Add metrics plots logs and reports

results/adam/resnet/log.csv

@@ -0,0 +1,25 @@
+epoch,train_loss,val_acc,val_auc,val_score,lr
+0,0.6890696138143539,0.475,0.3261648745519713,0.14264919305520904,0.000125
+1,0.6811029762029648,0.725,0.4623655913978494,0.32501875297975436,0.0002916666666666667
+2,0.6568811237812042,0.8,0.7992831541218638,0.6214050202455499,0.0004583333333333333
+3,0.6208048164844513,0.8,0.7240143369175627,0.5963154145107828,0.0004996859161456965
+4,0.5614998787641525,0.8,0.7168458781362007,0.5632248226620223,0.0004982915790812436
+5,0.47156069427728653,0.775,0.7060931899641577,0.4964012453781352,0.0004957883115509159
+6,0.3866201490163803,0.8,0.7491039426523297,0.5352577903290147,0.0004921872937551814
+7,0.30171431228518486,0.775,0.8028673835125448,0.479156658068799,0.000487504608713676
+8,0.23811906203627586,0.775,0.8028673835125448,0.479156658068799,0.00048176117043453436
+9,0.16715852729976177,0.775,0.8781362007168458,0.504246263803566,0.000474982630507352
+10,0.1148978192359209,0.825,0.8458781362007168,0.663197652654773,0.00046719926353695914
+11,0.11084206961095333,0.825,0.8172043010752688,0.653639707612957,0.00045844583192968674
+12,0.08324608393013477,0.775,0.8387096774193549,0.5406067411965342,0.00044876143063602076
+13,0.08638920076191425,0.85,0.8888888888888888,0.7479091995221028,0.00043818931254306284
+14,0.050563013181090355,0.825,0.8709677419354839,0.6957139245790865,0.00042677669529663686
+15,0.02975934650748968,0.775,0.8136200716845878,0.5708939951268827,0.0004145745504158204
+16,0.04655684158205986,0.8,0.8172043010752688,0.6729920575381941,0.0004016373756417668
+17,0.019254492479376495,0.85,0.8602150537634409,0.7383512544802867,0.00038802295153756415
+18,0.04804687201976776,0.775,0.7634408602150538,0.4660144836363019,0.0003737920834262134
+19,0.02171836607158184,0.8,0.6845878136200717,0.5137524139849287,0.0003590083298192957
+20,0.0381931948941201,0.8,0.7132616487455197,0.5233103590267446,0.0003437377185492303
+21,0.018138925079256296,0.8,0.7275985663082437,0.5280893315476526,0.0003280484518729466
+22,0.008973158313892782,0.8,0.7168458781362008,0.5245051021569717,0.00031201060186404833
+23,0.03882891917601228,0.775,0.7491039426523297,0.4612355111153939,0.0002956957974539226

results/adam/resnet/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 80,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.825,
+  "balanced_accuracy": 0.7885304659498208,
+  "precision_macro": 0.7523510971786834,
+  "recall_macro": 0.7885304659498208,
+  "f1_macro": 0.7666666666666666,
+  "precision_weighted": 0.8411442006269592,
+  "recall_weighted": 0.825,
+  "f1_weighted": 0.8308333333333333,
+  "cohen_kappa": 0.5348837209302326,
+  "quadratic_weighted_kappa": 0.5348837209302326,
+  "mcc": 0.5396701942924549,
+  "auroc": 0.9193548387096774,
+  "auprc": 0.8159779901898516,
+  "sensitivity": 0.7222222222222222,
+  "specificity": 0.8548387096774194,
+  "precision_pos": 0.5909090909090909,
+  "f1_pos": 0.65,
+  "per_class": {
+    "0": {
+      "precision": 0.9137931034482759,
+      "recall": 0.8548387096774194,
+      "f1-score": 0.8833333333333333,
+      "support": 62.0
+    },
+    "1": {
+      "precision": 0.5909090909090909,
+      "recall": 0.7222222222222222,
+      "f1-score": 0.65,
+      "support": 18.0
+    },
+    "accuracy": 0.825,
+    "macro avg": {
+      "precision": 0.7523510971786834,
+      "recall": 0.7885304659498208,
+      "f1-score": 0.7666666666666666,
+      "support": 80.0
+    },
+    "weighted avg": {
+      "precision": 0.8411442006269592,
+      "recall": 0.825,
+      "f1-score": 0.8308333333333333,
+      "support": 80.0
+    }
+  }
+}

results/adam/resnet/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc71c3bfe92bb42e70800536b1635bb8a2bc982678c561ff87ab1d2a12a7fd3d
+size 1790

results/adam/resnet/train.log

@@ -0,0 +1,128 @@
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:114: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = torch.cuda.amp.GradScaler()
+[resnet] train=280 val=40 test=80 classes=['0', '1']
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep0 loss=0.6891 val_acc=0.4750 val_auc=0.3262 score=0.1426
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep1 loss=0.6811 val_acc=0.7250 val_auc=0.4624 score=0.3250
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep2 loss=0.6569 val_acc=0.8000 val_auc=0.7993 score=0.6214
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep3 loss=0.6208 val_acc=0.8000 val_auc=0.7240 score=0.5963
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep4 loss=0.5615 val_acc=0.8000 val_auc=0.7168 score=0.5632
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep5 loss=0.4716 val_acc=0.7750 val_auc=0.7061 score=0.4964
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep6 loss=0.3866 val_acc=0.8000 val_auc=0.7491 score=0.5353
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep7 loss=0.3017 val_acc=0.7750 val_auc=0.8029 score=0.4792
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep8 loss=0.2381 val_acc=0.7750 val_auc=0.8029 score=0.4792
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep9 loss=0.1672 val_acc=0.7750 val_auc=0.8781 score=0.5042
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep10 loss=0.1149 val_acc=0.8250 val_auc=0.8459 score=0.6632
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep11 loss=0.1108 val_acc=0.8250 val_auc=0.8172 score=0.6536
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep12 loss=0.0832 val_acc=0.7750 val_auc=0.8387 score=0.5406
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep13 loss=0.0864 val_acc=0.8500 val_auc=0.8889 score=0.7479
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep14 loss=0.0506 val_acc=0.8250 val_auc=0.8710 score=0.6957
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep15 loss=0.0298 val_acc=0.7750 val_auc=0.8136 score=0.5709
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep16 loss=0.0466 val_acc=0.8000 val_auc=0.8172 score=0.6730
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep17 loss=0.0193 val_acc=0.8500 val_auc=0.8602 score=0.7384
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep18 loss=0.0480 val_acc=0.7750 val_auc=0.7634 score=0.4660
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep19 loss=0.0217 val_acc=0.8000 val_auc=0.6846 score=0.5138
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep20 loss=0.0382 val_acc=0.8000 val_auc=0.7133 score=0.5233
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep21 loss=0.0181 val_acc=0.8000 val_auc=0.7276 score=0.5281
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep22 loss=0.0090 val_acc=0.8000 val_auc=0.7168 score=0.5245
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep23 loss=0.0388 val_acc=0.7750 val_auc=0.7491 score=0.4612
+[resnet] early stop at ep23 (best ep13 score=0.7479)
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] DONE best_ep=13 best_val_score=0.7479 -> saved test_pred.npz (80 samples)
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/adam/resnet  acc=0.8250  auroc=0.9193548387096774  f1_macro=0.7667  qwk=0.5348837209302326

results/adam/retfound/log.txt

@@ -0,0 +1,50 @@
+{"train_lr": 2.7343749999999997e-05, "train_loss": 0.6853790283203125, "epoch": 0, "n_parameters": 303303682}
+{"train_lr": 8.984375e-05, "train_loss": 0.600438117980957, "epoch": 1, "n_parameters": 303303682}
+{"train_lr": 0.00015234375, "train_loss": 0.5224623680114746, "epoch": 2, "n_parameters": 303303682}
+{"train_lr": 0.00021484375, "train_loss": 0.5393064022064209, "epoch": 3, "n_parameters": 303303682}
+{"train_lr": 0.00027734375000000003, "train_loss": 0.5149924755096436, "epoch": 4, "n_parameters": 303303682}
+{"train_lr": 0.0003398437499999999, "train_loss": 0.482379674911499, "epoch": 5, "n_parameters": 303303682}
+{"train_lr": 0.00040234375, "train_loss": 0.46404457092285156, "epoch": 6, "n_parameters": 303303682}
+{"train_lr": 0.00046484375000000003, "train_loss": 0.46213990449905396, "epoch": 7, "n_parameters": 303303682}
+{"train_lr": 0.00052734375, "train_loss": 0.41957637667655945, "epoch": 8, "n_parameters": 303303682}
+{"train_lr": 0.00058984375, "train_loss": 0.47334104776382446, "epoch": 9, "n_parameters": 303303682}
+{"train_lr": 0.0006247369453830207, "train_loss": 0.4200931787490845, "epoch": 10, "n_parameters": 303303682}
+{"train_lr": 0.0006229352074360887, "train_loss": 0.4227827489376068, "epoch": 11, "n_parameters": 303303682}
+{"train_lr": 0.0006192226158713984, "train_loss": 0.3990233540534973, "epoch": 12, "n_parameters": 303303682}
+{"train_lr": 0.0006136220600505078, "train_loss": 0.39853784441947937, "epoch": 13, "n_parameters": 303303682}
+{"train_lr": 0.0006061680692624268, "train_loss": 0.37906284630298615, "epoch": 14, "n_parameters": 303303682}
+{"train_lr": 0.0005969065998390672, "train_loss": 0.3678862899541855, "epoch": 15, "n_parameters": 303303682}
+{"train_lr": 0.0005858947518191751, "train_loss": 0.3812587708234787, "epoch": 16, "n_parameters": 303303682}
+{"train_lr": 0.0005732004169076044, "train_loss": 0.37992827594280243, "epoch": 17, "n_parameters": 303303682}
+{"train_lr": 0.0005589018599003793, "train_loss": 0.29254330694675446, "epoch": 18, "n_parameters": 303303682}
+{"train_lr": 0.0005430872361562023, "train_loss": 0.33580098673701286, "epoch": 19, "n_parameters": 303303682}
+{"train_lr": 0.0005258540480893521, "train_loss": 0.3463967442512512, "epoch": 20, "n_parameters": 303303682}
+{"train_lr": 0.0005073085440348776, "train_loss": 0.31276238709688187, "epoch": 21, "n_parameters": 303303682}
+{"train_lr": 0.0004875650631922804, "train_loss": 0.31376905739307404, "epoch": 22, "n_parameters": 303303682}
+{"train_lr": 0.00046674533068632887, "train_loss": 0.3495359867811203, "epoch": 23, "n_parameters": 303303682}
+{"train_lr": 0.0004449777070911855, "train_loss": 0.32266679406166077, "epoch": 24, "n_parameters": 303303682}
+{"train_lr": 0.00042239639704478405, "train_loss": 0.2978495806455612, "epoch": 25, "n_parameters": 303303682}
+{"train_lr": 0.00039914062183260795, "train_loss": 0.2612730134278536, "epoch": 26, "n_parameters": 303303682}
+{"train_lr": 0.0003753537610421703, "train_loss": 0.32327523455023766, "epoch": 27, "n_parameters": 303303682}
+{"train_lr": 0.00035118246858017804, "train_loss": 0.25920983776450157, "epoch": 28, "n_parameters": 303303682}
+{"train_lr": 0.0003267757685024298, "train_loss": 0.29856251925230026, "epoch": 29, "n_parameters": 303303682}
+{"train_lr": 0.0003022841362309562, "train_loss": 0.27047010883688927, "epoch": 30, "n_parameters": 303303682}
+{"train_lr": 0.0002778585708230051, "train_loss": 0.2923644706606865, "epoch": 31, "n_parameters": 303303682}
+{"train_lr": 0.0002536496640116411, "train_loss": 0.28958597406744957, "epoch": 32, "n_parameters": 303303682}
+{"train_lr": 0.00022980667175763478, "train_loss": 0.2692524269223213, "epoch": 33, "n_parameters": 303303682}
+{"train_lr": 0.00020647659403683159, "train_loss": 0.26890668272972107, "epoch": 34, "n_parameters": 303303682}
+{"train_lr": 0.00018380326853641835, "train_loss": 0.2811972163617611, "epoch": 35, "n_parameters": 303303682}
+{"train_lr": 0.00016192648384775085, "train_loss": 0.2884734384715557, "epoch": 36, "n_parameters": 303303682}
+{"train_lr": 0.000140981117623204, "train_loss": 0.2802345249801874, "epoch": 37, "n_parameters": 303303682}
+{"train_lr": 0.00012109630501059311, "train_loss": 0.28150836005806923, "epoch": 38, "n_parameters": 303303682}
+{"train_lr": 0.00010239464249204608, "train_loss": 0.2860140986740589, "epoch": 39, "n_parameters": 303303682}
+{"train_lr": 8.499143203592362e-05, "train_loss": 0.27556246146559715, "epoch": 40, "n_parameters": 303303682}
+{"train_lr": 6.899397022184248e-05, "train_loss": 0.2808167040348053, "epoch": 41, "n_parameters": 303303682}
+{"train_lr": 5.450088672158235e-05, "train_loss": 0.24387812614440918, "epoch": 42, "n_parameters": 303303682}
+{"train_lr": 4.1601536214361626e-05, "train_loss": 0.23795588314533234, "epoch": 43, "n_parameters": 303303682}
+{"train_lr": 3.0375447485526644e-05, "train_loss": 0.2574918810278177, "epoch": 44, "n_parameters": 303303682}
+{"train_lr": 2.0891833105144005e-05, "train_loss": 0.2669173013418913, "epoch": 45, "n_parameters": 303303682}
+{"train_lr": 1.3209162709490563e-05, "train_loss": 0.2666049748659134, "epoch": 46, "n_parameters": 303303682}
+{"train_lr": 7.374802516302661e-06, "train_loss": 0.2570477966219187, "epoch": 47, "n_parameters": 303303682}
+{"train_lr": 3.4247232962929436e-06, "train_loss": 0.2644502613693476, "epoch": 48, "n_parameters": 303303682}
+{"train_lr": 1.3832786013874152e-06, "train_loss": 0.2804808057844639, "epoch": 49, "n_parameters": 303303682}

results/adam/retfound/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 80,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.925,
+  "balanced_accuracy": 0.89247311827957,
+  "precision_macro": 0.89247311827957,
+  "recall_macro": 0.89247311827957,
+  "f1_macro": 0.89247311827957,
+  "precision_weighted": 0.925,
+  "recall_weighted": 0.925,
+  "f1_weighted": 0.925,
+  "cohen_kappa": 0.7849462365591398,
+  "quadratic_weighted_kappa": 0.7849462365591398,
+  "mcc": 0.7849462365591398,
+  "auroc": 0.9516129032258065,
+  "auprc": 0.9214129072681705,
+  "sensitivity": 0.8333333333333334,
+  "specificity": 0.9516129032258065,
+  "precision_pos": 0.8333333333333334,
+  "f1_pos": 0.8333333333333334,
+  "per_class": {
+    "0": {
+      "precision": 0.9516129032258065,
+      "recall": 0.9516129032258065,
+      "f1-score": 0.9516129032258065,
+      "support": 62.0
+    },
+    "1": {
+      "precision": 0.8333333333333334,
+      "recall": 0.8333333333333334,
+      "f1-score": 0.8333333333333334,
+      "support": 18.0
+    },
+    "accuracy": 0.925,
+    "macro avg": {
+      "precision": 0.89247311827957,
+      "recall": 0.89247311827957,
+      "f1-score": 0.89247311827957,
+      "support": 80.0
+    },
+    "weighted avg": {
+      "precision": 0.925,
+      "recall": 0.925,
+      "f1-score": 0.925,
+      "support": 80.0
+    }
+  }
+}

results/adam/retfound/metrics_test.csv

@@ -0,0 +1,2 @@
+val_loss,accuracy,f1,roc_auc,hamming,jaccard,precision,recall,average_precision,kappa
+0.24544517199198404,0.925,0.89247311827957,0.9516129032258065,0.075,0.810989010989011,0.89247311827957,0.89247311827957,0.9511565422863844,0.7849462365591398

results/adam/retfound/metrics_val.csv

@@ -0,0 +1,51 @@
+val_loss,accuracy,f1,roc_auc,hamming,jaccard,precision,recall,average_precision,kappa
+0.6949386596679688,0.775,0.43661971830985913,0.7526881720430108,0.225,0.3875,0.3875,0.5,0.702140345769078,0.0
+0.7357521057128906,0.775,0.43661971830985913,0.7535842293906809,0.225,0.3875,0.3875,0.5,0.7023472297814682,0.0
+0.8536128997802734,0.775,0.43661971830985913,0.7974910394265233,0.225,0.3875,0.3875,0.5,0.7664684375686055,0.0
+0.8364524841308594,0.775,0.43661971830985913,0.8279569892473118,0.225,0.3875,0.3875,0.5,0.8171861361036572,0.0
+0.726959228515625,0.775,0.43661971830985913,0.85752688172043,0.225,0.3875,0.3875,0.5,0.8267116528482032,0.0
+0.6651697158813477,0.775,0.43661971830985913,0.870967741935484,0.225,0.3875,0.3875,0.5,0.8399610377402678,0.0
+0.5009684562683105,0.825,0.7584124245038826,0.8611111111111112,0.175,0.6278280542986425,0.75,0.7688172043010753,0.7919264893748371,0.5172413793103448
+0.6560912132263184,0.85,0.7058823529411764,0.870967741935484,0.15,0.5855855855855856,0.9189189189189189,0.6666666666666666,0.8455413148826051,0.43661971830985913
+0.4889563322067261,0.85,0.7849462365591398,0.881720430107527,0.15,0.6617647058823529,0.7849462365591398,0.7849462365591398,0.8786670749538394,0.5698924731182795
+0.5198326110839844,0.9,0.8268398268398269,0.8530465949820789,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.8587794130254447,0.6595744680851063
+0.7482777833938599,0.8,0.5428571428571428,0.8996415770609318,0.2,0.45299145299145294,0.8974358974358974,0.5555555555555556,0.8843861452756653,0.16230366492146608
+0.45795178413391113,0.9,0.8268398268398269,0.9068100358422939,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.8862751464020953,0.6595744680851063
+0.5212460160255432,0.9,0.8268398268398269,0.8996415770609318,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.881716849965241,0.6595744680851063
+0.5566548109054565,0.875,0.7948717948717949,0.8924731182795699,0.125,0.6785714285714286,0.857843137254902,0.7616487455197133,0.8804819825795989,0.5934959349593496
+0.6318426728248596,0.9,0.8268398268398269,0.8853046594982079,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.8765545253605149,0.6595744680851063
+0.6386967301368713,0.875,0.7703788748564868,0.8996415770609318,0.125,0.6527777777777778,0.9305555555555556,0.7222222222222222,0.8778435406015122,0.5535714285714286
+0.5159331560134888,0.9,0.8268398268398269,0.9175627240143369,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.906900466227048,0.6595744680851063
+0.39144355058670044,0.95,0.921875,0.9283154121863799,0.05,0.8585858585858586,0.9696969696969697,0.8888888888888888,0.9285526168442524,0.8443579766536965
+0.5359691083431244,0.875,0.7703788748564868,0.9292114695340502,0.125,0.6527777777777778,0.9305555555555556,0.7222222222222222,0.9285066651853976,0.5535714285714286
+0.4337925612926483,0.95,0.921875,0.9283154121863799,0.05,0.8585858585858586,0.9696969696969697,0.8888888888888888,0.9284603359145964,0.8443579766536965
+0.6121203303337097,0.875,0.7703788748564868,0.9283154121863799,0.125,0.6527777777777778,0.9305555555555556,0.7222222222222222,0.9284603359145964,0.5535714285714286
+0.42638447880744934,0.925,0.8769230769230769,0.9283154121863799,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9284603359145964,0.7560975609756098
+0.3997582793235779,0.925,0.8769230769230769,0.9283154121863799,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9284603359145963,0.7560975609756098
+0.4086921811103821,0.925,0.8769230769230769,0.9283154121863799,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9284603359145964,0.7560975609756098
+0.41143083572387695,0.9,0.8268398268398269,0.9283154121863799,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9284603359145964,0.6595744680851063
+0.5094860941171646,0.9,0.8268398268398269,0.931899641577061,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9305555924889939,0.6595744680851063
+0.5649861097335815,0.9,0.8268398268398269,0.9292114695340502,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9233688544403764,0.6595744680851063
+0.43275587260723114,0.9,0.8268398268398269,0.9390681003584229,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9251959935525449,0.6595744680851063
+0.21898877620697021,0.9,0.8566308243727598,0.9498207885304659,0.1,0.7575757575757576,0.8566308243727598,0.8566308243727598,0.9370107576036646,0.7132616487455197
+0.28504690527915955,0.925,0.8879551820728291,0.9498207885304659,0.075,0.8045454545454545,0.90625,0.8727598566308243,0.9346306558217193,0.7761194029850746
+0.3365369886159897,0.9,0.84375,0.9498207885304659,0.1,0.7411764705882353,0.8831168831168831,0.8172043010752688,0.940824081249271,0.688715953307393
+0.3427543491125107,0.925,0.8769230769230769,0.946236559139785,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9369379825912034,0.7560975609756098
+0.35773538053035736,0.925,0.8769230769230769,0.946236559139785,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9357159201324631,0.7560975609756098
+0.42521847784519196,0.9,0.8268398268398269,0.946236559139785,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9409427467992981,0.6595744680851063
+0.4029083847999573,0.925,0.8769230769230769,0.9390681003584229,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9352552443376021,0.7560975609756098
+0.3561532497406006,0.925,0.8769230769230769,0.9390681003584229,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9352552443376021,0.7560975609756098
+0.2910751849412918,0.925,0.8769230769230769,0.946236559139785,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9409427467992981,0.7560975609756098
+0.3142062872648239,0.925,0.8769230769230769,0.9498207885304659,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9431059973754814,0.7560975609756098
+0.2977108359336853,0.925,0.8769230769230769,0.953405017921147,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9454120273143385,0.7560975609756098
+0.2965516448020935,0.925,0.8769230769230769,0.953405017921147,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9454120273143386,0.7560975609756098
+0.3348654955625534,0.925,0.8769230769230769,0.9498207885304659,0.075,0.7892156862745098,0.9558823529411764,0.8333333333333333,0.9424153227047307,0.7560975609756098
+0.3686094284057617,0.9,0.8268398268398269,0.953405017921147,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9454425747239183,0.6595744680851063
+0.4043458551168442,0.9,0.8268398268398269,0.953405017921147,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9454120273143387,0.6595744680851063
+0.416122242808342,0.9,0.8268398268398269,0.956989247311828,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9487973403492624,0.6595744680851063
+0.4057278037071228,0.9,0.8268398268398269,0.956989247311828,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9487973403492624,0.6595744680851063
+0.38761037588119507,0.9,0.8268398268398269,0.956989247311828,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9487973403492622,0.6595744680851063
+0.3810284584760666,0.9,0.8268398268398269,0.956989247311828,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9487973403492622,0.6595744680851063
+0.3793119490146637,0.9,0.8268398268398269,0.9605734767025089,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9526834390073299,0.6595744680851063
+0.3780297338962555,0.9,0.8268398268398269,0.9605734767025089,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9526834390073299,0.6595744680851063
+0.37748883664608,0.9,0.8268398268398269,0.9605734767025089,0.1,0.7206349206349206,0.9428571428571428,0.7777777777777778,0.9526834390073299,0.6595744680851063

results/adam/retfound/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a9f339d43b3b03ac4ee519789551950bc677e6e276a250d13a0f02784aa9777
+size 1470

