: 0:00:18 lr: 0.000297 loss: 0.5898 (0.8496) grad: 0.4298 (0.5127) time: 0.4523 data: 0.0034 max mem: 22446
|
| 363 |
+
train: [5] [380/400] eta: 0:00:09 lr: 0.000297 loss: 0.6009 (0.8402) grad: 0.4299 (0.5086) time: 0.4528 data: 0.0034 max mem: 22446
|
| 364 |
+
train: [5] [399/400] eta: 0:00:00 lr: 0.000297 loss: 0.5956 (0.8293) grad: 0.4047 (0.5036) time: 0.4578 data: 0.0033 max mem: 22446
|
| 365 |
+
train: [5] Total time: 0:03:06 (0.4666 s / it)
|
| 366 |
+
train: [5] Summary: lr: 0.000297 loss: 0.5956 (0.8293) grad: 0.4047 (0.5036)
|
| 367 |
+
eval (validation): [5] [ 0/63] eta: 0:03:19 time: 3.1609 data: 2.9283 max mem: 22446
|
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+
eval (validation): [5] [20/63] eta: 0:00:20 time: 0.3435 data: 0.0038 max mem: 22446
|
| 369 |
+
eval (validation): [5] [40/63] eta: 0:00:09 time: 0.3325 data: 0.0027 max mem: 22446
|
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+
eval (validation): [5] [60/63] eta: 0:00:01 time: 0.3352 data: 0.0031 max mem: 22446
|
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+
eval (validation): [5] [62/63] eta: 0:00:00 time: 0.3333 data: 0.0030 max mem: 22446
|
| 372 |
+
eval (validation): [5] Total time: 0:00:24 (0.3859 s / it)
|
| 373 |
+
cv: [5] best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00') loss: 0.035 acc: 0.990 f1: 0.989
|
| 374 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 375 |
+
train: [6] [ 0/400] eta: 0:21:19 lr: nan time: 3.1987 data: 2.8558 max mem: 22446
|
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+
train: [6] [ 20/400] eta: 0:03:41 lr: 0.000296 loss: 0.4912 (0.4828) grad: 0.4037 (0.4424) time: 0.4517 data: 0.0032 max mem: 22446
|
| 377 |
+
train: [6] [ 40/400] eta: 0:03:08 lr: 0.000296 loss: 0.4912 (0.5739) grad: 0.3384 (0.3831) time: 0.4592 data: 0.0032 max mem: 22446
|
| 378 |
+
train: [6] [ 60/400] eta: 0:02:50 lr: 0.000296 loss: 0.5022 (0.6103) grad: 0.3878 (0.4024) time: 0.4607 data: 0.0033 max mem: 22446
|
| 379 |
+
train: [6] [ 80/400] eta: 0:02:38 lr: 0.000295 loss: 0.5816 (0.6194) grad: 0.4101 (0.4132) time: 0.4696 data: 0.0034 max mem: 22446
|
| 380 |
+
train: [6] [100/400] eta: 0:02:26 lr: 0.000295 loss: 0.5816 (0.6254) grad: 0.4209 (0.4165) time: 0.4613 data: 0.0033 max mem: 22446
|
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+
train: [6] [120/400] eta: 0:02:15 lr: 0.000295 loss: 0.5033 (0.6246) grad: 0.3849 (0.4093) time: 0.4558 data: 0.0032 max mem: 22446
|
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+
train: [6] [140/400] eta: 0:02:04 lr: 0.000294 loss: 0.4549 (0.6196) grad: 0.3754 (0.4054) time: 0.4625 data: 0.0030 max mem: 22446
|
| 383 |
+
train: [6] [160/400] eta: 0:01:54 lr: 0.000294 loss: 0.4549 (0.6063) grad: 0.3607 (0.3961) time: 0.4643 data: 0.0031 max mem: 22446
|
| 384 |
+
train: [6] [180/400] eta: 0:01:44 lr: 0.000293 loss: 0.5189 (0.6163) grad: 0.3587 (0.3977) time: 0.4546 data: 0.0031 max mem: 22446
|
| 385 |
+
train: [6] [200/400] eta: 0:01:34 lr: 0.000293 loss: 0.5189 (0.6381) grad: 0.3853 (0.3970) time: 0.4607 data: 0.0031 max mem: 22446
|
| 386 |
+
train: [6] [220/400] eta: 0:01:24 lr: 0.000292 loss: 0.3919 (0.6280) grad: 0.3710 (0.3972) time: 0.4484 data: 0.0032 max mem: 22446
|
| 387 |
+
train: [6] [240/400] eta: 0:01:15 lr: 0.000292 loss: 0.3863 (0.6138) grad: 0.3804 (0.3987) time: 0.4496 data: 0.0032 max mem: 22446
|
| 388 |
+
train: [6] [260/400] eta: 0:01:05 lr: 0.000291 loss: 0.3863 (0.6121) grad: 0.4260 (0.4042) time: 0.4622 data: 0.0033 max mem: 22446
|
| 389 |
+
train: [6] [280/400] eta: 0:00:56 lr: 0.000291 loss: 0.5560 (0.6266) grad: 0.4494 (0.4066) time: 0.4573 data: 0.0033 max mem: 22446
|
| 390 |
+
train: [6] [300/400] eta: 0:00:47 lr: 0.000290 loss: 0.5560 (0.6157) grad: 0.3737 (0.4046) time: 0.6236 data: 0.1774 max mem: 22446
|
| 391 |
+
train: [6] [320/400] eta: 0:00:38 lr: 0.000290 loss: 0.3876 (0.6034) grad: 0.3097 (0.3987) time: 0.4512 data: 0.0030 max mem: 22446
|
| 392 |
+
train: [6] [340/400] eta: 0:00:28 lr: 0.000289 loss: 0.3826 (0.5933) grad: 0.2976 (0.3923) time: 0.4583 data: 0.0033 max mem: 22446
|
| 393 |
+
train: [6] [360/400] eta: 0:00:18 lr: 0.000288 loss: 0.3527 (0.5795) grad: 0.2934 (0.3866) time: 0.4494 data: 0.0033 max mem: 22446
|
| 394 |
+
train: [6] [380/400] eta: 0:00:09 lr: 0.000288 loss: 0.3612 (0.5716) grad: 0.2878 (0.3825) time: 0.4645 data: 0.0035 max mem: 22446
|
| 395 |
+
train: [6] [399/400] eta: 0:00:00 lr: 0.000287 loss: 0.3905 (0.5616) grad: 0.3045 (0.3790) time: 0.4602 data: 0.0032 max mem: 22446
|
| 396 |
+
train: [6] Total time: 0:03:09 (0.4734 s / it)
|
| 397 |
+
train: [6] Summary: lr: 0.000287 loss: 0.3905 (0.5616) grad: 0.3045 (0.3790)
|
| 398 |
+
eval (validation): [6] [ 0/63] eta: 0:03:20 time: 3.1860 data: 2.9094 max mem: 22446
|
| 399 |
+
eval (validation): [6] [20/63] eta: 0:00:22 time: 0.3836 data: 0.0029 max mem: 22446
|
| 400 |
+
eval (validation): [6] [40/63] eta: 0:00:10 time: 0.3486 data: 0.0029 max mem: 22446
|
| 401 |
+
eval (validation): [6] [60/63] eta: 0:00:01 time: 0.3255 data: 0.0031 max mem: 22446
|
| 402 |
+
eval (validation): [6] [62/63] eta: 0:00:00 time: 0.3265 data: 0.0030 max mem: 22446
|
| 403 |
+
eval (validation): [6] Total time: 0:00:25 (0.4011 s / it)
|
| 404 |
+
cv: [6] best hparam: (1.6, 1.0) (027) ('027_lr1.6e+00_wd1.0e+00') loss: 0.027 acc: 0.992 f1: 0.991
|
| 405 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 406 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 407 |
+
train: [7] [ 0/400] eta: 0:23:45 lr: nan time: 3.5632 data: 3.1662 max mem: 22446
|
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+
train: [7] [ 20/400] eta: 0:03:43 lr: 0.000286 loss: 0.2975 (0.4163) grad: 0.3462 (0.3374) time: 0.4402 data: 0.0021 max mem: 22446
|
| 409 |
+
train: [7] [ 40/400] eta: 0:03:06 lr: 0.000286 loss: 0.2975 (0.3771) grad: 0.2873 (0.3083) time: 0.4434 data: 0.0033 max mem: 22446
|
| 410 |
+
train: [7] [ 60/400] eta: 0:02:47 lr: 0.000285 loss: 0.3036 (0.3994) grad: 0.2696 (0.3017) time: 0.4433 data: 0.0033 max mem: 22446
|
| 411 |
+
train: [7] [ 80/400] eta: 0:02:35 lr: 0.000284 loss: 0.2664 (0.3645) grad: 0.2579 (0.2977) time: 0.4648 data: 0.0041 max mem: 22446
|
| 412 |
+
train: [7] [100/400] eta: 0:02:24 lr: 0.000284 loss: 0.2448 (0.3709) grad: 0.3384 (0.3084) time: 0.4607 data: 0.0035 max mem: 22446
|
| 413 |
+
train: [7] [120/400] eta: 0:02:13 lr: 0.000283 loss: 0.2768 (0.3562) grad: 0.3077 (0.3048) time: 0.4525 data: 0.0035 max mem: 22446
|
| 414 |
+
train: [7] [140/400] eta: 0:02:02 lr: 0.000282 loss: 0.2992 (0.3604) grad: 0.2785 (0.3046) time: 0.4476 data: 0.0032 max mem: 22446
|
| 415 |
+
train: [7] [160/400] eta: 0:01:53 lr: 0.000282 loss: 0.3141 (0.3555) grad: 0.2821 (0.3033) time: 0.4720 data: 0.0033 max mem: 22446
|
| 416 |
+
train: [7] [180/400] eta: 0:01:43 lr: 0.000281 loss: 0.2698 (0.3555) grad: 0.2608 (0.3002) time: 0.4718 data: 0.0034 max mem: 22446
|
| 417 |
+
train: [7] [200/400] eta: 0:01:34 lr: 0.000280 loss: 0.2172 (0.3550) grad: 0.2608 (0.2996) time: 0.4592 data: 0.0034 max mem: 22446
|
| 418 |
+
train: [7] [220/400] eta: 0:01:24 lr: 0.000279 loss: 0.2172 (0.3595) grad: 0.2874 (0.2998) time: 0.4534 data: 0.0033 max mem: 22446
|
| 419 |
+
train: [7] [240/400] eta: 0:01:14 lr: 0.000278 loss: 0.4079 (0.3643) grad: 0.2981 (0.3030) time: 0.4538 data: 0.0033 max mem: 22446
|
| 420 |
+
train: [7] [260/400] eta: 0:01:05 lr: 0.000278 loss: 0.4079 (0.3640) grad: 0.3272 (0.3064) time: 0.4588 data: 0.0032 max mem: 22446
|
| 421 |
+
train: [7] [280/400] eta: 0:00:56 lr: 0.000277 loss: 0.2933 (0.3698) grad: 0.2888 (0.3051) time: 0.4609 data: 0.0034 max mem: 22446
|
| 422 |
+
train: [7] [300/400] eta: 0:00:47 lr: 0.000276 loss: 0.2917 (0.3705) grad: 0.2700 (0.3039) time: 0.6191 data: 0.1774 max mem: 22446
|
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+
train: [7] [320/400] eta: 0:00:38 lr: 0.000275 loss: 0.2917 (0.3684) grad: 0.2646 (0.3000) time: 0.4551 data: 0.0031 max mem: 22446
|
| 424 |
+
train: [7] [340/400] eta: 0:00:28 lr: 0.000274 loss: 0.2364 (0.3617) grad: 0.2422 (0.2975) time: 0.4470 data: 0.0033 max mem: 22446
|
| 425 |
+
train: [7] [360/400] eta: 0:00:18 lr: 0.000273 loss: 0.2273 (0.3592) grad: 0.2475 (0.2959) time: 0.4532 data: 0.0033 max mem: 22446
|
| 426 |
+
train: [7] [380/400] eta: 0:00:09 lr: 0.000272 loss: 0.2054 (0.3504) grad: 0.2554 (0.2944) time: 0.4441 data: 0.0033 max mem: 22446
|
| 427 |
+
train: [7] [399/400] eta: 0:00:00 lr: 0.000271 loss: 0.1894 (0.3457) grad: 0.2428 (0.2912) time: 0.4493 data: 0.0033 max mem: 22446
|
| 428 |
+
train: [7] Total time: 0:03:08 (0.4706 s / it)
|
| 429 |
+
train: [7] Summary: lr: 0.000271 loss: 0.1894 (0.3457) grad: 0.2428 (0.2912)
|
| 430 |
+
eval (validation): [7] [ 0/63] eta: 0:03:13 time: 3.0651 data: 2.7883 max mem: 22446
|
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eval (validation): [7] [20/63] eta: 0:00:19 time: 0.3293 data: 0.0094 max mem: 22446
|
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eval (validation): [7] [40/63] eta: 0:00:09 time: 0.3220 data: 0.0025 max mem: 22446
|
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eval (validation): [7] [60/63] eta: 0:00:01 time: 0.3169 data: 0.0028 max mem: 22446
