Environment:
	Python: 3.6.13
	PyTorch: 1.4.0
	Torchvision: 0.5.0
	CUDA: 10.1
	CUDNN: 7603
	NumPy: 1.19.5
	PIL: 8.1.0
Args:
	algorithm: GroupDRO
	batch_size: 8
	data: /data/GQA/MetaDataset-subpopulation-shift
	hparams: None
	hparams_seed: 0
	log_prefix: 
	num_classes: 2
	num_domains: 2
	output_dir: train_output
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	workers: 4
train_dataset.samples reverse: [('cat(indoor)', 840), ('dog(outdoor)', 840), ('cat(outdoor)', 10), ('dog(indoor)', 10)]
self.domain_to_groups {0: {'cat': ['cat(indoor)'], 'dog': ['dog(indoor)']}, 1: {'cat': ['cat(outdoor)'], 'dog': ['dog(outdoor)']}}
HParams:
	batch_size: 32
	class_balanced: False
	data_augmentation: True
	groupdro_eta: 0.01
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: True
	resnet_dropout: 0.0
	weight_decay: 0.0
step_vals {'loss': 0.769673228263855}
Iteration: 0
out-of-domain val
accuracy 0.583 	 roc_auc_score 0.666
confusion_matrix
[[ 83 205]
 [ 35 253]]
classification_report
              precision    recall  f1-score   support

           0       0.70      0.29      0.41       288
           1       0.55      0.88      0.68       288

    accuracy                           0.58       576
   macro avg       0.63      0.58      0.54       576
weighted avg       0.63      0.58      0.54       576

VAL * Acc@1 58.333
 * Acc@1 58.333 Acc@5 0.000
accuracy 0.882 	 size: 144 	 dog(outdoor)
accuracy 0.875 	 size: 144 	 dog(indoor)
accuracy 0.375 	 size: 144 	 cat(indoor)
accuracy 0.201 	 size: 144 	 cat(outdoor)
step_vals {'loss': 0.6207288503646851}
step_vals {'loss': 0.5357910394668579}
step_vals {'loss': 0.440571665763855}
step_vals {'loss': 0.3028791546821594}
step_vals {'loss': 0.37800607085227966}
step_vals {'loss': 0.3576801121234894}
step_vals {'loss': 0.2321661412715912}
step_vals {'loss': 0.18358871340751648}
step_vals {'loss': 0.18893148005008698}
step_vals {'loss': 0.2488023340702057}
step_vals {'loss': 0.11850664019584656}
step_vals {'loss': 0.1351444572210312}
step_vals {'loss': 0.10397308319807053}
step_vals {'loss': 0.07998864352703094}
step_vals {'loss': 0.29593732953071594}
step_vals {'loss': 0.2411603480577469}
step_vals {'loss': 0.46348094940185547}
step_vals {'loss': 0.17701032757759094}
step_vals {'loss': 0.22379344701766968}
step_vals {'loss': 0.2014141082763672}
Iteration: 20
out-of-domain val
accuracy 0.788 	 roc_auc_score 0.872
confusion_matrix
[[227  61]
 [ 61 227]]
classification_report
              precision    recall  f1-score   support

           0       0.79      0.79      0.79       288
           1       0.79      0.79      0.79       288

    accuracy                           0.79       576
   macro avg       0.79      0.79      0.79       576
weighted avg       0.79      0.79      0.79       576

VAL * Acc@1 78.819
 * Acc@1 78.819 Acc@5 0.000
accuracy 0.958 	 size: 144 	 dog(outdoor)
accuracy 0.910 	 size: 144 	 cat(indoor)
accuracy 0.667 	 size: 144 	 cat(outdoor)
accuracy 0.618 	 size: 144 	 dog(indoor)
step_vals {'loss': 0.05134323984384537}
step_vals {'loss': 0.27147024869918823}
step_vals {'loss': 0.4026072919368744}
step_vals {'loss': 0.12253815680742264}
step_vals {'loss': 0.3554787039756775}
step_vals {'loss': 0.16141553223133087}
step_vals {'loss': 0.20917600393295288}
step_vals {'loss': 0.1403145045042038}
step_vals {'loss': 0.11315645277500153}
step_vals {'loss': 0.13130752742290497}
step_vals {'loss': 0.27697619795799255}
step_vals {'loss': 0.2841462790966034}
step_vals {'loss': 0.2285439372062683}
step_vals {'loss': 0.21153907477855682}
step_vals {'loss': 0.2556278705596924}
step_vals {'loss': 0.17703047394752502}
step_vals {'loss': 0.17199987173080444}
step_vals {'loss': 0.051167748868465424}
step_vals {'loss': 0.09888001531362534}
step_vals {'loss': 0.1564222127199173}
Iteration: 40
out-of-domain val
accuracy 0.790 	 roc_auc_score 0.876
confusion_matrix
[[222  66]
 [ 55 233]]
classification_report
              precision    recall  f1-score   support