results/adam/retfound/train.log

@@ -0,0 +1,733 @@
+| distributed init (rank 0): env://, gpu 0
+[rank0]:[W615 13:52:22.761018092 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id.
+[13:52:24.578155] job dir: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound
+[13:52:24.578459] Namespace(batch_size=32,
+epochs=50,
+accum_iter=1,
+model='RETFound_mae',
+model_arch='retfound_mae',
+input_size=224,
+drop_path=0.2,
+global_pool=True,
+clip_grad=None,
+weight_decay=0.05,
+lr=None,
+blr=0.005,
+layer_decay=0.65,
+min_lr=1e-06,
+warmup_epochs=10,
+color_jitter=None,
+aa='rand-m9-mstd0.5-inc1',
+smoothing=0.1,
+reprob=0.25,
+remode='pixel',
+recount=1,
+resplit=False,
+mixup=0.0,
+cutmix=0.0,
+cutmix_minmax=None,
+mixup_prob=1.0,
+mixup_switch_prob=0.5,
+mixup_mode='batch',
+finetune='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth',
+task='retfound',
+adaptation='finetune',
+data_path='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Dataset/AMD/adamdataset',
+nb_classes=2,
+output_dir='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/adam',
+log_dir='./output_logs',
+dataratio='1.0',
+stratified=False,
+device='cuda',
+seed=0,
+resume='',
+start_epoch=0,
+eval=False,
+dist_eval=False,
+num_workers=10,
+pin_mem=True,
+world_size=1,
+local_rank=-1,
+dist_on_itp=False,
+dist_url='env://',
+savemodel=True,
+norm='IMAGENET',
+enhance=False,
+datasets_seed=2026,
+rank=0,
+gpu=0,
+distributed=True,
+dist_backend='nccl')
+[13:52:32.164895] Preparing to load pre-trained weights: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth
+[13:52:36.886727] Loaded pre-trained checkpoint from: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth
+[13:52:38.199921] Sampler_train = <torch.utils.data.distributed.DistributedSampler object at 0x7fd59c188510>
+[13:52:38.245683] len of train_set: 256
+[13:52:38.780666] [Adaptation] Full fine-tuning: training all parameters.
+[13:52:38.781731] number of trainable params (M): 303.30
+[13:52:38.781809] base lr: 5.00e-03
+[13:52:38.781866] actual lr: 6.25e-04
+[13:52:38.781915] accumulate grad iterations: 1
+[13:52:38.781968] effective batch size: 32
+[13:52:38.785609] criterion = CrossEntropyLoss()
+[13:52:38.785695] Start training for 50 epochs
+[13:52:38.787747] log_dir: ./output_logs/retfound
+[13:52:42.314240] Epoch: [0]  [0/8]  eta: 0:00:28  lr: 0.000000  loss: 0.6928 (0.6928)  time: 3.5255  data: 2.5943  max mem: 7340
+[13:52:43.413951] Epoch: [0]  [7/8]  eta: 0:00:00  lr: 0.000055  loss: 0.6831 (0.6854)  time: 0.5781  data: 0.3244  max mem: 9671
+[13:52:43.512082] Epoch: [0] Total time: 0:00:04 (0.5905 s / it)
+[13:52:43.521651] Averaged stats: lr: 0.000055  loss: 0.6831 (0.6854)
+[13:52:46.014370] val:  [0/2]  eta: 0:00:04  loss: 0.6396 (0.6396)  time: 2.4781  data: 2.4249  max mem: 9671
+[13:52:46.132148] val:  [1/2]  eta: 0:00:01  loss: 0.6396 (0.6949)  time: 1.2977  data: 1.2125  max mem: 9671
+[13:52:46.239339] val: Total time: 0:00:02 (1.3518 s / it)
+[13:52:46.251957] val loss: 0.6949386596679688
+[13:52:46.252128] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.7527, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.7021, Kappa: 0.0000, Score: 0.3964
+[13:52:47.965179] Best epoch = 0, Best score = 0.3964
+[13:52:48.038141] log_dir: ./output_logs/retfound
+[13:52:50.410461] Epoch: [1]  [0/8]  eta: 0:00:18  lr: 0.000063  loss: 0.6628 (0.6628)  time: 2.3713  data: 2.2170  max mem: 9671
+[13:52:51.430322] Epoch: [1]  [7/8]  eta: 0:00:00  lr: 0.000117  loss: 0.6032 (0.6004)  time: 0.4238  data: 0.2799  max mem: 9671
+[13:52:51.535674] Epoch: [1] Total time: 0:00:03 (0.4372 s / it)
+[13:52:51.545072] Averaged stats: lr: 0.000117  loss: 0.6032 (0.6004)
+[13:52:54.157044] val:  [0/2]  eta: 0:00:05  loss: 0.4481 (0.4481)  time: 2.5953  data: 2.5589  max mem: 9671
+[13:52:54.172452] val:  [1/2]  eta: 0:00:01  loss: 0.4481 (0.7358)  time: 1.3051  data: 1.2795  max mem: 9671
+[13:52:54.277531] val: Total time: 0:00:02 (1.3583 s / it)
+[13:52:54.286713] val loss: 0.7357521057128906
+[13:52:54.286912] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.7536, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.7023, Kappa: 0.0000, Score: 0.3967
+[13:52:55.923888] Best epoch = 1, Best score = 0.3967
+[13:52:55.990198] log_dir: ./output_logs/retfound
+[13:52:58.626576] Epoch: [2]  [0/8]  eta: 0:00:21  lr: 0.000125  loss: 0.6224 (0.6224)  time: 2.6348  data: 2.4891  max mem: 9671
+[13:52:59.620834] Epoch: [2]  [7/8]  eta: 0:00:00  lr: 0.000180  loss: 0.4999 (0.5225)  time: 0.4535  data: 0.3113  max mem: 9671
+[13:52:59.734195] Epoch: [2] Total time: 0:00:03 (0.4680 s / it)
+[13:52:59.743035] Averaged stats: lr: 0.000180  loss: 0.4999 (0.5225)
+[13:53:02.391033] val:  [0/2]  eta: 0:00:05  loss: 0.2783 (0.2783)  time: 2.6326  data: 2.5969  max mem: 9671
+[13:53:02.406238] val:  [1/2]  eta: 0:00:01  loss: 0.2783 (0.8536)  time: 1.3236  data: 1.2985  max mem: 9671
+[13:53:02.512965] val: Total time: 0:00:02 (1.3777 s / it)
+[13:53:02.522133] val loss: 0.8536128997802734
+[13:53:02.522307] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.7975, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.7665, Kappa: 0.0000, Score: 0.4114
+[13:53:04.204960] Best epoch = 2, Best score = 0.4114
+[13:53:04.282572] log_dir: ./output_logs/retfound
+[13:53:06.786738] Epoch: [3]  [0/8]  eta: 0:00:20  lr: 0.000188  loss: 0.6936 (0.6936)  time: 2.5028  data: 2.3583  max mem: 9671
+[13:53:07.780820] Epoch: [3]  [7/8]  eta: 0:00:00  lr: 0.000242  loss: 0.5377 (0.5393)  time: 0.4370  data: 0.2949  max mem: 9671
+[13:53:07.899014] Epoch: [3] Total time: 0:00:03 (0.4520 s / it)
+[13:53:07.908946] Averaged stats: lr: 0.000242  loss: 0.5377 (0.5393)
+[13:53:10.532422] val:  [0/2]  eta: 0:00:05  loss: 0.2665 (0.2665)  time: 2.6082  data: 2.5740  max mem: 9671
+[13:53:10.548060] val:  [1/2]  eta: 0:00:01  loss: 0.2665 (0.8365)  time: 1.3116  data: 1.2871  max mem: 9671
+[13:53:10.653453] val: Total time: 0:00:02 (1.3650 s / it)
+[13:53:10.663550] val loss: 0.8364524841308594
+[13:53:10.663729] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.8280, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.8172, Kappa: 0.0000, Score: 0.4215
+[13:53:12.317371] Best epoch = 3, Best score = 0.4215
+[13:53:12.384755] log_dir: ./output_logs/retfound
+[13:53:14.974660] Epoch: [4]  [0/8]  eta: 0:00:20  lr: 0.000250  loss: 0.6812 (0.6812)  time: 2.5889  data: 2.4463  max mem: 9671
+[13:53:15.968159] Epoch: [4]  [7/8]  eta: 0:00:00  lr: 0.000305  loss: 0.4633 (0.5150)  time: 0.4477  data: 0.3059  max mem: 9671
+[13:53:16.081679] Epoch: [4] Total time: 0:00:03 (0.4621 s / it)
+[13:53:16.083453] Averaged stats: lr: 0.000305  loss: 0.4633 (0.5150)
+[13:53:18.543742] val:  [0/2]  eta: 0:00:04  loss: 0.3205 (0.3205)  time: 2.4449  data: 2.4113  max mem: 9671
+[13:53:18.559316] val:  [1/2]  eta: 0:00:01  loss: 0.3205 (0.7270)  time: 1.2300  data: 1.2057  max mem: 9671
+[13:53:18.681860] val: Total time: 0:00:02 (1.2918 s / it)
+[13:53:18.691322] val loss: 0.726959228515625
+[13:53:18.691552] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.8575, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.8267, Kappa: 0.0000, Score: 0.4314
+[13:53:20.400076] Best epoch = 4, Best score = 0.4314
+[13:53:20.474867] log_dir: ./output_logs/retfound
+[13:53:22.996871] Epoch: [5]  [0/8]  eta: 0:00:20  lr: 0.000313  loss: 0.4578 (0.4578)  time: 2.5209  data: 2.3763  max mem: 9671
+[13:53:23.989126] Epoch: [5]  [7/8]  eta: 0:00:00  lr: 0.000367  loss: 0.4578 (0.4824)  time: 0.4391  data: 0.2971  max mem: 9671
+[13:53:24.104994] Epoch: [5] Total time: 0:00:03 (0.4537 s / it)
+[13:53:24.113508] Averaged stats: lr: 0.000367  loss: 0.4578 (0.4824)
+[13:53:26.614435] val:  [0/2]  eta: 0:00:04  loss: 0.2633 (0.2633)  time: 2.4870  data: 2.4526  max mem: 9671
+[13:53:26.630096] val:  [1/2]  eta: 0:00:01  loss: 0.2633 (0.6652)  time: 1.2511  data: 1.2264  max mem: 9671
+[13:53:26.737026] val: Total time: 0:00:02 (1.3052 s / it)
+[13:53:26.747038] val loss: 0.6651697158813477
+[13:53:26.747262] Accuracy: 0.7750, F1 Score: 0.4366, ROC AUC: 0.8710, Hamming Loss: 0.2250,
+ Jaccard Score: 0.3875, Precision: 0.3875, Recall: 0.5000,
+ Average Precision: 0.8400, Kappa: 0.0000, Score: 0.4359
+[13:53:28.383271] Best epoch = 5, Best score = 0.4359
+[13:53:28.445868] log_dir: ./output_logs/retfound
+[13:53:30.973733] Epoch: [6]  [0/8]  eta: 0:00:20  lr: 0.000375  loss: 0.3839 (0.3839)  time: 2.5270  data: 2.3822  max mem: 9671
+[13:53:31.960696] Epoch: [6]  [7/8]  eta: 0:00:00  lr: 0.000430  loss: 0.4587 (0.4640)  time: 0.4391  data: 0.2979  max mem: 9671
+[13:53:32.064165] Epoch: [6] Total time: 0:00:03 (0.4523 s / it)
+[13:53:32.072460] Averaged stats: lr: 0.000430  loss: 0.4587 (0.4640)
+[13:53:34.624255] val:  [0/2]  eta: 0:00:05  loss: 0.3132 (0.3132)  time: 2.5418  data: 2.5052  max mem: 9671
+[13:53:34.639707] val:  [1/2]  eta: 0:00:01  loss: 0.3132 (0.5010)  time: 1.2783  data: 1.2527  max mem: 9671
+[13:53:34.745214] val: Total time: 0:00:02 (1.3317 s / it)
+[13:53:34.754126] val loss: 0.5009684562683105
+[13:53:34.754313] Accuracy: 0.8250, F1 Score: 0.7584, ROC AUC: 0.8611, Hamming Loss: 0.1750,
+ Jaccard Score: 0.6278, Precision: 0.7500, Recall: 0.7688,
+ Average Precision: 0.7919, Kappa: 0.5172, Score: 0.7123
+[13:53:36.493464] Best epoch = 6, Best score = 0.7123
+[13:53:36.564071] log_dir: ./output_logs/retfound
+[13:53:39.042952] Epoch: [7]  [0/8]  eta: 0:00:19  lr: 0.000438  loss: 0.5242 (0.5242)  time: 2.4778  data: 2.3262  max mem: 9671
+[13:53:40.031939] Epoch: [7]  [7/8]  eta: 0:00:00  lr: 0.000492  loss: 0.4543 (0.4621)  time: 0.4332  data: 0.2909  max mem: 9671
+[13:53:40.137973] Epoch: [7] Total time: 0:00:03 (0.4467 s / it)
+[13:53:40.147565] Averaged stats: lr: 0.000492  loss: 0.4543 (0.4621)
+[13:53:42.640030] val:  [0/2]  eta: 0:00:04  loss: 0.1982 (0.1982)  time: 2.4775  data: 2.4483  max mem: 9671
+[13:53:42.655557] val:  [1/2]  eta: 0:00:01  loss: 0.1982 (0.6561)  time: 1.2462  data: 1.2242  max mem: 9671
+[13:53:42.758934] val: Total time: 0:00:02 (1.2986 s / it)
+[13:53:42.769226] val loss: 0.6560912132263184
+[13:53:42.769409] Accuracy: 0.8500, F1 Score: 0.7059, ROC AUC: 0.8710, Hamming Loss: 0.1500,
+ Jaccard Score: 0.5856, Precision: 0.9189, Recall: 0.6667,
+ Average Precision: 0.8455, Kappa: 0.4366, Score: 0.6712
+[13:53:42.814484] Best epoch = 6, Best score = 0.7123
+[13:53:43.081773] log_dir: ./output_logs/retfound
+[13:53:45.552484] Epoch: [8]  [0/8]  eta: 0:00:19  lr: 0.000500  loss: 0.5544 (0.5544)  time: 2.4697  data: 2.3249  max mem: 9671
+[13:53:46.541272] Epoch: [8]  [7/8]  eta: 0:00:00  lr: 0.000555  loss: 0.4068 (0.4196)  time: 0.4322  data: 0.2907  max mem: 9671
+[13:53:46.655118] Epoch: [8] Total time: 0:00:03 (0.4466 s / it)
+[13:53:46.662978] Averaged stats: lr: 0.000555  loss: 0.4068 (0.4196)
+[13:53:49.242886] val:  [0/2]  eta: 0:00:05  loss: 0.2713 (0.2713)  time: 2.5684  data: 2.5325  max mem: 9671
+[13:53:49.258470] val:  [1/2]  eta: 0:00:01  loss: 0.2713 (0.4890)  time: 1.2917  data: 1.2663  max mem: 9671
+[13:53:49.372089] val: Total time: 0:00:02 (1.3492 s / it)
+[13:53:49.380923] val loss: 0.4889563322067261
+[13:53:49.381102] Accuracy: 0.8500, F1 Score: 0.7849, ROC AUC: 0.8817, Hamming Loss: 0.1500,
+ Jaccard Score: 0.6618, Precision: 0.7849, Recall: 0.7849,
+ Average Precision: 0.8787, Kappa: 0.5699, Score: 0.7455
+[13:53:51.051394] Best epoch = 8, Best score = 0.7455
+[13:53:51.121339] log_dir: ./output_logs/retfound
+[13:53:53.520589] Epoch: [9]  [0/8]  eta: 0:00:19  lr: 0.000562  loss: 0.5163 (0.5163)  time: 2.3982  data: 2.2547  max mem: 9671
+[13:53:54.566192] Epoch: [9]  [7/8]  eta: 0:00:00  lr: 0.000617  loss: 0.4599 (0.4733)  time: 0.4304  data: 0.2886  max mem: 9671
+[13:53:54.688760] Epoch: [9] Total time: 0:00:03 (0.4459 s / it)
+[13:53:54.696927] Averaged stats: lr: 0.000617  loss: 0.4599 (0.4733)
+[13:53:57.125472] val:  [0/2]  eta: 0:00:04  loss: 0.2701 (0.2701)  time: 2.4178  data: 2.3866  max mem: 9671
+[13:53:57.140932] val:  [1/2]  eta: 0:00:01  loss: 0.2701 (0.5198)  time: 1.2163  data: 1.1934  max mem: 9671
+[13:53:57.253153] val: Total time: 0:00:02 (1.2731 s / it)
+[13:53:57.262050] val loss: 0.5198326110839844
+[13:53:57.262237] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.8530, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.8588, Kappa: 0.6596, Score: 0.7798
+[13:53:58.950254] Best epoch = 9, Best score = 0.7798
+[13:53:59.023367] log_dir: ./output_logs/retfound
+[13:54:01.410360] Epoch: [10]  [0/8]  eta: 0:00:19  lr: 0.000625  loss: 0.4179 (0.4179)  time: 2.3858  data: 2.2390  max mem: 9671
+[13:54:02.502642] Epoch: [10]  [7/8]  eta: 0:00:00  lr: 0.000624  loss: 0.4179 (0.4201)  time: 0.4347  data: 0.2928  max mem: 9671
+[13:54:02.623844] Epoch: [10] Total time: 0:00:03 (0.4500 s / it)
+[13:54:02.633329] Averaged stats: lr: 0.000624  loss: 0.4179 (0.4201)
+[13:54:05.117772] val:  [0/2]  eta: 0:00:04  loss: 0.1527 (0.1527)  time: 2.4700  data: 2.4356  max mem: 9671
+[13:54:05.133105] val:  [1/2]  eta: 0:00:01  loss: 0.1527 (0.7483)  time: 1.2424  data: 1.2179  max mem: 9671
+[13:54:05.236326] val: Total time: 0:00:02 (1.2948 s / it)
+[13:54:05.245930] val loss: 0.7482777833938599
+[13:54:05.246138] Accuracy: 0.8000, F1 Score: 0.5429, ROC AUC: 0.8996, Hamming Loss: 0.2000,
+ Jaccard Score: 0.4530, Precision: 0.8974, Recall: 0.5556,
+ Average Precision: 0.8844, Kappa: 0.1623, Score: 0.5349
+[13:54:05.291221] Best epoch = 9, Best score = 0.7798
+[13:54:05.569993] log_dir: ./output_logs/retfound
+[13:54:08.083871] Epoch: [11]  [0/8]  eta: 0:00:20  lr: 0.000624  loss: 0.3514 (0.3514)  time: 2.5129  data: 2.3671  max mem: 9671
+[13:54:09.077670] Epoch: [11]  [7/8]  eta: 0:00:00  lr: 0.000622  loss: 0.3698 (0.4228)  time: 0.4382  data: 0.2960  max mem: 9671
+[13:54:09.192574] Epoch: [11] Total time: 0:00:03 (0.4528 s / it)
+[13:54:09.202005] Averaged stats: lr: 0.000622  loss: 0.3698 (0.4228)
+[13:54:11.770500] val:  [0/2]  eta: 0:00:05  loss: 0.2029 (0.2029)  time: 2.5521  data: 2.5161  max mem: 9671
+[13:54:11.786192] val:  [1/2]  eta: 0:00:01  loss: 0.2029 (0.4580)  time: 1.2836  data: 1.2581  max mem: 9671
+[13:54:11.897793] val: Total time: 0:00:02 (1.3401 s / it)
+[13:54:11.906585] val loss: 0.45795178413391113
+[13:54:11.906772] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9068, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.8863, Kappa: 0.6596, Score: 0.7977
+[13:54:13.615563] Best epoch = 11, Best score = 0.7977
+[13:54:13.684221] log_dir: ./output_logs/retfound
+[13:54:16.320430] Epoch: [12]  [0/8]  eta: 0:00:21  lr: 0.000621  loss: 0.3098 (0.3098)  time: 2.6353  data: 2.4874  max mem: 9671
+[13:54:17.312384] Epoch: [12]  [7/8]  eta: 0:00:00  lr: 0.000617  loss: 0.3706 (0.3990)  time: 0.4533  data: 0.3110  max mem: 9671
+[13:54:17.456694] Epoch: [12] Total time: 0:00:03 (0.4715 s / it)
+[13:54:17.465963] Averaged stats: lr: 0.000617  loss: 0.3706 (0.3990)
+[13:54:20.043599] val:  [0/2]  eta: 0:00:05  loss: 0.1898 (0.1898)  time: 2.5621  data: 2.5275  max mem: 9671
+[13:54:20.058777] val:  [1/2]  eta: 0:00:01  loss: 0.1898 (0.5212)  time: 1.2883  data: 1.2638  max mem: 9671
+[13:54:20.170252] val: Total time: 0:00:02 (1.3449 s / it)
+[13:54:20.179390] val loss: 0.5212460160255432
+[13:54:20.179612] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.8996, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.8817, Kappa: 0.6596, Score: 0.7954
+[13:54:20.221615] Best epoch = 11, Best score = 0.7977
+[13:54:20.484237] log_dir: ./output_logs/retfound
+[13:54:23.015330] Epoch: [13]  [0/8]  eta: 0:00:20  lr: 0.000616  loss: 0.5697 (0.5697)  time: 2.5301  data: 2.3832  max mem: 9671
+[13:54:24.015100] Epoch: [13]  [7/8]  eta: 0:00:00  lr: 0.000611  loss: 0.3551 (0.3985)  time: 0.4411  data: 0.2981  max mem: 9671
+[13:54:24.133416] Epoch: [13] Total time: 0:00:03 (0.4561 s / it)
+[13:54:24.141941] Averaged stats: lr: 0.000611  loss: 0.3551 (0.3985)
+[13:54:26.708491] val:  [0/2]  eta: 0:00:05  loss: 0.1778 (0.1778)  time: 2.5494  data: 2.5153  max mem: 9671
+[13:54:26.723630] val:  [1/2]  eta: 0:00:01  loss: 0.1778 (0.5567)  time: 1.2820  data: 1.2577  max mem: 9671
+[13:54:26.830847] val: Total time: 0:00:02 (1.3364 s / it)
+[13:54:26.839850] val loss: 0.5566548109054565
+[13:54:26.840024] Accuracy: 0.8750, F1 Score: 0.7949, ROC AUC: 0.8925, Hamming Loss: 0.1250,
+ Jaccard Score: 0.6786, Precision: 0.8578, Recall: 0.7616,
+ Average Precision: 0.8805, Kappa: 0.5935, Score: 0.7603
+[13:54:26.888237] Best epoch = 11, Best score = 0.7977
+[13:54:27.147032] log_dir: ./output_logs/retfound
+[13:54:29.720747] Epoch: [14]  [0/8]  eta: 0:00:20  lr: 0.000610  loss: 0.4109 (0.4109)  time: 2.5726  data: 2.4270  max mem: 9671
+[13:54:30.716226] Epoch: [14]  [7/8]  eta: 0:00:00  lr: 0.000602  loss: 0.3012 (0.3791)  time: 0.4458  data: 0.3035  max mem: 9671
+[13:54:30.842805] Epoch: [14] Total time: 0:00:03 (0.4620 s / it)
+[13:54:30.851739] Averaged stats: lr: 0.000602  loss: 0.3012 (0.3791)
+[13:54:33.413005] val:  [0/2]  eta: 0:00:05  loss: 0.1636 (0.1636)  time: 2.5527  data: 2.5190  max mem: 9671
+[13:54:33.428677] val:  [1/2]  eta: 0:00:01  loss: 0.1636 (0.6318)  time: 1.2839  data: 1.2595  max mem: 9671
+[13:54:33.535470] val: Total time: 0:00:02 (1.3379 s / it)
+[13:54:33.547601] val loss: 0.6318426728248596
+[13:54:33.547804] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.8853, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.8766, Kappa: 0.6596, Score: 0.7906
+[13:54:33.599757] Best epoch = 11, Best score = 0.7977
+[13:54:33.838256] log_dir: ./output_logs/retfound
+[13:54:36.458393] Epoch: [15]  [0/8]  eta: 0:00:20  lr: 0.000601  loss: 0.4683 (0.4683)  time: 2.6190  data: 2.4740  max mem: 9671
+[13:54:37.452191] Epoch: [15]  [7/8]  eta: 0:00:00  lr: 0.000592  loss: 0.3347 (0.3679)  time: 0.4515  data: 0.3093  max mem: 9671
+[13:54:37.570649] Epoch: [15] Total time: 0:00:03 (0.4665 s / it)
+[13:54:37.580224] Averaged stats: lr: 0.000592  loss: 0.3347 (0.3679)
+[13:54:40.163649] val:  [0/2]  eta: 0:00:05  loss: 0.1511 (0.1511)  time: 2.5753  data: 2.5403  max mem: 9671
+[13:54:40.179134] val:  [1/2]  eta: 0:00:01  loss: 0.1511 (0.6387)  time: 1.2951  data: 1.2702  max mem: 9671
+[13:54:40.285602] val: Total time: 0:00:02 (1.3490 s / it)
+[13:54:40.294439] val loss: 0.6386967301368713
+[13:54:40.294604] Accuracy: 0.8750, F1 Score: 0.7704, ROC AUC: 0.8996, Hamming Loss: 0.1250,
+ Jaccard Score: 0.6528, Precision: 0.9306, Recall: 0.7222,
+ Average Precision: 0.8778, Kappa: 0.5536, Score: 0.7412
+[13:54:40.340349] Best epoch = 11, Best score = 0.7977
+[13:54:40.618860] log_dir: ./output_logs/retfound
+[13:54:43.157979] Epoch: [16]  [0/8]  eta: 0:00:20  lr: 0.000591  loss: 0.3331 (0.3331)  time: 2.5381  data: 2.4690  max mem: 9671
+[13:54:44.021078] Epoch: [16]  [7/8]  eta: 0:00:00  lr: 0.000581  loss: 0.2960 (0.3813)  time: 0.4251  data: 0.3087  max mem: 9671
+[13:54:44.133910] Epoch: [16] Total time: 0:00:03 (0.4394 s / it)
+[13:54:44.143191] Averaged stats: lr: 0.000581  loss: 0.2960 (0.3813)
+[13:54:46.605231] val:  [0/2]  eta: 0:00:04  loss: 0.1563 (0.1563)  time: 2.4550  data: 2.4191  max mem: 9671
+[13:54:46.620838] val:  [1/2]  eta: 0:00:01  loss: 0.1563 (0.5159)  time: 1.2350  data: 1.2096  max mem: 9671
+[13:54:46.728162] val: Total time: 0:00:02 (1.2893 s / it)
+[13:54:46.736911] val loss: 0.5159331560134888
+[13:54:46.737115] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9176, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9069, Kappa: 0.6596, Score: 0.8013
+[13:54:48.494622] Best epoch = 16, Best score = 0.8013
+[13:54:48.571901] log_dir: ./output_logs/retfound
+[13:54:50.996269] Epoch: [17]  [0/8]  eta: 0:00:19  lr: 0.000579  loss: 0.4858 (0.4858)  time: 2.4234  data: 2.2808  max mem: 9671
+[13:54:52.031801] Epoch: [17]  [7/8]  eta: 0:00:00  lr: 0.000567  loss: 0.3880 (0.3799)  time: 0.4322  data: 0.2906  max mem: 9671
+[13:54:52.150980] Epoch: [17] Total time: 0:00:03 (0.4474 s / it)
+[13:54:52.160055] Averaged stats: lr: 0.000567  loss: 0.3880 (0.3799)
+[13:54:54.695276] val:  [0/2]  eta: 0:00:05  loss: 0.1928 (0.1928)  time: 2.5192  data: 2.4843  max mem: 9671
+[13:54:54.708310] val:  [1/2]  eta: 0:00:01  loss: 0.1928 (0.3914)  time: 1.2658  data: 1.2422  max mem: 9671
+[13:54:54.822279] val: Total time: 0:00:02 (1.3235 s / it)
+[13:54:54.831482] val loss: 0.39144355058670044
+[13:54:54.831681] Accuracy: 0.9500, F1 Score: 0.9219, ROC AUC: 0.9283, Hamming Loss: 0.0500,
+ Jaccard Score: 0.8586, Precision: 0.9697, Recall: 0.8889,
+ Average Precision: 0.9286, Kappa: 0.8444, Score: 0.8982
+[13:54:56.620098] Best epoch = 17, Best score = 0.8982
+[13:54:56.694716] log_dir: ./output_logs/retfound
+[13:54:59.189733] Epoch: [18]  [0/8]  eta: 0:00:19  lr: 0.000565  loss: 0.2349 (0.2349)  time: 2.4940  data: 2.3495  max mem: 9671
+[13:55:00.181047] Epoch: [18]  [7/8]  eta: 0:00:00  lr: 0.000552  loss: 0.2442 (0.2925)  time: 0.4355  data: 0.2938  max mem: 9671
+[13:55:00.290467] Epoch: [18] Total time: 0:00:03 (0.4494 s / it)
+[13:55:00.298980] Averaged stats: lr: 0.000552  loss: 0.2442 (0.2925)
+[13:55:03.000826] val:  [0/2]  eta: 0:00:05  loss: 0.1443 (0.1443)  time: 2.6931  data: 2.6570  max mem: 9671
+[13:55:03.016392] val:  [1/2]  eta: 0:00:01  loss: 0.1443 (0.5360)  time: 1.3540  data: 1.3286  max mem: 9671
+[13:55:03.127080] val: Total time: 0:00:02 (1.4101 s / it)
+[13:55:03.136536] val loss: 0.5359691083431244
+[13:55:03.136716] Accuracy: 0.8750, F1 Score: 0.7704, ROC AUC: 0.9292, Hamming Loss: 0.1250,
+ Jaccard Score: 0.6528, Precision: 0.9306, Recall: 0.7222,
+ Average Precision: 0.9285, Kappa: 0.5536, Score: 0.7511
+[13:55:03.173665] Best epoch = 17, Best score = 0.8982
+[13:55:03.482112] log_dir: ./output_logs/retfound
+[13:55:06.095371] Epoch: [19]  [0/8]  eta: 0:00:20  lr: 0.000550  loss: 0.4059 (0.4059)  time: 2.6123  data: 2.4664  max mem: 9671
+[13:55:07.091937] Epoch: [19]  [7/8]  eta: 0:00:00  lr: 0.000536  loss: 0.2734 (0.3358)  time: 0.4510  data: 0.3084  max mem: 9671
+[13:55:07.199220] Epoch: [19] Total time: 0:00:03 (0.4646 s / it)
+[13:55:07.208456] Averaged stats: lr: 0.000536  loss: 0.2734 (0.3358)
+[13:55:09.758064] val:  [0/2]  eta: 0:00:05  loss: 0.1677 (0.1677)  time: 2.5429  data: 2.5071  max mem: 9671
+[13:55:09.773819] val:  [1/2]  eta: 0:00:01  loss: 0.1677 (0.4338)  time: 1.2791  data: 1.2536  max mem: 9671
+[13:55:09.884624] val: Total time: 0:00:02 (1.3351 s / it)
+[13:55:09.893282] val loss: 0.4337925612926483
+[13:55:09.893461] Accuracy: 0.9500, F1 Score: 0.9219, ROC AUC: 0.9283, Hamming Loss: 0.0500,
+ Jaccard Score: 0.8586, Precision: 0.9697, Recall: 0.8889,
+ Average Precision: 0.9285, Kappa: 0.8444, Score: 0.8982
+[13:55:09.934690] Best epoch = 17, Best score = 0.8982
+[13:55:10.199747] log_dir: ./output_logs/retfound
+[13:55:12.743926] Epoch: [20]  [0/8]  eta: 0:00:20  lr: 0.000534  loss: 0.3245 (0.3245)  time: 2.5432  data: 2.3978  max mem: 9671
+[13:55:13.733418] Epoch: [20]  [7/8]  eta: 0:00:00  lr: 0.000518  loss: 0.3245 (0.3464)  time: 0.4415  data: 0.2998  max mem: 9671
+[13:55:13.845317] Epoch: [20] Total time: 0:00:03 (0.4557 s / it)
+[13:55:13.853666] Averaged stats: lr: 0.000518  loss: 0.3245 (0.3464)
+[13:55:16.388226] val:  [0/2]  eta: 0:00:05  loss: 0.1523 (0.1523)  time: 2.5229  data: 2.4889  max mem: 9671
+[13:55:16.403753] val:  [1/2]  eta: 0:00:01  loss: 0.1523 (0.6121)  time: 1.2690  data: 1.2445  max mem: 9671
+[13:55:16.509932] val: Total time: 0:00:02 (1.3227 s / it)
+[13:55:16.518766] val loss: 0.6121203303337097
+[13:55:16.518938] Accuracy: 0.8750, F1 Score: 0.7704, ROC AUC: 0.9283, Hamming Loss: 0.1250,
+ Jaccard Score: 0.6528, Precision: 0.9306, Recall: 0.7222,
+ Average Precision: 0.9285, Kappa: 0.5536, Score: 0.7508
+[13:55:16.558346] Best epoch = 17, Best score = 0.8982
+[13:55:16.810840] log_dir: ./output_logs/retfound
+[13:55:19.504352] Epoch: [21]  [0/8]  eta: 0:00:21  lr: 0.000516  loss: 0.2419 (0.2419)  time: 2.6925  data: 2.5481  max mem: 9671
+[13:55:20.496562] Epoch: [21]  [7/8]  eta: 0:00:00  lr: 0.000499  loss: 0.2997 (0.3128)  time: 0.4605  data: 0.3186  max mem: 9671
+[13:55:20.613083] Epoch: [21] Total time: 0:00:03 (0.4753 s / it)
+[13:55:20.622872] Averaged stats: lr: 0.000499  loss: 0.2997 (0.3128)
+[13:55:23.312561] val:  [0/2]  eta: 0:00:05  loss: 0.1637 (0.1637)  time: 2.6733  data: 2.6381  max mem: 9671
+[13:55:23.327922] val:  [1/2]  eta: 0:00:01  loss: 0.1637 (0.4264)  time: 1.3440  data: 1.3191  max mem: 9671
+[13:55:23.434524] val: Total time: 0:00:02 (1.3980 s / it)
+[13:55:23.443528] val loss: 0.42638447880744934
+[13:55:23.443708] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9283, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9285, Kappa: 0.7561, Score: 0.8538
+[13:55:23.487013] Best epoch = 17, Best score = 0.8982
+[13:55:23.766583] log_dir: ./output_logs/retfound
+[13:55:26.376142] Epoch: [22]  [0/8]  eta: 0:00:20  lr: 0.000496  loss: 0.2969 (0.2969)  time: 2.6085  data: 2.4631  max mem: 9671
+[13:55:27.366310] Epoch: [22]  [7/8]  eta: 0:00:00  lr: 0.000479  loss: 0.3222 (0.3138)  time: 0.4497  data: 0.3080  max mem: 9671
+[13:55:27.486017] Epoch: [22] Total time: 0:00:03 (0.4649 s / it)
+[13:55:27.494899] Averaged stats: lr: 0.000479  loss: 0.3222 (0.3138)
+[13:55:30.091932] val:  [0/2]  eta: 0:00:05  loss: 0.1736 (0.1736)  time: 2.5837  data: 2.5485  max mem: 9671
+[13:55:30.107332] val:  [1/2]  eta: 0:00:01  loss: 0.1736 (0.3998)  time: 1.2993  data: 1.2743  max mem: 9671
+[13:55:30.212236] val: Total time: 0:00:02 (1.3524 s / it)
+[13:55:30.221133] val loss: 0.3997582793235779
+[13:55:30.221325] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9283, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9285, Kappa: 0.7561, Score: 0.8538
+[13:55:30.266705] Best epoch = 17, Best score = 0.8982
+[13:55:30.537400] log_dir: ./output_logs/retfound
+[13:55:33.166547] Epoch: [23]  [0/8]  eta: 0:00:21  lr: 0.000476  loss: 0.2342 (0.2342)  time: 2.6282  data: 2.4815  max mem: 9671
+[13:55:34.153806] Epoch: [23]  [7/8]  eta: 0:00:00  lr: 0.000457  loss: 0.3115 (0.3495)  time: 0.4518  data: 0.3103  max mem: 9671
+[13:55:34.270581] Epoch: [23] Total time: 0:00:03 (0.4666 s / it)
+[13:55:34.280309] Averaged stats: lr: 0.000457  loss: 0.3115 (0.3495)
+[13:55:36.882060] val:  [0/2]  eta: 0:00:05  loss: 0.1643 (0.1643)  time: 2.5911  data: 2.5562  max mem: 9671
+[13:55:36.897610] val:  [1/2]  eta: 0:00:01  loss: 0.1643 (0.4087)  time: 1.3030  data: 1.2782  max mem: 9671
+[13:55:37.008262] val: Total time: 0:00:02 (1.3590 s / it)
+[13:55:37.018835] val loss: 0.4086921811103821
+[13:55:37.019056] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9283, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9285, Kappa: 0.7561, Score: 0.8538
+[13:55:37.061416] Best epoch = 17, Best score = 0.8982
+[13:55:37.323509] log_dir: ./output_logs/retfound
+[13:55:40.024435] Epoch: [24]  [0/8]  eta: 0:00:21  lr: 0.000455  loss: 0.3266 (0.3266)  time: 2.6998  data: 2.5546  max mem: 9671