|
| 434 |
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eval (validation): [7] [62/63] eta: 0:00:00 time: 0.3168 data: 0.0029 max mem: 22446
|
| 435 |
+
eval (validation): [7] Total time: 0:00:23 (0.3705 s / it)
|
| 436 |
+
cv: [7] best hparam: (2.3, 1.0) (029) ('029_lr2.3e+00_wd1.0e+00') loss: 0.024 acc: 0.992 f1: 0.991
|
| 437 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 438 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 439 |
+
train: [8] [ 0/400] eta: 0:20:43 lr: nan time: 3.1076 data: 2.7705 max mem: 22446
|
| 440 |
+
train: [8] [ 20/400] eta: 0:03:38 lr: 0.000270 loss: 0.1870 (0.2060) grad: 0.1992 (0.1993) time: 0.4491 data: 0.0026 max mem: 22446
|
| 441 |
+
train: [8] [ 40/400] eta: 0:03:04 lr: 0.000270 loss: 0.1938 (0.2170) grad: 0.2050 (0.2212) time: 0.4456 data: 0.0033 max mem: 22446
|
| 442 |
+
train: [8] [ 60/400] eta: 0:02:46 lr: 0.000269 loss: 0.2024 (0.2179) grad: 0.2341 (0.2236) time: 0.4413 data: 0.0033 max mem: 22446
|
| 443 |
+
train: [8] [ 80/400] eta: 0:02:33 lr: 0.000268 loss: 0.1895 (0.2195) grad: 0.1966 (0.2162) time: 0.4503 data: 0.0033 max mem: 22446
|
| 444 |
+
train: [8] [100/400] eta: 0:02:21 lr: 0.000267 loss: 0.1885 (0.2186) grad: 0.1852 (0.2127) time: 0.4465 data: 0.0032 max mem: 22446
|
| 445 |
+
train: [8] [120/400] eta: 0:02:11 lr: 0.000266 loss: 0.2023 (0.2374) grad: 0.2229 (0.2239) time: 0.4489 data: 0.0033 max mem: 22446
|
| 446 |
+
train: [8] [140/400] eta: 0:02:00 lr: 0.000265 loss: 0.2386 (0.2458) grad: 0.2891 (0.2288) time: 0.4406 data: 0.0033 max mem: 22446
|
| 447 |
+
train: [8] [160/400] eta: 0:01:51 lr: 0.000264 loss: 0.1987 (0.2419) grad: 0.2179 (0.2271) time: 0.4650 data: 0.0035 max mem: 22446
|
| 448 |
+
train: [8] [180/400] eta: 0:01:41 lr: 0.000263 loss: 0.1813 (0.2412) grad: 0.2084 (0.2246) time: 0.4507 data: 0.0036 max mem: 22446
|
| 449 |
+
train: [8] [200/400] eta: 0:01:32 lr: 0.000262 loss: 0.1715 (0.2394) grad: 0.2084 (0.2233) time: 0.4543 data: 0.0033 max mem: 22446
|
| 450 |
+
train: [8] [220/400] eta: 0:01:23 lr: 0.000260 loss: 0.1734 (0.2362) grad: 0.2095 (0.2222) time: 0.4511 data: 0.0035 max mem: 22446
|
| 451 |
+
train: [8] [240/400] eta: 0:01:13 lr: 0.000259 loss: 0.1997 (0.2397) grad: 0.2095 (0.2223) time: 0.4542 data: 0.0034 max mem: 22446
|
| 452 |
+
train: [8] [260/400] eta: 0:01:04 lr: 0.000258 loss: 0.2149 (0.2386) grad: 0.2073 (0.2213) time: 0.4495 data: 0.0032 max mem: 22446
|
| 453 |
+
train: [8] [280/400] eta: 0:00:55 lr: 0.000257 loss: 0.1890 (0.2418) grad: 0.2441 (0.2228) time: 0.4428 data: 0.0032 max mem: 22446
|
| 454 |
+
train: [8] [300/400] eta: 0:00:47 lr: 0.000256 loss: 0.2309 (0.2487) grad: 0.2465 (0.2261) time: 0.6445 data: 0.1848 max mem: 22446
|
| 455 |
+
train: [8] [320/400] eta: 0:00:37 lr: 0.000255 loss: 0.2272 (0.2449) grad: 0.1887 (0.2235) time: 0.4558 data: 0.0032 max mem: 22446
|
| 456 |
+
train: [8] [340/400] eta: 0:00:28 lr: 0.000254 loss: 0.1515 (0.2414) grad: 0.1824 (0.2212) time: 0.4538 data: 0.0034 max mem: 22446
|
| 457 |
+
train: [8] [360/400] eta: 0:00:18 lr: 0.000253 loss: 0.1693 (0.2370) grad: 0.1762 (0.2178) time: 0.4542 data: 0.0033 max mem: 22446
|
| 458 |
+
train: [8] [380/400] eta: 0:00:09 lr: 0.000252 loss: 0.1556 (0.2325) grad: 0.1720 (0.2164) time: 0.4548 data: 0.0033 max mem: 22446
|
| 459 |
+
train: [8] [399/400] eta: 0:00:00 lr: 0.000250 loss: 0.1516 (0.2301) grad: 0.1720 (0.2157) time: 0.4529 data: 0.0031 max mem: 22446
|
| 460 |
+
train: [8] Total time: 0:03:06 (0.4672 s / it)
|
| 461 |
+
train: [8] Summary: lr: 0.000250 loss: 0.1516 (0.2301) grad: 0.1720 (0.2157)
|
| 462 |
+
eval (validation): [8] [ 0/63] eta: 0:03:18 time: 3.1567 data: 2.9178 max mem: 22446
|
| 463 |
+
eval (validation): [8] [20/63] eta: 0:00:21 time: 0.3747 data: 0.0038 max mem: 22446
|
| 464 |
+
eval (validation): [8] [40/63] eta: 0:00:09 time: 0.3423 data: 0.0030 max mem: 22446
|
| 465 |
+
eval (validation): [8] [60/63] eta: 0:00:01 time: 0.3290 data: 0.0030 max mem: 22446
|
| 466 |
+
eval (validation): [8] [62/63] eta: 0:00:00 time: 0.3275 data: 0.0030 max mem: 22446
|
| 467 |
+
eval (validation): [8] Total time: 0:00:25 (0.3976 s / it)
|
| 468 |
+
cv: [8] best hparam: (2.7, 1.0) (030) ('030_lr2.7e+00_wd1.0e+00') loss: 0.024 acc: 0.992 f1: 0.991
|
| 469 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 470 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 471 |
+
train: [9] [ 0/400] eta: 0:21:56 lr: nan time: 3.2901 data: 2.9161 max mem: 22446
|
| 472 |
+
train: [9] [ 20/400] eta: 0:03:41 lr: 0.000249 loss: 0.2105 (0.2570) grad: 0.1686 (0.1797) time: 0.4483 data: 0.0031 max mem: 22446
|
| 473 |
+
train: [9] [ 40/400] eta: 0:03:06 lr: 0.000248 loss: 0.1718 (0.2059) grad: 0.1745 (0.1868) time: 0.4498 data: 0.0029 max mem: 22446
|
| 474 |
+
train: [9] [ 60/400] eta: 0:02:48 lr: 0.000247 loss: 0.1380 (0.1996) grad: 0.1684 (0.1776) time: 0.4489 data: 0.0033 max mem: 22446
|
| 475 |
+
train: [9] [ 80/400] eta: 0:02:36 lr: 0.000246 loss: 0.1371 (0.1992) grad: 0.1577 (0.1703) time: 0.4754 data: 0.0035 max mem: 22446
|
| 476 |
+
train: [9] [100/400] eta: 0:02:25 lr: 0.000244 loss: 0.1430 (0.1889) grad: 0.1659 (0.1738) time: 0.4594 data: 0.0033 max mem: 22446
|
| 477 |
+
train: [9] [120/400] eta: 0:02:14 lr: 0.000243 loss: 0.1430 (0.1818) grad: 0.1581 (0.1693) time: 0.4528 data: 0.0033 max mem: 22446
|
| 478 |
+
train: [9] [140/400] eta: 0:02:04 lr: 0.000242 loss: 0.1332 (0.1766) grad: 0.1436 (0.1661) time: 0.4713 data: 0.0033 max mem: 22446
|
| 479 |
+
train: [9] [160/400] eta: 0:01:54 lr: 0.000241 loss: 0.1326 (0.1721) grad: 0.1374 (0.1662) time: 0.4643 data: 0.0033 max mem: 22446
|
| 480 |
+
train: [9] [180/400] eta: 0:01:44 lr: 0.000240 loss: 0.1193 (0.1684) grad: 0.1368 (0.1646) time: 0.4591 data: 0.0032 max mem: 22446
|
| 481 |
+
train: [9] [200/400] eta: 0:01:34 lr: 0.000238 loss: 0.1193 (0.1661) grad: 0.1595 (0.1655) time: 0.4525 data: 0.0032 max mem: 22446
|
| 482 |
+
train: [9] [220/400] eta: 0:01:24 lr: 0.000237 loss: 0.1289 (0.1639) grad: 0.1580 (0.1640) time: 0.4482 data: 0.0032 max mem: 22446
|
| 483 |
+
train: [9] [240/400] eta: 0:01:15 lr: 0.000236 loss: 0.1253 (0.1608) grad: 0.1407 (0.1612) time: 0.4604 data: 0.0033 max mem: 22446
|
| 484 |
+
train: [9] [260/400] eta: 0:01:05 lr: 0.000234 loss: 0.1141 (0.1611) grad: 0.1568 (0.1615) time: 0.4483 data: 0.0034 max mem: 22446
|
| 485 |
+
train: [9] [280/400] eta: 0:00:55 lr: 0.000233 loss: 0.1468 (0.1609) grad: 0.1568 (0.1607) time: 0.4528 data: 0.0033 max mem: 22446
|
| 486 |
+
train: [9] [300/400] eta: 0:00:47 lr: 0.000232 loss: 0.1332 (0.1602) grad: 0.1313 (0.1605) time: 0.6266 data: 0.1762 max mem: 22446
|
| 487 |
+
train: [9] [320/400] eta: 0:00:38 lr: 0.000230 loss: 0.1098 (0.1574) grad: 0.1479 (0.1604) time: 0.4629 data: 0.0029 max mem: 22446
|
| 488 |
+
train: [9] [340/400] eta: 0:00:28 lr: 0.000229 loss: 0.1193 (0.1581) grad: 0.1487 (0.1594) time: 0.4510 data: 0.0034 max mem: 22446
|
| 489 |
+
train: [9] [360/400] eta: 0:00:18 lr: 0.000228 loss: 0.1414 (0.1573) grad: 0.1324 (0.1573) time: 0.4551 data: 0.0034 max mem: 22446
|
| 490 |
+
train: [9] [380/400] eta: 0:00:09 lr: 0.000226 loss: 0.1262 (0.1555) grad: 0.1240 (0.1562) time: 0.4650 data: 0.0034 max mem: 22446
|
| 491 |
+
train: [9] [399/400] eta: 0:00:00 lr: 0.000225 loss: 0.1262 (0.1545) grad: 0.1326 (0.1553) time: 0.4724 data: 0.0032 max mem: 22446
|
| 492 |
+
train: [9] Total time: 0:03:09 (0.4735 s / it)
|
| 493 |
+
train: [9] Summary: lr: 0.000225 loss: 0.1262 (0.1545) grad: 0.1326 (0.1553)
|
| 494 |
+
eval (validation): [9] [ 0/63] eta: 0:03:21 time: 3.1959 data: 2.9254 max mem: 22446
|
| 495 |
+
eval (validation): [9] [20/63] eta: 0:00:21 time: 0.3569 data: 0.0041 max mem: 22446
|
| 496 |
+
eval (validation): [9] [40/63] eta: 0:00:09 time: 0.3391 data: 0.0027 max mem: 22446
|
| 497 |
+
eval (validation): [9] [60/63] eta: 0:00:01 time: 0.3280 data: 0.0029 max mem: 22446
|
| 498 |
+
eval (validation): [9] [62/63] eta: 0:00:00 time: 0.3297 data: 0.0029 max mem: 22446
|
| 499 |
+
eval (validation): [9] Total time: 0:00:24 (0.3904 s / it)
|
| 500 |
+
cv: [9] best hparam: (5.1, 1.0) (034) ('034_lr5.1e+00_wd1.0e+00') loss: 0.029 acc: 0.993 f1: 0.993
|
| 501 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 502 |
+
saving best checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 503 |
+
train: [10] [ 0/400] eta: 0:21:50 lr: nan time: 3.2773 data: 2.9260 max mem: 22446
|
| 504 |
+
train: [10] [ 20/400] eta: 0:03:45 lr: 0.000224 loss: 0.1476 (0.1700) grad: 0.1433 (0.1596) time: 0.4601 data: 0.0029 max mem: 22446
|
| 505 |
+
train: [10] [ 40/400] eta: 0:03:09 lr: 0.000222 loss: 0.1172 (0.1477) grad: 0.1257 (0.1466) time: 0.4552 data: 0.0033 max mem: 22446
|
| 506 |
+
train: [10] [ 60/400] eta: 0:02:51 lr: 0.000221 loss: 0.1125 (0.1502) grad: 0.1135 (0.1423) time: 0.4612 data: 0.0034 max mem: 22446
|
| 507 |
+
train: [10] [ 80/400] eta: 0:02:39 lr: 0.000220 loss: 0.1164 (0.1423) grad: 0.1478 (0.1478) time: 0.4842 data: 0.0035 max mem: 22446
|
| 508 |
+
train: [10] [100/400] eta: 0:02:27 lr: 0.000218 loss: 0.1023 (0.1393) grad: 0.1396 (0.1397) time: 0.4634 data: 0.0035 max mem: 22446
|
| 509 |
+