           0       0.80      0.77      0.79       288
           1       0.78      0.81      0.79       288

    accuracy                           0.79       576
   macro avg       0.79      0.79      0.79       576
weighted avg       0.79      0.79      0.79       576

VAL * Acc@1 78.993
 * Acc@1 78.993 Acc@5 0.000
accuracy 0.979 	 size: 144 	 dog(outdoor)
accuracy 0.910 	 size: 144 	 cat(indoor)
accuracy 0.639 	 size: 144 	 dog(indoor)
accuracy 0.632 	 size: 144 	 cat(outdoor)
step_vals {'loss': 0.327303946018219}
step_vals {'loss': 0.03542201593518257}
step_vals {'loss': 0.26455116271972656}
step_vals {'loss': 0.24976573884487152}
step_vals {'loss': 0.1438504159450531}
step_vals {'loss': 0.11260096728801727}
step_vals {'loss': 0.1280895173549652}
step_vals {'loss': 0.38621604442596436}
step_vals {'loss': 0.20265603065490723}
step_vals {'loss': 0.06435772776603699}
step_vals {'loss': 0.07763280719518661}
step_vals {'loss': 0.11652090400457382}
step_vals {'loss': 0.24059633910655975}
step_vals {'loss': 0.17217937111854553}
step_vals {'loss': 0.1454881727695465}
step_vals {'loss': 0.11419910192489624}
step_vals {'loss': 0.3407115936279297}
step_vals {'loss': 0.12978900969028473}
step_vals {'loss': 0.027611354365944862}
step_vals {'loss': 0.1394980251789093}
Iteration: 60
out-of-domain val
accuracy 0.745 	 roc_auc_score 0.850
confusion_matrix
[[248  40]
 [107 181]]
classification_report
              precision    recall  f1-score   support

           0       0.70      0.86      0.77       288
           1       0.82      0.63      0.71       288

    accuracy                           0.74       576
   macro avg       0.76      0.74      0.74       576
weighted avg       0.76      0.74      0.74       576

VAL * Acc@1 74.479
 * Acc@1 74.479 Acc@5 0.000
accuracy 0.986 	 size: 144 	 cat(indoor)
accuracy 0.882 	 size: 144 	 dog(outdoor)
accuracy 0.736 	 size: 144 	 cat(outdoor)
accuracy 0.375 	 size: 144 	 dog(indoor)
step_vals {'loss': 0.10950561612844467}
step_vals {'loss': 0.23005878925323486}
step_vals {'loss': 0.14282144606113434}
step_vals {'loss': 0.0846167579293251}
step_vals {'loss': 0.04586290195584297}
step_vals {'loss': 0.07680954039096832}
step_vals {'loss': 0.09169332683086395}
step_vals {'loss': 0.13942021131515503}
step_vals {'loss': 0.10001295059919357}
step_vals {'loss': 0.23394617438316345}
step_vals {'loss': 0.10765773057937622}
step_vals {'loss': 0.07748206704854965}
step_vals {'loss': 0.04961910843849182}
step_vals {'loss': 0.11333824694156647}
step_vals {'loss': 0.06998147815465927}
step_vals {'loss': 0.05794799327850342}
step_vals {'loss': 0.03238843381404877}
step_vals {'loss': 0.07187357544898987}
step_vals {'loss': 0.04307745397090912}
step_vals {'loss': 0.24166972935199738}
Iteration: 80
out-of-domain val
accuracy 0.762 	 roc_auc_score 0.850
confusion_matrix
[[220  68]
 [ 69 219]]
classification_report
              precision    recall  f1-score   support

           0       0.76      0.76      0.76       288
           1       0.76      0.76      0.76       288

    accuracy                           0.76       576
   macro avg       0.76      0.76      0.76       576
weighted avg       0.76      0.76      0.76       576

VAL * Acc@1 76.215
 * Acc@1 76.215 Acc@5 0.000
accuracy 0.944 	 size: 144 	 dog(outdoor)
accuracy 0.917 	 size: 144 	 cat(indoor)
accuracy 0.611 	 size: 144 	 cat(outdoor)
accuracy 0.576 	 size: 144 	 dog(indoor)