+[13:55:41.032889] Epoch: [24]  [7/8]  eta: 0:00:00  lr: 0.000435  loss: 0.3266 (0.3227)  time: 0.4634  data: 0.3210  max mem: 9671
+[13:55:41.148261] Epoch: [24] Total time: 0:00:03 (0.4781 s / it)
+[13:55:41.157306] Averaged stats: lr: 0.000435  loss: 0.3266 (0.3227)
+[13:55:43.769570] val:  [0/2]  eta: 0:00:05  loss: 0.1567 (0.1567)  time: 2.5970  data: 2.5624  max mem: 9671
+[13:55:43.785143] val:  [1/2]  eta: 0:00:01  loss: 0.1567 (0.4114)  time: 1.3060  data: 1.2813  max mem: 9671
+[13:55:44.078920] val: Total time: 0:00:02 (1.4535 s / it)
+[13:55:44.087839] val loss: 0.41143083572387695
+[13:55:44.088016] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9283, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9285, Kappa: 0.6596, Score: 0.8049
+[13:55:44.431151] Best epoch = 17, Best score = 0.8982
+[13:55:45.088004] log_dir: ./output_logs/retfound
+[13:55:47.741439] Epoch: [25]  [0/8]  eta: 0:00:21  lr: 0.000432  loss: 0.2123 (0.2123)  time: 2.6524  data: 2.5082  max mem: 9671
+[13:55:48.730249] Epoch: [25]  [7/8]  eta: 0:00:00  lr: 0.000412  loss: 0.2637 (0.2978)  time: 0.4551  data: 0.3136  max mem: 9671
+[13:55:48.840251] Epoch: [25] Total time: 0:00:03 (0.4690 s / it)
+[13:55:48.841157] Averaged stats: lr: 0.000412  loss: 0.2637 (0.2978)
+[13:55:51.401680] val:  [0/2]  eta: 0:00:05  loss: 0.1612 (0.1612)  time: 2.5498  data: 2.5137  max mem: 9671
+[13:55:51.417144] val:  [1/2]  eta: 0:00:01  loss: 0.1612 (0.5095)  time: 1.2823  data: 1.2569  max mem: 9671
+[13:55:51.521274] val: Total time: 0:00:02 (1.3351 s / it)
+[13:55:51.531217] val loss: 0.5094860941171646
+[13:55:51.531390] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9319, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9306, Kappa: 0.6596, Score: 0.8061
+[13:55:51.568777] Best epoch = 17, Best score = 0.8982
+[13:55:51.856946] log_dir: ./output_logs/retfound
+[13:55:54.556280] Epoch: [26]  [0/8]  eta: 0:00:21  lr: 0.000409  loss: 0.3678 (0.3678)  time: 2.6982  data: 2.5529  max mem: 9671
+[13:55:55.549899] Epoch: [26]  [7/8]  eta: 0:00:00  lr: 0.000389  loss: 0.2022 (0.2613)  time: 0.4614  data: 0.3192  max mem: 9671
+[13:55:55.668925] Epoch: [26] Total time: 0:00:03 (0.4765 s / it)
+[13:55:55.677318] Averaged stats: lr: 0.000389  loss: 0.2022 (0.2613)
+[13:55:58.327322] val:  [0/2]  eta: 0:00:05  loss: 0.1778 (0.1778)  time: 2.6385  data: 2.6048  max mem: 9671
+[13:55:58.342718] val:  [1/2]  eta: 0:00:01  loss: 0.1778 (0.5650)  time: 1.3267  data: 1.3025  max mem: 9671
+[13:55:58.451513] val: Total time: 0:00:02 (1.3817 s / it)
+[13:55:58.460350] val loss: 0.5649861097335815
+[13:55:58.460536] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9292, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9234, Kappa: 0.6596, Score: 0.8052
+[13:55:58.503458] Best epoch = 17, Best score = 0.8982
+[13:55:58.775419] log_dir: ./output_logs/retfound
+[13:56:01.560598] Epoch: [27]  [0/8]  eta: 0:00:22  lr: 0.000386  loss: 0.3435 (0.3435)  time: 2.7842  data: 2.6406  max mem: 9671
+[13:56:02.553688] Epoch: [27]  [7/8]  eta: 0:00:00  lr: 0.000365  loss: 0.3040 (0.3233)  time: 0.4721  data: 0.3302  max mem: 9671
+[13:56:02.670899] Epoch: [27] Total time: 0:00:03 (0.4869 s / it)
+[13:56:02.680284] Averaged stats: lr: 0.000365  loss: 0.3040 (0.3233)
+[13:56:05.332181] val:  [0/2]  eta: 0:00:05  loss: 0.1657 (0.1657)  time: 2.6396  data: 2.6043  max mem: 9671
+[13:56:05.347827] val:  [1/2]  eta: 0:00:01  loss: 0.1657 (0.4328)  time: 1.3273  data: 1.3022  max mem: 9671
+[13:56:05.456625] val: Total time: 0:00:02 (1.3824 s / it)
+[13:56:05.465596] val loss: 0.43275587260723114
+[13:56:05.465841] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9391, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9252, Kappa: 0.6596, Score: 0.8085
+[13:56:05.506627] Best epoch = 17, Best score = 0.8982
+[13:56:05.768898] log_dir: ./output_logs/retfound
+[13:56:08.273372] Epoch: [28]  [0/8]  eta: 0:00:20  lr: 0.000362  loss: 0.2153 (0.2153)  time: 2.5035  data: 2.3590  max mem: 9671
+[13:56:09.265644] Epoch: [28]  [7/8]  eta: 0:00:00  lr: 0.000341  loss: 0.2252 (0.2592)  time: 0.4369  data: 0.2950  max mem: 9671
+[13:56:09.378523] Epoch: [28] Total time: 0:00:03 (0.4512 s / it)
+[13:56:09.386861] Averaged stats: lr: 0.000341  loss: 0.2252 (0.2592)
+[13:56:12.018122] val:  [0/2]  eta: 0:00:05  loss: 0.2523 (0.2523)  time: 2.6136  data: 2.5779  max mem: 9671
+[13:56:12.033706] val:  [1/2]  eta: 0:00:01  loss: 0.1857 (0.2190)  time: 1.3143  data: 1.2890  max mem: 9671
+[13:56:12.142252] val: Total time: 0:00:02 (1.3693 s / it)
+[13:56:12.151085] val loss: 0.21898877620697021
+[13:56:12.151264] Accuracy: 0.9000, F1 Score: 0.8566, ROC AUC: 0.9498, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7576, Precision: 0.8566, Recall: 0.8566,
+ Average Precision: 0.9370, Kappa: 0.7133, Score: 0.8399
+[13:56:12.189265] Best epoch = 17, Best score = 0.8982
+[13:56:12.455804] log_dir: ./output_logs/retfound
+[13:56:14.814913] Epoch: [29]  [0/8]  eta: 0:00:18  lr: 0.000337  loss: 0.2674 (0.2674)  time: 2.3578  data: 2.2118  max mem: 9671
+[13:56:16.083719] Epoch: [29]  [7/8]  eta: 0:00:00  lr: 0.000316  loss: 0.2900 (0.2986)  time: 0.4532  data: 0.3119  max mem: 9671
+[13:56:16.195376] Epoch: [29] Total time: 0:00:03 (0.4674 s / it)
+[13:56:16.203868] Averaged stats: lr: 0.000316  loss: 0.2900 (0.2986)
+[13:56:18.768094] val:  [0/2]  eta: 0:00:05  loss: 0.1922 (0.1922)  time: 2.5473  data: 2.5170  max mem: 9671
+[13:56:18.780819] val:  [1/2]  eta: 0:00:01  loss: 0.1922 (0.2850)  time: 1.2797  data: 1.2585  max mem: 9671
+[13:56:18.892676] val: Total time: 0:00:02 (1.3363 s / it)
+[13:56:18.902135] val loss: 0.28504690527915955
+[13:56:18.902386] Accuracy: 0.9250, F1 Score: 0.8880, ROC AUC: 0.9498, Hamming Loss: 0.0750,
+ Jaccard Score: 0.8045, Precision: 0.9062, Recall: 0.8728,
+ Average Precision: 0.9346, Kappa: 0.7761, Score: 0.8713
+[13:56:18.948168] Best epoch = 17, Best score = 0.8982
+[13:56:19.202588] log_dir: ./output_logs/retfound
+[13:56:21.767984] Epoch: [30]  [0/8]  eta: 0:00:20  lr: 0.000313  loss: 0.2006 (0.2006)  time: 2.5642  data: 2.4200  max mem: 9671
+[13:56:22.758710] Epoch: [30]  [7/8]  eta: 0:00:00  lr: 0.000292  loss: 0.2116 (0.2705)  time: 0.4443  data: 0.3026  max mem: 9671
+[13:56:22.870294] Epoch: [30] Total time: 0:00:03 (0.4584 s / it)
+[13:56:22.878863] Averaged stats: lr: 0.000292  loss: 0.2116 (0.2705)
+[13:56:25.501204] val:  [0/2]  eta: 0:00:05  loss: 0.1967 (0.1967)  time: 2.6052  data: 2.5712  max mem: 9671
+[13:56:25.516928] val:  [1/2]  eta: 0:00:01  loss: 0.1967 (0.3365)  time: 1.3102  data: 1.2857  max mem: 9671
+[13:56:25.618390] val: Total time: 0:00:02 (1.3615 s / it)
+[13:56:25.627316] val loss: 0.3365369886159897
+[13:56:25.627495] Accuracy: 0.9000, F1 Score: 0.8438, ROC AUC: 0.9498, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7412, Precision: 0.8831, Recall: 0.8172,
+ Average Precision: 0.9408, Kappa: 0.6887, Score: 0.8274
+[13:56:25.666863] Best epoch = 17, Best score = 0.8982
+[13:56:25.905752] log_dir: ./output_logs/retfound
+[13:56:28.512268] Epoch: [31]  [0/8]  eta: 0:00:20  lr: 0.000289  loss: 0.5076 (0.5076)  time: 2.6055  data: 2.4603  max mem: 9671
+[13:56:29.504432] Epoch: [31]  [7/8]  eta: 0:00:00  lr: 0.000267  loss: 0.2211 (0.2924)  time: 0.4496  data: 0.3076  max mem: 9671
+[13:56:29.615107] Epoch: [31] Total time: 0:00:03 (0.4636 s / it)
+[13:56:29.624337] Averaged stats: lr: 0.000267  loss: 0.2211 (0.2924)
+[13:56:32.263759] val:  [0/2]  eta: 0:00:05  loss: 0.1812 (0.1812)  time: 2.6227  data: 2.5889  max mem: 9671
+[13:56:32.276817] val:  [1/2]  eta: 0:00:01  loss: 0.1812 (0.3428)  time: 1.3176  data: 1.2945  max mem: 9671
+[13:56:32.382412] val: Total time: 0:00:02 (1.3710 s / it)
+[13:56:32.391100] val loss: 0.3427543491125107
+[13:56:32.391293] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9462, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9369, Kappa: 0.7561, Score: 0.8598
+[13:56:32.427747] Best epoch = 17, Best score = 0.8982
+[13:56:32.701895] log_dir: ./output_logs/retfound
+[13:56:35.191139] Epoch: [32]  [0/8]  eta: 0:00:19  lr: 0.000264  loss: 0.1763 (0.1763)  time: 2.4883  data: 2.3441  max mem: 9671
+[13:56:36.182969] Epoch: [32]  [7/8]  eta: 0:00:00  lr: 0.000243  loss: 0.3075 (0.2896)  time: 0.4349  data: 0.2931  max mem: 9671
+[13:56:36.297918] Epoch: [32] Total time: 0:00:03 (0.4495 s / it)
+[13:56:36.307398] Averaged stats: lr: 0.000243  loss: 0.3075 (0.2896)
+[13:56:39.028118] val:  [0/2]  eta: 0:00:05  loss: 0.1718 (0.1718)  time: 2.7050  data: 2.6708  max mem: 9671
+[13:56:39.043524] val:  [1/2]  eta: 0:00:01  loss: 0.1718 (0.3577)  time: 1.3599  data: 1.3355  max mem: 9671
+[13:56:39.147275] val: Total time: 0:00:02 (1.4125 s / it)
+[13:56:39.156031] val loss: 0.35773538053035736
+[13:56:39.156229] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9462, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9357, Kappa: 0.7561, Score: 0.8598
+[13:56:39.198000] Best epoch = 17, Best score = 0.8982
+[13:56:39.453581] log_dir: ./output_logs/retfound
+[13:56:41.822783] Epoch: [33]  [0/8]  eta: 0:00:18  lr: 0.000240  loss: 0.1372 (0.1372)  time: 2.3680  data: 2.2236  max mem: 9671
+[13:56:42.866195] Epoch: [33]  [7/8]  eta: 0:00:00  lr: 0.000220  loss: 0.2494 (0.2693)  time: 0.4263  data: 0.2845  max mem: 9671
+[13:56:42.973127] Epoch: [33] Total time: 0:00:03 (0.4399 s / it)
+[13:56:42.980214] Averaged stats: lr: 0.000220  loss: 0.2494 (0.2693)
+[13:56:45.685841] val:  [0/2]  eta: 0:00:05  loss: 0.1649 (0.1649)  time: 2.6894  data: 2.6553  max mem: 9671
+[13:56:45.701073] val:  [1/2]  eta: 0:00:01  loss: 0.1649 (0.4252)  time: 1.3519  data: 1.3277  max mem: 9671
+[13:56:45.808981] val: Total time: 0:00:02 (1.4068 s / it)
+[13:56:45.820281] val loss: 0.42521847784519196
+[13:56:45.820468] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9462, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9409, Kappa: 0.6596, Score: 0.8109
+[13:56:45.861508] Best epoch = 17, Best score = 0.8982
+[13:56:46.149671] log_dir: ./output_logs/retfound
+[13:56:48.772298] Epoch: [34]  [0/8]  eta: 0:00:20  lr: 0.000217  loss: 0.2293 (0.2293)  time: 2.6219  data: 2.4779  max mem: 9671
+[13:56:49.762117] Epoch: [34]  [7/8]  eta: 0:00:00  lr: 0.000196  loss: 0.2456 (0.2689)  time: 0.4514  data: 0.3099  max mem: 9671
+[13:56:49.877441] Epoch: [34] Total time: 0:00:03 (0.4660 s / it)
+[13:56:49.886558] Averaged stats: lr: 0.000196  loss: 0.2456 (0.2689)
+[13:56:52.464416] val:  [0/2]  eta: 0:00:05  loss: 0.1657 (0.1657)  time: 2.5624  data: 2.5281  max mem: 9671
+[13:56:52.480069] val:  [1/2]  eta: 0:00:01  loss: 0.1657 (0.4029)  time: 1.2887  data: 1.2641  max mem: 9671
+[13:56:52.584949] val: Total time: 0:00:02 (1.3418 s / it)
+[13:56:52.593747] val loss: 0.4029083847999573
+[13:56:52.593937] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9391, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9353, Kappa: 0.7561, Score: 0.8574
+[13:56:52.637970] Best epoch = 17, Best score = 0.8982
+[13:56:52.908137] log_dir: ./output_logs/retfound
+[13:56:55.634125] Epoch: [35]  [0/8]  eta: 0:00:21  lr: 0.000194  loss: 0.4496 (0.4496)  time: 2.7249  data: 2.5847  max mem: 9671
+[13:56:56.632537] Epoch: [35]  [7/8]  eta: 0:00:00  lr: 0.000174  loss: 0.2822 (0.2812)  time: 0.4653  data: 0.3232  max mem: 9671
+[13:56:56.743234] Epoch: [35] Total time: 0:00:03 (0.4794 s / it)
+[13:56:56.752586] Averaged stats: lr: 0.000174  loss: 0.2822 (0.2812)
+[13:56:59.396594] val:  [0/2]  eta: 0:00:05  loss: 0.1606 (0.1606)  time: 2.6246  data: 2.5896  max mem: 9671
+[13:56:59.412344] val:  [1/2]  eta: 0:00:01  loss: 0.1606 (0.3562)  time: 1.3198  data: 1.2949  max mem: 9671
+[13:56:59.523423] val: Total time: 0:00:02 (1.3761 s / it)
+[13:56:59.532239] val loss: 0.3561532497406006
+[13:56:59.532441] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9391, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9353, Kappa: 0.7561, Score: 0.8574
+[13:56:59.573985] Best epoch = 17, Best score = 0.8982
+[13:56:59.835305] log_dir: ./output_logs/retfound
+[13:57:02.358442] Epoch: [36]  [0/8]  eta: 0:00:20  lr: 0.000171  loss: 0.4470 (0.4470)  time: 2.5222  data: 2.3781  max mem: 9671
+[13:57:03.348156] Epoch: [36]  [7/8]  eta: 0:00:00  lr: 0.000153  loss: 0.2518 (0.2885)  time: 0.4389  data: 0.2973  max mem: 9671
+[13:57:03.475106] Epoch: [36] Total time: 0:00:03 (0.4550 s / it)
+[13:57:03.485225] Averaged stats: lr: 0.000153  loss: 0.2518 (0.2885)
+[13:57:06.118664] val:  [0/2]  eta: 0:00:05  loss: 0.1652 (0.1652)  time: 2.6214  data: 2.5865  max mem: 9671
+[13:57:06.134092] val:  [1/2]  eta: 0:00:01  loss: 0.1652 (0.2911)  time: 1.3181  data: 1.2933  max mem: 9671
+[13:57:06.238176] val: Total time: 0:00:02 (1.3709 s / it)
+[13:57:06.246976] val loss: 0.2910751849412918
+[13:57:06.247172] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9462, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9409, Kappa: 0.7561, Score: 0.8598
+[13:57:06.292415] Best epoch = 17, Best score = 0.8982
+[13:57:06.545620] log_dir: ./output_logs/retfound
+[13:57:09.305220] Epoch: [37]  [0/8]  eta: 0:00:22  lr: 0.000150  loss: 0.3017 (0.3017)  time: 2.7585  data: 2.6143  max mem: 9671
+[13:57:10.289578] Epoch: [37]  [7/8]  eta: 0:00:00  lr: 0.000132  loss: 0.2246 (0.2802)  time: 0.4678  data: 0.3269  max mem: 9671
+[13:57:10.402850] Epoch: [37] Total time: 0:00:03 (0.4821 s / it)
+[13:57:10.411895] Averaged stats: lr: 0.000132  loss: 0.2246 (0.2802)
+[13:57:12.977439] val:  [0/2]  eta: 0:00:05  loss: 0.1532 (0.1532)  time: 2.5546  data: 2.5194  max mem: 9671
+[13:57:12.993077] val:  [1/2]  eta: 0:00:01  loss: 0.1532 (0.3142)  time: 1.2847  data: 1.2598  max mem: 9671
+[13:57:13.101946] val: Total time: 0:00:02 (1.3400 s / it)
+[13:57:13.111613] val loss: 0.3142062872648239
+[13:57:13.111822] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9498, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9431, Kappa: 0.7561, Score: 0.8609
+[13:57:13.155390] Best epoch = 17, Best score = 0.8982
+[13:57:13.419455] log_dir: ./output_logs/retfound
+[13:57:15.735752] Epoch: [38]  [0/8]  eta: 0:00:18  lr: 0.000130  loss: 0.1331 (0.1331)  time: 2.3153  data: 2.1710  max mem: 9671
+[13:57:16.860145] Epoch: [38]  [7/8]  eta: 0:00:00  lr: 0.000113  loss: 0.2899 (0.2815)  time: 0.4299  data: 0.2878  max mem: 9671
+[13:57:16.974810] Epoch: [38] Total time: 0:00:03 (0.4444 s / it)
+[13:57:16.982721] Averaged stats: lr: 0.000113  loss: 0.2899 (0.2815)
+[13:57:19.584262] val:  [0/2]  eta: 0:00:05  loss: 0.1554 (0.1554)  time: 2.5887  data: 2.5524  max mem: 9671
+[13:57:19.599922] val:  [1/2]  eta: 0:00:01  loss: 0.1554 (0.2977)  time: 1.3018  data: 1.2763  max mem: 9671
+[13:57:19.714194] val: Total time: 0:00:02 (1.3597 s / it)
+[13:57:19.723596] val loss: 0.2977108359336853
+[13:57:19.723793] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9534, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9454, Kappa: 0.7561, Score: 0.8621
+[13:57:19.762774] Best epoch = 17, Best score = 0.8982
+[13:57:20.029088] log_dir: ./output_logs/retfound
+[13:57:22.694535] Epoch: [39]  [0/8]  eta: 0:00:21  lr: 0.000110  loss: 0.2601 (0.2601)  time: 2.6645  data: 2.5190  max mem: 9671
+[13:57:23.691181] Epoch: [39]  [7/8]  eta: 0:00:00  lr: 0.000095  loss: 0.2684 (0.2860)  time: 0.4575  data: 0.3150  max mem: 9671
+[13:57:23.801353] Epoch: [39] Total time: 0:00:03 (0.4715 s / it)
+[13:57:23.809432] Averaged stats: lr: 0.000095  loss: 0.2684 (0.2860)
+[13:57:26.361756] val:  [0/2]  eta: 0:00:05  loss: 0.1555 (0.1555)  time: 2.5356  data: 2.5008  max mem: 9671
+[13:57:26.377155] val:  [1/2]  eta: 0:00:01  loss: 0.1555 (0.2966)  time: 1.2752  data: 1.2505  max mem: 9671
+[13:57:26.480480] val: Total time: 0:00:02 (1.3276 s / it)
+[13:57:26.489272] val loss: 0.2965516448020935
+[13:57:26.489492] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9534, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9454, Kappa: 0.7561, Score: 0.8621
+[13:57:26.525424] Best epoch = 17, Best score = 0.8982
+[13:57:26.832626] log_dir: ./output_logs/retfound
+[13:57:29.340345] Epoch: [40]  [0/8]  eta: 0:00:20  lr: 0.000092  loss: 0.2017 (0.2017)  time: 2.5066  data: 2.3740  max mem: 9671
+[13:57:30.337085] Epoch: [40]  [7/8]  eta: 0:00:00  lr: 0.000078  loss: 0.2769 (0.2756)  time: 0.4378  data: 0.2969  max mem: 9671
+[13:57:30.455456] Epoch: [40] Total time: 0:00:03 (0.4528 s / it)
+[13:57:30.465251] Averaged stats: lr: 0.000078  loss: 0.2769 (0.2756)
+[13:57:33.157634] val:  [0/2]  eta: 0:00:05  loss: 0.1451 (0.1451)  time: 2.6779  data: 2.6430  max mem: 9671
+[13:57:33.173109] val:  [1/2]  eta: 0:00:01  loss: 0.1451 (0.3349)  time: 1.3464  data: 1.3216  max mem: 9671
+[13:57:33.286367] val: Total time: 0:00:02 (1.4037 s / it)
+[13:57:33.295163] val loss: 0.3348654955625534
+[13:57:33.295354] Accuracy: 0.9250, F1 Score: 0.8769, ROC AUC: 0.9498, Hamming Loss: 0.0750,
+ Jaccard Score: 0.7892, Precision: 0.9559, Recall: 0.8333,
+ Average Precision: 0.9424, Kappa: 0.7561, Score: 0.8609
+[13:57:33.343824] Best epoch = 17, Best score = 0.8982
+[13:57:33.603696] log_dir: ./output_logs/retfound
+[13:57:36.339120] Epoch: [41]  [0/8]  eta: 0:00:21  lr: 0.000076  loss: 0.2176 (0.2176)  time: 2.7342  data: 2.5890  max mem: 9671
+[13:57:37.326870] Epoch: [41]  [7/8]  eta: 0:00:00  lr: 0.000062  loss: 0.2356 (0.2808)  time: 0.4652  data: 0.3238  max mem: 9671
+[13:57:37.452503] Epoch: [41] Total time: 0:00:03 (0.4811 s / it)
+[13:57:37.461739] Averaged stats: lr: 0.000062  loss: 0.2356 (0.2808)
+[13:57:40.106831] val:  [0/2]  eta: 0:00:05  loss: 0.1402 (0.1402)  time: 2.6276  data: 2.5935  max mem: 9671
+[13:57:40.122170] val:  [1/2]  eta: 0:00:01  loss: 0.1402 (0.3686)  time: 1.3212  data: 1.2968  max mem: 9671
+[13:57:40.236666] val: Total time: 0:00:02 (1.3791 s / it)
+[13:57:40.245478] val loss: 0.3686094284057617
+[13:57:40.245655] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9534, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9454, Kappa: 0.6596, Score: 0.8133
+[13:57:40.289347] Best epoch = 17, Best score = 0.8982
+[13:57:40.540190] log_dir: ./output_logs/retfound
+[13:57:43.045722] Epoch: [42]  [0/8]  eta: 0:00:20  lr: 0.000061  loss: 0.2027 (0.2027)  time: 2.5042  data: 2.3563  max mem: 9671
+[13:57:44.034881] Epoch: [42]  [7/8]  eta: 0:00:00  lr: 0.000049  loss: 0.2027 (0.2439)  time: 0.4366  data: 0.2946  max mem: 9671
+[13:57:44.153749] Epoch: [42] Total time: 0:00:03 (0.4517 s / it)
+[13:57:44.163441] Averaged stats: lr: 0.000049  loss: 0.2027 (0.2439)
+[13:57:46.812922] val:  [0/2]  eta: 0:00:05  loss: 0.1391 (0.1391)  time: 2.6341  data: 2.5981  max mem: 9671
+[13:57:46.828705] val:  [1/2]  eta: 0:00:01  loss: 0.1391 (0.4043)  time: 1.3246  data: 1.2991  max mem: 9671
+[13:57:46.938953] val: Total time: 0:00:02 (1.3805 s / it)
+[13:57:46.947754] val loss: 0.4043458551168442
+[13:57:46.947939] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9534, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9454, Kappa: 0.6596, Score: 0.8133
+[13:57:46.998329] Best epoch = 17, Best score = 0.8982
+[13:57:47.255679] log_dir: ./output_logs/retfound
+[13:57:49.865228] Epoch: [43]  [0/8]  eta: 0:00:20  lr: 0.000047  loss: 0.1854 (0.1854)  time: 2.6086  data: 2.4643  max mem: 9671
+[13:57:50.857479] Epoch: [43]  [7/8]  eta: 0:00:00  lr: 0.000036  loss: 0.2205 (0.2380)  time: 0.4500  data: 0.3081  max mem: 9671
+[13:57:50.967578] Epoch: [43] Total time: 0:00:03 (0.4640 s / it)
+[13:57:50.975726] Averaged stats: lr: 0.000036  loss: 0.2205 (0.2380)
+[13:57:53.598847] val:  [0/2]  eta: 0:00:05  loss: 0.1402 (0.1402)  time: 2.6119  data: 2.5781  max mem: 9671
+[13:57:53.614524] val:  [1/2]  eta: 0:00:01  loss: 0.1402 (0.4161)  time: 1.3135  data: 1.2891  max mem: 9671
+[13:57:53.722129] val: Total time: 0:00:02 (1.3680 s / it)
+[13:57:53.730996] val loss: 0.416122242808342
+[13:57:53.731195] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9570, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9488, Kappa: 0.6596, Score: 0.8145
+[13:57:53.768099] Best epoch = 17, Best score = 0.8982
+[13:57:54.041269] log_dir: ./output_logs/retfound
+[13:57:56.740457] Epoch: [44]  [0/8]  eta: 0:00:21  lr: 0.000035  loss: 0.3020 (0.3020)  time: 2.6982  data: 2.5546  max mem: 9671
+[13:57:57.730640] Epoch: [44]  [7/8]  eta: 0:00:00  lr: 0.000026  loss: 0.2097 (0.2575)  time: 0.4610  data: 0.3194  max mem: 9671
+[13:57:57.841698] Epoch: [44] Total time: 0:00:03 (0.4750 s / it)
+[13:57:57.850816] Averaged stats: lr: 0.000026  loss: 0.2097 (0.2575)
+[13:58:00.402354] val:  [0/2]  eta: 0:00:05  loss: 0.1411 (0.1411)  time: 2.5351  data: 2.5013  max mem: 9671
+[13:58:00.418008] val:  [1/2]  eta: 0:00:01  loss: 0.1411 (0.4057)  time: 1.2751  data: 1.2507  max mem: 9671
+[13:58:00.527311] val: Total time: 0:00:02 (1.3304 s / it)
+[13:58:00.536186] val loss: 0.4057278037071228
+[13:58:00.536388] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9570, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9488, Kappa: 0.6596, Score: 0.8145
+[13:58:00.580468] Best epoch = 17, Best score = 0.8982
+[13:58:00.844867] log_dir: ./output_logs/retfound
+[13:58:03.374393] Epoch: [45]  [0/8]  eta: 0:00:20  lr: 0.000025  loss: 0.2266 (0.2266)  time: 2.5286  data: 2.3811  max mem: 9671
+[13:58:04.366539] Epoch: [45]  [7/8]  eta: 0:00:00  lr: 0.000017  loss: 0.2266 (0.2669)  time: 0.4400  data: 0.2977  max mem: 9671
+[13:58:04.509172] Epoch: [45] Total time: 0:00:03 (0.4580 s / it)
+[13:58:04.517954] Averaged stats: lr: 0.000017  loss: 0.2266 (0.2669)
+[13:58:07.050917] val:  [0/2]  eta: 0:00:05  loss: 0.1414 (0.1414)  time: 2.5190  data: 2.4836  max mem: 9671
+[13:58:07.066464] val:  [1/2]  eta: 0:00:01  loss: 0.1414 (0.3876)  time: 1.2670  data: 1.2419  max mem: 9671
+[13:58:07.170584] val: Total time: 0:00:02 (1.3197 s / it)
+[13:58:07.179685] val loss: 0.38761037588119507
+[13:58:07.179893] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9570, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9488, Kappa: 0.6596, Score: 0.8145
+[13:58:07.223819] Best epoch = 17, Best score = 0.8982
+[13:58:07.484293] log_dir: ./output_logs/retfound
+[13:58:10.183208] Epoch: [46]  [0/8]  eta: 0:00:21  lr: 0.000016  loss: 0.2934 (0.2934)  time: 2.6979  data: 2.5539  max mem: 9671
+[13:58:11.180959] Epoch: [46]  [7/8]  eta: 0:00:00  lr: 0.000010  loss: 0.2377 (0.2666)  time: 0.4618  data: 0.3193  max mem: 9671
+[13:58:11.289298] Epoch: [46] Total time: 0:00:03 (0.4756 s / it)
+[13:58:11.297206] Averaged stats: lr: 0.000010  loss: 0.2377 (0.2666)
+[13:58:13.894239] val:  [0/2]  eta: 0:00:05  loss: 0.1412 (0.1412)  time: 2.5814  data: 2.5478  max mem: 9671
+[13:58:13.909786] val:  [1/2]  eta: 0:00:01  loss: 0.1412 (0.3810)  time: 1.2982  data: 1.2740  max mem: 9671
+[13:58:14.014601] val: Total time: 0:00:02 (1.3513 s / it)
+[13:58:14.024088] val loss: 0.3810284584760666
+[13:58:14.024264] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9570, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9488, Kappa: 0.6596, Score: 0.8145
+[13:58:14.059744] Best epoch = 17, Best score = 0.8982
+[13:58:14.332951] log_dir: ./output_logs/retfound
+[13:58:16.822406] Epoch: [47]  [0/8]  eta: 0:00:19  lr: 0.000010  loss: 0.2115 (0.2115)  time: 2.4885  data: 2.3437  max mem: 9671
+[13:58:17.876939] Epoch: [47]  [7/8]  eta: 0:00:00  lr: 0.000005  loss: 0.2115 (0.2570)  time: 0.4428  data: 0.3010  max mem: 9671
+[13:58:17.995207] Epoch: [47] Total time: 0:00:03 (0.4578 s / it)
+[13:58:18.003435] Averaged stats: lr: 0.000005  loss: 0.2115 (0.2570)
+[13:58:20.696537] val:  [0/2]  eta: 0:00:05  loss: 0.1411 (0.1411)  time: 2.6646  data: 2.6306  max mem: 9671
+[13:58:20.712303] val:  [1/2]  eta: 0:00:01  loss: 0.1411 (0.3793)  time: 1.3398  data: 1.3154  max mem: 9671
+[13:58:20.818223] val: Total time: 0:00:02 (1.3935 s / it)
+[13:58:20.833242] val loss: 0.3793119490146637
+[13:58:20.833492] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9606, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9527, Kappa: 0.6596, Score: 0.8157
+[13:58:20.873481] Best epoch = 17, Best score = 0.8982
+[13:58:21.160139] log_dir: ./output_logs/retfound
+[13:58:23.571144] Epoch: [48]  [0/8]  eta: 0:00:19  lr: 0.000005  loss: 0.3700 (0.3700)  time: 2.4099  data: 2.2657  max mem: 9671
+[13:58:24.560774] Epoch: [48]  [7/8]  eta: 0:00:00  lr: 0.000002  loss: 0.2148 (0.2645)  time: 0.4248  data: 0.2835  max mem: 9671
+[13:58:24.677673] Epoch: [48] Total time: 0:00:03 (0.4397 s / it)
+[13:58:24.687313] Averaged stats: lr: 0.000002  loss: 0.2148 (0.2645)
+[13:58:27.307905] val:  [0/2]  eta: 0:00:05  loss: 0.1411 (0.1411)  time: 2.6085  data: 2.5741  max mem: 9671
+[13:58:27.323530] val:  [1/2]  eta: 0:00:01  loss: 0.1411 (0.3780)  time: 1.3117  data: 1.2871  max mem: 9671
+[13:58:27.428244] val: Total time: 0:00:02 (1.3648 s / it)
+[13:58:27.437292] val loss: 0.3780297338962555
+[13:58:27.437497] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9606, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9527, Kappa: 0.6596, Score: 0.8157
+[13:58:27.475820] Best epoch = 17, Best score = 0.8982
+[13:58:27.737422] log_dir: ./output_logs/retfound
+[13:58:30.133935] Epoch: [49]  [0/8]  eta: 0:00:19  lr: 0.000002  loss: 0.1158 (0.1158)  time: 2.3955  data: 2.2504  max mem: 9671
+[13:58:31.123119] Epoch: [49]  [7/8]  eta: 0:00:00  lr: 0.000001  loss: 0.2527 (0.2805)  time: 0.4230  data: 0.2814  max mem: 9671
+[13:58:31.242759] Epoch: [49] Total time: 0:00:03 (0.4381 s / it)
+[13:58:31.251532] Averaged stats: lr: 0.000001  loss: 0.2527 (0.2805)
+[13:58:33.827005] val:  [0/2]  eta: 0:00:05  loss: 0.1411 (0.1411)  time: 2.5645  data: 2.5297  max mem: 9671
+[13:58:33.842600] val:  [1/2]  eta: 0:00:01  loss: 0.1411 (0.3775)  time: 1.2898  data: 1.2649  max mem: 9671
+[13:58:33.957716] val: Total time: 0:00:02 (1.3480 s / it)
+[13:58:33.966648] val loss: 0.37748883664608
+[13:58:33.966878] Accuracy: 0.9000, F1 Score: 0.8268, ROC AUC: 0.9606, Hamming Loss: 0.1000,
+ Jaccard Score: 0.7206, Precision: 0.9429, Recall: 0.7778,
+ Average Precision: 0.9527, Kappa: 0.6596, Score: 0.8157
+[13:58:34.007113] Best epoch = 17, Best score = 0.8982
+[13:58:37.849680] Test with the best model, epoch = 17:
+[13:58:40.407636] test:  [0/3]  eta: 0:00:07  loss: 0.2582 (0.2582)  time: 2.5484  data: 2.5134  max mem: 9671
+[13:58:40.550538] test:  [2/3]  eta: 0:00:00  loss: 0.2582 (0.2454)  time: 0.8969  data: 0.8379  max mem: 9671
+[13:58:40.645493] test: Total time: 0:00:02 (0.9290 s / it)
+[13:58:40.654785] val loss: 0.24544517199198404
+[13:58:40.654887] Accuracy: 0.9250, F1 Score: 0.8925, ROC AUC: 0.9516, Hamming Loss: 0.0750,
+ Jaccard Score: 0.8110, Precision: 0.8925, Recall: 0.8925,
+ Average Precision: 0.9512, Kappa: 0.7849, Score: 0.8763
+[13:58:41.372124] Training time 0:06:02
+[rank0]:[W615 13:58:41.789217613 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/adam/retfound  acc=0.9250  auroc=0.9516129032258065  f1_macro=0.8925  qwk=0.7849462365591398