train: [10] [120/400] eta: 0:02:16 lr: 0.000217 loss: 0.1023 (0.1346) grad: 0.1293 (0.1395) time: 0.4535 data: 0.0032 max mem: 22446
|
| 510 |
+
train: [10] [140/400] eta: 0:02:05 lr: 0.000215 loss: 0.1067 (0.1318) grad: 0.1309 (0.1358) time: 0.4642 data: 0.0032 max mem: 22446
|
| 511 |
+
train: [10] [160/400] eta: 0:01:55 lr: 0.000214 loss: 0.1157 (0.1298) grad: 0.1095 (0.1345) time: 0.4588 data: 0.0032 max mem: 22446
|
| 512 |
+
train: [10] [180/400] eta: 0:01:44 lr: 0.000213 loss: 0.1137 (0.1283) grad: 0.1057 (0.1321) time: 0.4518 data: 0.0032 max mem: 22446
|
| 513 |
+
train: [10] [200/400] eta: 0:01:34 lr: 0.000211 loss: 0.1049 (0.1301) grad: 0.1276 (0.1347) time: 0.4533 data: 0.0032 max mem: 22446
|
| 514 |
+
train: [10] [220/400] eta: 0:01:25 lr: 0.000210 loss: 0.1049 (0.1295) grad: 0.1040 (0.1322) time: 0.4518 data: 0.0032 max mem: 22446
|
| 515 |
+
train: [10] [240/400] eta: 0:01:15 lr: 0.000208 loss: 0.1043 (0.1295) grad: 0.1040 (0.1328) time: 0.4506 data: 0.0031 max mem: 22446
|
| 516 |
+
train: [10] [260/400] eta: 0:01:05 lr: 0.000207 loss: 0.1005 (0.1291) grad: 0.1464 (0.1340) time: 0.4515 data: 0.0032 max mem: 22446
|
| 517 |
+
train: [10] [280/400] eta: 0:00:56 lr: 0.000205 loss: 0.0957 (0.1269) grad: 0.1052 (0.1315) time: 0.4505 data: 0.0032 max mem: 22446
|
| 518 |
+
train: [10] [300/400] eta: 0:00:47 lr: 0.000204 loss: 0.0945 (0.1262) grad: 0.1059 (0.1307) time: 0.6161 data: 0.1706 max mem: 22446
|
| 519 |
+
train: [10] [320/400] eta: 0:00:38 lr: 0.000202 loss: 0.0850 (0.1243) grad: 0.1222 (0.1301) time: 0.4480 data: 0.0029 max mem: 22446
|
| 520 |
+
train: [10] [340/400] eta: 0:00:28 lr: 0.000201 loss: 0.0889 (0.1226) grad: 0.1111 (0.1289) time: 0.4542 data: 0.0034 max mem: 22446
|
| 521 |
+
train: [10] [360/400] eta: 0:00:18 lr: 0.000199 loss: 0.0935 (0.1219) grad: 0.0904 (0.1265) time: 0.4596 data: 0.0033 max mem: 22446
|
| 522 |
+
train: [10] [380/400] eta: 0:00:09 lr: 0.000198 loss: 0.0940 (0.1218) grad: 0.0834 (0.1247) time: 0.4526 data: 0.0031 max mem: 22446
|
| 523 |
+
train: [10] [399/400] eta: 0:00:00 lr: 0.000196 loss: 0.0980 (0.1204) grad: 0.0818 (0.1221) time: 0.4505 data: 0.0033 max mem: 22446
|
| 524 |
+
train: [10] Total time: 0:03:08 (0.4719 s / it)
|
| 525 |
+
train: [10] Summary: lr: 0.000196 loss: 0.0980 (0.1204) grad: 0.0818 (0.1221)
|
| 526 |
+
eval (validation): [10] [ 0/63] eta: 0:03:19 time: 3.1697 data: 2.9331 max mem: 22446
|
| 527 |
+
eval (validation): [10] [20/63] eta: 0:00:20 time: 0.3377 data: 0.0032 max mem: 22446
|
| 528 |
+
eval (validation): [10] [40/63] eta: 0:00:09 time: 0.3352 data: 0.0027 max mem: 22446
|
| 529 |
+
eval (validation): [10] [60/63] eta: 0:00:01 time: 0.3202 data: 0.0027 max mem: 22446
|
| 530 |
+
eval (validation): [10] [62/63] eta: 0:00:00 time: 0.3202 data: 0.0030 max mem: 22446
|
| 531 |
+
eval (validation): [10] Total time: 0:00:24 (0.3812 s / it)
|
| 532 |
+
cv: [10] best hparam: (4.3, 1.0) (033) ('033_lr4.3e+00_wd1.0e+00') loss: 0.028 acc: 0.992 f1: 0.991
|
| 533 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 534 |
+
train: [11] [ 0/400] eta: 0:21:28 lr: nan time: 3.2205 data: 2.8383 max mem: 22446
|
| 535 |
+
train: [11] [ 20/400] eta: 0:03:48 lr: 0.000195 loss: 0.0925 (0.0973) grad: 0.0473 (0.0630) time: 0.4693 data: 0.0028 max mem: 22446
|
| 536 |
+
train: [11] [ 40/400] eta: 0:03:11 lr: 0.000193 loss: 0.0925 (0.1001) grad: 0.0764 (0.0750) time: 0.4598 data: 0.0033 max mem: 22446
|
| 537 |
+
train: [11] [ 60/400] eta: 0:02:52 lr: 0.000192 loss: 0.0892 (0.1000) grad: 0.0848 (0.0775) time: 0.4586 data: 0.0032 max mem: 22446
|
| 538 |
+
train: [11] [ 80/400] eta: 0:02:39 lr: 0.000190 loss: 0.0919 (0.0991) grad: 0.0843 (0.0772) time: 0.4671 data: 0.0033 max mem: 22446
|
| 539 |
+
train: [11] [100/400] eta: 0:02:26 lr: 0.000189 loss: 0.0845 (0.0967) grad: 0.0810 (0.0772) time: 0.4548 data: 0.0033 max mem: 22446
|
| 540 |
+
train: [11] [120/400] eta: 0:02:15 lr: 0.000187 loss: 0.0794 (0.0951) grad: 0.0859 (0.0808) time: 0.4539 data: 0.0033 max mem: 22446
|
| 541 |
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train: [11] [140/400] eta: 0:02:05 lr: 0.000186 loss: 0.0874 (0.0964) grad: 0.0920 (0.0823) time: 0.4852 data: 0.0032 max mem: 22446
|
| 542 |
+
train: [11] [160/400] eta: 0:01:56 lr: 0.000184 loss: 0.0922 (0.0977) grad: 0.0920 (0.0842) time: 0.4851 data: 0.0035 max mem: 22446
|
| 543 |
+
train: [11] [180/400] eta: 0:01:46 lr: 0.000183 loss: 0.0909 (0.0980) grad: 0.0991 (0.0885) time: 0.4668 data: 0.0034 max mem: 22446
|
| 544 |
+
train: [11] [200/400] eta: 0:01:36 lr: 0.000181 loss: 0.0884 (0.0983) grad: 0.0991 (0.0898) time: 0.4818 data: 0.0035 max mem: 22446
|
| 545 |
+
train: [11] [220/400] eta: 0:01:26 lr: 0.000180 loss: 0.0892 (0.0974) grad: 0.0985 (0.0910) time: 0.4919 data: 0.0037 max mem: 22446
|
| 546 |
+
train: [11] [240/400] eta: 0:01:17 lr: 0.000178 loss: 0.0948 (0.0972) grad: 0.0975 (0.0914) time: 0.5159 data: 0.0039 max mem: 22446
|
| 547 |
+
train: [11] [260/400] eta: 0:01:07 lr: 0.000177 loss: 0.0956 (0.0977) grad: 0.0853 (0.0909) time: 0.4761 data: 0.0037 max mem: 22446
|
| 548 |
+
train: [11] [280/400] eta: 0:00:58 lr: 0.000175 loss: 0.1008 (0.0982) grad: 0.0853 (0.0915) time: 0.4970 data: 0.0037 max mem: 22446
|
| 549 |
+
train: [11] [300/400] eta: 0:00:49 lr: 0.000174 loss: 0.0897 (0.0980) grad: 0.0759 (0.0907) time: 0.6872 data: 0.2224 max mem: 22446
|
| 550 |
+
train: [11] [320/400] eta: 0:00:39 lr: 0.000172 loss: 0.0863 (0.0973) grad: 0.0691 (0.0901) time: 0.4720 data: 0.0027 max mem: 22446
|
| 551 |
+
train: [11] [340/400] eta: 0:00:29 lr: 0.000170 loss: 0.0829 (0.0968) grad: 0.0777 (0.0893) time: 0.4602 data: 0.0033 max mem: 22446
|
| 552 |
+
train: [11] [360/400] eta: 0:00:19 lr: 0.000169 loss: 0.0850 (0.0961) grad: 0.0754 (0.0890) time: 0.4749 data: 0.0033 max mem: 22446
|
| 553 |
+
train: [11] [380/400] eta: 0:00:09 lr: 0.000167 loss: 0.0812 (0.0954) grad: 0.0712 (0.0881) time: 0.4618 data: 0.0033 max mem: 22446
|
| 554 |
+
train: [11] [399/400] eta: 0:00:00 lr: 0.000166 loss: 0.0812 (0.0950) grad: 0.0700 (0.0872) time: 0.4584 data: 0.0031 max mem: 22446
|
| 555 |
+
train: [11] Total time: 0:03:16 (0.4911 s / it)
|
| 556 |
+
train: [11] Summary: lr: 0.000166 loss: 0.0812 (0.0950) grad: 0.0700 (0.0872)
|
| 557 |
+
eval (validation): [11] [ 0/63] eta: 0:03:18 time: 3.1526 data: 2.9095 max mem: 22446
|
| 558 |
+
eval (validation): [11] [20/63] eta: 0:00:20 time: 0.3537 data: 0.0032 max mem: 22446
|
| 559 |
+
eval (validation): [11] [40/63] eta: 0:00:09 time: 0.3382 data: 0.0025 max mem: 22446
|
| 560 |
+
eval (validation): [11] [60/63] eta: 0:00:01 time: 0.3403 data: 0.0032 max mem: 22446
|
| 561 |
+
eval (validation): [11] [62/63] eta: 0:00:00 time: 0.3414 data: 0.0032 max mem: 22446
|
| 562 |
+
eval (validation): [11] Total time: 0:00:24 (0.3932 s / it)
|
| 563 |
+
cv: [11] best hparam: (1.9, 1.0) (028) ('028_lr1.9e+00_wd1.0e+00') loss: 0.025 acc: 0.993 f1: 0.992
|
| 564 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 565 |
+
train: [12] [ 0/400] eta: 0:21:39 lr: nan time: 3.2487 data: 2.8877 max mem: 22446
|
| 566 |
+
train: [12] [ 20/400] eta: 0:03:48 lr: 0.000164 loss: 0.0833 (0.0875) grad: 0.0763 (0.0724) time: 0.4689 data: 0.0031 max mem: 22446
|
| 567 |
+
train: [12] [ 40/400] eta: 0:03:11 lr: 0.000163 loss: 0.0779 (0.0823) grad: 0.0735 (0.0672) time: 0.4578 data: 0.0034 max mem: 22446
|
| 568 |
+
train: [12] [ 60/400] eta: 0:02:53 lr: 0.000161 loss: 0.0721 (0.0795) grad: 0.0466 (0.0622) time: 0.4703 data: 0.0034 max mem: 22446
|
| 569 |
+
train: [12] [ 80/400] eta: 0:02:40 lr: 0.000160 loss: 0.0686 (0.0790) grad: 0.0443 (0.0590) time: 0.4676 data: 0.0033 max mem: 22446
|
| 570 |
+
train: [12] [100/400] eta: 0:02:27 lr: 0.000158 loss: 0.0772 (0.0805) grad: 0.0552 (0.0595) time: 0.4624 data: 0.0032 max mem: 22446
|
| 571 |
+
train: [12] [120/400] eta: 0:02:16 lr: 0.000156 loss: 0.0790 (0.0823) grad: 0.0639 (0.0607) time: 0.4491 data: 0.0032 max mem: 22446
|
| 572 |
+
train: [12] [140/400] eta: 0:02:05 lr: 0.000155 loss: 0.0727 (0.0826) grad: 0.0609 (0.0601) time: 0.4668 data: 0.0031 max mem: 22446
|
| 573 |
+
train: [12] [160/400] eta: 0:01:55 lr: 0.000153 loss: 0.0727 (0.0816) grad: 0.0445 (0.0594) time: 0.4535 data: 0.0033 max mem: 22446
|
| 574 |
+
train: [12] [180/400] eta: 0:01:44 lr: 0.000152 loss: 0.0722 (0.0815) grad: 0.0429 (0.0587) time: 0.4514 data: 0.0032 max mem: 22446
|
| 575 |
+
train: [12] [200/400] eta: 0:01:34 lr: 0.000150 loss: 0.0722 (0.0806) grad: 0.0423 (0.0584) time: 0.4548 data: 0.0033 max mem: 22446
|
| 576 |
+
train: [12] [220/400] eta: 0:01:25 lr: 0.000149 loss: 0.0705 (0.0819) grad: 0.0577 (0.0592) time: 0.4598 data: 0.0035 max mem: 22446
|
| 577 |
+
train: [12] [240/400] eta: 0:01:15 lr: 0.000147 loss: 0.0771 (0.0824) grad: 0.0567 (0.0591) time: 0.4549 data: 0.0033 max mem: 22446
|
| 578 |
+
train: [12] [260/400] eta: 0:01:05 lr: 0.000145 loss: 0.0771 (0.0822) grad: 0.0567 (0.0592) time: 0.4619 data: 0.0032 max mem: 22446
|
| 579 |
+
train: [12] [280/400] eta: 0:00:56 lr: 0.000144 loss: 0.0754 (0.0815) grad: 0.0705 (0.0609) time: 0.4522 data: 0.0033 max mem: 22446
|
| 580 |
+
train: [12] [300/400] eta: 0:00:48 lr: 0.000142 loss: 0.0714 (0.0817) grad: 0.0740 (0.0613) time: 0.6373 data: 0.1790 max mem: 22446
