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)', 750), ('dog(outdoor)', 750), ('cat(outdoor)', 100), ('dog(indoor)', 100)]
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': 0.782974362373352}
Iteration: 0
out-of-domain val
accuracy 0.585 	 roc_auc_score 0.679
confusion_matrix
[[ 72 216]
 [ 23 265]]
classification_report
              precision    recall  f1-score   support

           0       0.76      0.25      0.38       288
           1       0.55      0.92      0.69       288

    accuracy                           0.59       576
   macro avg       0.65      0.59      0.53       576
weighted avg       0.65      0.59      0.53       576

VAL * Acc@1 58.507
 * Acc@1 58.507 Acc@5 0.000
accuracy 0.931 	 size: 144 	 dog(outdoor)
accuracy 0.910 	 size: 144 	 dog(indoor)
accuracy 0.312 	 size: 144 	 cat(indoor)
accuracy 0.188 	 size: 144 	 cat(outdoor)
step_vals {'loss': 0.6923204660415649}
step_vals {'loss': 0.6442362070083618}
step_vals {'loss': 0.45730966329574585}
step_vals {'loss': 0.5358803272247314}
step_vals {'loss': 0.45583945512771606}
step_vals {'loss': 0.40717288851737976}
step_vals {'loss': 0.5108343362808228}
step_vals {'loss': 0.5260456800460815}
step_vals {'loss': 0.4663091003894806}
step_vals {'loss': 0.5115252733230591}
step_vals {'loss': 0.3681630492210388}
step_vals {'loss': 0.5068610906600952}
step_vals {'loss': 0.39616113901138306}
step_vals {'loss': 0.48587873578071594}
step_vals {'loss': 0.4542102813720703}
step_vals {'loss': 0.5912470817565918}
step_vals {'loss': 0.3631542921066284}
step_vals {'loss': 0.35279640555381775}
step_vals {'loss': 0.6006330251693726}
step_vals {'loss': 0.3422411382198334}
Iteration: 20
out-of-domain val
accuracy 0.816 	 roc_auc_score 0.896
confusion_matrix
[[217  71]
 [ 35 253]]
classification_report
              precision    recall  f1-score   support

           0       0.86      0.75      0.80       288
           1       0.78      0.88      0.83       288

    accuracy                           0.82       576
   macro avg       0.82      0.82      0.82       576
weighted avg       0.82      0.82      0.82       576

VAL * Acc@1 81.597
 * Acc@1 81.597 Acc@5 0.000
accuracy 0.965 	 size: 144 	 dog(outdoor)
accuracy 0.812 	 size: 144 	 cat(indoor)
accuracy 0.792 	 size: 144 	 dog(indoor)
accuracy 0.694 	 size: 144 	 cat(outdoor)
step_vals {'loss': 0.29817843437194824}
step_vals {'loss': 0.3312833309173584}
step_vals {'loss': 0.6254711747169495}
step_vals {'loss': 0.435773104429245}
step_vals {'loss': 0.3073948919773102}
step_vals {'loss': 0.529425859451294}
step_vals {'loss': 0.3535246253013611}
step_vals {'loss': 0.3677806258201599}
step_vals {'loss': 0.29966628551483154}
step_vals {'loss': 0.501183032989502}
step_vals {'loss': 0.2321731001138687}
step_vals {'loss': 0.36158931255340576}
step_vals {'loss': 0.5028289556503296}
step_vals {'loss': 0.3512852191925049}
step_vals {'loss': 0.45020776987075806}
step_vals {'loss': 0.3321511745452881}
step_vals {'loss': 0.25692296028137207}
step_vals {'loss': 0.3942145109176636}
step_vals {'loss': 0.49481824040412903}
step_vals {'loss': 0.42649346590042114}
Iteration: 40
out-of-domain val
accuracy 0.819 	 roc_auc_score 0.917
confusion_matrix
[[237  51]
 [ 53 235]]
classification_report
              precision    recall  f1-score   support