results/adam/vit/log.csv

@@ -0,0 +1,33 @@
+epoch,train_loss,val_acc,val_auc,val_score,lr
+0,0.8326617628335953,0.5,0.37275985663082434,0.20603382384588553,5.545808836528073e-08
+1,0.7702071815729141,0.525,0.7204301075268817,0.45818926807259003,1.29402206185655e-07
+2,0.6546564847230911,0.7,0.7849462365591398,0.5773837351677463,2.0334632400602932e-07
+3,0.6182350814342499,0.75,0.870967741935484,0.6940345499098299,2.7729044182640365e-07
+4,0.5662039071321487,0.75,0.8960573476702509,0.6544608924687236,3.512345596467779e-07
+5,0.5842887908220291,0.725,0.8745519713261649,0.6756845538858233,3.694672444929302e-07
+6,0.5582687333226204,0.825,0.899641577060932,0.7416449759260039,3.683426684987483e-07
+7,0.4713408350944519,0.775,0.9175627240143369,0.7299333069266117,3.663241848222764e-07
+8,0.5112659633159637,0.775,0.9175627240143369,0.7299333069266117,3.6342162731308893e-07
+9,0.465877428650856,0.9,0.9139784946236559,0.8279569892473119,3.5964913693960667e-07
+10,0.4645930454134941,0.75,0.9247311827956989,0.6824673300830383,3.550250928957199e-07
+11,0.4524441137909889,0.775,0.946236559139785,0.7109049537056887,3.495720230592029e-07
+12,0.4489521011710167,0.8,0.942652329749104,0.7321708645126134,3.4331649423815874e-07
+13,0.4123004525899887,0.825,0.946236559139785,0.7571766366189548,3.362889827402e-07
+14,0.412119522690773,0.9,0.9569892473118279,0.8422939068100358,3.285237258949416e-07
+15,0.38696958124637604,0.8,0.9641577060931898,0.7667567252402153,3.2005855525317277e-07
+16,0.3891528472304344,0.9,0.946236559139785,0.8387096774193549,3.1093471227534435e-07
+17,0.38670214265584946,0.775,0.9283154121863799,0.7049312380545537,3.0119664740731875e-07
+18,0.4111583083868027,0.925,0.935483870967742,0.866519485341882,2.908918035222662e-07
+19,0.40045975893735886,0.875,0.942652329749104,0.8084207298721364,2.800703847837553e-07
+20,0.38688723742961884,0.85,0.96415770609319,0.7884955365825584,2.687851120561149e-07
+21,0.3998527154326439,0.925,0.9605734767025089,0.8748826872534708,2.570909660536824e-07
+22,0.37270648032426834,0.875,0.9569892473118279,0.8131997023930445,2.450449194802903e-07
+23,0.36275216937065125,0.9,0.9569892473118279,0.8422939068100358,2.3270565946397963e-07
+24,0.39110884070396423,0.825,0.9498207885304659,0.7723605633561171,2.2013330163921197e-07
+25,0.39257583022117615,0.875,0.9283154121863799,0.8036417573512283,2.0738909726954043e-07
+26,0.3709259107708931,0.875,0.9283154121863799,0.8036417573512283,1.9453513483761127e-07
+27,0.35911235213279724,0.9,0.931899641577061,0.8339307048984468,1.8163403755631687e-07
+28,0.350225068628788,0.9,0.935483870967742,0.8351254480286738,1.6874865827479806e-07
+29,0.3573886975646019,0.875,0.9390681003584229,0.8072259867419094,1.5594177326568057e-07
+30,0.3484802022576332,0.925,0.9318996415770608,0.8653247422116549,1.4327577638538533e-07
+31,0.3666532337665558,0.875,0.942652329749104,0.8084207298721364,1.3081237509753143e-07

results/adam/vit/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 80,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.9125,
+  "balanced_accuracy": 0.8844086021505376,
+  "precision_macro": 0.8701466781708369,
+  "recall_macro": 0.8844086021505376,
+  "f1_macro": 0.8769501208525599,
+  "precision_weighted": 0.9145168248490079,
+  "recall_weighted": 0.9125,
+  "f1_weighted": 0.9133267413755218,
+  "cohen_kappa": 0.7539543057996485,
+  "quadratic_weighted_kappa": 0.7539543057996485,
+  "mcc": 0.7544204852635336,
+  "auroc": 0.9318996415770608,
+  "auprc": 0.885447451927631,
+  "sensitivity": 0.8333333333333334,
+  "specificity": 0.9354838709677419,
+  "precision_pos": 0.7894736842105263,
+  "f1_pos": 0.8108108108108109,
+  "per_class": {
+    "0": {
+      "precision": 0.9508196721311475,
+      "recall": 0.9354838709677419,
+      "f1-score": 0.943089430894309,
+      "support": 62.0
+    },
+    "1": {
+      "precision": 0.7894736842105263,
+      "recall": 0.8333333333333334,
+      "f1-score": 0.8108108108108109,
+      "support": 18.0
+    },
+    "accuracy": 0.9125,
+    "macro avg": {
+      "precision": 0.8701466781708369,
+      "recall": 0.8844086021505376,
+      "f1-score": 0.8769501208525599,
+      "support": 80.0
+    },
+    "weighted avg": {
+      "precision": 0.9145168248490079,
+      "recall": 0.9125,
+      "f1-score": 0.9133267413755218,
+      "support": 80.0
+    }
+  }
+}

results/adam/vit/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f70c97d3201c9d3359e705ebe55c3068c2b42b85f9d461b377bf407028d9864
+size 1790

results/adam/vit/train.log

@@ -0,0 +1,169 @@
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:154: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = torch.cuda.amp.GradScaler()
+[vit] train=280 val=40 test=80 classes=['0', '1']
+[vit] optim groups=28 layer_decay=0.65 drop_path=0.1 ls=0.1 lr=0.0001
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep0 loss=0.8327 val_acc=0.5000 val_auc=0.3728 score=0.2060
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep1 loss=0.7702 val_acc=0.5250 val_auc=0.7204 score=0.4582
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep2 loss=0.6547 val_acc=0.7000 val_auc=0.7849 score=0.5774
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep3 loss=0.6182 val_acc=0.7500 val_auc=0.8710 score=0.6940
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep4 loss=0.5662 val_acc=0.7500 val_auc=0.8961 score=0.6545
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep5 loss=0.5843 val_acc=0.7250 val_auc=0.8746 score=0.6757
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep6 loss=0.5583 val_acc=0.8250 val_auc=0.8996 score=0.7416
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep7 loss=0.4713 val_acc=0.7750 val_auc=0.9176 score=0.7299
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep8 loss=0.5113 val_acc=0.7750 val_auc=0.9176 score=0.7299
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep9 loss=0.4659 val_acc=0.9000 val_auc=0.9140 score=0.8280
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep10 loss=0.4646 val_acc=0.7500 val_auc=0.9247 score=0.6825
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep11 loss=0.4524 val_acc=0.7750 val_auc=0.9462 score=0.7109
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep12 loss=0.4490 val_acc=0.8000 val_auc=0.9427 score=0.7322
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep13 loss=0.4123 val_acc=0.8250 val_auc=0.9462 score=0.7572
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep14 loss=0.4121 val_acc=0.9000 val_auc=0.9570 score=0.8423
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep15 loss=0.3870 val_acc=0.8000 val_auc=0.9642 score=0.7668
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep16 loss=0.3892 val_acc=0.9000 val_auc=0.9462 score=0.8387
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep17 loss=0.3867 val_acc=0.7750 val_auc=0.9283 score=0.7049
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep18 loss=0.4112 val_acc=0.9250 val_auc=0.9355 score=0.8665
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep19 loss=0.4005 val_acc=0.8750 val_auc=0.9427 score=0.8084
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep20 loss=0.3869 val_acc=0.8500 val_auc=0.9642 score=0.7885
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep21 loss=0.3999 val_acc=0.9250 val_auc=0.9606 score=0.8749
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep22 loss=0.3727 val_acc=0.8750 val_auc=0.9570 score=0.8132
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep23 loss=0.3628 val_acc=0.9000 val_auc=0.9570 score=0.8423
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep24 loss=0.3911 val_acc=0.8250 val_auc=0.9498 score=0.7724
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep25 loss=0.3926 val_acc=0.8750 val_auc=0.9283 score=0.8036
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep26 loss=0.3709 val_acc=0.8750 val_auc=0.9283 score=0.8036
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep27 loss=0.3591 val_acc=0.9000 val_auc=0.9319 score=0.8339
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep28 loss=0.3502 val_acc=0.9000 val_auc=0.9355 score=0.8351
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep29 loss=0.3574 val_acc=0.8750 val_auc=0.9391 score=0.8072
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep30 loss=0.3485 val_acc=0.9250 val_auc=0.9319 score=0.8653
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep31 loss=0.3667 val_acc=0.8750 val_auc=0.9427 score=0.8084
+[vit] early stop at ep31 (best ep21 score=0.8749)
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] DONE best_ep=21 best_val_score=0.8749 -> saved test_pred.npz (80 samples)
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/adam/vit  acc=0.9125  auroc=0.9318996415770608  f1_macro=0.8770  qwk=0.7539543057996485

results/airogs/resnet/log.csv

@@ -0,0 +1,31 @@
+epoch,train_loss,val_acc,val_auc,val_score,lr
+0,0.6808460920284956,0.6759259259259259,0.746721536351166,0.5907443539542305,0.00016452991452991454
+1,0.4906524530588052,0.7814814814814814,0.8890123456790124,0.7427663474923492,0.0003311965811965812
+2,0.33204989918531513,0.8425925925925926,0.93039780521262,0.8192603594033093,0.0004978632478632479
+3,0.2671536950346751,0.8648148148148148,0.9504252400548697,0.8482860311849528,0.0004983526080898481
+4,0.22774717135307115,0.8796296296296297,0.9583333333333334,0.8655903352674731,0.0004933469513590679
+5,0.2098067749578219,0.8148148148148148,0.9573113854595336,0.7986563461528249,0.00048505044456893006
+6,0.19355002456368545,0.8962962962962963,0.9551851851851852,0.8813409456887719,0.00047357528374124746
+7,0.17832664304818863,0.9055555555555556,0.961673525377229,0.8927669952883797,0.00045907665074076113
+8,0.15717215769183943,0.8907407407407407,0.9637722908093278,0.878473874372551,0.0004417506147066312
+9,0.13631468368932986,0.9055555555555556,0.9600891632373114,0.8922466523141829,0.0004218314805536838
+10,0.12444841427107652,0.8981481481481481,0.9627366255144033,0.8857073354541486,0.00039958862040038377
+11,0.1062700557641876,0.8944444444444445,0.9630315500685871,0.8821186109794805,0.0003753228307730364
+12,0.10710507903534633,0.9037037037037037,0.9671604938271605,0.8927289968986077,0.0003493622648487805
+13,0.09017811846943238,0.8962962962962963,0.9655349794238683,0.8847960974760416,0.00032205799474680896
+14,0.07573812407178757,0.9055555555555556,0.9629080932784636,0.8930874928973532,0.00029377926388021573
+15,0.06202182715806442,0.9,0.965281207133059,0.8884266117907099,0.0002649084935722644
+16,0.06268224585801363,0.9018518518518519,0.9617764060356653,0.8890970726384194,0.00023583611146402853
+17,0.051908947073687345,0.9018518518518519,0.9618792866941014,0.8891448352212108,0.00020695527165031925
+18,0.0447326914813274,0.8981481481481481,0.9648628257887517,0.8864347088534984,0.00017865653794500142
+19,0.040650204612085454,0.9055555555555556,0.9656995884773661,0.8941211133673578,0.0001513226021754179
+20,0.038443614943669394,0.9018518518518519,0.9647599451303155,0.8900727103856783,0.00012532310893192616
+21,0.029121919320179865,0.9074074074074074,0.9637448559670783,0.8953155838742269,0.00010100965675893233
+22,0.024985664142056916,0.912962962962963,0.9632510288065844,0.9007084298907317,7.871104338774113e-05
+23,0.02308889929778301,0.9074074074074074,0.9619478737997257,0.8947195544754152,5.872881931128568e-05
+24,0.022915765022238094,0.8962962962962963,0.9614814814814815,0.883444931495246,4.133320983101688e-05
+25,0.018773427710701257,0.9018518518518519,0.9620644718792868,0.8892011773347791,2.675946072326743e-05
+26,0.019186586308746766,0.9074074074074074,0.9629561042524006,0.8950577485564994,1.5204656943687972e-05
+27,0.019509275467732012,0.9055555555555556,0.9618724279835391,0.8928436656223644,6.825057391317641e-06
+28,0.017345010422361203,0.9092592592592592,0.9616666666666667,0.8964788880738178,1.733981775034199e-06
+29,0.018072081849170037,0.9055555555555556,0.9617009602194787,0.8927865097010109,2.781588896993981e-10

results/airogs/resnet/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 1000,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.9,
+  "balanced_accuracy": 0.9,
+  "precision_macro": 0.9001024262211126,
+  "recall_macro": 0.9,
+  "f1_macro": 0.8999935995903738,
+  "precision_weighted": 0.9001024262211126,
+  "recall_weighted": 0.9,
+  "f1_weighted": 0.8999935995903738,
+  "cohen_kappa": 0.8,
+  "quadratic_weighted_kappa": 0.8,
+  "mcc": 0.8001024196649953,
+  "auroc": 0.961382,
+  "auprc": 0.9595785211616094,
+  "sensitivity": 0.908,
+  "specificity": 0.892,
+  "precision_pos": 0.8937007874015748,
+  "f1_pos": 0.9007936507936508,
+  "per_class": {
+    "0": {
+      "precision": 0.9065040650406504,
+      "recall": 0.892,
+      "f1-score": 0.8991935483870968,
+      "support": 500.0
+    },
+    "1": {
+      "precision": 0.8937007874015748,
+      "recall": 0.908,
+      "f1-score": 0.9007936507936508,
+      "support": 500.0
+    },
+    "accuracy": 0.9,
+    "macro avg": {
+      "precision": 0.9001024262211126,
+      "recall": 0.9,
+      "f1-score": 0.8999935995903738,
+      "support": 1000.0
+    },
+    "weighted avg": {
+      "precision": 0.9001024262211126,
+      "recall": 0.9,
+      "f1-score": 0.8999935995903738,
+      "support": 1000.0
+    }
+  }
+}

results/airogs/resnet/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f70e1306ddc5c4e23bbf67aad991d222fe6c77648d37c5e7e7fbec79e162377d
+size 16510

results/airogs/resnet/train.log

@@ -0,0 +1,157 @@
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:114: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = torch.cuda.amp.GradScaler()
+[resnet] train=5000 val=540 test=1000 classes=['0', '1']
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep0 loss=0.6808 val_acc=0.6759 val_auc=0.7467 score=0.5907
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep1 loss=0.4907 val_acc=0.7815 val_auc=0.8890 score=0.7428
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep2 loss=0.3320 val_acc=0.8426 val_auc=0.9304 score=0.8193
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep3 loss=0.2672 val_acc=0.8648 val_auc=0.9504 score=0.8483
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep4 loss=0.2277 val_acc=0.8796 val_auc=0.9583 score=0.8656
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep5 loss=0.2098 val_acc=0.8148 val_auc=0.9573 score=0.7987
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep6 loss=0.1936 val_acc=0.8963 val_auc=0.9552 score=0.8813
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep7 loss=0.1783 val_acc=0.9056 val_auc=0.9617 score=0.8928
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep8 loss=0.1572 val_acc=0.8907 val_auc=0.9638 score=0.8785
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep9 loss=0.1363 val_acc=0.9056 val_auc=0.9601 score=0.8922
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep10 loss=0.1244 val_acc=0.8981 val_auc=0.9627 score=0.8857
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep11 loss=0.1063 val_acc=0.8944 val_auc=0.9630 score=0.8821
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep12 loss=0.1071 val_acc=0.9037 val_auc=0.9672 score=0.8927
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep13 loss=0.0902 val_acc=0.8963 val_auc=0.9655 score=0.8848
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep14 loss=0.0757 val_acc=0.9056 val_auc=0.9629 score=0.8931
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep15 loss=0.0620 val_acc=0.9000 val_auc=0.9653 score=0.8884
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep16 loss=0.0627 val_acc=0.9019 val_auc=0.9618 score=0.8891
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep17 loss=0.0519 val_acc=0.9019 val_auc=0.9619 score=0.8891
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep18 loss=0.0447 val_acc=0.8981 val_auc=0.9649 score=0.8864
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep19 loss=0.0407 val_acc=0.9056 val_auc=0.9657 score=0.8941
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep20 loss=0.0384 val_acc=0.9019 val_auc=0.9648 score=0.8901
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep21 loss=0.0291 val_acc=0.9074 val_auc=0.9637 score=0.8953
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep22 loss=0.0250 val_acc=0.9130 val_auc=0.9633 score=0.9007
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep23 loss=0.0231 val_acc=0.9074 val_auc=0.9619 score=0.8947
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep24 loss=0.0229 val_acc=0.8963 val_auc=0.9615 score=0.8834
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep25 loss=0.0188 val_acc=0.9019 val_auc=0.9621 score=0.8892
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep26 loss=0.0192 val_acc=0.9074 val_auc=0.9630 score=0.8951
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep27 loss=0.0195 val_acc=0.9056 val_auc=0.9619 score=0.8928
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep28 loss=0.0173 val_acc=0.9093 val_auc=0.9617 score=0.8965
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:136: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] ep29 loss=0.0181 val_acc=0.9056 val_auc=0.9617 score=0.8928
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[resnet] DONE best_ep=22 best_val_score=0.9007 -> saved test_pred.npz (1000 samples)
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/airogs/resnet  acc=0.9000  auroc=0.961382  f1_macro=0.9000  qwk=0.8