|
| 581 |
+
train: [12] [320/400] eta: 0:00:38 lr: 0.000141 loss: 0.0794 (0.0813) grad: 0.0509 (0.0613) time: 0.4524 data: 0.0025 max mem: 22446
|
| 582 |
+
train: [12] [340/400] eta: 0:00:28 lr: 0.000139 loss: 0.0776 (0.0813) grad: 0.0620 (0.0615) time: 0.4459 data: 0.0032 max mem: 22446
|
| 583 |
+
train: [12] [360/400] eta: 0:00:19 lr: 0.000138 loss: 0.0766 (0.0808) grad: 0.0549 (0.0611) time: 0.4446 data: 0.0032 max mem: 22446
|
| 584 |
+
train: [12] [380/400] eta: 0:00:09 lr: 0.000136 loss: 0.0683 (0.0810) grad: 0.0479 (0.0614) time: 0.4625 data: 0.0032 max mem: 22446
|
| 585 |
+
train: [12] [399/400] eta: 0:00:00 lr: 0.000134 loss: 0.0683 (0.0808) grad: 0.0437 (0.0607) time: 0.4560 data: 0.0030 max mem: 22446
|
| 586 |
+
train: [12] Total time: 0:03:09 (0.4737 s / it)
|
| 587 |
+
train: [12] Summary: lr: 0.000134 loss: 0.0683 (0.0808) grad: 0.0437 (0.0607)
|
| 588 |
+
eval (validation): [12] [ 0/63] eta: 0:03:16 time: 3.1129 data: 2.8347 max mem: 22446
|
| 589 |
+
eval (validation): [12] [20/63] eta: 0:00:20 time: 0.3490 data: 0.0029 max mem: 22446
|
| 590 |
+
eval (validation): [12] [40/63] eta: 0:00:09 time: 0.3349 data: 0.0032 max mem: 22446
|
| 591 |
+
eval (validation): [12] [60/63] eta: 0:00:01 time: 0.3291 data: 0.0031 max mem: 22446
|
| 592 |
+
eval (validation): [12] [62/63] eta: 0:00:00 time: 0.3281 data: 0.0031 max mem: 22446
|
| 593 |
+
eval (validation): [12] Total time: 0:00:24 (0.3855 s / it)
|
| 594 |
+
cv: [12] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.041 acc: 0.993 f1: 0.991
|
| 595 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 596 |
+
train: [13] [ 0/400] eta: 0:22:31 lr: nan time: 3.3783 data: 2.9854 max mem: 22446
|
| 597 |
+
train: [13] [ 20/400] eta: 0:04:01 lr: 0.000133 loss: 0.0717 (0.0732) grad: 0.0381 (0.0434) time: 0.4997 data: 0.0028 max mem: 22446
|
| 598 |
+
train: [13] [ 40/400] eta: 0:03:25 lr: 0.000131 loss: 0.0713 (0.0710) grad: 0.0376 (0.0473) time: 0.4995 data: 0.0032 max mem: 22446
|
| 599 |
+
train: [13] [ 60/400] eta: 0:03:04 lr: 0.000130 loss: 0.0695 (0.0709) grad: 0.0396 (0.0507) time: 0.4901 data: 0.0037 max mem: 22446
|
| 600 |
+
train: [13] [ 80/400] eta: 0:02:52 lr: 0.000128 loss: 0.0689 (0.0716) grad: 0.0423 (0.0508) time: 0.5197 data: 0.0038 max mem: 22446
|
| 601 |
+
train: [13] [100/400] eta: 0:02:38 lr: 0.000127 loss: 0.0683 (0.0712) grad: 0.0417 (0.0510) time: 0.4913 data: 0.0036 max mem: 22446
|
| 602 |
+
train: [13] [120/400] eta: 0:02:27 lr: 0.000125 loss: 0.0650 (0.0706) grad: 0.0417 (0.0518) time: 0.5077 data: 0.0035 max mem: 22446
|
| 603 |
+
train: [13] [140/400] eta: 0:02:14 lr: 0.000124 loss: 0.0643 (0.0713) grad: 0.0459 (0.0511) time: 0.4697 data: 0.0032 max mem: 22446
|
| 604 |
+
train: [13] [160/400] eta: 0:02:03 lr: 0.000122 loss: 0.0681 (0.0707) grad: 0.0458 (0.0509) time: 0.4979 data: 0.0035 max mem: 22446
|
| 605 |
+
train: [13] [180/400] eta: 0:01:52 lr: 0.000120 loss: 0.0728 (0.0719) grad: 0.0399 (0.0509) time: 0.4761 data: 0.0035 max mem: 22446
|
| 606 |
+
train: [13] [200/400] eta: 0:01:41 lr: 0.000119 loss: 0.0728 (0.0723) grad: 0.0377 (0.0502) time: 0.4760 data: 0.0033 max mem: 22446
|
| 607 |
+
train: [13] [220/400] eta: 0:01:30 lr: 0.000117 loss: 0.0670 (0.0726) grad: 0.0412 (0.0504) time: 0.4701 data: 0.0033 max mem: 22446
|
| 608 |
+
train: [13] [240/400] eta: 0:01:20 lr: 0.000116 loss: 0.0655 (0.0724) grad: 0.0493 (0.0518) time: 0.4707 data: 0.0033 max mem: 22446
|
| 609 |
+
train: [13] [260/400] eta: 0:01:09 lr: 0.000114 loss: 0.0634 (0.0720) grad: 0.0470 (0.0512) time: 0.4535 data: 0.0033 max mem: 22446
|
| 610 |
+
train: [13] [280/400] eta: 0:00:59 lr: 0.000113 loss: 0.0618 (0.0715) grad: 0.0450 (0.0511) time: 0.4545 data: 0.0033 max mem: 22446
|
| 611 |
+
train: [13] [300/400] eta: 0:00:50 lr: 0.000111 loss: 0.0646 (0.0717) grad: 0.0459 (0.0507) time: 0.6234 data: 0.1744 max mem: 22446
|
| 612 |
+
train: [13] [320/400] eta: 0:00:39 lr: 0.000110 loss: 0.0661 (0.0717) grad: 0.0439 (0.0505) time: 0.4544 data: 0.0030 max mem: 22446
|
| 613 |
+
train: [13] [340/400] eta: 0:00:29 lr: 0.000108 loss: 0.0683 (0.0715) grad: 0.0417 (0.0504) time: 0.4585 data: 0.0032 max mem: 22446
|
| 614 |
+
train: [13] [360/400] eta: 0:00:19 lr: 0.000107 loss: 0.0595 (0.0708) grad: 0.0395 (0.0498) time: 0.4467 data: 0.0032 max mem: 22446
|
| 615 |
+
train: [13] [380/400] eta: 0:00:09 lr: 0.000105 loss: 0.0668 (0.0709) grad: 0.0374 (0.0497) time: 0.4450 data: 0.0032 max mem: 22446
|
| 616 |
+
train: [13] [399/400] eta: 0:00:00 lr: 0.000104 loss: 0.0732 (0.0708) grad: 0.0383 (0.0496) time: 0.4440 data: 0.0033 max mem: 22446
|
| 617 |
+
train: [13] Total time: 0:03:16 (0.4900 s / it)
|
| 618 |
+
train: [13] Summary: lr: 0.000104 loss: 0.0732 (0.0708) grad: 0.0383 (0.0496)
|
| 619 |
+
eval (validation): [13] [ 0/63] eta: 0:03:11 time: 3.0424 data: 2.8144 max mem: 22446
|
| 620 |
+
eval (validation): [13] [20/63] eta: 0:00:20 time: 0.3502 data: 0.0030 max mem: 22446
|
| 621 |
+
eval (validation): [13] [40/63] eta: 0:00:09 time: 0.3298 data: 0.0028 max mem: 22446
|
| 622 |
+
eval (validation): [13] [60/63] eta: 0:00:01 time: 0.3230 data: 0.0029 max mem: 22446
|
| 623 |
+
eval (validation): [13] [62/63] eta: 0:00:00 time: 0.3209 data: 0.0029 max mem: 22446
|
| 624 |
+
eval (validation): [13] Total time: 0:00:24 (0.3812 s / it)
|
| 625 |
+
cv: [13] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.041 acc: 0.993 f1: 0.992
|
| 626 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 627 |
+
train: [14] [ 0/400] eta: 0:21:16 lr: nan time: 3.1912 data: 2.8204 max mem: 22446
|
| 628 |
+
train: [14] [ 20/400] eta: 0:03:34 lr: 0.000102 loss: 0.0726 (0.0735) grad: 0.0399 (0.0424) time: 0.4345 data: 0.0028 max mem: 22446
|
| 629 |
+
train: [14] [ 40/400] eta: 0:03:03 lr: 0.000101 loss: 0.0664 (0.0701) grad: 0.0399 (0.0438) time: 0.4524 data: 0.0031 max mem: 22446
|
| 630 |
+
train: [14] [ 60/400] eta: 0:02:47 lr: 0.000099 loss: 0.0614 (0.0673) grad: 0.0391 (0.0435) time: 0.4574 data: 0.0033 max mem: 22446
|
| 631 |
+
train: [14] [ 80/400] eta: 0:02:35 lr: 0.000098 loss: 0.0574 (0.0670) grad: 0.0383 (0.0432) time: 0.4580 data: 0.0033 max mem: 22446
|
| 632 |
+
train: [14] [100/400] eta: 0:02:23 lr: 0.000096 loss: 0.0578 (0.0665) grad: 0.0374 (0.0426) time: 0.4499 data: 0.0032 max mem: 22446
|
| 633 |
+
train: [14] [120/400] eta: 0:02:13 lr: 0.000095 loss: 0.0629 (0.0678) grad: 0.0371 (0.0423) time: 0.4665 data: 0.0032 max mem: 22446
|
| 634 |
+
train: [14] [140/400] eta: 0:02:02 lr: 0.000093 loss: 0.0703 (0.0694) grad: 0.0453 (0.0432) time: 0.4521 data: 0.0031 max mem: 22446
|
| 635 |
+
train: [14] [160/400] eta: 0:01:53 lr: 0.000092 loss: 0.0615 (0.0683) grad: 0.0392 (0.0425) time: 0.4640 data: 0.0032 max mem: 22446
|
| 636 |
+
train: [14] [180/400] eta: 0:01:43 lr: 0.000090 loss: 0.0543 (0.0674) grad: 0.0337 (0.0423) time: 0.4649 data: 0.0032 max mem: 22446
|
| 637 |
+
train: [14] [200/400] eta: 0:01:34 lr: 0.000089 loss: 0.0566 (0.0675) grad: 0.0358 (0.0427) time: 0.4680 data: 0.0033 max mem: 22446
|
| 638 |
+
train: [14] [220/400] eta: 0:01:24 lr: 0.000088 loss: 0.0602 (0.0670) grad: 0.0359 (0.0421) time: 0.4611 data: 0.0032 max mem: 22446
|
| 639 |
+
train: [14] [240/400] eta: 0:01:14 lr: 0.000086 loss: 0.0611 (0.0668) grad: 0.0374 (0.0422) time: 0.4548 data: 0.0032 max mem: 22446
|
| 640 |
+
train: [14] [260/400] eta: 0:01:05 lr: 0.000085 loss: 0.0591 (0.0661) grad: 0.0398 (0.0426) time: 0.4539 data: 0.0032 max mem: 22446
|
| 641 |
+
train: [14] [280/400] eta: 0:00:56 lr: 0.000083 loss: 0.0669 (0.0669) grad: 0.0383 (0.0423) time: 0.4653 data: 0.0033 max mem: 22446
|
| 642 |
+
train: [14] [300/400] eta: 0:00:47 lr: 0.000082 loss: 0.0683 (0.0670) grad: 0.0383 (0.0427) time: 0.6305 data: 0.1747 max mem: 22446
|
| 643 |
+
train: [14] [320/400] eta: 0:00:38 lr: 0.000081 loss: 0.0624 (0.0668) grad: 0.0365 (0.0422) time: 0.4447 data: 0.0029 max mem: 22446
|
| 644 |
+
train: [14] [340/400] eta: 0:00:28 lr: 0.000079 loss: 0.0660 (0.0671) grad: 0.0355 (0.0418) time: 0.4602 data: 0.0033 max mem: 22446
|
| 645 |
+
train: [14] [360/400] eta: 0:00:18 lr: 0.000078 loss: 0.0727 (0.0671) grad: 0.0367 (0.0417) time: 0.4535 data: 0.0032 max mem: 22446
|
| 646 |
+
train: [14] [380/400] eta: 0:00:09 lr: 0.000076 loss: 0.0682 (0.0672) grad: 0.0384 (0.0415) time: 0.4779 data: 0.0034 max mem: 22446
|
| 647 |
+
train: [14] [399/400] eta: 0:00:00 lr: 0.000075 loss: 0.0614 (0.0673) grad: 0.0356 (0.0414) time: 0.4682 data: 0.0034 max mem: 22446
|
| 648 |
+
train: [14] Total time: 0:03:09 (0.4740 s / it)
|
| 649 |
+
train: [14] Summary: lr: 0.000075 loss: 0.0614 (0.0673) grad: 0.0356 (0.0414)
|
| 650 |
+
eval (validation): [14] [ 0/63] eta: 0:03:23 time: 3.2334 data: 3.0026 max mem: 22446
|
| 651 |
+
eval (validation): [14] [20/63] eta: 0:00:21 time: 0.3718 data: 0.0035 max mem: 22446
|
| 652 |
+
eval (validation): [14] [40/63] eta: 0:00:10 time: 0.4044 data: 0.0032 max mem: 22446
|
| 653 |
+
eval (validation): [14] [60/63] eta: 0:00:01 time: 0.3471 data: 0.0032 max mem: 22446
|
| 654 |
+
eval (validation): [14] [62/63] eta: 0:00:00 time: 0.3445 data: 0.0031 max mem: 22446
|
| 655 |
+
eval (validation): [14] Total time: 0:00:26 (0.4232 s / it)
|