           0       0.82      0.82      0.82       288
           1       0.82      0.82      0.82       288

    accuracy                           0.82       576
   macro avg       0.82      0.82      0.82       576
weighted avg       0.82      0.82      0.82       576

VAL * Acc@1 81.944
 * Acc@1 81.944 Acc@5 0.000
accuracy 0.931 	 size: 144 	 dog(outdoor)
accuracy 0.896 	 size: 144 	 cat(indoor)
accuracy 0.750 	 size: 144 	 cat(outdoor)
accuracy 0.701 	 size: 144 	 dog(indoor)
step_vals {'loss': 0.23008692264556885}
step_vals {'loss': 0.3235171437263489}
step_vals {'loss': 0.33202052116394043}
step_vals {'loss': 0.4962618350982666}
step_vals {'loss': 0.3600156903266907}
step_vals {'loss': 0.3005039691925049}
step_vals {'loss': 0.3051591217517853}
step_vals {'loss': 0.38770511746406555}
step_vals {'loss': 0.3727878928184509}
step_vals {'loss': 0.3734447956085205}
step_vals {'loss': 0.32768869400024414}
step_vals {'loss': 0.2666164040565491}
step_vals {'loss': 0.3291853070259094}
step_vals {'loss': 0.28314411640167236}
step_vals {'loss': 0.2175900638103485}
step_vals {'loss': 0.22955277562141418}
step_vals {'loss': 0.3654421865940094}
step_vals {'loss': 0.27757081389427185}
step_vals {'loss': 0.2964366674423218}
step_vals {'loss': 0.5230903625488281}
Iteration: 60
out-of-domain val
accuracy 0.819 	 roc_auc_score 0.901
confusion_matrix
[[242  46]
 [ 58 230]]
classification_report
              precision    recall  f1-score   support

           0       0.81      0.84      0.82       288
           1       0.83      0.80      0.82       288

    accuracy                           0.82       576
   macro avg       0.82      0.82      0.82       576
weighted avg       0.82      0.82      0.82       576

VAL * Acc@1 81.944
 * Acc@1 81.944 Acc@5 0.000
accuracy 0.889 	 size: 144 	 cat(indoor)
accuracy 0.882 	 size: 144 	 dog(outdoor)
accuracy 0.792 	 size: 144 	 cat(outdoor)
accuracy 0.715 	 size: 144 	 dog(indoor)
step_vals {'loss': 0.25623399019241333}
step_vals {'loss': 0.2000294327735901}
step_vals {'loss': 0.3392483592033386}
step_vals {'loss': 0.41172948479652405}
step_vals {'loss': 0.18189185857772827}
step_vals {'loss': 0.36257216334342957}
step_vals {'loss': 0.39520126581192017}
step_vals {'loss': 0.18444277346134186}
step_vals {'loss': 0.2461710274219513}
step_vals {'loss': 0.3384338617324829}
step_vals {'loss': 0.19284185767173767}
step_vals {'loss': 0.2744177579879761}
step_vals {'loss': 0.30866289138793945}
step_vals {'loss': 0.19185581803321838}
step_vals {'loss': 0.3034110367298126}
step_vals {'loss': 0.20398275554180145}
step_vals {'loss': 0.2240423560142517}
step_vals {'loss': 0.20191949605941772}
step_vals {'loss': 0.2462407350540161}
step_vals {'loss': 0.16310490667819977}
Iteration: 80
out-of-domain val
accuracy 0.839 	 roc_auc_score 0.913
confusion_matrix
[[250  38]
 [ 55 233]]
classification_report
              precision    recall  f1-score   support

           0       0.82      0.87      0.84       288
           1       0.86      0.81      0.83       288

    accuracy                           0.84       576
   macro avg       0.84      0.84      0.84       576
weighted avg       0.84      0.84      0.84       576

VAL * Acc@1 83.854
 * Acc@1 83.854 Acc@5 0.000
accuracy 0.938 	 size: 144 	 cat(indoor)
accuracy 0.917 	 size: 144 	 dog(outdoor)
accuracy 0.799 	 size: 144 	 cat(outdoor)
accuracy 0.701 	 size: 144 	 dog(indoor)