results/airogs/retfound/log.txt

@@ -0,0 +1,30 @@
+{"train_lr": 3.104967948717949e-05, "train_loss": 0.6869127811529697, "epoch": 0, "n_parameters": 303303682}
+{"train_lr": 9.354967948717946e-05, "train_loss": 0.6098002164791791, "epoch": 1, "n_parameters": 303303682}
+{"train_lr": 0.0001560496794871795, "train_loss": 0.5515275139075059, "epoch": 2, "n_parameters": 303303682}
+{"train_lr": 0.00021854967948717953, "train_loss": 0.5289115004050426, "epoch": 3, "n_parameters": 303303682}
+{"train_lr": 0.0002810496794871795, "train_loss": 0.5009684100365027, "epoch": 4, "n_parameters": 303303682}
+{"train_lr": 0.00034354967948717954, "train_loss": 0.5024133649392005, "epoch": 5, "n_parameters": 303303682}
+{"train_lr": 0.00040604967948717954, "train_loss": 0.49966024053402436, "epoch": 6, "n_parameters": 303303682}
+{"train_lr": 0.0004685496794871794, "train_loss": 0.4900955099325914, "epoch": 7, "n_parameters": 303303682}
+{"train_lr": 0.0005310496794871795, "train_loss": 0.48253656178712845, "epoch": 8, "n_parameters": 303303682}
+{"train_lr": 0.0005935496794871795, "train_loss": 0.4739962577437743, "epoch": 9, "n_parameters": 303303682}
+{"train_lr": 0.0006237308188025447, "train_loss": 0.4772948145102232, "epoch": 10, "n_parameters": 303303682}
+{"train_lr": 0.0006161042181059726, "train_loss": 0.4518326301223192, "epoch": 11, "n_parameters": 303303682}
+{"train_lr": 0.0006010141856144047, "train_loss": 0.46381485901581937, "epoch": 12, "n_parameters": 303303682}
+{"train_lr": 0.0005788322880087305, "train_loss": 0.4572136507202417, "epoch": 13, "n_parameters": 303303682}
+{"train_lr": 0.0005501047172254988, "train_loss": 0.4376392687360446, "epoch": 14, "n_parameters": 303303682}
+{"train_lr": 0.0005155388413987338, "train_loss": 0.4237176972704056, "epoch": 15, "n_parameters": 303303682}
+{"train_lr": 0.0004759857871088555, "train_loss": 0.43037567211267275, "epoch": 16, "n_parameters": 303303682}
+{"train_lr": 0.0004324194818215422, "train_loss": 0.4216204109864357, "epoch": 17, "n_parameters": 303303682}
+{"train_lr": 0.0003859126725612448, "train_loss": 0.42659685015678406, "epoch": 18, "n_parameters": 303303682}
+{"train_lr": 0.0003376105113192029, "train_loss": 0.40269207667845947, "epoch": 19, "n_parameters": 303303682}
+{"train_lr": 0.000288702357610878, "train_loss": 0.4008018556886759, "epoch": 20, "n_parameters": 303303682}
+{"train_lr": 0.00024039249249742462, "train_loss": 0.3933946737685265, "epoch": 21, "n_parameters": 303303682}
+{"train_lr": 0.0001938704651891872, "train_loss": 0.38881010562181473, "epoch": 22, "n_parameters": 303303682}
+{"train_lr": 0.00015028180239625032, "train_loss": 0.381559801980471, "epoch": 23, "n_parameters": 303303682}
+{"train_lr": 0.00011069980165905447, "train_loss": 0.38073944586973923, "epoch": 24, "n_parameters": 303303682}
+{"train_lr": 7.609910320087817e-05, "train_loss": 0.38250331026621354, "epoch": 25, "n_parameters": 303303682}
+{"train_lr": 4.733169105089629e-05, "train_loss": 0.37621724328551537, "epoch": 26, "n_parameters": 303303682}
+{"train_lr": 2.5105914369808592e-05, "train_loss": 0.37985354212996286, "epoch": 27, "n_parameters": 303303682}
+{"train_lr": 9.969045542627247e-06, "train_loss": 0.37229995333995575, "epoch": 28, "n_parameters": 303303682}
+{"train_lr": 2.2938045162649166e-06, "train_loss": 0.36475676097548926, "epoch": 29, "n_parameters": 303303682}

results/airogs/retfound/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 1000,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.908,
+  "balanced_accuracy": 0.908,
+  "precision_macro": 0.9084182202575437,
+  "recall_macro": 0.908,
+  "f1_macro": 0.9079764419691441,
+  "precision_weighted": 0.9084182202575437,
+  "recall_weighted": 0.908,
+  "f1_weighted": 0.9079764419691442,
+  "cohen_kappa": 0.8160000000000001,
+  "quadratic_weighted_kappa": 0.8160000000000001,
+  "mcc": 0.8164181131383057,
+  "auroc": 0.970758,
+  "auprc": 0.9715458663728014,
+  "sensitivity": 0.892,
+  "specificity": 0.924,
+  "precision_pos": 0.9214876033057852,
+  "f1_pos": 0.9065040650406504,
+  "per_class": {
+    "0": {
+      "precision": 0.8953488372093024,
+      "recall": 0.924,
+      "f1-score": 0.9094488188976378,
+      "support": 500.0
+    },
+    "1": {
+      "precision": 0.9214876033057852,
+      "recall": 0.892,
+      "f1-score": 0.9065040650406504,
+      "support": 500.0
+    },
+    "accuracy": 0.908,
+    "macro avg": {
+      "precision": 0.9084182202575437,
+      "recall": 0.908,
+      "f1-score": 0.9079764419691441,
+      "support": 1000.0
+    },
+    "weighted avg": {
+      "precision": 0.9084182202575437,
+      "recall": 0.908,
+      "f1-score": 0.9079764419691442,
+      "support": 1000.0
+    }
+  }
+}

results/airogs/retfound/metrics_test.csv

@@ -0,0 +1,2 @@
+val_loss,accuracy,f1,roc_auc,hamming,jaccard,precision,recall,average_precision,kappa
+0.2516249555628747,0.908,0.9079764419691441,0.9707290000000001,0.092,0.8314656502892113,0.9084182202575437,0.908,0.9703819077045409,0.8160000000000001

results/airogs/retfound/metrics_val.csv

@@ -0,0 +1,31 @@
+val_loss,accuracy,f1,roc_auc,hamming,jaccard,precision,recall,average_precision,kappa
+0.6267830974915448,0.7444444444444445,0.7425764463666387,0.8363786008230453,0.25555555555555554,0.5910378348136234,0.7517518083182639,0.7444444444444445,0.8347590615489223,0.48888888888888893
+0.36065684171283946,0.85,0.8499377313631513,0.923576817558299,0.15,0.739048557792073,0.8505818986246032,0.85,0.923376540042333,0.7
+0.30178015372332406,0.8796296296296297,0.8794473122932831,0.9466872427983539,0.12037037037037036,0.7848647643222898,0.8819401316606632,0.8796296296296297,0.946405607115115,0.7592592592592593
+0.26234125126810637,0.8888888888888888,0.8888873647100783,0.9587654320987654,0.1111111111111111,0.7999977777530862,0.8889102282704127,0.8888888888888888,0.9599995369798717,0.7777777777777778
+0.2907397606793572,0.8814814814814815,0.8813186813186813,0.9527469135802469,0.11851851851851852,0.787846856340007,0.8835862068965518,0.8814814814814815,0.9541414159026794,0.762962962962963
+0.27061493037378087,0.8944444444444445,0.8941798941798942,0.9617318244170097,0.10555555555555556,0.8086538461538462,0.8984287317620651,0.8944444444444444,0.9627803186031139,0.7888888888888889
+0.25615671759142594,0.8925925925925926,0.8925557461406518,0.96340877914952,0.10740740740740741,0.8059658073755818,0.8931318681318681,0.8925925925925926,0.9640757048493638,0.7851851851851852
+0.24167594839544856,0.8907407407407407,0.8905752526969799,0.9655452674897119,0.10925925925925926,0.8027625851099454,0.8931188672214632,0.8907407407407407,0.9666023085851674,0.7814814814814814
+0.25277270420509224,0.9018518518518519,0.9017031931006769,0.9657064471879286,0.09814814814814815,0.821023439101615,0.9042976027822631,0.9018518518518519,0.9664421166860533,0.8037037037037037
+0.26290999637807116,0.8907407407407407,0.8905060572214107,0.9655521262002744,0.10925925925925926,0.8026612615027249,0.8941196817710135,0.8907407407407407,0.9669754289687871,0.7814814814814814
+0.25161123801680174,0.9018518518518519,0.9018353564213465,0.9649142661179699,0.09814814814814815,0.8212229806598408,0.9021221397098187,0.9018518518518519,0.9658003944161854,0.8037037037037037
+0.23271823586786494,0.912962962962963,0.9129483349396846,0.9687551440329218,0.08703703703703704,0.8398409381168002,0.9132407242179242,0.912962962962963,0.9692950646828473,0.825925925925926
+0.22827284958432703,0.9037037037037037,0.903638934263085,0.9681035665294924,0.0962962962962963,0.8242260212180388,0.9047920334507042,0.9037037037037037,0.9682872052596343,0.8074074074074074
+0.23409848265788136,0.912962962962963,0.9129483349396846,0.9672187928669409,0.08703703703703704,0.8398409381168002,0.9132407242179242,0.912962962962963,0.9680735164326912,0.825925925925926
+0.23617559057824752,0.9,0.8999876527966415,0.9679492455418381,0.1,0.8181632653061224,0.900197628458498,0.8999999999999999,0.9691229672185688,0.8
+0.22935323767802296,0.9185185185185185,0.9185084578343006,0.9698319615912209,0.08148148148148149,0.8492991613395109,0.9187252964426877,0.9185185185185185,0.9709637428818148,0.837037037037037
+0.2348311854635968,0.9203703703703704,0.9203241933768199,0.9689231824417011,0.07962962962962963,0.8524137525020605,0.9213471559582571,0.9203703703703703,0.9700474266686755,0.8407407407407408
+0.25106555968523026,0.9074074074074074,0.9074061372583986,0.967681755829904,0.09259259259259259,0.8305065269350984,0.9074297629499561,0.9074074074074074,0.9684479216576163,0.8148148148148149
+0.23531180181924036,0.9203703703703704,0.9202913724507484,0.9707510288065844,0.07962962962962963,0.8523616018845701,0.9220434920328875,0.9203703703703703,0.9716494849123236,0.8407407407407408
+0.21684428611222437,0.9148148148148149,0.9148148148148149,0.9712791495198903,0.08518518518518518,0.8430034129692833,0.9148148148148149,0.9148148148148149,0.9723191297072907,0.8296296296296296
+0.2322498717728783,0.9111111111111111,0.9111098917680627,0.9698285322359397,0.08888888888888889,0.8367328049979754,0.9111336698858647,0.9111111111111112,0.9704452446619843,0.8222222222222222
+0.23778783990179791,0.9166666666666666,0.9165404469722729,0.9706310013717421,0.08333333333333333,0.8459553402148725,0.9192025835299962,0.9166666666666667,0.9709067553336702,0.8333333333333334
+0.2386385027100058,0.9185185185185185,0.9185084578343006,0.9701268861454047,0.08148148148148149,0.8492991613395109,0.9187252964426877,0.9185185185185185,0.970491594177331,0.837037037037037
+0.23999610104981592,0.9222222222222223,0.9221699084432609,0.9707338820301783,0.07777777777777778,0.8555865393704509,0.9233604753521127,0.9222222222222223,0.9711381442387186,0.8444444444444444
+0.23967866029809504,0.9203703703703704,0.9203088803088804,0.9703326474622771,0.07962962962962963,0.8523894201328371,0.9216718266253869,0.9203703703703704,0.9701346387665579,0.8407407407407408
+0.23302041739225388,0.9166666666666666,0.9166663808860798,0.9717661179698217,0.08333333333333333,0.846153396605732,0.9166723823372063,0.9166666666666667,0.9719504025999759,0.8333333333333334
+0.23026803880929947,0.924074074074074,0.9240529776789849,0.9718792866941015,0.07592592592592592,0.8588301528979495,0.9245457916203189,0.924074074074074,0.9721254668793979,0.8481481481481481
+0.22829091548919678,0.9277777777777778,0.927757710475132,0.9717832647462278,0.07222222222222222,0.8652524167561761,0.9282536151279199,0.9277777777777778,0.9718737444256429,0.8555555555555556
+0.22767887702759573,0.9277777777777778,0.927757710475132,0.971786694101509,0.07222222222222222,0.8652524167561761,0.9282536151279199,0.9277777777777778,0.971843318291873,0.8555555555555556
+0.2282544427058276,0.9277777777777778,0.927757710475132,0.9718518518518517,0.07222222222222222,0.8652524167561761,0.9282536151279199,0.9277777777777778,0.9719820208335395,0.8555555555555556

results/airogs/retfound/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:465d825381cbf129f91200b2a0d6f9cd0c6592b691865e383f9f66e830b2ab0c
+size 12510