| 656 |
+
cv: [14] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 657 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 658 |
+
train: [15] [ 0/400] eta: 0:26:05 lr: nan time: 3.9148 data: 3.5514 max mem: 22446
|
| 659 |
+
train: [15] [ 20/400] eta: 0:04:04 lr: 0.000074 loss: 0.0665 (0.0681) grad: 0.0338 (0.0366) time: 0.4806 data: 0.0030 max mem: 22446
|
| 660 |
+
train: [15] [ 40/400] eta: 0:03:29 lr: 0.000072 loss: 0.0665 (0.0670) grad: 0.0383 (0.0385) time: 0.5188 data: 0.0042 max mem: 22446
|
| 661 |
+
train: [15] [ 60/400] eta: 0:03:07 lr: 0.000071 loss: 0.0591 (0.0631) grad: 0.0371 (0.0378) time: 0.4825 data: 0.0036 max mem: 22446
|
| 662 |
+
train: [15] [ 80/400] eta: 0:02:49 lr: 0.000070 loss: 0.0529 (0.0622) grad: 0.0329 (0.0370) time: 0.4695 data: 0.0035 max mem: 22446
|
| 663 |
+
train: [15] [100/400] eta: 0:02:36 lr: 0.000068 loss: 0.0542 (0.0619) grad: 0.0335 (0.0374) time: 0.4850 data: 0.0035 max mem: 22446
|
| 664 |
+
train: [15] [120/400] eta: 0:02:24 lr: 0.000067 loss: 0.0546 (0.0616) grad: 0.0335 (0.0371) time: 0.4837 data: 0.0035 max mem: 22446
|
| 665 |
+
train: [15] [140/400] eta: 0:02:11 lr: 0.000066 loss: 0.0610 (0.0618) grad: 0.0355 (0.0368) time: 0.4555 data: 0.0032 max mem: 22446
|
| 666 |
+
train: [15] [160/400] eta: 0:02:00 lr: 0.000064 loss: 0.0618 (0.0617) grad: 0.0362 (0.0373) time: 0.4809 data: 0.0034 max mem: 22446
|
| 667 |
+
train: [15] [180/400] eta: 0:01:49 lr: 0.000063 loss: 0.0555 (0.0621) grad: 0.0373 (0.0371) time: 0.4604 data: 0.0034 max mem: 22446
|
| 668 |
+
train: [15] [200/400] eta: 0:01:38 lr: 0.000062 loss: 0.0664 (0.0628) grad: 0.0340 (0.0372) time: 0.4501 data: 0.0033 max mem: 22446
|
| 669 |
+
train: [15] [220/400] eta: 0:01:28 lr: 0.000061 loss: 0.0633 (0.0624) grad: 0.0377 (0.0375) time: 0.4488 data: 0.0033 max mem: 22446
|
| 670 |
+
train: [15] [240/400] eta: 0:01:17 lr: 0.000059 loss: 0.0566 (0.0621) grad: 0.0358 (0.0375) time: 0.4443 data: 0.0033 max mem: 22446
|
| 671 |
+
train: [15] [260/400] eta: 0:01:07 lr: 0.000058 loss: 0.0563 (0.0622) grad: 0.0348 (0.0375) time: 0.4488 data: 0.0032 max mem: 22446
|
| 672 |
+
train: [15] [280/400] eta: 0:00:57 lr: 0.000057 loss: 0.0550 (0.0621) grad: 0.0328 (0.0374) time: 0.4436 data: 0.0033 max mem: 22446
|
| 673 |
+
train: [15] [300/400] eta: 0:00:48 lr: 0.000056 loss: 0.0564 (0.0620) grad: 0.0347 (0.0373) time: 0.6191 data: 0.1664 max mem: 22446
|
| 674 |
+
train: [15] [320/400] eta: 0:00:38 lr: 0.000054 loss: 0.0597 (0.0618) grad: 0.0347 (0.0373) time: 0.4325 data: 0.0028 max mem: 22446
|
| 675 |
+
train: [15] [340/400] eta: 0:00:29 lr: 0.000053 loss: 0.0605 (0.0619) grad: 0.0378 (0.0374) time: 0.4457 data: 0.0033 max mem: 22446
|
| 676 |
+
train: [15] [360/400] eta: 0:00:19 lr: 0.000052 loss: 0.0539 (0.0619) grad: 0.0378 (0.0374) time: 0.4416 data: 0.0033 max mem: 22446
|
| 677 |
+
train: [15] [380/400] eta: 0:00:09 lr: 0.000051 loss: 0.0539 (0.0615) grad: 0.0358 (0.0373) time: 0.4392 data: 0.0033 max mem: 22446
|
| 678 |
+
train: [15] [399/400] eta: 0:00:00 lr: 0.000050 loss: 0.0593 (0.0620) grad: 0.0333 (0.0372) time: 0.4403 data: 0.0032 max mem: 22446
|
| 679 |
+
train: [15] Total time: 0:03:11 (0.4775 s / it)
|
| 680 |
+
train: [15] Summary: lr: 0.000050 loss: 0.0593 (0.0620) grad: 0.0333 (0.0372)
|
| 681 |
+
eval (validation): [15] [ 0/63] eta: 0:03:13 time: 3.0700 data: 2.7990 max mem: 22446
|
| 682 |
+
eval (validation): [15] [20/63] eta: 0:00:21 time: 0.3611 data: 0.0032 max mem: 22446
|
| 683 |
+
eval (validation): [15] [40/63] eta: 0:00:09 time: 0.3409 data: 0.0031 max mem: 22446
|
| 684 |
+
eval (validation): [15] [60/63] eta: 0:00:01 time: 0.3225 data: 0.0031 max mem: 22446
|
| 685 |
+
eval (validation): [15] [62/63] eta: 0:00:00 time: 0.3230 data: 0.0031 max mem: 22446
|
| 686 |
+
eval (validation): [15] Total time: 0:00:24 (0.3886 s / it)
|
| 687 |
+
cv: [15] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 688 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 689 |
+
train: [16] [ 0/400] eta: 0:20:42 lr: nan time: 3.1065 data: 2.7722 max mem: 22446
|
| 690 |
+
train: [16] [ 20/400] eta: 0:03:38 lr: 0.000048 loss: 0.0582 (0.0648) grad: 0.0335 (0.0338) time: 0.4492 data: 0.0022 max mem: 22446
|
| 691 |
+
train: [16] [ 40/400] eta: 0:03:03 lr: 0.000047 loss: 0.0591 (0.0630) grad: 0.0335 (0.0346) time: 0.4421 data: 0.0033 max mem: 22446
|
| 692 |
+
train: [16] [ 60/400] eta: 0:02:46 lr: 0.000046 loss: 0.0563 (0.0611) grad: 0.0314 (0.0336) time: 0.4466 data: 0.0034 max mem: 22446
|
| 693 |
+
train: [16] [ 80/400] eta: 0:02:35 lr: 0.000045 loss: 0.0576 (0.0623) grad: 0.0320 (0.0337) time: 0.4709 data: 0.0033 max mem: 22446
|
| 694 |
+
train: [16] [100/400] eta: 0:02:23 lr: 0.000044 loss: 0.0597 (0.0625) grad: 0.0327 (0.0342) time: 0.4586 data: 0.0032 max mem: 22446
|
| 695 |
+
train: [16] [120/400] eta: 0:02:12 lr: 0.000043 loss: 0.0548 (0.0620) grad: 0.0365 (0.0349) time: 0.4469 data: 0.0034 max mem: 22446
|
| 696 |
+
train: [16] [140/400] eta: 0:02:01 lr: 0.000042 loss: 0.0548 (0.0621) grad: 0.0365 (0.0353) time: 0.4353 data: 0.0032 max mem: 22446
|
| 697 |
+
train: [16] [160/400] eta: 0:01:52 lr: 0.000041 loss: 0.0554 (0.0620) grad: 0.0354 (0.0354) time: 0.4757 data: 0.0033 max mem: 22446
|
| 698 |
+
train: [16] [180/400] eta: 0:01:42 lr: 0.000040 loss: 0.0584 (0.0620) grad: 0.0354 (0.0353) time: 0.4536 data: 0.0032 max mem: 22446
|
| 699 |
+
train: [16] [200/400] eta: 0:01:33 lr: 0.000039 loss: 0.0616 (0.0628) grad: 0.0385 (0.0359) time: 0.4594 data: 0.0032 max mem: 22446
|
| 700 |
+
train: [16] [220/400] eta: 0:01:23 lr: 0.000038 loss: 0.0638 (0.0627) grad: 0.0347 (0.0358) time: 0.4562 data: 0.0033 max mem: 22446
|
| 701 |
+
train: [16] [240/400] eta: 0:01:14 lr: 0.000036 loss: 0.0592 (0.0623) grad: 0.0316 (0.0354) time: 0.4525 data: 0.0032 max mem: 22446
|
| 702 |
+
train: [16] [260/400] eta: 0:01:05 lr: 0.000035 loss: 0.0592 (0.0626) grad: 0.0327 (0.0355) time: 0.4788 data: 0.0033 max mem: 22446
|
| 703 |
+
train: [16] [280/400] eta: 0:00:55 lr: 0.000034 loss: 0.0562 (0.0622) grad: 0.0370 (0.0357) time: 0.4702 data: 0.0035 max mem: 22446
|
| 704 |
+
train: [16] [300/400] eta: 0:00:47 lr: 0.000033 loss: 0.0560 (0.0623) grad: 0.0347 (0.0360) time: 0.6442 data: 0.1830 max mem: 22446
|
| 705 |
+
train: [16] [320/400] eta: 0:00:38 lr: 0.000032 loss: 0.0618 (0.0622) grad: 0.0382 (0.0360) time: 0.4611 data: 0.0034 max mem: 22446
|
| 706 |
+
train: [16] [340/400] eta: 0:00:28 lr: 0.000031 loss: 0.0660 (0.0627) grad: 0.0351 (0.0361) time: 0.4796 data: 0.0031 max mem: 22446
|
| 707 |
+
train: [16] [360/400] eta: 0:00:19 lr: 0.000031 loss: 0.0620 (0.0625) grad: 0.0343 (0.0360) time: 0.4621 data: 0.0034 max mem: 22446
|
| 708 |
+
train: [16] [380/400] eta: 0:00:09 lr: 0.000030 loss: 0.0548 (0.0619) grad: 0.0328 (0.0359) time: 0.4633 data: 0.0036 max mem: 22446
|
| 709 |
+
train: [16] [399/400] eta: 0:00:00 lr: 0.000029 loss: 0.0523 (0.0620) grad: 0.0375 (0.0362) time: 0.4775 data: 0.0036 max mem: 22446
|
| 710 |
+
train: [16] Total time: 0:03:10 (0.4761 s / it)
|
| 711 |
+
train: [16] Summary: lr: 0.000029 loss: 0.0523 (0.0620) grad: 0.0375 (0.0362)
|
| 712 |
+
eval (validation): [16] [ 0/63] eta: 0:03:27 time: 3.2894 data: 3.0019 max mem: 22446
|
| 713 |
+
eval (validation): [16] [20/63] eta: 0:00:23 time: 0.4082 data: 0.0253 max mem: 22446
|
| 714 |
+
eval (validation): [16] [40/63] eta: 0:00:10 time: 0.3733 data: 0.0034 max mem: 22446
|
| 715 |
+
eval (validation): [16] [60/63] eta: 0:00:01 time: 0.3294 data: 0.0033 max mem: 22446
|
| 716 |
+
eval (validation): [16] [62/63] eta: 0:00:00 time: 0.3283 data: 0.0033 max mem: 22446
|
| 717 |
+
eval (validation): [16] Total time: 0:00:26 (0.4196 s / it)
|
| 718 |
+
cv: [16] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 719 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 720 |
+
train: [17] [ 0/400] eta: 0:21:18 lr: nan time: 3.1957 data: 2.8198 max mem: 22446
|
| 721 |
+
train: [17] [ 20/400] eta: 0:03:58 lr: 0.000028 loss: 0.0475 (0.0557) grad: 0.0348 (0.0358) time: 0.4985 data: 0.0034 max mem: 22446
|
| 722 |
+
train: [17] [ 40/400] eta: 0:03:19 lr: 0.000027 loss: 0.0590 (0.0604) grad: 0.0348 (0.0370) time: 0.4790 data: 0.0033 max mem: 22446
|
| 723 |
+
train: [17] [ 60/400] eta: 0:02:58 lr: 0.000026 loss: 0.0661 (0.0629) grad: 0.0356 (0.0364) time: 0.4648 data: 0.0034 max mem: 22446
|
| 724 |
+
train: [17] [ 80/400] eta: 0:02:44 lr: 0.000025 loss: 0.0674 (0.0640) grad: 0.0356 (0.0361) time: 0.4773 data: 0.0033 max mem: 22446
|
| 725 |
+
train: [17] [100/400] eta: 0:02:31 lr: 0.000024 loss: 0.0614 (0.0629) grad: 0.0337 (0.0357) time: 0.4630 data: 0.0033 max mem: 22446
|
| 726 |
+
train: [17] [120/400] eta: 0:02:18 lr: 0.000023 loss: 0.0591 (0.0627) grad: 0.0333 (0.0353) time: 0.4546 data: 0.0033 max mem: 22446
|
| 727 |
+
train: [17] [140/400] eta: 0:02:06 lr: 0.000023 loss: 0.0589 (0.0624) grad: 0.0317 (0.0352) time: 0.4399 data: 0.0030 max mem: 22446
|
| 728 |
+
train: [17] [160/400] eta: 0:01:56 lr: 0.000022 loss: 0.0645 (0.0625) grad: 0.0326 (0.0353) time: 0.4627 data: 0.0032 max mem: 22446
|
| 729 |
+
train: [17] [180/400] eta: 0:01:45 lr: 0.000021 loss: 0.0580 (0.0618) grad: 0.0350 (0.0352) time: 0.4499 data: 0.0034 max mem: 22446
|
| 730 |
+
train: [17] [200/400] eta: 0:01:35 lr: 0.000020 loss: 0.0516 (0.0615) grad: 0.0337 (0.0352) time: 0.4624 data: 0.0033 max mem: 22446
|
| 731 |
+
train: [17] [220/400] eta: 0:01:25 lr: 0.000019 loss: 0.0557 (0.0617) grad: 0.0337 (0.0352) time: 0.4610 data: 0.0033 max mem: 22446
|
| 732 |
+
train: [17] [240/400] eta: 0:01:15 lr: 0.000019 loss: 0.0552 (0.0616) grad: 0.0311 (0.0353) time: 0.4490 data: 0.0032 max mem: 22446
|
| 733 |
+
train: [17] [260/400] eta: 0:01:06 lr: 0.000018 loss: 0.0537 (0.0616) grad: 0.0339 (0.0353) time: 0.4437 data: 0.0032 max mem: 22446
|
| 734 |
+
train: [17] [280/400] eta: 0:00:56 lr: 0.000017 loss: 0.0559 (0.0613) grad: 0.0339 (0.0350) time: 0.4486 data: 0.0033 max mem: 22446
|
| 735 |
+
train: [17] [300/400] eta: 0:00:48 lr: 0.000016 loss: 0.0561 (0.0616) grad: 0.0342 (0.0353) time: 0.6409 data: 0.1830 max mem: 22446
|
| 736 |
+
train: [17] [320/400] eta: 0:00:38 lr: 0.000016 loss: 0.0581 (0.0616) grad: 0.0364 (0.0355) time: 0.4486 data: 0.0029 max mem: 22446
|
| 737 |
+
train: [17] [340/400] eta: 0:00:28 lr: 0.000015 loss: 0.0581 (0.0617) grad: 0.0327 (0.0353) time: 0.4582 data: 0.0033 max mem: 22446
|
| 738 |
+
train: [17] [360/400] eta: 0:00:19 lr: 0.000014 loss: 0.0567 (0.0613) grad: 0.0309 (0.0351) time: 0.4514 data: 0.0032 max mem: 22446
|
| 739 |
+
train: [17] [380/400] eta: 0:00:09 lr: 0.000014 loss: 0.0526 (0.0614) grad: 0.0329 (0.0350) time: 0.4563 data: 0.0032 max mem: 22446
|
| 740 |
+
train: [17] [399/400] eta: 0:00:00 lr: 0.000013 loss: 0.0526 (0.0613) grad: 0.0335 (0.0350) time: 0.4484 data: 0.0031 max mem: 22446
|
| 741 |
+
train: [17] Total time: 0:03:10 (0.4750 s / it)
|
| 742 |
+
train: [17] Summary: lr: 0.000013 loss: 0.0526 (0.0613) grad: 0.0335 (0.0350)
|
| 743 |
+
eval (validation): [17] [ 0/63] eta: 0:03:15 time: 3.1067 data: 2.8431 max mem: 22446
|
| 744 |
+
eval (validation): [17] [20/63] eta: 0:00:20 time: 0.3550 data: 0.0030 max mem: 22446
|
| 745 |
+
eval (validation): [17] [40/63] eta: 0:00:09 time: 0.3348 data: 0.0032 max mem: 22446
|
| 746 |
+
eval (validation): [17] [60/63] eta: 0:00:01 time: 0.3258 data: 0.0030 max mem: 22446
|
| 747 |
+
eval (validation): [17] [62/63] eta: 0:00:00 time: 0.3260 data: 0.0030 max mem: 22446
|
| 748 |
+
eval (validation): [17] Total time: 0:00:24 (0.3865 s / it)
|
| 749 |
+
cv: [17] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 750 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 751 |
+
train: [18] [ 0/400] eta: 0:21:25 lr: nan time: 3.2130 data: 2.8335 max mem: 22446
|
| 752 |
+
train: [18] [ 20/400] eta: 0:03:45 lr: 0.000012 loss: 0.0549 (0.0593) grad: 0.0301 (0.0340) time: 0.4622 data: 0.0046 max mem: 22446
|
| 753 |
+
train: [18] [ 40/400] eta: 0:03:08 lr: 0.000012 loss: 0.0552 (0.0616) grad: 0.0344 (0.0352) time: 0.4514 data: 0.0035 max mem: 22446
|
| 754 |
+
train: [18] [ 60/400] eta: 0:02:50 lr: 0.000011 loss: 0.0537 (0.0608) grad: 0.0353 (0.0350) time: 0.4554 data: 0.0035 max mem: 22446
|
| 755 |
+
train: [18] [ 80/400] eta: 0:02:38 lr: 0.000011 loss: 0.0569 (0.0616) grad: 0.0350 (0.0349) time: 0.4714 data: 0.0035 max mem: 22446
|
| 756 |
+
train: [18] [100/400] eta: 0:02:26 lr: 0.000010 loss: 0.0570 (0.0622) grad: 0.0355 (0.0351) time: 0.4700 data: 0.0035 max mem: 22446
|
| 757 |
+
train: [18] [120/400] eta: 0:02:15 lr: 0.000009 loss: 0.0551 (0.0604) grad: 0.0324 (0.0349) time: 0.4522 data: 0.0033 max mem: 22446
|
| 758 |
+
train: [18] [140/400] eta: 0:02:05 lr: 0.000009 loss: 0.0593 (0.0613) grad: 0.0325 (0.0350) time: 0.4678 data: 0.0034 max mem: 22446
|
| 759 |
+
train: [18] [160/400] eta: 0:01:55 lr: 0.000008 loss: 0.0693 (0.0610) grad: 0.0343 (0.0350) time: 0.4854 data: 0.0035 max mem: 22446
|
| 760 |
+
train: [18] [180/400] eta: 0:01:45 lr: 0.000008 loss: 0.0520 (0.0601) grad: 0.0348 (0.0352) time: 0.4631 data: 0.0032 max mem: 22446
|
| 761 |
+
train: [18] [200/400] eta: 0:01:35 lr: 0.000007 loss: 0.0543 (0.0601) grad: 0.0339 (0.0351) time: 0.4646 data: 0.0032 max mem: 22446
|
| 762 |
+
train: [18] [220/400] eta: 0:01:25 lr: 0.000007 loss: 0.0543 (0.0597) grad: 0.0334 (0.0350) time: 0.4665 data: 0.0032 max mem: 22446
|
| 763 |
+
train: [18] [240/400] eta: 0:01:16 lr: 0.000006 loss: 0.0517 (0.0595) grad: 0.0342 (0.0350) time: 0.4603 data: 0.0031 max mem: 22446
|
| 764 |
+
train: [18] [260/400] eta: 0:01:06 lr: 0.000006 loss: 0.0549 (0.0598) grad: 0.0321 (0.0348) time: 0.4562 data: 0.0032 max mem: 22446
|
| 765 |
+
train: [18] [280/400] eta: 0:00:56 lr: 0.000006 loss: 0.0594 (0.0599) grad: 0.0346 (0.0350) time: 0.4649 data: 0.0036 max mem: 22446
|
| 766 |
+
train: [18] [300/400] eta: 0:00:48 lr: 0.000005 loss: 0.0537 (0.0595) grad: 0.0349 (0.0350) time: 0.6192 data: 0.1821 max mem: 22446
|
| 767 |
+
train: [18] [320/400] eta: 0:00:38 lr: 0.000005 loss: 0.0545 (0.0596) grad: 0.0341 (0.0350) time: 0.4477 data: 0.0031 max mem: 22446
|
| 768 |
+
train: [18] [340/400] eta: 0:00:28 lr: 0.000004 loss: 0.0556 (0.0597) grad: 0.0346 (0.0350) time: 0.4564 data: 0.0034 max mem: 22446
|
| 769 |
+
train: [18] [360/400] eta: 0:00:19 lr: 0.000004 loss: 0.0559 (0.0595) grad: 0.0334 (0.0352) time: 0.4594 data: 0.0034 max mem: 22446
|
| 770 |
+
train: [18] [380/400] eta: 0:00:09 lr: 0.000004 loss: 0.0549 (0.0590) grad: 0.0326 (0.0350) time: 0.4547 data: 0.0033 max mem: 22446
|
| 771 |
+
train: [18] [399/400] eta: 0:00:00 lr: 0.000003 loss: 0.0508 (0.0589) grad: 0.0332 (0.0349) time: 0.4577 data: 0.0032 max mem: 22446
|
| 772 |
+
train: [18] Total time: 0:03:10 (0.4766 s / it)
|
| 773 |
+
train: [18] Summary: lr: 0.000003 loss: 0.0508 (0.0589) grad: 0.0332 (0.0349)
|
| 774 |
+
eval (validation): [18] [ 0/63] eta: 0:03:21 time: 3.1988 data: 2.9576 max mem: 22446
|
| 775 |
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eval (validation): [18] [20/63] eta: 0:00:21 time: 0.3578 data: 0.0029 max mem: 22446
|
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eval (validation): [18] [40/63] eta: 0:00:09 time: 0.3292 data: 0.0026 max mem: 22446
|
| 777 |
+
eval (validation): [18] [60/63] eta: 0:00:01 time: 0.3224 data: 0.0031 max mem: 22446
|
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+
eval (validation): [18] [62/63] eta: 0:00:00 time: 0.3214 data: 0.0031 max mem: 22446
|
| 779 |
+
eval (validation): [18] Total time: 0:00:24 (0.3853 s / it)
|
| 780 |
+
cv: [18] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 781 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 782 |
+
train: [19] [ 0/400] eta: 0:21:22 lr: nan time: 3.2060 data: 2.8659 max mem: 22446
|
| 783 |
+
train: [19] [ 20/400] eta: 0:03:44 lr: 0.000003 loss: 0.0546 (0.0573) grad: 0.0320 (0.0328) time: 0.4594 data: 0.0030 max mem: 22446
|
| 784 |
+
train: [19] [ 40/400] eta: 0:03:07 lr: 0.000003 loss: 0.0553 (0.0571) grad: 0.0328 (0.0341) time: 0.4465 data: 0.0031 max mem: 22446
|
| 785 |
+
train: [19] [ 60/400] eta: 0:02:48 lr: 0.000002 loss: 0.0573 (0.0573) grad: 0.0331 (0.0337) time: 0.4455 data: 0.0032 max mem: 22446
|
| 786 |
+
train: [19] [ 80/400] eta: 0:02:35 lr: 0.000002 loss: 0.0579 (0.0587) grad: 0.0344 (0.0343) time: 0.4605 data: 0.0033 max mem: 22446
|
| 787 |
+
train: [19] [100/400] eta: 0:02:24 lr: 0.000002 loss: 0.0599 (0.0600) grad: 0.0360 (0.0341) time: 0.4611 data: 0.0031 max mem: 22446
|
| 788 |
+
train: [19] [120/400] eta: 0:02:13 lr: 0.000002 loss: 0.0583 (0.0595) grad: 0.0343 (0.0343) time: 0.4424 data: 0.0032 max mem: 22446
|
| 789 |
+
train: [19] [140/400] eta: 0:02:01 lr: 0.000001 loss: 0.0555 (0.0585) grad: 0.0329 (0.0342) time: 0.4316 data: 0.0032 max mem: 22446
|
| 790 |
+
train: [19] [160/400] eta: 0:01:53 lr: 0.000001 loss: 0.0555 (0.0590) grad: 0.0333 (0.0341) time: 0.4841 data: 0.0032 max mem: 22446
|
| 791 |
+
train: [19] [180/400] eta: 0:01:43 lr: 0.000001 loss: 0.0561 (0.0589) grad: 0.0335 (0.0341) time: 0.4480 data: 0.0033 max mem: 22446
|
| 792 |
+
train: [19] [200/400] eta: 0:01:33 lr: 0.000001 loss: 0.0535 (0.0592) grad: 0.0321 (0.0342) time: 0.4492 data: 0.0032 max mem: 22446
|
| 793 |
+
train: [19] [220/400] eta: 0:01:23 lr: 0.000001 loss: 0.0597 (0.0599) grad: 0.0347 (0.0344) time: 0.4499 data: 0.0032 max mem: 22446
|
| 794 |
+
train: [19] [240/400] eta: 0:01:14 lr: 0.000001 loss: 0.0597 (0.0595) grad: 0.0338 (0.0343) time: 0.4518 data: 0.0031 max mem: 22446
|
| 795 |
+
train: [19] [260/400] eta: 0:01:04 lr: 0.000000 loss: 0.0539 (0.0594) grad: 0.0324 (0.0342) time: 0.4667 data: 0.0033 max mem: 22446
|
| 796 |
+
train: [19] [280/400] eta: 0:00:55 lr: 0.000000 loss: 0.0553 (0.0596) grad: 0.0330 (0.0344) time: 0.4600 data: 0.0032 max mem: 22446
|
| 797 |
+
train: [19] [300/400] eta: 0:00:47 lr: 0.000000 loss: 0.0584 (0.0596) grad: 0.0354 (0.0344) time: 0.6068 data: 0.1683 max mem: 22446
|
| 798 |
+
train: [19] [320/400] eta: 0:00:37 lr: 0.000000 loss: 0.0561 (0.0594) grad: 0.0366 (0.0346) time: 0.4567 data: 0.0037 max mem: 22446
|
| 799 |
+
train: [19] [340/400] eta: 0:00:28 lr: 0.000000 loss: 0.0560 (0.0593) grad: 0.0346 (0.0347) time: 0.4552 data: 0.0033 max mem: 22446
|
| 800 |
+
train: [19] [360/400] eta: 0:00:18 lr: 0.000000 loss: 0.0530 (0.0592) grad: 0.0344 (0.0347) time: 0.4764 data: 0.0036 max mem: 22446
|
| 801 |
+