results/airogs/retfound/train.log

@@ -0,0 +1,716 @@
+| distributed init (rank 0): env://, gpu 0
+[rank0]:[W615 13:52:23.093345041 ProcessGroupNCCL.cpp:4115] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device,or call init_process_group() with a device_id.
+[13:52:24.582098] job dir: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound
+[13:52:24.582378] Namespace(batch_size=32,
+epochs=30,
+accum_iter=1,
+model='RETFound_mae',
+model_arch='retfound_mae',
+input_size=224,
+drop_path=0.2,
+global_pool=True,
+clip_grad=None,
+weight_decay=0.05,
+lr=None,
+blr=0.005,
+layer_decay=0.65,
+min_lr=1e-06,
+warmup_epochs=10,
+color_jitter=None,
+aa='rand-m9-mstd0.5-inc1',
+smoothing=0.1,
+reprob=0.25,
+remode='pixel',
+recount=1,
+resplit=False,
+mixup=0.0,
+cutmix=0.0,
+cutmix_minmax=None,
+mixup_prob=1.0,
+mixup_switch_prob=0.5,
+mixup_mode='batch',
+finetune='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth',
+task='retfound',
+adaptation='finetune',
+data_path='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Dataset/Glaucoma/eyepacs-airogs-light',
+nb_classes=2,
+output_dir='/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/airogs',
+log_dir='./output_logs',
+dataratio='1.0',
+stratified=False,
+device='cuda',
+seed=0,
+resume='',
+start_epoch=0,
+eval=False,
+dist_eval=False,
+num_workers=10,
+pin_mem=True,
+world_size=1,
+local_rank=-1,
+dist_on_itp=False,
+dist_url='env://',
+savemodel=True,
+norm='IMAGENET',
+enhance=False,
+datasets_seed=2026,
+rank=0,
+gpu=0,
+distributed=True,
+dist_backend='nccl')
+[13:52:31.637533] Preparing to load pre-trained weights: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth
+[13:52:36.886581] Loaded pre-trained checkpoint from: /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/RETFound/RETFound_mae_natureCFP/RETFound_mae_natureCFP.pth
+[13:52:38.245461] Sampler_train = <torch.utils.data.distributed.DistributedSampler object at 0x7f889f2296d0>
+[13:52:38.288706] len of train_set: 4992
+[13:52:38.960732] [Adaptation] Full fine-tuning: training all parameters.
+[13:52:38.962191] number of trainable params (M): 303.30
+[13:52:38.962291] base lr: 5.00e-03
+[13:52:38.962369] actual lr: 6.25e-04
+[13:52:38.962438] accumulate grad iterations: 1
+[13:52:38.962508] effective batch size: 32
+[13:52:38.966035] criterion = CrossEntropyLoss()
+[13:52:38.966141] Start training for 30 epochs
+[13:52:38.968658] log_dir: ./output_logs/retfound
+[13:52:41.395490] Epoch: [0]  [  0/156]  eta: 0:06:18  lr: 0.000000  loss: 0.6929 (0.6929)  time: 2.4232  data: 1.0332  max mem: 7340
+[13:52:44.363108] Epoch: [0]  [ 20/156]  eta: 0:00:34  lr: 0.000008  loss: 0.6930 (0.6929)  time: 0.1483  data: 0.0002  max mem: 9669
+[13:52:47.214439] Epoch: [0]  [ 40/156]  eta: 0:00:23  lr: 0.000016  loss: 0.6920 (0.6928)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:52:50.063763] Epoch: [0]  [ 60/156]  eta: 0:00:17  lr: 0.000024  loss: 0.6920 (0.6928)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:52:52.898232] Epoch: [0]  [ 80/156]  eta: 0:00:13  lr: 0.000032  loss: 0.6909 (0.6920)  time: 0.1417  data: 0.0002  max mem: 9669
+[13:52:55.387001] Epoch: [0]  [100/156]  eta: 0:00:09  lr: 0.000040  loss: 0.6899 (0.6917)  time: 0.1244  data: 0.0002  max mem: 9669
+[13:52:58.232687] Epoch: [0]  [120/156]  eta: 0:00:05  lr: 0.000048  loss: 0.6860 (0.6908)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:53:01.079729] Epoch: [0]  [140/156]  eta: 0:00:02  lr: 0.000056  loss: 0.6767 (0.6888)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:53:03.216255] Epoch: [0]  [155/156]  eta: 0:00:00  lr: 0.000062  loss: 0.6643 (0.6869)  time: 0.1426  data: 0.0001  max mem: 9669
+[13:53:03.363542] Epoch: [0] Total time: 0:00:24 (0.1564 s / it)
+[13:53:03.372222] Averaged stats: lr: 0.000062  loss: 0.6643 (0.6869)
+[13:53:04.338277] val:  [ 0/17]  eta: 0:00:16  loss: 0.6420 (0.6420)  time: 0.9530  data: 0.9048  max mem: 9669
+[13:53:04.689602] val:  [10/17]  eta: 0:00:00  loss: 0.6479 (0.6470)  time: 0.1185  data: 0.0827  max mem: 9669
+[13:53:04.974467] val:  [16/17]  eta: 0:00:00  loss: 0.6328 (0.6268)  time: 0.0934  data: 0.0536  max mem: 9669
+[13:53:05.083131] val: Total time: 0:00:01 (0.0999 s / it)
+[13:53:05.102235] val loss: 0.6267830974915448
+[13:53:05.102488] Accuracy: 0.7444, F1 Score: 0.7426, ROC AUC: 0.8364, Hamming Loss: 0.2556,
+ Jaccard Score: 0.5910, Precision: 0.7518, Recall: 0.7444,
+ Average Precision: 0.8348, Kappa: 0.4889, Score: 0.6893
+[13:53:06.912673] Best epoch = 0, Best score = 0.6893
+[13:53:06.990615] log_dir: ./output_logs/retfound
+[13:53:08.122482] Epoch: [1]  [  0/156]  eta: 0:02:56  lr: 0.000063  loss: 0.6670 (0.6670)  time: 1.1309  data: 0.9324  max mem: 9669
+[13:53:10.974458] Epoch: [1]  [ 20/156]  eta: 0:00:25  lr: 0.000071  loss: 0.6449 (0.6479)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:53:13.811828] Epoch: [1]  [ 40/156]  eta: 0:00:19  lr: 0.000079  loss: 0.6459 (0.6417)  time: 0.1418  data: 0.0001  max mem: 9669
+[13:53:16.656392] Epoch: [1]  [ 60/156]  eta: 0:00:15  lr: 0.000087  loss: 0.6346 (0.6419)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:53:19.504205] Epoch: [1]  [ 80/156]  eta: 0:00:11  lr: 0.000095  loss: 0.6075 (0.6338)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:53:22.362911] Epoch: [1]  [100/156]  eta: 0:00:08  lr: 0.000103  loss: 0.5754 (0.6276)  time: 0.1429  data: 0.0002  max mem: 9669
+[13:53:25.215046] Epoch: [1]  [120/156]  eta: 0:00:05  lr: 0.000111  loss: 0.5869 (0.6222)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:53:28.060807] Epoch: [1]  [140/156]  eta: 0:00:02  lr: 0.000119  loss: 0.5824 (0.6155)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:53:30.192188] Epoch: [1]  [155/156]  eta: 0:00:00  lr: 0.000125  loss: 0.5344 (0.6098)  time: 0.1421  data: 0.0001  max mem: 9669
+[13:53:30.321490] Epoch: [1] Total time: 0:00:23 (0.1496 s / it)
+[13:53:30.331294] Averaged stats: lr: 0.000125  loss: 0.5344 (0.6098)
+[13:53:31.278994] val:  [ 0/17]  eta: 0:00:15  loss: 0.2565 (0.2565)  time: 0.9393  data: 0.9057  max mem: 9669
+[13:53:31.625790] val:  [10/17]  eta: 0:00:00  loss: 0.3011 (0.3592)  time: 0.1169  data: 0.0825  max mem: 9669
+[13:53:31.820467] val:  [16/17]  eta: 0:00:00  loss: 0.3327 (0.3607)  time: 0.0870  data: 0.0534  max mem: 9669
+[13:53:31.945759] val: Total time: 0:00:01 (0.0945 s / it)
+[13:53:31.961685] val loss: 0.36065684171283946
+[13:53:31.962004] Accuracy: 0.8500, F1 Score: 0.8499, ROC AUC: 0.9236, Hamming Loss: 0.1500,
+ Jaccard Score: 0.7390, Precision: 0.8506, Recall: 0.8500,
+ Average Precision: 0.9234, Kappa: 0.7000, Score: 0.8245
+[13:53:33.782439] Best epoch = 1, Best score = 0.8245
+[13:53:33.858897] log_dir: ./output_logs/retfound
+[13:53:35.052659] Epoch: [2]  [  0/156]  eta: 0:03:06  lr: 0.000125  loss: 0.5732 (0.5732)  time: 1.1927  data: 1.0531  max mem: 9669
+[13:53:37.907418] Epoch: [2]  [ 20/156]  eta: 0:00:26  lr: 0.000133  loss: 0.5497 (0.5636)  time: 0.1427  data: 0.0002  max mem: 9669
+[13:53:40.759191] Epoch: [2]  [ 40/156]  eta: 0:00:19  lr: 0.000141  loss: 0.5511 (0.5642)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:53:43.599907] Epoch: [2]  [ 60/156]  eta: 0:00:15  lr: 0.000149  loss: 0.5691 (0.5663)  time: 0.1420  data: 0.0002  max mem: 9669
+[13:53:46.445910] Epoch: [2]  [ 80/156]  eta: 0:00:11  lr: 0.000157  loss: 0.5262 (0.5577)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:53:49.305334] Epoch: [2]  [100/156]  eta: 0:00:08  lr: 0.000165  loss: 0.5398 (0.5551)  time: 0.1429  data: 0.0002  max mem: 9669
+[13:53:52.148579] Epoch: [2]  [120/156]  eta: 0:00:05  lr: 0.000173  loss: 0.5339 (0.5563)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:53:54.994128] Epoch: [2]  [140/156]  eta: 0:00:02  lr: 0.000181  loss: 0.5275 (0.5525)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:53:57.128410] Epoch: [2]  [155/156]  eta: 0:00:00  lr: 0.000187  loss: 0.5123 (0.5515)  time: 0.1425  data: 0.0001  max mem: 9669
+[13:53:57.245475] Epoch: [2] Total time: 0:00:23 (0.1499 s / it)
+[13:53:57.254263] Averaged stats: lr: 0.000187  loss: 0.5123 (0.5515)
+[13:53:58.278272] val:  [ 0/17]  eta: 0:00:17  loss: 0.1746 (0.1746)  time: 1.0076  data: 0.9732  max mem: 9669
+[13:53:58.626204] val:  [10/17]  eta: 0:00:00  loss: 0.2856 (0.2840)  time: 0.1232  data: 0.0886  max mem: 9669
+[13:53:58.831212] val:  [16/17]  eta: 0:00:00  loss: 0.3181 (0.3018)  time: 0.0917  data: 0.0574  max mem: 9669
+[13:53:58.954388] val: Total time: 0:00:01 (0.0991 s / it)
+[13:53:58.977600] val loss: 0.30178015372332406
+[13:53:58.977812] Accuracy: 0.8796, F1 Score: 0.8794, ROC AUC: 0.9467, Hamming Loss: 0.1204,
+ Jaccard Score: 0.7849, Precision: 0.8819, Recall: 0.8796,
+ Average Precision: 0.9464, Kappa: 0.7593, Score: 0.8618
+[13:54:00.882868] Best epoch = 2, Best score = 0.8618
+[13:54:00.954549] log_dir: ./output_logs/retfound
+[13:54:02.025738] Epoch: [3]  [  0/156]  eta: 0:02:46  lr: 0.000188  loss: 0.5378 (0.5378)  time: 1.0702  data: 0.9241  max mem: 9669
+[13:54:04.875556] Epoch: [3]  [ 20/156]  eta: 0:00:25  lr: 0.000196  loss: 0.5189 (0.5252)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:54:07.724782] Epoch: [3]  [ 40/156]  eta: 0:00:19  lr: 0.000204  loss: 0.4852 (0.5192)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:54:10.581770] Epoch: [3]  [ 60/156]  eta: 0:00:15  lr: 0.000212  loss: 0.5251 (0.5225)  time: 0.1428  data: 0.0002  max mem: 9669
+[13:54:13.432572] Epoch: [3]  [ 80/156]  eta: 0:00:11  lr: 0.000220  loss: 0.5494 (0.5297)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:54:16.281449] Epoch: [3]  [100/156]  eta: 0:00:08  lr: 0.000228  loss: 0.4800 (0.5266)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:54:19.131196] Epoch: [3]  [120/156]  eta: 0:00:05  lr: 0.000236  loss: 0.4913 (0.5219)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:54:21.976702] Epoch: [3]  [140/156]  eta: 0:00:02  lr: 0.000244  loss: 0.5545 (0.5290)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:54:24.116737] Epoch: [3]  [155/156]  eta: 0:00:00  lr: 0.000250  loss: 0.5226 (0.5289)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:54:24.237422] Epoch: [3] Total time: 0:00:23 (0.1492 s / it)
+[13:54:24.245365] Averaged stats: lr: 0.000250  loss: 0.5226 (0.5289)
+[13:54:25.272937] val:  [ 0/17]  eta: 0:00:17  loss: 0.1953 (0.1953)  time: 1.0139  data: 0.9788  max mem: 9669
+[13:54:25.618721] val:  [10/17]  eta: 0:00:00  loss: 0.2957 (0.2924)  time: 0.1235  data: 0.0891  max mem: 9669
+[13:54:25.822904] val:  [16/17]  eta: 0:00:00  loss: 0.2227 (0.2623)  time: 0.0919  data: 0.0577  max mem: 9669
+[13:54:25.945494] val: Total time: 0:00:01 (0.0993 s / it)
+[13:54:25.969642] val loss: 0.26234125126810637
+[13:54:25.969964] Accuracy: 0.8889, F1 Score: 0.8889, ROC AUC: 0.9588, Hamming Loss: 0.1111,
+ Jaccard Score: 0.8000, Precision: 0.8889, Recall: 0.8889,
+ Average Precision: 0.9600, Kappa: 0.7778, Score: 0.8751
+[13:54:27.800378] Best epoch = 3, Best score = 0.8751
+[13:54:27.864235] log_dir: ./output_logs/retfound
+[13:54:29.013635] Epoch: [4]  [  0/156]  eta: 0:02:59  lr: 0.000250  loss: 0.5731 (0.5731)  time: 1.1484  data: 1.0049  max mem: 9669
+[13:54:31.859977] Epoch: [4]  [ 20/156]  eta: 0:00:25  lr: 0.000258  loss: 0.5041 (0.5161)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:54:34.707968] Epoch: [4]  [ 40/156]  eta: 0:00:19  lr: 0.000266  loss: 0.5131 (0.5237)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:54:37.550266] Epoch: [4]  [ 60/156]  eta: 0:00:15  lr: 0.000274  loss: 0.5255 (0.5249)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:54:40.392898] Epoch: [4]  [ 80/156]  eta: 0:00:11  lr: 0.000282  loss: 0.4897 (0.5184)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:54:43.238037] Epoch: [4]  [100/156]  eta: 0:00:08  lr: 0.000290  loss: 0.4656 (0.5108)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:54:46.085535] Epoch: [4]  [120/156]  eta: 0:00:05  lr: 0.000298  loss: 0.4011 (0.4958)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:54:48.938586] Epoch: [4]  [140/156]  eta: 0:00:02  lr: 0.000306  loss: 0.5319 (0.5011)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:54:51.070452] Epoch: [4]  [155/156]  eta: 0:00:00  lr: 0.000312  loss: 0.4962 (0.5010)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:54:51.199201] Epoch: [4] Total time: 0:00:23 (0.1496 s / it)
+[13:54:51.208901] Averaged stats: lr: 0.000312  loss: 0.4962 (0.5010)
+[13:54:52.138874] val:  [ 0/17]  eta: 0:00:15  loss: 0.1446 (0.1446)  time: 0.9229  data: 0.8897  max mem: 9669
+[13:54:52.489329] val:  [10/17]  eta: 0:00:00  loss: 0.2819 (0.2813)  time: 0.1157  data: 0.0816  max mem: 9669
+[13:54:52.695387] val:  [16/17]  eta: 0:00:00  loss: 0.2819 (0.2907)  time: 0.0869  data: 0.0529  max mem: 9669
+[13:54:52.823107] val: Total time: 0:00:01 (0.0946 s / it)
+[13:54:52.837677] val loss: 0.2907397606793572
+[13:54:52.837967] Accuracy: 0.8815, F1 Score: 0.8813, ROC AUC: 0.9527, Hamming Loss: 0.1185,
+ Jaccard Score: 0.7878, Precision: 0.8836, Recall: 0.8815,
+ Average Precision: 0.9541, Kappa: 0.7630, Score: 0.8657
+[13:54:52.880181] Best epoch = 3, Best score = 0.8751
+[13:54:53.150498] log_dir: ./output_logs/retfound
+[13:54:54.235572] Epoch: [5]  [  0/156]  eta: 0:02:49  lr: 0.000313  loss: 0.5227 (0.5227)  time: 1.0842  data: 0.9375  max mem: 9669
+[13:54:57.087326] Epoch: [5]  [ 20/156]  eta: 0:00:25  lr: 0.000321  loss: 0.4854 (0.4888)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:54:59.933755] Epoch: [5]  [ 40/156]  eta: 0:00:19  lr: 0.000329  loss: 0.4325 (0.4803)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:55:02.778826] Epoch: [5]  [ 60/156]  eta: 0:00:15  lr: 0.000337  loss: 0.4913 (0.4887)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:55:05.303518] Epoch: [5]  [ 80/156]  eta: 0:00:11  lr: 0.000345  loss: 0.5478 (0.5029)  time: 0.1262  data: 0.0001  max mem: 9669
+[13:55:08.151695] Epoch: [5]  [100/156]  eta: 0:00:08  lr: 0.000353  loss: 0.4967 (0.4999)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:55:11.001042] Epoch: [5]  [120/156]  eta: 0:00:05  lr: 0.000361  loss: 0.5006 (0.5023)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:55:13.849678] Epoch: [5]  [140/156]  eta: 0:00:02  lr: 0.000369  loss: 0.4839 (0.5014)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:55:15.992142] Epoch: [5]  [155/156]  eta: 0:00:00  lr: 0.000375  loss: 0.5141 (0.5024)  time: 0.1425  data: 0.0001  max mem: 9669
+[13:55:16.130040] Epoch: [5] Total time: 0:00:22 (0.1473 s / it)
+[13:55:16.139584] Averaged stats: lr: 0.000375  loss: 0.5141 (0.5024)
+[13:55:16.960636] val:  [ 0/17]  eta: 0:00:13  loss: 0.3622 (0.3622)  time: 0.8045  data: 0.7711  max mem: 9669
+[13:55:17.432604] val:  [10/17]  eta: 0:00:00  loss: 0.3603 (0.3440)  time: 0.1160  data: 0.0815  max mem: 9669
+[13:55:17.635012] val:  [16/17]  eta: 0:00:00  loss: 0.2352 (0.2706)  time: 0.0869  data: 0.0528  max mem: 9669
+[13:55:17.749395] val: Total time: 0:00:01 (0.0938 s / it)
+[13:55:17.763496] val loss: 0.27061493037378087
+[13:55:17.763760] Accuracy: 0.8944, F1 Score: 0.8942, ROC AUC: 0.9617, Hamming Loss: 0.1056,
+ Jaccard Score: 0.8087, Precision: 0.8984, Recall: 0.8944,
+ Average Precision: 0.9628, Kappa: 0.7889, Score: 0.8816
+[13:55:19.751322] Best epoch = 5, Best score = 0.8816
+[13:55:19.809192] log_dir: ./output_logs/retfound
+[13:55:20.924269] Epoch: [6]  [  0/156]  eta: 0:02:53  lr: 0.000375  loss: 0.5117 (0.5117)  time: 1.1141  data: 0.9664  max mem: 9669
+[13:55:23.772400] Epoch: [6]  [ 20/156]  eta: 0:00:25  lr: 0.000383  loss: 0.4926 (0.5112)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:55:26.619109] Epoch: [6]  [ 40/156]  eta: 0:00:19  lr: 0.000391  loss: 0.4482 (0.4945)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:55:29.440913] Epoch: [6]  [ 60/156]  eta: 0:00:15  lr: 0.000399  loss: 0.5284 (0.5101)  time: 0.1410  data: 0.0002  max mem: 9669
+[13:55:32.298061] Epoch: [6]  [ 80/156]  eta: 0:00:11  lr: 0.000407  loss: 0.4940 (0.5119)  time: 0.1428  data: 0.0002  max mem: 9669
+[13:55:35.141464] Epoch: [6]  [100/156]  eta: 0:00:08  lr: 0.000415  loss: 0.4631 (0.5055)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:55:37.985736] Epoch: [6]  [120/156]  eta: 0:00:05  lr: 0.000423  loss: 0.4742 (0.5046)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:55:40.835281] Epoch: [6]  [140/156]  eta: 0:00:02  lr: 0.000431  loss: 0.4733 (0.5021)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:55:42.966908] Epoch: [6]  [155/156]  eta: 0:00:00  lr: 0.000437  loss: 0.4729 (0.4997)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:55:43.104983] Epoch: [6] Total time: 0:00:23 (0.1493 s / it)
+[13:55:43.114456] Averaged stats: lr: 0.000437  loss: 0.4729 (0.4997)
+[13:55:43.885626] val:  [ 0/17]  eta: 0:00:12  loss: 0.3536 (0.3536)  time: 0.7597  data: 0.7251  max mem: 9669
+[13:55:44.252320] val:  [10/17]  eta: 0:00:00  loss: 0.2742 (0.3143)  time: 0.1023  data: 0.0679  max mem: 9669
+[13:55:44.450208] val:  [16/17]  eta: 0:00:00  loss: 0.2401 (0.2562)  time: 0.0778  data: 0.0440  max mem: 9669
+[13:55:44.812462] val: Total time: 0:00:01 (0.0992 s / it)
+[13:55:44.826756] val loss: 0.25615671759142594
+[13:55:44.827058] Accuracy: 0.8926, F1 Score: 0.8926, ROC AUC: 0.9634, Hamming Loss: 0.1074,
+ Jaccard Score: 0.8060, Precision: 0.8931, Recall: 0.8926,
+ Average Precision: 0.9641, Kappa: 0.7852, Score: 0.8804
+[13:55:44.871860] Best epoch = 5, Best score = 0.8816
+[13:55:45.141890] log_dir: ./output_logs/retfound
+[13:55:46.206399] Epoch: [7]  [  0/156]  eta: 0:02:45  lr: 0.000438  loss: 0.5038 (0.5038)  time: 1.0634  data: 0.9182  max mem: 9669
+[13:55:49.058186] Epoch: [7]  [ 20/156]  eta: 0:00:25  lr: 0.000446  loss: 0.5144 (0.5279)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:55:51.906508] Epoch: [7]  [ 40/156]  eta: 0:00:19  lr: 0.000454  loss: 0.4662 (0.5053)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:55:54.759114] Epoch: [7]  [ 60/156]  eta: 0:00:15  lr: 0.000462  loss: 0.5224 (0.5106)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:55:57.613879] Epoch: [7]  [ 80/156]  eta: 0:00:11  lr: 0.000470  loss: 0.4508 (0.5010)  time: 0.1427  data: 0.0001  max mem: 9669
+[13:56:00.469639] Epoch: [7]  [100/156]  eta: 0:00:08  lr: 0.000478  loss: 0.4589 (0.4949)  time: 0.1427  data: 0.0002  max mem: 9669
+[13:56:03.318132] Epoch: [7]  [120/156]  eta: 0:00:05  lr: 0.000486  loss: 0.4859 (0.4906)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:56:06.177076] Epoch: [7]  [140/156]  eta: 0:00:02  lr: 0.000494  loss: 0.4952 (0.4913)  time: 0.1429  data: 0.0002  max mem: 9669
+[13:56:08.315388] Epoch: [7]  [155/156]  eta: 0:00:00  lr: 0.000500  loss: 0.4568 (0.4901)  time: 0.1428  data: 0.0001  max mem: 9669
+[13:56:08.436039] Epoch: [7] Total time: 0:00:23 (0.1493 s / it)
+[13:56:08.444602] Averaged stats: lr: 0.000500  loss: 0.4568 (0.4901)
+[13:56:09.373712] val:  [ 0/17]  eta: 0:00:15  loss: 0.3089 (0.3089)  time: 0.9205  data: 0.8858  max mem: 9669
+[13:56:09.715665] val:  [10/17]  eta: 0:00:00  loss: 0.2991 (0.2929)  time: 0.1147  data: 0.0809  max mem: 9669
+[13:56:09.920766] val:  [16/17]  eta: 0:00:00  loss: 0.2143 (0.2417)  time: 0.0863  data: 0.0524  max mem: 9669
+[13:56:10.057973] val: Total time: 0:00:01 (0.0944 s / it)
+[13:56:10.072173] val loss: 0.24167594839544856
+[13:56:10.072416] Accuracy: 0.8907, F1 Score: 0.8906, ROC AUC: 0.9655, Hamming Loss: 0.1093,
+ Jaccard Score: 0.8028, Precision: 0.8931, Recall: 0.8907,
+ Average Precision: 0.9666, Kappa: 0.7815, Score: 0.8792
+[13:56:10.114218] Best epoch = 5, Best score = 0.8816
+[13:56:10.395372] log_dir: ./output_logs/retfound
+[13:56:11.550437] Epoch: [8]  [  0/156]  eta: 0:03:00  lr: 0.000500  loss: 0.3634 (0.3634)  time: 1.1541  data: 1.0095  max mem: 9669
+[13:56:14.399205] Epoch: [8]  [ 20/156]  eta: 0:00:25  lr: 0.000508  loss: 0.4905 (0.4941)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:56:17.244542] Epoch: [8]  [ 40/156]  eta: 0:00:19  lr: 0.000516  loss: 0.4808 (0.4790)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:56:20.089234] Epoch: [8]  [ 60/156]  eta: 0:00:15  lr: 0.000524  loss: 0.4981 (0.4807)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:56:22.955967] Epoch: [8]  [ 80/156]  eta: 0:00:11  lr: 0.000532  loss: 0.5331 (0.4941)  time: 0.1433  data: 0.0002  max mem: 9669
+[13:56:25.809101] Epoch: [8]  [100/156]  eta: 0:00:08  lr: 0.000540  loss: 0.4574 (0.4899)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:56:28.660286] Epoch: [8]  [120/156]  eta: 0:00:05  lr: 0.000548  loss: 0.4651 (0.4857)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:56:31.515312] Epoch: [8]  [140/156]  eta: 0:00:02  lr: 0.000556  loss: 0.4696 (0.4866)  time: 0.1427  data: 0.0002  max mem: 9669
+[13:56:33.646683] Epoch: [8]  [155/156]  eta: 0:00:00  lr: 0.000562  loss: 0.4585 (0.4825)  time: 0.1421  data: 0.0001  max mem: 9669
+[13:56:33.775728] Epoch: [8] Total time: 0:00:23 (0.1499 s / it)
+[13:56:33.784229] Averaged stats: lr: 0.000562  loss: 0.4585 (0.4825)
+[13:56:34.757027] val:  [ 0/17]  eta: 0:00:16  loss: 0.2019 (0.2019)  time: 0.9608  data: 0.9268  max mem: 9669
+[13:56:35.098065] val:  [10/17]  eta: 0:00:00  loss: 0.2579 (0.3103)  time: 0.1183  data: 0.0844  max mem: 9669
+[13:56:35.303058] val:  [16/17]  eta: 0:00:00  loss: 0.2253 (0.2528)  time: 0.0886  data: 0.0546  max mem: 9669
+[13:56:35.435163] val: Total time: 0:00:01 (0.0965 s / it)
+[13:56:35.449333] val loss: 0.25277270420509224
+[13:56:35.449567] Accuracy: 0.9019, F1 Score: 0.9017, ROC AUC: 0.9657, Hamming Loss: 0.0981,
+ Jaccard Score: 0.8210, Precision: 0.9043, Recall: 0.9019,
+ Average Precision: 0.9664, Kappa: 0.8037, Score: 0.8904
+[13:56:37.274087] Best epoch = 8, Best score = 0.8904
+[13:56:37.338324] log_dir: ./output_logs/retfound
+[13:56:38.450234] Epoch: [9]  [  0/156]  eta: 0:02:53  lr: 0.000562  loss: 0.4432 (0.4432)  time: 1.1108  data: 0.9656  max mem: 9669
+[13:56:41.295205] Epoch: [9]  [ 20/156]  eta: 0:00:25  lr: 0.000571  loss: 0.4576 (0.4668)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:56:44.148284] Epoch: [9]  [ 40/156]  eta: 0:00:19  lr: 0.000579  loss: 0.4368 (0.4555)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:56:46.992058] Epoch: [9]  [ 60/156]  eta: 0:00:15  lr: 0.000587  loss: 0.4287 (0.4578)  time: 0.1421  data: 0.0001  max mem: 9669
+[13:56:49.826542] Epoch: [9]  [ 80/156]  eta: 0:00:11  lr: 0.000595  loss: 0.4820 (0.4673)  time: 0.1417  data: 0.0002  max mem: 9669
+[13:56:52.668918] Epoch: [9]  [100/156]  eta: 0:00:08  lr: 0.000603  loss: 0.4720 (0.4682)  time: 0.1421  data: 0.0001  max mem: 9669
+[13:56:55.517904] Epoch: [9]  [120/156]  eta: 0:00:05  lr: 0.000611  loss: 0.5268 (0.4759)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:56:58.362021] Epoch: [9]  [140/156]  eta: 0:00:02  lr: 0.000619  loss: 0.4779 (0.4769)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:57:00.495031] Epoch: [9]  [155/156]  eta: 0:00:00  lr: 0.000625  loss: 0.4439 (0.4740)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:57:00.618510] Epoch: [9] Total time: 0:00:23 (0.1492 s / it)
+[13:57:00.628174] Averaged stats: lr: 0.000625  loss: 0.4439 (0.4740)
+[13:57:01.591549] val:  [ 0/17]  eta: 0:00:16  loss: 0.3370 (0.3370)  time: 0.9456  data: 0.9096  max mem: 9669
+[13:57:01.939226] val:  [10/17]  eta: 0:00:00  loss: 0.2942 (0.3432)  time: 0.1175  data: 0.0828  max mem: 9669
+[13:57:02.144639] val:  [16/17]  eta: 0:00:00  loss: 0.2737 (0.2629)  time: 0.0881  data: 0.0537  max mem: 9669
+[13:57:02.270073] val: Total time: 0:00:01 (0.0956 s / it)
+[13:57:02.284546] val loss: 0.26290999637807116
+[13:57:02.284829] Accuracy: 0.8907, F1 Score: 0.8905, ROC AUC: 0.9656, Hamming Loss: 0.1093,
+ Jaccard Score: 0.8027, Precision: 0.8941, Recall: 0.8907,
+ Average Precision: 0.9670, Kappa: 0.7815, Score: 0.8792
+[13:57:02.326756] Best epoch = 8, Best score = 0.8904
+[13:57:02.615162] log_dir: ./output_logs/retfound
+[13:57:03.661265] Epoch: [10]  [  0/156]  eta: 0:02:43  lr: 0.000625  loss: 0.4573 (0.4573)  time: 1.0451  data: 0.8999  max mem: 9669
+[13:57:06.508360] Epoch: [10]  [ 20/156]  eta: 0:00:25  lr: 0.000625  loss: 0.4759 (0.5153)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:57:09.364293] Epoch: [10]  [ 40/156]  eta: 0:00:19  lr: 0.000625  loss: 0.4536 (0.4997)  time: 0.1428  data: 0.0002  max mem: 9669
+[13:57:12.212589] Epoch: [10]  [ 60/156]  eta: 0:00:15  lr: 0.000624  loss: 0.4371 (0.4820)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:57:15.060206] Epoch: [10]  [ 80/156]  eta: 0:00:11  lr: 0.000624  loss: 0.4576 (0.4800)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:57:17.603254] Epoch: [10]  [100/156]  eta: 0:00:08  lr: 0.000623  loss: 0.4665 (0.4773)  time: 0.1271  data: 0.0002  max mem: 9669
+[13:57:20.428375] Epoch: [10]  [120/156]  eta: 0:00:05  lr: 0.000623  loss: 0.4646 (0.4758)  time: 0.1412  data: 0.0002  max mem: 9669
+[13:57:23.265496] Epoch: [10]  [140/156]  eta: 0:00:02  lr: 0.000622  loss: 0.4452 (0.4737)  time: 0.1418  data: 0.0002  max mem: 9669
+[13:57:25.401664] Epoch: [10]  [155/156]  eta: 0:00:00  lr: 0.000621  loss: 0.4679 (0.4773)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:57:25.527935] Epoch: [10] Total time: 0:00:22 (0.1469 s / it)
+[13:57:25.536317] Averaged stats: lr: 0.000621  loss: 0.4679 (0.4773)
+[13:57:26.531190] val:  [ 0/17]  eta: 0:00:16  loss: 0.1854 (0.1854)  time: 0.9782  data: 0.9439  max mem: 9669
+[13:57:26.871659] val:  [10/17]  eta: 0:00:00  loss: 0.2088 (0.2325)  time: 0.1198  data: 0.0859  max mem: 9669
+[13:57:27.076608] val:  [16/17]  eta: 0:00:00  loss: 0.2786 (0.2516)  time: 0.0895  data: 0.0557  max mem: 9669
+[13:57:27.203034] val: Total time: 0:00:01 (0.0971 s / it)
+[13:57:27.217059] val loss: 0.25161123801680174
+[13:57:27.217317] Accuracy: 0.9019, F1 Score: 0.9018, ROC AUC: 0.9649, Hamming Loss: 0.0981,
+ Jaccard Score: 0.8212, Precision: 0.9021, Recall: 0.9019,
+ Average Precision: 0.9658, Kappa: 0.8037, Score: 0.8902
+[13:57:27.256969] Best epoch = 8, Best score = 0.8904
+[13:57:27.596756] log_dir: ./output_logs/retfound
+[13:57:28.743864] Epoch: [11]  [  0/156]  eta: 0:02:58  lr: 0.000621  loss: 0.4665 (0.4665)  time: 1.1458  data: 0.9998  max mem: 9669
+[13:57:31.595558] Epoch: [11]  [ 20/156]  eta: 0:00:25  lr: 0.000620  loss: 0.4409 (0.4736)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:57:34.437466] Epoch: [11]  [ 40/156]  eta: 0:00:19  lr: 0.000619  loss: 0.4341 (0.4579)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:57:37.289257] Epoch: [11]  [ 60/156]  eta: 0:00:15  lr: 0.000618  loss: 0.4596 (0.4576)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:57:40.129571] Epoch: [11]  [ 80/156]  eta: 0:00:11  lr: 0.000616  loss: 0.4355 (0.4541)  time: 0.1420  data: 0.0002  max mem: 9669
+[13:57:42.975441] Epoch: [11]  [100/156]  eta: 0:00:08  lr: 0.000615  loss: 0.4711 (0.4532)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:57:45.824675] Epoch: [11]  [120/156]  eta: 0:00:05  lr: 0.000613  loss: 0.4498 (0.4599)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:57:48.671048] Epoch: [11]  [140/156]  eta: 0:00:02  lr: 0.000611  loss: 0.3991 (0.4530)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:57:50.806850] Epoch: [11]  [155/156]  eta: 0:00:00  lr: 0.000610  loss: 0.3987 (0.4518)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:57:50.931587] Epoch: [11] Total time: 0:00:23 (0.1496 s / it)
+[13:57:50.939546] Averaged stats: lr: 0.000610  loss: 0.3987 (0.4518)
+[13:57:51.845585] val:  [ 0/17]  eta: 0:00:15  loss: 0.2339 (0.2339)  time: 0.8892  data: 0.8541  max mem: 9669
+[13:57:52.320911] val:  [10/17]  eta: 0:00:00  loss: 0.2979 (0.2607)  time: 0.1240  data: 0.0894  max mem: 9669
+[13:57:52.520232] val:  [16/17]  eta: 0:00:00  loss: 0.2054 (0.2327)  time: 0.0919  data: 0.0579  max mem: 9669
+[13:57:52.640729] val: Total time: 0:00:01 (0.0991 s / it)
+[13:57:52.655238] val loss: 0.23271823586786494
+[13:57:52.655435] Accuracy: 0.9130, F1 Score: 0.9129, ROC AUC: 0.9688, Hamming Loss: 0.0870,
+ Jaccard Score: 0.8398, Precision: 0.9132, Recall: 0.9130,
+ Average Precision: 0.9693, Kappa: 0.8259, Score: 0.9025
+[13:57:54.498736] Best epoch = 11, Best score = 0.9025
+[13:57:54.572853] log_dir: ./output_logs/retfound
+[13:57:55.711471] Epoch: [12]  [  0/156]  eta: 0:02:57  lr: 0.000610  loss: 0.4175 (0.4175)  time: 1.1374  data: 0.9923  max mem: 9669
+[13:57:58.558032] Epoch: [12]  [ 20/156]  eta: 0:00:25  lr: 0.000608  loss: 0.4299 (0.4135)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:58:01.407707] Epoch: [12]  [ 40/156]  eta: 0:00:19  lr: 0.000606  loss: 0.4877 (0.4603)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:58:04.254639] Epoch: [12]  [ 60/156]  eta: 0:00:15  lr: 0.000603  loss: 0.4499 (0.4613)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:58:07.102371] Epoch: [12]  [ 80/156]  eta: 0:00:11  lr: 0.000601  loss: 0.4070 (0.4535)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:58:09.955830] Epoch: [12]  [100/156]  eta: 0:00:08  lr: 0.000599  loss: 0.4636 (0.4610)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:58:12.803260] Epoch: [12]  [120/156]  eta: 0:00:05  lr: 0.000596  loss: 0.4434 (0.4627)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:58:15.647844] Epoch: [12]  [140/156]  eta: 0:00:02  lr: 0.000593  loss: 0.4253 (0.4616)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:58:17.785967] Epoch: [12]  [155/156]  eta: 0:00:00  lr: 0.000591  loss: 0.4490 (0.4638)  time: 0.1423  data: 0.0001  max mem: 9669
+[13:58:17.924603] Epoch: [12] Total time: 0:00:23 (0.1497 s / it)
+[13:58:17.933376] Averaged stats: lr: 0.000591  loss: 0.4490 (0.4638)
+[13:58:18.953285] val:  [ 0/17]  eta: 0:00:17  loss: 0.2895 (0.2895)  time: 1.0063  data: 0.9696  max mem: 9669
+[13:58:19.297278] val:  [10/17]  eta: 0:00:00  loss: 0.2840 (0.2778)  time: 0.1227  data: 0.0883  max mem: 9669
+[13:58:19.501653] val:  [16/17]  eta: 0:00:00  loss: 0.1948 (0.2283)  time: 0.0914  data: 0.0572  max mem: 9669
+[13:58:19.629774] val: Total time: 0:00:01 (0.0991 s / it)
+[13:58:19.644571] val loss: 0.22827284958432703
+[13:58:19.644827] Accuracy: 0.9037, F1 Score: 0.9036, ROC AUC: 0.9681, Hamming Loss: 0.0963,
+ Jaccard Score: 0.8242, Precision: 0.9048, Recall: 0.9037,
+ Average Precision: 0.9683, Kappa: 0.8074, Score: 0.8930
+[13:58:19.681919] Best epoch = 11, Best score = 0.9025
+[13:58:19.965673] log_dir: ./output_logs/retfound
+[13:58:21.094196] Epoch: [13]  [  0/156]  eta: 0:02:55  lr: 0.000591  loss: 0.3888 (0.3888)  time: 1.1273  data: 0.9827  max mem: 9669
+[13:58:23.942487] Epoch: [13]  [ 20/156]  eta: 0:00:25  lr: 0.000588  loss: 0.4828 (0.4831)  time: 0.1424  data: 0.0001  max mem: 9669
+[13:58:26.787334] Epoch: [13]  [ 40/156]  eta: 0:00:19  lr: 0.000585  loss: 0.4745 (0.4803)  time: 0.1422  data: 0.0001  max mem: 9669
+[13:58:29.636195] Epoch: [13]  [ 60/156]  eta: 0:00:15  lr: 0.000582  loss: 0.4478 (0.4713)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:58:32.486502] Epoch: [13]  [ 80/156]  eta: 0:00:11  lr: 0.000579  loss: 0.4615 (0.4669)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:58:35.337158] Epoch: [13]  [100/156]  eta: 0:00:08  lr: 0.000575  loss: 0.4323 (0.4634)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:58:38.183163] Epoch: [13]  [120/156]  eta: 0:00:05  lr: 0.000572  loss: 0.3835 (0.4549)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:58:41.035947] Epoch: [13]  [140/156]  eta: 0:00:02  lr: 0.000568  loss: 0.4419 (0.4550)  time: 0.1426  data: 0.0002  max mem: 9669
+[13:58:43.170754] Epoch: [13]  [155/156]  eta: 0:00:00  lr: 0.000566  loss: 0.4427 (0.4572)  time: 0.1426  data: 0.0001  max mem: 9669
+[13:58:43.300912] Epoch: [13] Total time: 0:00:23 (0.1496 s / it)
+[13:58:43.309737] Averaged stats: lr: 0.000566  loss: 0.4427 (0.4572)
+[13:58:44.302035] val:  [ 0/17]  eta: 0:00:16  loss: 0.2312 (0.2312)  time: 0.9759  data: 0.9421  max mem: 9669
+[13:58:44.649432] val:  [10/17]  eta: 0:00:00  loss: 0.2646 (0.2644)  time: 0.1202  data: 0.0858  max mem: 9669
+[13:58:44.854202] val:  [16/17]  eta: 0:00:00  loss: 0.2106 (0.2341)  time: 0.0898  data: 0.0556  max mem: 9669
+[13:58:44.983550] val: Total time: 0:00:01 (0.0975 s / it)
+[13:58:44.997890] val loss: 0.23409848265788136
+[13:58:44.998218] Accuracy: 0.9130, F1 Score: 0.9129, ROC AUC: 0.9672, Hamming Loss: 0.0870,
+ Jaccard Score: 0.8398, Precision: 0.9132, Recall: 0.9130,
+ Average Precision: 0.9681, Kappa: 0.8259, Score: 0.9020
+[13:58:45.038269] Best epoch = 11, Best score = 0.9025
+[13:58:45.342678] log_dir: ./output_logs/retfound
+[13:58:46.534879] Epoch: [14]  [  0/156]  eta: 0:03:05  lr: 0.000565  loss: 0.4599 (0.4599)  time: 1.1909  data: 1.0462  max mem: 9669
+[13:58:49.373496] Epoch: [14]  [ 20/156]  eta: 0:00:26  lr: 0.000562  loss: 0.4364 (0.4398)  time: 0.1419  data: 0.0001  max mem: 9669
+[13:58:52.219422] Epoch: [14]  [ 40/156]  eta: 0:00:19  lr: 0.000558  loss: 0.4232 (0.4337)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:58:55.071447] Epoch: [14]  [ 60/156]  eta: 0:00:15  lr: 0.000554  loss: 0.4154 (0.4344)  time: 0.1426  data: 0.0001  max mem: 9669
+[13:58:57.916497] Epoch: [14]  [ 80/156]  eta: 0:00:11  lr: 0.000550  loss: 0.4435 (0.4407)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:59:00.766092] Epoch: [14]  [100/156]  eta: 0:00:08  lr: 0.000546  loss: 0.4026 (0.4389)  time: 0.1424  data: 0.0003  max mem: 9669
+[13:59:03.610302] Epoch: [14]  [120/156]  eta: 0:00:05  lr: 0.000541  loss: 0.4247 (0.4368)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:59:06.463103] Epoch: [14]  [140/156]  eta: 0:00:02  lr: 0.000537  loss: 0.3900 (0.4370)  time: 0.1426  data: 0.0001  max mem: 9669
+[13:59:08.602426] Epoch: [14]  [155/156]  eta: 0:00:00  lr: 0.000534  loss: 0.4216 (0.4376)  time: 0.1427  data: 0.0001  max mem: 9669
+[13:59:08.742351] Epoch: [14] Total time: 0:00:23 (0.1500 s / it)
+[13:59:08.750941] Averaged stats: lr: 0.000534  loss: 0.4216 (0.4376)
+[13:59:09.733719] val:  [ 0/17]  eta: 0:00:16  loss: 0.2281 (0.2281)  time: 0.9659  data: 0.9308  max mem: 9669