train: [19] [380/400] eta: 0:00:09 lr: 0.000000 loss: 0.0549 (0.0591) grad: 0.0344 (0.0347) time: 0.4581 data: 0.0035 max mem: 22446
|
| 802 |
+
train: [19] [399/400] eta: 0:00:00 lr: 0.000000 loss: 0.0549 (0.0590) grad: 0.0339 (0.0347) time: 0.4596 data: 0.0034 max mem: 22446
|
| 803 |
+
train: [19] Total time: 0:03:08 (0.4706 s / it)
|
| 804 |
+
train: [19] Summary: lr: 0.000000 loss: 0.0549 (0.0590) grad: 0.0339 (0.0347)
|
| 805 |
+
eval (validation): [19] [ 0/63] eta: 0:04:18 time: 4.1028 data: 3.8491 max mem: 22446
|
| 806 |
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eval (validation): [19] [20/63] eta: 0:00:22 time: 0.3403 data: 0.0043 max mem: 22446
|
| 807 |
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eval (validation): [19] [40/63] eta: 0:00:10 time: 0.3693 data: 0.0033 max mem: 22446
|
| 808 |
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eval (validation): [19] [60/63] eta: 0:00:01 time: 0.3475 data: 0.0029 max mem: 22446
|
| 809 |
+
eval (validation): [19] [62/63] eta: 0:00:00 time: 0.3430 data: 0.0032 max mem: 22446
|
| 810 |
+
eval (validation): [19] Total time: 0:00:26 (0.4157 s / it)
|
| 811 |
+
cv: [19] best hparam: (6, 1.0) (035) ('035_lr6.0e+00_wd1.0e+00') loss: 0.040 acc: 0.993 f1: 0.992
|
| 812 |
+
saving checkpoint experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 813 |
+
evaluating last checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-last.pth
|
| 814 |
+
eval model info:
|
| 815 |
+
{"score": 0.9930555555555556, "hparam": [6, 1.0], "hparam_id": 35, "epoch": 19, "is_best": false, "best_score": 0.9933035714285714}
|
| 816 |
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eval (train): [20] [ 0/297] eta: 0:14:58 time: 3.0244 data: 2.7876 max mem: 22446
|
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eval (train): [20] [ 20/297] eta: 0:02:12 time: 0.3508 data: 0.0039 max mem: 22446
|
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eval (train): [20] [ 40/297] eta: 0:01:47 time: 0.3574 data: 0.0035 max mem: 22446
|
| 819 |
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eval (train): [20] [ 60/297] eta: 0:01:34 time: 0.3604 data: 0.0031 max mem: 22446
|
| 820 |
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eval (train): [20] [ 80/297] eta: 0:01:27 time: 0.4162 data: 0.0038 max mem: 22446
|
| 821 |
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eval (train): [20] [100/297] eta: 0:01:21 time: 0.4438 data: 0.0038 max mem: 22446
|
| 822 |
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eval (train): [20] [120/297] eta: 0:01:13 time: 0.4192 data: 0.0040 max mem: 22446
|
| 823 |
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eval (train): [20] [140/297] eta: 0:01:04 time: 0.4074 data: 0.0038 max mem: 22446
|
| 824 |
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eval (train): [20] [160/297] eta: 0:00:56 time: 0.3942 data: 0.0036 max mem: 22446
|
| 825 |
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eval (train): [20] [180/297] eta: 0:00:47 time: 0.3705 data: 0.0035 max mem: 22446
|
| 826 |
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eval (train): [20] [200/297] eta: 0:00:39 time: 0.4260 data: 0.0036 max mem: 22446
|
| 827 |
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eval (train): [20] [220/297] eta: 0:00:31 time: 0.4183 data: 0.0040 max mem: 22446
|
| 828 |
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eval (train): [20] [240/297] eta: 0:00:23 time: 0.3947 data: 0.0036 max mem: 22446
|
| 829 |
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eval (train): [20] [260/297] eta: 0:00:15 time: 0.4341 data: 0.0037 max mem: 22446
|
| 830 |
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eval (train): [20] [280/297] eta: 0:00:06 time: 0.4045 data: 0.0034 max mem: 22446
|
| 831 |
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eval (train): [20] [296/297] eta: 0:00:00 time: 0.3480 data: 0.0031 max mem: 22446
|
| 832 |
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eval (train): [20] Total time: 0:02:01 (0.4080 s / it)
|
| 833 |
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eval (validation): [20] [ 0/63] eta: 0:03:19 time: 3.1591 data: 2.8600 max mem: 22446
|
| 834 |
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eval (validation): [20] [20/63] eta: 0:00:25 time: 0.4731 data: 0.0039 max mem: 22446
|
| 835 |
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eval (validation): [20] [40/63] eta: 0:00:11 time: 0.4310 data: 0.0036 max mem: 22446
|
| 836 |
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eval (validation): [20] [60/63] eta: 0:00:01 time: 0.3536 data: 0.0033 max mem: 22446
|
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eval (validation): [20] [62/63] eta: 0:00:00 time: 0.3559 data: 0.0033 max mem: 22446
|
| 838 |
+
eval (validation): [20] Total time: 0:00:29 (0.4655 s / it)
|
| 839 |
+
eval (test): [20] [ 0/79] eta: 0:04:10 time: 3.1767 data: 2.8873 max mem: 22446
|
| 840 |
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eval (test): [20] [20/79] eta: 0:00:31 time: 0.4014 data: 0.0044 max mem: 22446
|
| 841 |
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eval (test): [20] [40/79] eta: 0:00:17 time: 0.3547 data: 0.0030 max mem: 22446
|
| 842 |
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eval (test): [20] [60/79] eta: 0:00:08 time: 0.3746 data: 0.0034 max mem: 22446
|
| 843 |
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eval (test): [20] [78/79] eta: 0:00:00 time: 0.3274 data: 0.0030 max mem: 22446
|
| 844 |
+
eval (test): [20] Total time: 0:00:31 (0.4033 s / it)
|
| 845 |
+
evaluating best checkpoint: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/checkpoint-best.pth
|
| 846 |
+
eval model info:
|
| 847 |
+
{"score": 0.9933035714285714, "hparam": [5.1, 1.0], "hparam_id": 34, "epoch": 9, "is_best": true, "best_score": 0.9933035714285714}
|
| 848 |
+
eval (train): [20] [ 0/297] eta: 0:15:23 time: 3.1093 data: 2.7973 max mem: 22446
|
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eval (train): [20] [ 20/297] eta: 0:02:15 time: 0.3586 data: 0.0036 max mem: 22446
|
| 850 |
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eval (train): [20] [ 40/297] eta: 0:01:47 time: 0.3456 data: 0.0028 max mem: 22446
|
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eval (train): [20] [ 60/297] eta: 0:01:39 time: 0.4184 data: 0.0042 max mem: 22446
|
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eval (train): [20] [ 80/297] eta: 0:01:27 time: 0.3569 data: 0.0026 max mem: 22446
|
| 853 |
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eval (train): [20] [100/297] eta: 0:01:18 time: 0.3760 data: 0.0036 max mem: 22446
|
| 854 |
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eval (train): [20] [120/297] eta: 0:01:10 time: 0.3874 data: 0.0035 max mem: 22446
|
| 855 |
+
eval (train): [20] [140/297] eta: 0:01:01 time: 0.3625 data: 0.0033 max mem: 22446
|
| 856 |
+
eval (train): [20] [160/297] eta: 0:00:52 time: 0.3486 data: 0.0033 max mem: 22446
|
| 857 |
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eval (train): [20] [180/297] eta: 0:00:44 time: 0.3526 data: 0.0034 max mem: 22446
|
| 858 |
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eval (train): [20] [200/297] eta: 0:00:36 time: 0.3377 data: 0.0033 max mem: 22446
|
| 859 |
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eval (train): [20] [220/297] eta: 0:00:28 time: 0.3564 data: 0.0033 max mem: 22446
|
| 860 |
+
eval (train): [20] [240/297] eta: 0:00:21 time: 0.3333 data: 0.0031 max mem: 22446
|
| 861 |
+
eval (train): [20] [260/297] eta: 0:00:13 time: 0.3408 data: 0.0032 max mem: 22446
|
| 862 |
+
eval (train): [20] [280/297] eta: 0:00:06 time: 0.3487 data: 0.0031 max mem: 22446
|
| 863 |
+
eval (train): [20] [296/297] eta: 0:00:00 time: 0.3329 data: 0.0031 max mem: 22446
|
| 864 |
+
eval (train): [20] Total time: 0:01:49 (0.3681 s / it)
|
| 865 |
+
eval (validation): [20] [ 0/63] eta: 0:03:50 time: 3.6631 data: 3.3699 max mem: 22446
|
| 866 |
+
eval (validation): [20] [20/63] eta: 0:00:21 time: 0.3401 data: 0.0024 max mem: 22446
|
| 867 |
+
eval (validation): [20] [40/63] eta: 0:00:09 time: 0.3373 data: 0.0028 max mem: 22446
|
| 868 |
+
eval (validation): [20] [60/63] eta: 0:00:01 time: 0.3370 data: 0.0032 max mem: 22446
|
| 869 |
+
eval (validation): [20] [62/63] eta: 0:00:00 time: 0.3342 data: 0.0032 max mem: 22446
|
| 870 |
+
eval (validation): [20] Total time: 0:00:24 (0.3944 s / it)
|
| 871 |
+
eval (test): [20] [ 0/79] eta: 0:03:54 time: 2.9678 data: 2.7377 max mem: 22446
|
| 872 |
+
eval (test): [20] [20/79] eta: 0:00:27 time: 0.3332 data: 0.0035 max mem: 22446
|
| 873 |
+
eval (test): [20] [40/79] eta: 0:00:15 time: 0.3401 data: 0.0030 max mem: 22446
|
| 874 |
+
eval (test): [20] [60/79] eta: 0:00:07 time: 0.3106 data: 0.0026 max mem: 22446
|
| 875 |
+
eval (test): [20] [78/79] eta: 0:00:00 time: 0.3251 data: 0.0030 max mem: 22446
|
| 876 |
+
eval (test): [20] Total time: 0:00:28 (0.3642 s / it)
|
| 877 |
+
eval results:
|
| 878 |
+
|
| 879 |
+
| model | repr | clf | dataset | ckpt | epoch | lr | wd | hparam_id | hparam | split | loss | acc | acc_std | f1 | f1_std |
|
| 880 |
+