+[13:59:10.080707] val:  [10/17]  eta: 0:00:00  loss: 0.2639 (0.2634)  time: 0.1193  data: 0.0847  max mem: 9669
+[13:59:10.279523] val:  [16/17]  eta: 0:00:00  loss: 0.2171 (0.2362)  time: 0.0888  data: 0.0549  max mem: 9669
+[13:59:10.401738] val: Total time: 0:00:01 (0.0962 s / it)
+[13:59:10.415748] val loss: 0.23617559057824752
+[13:59:10.415960] Accuracy: 0.9000, F1 Score: 0.9000, ROC AUC: 0.9679, Hamming Loss: 0.1000,
+ Jaccard Score: 0.8182, Precision: 0.9002, Recall: 0.9000,
+ Average Precision: 0.9691, Kappa: 0.8000, Score: 0.8893
+[13:59:10.452709] Best epoch = 11, Best score = 0.9025
+[13:59:10.752824] log_dir: ./output_logs/retfound
+[13:59:11.895015] Epoch: [15]  [  0/156]  eta: 0:02:58  lr: 0.000534  loss: 0.5035 (0.5035)  time: 1.1411  data: 0.9952  max mem: 9669
+[13:59:14.743822] Epoch: [15]  [ 20/156]  eta: 0:00:25  lr: 0.000529  loss: 0.4108 (0.4235)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:59:17.595554] Epoch: [15]  [ 40/156]  eta: 0:00:19  lr: 0.000525  loss: 0.4453 (0.4318)  time: 0.1425  data: 0.0002  max mem: 9669
+[13:59:20.450100] Epoch: [15]  [ 60/156]  eta: 0:00:15  lr: 0.000520  loss: 0.4378 (0.4427)  time: 0.1427  data: 0.0002  max mem: 9669
+[13:59:22.867269] Epoch: [15]  [ 80/156]  eta: 0:00:11  lr: 0.000515  loss: 0.3557 (0.4318)  time: 0.1208  data: 0.0001  max mem: 9669
+[13:59:25.712012] Epoch: [15]  [100/156]  eta: 0:00:08  lr: 0.000510  loss: 0.3944 (0.4249)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:59:28.556268] Epoch: [15]  [120/156]  eta: 0:00:05  lr: 0.000505  loss: 0.4038 (0.4225)  time: 0.1422  data: 0.0002  max mem: 9669
+[13:59:31.403837] Epoch: [15]  [140/156]  eta: 0:00:02  lr: 0.000500  loss: 0.4266 (0.4221)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:59:33.539935] Epoch: [15]  [155/156]  eta: 0:00:00  lr: 0.000497  loss: 0.4199 (0.4237)  time: 0.1425  data: 0.0001  max mem: 9669
+[13:59:33.679693] Epoch: [15] Total time: 0:00:22 (0.1470 s / it)
+[13:59:33.689206] Averaged stats: lr: 0.000497  loss: 0.4199 (0.4237)
+[13:59:34.703761] val:  [ 0/17]  eta: 0:00:17  loss: 0.2239 (0.2239)  time: 1.0018  data: 0.9658  max mem: 9669
+[13:59:35.084875] val:  [10/17]  eta: 0:00:00  loss: 0.2239 (0.2526)  time: 0.1257  data: 0.0912  max mem: 9669
+[13:59:35.290368] val:  [16/17]  eta: 0:00:00  loss: 0.2040 (0.2294)  time: 0.0934  data: 0.0590  max mem: 9669
+[13:59:35.405531] val: Total time: 0:00:01 (0.1003 s / it)
+[13:59:35.419759] val loss: 0.22935323767802296
+[13:59:35.420030] Accuracy: 0.9185, F1 Score: 0.9185, ROC AUC: 0.9698, Hamming Loss: 0.0815,
+ Jaccard Score: 0.8493, Precision: 0.9187, Recall: 0.9185,
+ Average Precision: 0.9710, Kappa: 0.8370, Score: 0.9085
+[13:59:37.239327] Best epoch = 15, Best score = 0.9085
+[13:59:37.312410] log_dir: ./output_logs/retfound
+[13:59:38.538692] Epoch: [16]  [  0/156]  eta: 0:03:11  lr: 0.000496  loss: 0.3148 (0.3148)  time: 1.2253  data: 1.0807  max mem: 9669
+[13:59:41.387779] Epoch: [16]  [ 20/156]  eta: 0:00:26  lr: 0.000491  loss: 0.3946 (0.4188)  time: 0.1424  data: 0.0002  max mem: 9669
+[13:59:44.235358] Epoch: [16]  [ 40/156]  eta: 0:00:19  lr: 0.000486  loss: 0.4368 (0.4331)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:59:47.077935] Epoch: [16]  [ 60/156]  eta: 0:00:15  lr: 0.000481  loss: 0.4131 (0.4302)  time: 0.1421  data: 0.0002  max mem: 9669
+[13:59:49.925043] Epoch: [16]  [ 80/156]  eta: 0:00:11  lr: 0.000475  loss: 0.4221 (0.4310)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:59:52.788471] Epoch: [16]  [100/156]  eta: 0:00:08  lr: 0.000470  loss: 0.3896 (0.4337)  time: 0.1431  data: 0.0002  max mem: 9669
+[13:59:55.636437] Epoch: [16]  [120/156]  eta: 0:00:05  lr: 0.000465  loss: 0.4177 (0.4348)  time: 0.1423  data: 0.0002  max mem: 9669
+[13:59:58.488572] Epoch: [16]  [140/156]  eta: 0:00:02  lr: 0.000459  loss: 0.3996 (0.4352)  time: 0.1426  data: 0.0001  max mem: 9669
+[14:00:00.625654] Epoch: [16]  [155/156]  eta: 0:00:00  lr: 0.000455  loss: 0.3996 (0.4304)  time: 0.1424  data: 0.0001  max mem: 9669
+[14:00:00.753079] Epoch: [16] Total time: 0:00:23 (0.1503 s / it)
+[14:00:00.758859] Averaged stats: lr: 0.000455  loss: 0.3996 (0.4304)
+[14:00:01.787699] val:  [ 0/17]  eta: 0:00:17  loss: 0.3264 (0.3264)  time: 1.0129  data: 0.9787  max mem: 9669
+[14:00:02.129569] val:  [10/17]  eta: 0:00:00  loss: 0.2887 (0.2838)  time: 0.1231  data: 0.0891  max mem: 9669
+[14:00:02.334474] val:  [16/17]  eta: 0:00:00  loss: 0.1784 (0.2348)  time: 0.0917  data: 0.0577  max mem: 9669
+[14:00:02.450112] val: Total time: 0:00:01 (0.0986 s / it)
+[14:00:02.464116] val loss: 0.2348311854635968
+[14:00:02.464374] Accuracy: 0.9204, F1 Score: 0.9203, ROC AUC: 0.9689, Hamming Loss: 0.0796,
+ Jaccard Score: 0.8524, Precision: 0.9213, Recall: 0.9204,
+ Average Precision: 0.9700, Kappa: 0.8407, Score: 0.9100
+[14:00:04.374135] Best epoch = 16, Best score = 0.9100
+[14:00:04.447274] log_dir: ./output_logs/retfound
+[14:00:05.651532] Epoch: [17]  [  0/156]  eta: 0:03:07  lr: 0.000455  loss: 0.3511 (0.3511)  time: 1.2031  data: 1.0565  max mem: 9669
+[14:00:08.496356] Epoch: [17]  [ 20/156]  eta: 0:00:26  lr: 0.000449  loss: 0.3710 (0.3823)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:00:11.341458] Epoch: [17]  [ 40/156]  eta: 0:00:19  lr: 0.000443  loss: 0.3868 (0.3839)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:00:14.187970] Epoch: [17]  [ 60/156]  eta: 0:00:15  lr: 0.000438  loss: 0.4111 (0.4096)  time: 0.1423  data: 0.0001  max mem: 9669
+[14:00:17.028949] Epoch: [17]  [ 80/156]  eta: 0:00:11  lr: 0.000432  loss: 0.4047 (0.4131)  time: 0.1420  data: 0.0002  max mem: 9669
+[14:00:19.481576] Epoch: [17]  [100/156]  eta: 0:00:08  lr: 0.000426  loss: 0.4097 (0.4126)  time: 0.1226  data: 0.0002  max mem: 9669
+[14:00:22.328286] Epoch: [17]  [120/156]  eta: 0:00:05  lr: 0.000420  loss: 0.3984 (0.4150)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:00:25.171976] Epoch: [17]  [140/156]  eta: 0:00:02  lr: 0.000414  loss: 0.4486 (0.4206)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:00:27.304013] Epoch: [17]  [155/156]  eta: 0:00:00  lr: 0.000410  loss: 0.4067 (0.4216)  time: 0.1423  data: 0.0001  max mem: 9669
+[14:00:27.433555] Epoch: [17] Total time: 0:00:22 (0.1473 s / it)
+[14:00:27.442395] Averaged stats: lr: 0.000410  loss: 0.4067 (0.4216)
+[14:00:28.459035] val:  [ 0/17]  eta: 0:00:16  loss: 0.1918 (0.1918)  time: 0.9995  data: 0.9645  max mem: 9669
+[14:00:28.804998] val:  [10/17]  eta: 0:00:00  loss: 0.2945 (0.2638)  time: 0.1222  data: 0.0878  max mem: 9669
+[14:00:29.009728] val:  [16/17]  eta: 0:00:00  loss: 0.2452 (0.2511)  time: 0.0911  data: 0.0569  max mem: 9669
+[14:00:29.128985] val: Total time: 0:00:01 (0.0982 s / it)
+[14:00:29.143987] val loss: 0.25106555968523026
+[14:00:29.144204] Accuracy: 0.9074, F1 Score: 0.9074, ROC AUC: 0.9677, Hamming Loss: 0.0926,
+ Jaccard Score: 0.8305, Precision: 0.9074, Recall: 0.9074,
+ Average Precision: 0.9684, Kappa: 0.8148, Score: 0.8966
+[14:00:29.193039] Best epoch = 16, Best score = 0.9100
+[14:00:29.468915] log_dir: ./output_logs/retfound
+[14:00:30.640126] Epoch: [18]  [  0/156]  eta: 0:03:02  lr: 0.000409  loss: 0.6675 (0.6675)  time: 1.1702  data: 1.0284  max mem: 9669
+[14:00:33.486891] Epoch: [18]  [ 20/156]  eta: 0:00:26  lr: 0.000403  loss: 0.4165 (0.4052)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:00:36.333861] Epoch: [18]  [ 40/156]  eta: 0:00:19  lr: 0.000397  loss: 0.4269 (0.4197)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:00:39.186443] Epoch: [18]  [ 60/156]  eta: 0:00:15  lr: 0.000391  loss: 0.4218 (0.4280)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:00:42.035752] Epoch: [18]  [ 80/156]  eta: 0:00:11  lr: 0.000385  loss: 0.3917 (0.4246)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:00:44.878919] Epoch: [18]  [100/156]  eta: 0:00:08  lr: 0.000379  loss: 0.4344 (0.4275)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:00:47.733584] Epoch: [18]  [120/156]  eta: 0:00:05  lr: 0.000373  loss: 0.4358 (0.4303)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:00:50.581069] Epoch: [18]  [140/156]  eta: 0:00:02  lr: 0.000367  loss: 0.4121 (0.4294)  time: 0.1423  data: 0.0001  max mem: 9669
+[14:00:52.714428] Epoch: [18]  [155/156]  eta: 0:00:00  lr: 0.000362  loss: 0.3924 (0.4266)  time: 0.1421  data: 0.0001  max mem: 9669
+[14:00:52.853426] Epoch: [18] Total time: 0:00:23 (0.1499 s / it)
+[14:00:52.861954] Averaged stats: lr: 0.000362  loss: 0.3924 (0.4266)
+[14:00:53.826973] val:  [ 0/17]  eta: 0:00:16  loss: 0.2619 (0.2619)  time: 0.9504  data: 0.9148  max mem: 9669
+[14:00:54.180927] val:  [10/17]  eta: 0:00:00  loss: 0.2619 (0.2848)  time: 0.1185  data: 0.0839  max mem: 9669
+[14:00:54.384626] val:  [16/17]  eta: 0:00:00  loss: 0.1932 (0.2353)  time: 0.0886  data: 0.0543  max mem: 9669
+[14:00:54.501586] val: Total time: 0:00:01 (0.0957 s / it)
+[14:00:54.515711] val loss: 0.23531180181924036
+[14:00:54.516049] Accuracy: 0.9204, F1 Score: 0.9203, ROC AUC: 0.9708, Hamming Loss: 0.0796,
+ Jaccard Score: 0.8524, Precision: 0.9220, Recall: 0.9204,
+ Average Precision: 0.9716, Kappa: 0.8407, Score: 0.9106
+[14:00:56.307530] Best epoch = 18, Best score = 0.9106
+[14:00:56.388744] log_dir: ./output_logs/retfound
+[14:00:57.542420] Epoch: [19]  [  0/156]  eta: 0:02:59  lr: 0.000362  loss: 0.4672 (0.4672)  time: 1.1526  data: 1.0071  max mem: 9669
+[14:01:00.384638] Epoch: [19]  [ 20/156]  eta: 0:00:25  lr: 0.000356  loss: 0.4151 (0.4028)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:01:03.227846] Epoch: [19]  [ 40/156]  eta: 0:00:19  lr: 0.000349  loss: 0.3841 (0.3963)  time: 0.1421  data: 0.0001  max mem: 9669
+[14:01:06.067925] Epoch: [19]  [ 60/156]  eta: 0:00:15  lr: 0.000343  loss: 0.4096 (0.4003)  time: 0.1420  data: 0.0004  max mem: 9669
+[14:01:08.914592] Epoch: [19]  [ 80/156]  eta: 0:00:11  lr: 0.000337  loss: 0.3970 (0.3987)  time: 0.1423  data: 0.0001  max mem: 9669
+[14:01:11.742160] Epoch: [19]  [100/156]  eta: 0:00:08  lr: 0.000331  loss: 0.4069 (0.3998)  time: 0.1413  data: 0.0002  max mem: 9669
+[14:01:14.586360] Epoch: [19]  [120/156]  eta: 0:00:05  lr: 0.000324  loss: 0.3498 (0.3965)  time: 0.1422  data: 0.0001  max mem: 9669
+[14:01:17.430512] Epoch: [19]  [140/156]  eta: 0:00:02  lr: 0.000318  loss: 0.4158 (0.3991)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:01:19.564756] Epoch: [19]  [155/156]  eta: 0:00:00  lr: 0.000313  loss: 0.4382 (0.4027)  time: 0.1422  data: 0.0001  max mem: 9669
+[14:01:19.693524] Epoch: [19] Total time: 0:00:23 (0.1494 s / it)
+[14:01:19.700988] Averaged stats: lr: 0.000313  loss: 0.4382 (0.4027)
+[14:01:20.725514] val:  [ 0/17]  eta: 0:00:17  loss: 0.1684 (0.1684)  time: 1.0067  data: 0.9705  max mem: 9669
+[14:01:21.066854] val:  [10/17]  eta: 0:00:00  loss: 0.2131 (0.2261)  time: 0.1225  data: 0.0884  max mem: 9669
+[14:01:21.273605] val:  [16/17]  eta: 0:00:00  loss: 0.2131 (0.2168)  time: 0.0914  data: 0.0573  max mem: 9669
+[14:01:21.387809] val: Total time: 0:00:01 (0.0982 s / it)
+[14:01:21.402148] val loss: 0.21684428611222437
+[14:01:21.402412] Accuracy: 0.9148, F1 Score: 0.9148, ROC AUC: 0.9713, Hamming Loss: 0.0852,
+ Jaccard Score: 0.8430, Precision: 0.9148, Recall: 0.9148,
+ Average Precision: 0.9723, Kappa: 0.8296, Score: 0.9052
+[14:01:21.449695] Best epoch = 18, Best score = 0.9106
+[14:01:21.719157] log_dir: ./output_logs/retfound
+[14:01:22.953279] Epoch: [20]  [  0/156]  eta: 0:03:12  lr: 0.000313  loss: 0.4803 (0.4803)  time: 1.2331  data: 1.0823  max mem: 9669
+[14:01:25.798169] Epoch: [20]  [ 20/156]  eta: 0:00:26  lr: 0.000307  loss: 0.3682 (0.3869)  time: 0.1422  data: 0.0001  max mem: 9669
+[14:01:28.626670] Epoch: [20]  [ 40/156]  eta: 0:00:19  lr: 0.000300  loss: 0.3869 (0.3848)  time: 0.1414  data: 0.0002  max mem: 9669
+[14:01:31.466087] Epoch: [20]  [ 60/156]  eta: 0:00:15  lr: 0.000294  loss: 0.3871 (0.3932)  time: 0.1419  data: 0.0002  max mem: 9669
+[14:01:34.297973] Epoch: [20]  [ 80/156]  eta: 0:00:11  lr: 0.000288  loss: 0.4182 (0.3983)  time: 0.1415  data: 0.0002  max mem: 9669
+[14:01:37.135112] Epoch: [20]  [100/156]  eta: 0:00:08  lr: 0.000282  loss: 0.3950 (0.4008)  time: 0.1418  data: 0.0001  max mem: 9669
+[14:01:39.973376] Epoch: [20]  [120/156]  eta: 0:00:05  lr: 0.000275  loss: 0.4196 (0.4051)  time: 0.1419  data: 0.0002  max mem: 9669
+[14:01:42.822632] Epoch: [20]  [140/156]  eta: 0:00:02  lr: 0.000269  loss: 0.3612 (0.4010)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:01:44.955935] Epoch: [20]  [155/156]  eta: 0:00:00  lr: 0.000265  loss: 0.4231 (0.4008)  time: 0.1421  data: 0.0001  max mem: 9669
+[14:01:45.080502] Epoch: [20] Total time: 0:00:23 (0.1498 s / it)
+[14:01:45.089459] Averaged stats: lr: 0.000265  loss: 0.4231 (0.4008)
+[14:01:46.151168] val:  [ 0/17]  eta: 0:00:17  loss: 0.1891 (0.1891)  time: 1.0490  data: 1.0150  max mem: 9669
+[14:01:46.539146] val:  [10/17]  eta: 0:00:00  loss: 0.2415 (0.2510)  time: 0.1306  data: 0.0964  max mem: 9669
+[14:01:46.745093] val:  [16/17]  eta: 0:00:00  loss: 0.2240 (0.2322)  time: 0.0966  data: 0.0625  max mem: 9669
+[14:01:46.876490] val: Total time: 0:00:01 (0.1044 s / it)
+[14:01:46.900138] val loss: 0.2322498717728783
+[14:01:46.900377] Accuracy: 0.9111, F1 Score: 0.9111, ROC AUC: 0.9698, Hamming Loss: 0.0889,
+ Jaccard Score: 0.8367, Precision: 0.9111, Recall: 0.9111,
+ Average Precision: 0.9704, Kappa: 0.8222, Score: 0.9011
+[14:01:46.944824] Best epoch = 18, Best score = 0.9106
+[14:01:47.219331] log_dir: ./output_logs/retfound
+[14:01:48.487106] Epoch: [21]  [  0/156]  eta: 0:03:17  lr: 0.000264  loss: 0.3076 (0.3076)  time: 1.2667  data: 1.1222  max mem: 9669
+[14:01:51.335364] Epoch: [21]  [ 20/156]  eta: 0:00:26  lr: 0.000258  loss: 0.3947 (0.3811)  time: 0.1424  data: 0.0003  max mem: 9669
+[14:01:54.183864] Epoch: [21]  [ 40/156]  eta: 0:00:19  lr: 0.000252  loss: 0.3580 (0.3706)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:01:57.035109] Epoch: [21]  [ 60/156]  eta: 0:00:15  lr: 0.000246  loss: 0.3954 (0.3747)  time: 0.1425  data: 0.0003  max mem: 9669
+[14:01:59.886513] Epoch: [21]  [ 80/156]  eta: 0:00:11  lr: 0.000240  loss: 0.4262 (0.3865)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:02:02.741869] Epoch: [21]  [100/156]  eta: 0:00:08  lr: 0.000233  loss: 0.4166 (0.3932)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:02:05.591608] Epoch: [21]  [120/156]  eta: 0:00:05  lr: 0.000227  loss: 0.3974 (0.3968)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:02:08.442491] Epoch: [21]  [140/156]  eta: 0:00:02  lr: 0.000221  loss: 0.3612 (0.3919)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:02:10.580026] Epoch: [21]  [155/156]  eta: 0:00:00  lr: 0.000217  loss: 0.3752 (0.3934)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:02:10.711240] Epoch: [21] Total time: 0:00:23 (0.1506 s / it)
+[14:02:10.720374] Averaged stats: lr: 0.000217  loss: 0.3752 (0.3934)
+[14:02:11.949526] val:  [ 0/17]  eta: 0:00:20  loss: 0.3412 (0.3412)  time: 1.2106  data: 1.1743  max mem: 9669
+[14:02:12.297074] val:  [10/17]  eta: 0:00:00  loss: 0.2534 (0.3023)  time: 0.1416  data: 0.1069  max mem: 9669
+[14:02:12.501152] val:  [16/17]  eta: 0:00:00  loss: 0.1760 (0.2378)  time: 0.1036  data: 0.0692  max mem: 9669
+[14:02:12.612917] val: Total time: 0:00:01 (0.1103 s / it)
+[14:02:12.628199] val loss: 0.23778783990179791
+[14:02:12.628423] Accuracy: 0.9167, F1 Score: 0.9165, ROC AUC: 0.9706, Hamming Loss: 0.0833,
+ Jaccard Score: 0.8460, Precision: 0.9192, Recall: 0.9167,
+ Average Precision: 0.9709, Kappa: 0.8333, Score: 0.9068
+[14:02:12.670375] Best epoch = 18, Best score = 0.9106
+[14:02:12.951894] log_dir: ./output_logs/retfound
+[14:02:14.307803] Epoch: [22]  [  0/156]  eta: 0:03:31  lr: 0.000217  loss: 0.2743 (0.2743)  time: 1.3544  data: 1.2081  max mem: 9669
+[14:02:17.162130] Epoch: [22]  [ 20/156]  eta: 0:00:27  lr: 0.000211  loss: 0.3164 (0.3383)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:02:19.669448] Epoch: [22]  [ 40/156]  eta: 0:00:18  lr: 0.000205  loss: 0.3849 (0.3751)  time: 0.1253  data: 0.0002  max mem: 9669
+[14:02:22.514322] Epoch: [22]  [ 60/156]  eta: 0:00:15  lr: 0.000199  loss: 0.3804 (0.3815)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:02:25.368939] Epoch: [22]  [ 80/156]  eta: 0:00:11  lr: 0.000193  loss: 0.3572 (0.3854)  time: 0.1427  data: 0.0001  max mem: 9669
+[14:02:28.221028] Epoch: [22]  [100/156]  eta: 0:00:08  lr: 0.000187  loss: 0.4054 (0.3910)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:02:31.065958] Epoch: [22]  [120/156]  eta: 0:00:05  lr: 0.000182  loss: 0.3655 (0.3888)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:02:33.908281] Epoch: [22]  [140/156]  eta: 0:00:02  lr: 0.000176  loss: 0.3798 (0.3888)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:02:36.049417] Epoch: [22]  [155/156]  eta: 0:00:00  lr: 0.000172  loss: 0.3645 (0.3888)  time: 0.1423  data: 0.0001  max mem: 9669
+[14:02:36.200619] Epoch: [22] Total time: 0:00:23 (0.1490 s / it)
+[14:02:36.203377] Averaged stats: lr: 0.000172  loss: 0.3645 (0.3888)
+[14:02:37.398164] val:  [ 0/17]  eta: 0:00:20  loss: 0.2359 (0.2359)  time: 1.1774  data: 1.1408  max mem: 9669
+[14:02:37.745100] val:  [10/17]  eta: 0:00:00  loss: 0.2614 (0.2715)  time: 0.1385  data: 0.1039  max mem: 9669
+[14:02:37.944934] val:  [16/17]  eta: 0:00:00  loss: 0.2143 (0.2386)  time: 0.1013  data: 0.0673  max mem: 9669
+[14:02:38.066292] val: Total time: 0:00:01 (0.1086 s / it)
+[14:02:38.082495] val loss: 0.2386385027100058
+[14:02:38.082693] Accuracy: 0.9185, F1 Score: 0.9185, ROC AUC: 0.9701, Hamming Loss: 0.0815,
+ Jaccard Score: 0.8493, Precision: 0.9187, Recall: 0.9185,
+ Average Precision: 0.9705, Kappa: 0.8370, Score: 0.9086
+[14:02:38.127692] Best epoch = 18, Best score = 0.9106
+[14:02:38.441539] log_dir: ./output_logs/retfound
+[14:02:39.670705] Epoch: [23]  [  0/156]  eta: 0:03:11  lr: 0.000171  loss: 0.3711 (0.3711)  time: 1.2281  data: 1.0819  max mem: 9669
+[14:02:42.511175] Epoch: [23]  [ 20/156]  eta: 0:00:26  lr: 0.000166  loss: 0.3567 (0.3692)  time: 0.1420  data: 0.0002  max mem: 9669
+[14:02:45.361456] Epoch: [23]  [ 40/156]  eta: 0:00:19  lr: 0.000160  loss: 0.3816 (0.3839)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:02:48.206160] Epoch: [23]  [ 60/156]  eta: 0:00:15  lr: 0.000155  loss: 0.3849 (0.3865)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:02:51.051731] Epoch: [23]  [ 80/156]  eta: 0:00:11  lr: 0.000149  loss: 0.3736 (0.3917)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:02:53.899827] Epoch: [23]  [100/156]  eta: 0:00:08  lr: 0.000144  loss: 0.3504 (0.3864)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:02:56.748532] Epoch: [23]  [120/156]  eta: 0:00:05  lr: 0.000139  loss: 0.3553 (0.3820)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:02:59.601194] Epoch: [23]  [140/156]  eta: 0:00:02  lr: 0.000134  loss: 0.3917 (0.3831)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:03:01.736159] Epoch: [23]  [155/156]  eta: 0:00:00  lr: 0.000130  loss: 0.3600 (0.3816)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:03:01.879512] Epoch: [23] Total time: 0:00:23 (0.1502 s / it)
+[14:03:01.889415] Averaged stats: lr: 0.000130  loss: 0.3600 (0.3816)
+[14:03:03.047057] val:  [ 0/17]  eta: 0:00:19  loss: 0.3133 (0.3133)  time: 1.1446  data: 1.1097  max mem: 9669
+[14:03:03.407535] val:  [10/17]  eta: 0:00:00  loss: 0.2837 (0.2940)  time: 0.1368  data: 0.1020  max mem: 9669
+[14:03:03.611438] val:  [16/17]  eta: 0:00:00  loss: 0.1864 (0.2400)  time: 0.1004  data: 0.0661  max mem: 9669
+[14:03:03.745928] val: Total time: 0:00:01 (0.1085 s / it)
+[14:03:03.768859] val loss: 0.23999610104981592
+[14:03:03.769106] Accuracy: 0.9222, F1 Score: 0.9222, ROC AUC: 0.9707, Hamming Loss: 0.0778,
+ Jaccard Score: 0.8556, Precision: 0.9234, Recall: 0.9222,
+ Average Precision: 0.9711, Kappa: 0.8444, Score: 0.9124
+[14:03:05.544551] Best epoch = 23, Best score = 0.9124
+[14:03:05.627528] log_dir: ./output_logs/retfound
+[14:03:06.917559] Epoch: [24]  [  0/156]  eta: 0:03:21  lr: 0.000130  loss: 0.4154 (0.4154)  time: 1.2889  data: 1.1416  max mem: 9669
+[14:03:09.772975] Epoch: [24]  [ 20/156]  eta: 0:00:26  lr: 0.000125  loss: 0.3271 (0.3633)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:03:12.624910] Epoch: [24]  [ 40/156]  eta: 0:00:19  lr: 0.000120  loss: 0.3947 (0.3858)  time: 0.1425  data: 0.0001  max mem: 9669
+[14:03:15.478619] Epoch: [24]  [ 60/156]  eta: 0:00:15  lr: 0.000115  loss: 0.3738 (0.3892)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:03:18.328566] Epoch: [24]  [ 80/156]  eta: 0:00:11  lr: 0.000110  loss: 0.3735 (0.3839)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:03:20.830644] Epoch: [24]  [100/156]  eta: 0:00:08  lr: 0.000105  loss: 0.3391 (0.3819)  time: 0.1250  data: 0.0002  max mem: 9669
+[14:03:23.678090] Epoch: [24]  [120/156]  eta: 0:00:05  lr: 0.000101  loss: 0.3435 (0.3804)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:03:26.523163] Epoch: [24]  [140/156]  eta: 0:00:02  lr: 0.000096  loss: 0.3509 (0.3794)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:03:28.659454] Epoch: [24]  [155/156]  eta: 0:00:00  lr: 0.000093  loss: 0.3651 (0.3807)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:03:28.805777] Epoch: [24] Total time: 0:00:23 (0.1486 s / it)
+[14:03:28.815165] Averaged stats: lr: 0.000093  loss: 0.3651 (0.3807)
+[14:03:30.028510] val:  [ 0/17]  eta: 0:00:20  loss: 0.2879 (0.2879)  time: 1.1999  data: 1.1658  max mem: 9669
+[14:03:30.381705] val:  [10/17]  eta: 0:00:00  loss: 0.2879 (0.2952)  time: 0.1411  data: 0.1062  max mem: 9669
+[14:03:30.586868] val:  [16/17]  eta: 0:00:00  loss: 0.1953 (0.2397)  time: 0.1033  data: 0.0688  max mem: 9669
+[14:03:30.713017] val: Total time: 0:00:01 (0.1109 s / it)
+[14:03:30.728328] val loss: 0.23967866029809504
+[14:03:30.728532] Accuracy: 0.9204, F1 Score: 0.9203, ROC AUC: 0.9703, Hamming Loss: 0.0796,
+ Jaccard Score: 0.8524, Precision: 0.9217, Recall: 0.9204,
+ Average Precision: 0.9701, Kappa: 0.8407, Score: 0.9105
+[14:03:30.768583] Best epoch = 23, Best score = 0.9124
+[14:03:31.061672] log_dir: ./output_logs/retfound
+[14:03:32.411416] Epoch: [25]  [  0/156]  eta: 0:03:30  lr: 0.000092  loss: 0.2706 (0.2706)  time: 1.3486  data: 1.2017  max mem: 9669
+[14:03:35.254649] Epoch: [25]  [ 20/156]  eta: 0:00:27  lr: 0.000088  loss: 0.4050 (0.3961)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:03:38.105113] Epoch: [25]  [ 40/156]  eta: 0:00:19  lr: 0.000084  loss: 0.3465 (0.3772)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:03:40.946787] Epoch: [25]  [ 60/156]  eta: 0:00:15  lr: 0.000079  loss: 0.4074 (0.3937)  time: 0.1420  data: 0.0002  max mem: 9669
+[14:03:43.801000] Epoch: [25]  [ 80/156]  eta: 0:00:11  lr: 0.000075  loss: 0.4057 (0.3941)  time: 0.1427  data: 0.0003  max mem: 9669
+[14:03:46.652036] Epoch: [25]  [100/156]  eta: 0:00:08  lr: 0.000071  loss: 0.3420 (0.3892)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:03:49.501739] Epoch: [25]  [120/156]  eta: 0:00:05  lr: 0.000067  loss: 0.3155 (0.3838)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:03:52.363150] Epoch: [25]  [140/156]  eta: 0:00:02  lr: 0.000064  loss: 0.3565 (0.3815)  time: 0.1430  data: 0.0002  max mem: 9669
+[14:03:54.500214] Epoch: [25]  [155/156]  eta: 0:00:00  lr: 0.000061  loss: 0.3476 (0.3825)  time: 0.1426  data: 0.0001  max mem: 9669
+[14:03:54.652508] Epoch: [25] Total time: 0:00:23 (0.1512 s / it)
+[14:03:54.661502] Averaged stats: lr: 0.000061  loss: 0.3476 (0.3825)
+[14:03:55.853591] val:  [ 0/17]  eta: 0:00:19  loss: 0.1976 (0.1976)  time: 1.1761  data: 1.1401  max mem: 9669
+[14:03:56.201969] val:  [10/17]  eta: 0:00:00  loss: 0.2580 (0.2567)  time: 0.1385  data: 0.1038  max mem: 9669
+[14:03:56.405115] val:  [16/17]  eta: 0:00:00  loss: 0.2172 (0.2330)  time: 0.1015  data: 0.0672  max mem: 9669
+[14:03:56.538552] val: Total time: 0:00:01 (0.1095 s / it)
+[14:03:56.563147] val loss: 0.23302041739225388
+[14:03:56.563377] Accuracy: 0.9167, F1 Score: 0.9167, ROC AUC: 0.9718, Hamming Loss: 0.0833,
+ Jaccard Score: 0.8462, Precision: 0.9167, Recall: 0.9167,
+ Average Precision: 0.9720, Kappa: 0.8333, Score: 0.9073
+[14:03:56.609391] Best epoch = 23, Best score = 0.9124
+[14:03:56.895992] log_dir: ./output_logs/retfound
+[14:03:58.193751] Epoch: [26]  [  0/156]  eta: 0:03:22  lr: 0.000061  loss: 0.5383 (0.5383)  time: 1.2967  data: 1.1511  max mem: 9669
+[14:04:01.039682] Epoch: [26]  [ 20/156]  eta: 0:00:26  lr: 0.000057  loss: 0.3742 (0.3995)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:04:03.887424] Epoch: [26]  [ 40/156]  eta: 0:00:19  lr: 0.000053  loss: 0.3442 (0.3819)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:04:06.732947] Epoch: [26]  [ 60/156]  eta: 0:00:15  lr: 0.000050  loss: 0.3598 (0.3880)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:04:09.587617] Epoch: [26]  [ 80/156]  eta: 0:00:11  lr: 0.000047  loss: 0.3191 (0.3707)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:04:12.435555] Epoch: [26]  [100/156]  eta: 0:00:08  lr: 0.000043  loss: 0.3691 (0.3724)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:04:15.275237] Epoch: [26]  [120/156]  eta: 0:00:05  lr: 0.000040  loss: 0.3316 (0.3727)  time: 0.1419  data: 0.0002  max mem: 9669
+[14:04:18.117718] Epoch: [26]  [140/156]  eta: 0:00:02  lr: 0.000037  loss: 0.3928 (0.3753)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:04:20.261207] Epoch: [26]  [155/156]  eta: 0:00:00  lr: 0.000035  loss: 0.3924 (0.3762)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:04:20.391298] Epoch: [26] Total time: 0:00:23 (0.1506 s / it)
+[14:04:20.399717] Averaged stats: lr: 0.000035  loss: 0.3924 (0.3762)
+[14:04:21.700179] val:  [ 0/17]  eta: 0:00:21  loss: 0.2516 (0.2516)  time: 1.2881  data: 1.2523  max mem: 9669
+[14:04:22.041464] val:  [10/17]  eta: 0:00:01  loss: 0.2528 (0.2728)  time: 0.1481  data: 0.1140  max mem: 9669
+[14:04:22.246625] val:  [16/17]  eta: 0:00:00  loss: 0.2173 (0.2303)  time: 0.1078  data: 0.0738  max mem: 9669
+[14:04:22.382159] val: Total time: 0:00:01 (0.1159 s / it)
+[14:04:22.396778] val loss: 0.23026803880929947
+[14:04:22.396985] Accuracy: 0.9241, F1 Score: 0.9241, ROC AUC: 0.9719, Hamming Loss: 0.0759,
+ Jaccard Score: 0.8588, Precision: 0.9245, Recall: 0.9241,
+ Average Precision: 0.9721, Kappa: 0.8481, Score: 0.9147
+[14:04:24.378716] Best epoch = 26, Best score = 0.9147
+[14:04:24.454489] log_dir: ./output_logs/retfound
+[14:04:25.791357] Epoch: [27]  [  0/156]  eta: 0:03:28  lr: 0.000035  loss: 0.3272 (0.3272)  time: 1.3359  data: 1.1909  max mem: 9669
+[14:04:28.944094] Epoch: [27]  [ 20/156]  eta: 0:00:29  lr: 0.000032  loss: 0.3556 (0.3730)  time: 0.1576  data: 0.0001  max mem: 9669
+[14:04:31.798287] Epoch: [27]  [ 40/156]  eta: 0:00:20  lr: 0.000030  loss: 0.3624 (0.3686)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:04:34.641258] Epoch: [27]  [ 60/156]  eta: 0:00:16  lr: 0.000027  loss: 0.3282 (0.3590)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:04:37.485865] Epoch: [27]  [ 80/156]  eta: 0:00:12  lr: 0.000025  loss: 0.3779 (0.3642)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:04:40.332607] Epoch: [27]  [100/156]  eta: 0:00:08  lr: 0.000022  loss: 0.3637 (0.3665)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:04:43.180707] Epoch: [27]  [120/156]  eta: 0:00:05  lr: 0.000020  loss: 0.3731 (0.3708)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:04:46.024253] Epoch: [27]  [140/156]  eta: 0:00:02  lr: 0.000018  loss: 0.3775 (0.3725)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:04:48.156757] Epoch: [27]  [155/156]  eta: 0:00:00  lr: 0.000016  loss: 0.4017 (0.3799)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:04:48.310432] Epoch: [27] Total time: 0:00:23 (0.1529 s / it)
+[14:04:48.318681] Averaged stats: lr: 0.000016  loss: 0.4017 (0.3799)
+[14:04:49.430976] val:  [ 0/17]  eta: 0:00:18  loss: 0.2363 (0.2363)  time: 1.1001  data: 1.0653  max mem: 9669
+[14:04:49.801246] val:  [10/17]  eta: 0:00:00  loss: 0.2602 (0.2659)  time: 0.1336  data: 0.0992  max mem: 9669
+[14:04:50.004692] val:  [16/17]  eta: 0:00:00  loss: 0.2213 (0.2283)  time: 0.0984  data: 0.0642  max mem: 9669
+[14:04:50.137586] val: Total time: 0:00:01 (0.1063 s / it)
+[14:04:50.152642] val loss: 0.22829091548919678
+[14:04:50.152914] Accuracy: 0.9278, F1 Score: 0.9278, ROC AUC: 0.9718, Hamming Loss: 0.0722,
+ Jaccard Score: 0.8653, Precision: 0.9283, Recall: 0.9278,
+ Average Precision: 0.9719, Kappa: 0.8556, Score: 0.9184
+[14:04:52.178398] Best epoch = 27, Best score = 0.9184
+[14:04:52.246907] log_dir: ./output_logs/retfound
+[14:04:53.620607] Epoch: [28]  [  0/156]  eta: 0:03:34  lr: 0.000016  loss: 0.5803 (0.5803)  time: 1.3725  data: 1.2181  max mem: 9669
+[14:04:56.467381] Epoch: [28]  [ 20/156]  eta: 0:00:27  lr: 0.000014  loss: 0.3818 (0.3711)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:04:59.310740] Epoch: [28]  [ 40/156]  eta: 0:00:19  lr: 0.000013  loss: 0.3826 (0.3896)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:05:02.150665] Epoch: [28]  [ 60/156]  eta: 0:00:15  lr: 0.000011  loss: 0.3227 (0.3742)  time: 0.1419  data: 0.0003  max mem: 9669
+[14:05:04.999963] Epoch: [28]  [ 80/156]  eta: 0:00:11  lr: 0.000009  loss: 0.3472 (0.3726)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:05:07.842091] Epoch: [28]  [100/156]  eta: 0:00:08  lr: 0.000008  loss: 0.3824 (0.3784)  time: 0.1421  data: 0.0002  max mem: 9669
+[14:05:10.689542] Epoch: [28]  [120/156]  eta: 0:00:05  lr: 0.000007  loss: 0.3396 (0.3717)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:05:13.539219] Epoch: [28]  [140/156]  eta: 0:00:02  lr: 0.000006  loss: 0.3599 (0.3713)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:05:15.678783] Epoch: [28]  [155/156]  eta: 0:00:00  lr: 0.000005  loss: 0.3999 (0.3723)  time: 0.1425  data: 0.0001  max mem: 9669
+[14:05:15.848117] Epoch: [28] Total time: 0:00:23 (0.1513 s / it)
+[14:05:15.857348] Averaged stats: lr: 0.000005  loss: 0.3999 (0.3723)
+[14:05:17.182925] val:  [ 0/17]  eta: 0:00:22  loss: 0.2405 (0.2405)  time: 1.3076  data: 1.2726  max mem: 9669
+[14:05:17.531097] val:  [10/17]  eta: 0:00:01  loss: 0.2569 (0.2667)  time: 0.1504  data: 0.1158  max mem: 9669
+[14:05:17.736291] val:  [16/17]  eta: 0:00:00  loss: 0.2159 (0.2277)  time: 0.1094  data: 0.0750  max mem: 9669
+[14:05:17.879076] val: Total time: 0:00:02 (0.1179 s / it)
+[14:05:17.894058] val loss: 0.22767887702759573
+[14:05:17.894287] Accuracy: 0.9278, F1 Score: 0.9278, ROC AUC: 0.9718, Hamming Loss: 0.0722,
+ Jaccard Score: 0.8653, Precision: 0.9283, Recall: 0.9278,
+ Average Precision: 0.9718, Kappa: 0.8556, Score: 0.9184
+[14:05:19.736814] Best epoch = 28, Best score = 0.9184
+[14:05:19.808695] log_dir: ./output_logs/retfound
+[14:05:21.151897] Epoch: [29]  [  0/156]  eta: 0:03:29  lr: 0.000005  loss: 0.3711 (0.3711)  time: 1.3421  data: 1.1970  max mem: 9669
+[14:05:24.001202] Epoch: [29]  [ 20/156]  eta: 0:00:27  lr: 0.000004  loss: 0.3350 (0.3650)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:05:26.837594] Epoch: [29]  [ 40/156]  eta: 0:00:19  lr: 0.000003  loss: 0.3628 (0.3587)  time: 0.1418  data: 0.0003  max mem: 9669
+[14:05:29.689098] Epoch: [29]  [ 60/156]  eta: 0:00:15  lr: 0.000002  loss: 0.3660 (0.3623)  time: 0.1425  data: 0.0002  max mem: 9669
+[14:05:32.538475] Epoch: [29]  [ 80/156]  eta: 0:00:11  lr: 0.000002  loss: 0.3133 (0.3579)  time: 0.1424  data: 0.0002  max mem: 9669
+[14:05:35.392030] Epoch: [29]  [100/156]  eta: 0:00:08  lr: 0.000001  loss: 0.3825 (0.3635)  time: 0.1426  data: 0.0002  max mem: 9669
+[14:05:38.247857] Epoch: [29]  [120/156]  eta: 0:00:05  lr: 0.000001  loss: 0.4103 (0.3692)  time: 0.1427  data: 0.0002  max mem: 9669
+[14:05:41.094588] Epoch: [29]  [140/156]  eta: 0:00:02  lr: 0.000001  loss: 0.3245 (0.3648)  time: 0.1423  data: 0.0002  max mem: 9669
+[14:05:43.236370] Epoch: [29]  [155/156]  eta: 0:00:00  lr: 0.000001  loss: 0.3686 (0.3648)  time: 0.1422  data: 0.0002  max mem: 9669
+[14:05:43.390679] Epoch: [29] Total time: 0:00:23 (0.1512 s / it)
+[14:05:43.398674] Averaged stats: lr: 0.000001  loss: 0.3686 (0.3648)
+[14:05:44.522495] val:  [ 0/17]  eta: 0:00:18  loss: 0.2422 (0.2422)  time: 1.1064  data: 1.0713  max mem: 9669
+[14:05:44.871971] val:  [10/17]  eta: 0:00:00  loss: 0.2575 (0.2679)  time: 0.1323  data: 0.0978  max mem: 9669
+[14:05:45.075080] val:  [16/17]  eta: 0:00:00  loss: 0.2153 (0.2283)  time: 0.0975  data: 0.0633  max mem: 9669
+[14:05:45.206529] val: Total time: 0:00:01 (0.1054 s / it)
+[14:05:45.221314] val loss: 0.2282544427058276
+[14:05:45.221543] Accuracy: 0.9278, F1 Score: 0.9278, ROC AUC: 0.9719, Hamming Loss: 0.0722,
+ Jaccard Score: 0.8653, Precision: 0.9283, Recall: 0.9278,
+ Average Precision: 0.9720, Kappa: 0.8556, Score: 0.9184
+[14:05:47.072535] Best epoch = 29, Best score = 0.9184
+[14:05:50.206771] Test with the best model, epoch = 29:
+[14:05:51.178915] test:  [ 0/32]  eta: 0:00:30  loss: 0.1497 (0.1497)  time: 0.9565  data: 0.9186  max mem: 9669
+[14:05:51.526147] test:  [10/32]  eta: 0:00:02  loss: 0.2625 (0.2276)  time: 0.1184  data: 0.0837  max mem: 9669
+[14:05:51.870804] test:  [20/32]  eta: 0:00:00  loss: 0.2398 (0.2350)  time: 0.0345  data: 0.0002  max mem: 9669
+[14:05:52.219085] test:  [30/32]  eta: 0:00:00  loss: 0.2097 (0.2370)  time: 0.0346  data: 0.0002  max mem: 9669
+[14:05:52.333496] test:  [31/32]  eta: 0:00:00  loss: 0.2398 (0.2516)  time: 0.0385  data: 0.0002  max mem: 9669
+[14:05:52.450048] test: Total time: 0:00:02 (0.0696 s / it)
+[14:05:52.471620] val loss: 0.2516249555628747
+[14:05:52.471780] Accuracy: 0.9080, F1 Score: 0.9080, ROC AUC: 0.9707, Hamming Loss: 0.0920,
+ Jaccard Score: 0.8315, Precision: 0.9084, Recall: 0.9080,
+ Average Precision: 0.9704, Kappa: 0.8160, Score: 0.8982
+[14:05:53.253630] Training time 0:13:14
+[rank0]:[W615 14:05:53.798870046 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4 (function operator())
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/airogs/retfound  acc=0.9080  auroc=0.970758  f1_macro=0.9080  qwk=0.8160000000000001