|:---------|:-------|:------|:-------------|:-------|--------:|--------:|-----:|------------:|:-----------|:-----------|----------:|--------:|-----------:|--------:|-----------:|
|
| 881 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 9 | 0.00153 | 0.05 | 34 | [5.1, 1.0] | train | 0.0008329 | 0.99989 | 7.6798e-05 | 0.9999 | 7.6843e-05 |
|
| 882 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 9 | 0.00153 | 0.05 | 34 | [5.1, 1.0] | validation | 0.029025 | 0.9933 | 0.0013547 | 0.9929 | 0.001544 |
|
| 883 |
+
| flat_mae | patch | attn | hcpya_task21 | best | 9 | 0.00153 | 0.05 | 34 | [5.1, 1.0] | test | 0.052616 | 0.9877 | 0.0015318 | 0.98688 | 0.0018109 |
|
| 884 |
+
|
| 885 |
+
|
| 886 |
+
done! total time: 1:19:10
|
input_space_v3/flat_lr1e-3_8/eval_v2/hcpya_task21__patch__attn/train_log.json
ADDED
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input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/config.yaml
ADDED
|
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| 1 |
+
output_root: experiments/input_space_v3/output
|
| 2 |
+
name_prefix: eval_probe
|
| 3 |
+
remote_root: null
|
| 4 |
+
notes: input_space ablation v3 flat_lr1e-3_8; eval v2 (nsd_cococlip patch attn)
|
| 5 |
+
model_kwargs:
|
| 6 |
+
ckpt_path: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/pretrain/checkpoint-last.pth
|
| 7 |
+
dataset_kwargs: {}
|
| 8 |
+
classifier_kwargs:
|
| 9 |
+
embed_dim: null
|
| 10 |
+
dropout: 0.0
|
| 11 |
+
xavier_init: true
|
| 12 |
+
norm: true
|
| 13 |
+
lr_scale_grid:
|
| 14 |
+
- 0.02
|
| 15 |
+
- 0.023
|
| 16 |
+
- 0.028
|
| 17 |
+
- 0.033
|
| 18 |
+
- 0.038
|
| 19 |
+
- 0.045
|
| 20 |
+
- 0.053
|
| 21 |
+
- 0.062
|
| 22 |
+
- 0.074
|
| 23 |
+
- 0.087
|
| 24 |
+
- 0.1
|
| 25 |
+
- 0.12
|
| 26 |
+
- 0.14
|
| 27 |
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- 0.17
|
| 28 |
+
- 0.2
|
| 29 |
+
- 0.23
|
| 30 |
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- 0.27
|
| 31 |
+
- 0.32
|
| 32 |
+
- 0.38
|
| 33 |
+
- 0.44
|
| 34 |
+
- 0.52
|
| 35 |
+
- 0.61
|
| 36 |
+
- 0.72
|
| 37 |
+
- 0.85
|
| 38 |
+
- 1
|
| 39 |
+
- 1.2
|
| 40 |
+
- 1.4
|
| 41 |
+
- 1.6
|
| 42 |
+
- 1.9
|
| 43 |
+
- 2.3
|
| 44 |
+
- 2.7
|
| 45 |
+
- 3.1
|
| 46 |
+
- 3.7
|
| 47 |
+
- 4.3
|
| 48 |
+
- 5.1
|
| 49 |
+
- 6
|
| 50 |
+
- 7.1
|
| 51 |
+
- 8.3
|
| 52 |
+
- 9.8
|
| 53 |
+
- 12
|
| 54 |
+
- 14
|
| 55 |
+
- 16
|
| 56 |
+
- 19
|
| 57 |
+
- 22
|
| 58 |
+
- 26
|
| 59 |
+
- 31
|
| 60 |
+
- 36
|
| 61 |
+
- 43
|
| 62 |
+
- 50
|
| 63 |
+
wd_scale_grid:
|
| 64 |
+
- 1.0
|
| 65 |
+
num_workers: 8
|
| 66 |
+
prefetch_factor: null
|
| 67 |
+
balanced_sampling: false
|
| 68 |
+
epochs: 20
|
| 69 |
+
steps_per_epoch: 200
|
| 70 |
+
batch_size: 64
|
| 71 |
+
accum_iter: 2
|
| 72 |
+
lr: 0.0003
|
| 73 |
+
warmup_epochs: 5
|
| 74 |
+
no_decay: false
|
| 75 |
+
weight_decay: 0.05
|
| 76 |
+
clip_grad: 1.0
|
| 77 |
+
metrics:
|
| 78 |
+
- acc
|
| 79 |
+
- f1
|
| 80 |
+
cv_metric: acc
|
| 81 |
+
early_stopping: true
|
| 82 |
+
amp: true
|
| 83 |
+
device: cuda
|
| 84 |
+
seed: 4466
|
| 85 |
+
debug: false
|
| 86 |
+
wandb: false
|
| 87 |
+
wandb_entity: null
|
| 88 |
+
wandb_project: fMRI-fm-eval
|
| 89 |
+
name: input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn
|
| 90 |
+
model: flat_mae
|
| 91 |
+
representation: patch
|
| 92 |
+
classifier: attn
|
| 93 |
+
dataset: nsd_cococlip
|
| 94 |
+
distributed: false
|
| 95 |
+
output_dir: experiments/input_space_v3/output/input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn
|
| 96 |
+
remote_dir: null
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/epoch": 6, "eval/id_best": 22, "eval/lr_best": 0.00021599999999999996, "eval/wd_best": 0.05, "eval/train/loss": 2.065432548522949, "eval/train/acc": 0.3779464642429085, "eval/train/acc_std": 0.002321442675005775, "eval/train/f1": 0.32019069749667856, "eval/train/f1_std": 0.002350462722964266, "eval/validation/loss": 2.3665122985839844, "eval/validation/acc": 0.29328165374677, "eval/validation/acc_std": 0.005524132117109234, "eval/validation/f1": 0.23446754601497014, "eval/validation/f1_std": 0.005218988478729958, "eval/test/loss": 2.241274356842041, "eval/test/acc": 0.3198515769944341, "eval/test/acc_std": 0.005570334517758026, "eval/test/f1": 0.25387155181344573, "eval/test/f1_std": 0.00547651374767538, "eval/testid/loss": 2.2497355937957764, "eval/testid/acc": 0.30884904569115096, "eval/testid/acc_std": 0.005845100096278563, "eval/testid/f1": 0.2510447501983926, "eval/testid/f1_std": 0.00535473863073123}
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log_best.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/best/epoch": 6, "eval/best/id_best": 22, "eval/best/lr_best": 0.00021599999999999996, "eval/best/wd_best": 0.05, "eval/best/train/loss": 2.065432548522949, "eval/best/train/acc": 0.3779464642429085, "eval/best/train/acc_std": 0.002321442675005775, "eval/best/train/f1": 0.32019069749667856, "eval/best/train/f1_std": 0.002350462722964266, "eval/best/validation/loss": 2.3665122985839844, "eval/best/validation/acc": 0.29328165374677, "eval/best/validation/acc_std": 0.005524132117109234, "eval/best/validation/f1": 0.23446754601497014, "eval/best/validation/f1_std": 0.005218988478729958, "eval/best/test/loss": 2.241274356842041, "eval/best/test/acc": 0.3198515769944341, "eval/best/test/acc_std": 0.005570334517758026, "eval/best/test/f1": 0.25387155181344573, "eval/best/test/f1_std": 0.00547651374767538, "eval/best/testid/loss": 2.2497355937957764, "eval/best/testid/acc": 0.30884904569115096, "eval/best/testid/acc_std": 0.005845100096278563, "eval/best/testid/f1": 0.2510447501983926, "eval/best/testid/f1_std": 0.00535473863073123}
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_log_last.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"eval/last/epoch": 19, "eval/last/id_best": 15, "eval/last/lr_best": 6.9e-05, "eval/last/wd_best": 0.05, "eval/last/train/loss": 2.1982474327087402, "eval/last/train/acc": 0.34155935953778543, "eval/last/train/acc_std": 0.0022814716019880304, "eval/last/train/f1": 0.2772496250712718, "eval/last/train/f1_std": 0.002314194008349156, "eval/last/validation/loss": 2.390402317047119, "eval/last/validation/acc": 0.2847914359542267, "eval/last/validation/acc_std": 0.005251641454491776, "eval/last/validation/f1": 0.2163707758801887, "eval/last/validation/f1_std": 0.004726969551552898, "eval/last/test/loss": 2.2590157985687256, "eval/last/test/acc": 0.30890538033395176, "eval/last/test/acc_std": 0.005310953066651233, "eval/last/test/f1": 0.23348941044502958, "eval/last/test/f1_std": 0.005284242677719361, "eval/last/testid/loss": 2.3171467781066895, "eval/last/testid/acc": 0.29631771737034895, "eval/last/testid/acc_std": 0.005668286604897441, "eval/last/testid/f1": 0.23190373429806357, "eval/last/testid/f1_std": 0.005451936486945506}
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",train,2.065432548522949,0.3779464642429085,0.002321442675005775,0.32019069749667856,0.002350462722964266
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",validation,2.3665122985839844,0.29328165374677,0.005524132117109234,0.23446754601497014,0.005218988478729958
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",test,2.241274356842041,0.3198515769944341,0.005570334517758026,0.25387155181344573,0.00547651374767538
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",testid,2.2497355937957764,0.30884904569115096,0.005845100096278563,0.2510447501983926,0.00535473863073123
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table_best.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",train,2.065432548522949,0.3779464642429085,0.002321442675005775,0.32019069749667856,0.002350462722964266
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",validation,2.3665122985839844,0.29328165374677,0.005524132117109234,0.23446754601497014,0.005218988478729958
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",test,2.241274356842041,0.3198515769944341,0.005570334517758026,0.25387155181344573,0.00547651374767538
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,best,6,0.00021599999999999996,0.05,22,"[0.72, 1.0]",testid,2.2497355937957764,0.30884904569115096,0.005845100096278563,0.2510447501983926,0.00535473863073123
|
input_space_v3/flat_lr1e-3_8/eval_v2/nsd_cococlip__patch__attn/eval_table_last.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model,repr,clf,dataset,ckpt,epoch,lr,wd,hparam_id,hparam,split,loss,acc,acc_std,f1,f1_std
|
| 2 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,6.9e-05,0.05,15,"[0.23, 1.0]",train,2.1982474327087402,0.34155935953778543,0.0022814716019880304,0.2772496250712718,0.002314194008349156
|
| 3 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,6.9e-05,0.05,15,"[0.23, 1.0]",validation,2.390402317047119,0.2847914359542267,0.005251641454491776,0.2163707758801887,0.004726969551552898
|
| 4 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,6.9e-05,0.05,15,"[0.23, 1.0]",test,2.2590157985687256,0.30890538033395176,0.005310953066651233,0.23348941044502958,0.005284242677719361
|
| 5 |
+
flat_mae,patch,attn,nsd_cococlip,last,19,6.9e-05,0.05,15,"[0.23, 1.0]",testid,2.3171467781066895,0.29631771737034895,0.005668286604897441,0.23190373429806357,0.005451936486945506
|