results/airogs/vit/log.csv

@@ -0,0 +1,31 @@
+epoch,train_loss,val_acc,val_auc,val_score,lr
+0,0.5149044979077119,0.7555555555555555,0.9283127572016461,0.7275739375598578,0.00016452991452991454
+1,0.4293446271465375,0.7777777777777778,0.8612139917695473,0.730651257121246,0.0003311965811965812
+2,0.47342362388586384,0.8314814814814815,0.9137174211248286,0.8026648946418073,0.0004978632478632479
+3,0.4123932318045543,0.7203703703703703,0.897798353909465,0.6806619301131271,0.0004983526080898481
+4,0.4123909076054891,0.7685185185185185,0.9021193415637859,0.7329783871837222,0.0004933469513590679
+5,0.3883578437261092,0.8018518518518518,0.8913443072702333,0.7655822842242596,0.00048505044456893006
+6,0.3691409961917462,0.825925925925926,0.9223113854595336,0.7994866233365525,0.00047357528374124746
+7,0.3690838359105281,0.8222222222222222,0.9215363511659809,0.7954690942345263,0.00045907665074076113
+8,0.36253918898411286,0.8314814814814815,0.9298148148148148,0.807657637232313,0.0004417506147066312
+9,0.36002588711487943,0.8222222222222222,0.9029149519890262,0.7897228429051469,0.0004218314805536838
+10,0.35260091225306195,0.8425925925925926,0.9319135802469136,0.8194615078938696,0.00039958862040038377
+11,0.3455605086607811,0.8333333333333334,0.928840877914952,0.8093656100986347,0.0003753228307730364
+12,0.3429072205072794,0.825925925925926,0.9123045267489712,0.7963409516049861,0.0003493622648487805
+13,0.32870546518227994,0.8555555555555555,0.9356172839506173,0.8339249744455345,0.00032205799474680896
+14,0.3155752603824322,0.8444444444444444,0.9200617283950617,0.8177271288382398,0.00029377926388021573
+15,0.302010148190535,0.837037037037037,0.9320370370370371,0.8140830140485313,0.0002649084935722644
+16,0.2751752075094443,0.8611111111111112,0.9386351165980795,0.8403607175132226,0.00023583611146402853
+17,0.27313649979157323,0.8555555555555555,0.936042524005487,0.8342040121704085,0.00020695527165031925
+18,0.2583170288648361,0.8592592592592593,0.9443072702331962,0.8406892234885023,0.00017865653794500142
+19,0.23375292361164704,0.8703703703703703,0.9430864197530865,0.8513273376183618,0.0001513226021754179
+20,0.21957338123749465,0.8685185185185185,0.9455624142661181,0.8502628140997213,0.00012532310893192616
+21,0.2069929434129825,0.8648148148148148,0.9504389574759945,0.8481976769287843,0.00010100965675893233
+22,0.1835992902230758,0.8777777777777778,0.947002743484225,0.8600313907899384,7.871104338774113e-05
+23,0.17139388950398335,0.8740740740740741,0.9502606310013718,0.8573811247399808,5.872881931128568e-05
+24,0.15429930737576422,0.8759259259259259,0.9511522633744856,0.8596261783319527,4.133320983101688e-05
+25,0.1345765313658959,0.8685185185185185,0.9509807956104253,0.8521353042468528,2.675946072326743e-05
+26,0.12600348491030625,0.8685185185185185,0.9509053497942387,0.852087252906954,1.5204656943687972e-05
+27,0.12048482914001514,0.8722222222222222,0.9517832647462279,0.8560584903419605,6.825057391317641e-06
+28,0.10587343633270417,0.8796296296296297,0.9514883401920439,0.8633983039148619,1.733981775034199e-06
+29,0.10432042496708724,0.8796296296296297,0.9512688614540467,0.8633251443355295,2.781588896993981e-10

results/airogs/vit/metrics.json

@@ -0,0 +1,49 @@
+{
+  "n_test": 1000,
+  "n_classes": 2,
+  "task": "binary",
+  "accuracy": 0.9,
+  "balanced_accuracy": 0.9,
+  "precision_macro": 0.9000576082955947,
+  "recall_macro": 0.9,
+  "f1_macro": 0.8999963998703953,
+  "precision_weighted": 0.9000576082955946,
+  "recall_weighted": 0.9,
+  "f1_weighted": 0.8999963998703953,
+  "cohen_kappa": 0.8,
+  "quadratic_weighted_kappa": 0.8,
+  "mcc": 0.8000576062215466,
+  "auroc": 0.9599679999999999,
+  "auprc": 0.962499989751263,
+  "sensitivity": 0.906,
+  "specificity": 0.894,
+  "precision_pos": 0.8952569169960475,
+  "f1_pos": 0.9005964214711729,
+  "per_class": {
+    "0": {
+      "precision": 0.9048582995951417,
+      "recall": 0.894,
+      "f1-score": 0.8993963782696177,
+      "support": 500.0
+    },
+    "1": {
+      "precision": 0.8952569169960475,
+      "recall": 0.906,
+      "f1-score": 0.9005964214711729,
+      "support": 500.0
+    },
+    "accuracy": 0.9,
+    "macro avg": {
+      "precision": 0.9000576082955947,
+      "recall": 0.9,
+      "f1-score": 0.8999963998703953,
+      "support": 1000.0
+    },
+    "weighted avg": {
+      "precision": 0.9000576082955946,
+      "recall": 0.9,
+      "f1-score": 0.8999963998703953,
+      "support": 1000.0
+    }
+  }
+}

results/airogs/vit/test_pred.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95914c1c34fc1fa6ada2fe1f6724299f448eae40085cffc4e8660442c11c69ad
+size 16510

results/airogs/vit/train.log

@@ -0,0 +1,124 @@
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:154: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
+  scaler = torch.cuda.amp.GradScaler()
+[vit] train=5000 val=540 test=1000 classes=['0', '1']
+[vit] optim groups=28 layer_decay=0.65 drop_path=0.1 ls=0.1 lr=0.0001
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep0 loss=0.6943 val_acc=0.7093 val_auc=0.7683 score=0.6320
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep1 loss=0.5653 val_acc=0.7852 val_auc=0.8664 score=0.7405
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep2 loss=0.5144 val_acc=0.8019 val_auc=0.8899 score=0.7644
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep3 loss=0.4824 val_acc=0.8278 val_auc=0.9118 score=0.7984
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep4 loss=0.4513 val_acc=0.8481 val_auc=0.9219 score=0.8221
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep5 loss=0.4164 val_acc=0.8463 val_auc=0.9283 score=0.8221
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep6 loss=0.3999 val_acc=0.8537 val_auc=0.9365 score=0.8320
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep7 loss=0.3716 val_acc=0.8704 val_auc=0.9364 score=0.8489
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep8 loss=0.3723 val_acc=0.8611 val_auc=0.9361 score=0.8394
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep9 loss=0.3535 val_acc=0.8611 val_auc=0.9355 score=0.8396
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep10 loss=0.3337 val_acc=0.8704 val_auc=0.9292 score=0.8465
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep11 loss=0.3219 val_acc=0.8556 val_auc=0.9335 score=0.8327
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep12 loss=0.3160 val_acc=0.8815 val_auc=0.9400 score=0.8614
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep13 loss=0.3047 val_acc=0.8759 val_auc=0.9381 score=0.8551
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep14 loss=0.2920 val_acc=0.8741 val_auc=0.9396 score=0.8539
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep15 loss=0.2911 val_acc=0.8685 val_auc=0.9397 score=0.8484
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep16 loss=0.2848 val_acc=0.8759 val_auc=0.9394 score=0.8557
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep17 loss=0.2747 val_acc=0.8722 val_auc=0.9328 score=0.8497
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep18 loss=0.2690 val_acc=0.8759 val_auc=0.9359 score=0.8544
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep19 loss=0.2715 val_acc=0.8759 val_auc=0.9286 score=0.8520
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep20 loss=0.2643 val_acc=0.8778 val_auc=0.9328 score=0.8554
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep21 loss=0.2558 val_acc=0.8759 val_auc=0.9286 score=0.8520
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:176: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] ep22 loss=0.2579 val_acc=0.8833 val_auc=0.9300 score=0.8599
+[vit] early stop at ep22 (best ep12 score=0.8614)
+/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Code/train_cnn_vit.py:58: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast():
+[vit] DONE best_ep=12 best_val_score=0.8614 -> saved test_pred.npz (1000 samples)
+[evaluate] /mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/results/airogs/vit  acc=0.8730  auroc=0.945154  f1_macro=0.8730  qwk=0.746

results/aptos/resnet/log.csv

@@ -0,0 +1,31 @@
+epoch,train_loss,val_acc,val_auc,val_score,lr
+0,1.6022112846374512,0.546448087431694,0.707079345096656,0.4578987430807475,0.00016296296296296295
+1,1.481985298792521,0.5437158469945356,0.8583620293281019,0.5206668333626183,0.00032962962962962964
+2,1.0903354975912305,0.5737704918032787,0.8030857451883981,0.5153118263583831,0.0004962962962962963
+3,0.8820486081971063,0.674863387978142,0.8618185474159583,0.628819203940555,0.0004983838038674314
+4,0.798465426100625,0.7486338797814208,0.8958376298611948,0.7164580866024649,0.0004934094785985891
+5,0.7188928928640154,0.7213114754098361,0.9134211122196007,0.6993799339529772,0.00048514345769673907
+6,0.5981913288434346,0.7868852459016393,0.9134103285436345,0.7240562027491079,0.0004736975249143533
+7,0.5255022015836504,0.7786885245901639,0.9108596857061437,0.7433230297988072,0.0004592264668570001
+8,0.46181285315089754,0.7622950819672131,0.9222729901925139,0.7318364302587627,0.0004419259797600385
+9,0.4122929010126326,0.7923497267759563,0.9224038254664986,0.7557271324289929,0.00042203002303274785
+10,0.35467921131187014,0.76775956284153,0.9131709330618574,0.7328492877585223,0.00039980765535867175
+11,0.2823121948374642,0.7896174863387978,0.9193939034097734,0.7542423123454284,0.0003755593961384883
+12,0.26580536630418566,0.7896174863387978,0.9154692930884206,0.741548401515562,0.0003496131614806527
+13,0.214219435552756,0.7868852459016393,0.920296111622056,0.7479175198248273,0.0003223198296985647
+14,0.20638181881772147,0.8087431693989071,0.9245912696369467,0.7664348039087657,0.000294048496283306
+15,0.16843497256437936,0.8060109289617486,0.916837076808525,0.7615347065821667,0.0002651814825203012
+16,0.14184371584819422,0.8060109289617486,0.9198374109698909,0.7597350478156475,0.0002361091652497979
+17,0.11139711323711607,0.8142076502732241,0.9186266407366391,0.7728060463495772,0.00020722469768978796
+18,0.11305203234983816,0.8005464480874317,0.9196866300613529,0.7630379030488843,0.00017891869271318173
+19,0.09528732962078518,0.8032786885245902,0.9182721864845776,0.7610588035421563,0.0001515739404787926
+20,0.08816374635530842,0.8060109289617486,0.9154554612100976,0.7638113289083576,0.00012556023185110866
+21,0.09019071285923322,0.8060109289617486,0.9157080960336517,0.7626259843203638,0.00010122935761322141
+22,0.08065369634164704,0.825136612021858,0.9163249911242524,0.7821297779496253,7.891035109997563e-05
+23,0.08097618880371253,0.8169398907103825,0.9175168093315029,0.7692978448027649,5.890503858656696e-05
+24,0.07066128568516837,0.8087431693989071,0.9168501427524222,0.763592717697461,4.148395760594798e-05
+25,0.07508751153945922,0.8114754098360656,0.9148993859567416,0.766509643802978,2.688269839279783e-05
+26,0.059848319242397946,0.8169398907103825,0.9143473537667471,0.7732221777097076,1.5298717929749516e-05
+27,0.06323818796210819,0.8278688524590164,0.9161340559730864,0.7812288586535097,6.888669680433918e-06
+28,0.06027366067800257,0.825136612021858,0.9152360310739749,0.777074034392648,1.7662851201208087e-06
+29,0.06106692339397139,0.819672131147541,0.9148171856006198,0.7728869213285655,8.357126202174214e-10